Eurasian Business and Economics Perspectives: Proceedings of the 38th Eurasia Business and Economics Society Conference (Eurasian Studies in Business and Economics, 25) 3031362853, 9783031362859

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Eurasian Studies in Business and Economics  25 Series Editors: Mehmet Huseyin Bilgin · Hakan Danis

Mehmet Hüseyin Bilgin · Hakan Danis · Ender Demir · Leszek Wincenciak · S. Tolga Er   Editors

Eurasian Business and Economics Perspectives Proceedings of the 38th Eurasia Business and Economics Society Conference

Eurasian Studies in Business and Economics Volume 25

Series Editors Mehmet Huseyin Bilgin, Faculty of Political Sciences, Istanbul Medeniyet University, Istanbul, Türkiye Hakan Danis, EBES, San Francisco, CA, USA

Eurasian Studies in Business and Economics is the official book series of the Eurasia Business and Economics Society (www.ebesweb.org). Each issue of the series includes selected papers from the EBES conferences. The EBES conferences, which are being held three times a year, have been intellectual hub for academic discussion in economics, finance, and business fields and provide network opportunities for participants to make long lasting academic cooperation. Each conference features around 250 research articles presented and attended by almost 500 researchers from more than 60 countries around the World. Theoretical and empirical papers in the series cover diverse areas of business, economics, and finance from many different countries, providing a valuable opportunity to researchers, professionals, and students to catch up with the most recent studies in a diverse set of fields across many countries and regions.

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Mehmet Hüseyin Bilgin • Hakan Danis • Ender Demir • Leszek Wincenciak • S. Tolga Er Editors

Eurasian Business and Economics Perspectives Proceedings of the 38th Eurasia Business and Economics Society Conference

Editors Mehmet Hüseyin Bilgin Faculty of Political Sciences Istanbul Medeniyet University Istanbul, Türkiye Ender Demir Business Administration Reykjavik University Reykjavík, Iceland

Hakan Danis EBES San Francisco, CA, USA Leszek Wincenciak University of Warsaw Warsaw, Poland

S. Tolga Er Universität Hamburg Hamburg, Germany

ISSN 2364-5067 ISSN 2364-5075 (electronic) Eurasian Studies in Business and Economics ISBN 978-3-031-36285-9 ISBN 978-3-031-36286-6 (eBook) https://doi.org/10.1007/978-3-031-36286-6 The authors of individual papers are responsible for technical, content, and linguistic correctness. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This is the 26th issue of the Springer’s series Eurasian Studies in Business and Economics, which is the official book series of the Eurasia Business and Economics Society (EBES, www.ebesweb.org). This issue includes selected papers presented at the 38th EBES Conference—Warsaw, which was held on January 12, 13, and 14, 2022, with the support of the Istanbul Economic Research Association. It was hosted by the Faculty of Economic Sciences, University of Warsaw, Poland. Due to the pandemic, the conference was hybrid which gave participants to join the conference either in person or virtually. We are honored to have received top-tier papers from distinguished scholars from all over the world. We regret that we were unable to accept more papers. In the conference, 197 papers will be presented and 439 colleagues from 50 countries will attend the conference. Distinguished colleagues Christos Kollias from the University of Thessaly (Greece), M. Kabir Hassan from the University of New Orleans (USA), Klaus Zimmermann from GLO (Germany) and EBES, and Christopher A. Hartwell from the Zurich University of Applied Sciences and Arts (Switzerland) joined the conference as invited editors and/or keynote speakers. In addition to publication opportunities in EBES journals (Eurasian Business Review and Eurasian Economic Review, which are also published by Springer), conference participants were given the opportunity to submit their full papers for this issue. Theoretical and empirical papers in the series cover diverse areas of business, economics, and finance from many different countries, providing a valuable opportunity to researchers, professionals, and students to catch up with the most recent studies in a diverse set of fields across many countries and regions. The aim of the EBES conferences is to bring together scientists from business, finance, and economics fields, attract original research papers, and provide them with publication opportunities. Each issue of the Eurasian Studies in Business and Economics covers a wide variety of topics from business and economics and provides empirical results from many different countries and regions that are less investigated in the existing literature. All accepted papers for the issue went through a peer review process and benefited from the comments made during the conference v

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Preface

as well. The current issue is entitled Eurasian Business and Economics Perspectives and covers fields such as corporate governance, entrepreneurship, sustainability, banking, finance, and tourism. Although the papers in this issue may provide empirical results for a specific county or regions, we believe that the readers would have an opportunity to catch up with the most recent studies in a diverse set of fields across many countries and regions and empirical support for the existing literature. In addition, the findings from these papers could be valid for similar economies or regions. On behalf of the series editors, volume editors, and EBES officers, I would like to thank all the presenters, participants, board members, and keynote speakers, and we are looking forward to seeing you at the upcoming EBES conferences. Best regards, Reykjavík, Iceland

Ender Demir

Eurasia Business and Economics Society (EBES)

EBES is a scholarly association for scholars involved in the practice and study of economics, finance, and business worldwide. EBES was founded in 2008 with the purpose of not only promoting academic research in the field of business and economics but also encouraging the intellectual development of scholars. In spite of the term “Eurasia,” the scope should be understood in its broadest terms as having a global emphasis. EBES aims to bring worldwide researchers and professionals together through organizing conferences and publishing academic journals and increase economics, finance, and business knowledge through academic discussions. Any scholar or professional interested in economics, finance, and business is welcome to attend EBES conferences. Since our first conference in 2009, around 16,198 colleagues from 102 countries have joined our conferences and 8861 academic papers have been presented. EBES has reached 2869 members from 87 countries. Since 2011, EBES has been publishing two journals. One of those journals, Eurasian Business Review—EABR, is in the fields of industrial organization, innovation, and management science, and the other one, Eurasian Economic Review—EAER, is in the fields of applied macroeconomics and finance. Both journals are published quarterly by Springer and indexed in Scopus. In addition, EAER is indexed in the Emerging Sources Citation Index (Clarivate Analytics), and EABR is indexed in the Social Science Citation Index (SSCI). EABR has an impact factor of 3.574 (2021 JCR Impact Factor). Furthermore, since 2014 Springer has started to publish a new conference proceedings series (Eurasian Studies in Business and Economics) which includes selected papers from the EBES conferences. The series has been recently indexed by SCOPUS. In addition, the 10th, 11th, 12th, 13th, 14th, 15th, 16th, 17th, 18th, 19th, 20th, 21st, 22nd, 23rd, 24th, 25th, 26th, 27th, 28th, 29th (Vol. 1), and 30th EBES Conference Proceedings have already been accepted for inclusion in the Conference Proceedings Citation Index—Social Science & Humanities (CPCI-SSH). Other conference proceedings are in progress.

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Eurasia Business and Economics Society (EBES)

On behalf of all EBES officers, I sincerely thank you for all your support in the past. We look forward to seeing you at our forthcoming conferences. We very much welcome your comments and suggestions in order to improve our future events. Our success is only possible with your valuable feedback and support! I hope you enjoy the conference! With my very best wishes, Klaus F. Zimmermann President EBES Executive Board Klaus F. Zimmermann, UNU-MERIT, Maastricht, and Free University Berlin, Germany Mehmet Huseyin Bilgin, Istanbul Medeniyet University, Turkey Jonathan Batten, RMIT University, Australia Iftekhar Hasan, Fordham University, USA Euston Quah, Nanyang Technological University, Singapore John Rust, Georgetown University, USA Dorothea Schafer, German Institute for Economic Research DIW Berlin, Germany Marco Vivarelli, Università Cattolica del Sacro Cuore, Italy EBES Advisory Board Ahmet Faruk Aysan, Hamad Bin Khalifa University, Qatar Michael R. Baye, Kelley School of Business, Indiana University, USA Mohamed Hegazy, School of Management, Economics and Communication, The American University in Cairo, Egypt Cheng Hsiao, Department of Economics, University of Southern California, USA Noor Azina Ismail, University of Malaya, Malaysia Irina Ivashkovskaya, State University—Higher School of Economics, Russia Christos Kollias, Department of Economics, University of Thessaly, Greece Wolfgang Kürsten, Friedrich Schiller University Jena, Germany William D. Lastrapes, Terry College of Business, University of Georgia, USA Sungho Lee, University of Seoul, South Korea Justin Y. Lin, Peking University, China Brian Lucey, The University of Dublin, Ireland Rita Martenson, School of Business, Economics and Law, University of Gothenburg, Sweden Steven Ongena, University of Zurich, Switzerland Peter Rangazas, Indiana University-Purdue University Indianapolis, USA Peter Szilagyi, EDHEC Business School, France Amine Tarazi, University of Limoges, France Russ Vince, University of Bath, UK

Eurasia Business and Economics Society (EBES)

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Adrian Wilkinson, Griffith University, Australia Naoyuki Yoshino, Faculty of Economics, Keio University, Japan Organizing Committee Klaus F. Zimmermann, PhD, UNU-MERIT, Maastricht, and Free University Berlin, Germany Mehmet Huseyin Bilgin, PhD, Istanbul Medeniyet University, Turkey Hakan Danis, PhD, Union Bank, USA Alina Klonowska, PhD, Cracow University of Economics, Poland Orhun Guldiken, PhD, University of Arkansas, USA Ender Demir, PhD, Reykjavik University, Iceland Sofia Vale, PhD, ISCTE Business School, Portugal Jonathan Tan, PhD, Nanyang Technological University, Singapore Ugur Can, EBES, Turkey Tolga Er, EBES, Turkey Reviewers Sagi Akron, PhD, University of Haifa, Israel Mehmet Huseyin Bilgin, PhD, Istanbul Medeniyet University, Turkey Andrzej Cieślik, PhD, University of Warsaw, Poland Hakan Danis, PhD, Union Bank, USA Ender Demir, PhD, Reykjavik University, Iceland Emanuele Giovannetti, PhD, Anglia Ruskin University, UK Oguz Ersan, PhD, Kadir Has University, Turkey Conrado Diego García-Gómez, PhD, Universidad de Valladolid, Spain Orhun Guldiken, PhD, University of Arkansas, USA Peter Harris, PhD, New York Institute of Technology, USA Mohamed Hegazy, PhD, The American University in Cairo, Egypt Gokhan Karabulut, PhD, Istanbul University, Turkey Alexander M. Karminsky, PhD, National Research University, Russia Christos Kollias, PhD, University of Thessaly, Greece Davor Labaš, PhD, University of Zagreb, Croatia Veljko M. Mijušković, PhD, University of Belgrade, Serbia Ghulam Mustafa, PhD, Norwegian University of Science and Technology, Norway Nidžara Osmanagić-Bedenik, PhD, University of Zagreb, Croatia Euston Quah, PhD, Nanyang Technological University, Singapore Peter Rangazas, PhD, Indiana University-Purdue University Indianapolis, USA Ralph Sonenshine, PhD, American University, USA Doojin Ryu, PhD, Chung-Ang University, South Korea Dorothea Schafer, PhD, German Institute for Economic Research DIW Berlin, Germany Uchenna Tony-Okeke, PhD, Coventry University, UK Sofia Vale, PhD, ISCTE Business School, Portugal Marco Vivarelli, PhD, Università Cattolica del Sacro Cuore, Italy

Contents

Part I

Eurasian Business Perspectives: Corporate Governance

Board Diversity and Sustainability: Indonesian Evidence . . . . . . . . . . . . Zuraida Zuraida and Said Musnadi The Impact of Audit Committee Composition on Corporate Risk Disclosure in Emerging Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Musa Uba Adamu and Irina Ivashkovskaya Part II

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Eurasian Business Perspectives: Entrepreneurship

The Dynamic Capability and Ambidexterity in the Early-Stage Startups: A Hierarchical Component Model Approach . . . . . . . . . . . . . Prio Utomo and Florentina Kurniasari

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The Impact of Institutional Framework On Entrepreneurship in OECD Members Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ante Zdilar

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Part III

Eurasian Business Perspectives: Management Information System

Combining Robotic Process Automation with Artificial Intelligence: Applications, Terminology, Benefits, and Challenges . . . . . . . . . . . . . . . Lewin Schaudt and Dennis Schlegel

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Google Analytics Best Practices in Slovak and Czech Online Business . . 101 Miroslav Reiter and Andrej Miklosik Part IV

Eurasian Business Perspectives: Sustainability

Product Sustainability in Spatial Competition with Consumer Environmental Awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Hamid Hamoudi and Carmen Aviles-Palacios xi

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Is the Green Economy the Key Factor in Reducing Urban Pollution in Romania? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Alin-Cristian Maricuț and Giani-Ionel Grădinaru Part V

Eurasian Economic Perspectives: Agricultural Economics

Rural Development in the Context of Agricultural Models: Evidence from Bulgaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Julia Doitchinova and Albena Miteva Risk Management in Agriculture: Lesson from Bulgaria . . . . . . . . . . . . 167 Hristina Harizanova-Bartos and Zornitsa Stoyanova Part VI

Eurasian Economic Perspectives: Banking

Impact of Merger Announcements on Stock Price of Participating Banks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Tamy Al-Binali Impact of Macroprudentiality on Customer Protection of Banking Services: The Case of the Republic of Moldova . . . . . . . . . . . . . . . . . . . 195 Cociug Victoria and Turcan-Munteanu Natalia Part VII

Eurasian Economic Perspectives: Empirical Studies on Finance and Economics

The Lack of Public Health Spending and Economic Growth in Russia: A Regional Aspect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Olga Demidova, Elena Kayasheva, and Artem Demyanenko Analysing the Asymmetric Effect of Oil Price Shock on Inflationary at the Aggregate and Disaggregated Levels in Malaysia . . . . . . . . . . . . . 233 Wong Hock Tsen, Kasim Mansur, and Cheong Jia Qi Qatari Real Estate Market and Its Response to Shocks . . . . . . . . . . . . . 251 Alanoud Hamad Fetais Part VIII

Eurasian Economic Perspectives: Investment

Investor Sentiment and Efficiency of the Cryptocurrency Market: The Case of the Crypto Fear & Greed Index . . . . . . . . . . . . . . . . . . . . . 271 Blanka Łęt, Konrad Sobański, Wojciech Świder, and Katarzyna Włosik The Key Determinants of Financial Risk Tolerance Among Gen-Z Investors: Propensity for Regret, Propensity for Overconfidence and Income Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Florentina Kurniasari and Prio Utomo

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Profiling the Victims of Ponzi Schemes: The Role of Financial Literacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Abdur Rafik, Dwipraptono Agus Harjito, Bagus Panuntun, and Anisa Rahmadani Part IX

Eurasian Economic Perspectives: Public Economics

Chosen Central European Countries Compared in Measuring Efficiency of General Government Expenditures . . . . . . . . . . . . . . . . . . 313 Petr Makovský and František Hřebík A Contribution to the International Trade Theory . . . . . . . . . . . . . . . . . 329 Truong Hong Trinh and Tran Thi Ngoc Duy Part X

Eurasian Economic Perspectives: Tourism

COVID-19: How Do Companies in the Tourism Sector React? The Case of Riccione . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Stefania Vignini Analysis of Social Capital in Aragon’s Tourism Cluster: A Social Network Resources Perspective on Twitter . . . . . . . . . . . . . . . . . . . . . . . 369 Natalia Sánchez-Arrieta, Ferran Sabate, Antonio Cañabate, and Umair Tehami The Economic Value of Recreational Assets During the COVID-19 Pandemic on the Example of Bóbr Valley Railway “Bobertalbahn” . . . . 395 Paweł Piepiora, Justyna Bagińska, and Zbigniew Piepiora

Contributors

Musa Uba Adamu School of Finance, National Research University Higher School of Economics, Moscow, Russia Tamy Al-Binali College of Islamic Finance, Hamad Bin Khalifa University, Doha, Qatar Carmen Aviles-Palacios Dpto. Ingeniería de Organización, Administración de Empresas y Estadística, E.T.S.I. Montes, Forestal y del Medio Natural. Universidad Politécnica de Madrid, Madrid, Spain Justyna Bagińska Wroclaw Business University of Applied Sciences, Wrocław, Poland Antonio Cañabate Department of Management (OE), Universitat Politècnica de Catalunya, BarcelonaTech (UPC), Barcelona, Spain Olga Demidova Department of Applied Economics, HSE University, Moscow, Russian Federation Artem Demyanenko Department of Applied Economics, HSE University, Moscow, Russian Federation Julia Doitchinova Department of Natural Resources Economics, University of National and World Economy, Sofia, Bulgaria Tran Thi Ngoc Duy Faculty of International Business, The University of Danang – University of Economics, Danang, Vietnam Alanoud Hamad Fetais College of Islamic Studies, Hamad Bin Khalifa University, Doha, Qatar Giani-Ionel Grădinaru Department of Statistics and Econometrics, The Bucharest University of Economics Studies, Bucharest, Romania Hamid Hamoudi Dpto. Fundamentos de Análisis Económico, Universidad Rey Juan Carlos-URJC, Madrid, Spain xv

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Hristina Harizanova-Bartos Economics of Natural Resources Department, University of National and World Economy, Sofia, Bulgaria Dwipraptono Agus Harjito Department of Management, Faculty of Business and Economics, Universitas Islam Indonesia, Yogyakarta, Indonesia František Hřebík Masaryk Institute of Advanced Studies, Czech Technical University Prague, Prague, Czechia Irina Ivashkovskaya School of Finance, National Research University Higher School of Economics, Moscow, Russia Elena Kayasheva Department of Theoretical Economics, HSE University, Moscow, Russian Federation Florentina Kurniasari Technology Management Department, Universitas Multimedia Nusantara, Tangerang, Banten, Indonesia Blanka Łęt Department of Applied Mathematics, Poznań University of Economics and Business, Poznań, Poland Petr Makovský Masaryk Institute of Advanced Studies, Czech Technical University Prague, Prague, Czechia Kasim Mansur Faculty of Business, Economics and Accountancy, University Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia Alin-Cristian Maricuț Department of Statistics and Econometrics, The Bucharest University of Economics Studies, Bucharest, Romania Andrej Miklosik Faculty of Management, Comenius University in Bratislava, Bratislava, Slovak Republic Albena Miteva Department of Natural Resources Economics, University of National and World Economy, Sofia, Bulgaria Said Musnadi Department of Management, Faculty of Economics and Business, Syiah Kuala University, Banda Aceh, Indonesia Turcan-Munteanu Natalia The Academy of Economic Studies of Moldova, Chisinau, Republic of Moldova Bagus Panuntun Department of Management, Faculty of Business and Economics, Universitas Islam Indonesia, Yogyakarta, Indonesia Paweł Piepiora Department of Sports Didactics, Wroclaw University of Health and Sport Sciences, Wrocław, Poland Zbigniew Piepiora Institute of Spatial Management, Wrocław University of Environmental and Life Sciences, Wrocław, Poland Cheong Jia Qi Faculty of Business, Economics and Accountancy, University Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia

Contributors

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Abdur Rafik Department of Management, Faculty of Business and Economics, Universitas Islam Indonesia, Yogyakarta, Indonesia Anisa Rahmadani Department of Management, Faculty of Business and Economics, Universitas Islam Indonesia, Yogyakarta, Indonesia Miroslav Reiter Faculty of Management, Comenius University in Bratislava, Bratislava, Slovak Republic Ferran Sabate Department of Management (OE), Universitat Politècnica de Catalunya, BarcelonaTech (UPC), Barcelona, Spain Natalia Sánchez-Arrieta Department of Management (OE), Universitat Politècnica de Catalunya, BarcelonaTech (UPC), Barcelona, Spain Programme “Colombia Científica” - Component: “Pasaporte a la Ciencia”, Instituto Colombiano de Crédito Educativo y Estudios Técnicos en el Exterior (ICETEX), Bogotá D.C., Colombia Lewin Schaudt School of Informatics, Reutlingen University, Reutlingen, Germany Dennis Schlegel School of Informatics, Reutlingen University, Reutlingen, Germany Konrad Sobański Department of International Finance, Poznań University of Economics and Business, Poznań, Poland Zornitsa Stoyanova Economics of Natural Resources Department, University of National and World Economy, Sofia, Bulgaria Wojciech Świder Department of Public Finance, Poznań University of Economics and Business, Poznań, Poland Umair Tehami Department of Management (OE), Universitat Politècnica de Catalunya, BarcelonaTech (UPC), Barcelona, Spain Truong Hong Trinh Faculty of Finance, The University of Danang – University of Economics, Danang, Vietnam Wong Hock Tsen Faculty of Business, Economics and Accountancy, University Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia Prio Utomo Technology Management Department, Universitas Multimedia Nusantara, Tangerang, Banten, Indonesia Cociug Victoria Department of Finance, The Academy of Economic Studies of Moldova, Chisinau, Republic of Moldova Stefania Vignini Department of Management, University of Bologna, Bologna, Italy

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Contributors

Katarzyna Włosik Department of Investment and Financial Markets, Poznań University of Economics and Business, Poznań, Poland Ante Zdilar The Department of Economics and Business, University of Dubrovnik, Dubrovnik, Croatia Zuraida Zuraida Department of Accounting, Faculty of Economics and Business, Syiah Kuala University, Banda Aceh, Indonesia

Part I

Eurasian Business Perspectives: Corporate Governance

Board Diversity and Sustainability: Indonesian Evidence Zuraida Zuraida

and Said Musnadi

Abstract This study examines the relationship between board diversity and sustainability. The research sample consists of 38 companies listed on the Indonesia Stock Exchange from 2012 to 2018. Research data was collected from annual and sustainability reports and then analyzed using multiple regressions. We find that the level of sustainability disclosure in the sample companies is still relatively low, with an average of 17.49 points out of the 83 points listed in the Global Reporting Initiative standards. We find that the nationality and education level of the board of directors have a positive and significant relationship with sustainability disclosure. In contrast, board gender, age, and independent directors have a negative and significant relationship with sustainability disclosure. We also find that board education level has a positive and significant relationship with sustainability spending. However, gender has a negative and significant relationship with sustainability spending. We further find that the amount spent on sustainability initiatives does not match the level of sustainability disclosure. This suggests that companies disclosing higher sustainability factors do not necessarily spend more money on sustainability activities. Thus, in line with agency theory, there appears to be a practice of managerial entrenchment by outlining sustainability activities more extensively than the value invested. This study sheds light on the hidden lack of commitment to corporate sustainability, contributing to the debate in the sustainability literature. Keywords Board diversity · Corporate governance · Disclosure · Expenditure · Indonesia · Sustainability Z. Zuraida (✉) Department of Accounting, Faculty of Economics and Business, Syiah Kuala University, Banda Aceh, Indonesia e-mail: [email protected] S. Musnadi Department of Management, Faculty of Economics and Business, Syiah Kuala University, Banda Aceh, Indonesia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_1

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1 Introduction The importance of good corporate governance has been learned from major corporate scandals, such as the fall of Enron Corporation (Fajri, 2016; Li, 2010; Yarram & Adapa, 2021) and the occurrence of the 2008 world financial crisis (Manita et al., 2018, Pritchard, 2018), which were partly the result of poor corporate governance (Financial Crisis Inquiry Commission, 2011). Since these major cases and many other corporate failures, corporate governance has received global attention with the increasing number of academic studies on various aspects of corporate governance. One of them is research on board diversity and its role in driving sustainability. A consensus has been established in the literature that higher levels of board diversity represent better corporate governance for the board’s diverse background, suggesting a broader cognitive influence on various aspects of corporate governance (Ararat et al., 2010). The literature has also agreed that the diversity of directors can be measured by various dimensions, including nationality, race, gender, education level, educational background, and experience (Saidu et al., 2020). Although research on the relationship between board diversity and sustainability has been performed in various countries, the number of studies in certain parts of the world is still limited, with the slow growth of its literature (Ahmed, 2016). Likewise, research findings have not reached a consensus, and a lot of their results are not in accordance with the underlying theories. Several studies demonstrate positive and significant results (Bing & Amran, 2017; Biswas Pallab et al., 2018; Harjoto et al., 2015; Hu & Loh, 2018; Kılıç & Uyar, 2014; Moses et al., 2020; Zahid et al., 2020), others indicate mixed outcomes (Buallay et al., 2022; Naciti, 2019; Saidu et al., 2020; Shamil et al., 2014), and the rest show no relationship (Adeniyi & Fadipe, 2018; Zaid et al., 2020). The inconclusive research stage on the relationship between board diversity and sustainability highlights the necessity of further research regarding the impact of board diversity on various aspects of corporate governance. Our study examining eight board diversity factors’ role in sustainability in Indonesia fills this gap. We investigate the relationship between board diversity and sustainability through the following stages. First, we obtain information on board diversity, including the gender representation of the sample companies listed on the Indonesia Stock Exchange (IDX). Second, we gather information about the extent to which these companies follow sustainability practices, including the level of disclosure in their sustainability reports and their spending on these important issues. Third, we examine the relationship between board diversity and sustainability from the perspective of both descriptive and spending disclosures. This relationship is tested empirically utilizing the relevant statistical analysis, descriptive statistics, Pearson’s correlation, and multiple regressions. Sustainability disclosure in Indonesia’s best companies is still relatively low, with an average of 17.49 points out of the 83 points listed in the GRI standards. Of the eight dimensions of board diversity considered to be related to sustainability, only nationality and education level of directors are positively and significantly related to

Board Diversity and Sustainability: Indonesian Evidence

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sustainability disclosure. Meanwhile, gender, age, and independent directors are negatively and significantly associated with sustainability disclosure. The results also show that board education level is positively related to sustainability spending and gender has a negative and significant relationship with sustainability spending. We further find that the amount spent on sustainability initiatives does not match the level of sustainability disclosure. This study contributes to and broadens the scope of the extant literature on corporate governance and sustainability (Adeniyi & Fadipe, 2018; Anazonwu et al., 2018; Ararat et al., 2010; Bear et al., 2010; Bing & Amran, 2017; Harjoto et al., 2015; Juwita & Honggowati, 2022; Li & He, 2021; Margaretha & Isnaini, 2014) given that our investigation not only examines the relationship of board diversity and sustainability disclosure items but also links it to sustainability spending. Unlike the existing studies, our research focuses on the best practices of companies to validate their commitment to corporate governance and sustainability. Our paper is organized in this sequence. Following the introduction, we review the selected literature and develop hypotheses. Section 3 appraises the research methodology, followed by presentations of the results and discussion in Sect. 4. Section 5 summarizes our findings, highlights our contributions, and outlines our research limitations. We also provide this section with directions for future inquiries and suggest the potential implications of our study. References are provided afterward.

2 Literature Review and Hypothesis Development Corporate governance and sustainable development are two agendas of the United Nations which require global cooperation through their adoption and integration into business strategies and practices. Our study examines these two materialized topics, known in the business world as corporate governance and sustainability. This section reviews the literature relating to these two issues.

2.1

Corporate Governance

The business world’s attention to the importance of good corporate governance not only represents moral awareness as a reflection of being a good corporate citizen but also, more importantly, is based on valuable lessons learned from major corporate scandals (Fajri, 2016). The case of Enron Corporation, which went bankrupt due to weak corporate governance, resulting in fraud cases that ultimately destroyed this giant company, is a good example. The literature has documented how this “American Giant” lost market confidence, causing its stock price to drop drastically before being declared bankrupt (Segal, 2018).

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The experience at WorldCom, which contributed to the bankruptcy of the world’s largest Auditor Company Arthur Andersen, is another example (Tran, 2010). Likewise, the Subprime Mortgage case, which led to the world financial crisis of 2008 (Pritchard, 2018 ), is still fresh in our minds. An investigation by the Financial Crisis Inquiry Commission demonstrates that such crises occur due to a lack of transparency and poor corporate governance in American financial institutions (Financial Crisis Inquiry Commission, 2011). Following these major cases, the business world has begun to pay more attention to corporate governance (Ferrero-Ferrero et al., 2015). In Indonesia, attention to the importance of corporate governance started in 1999, when the Financial Services Authority (OJK) and the organization that preceded it, namely, the Capital Market Supervisory Agency, began to socialize the implementation of Good Corporate Governance (GCG), and in February 2014, the OJK officially issued a Roadmap for Good Corporate Governance reflecting the ASEAN Corporate Governance Scorecard standards (Fajri, 2016). Researchers propose a variety of definitions for corporate governance. However, generally, it refers to the system through which companies are managed and controlled (Kocmanová et al., 2011a, Naciti et al., 2021) or a system of how a company is directed and controlled. According to Klírová (2001) in Kocmanova et al. (2011b), corporate governance is a key element in efforts to achieve efficiency, economic growth, and investor confidence. Therefore, corporate governance covers a variety of concerns that emerge from the relationship between company management, shareholders, and other stakeholders. As such, corporate governance can be analyzed based on different factors, including the issue of board diversity. Board diversity is the core of corporate governance theory because a good board provides direction for a company’s effective decision-making and proper strategic oversight (Djulic & Kuzman, 2013). The concept of board diversity has been defined and categorized in various ways in the corporate governance literature. At the beginning of the development of research on corporate governance, the notion of board diversity has been classified into two dimensions: the demographic dimension, which is observable, i.e., age, and the gender and cognitive dimension, which is unobservable, i.e. educational background (Ararat et al., 2010; Li & He, 2021). Other researchers classify board diversity into three different dimensions: personality, demographics, and traits (McGrath et al., 1995). In general, board diversity refers to the variation of human resources serving as board of directors who carry out the corporate governance function. The higher the level of diversity is, the better it is for corporate governance because high diversity provides a wider range of cognitive influence on companies (Ararat et al., 2010). Saidu et al. (2020) summarize the notion of board diversity in the dimensions of nationality, race, gender, education level, educational background, and experience. Our study explores the eight board diversity dimensions of size, gender, ethnicity, age, independence, level of education, country of education, and CEO duality.

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7

Sustainability

Since the 1990s, the issue of corporate sustainability has become one of the temporal issues that have developed and been discussed, especially regarding sustainability performance. The definition of sustainability is quite diverse, but generally, it is mentioned as the ability to sustain or endure changes in the business world (Sustainability, 2018). Given the absence of a consensus understanding, the term sustainability has been used interchangeably with several other terms, such as corporate social responsibility (CSR), eco-efficiency, and corporate philanthropy. However, when researchers employ these terms, they tend to signify different functions. Some focus on environmental factors alone, whereas others focus on social factors alone or even a combination of the two. In the last decade, a number of sustainability reporting guidelines have been issued by various institutions, and one of the most globally accepted guidelines for measuring corporate sustainability was issued by the Global Reporting Initiative (GRI), which includes the GRI Guidelines [G1] issued in 2000, followed by GRI-G2 in 2002, GRI-G3 in 2006, and GRI-G4 in 2013, which were later updated to the GRI standards in 2016. The 2016 GRI standards have also undergone a number of revisions. In 2018, the GRI updated GRI-303 to provide a holistic perspective on the organization’s impact on water resources and GRI 403 to provide Occupational Health and Safety Standards. In 2019, the GRI again updated GRI-207 to provide a standard on company management approach to taxes (Global Reporting Initiative, 2021). In October 2021, a new version of the GRI standards was published that is effective for 2023 sustainability reporting. Some significant changes to the 2021 GRI standards from the 2016 GRI standards include removing the core disclosure option so that more material topics are required to be reported and introducing sectorspecific reporting, i.e. the oil and gas sector standards (Global Reporting Initiative, 2021). The GRI standards measure sustainability based on three specific factors, namely, economic, environmental, and social sustainability. Furthermore, the GRI standards also list governance factors as part of the general disclosure requirements. This study examines sustainability in the context of the following factors: Economic and environmental, social, and governance (ESG) factors.

2.3

Board Diversity and Sustainability

Research investigating the relationship between board diversity and sustainability has been progressing slowly and is still in the early stage of discussion. In particular, this branch of research is still limited in many countries, including Indonesia. This section will review some of those studies. A number of studies have been conducted in several countries on the effect of board diversity on sustainability disclosure and

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have yielded mixed results. Nevertheless, most previous studies have reported positive results. The following studies have shown positive results. Zahid et al. (2020) examine the effect of boardroom gender diversity, the Malaysian Code of Corporate Governance MCCG, and company-specific characteristics on the sustainability disclosure of Malaysian companies. They reported a significant positive relationship between boardroom gender diversity and corporate social disclosure, with this relationship becoming even stronger after the implementation of the 2012 MCCG code. This conclusion is supported by Moses et al. (2020). They reviewed the previous literature on sustainability reporting practices to conceptually examine the relationship between board governance mechanisms and sustainability reporting quality in Malaysia. They found a positive relationship between the elements of board governance examined and sustainability reporting quality based on various theories. However, an earlier study by Bing and Amran (2017), who also reviewed the previous literature on the role of board diversity on the disclosure of materiality in sustainability reporting, found that this aspect was generally still relatively low in Malaysia and concluded that sustainability reporting is still in its early stages in Malaysia. In addition, Hu and Loh (2018) report a significant positive relationship between board governance and sustainability disclosure in Singapore. They find that larger board sizes and higher board independence positively impact the likelihood of sustainability reporting and the quality of sustainability reporting in Singapore. Meanwhile, board incentives can significantly increase the likelihood and quality of sustainability reporting. Furthermore, this result is supported by Biswas Pallab et al. (2018). They show that a higher board gender composition, higher board independence, and the presence of a sustainability committee tend to be associated with better social and environmental performance in Australian companies. This positive relationship also applies to the sub-dimensional social and environmental performance. Those studies are supported by Harjoto et al. (2015), who explore the impact of board diversity on the corporate social responsibility (CSR) performance of 1489 US companies from 1999 to 2011. They found that board diversity had a positive effect on CSR performance. Furthermore, Ibrahim and Hanefah (2016) also found a positive relationship between the level of disclosure of sustainability factors and the plurality of board members in Jordanian companies. Fuente et al. (2017) also support that Spanish companies with high board diversity are directly related to sustainability reporting. However, subsequent studies have reported mixed results. For example, Shamil et al. (2014) investigated the effect of board characteristics on the sustainability reporting of Sri Lankan listed companies. They documented that board size and multiple leadership have a significant positive effect on sustainability reporting, whereas boards with female directors have a negative effect on sustainability reporting. A study by Saidu et al. (2020) also produced mixed results. The study investigated the effect of board diversity on sustainability reporting in industrial goods companies listed on the Nigerian Stock Exchange from 2014 to 2018. They

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found that board diversity affected the level of sustainability reporting in Nigerian listed companies. Several cross-country studies have also reported mixed results. Buallay et al. (2022) examine the relationship between board gender diversity and the sustainability reporting of 2116 listed banks worldwide over 10 years (2007–2016). Descriptively, they found that board diversity was higher in low-debt and high-asset banks. Central America has the highest level of diversity, whereas Europe has the highest level of environmental and social diversity among other banks; furthermore, the level of governance is highest in Australia. The results of the regression analysis also show that the diversity of board members has a positive and significant effect on the level of sustainability disclosure when the proportion of female board members ranges from 22 to 50% and a significant negative effect between member diversity and sustainability when the proportion of women on the board is more than 50%. Additionally, Naciti (2019) analyzes the effect of the composition of the board of directors as proxied by board diversity, board independence, and CEO duality on the company’s sustainability performance. This study analyzed data from 362 companies from 46 countries extracted from the Sustainalytics and Compustat databases. They found that companies with high levels of board diversity and a separation between the roles of chairman and CEO had higher sustainability performance. In addition, a higher number of independent directors results in lower sustainability performance. Some studies reported no relationship between board diversity and sustainability reporting. Zaid et al. (2020) empirically investigated the effect of board diversity on the level of the sustainability performance of listed companies in Palestine from 2013 to 2018. The results show limited evidence of this relationship characterized by positive and insignificant effects of national and gender differences on sustainability performance. Similarly, Adeniyi and Fadipe (2018) examined the effect of board diversity on sustainability reporting in Nigerian breweries. This study found that board gender diversity did not significantly affect sustainability reporting. They also found that the proportion of female board of directors was only 1%, especially in Champion Brewery Nigeria Plc. The maximum female board of directors in the sample company was only three. The results of this study are also in line with the results of the study conducted by Anazonwu et al. (2018), which examined the relationship between corporate board diversity and sustainability reporting in manufacturing companies listed on the Nigerian Stock Exchange. The results showed that the citizenship of the board members had no significant positive effect on sustainability reporting. In contrast, the proportion of female directors, non-executive directors, and several directors significantly affected sustainability reporting. While the effect of board diversity on sustainability disclosure in studies conducted in various countries shows mixed results, research investigating this stand of research in Indonesia is also still limited, and the results are also mixed. Research by Margaretha and Isnaini (2014) examines the effect of board diversity and the composition of directors on CSR performance and company reputation. They found that board diversity has no effect on CSR performance. Likewise, Juwita and Honggowati (2022) investigated the influence of board diversity on

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sustainability reporting in 70 Indonesian listed companies in the pre-pandemic period and 52 companies during the pandemic. They found that the presence of board diversity only affects sustainability reporting in the pre-pandemic period. Overall, the results of previous studies have not provided conclusive evidence on the effects of board diversity and sustainability in Indonesia. In addition to the limited number of previous studies and inconsistencies in the research findings, previous studies of the board diversity and sustainability literature have also been criticized for focusing heavily on the board gender diversity. This series of studies was conducted by, among others, Williams (2003), who showed that the proportion of female directors on the company’s board of directors had a positive effect on providing corporate assistance to the community. Similarly, Bear et al. (2010) found a positive relationship between gender diversity in the board of directors and the strength of the company’s social performance. Khidmat and Awan (2021) also linked the role of female directors and the firm’s innovation level and found a positive and significant relationship. Additionally, Mirza et al. (2020) studied the role of female directors in a competitive environment in China to create investment efficiency. They also found that female directors’ presence increased management’s monitoring role, thereby reducing agency problems and ultimately creating increased investment efficiency. However, Francoeur et al. (2008) found that only firms operating in complex environments benefited from a high proportion of female officers. They argue that women often bring new perspectives on complex problems, which can correct information bias in choosing strategies to solve them. Furthermore, Handajani et al. (2014) reported that female boards significantly negatively affected corporate social disclosure. They argue that the existence of female boards without adequate expertise or experience will not be able to formulate new perspectives on ethical and environmental issues let alone encourage altruistic behavior. In addition to the disproportionate focus of research on all dimensions of board diversity, the literature also indicates that progress toward achieving board diversity has been relatively slow because, so far, board diversity has not been on the company’s priority agenda, as it is considered to have less impact on the bottom line (Ahmed, 2016). Therefore, further research is needed to examine the role of board diversity in various aspects of corporate governance. This research fills this gap by focusing on the role of the eight board diversity factors in sustainability disclosure, namely the board size, gender, ethnicity, age, independence, level of education, country of education, and CEO duality.

2.4

Hypotheses

We use agency theory as a supporting theory to explain the relationship between board diversity and sustainability disclosure. Agency theory considers that managers can act opportunistically for his/her utility (Beji et al., 2020). Therefore, shareholders may impose monitoring measures to control the potential opportunistic behavior (Ferris et al., 2003; Pucheta-Martínez & Gallego-Álvarez, 2019). Diverse boards of

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directors can provide effective monitoring of companies through their pressure to motivate and coerce managers, for example to disclose additional information besides financial information to reduce information asymmetry and monitoring costs. Thus, agency theory supports the role of the board of directors in monitoring managers to comply with regulations and minimize agency conflicts with stakeholder interests (Mirza et al., 2020). Thus, we propose the following research hypotheses: H1: Each of the eight board diversity factors positively affects sustainability disclosure. H2: Each of the eight board diversity factors positively affects spending on sustainability activities.

3 Method 3.1

Sampling

There are currently over 800 companies listed on the IDX as of 10 September 2022 (Handayani, 2022).1 However, the population in this research is 50 listed companies that are included in the OJK’s 50 best company index.2 This index was issued by the Financial Services Authority (OJK) in collaboration with the Indonesia Institute for Corporate Directorship (IICD) to generalize corporate governance performance in Indonesia to the public. They selected 50 Indonesian public companies based on the implementation of the ASEAN Corporate Governance Scorecard. The selection of this index as our study population allows us to focus on companies with a good degree of corporate governance to avoid scale and selection bias when making inferences about its relationship to sustainability. The study sample is selected from companies that publish annual and sustainability reports and remain in the index for the entire study period. A total of 10 companies did not yield complete data and were therefore excluded from our research sample. We performed multivariate outlier diagnostics using the Mahalanobis distance on the remaining dataset and 41 observations were considered as multivariate outliers and then removed. Thus, the final research sample consisted of 38 companies with 239 firm-year observations for the sustainability disclosure test and 191 observations from 34 companies for the sustainability expenditure test.

1

The listed companies can be seen on the IDX website:https://www.idx.co.id/en-us/market-data/ stocks-data/list-of-stocks/ 2 http://crmsindonesia.org/publications/ini-dia-50-perusahaan-terbaik-versi-ojk/.

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Table 1 Measurement of variable No 1 2 3 4 5 6 7 8 9 10 11 12

Variable CSRI Expend Board Size Women Nation Age Independent Director Educational Level Educational Country CEO Duality Size ROA

Description Total disclosed items/total index Natural logarithm of total CSR expenditure Number of directors and commissioners on the board Number of women on board/board size Number of the nation (non-Indonesian citizenship) on board/ board size Number of ages (less than 40 years) on board/board size Number of independent directors and commissioners on board/board size Number of educational level (post graduate level) on board/ board size Number of the educational country (graduate from overseas) on board/board size Number of CEO duality on board Natural logarithm of firm total assets Return on asset

Type Ratio Nominal Nominal Ratio Ratio Ratio Ratio Ratio Ratio Dummy Nominal Ratio

Source: Own work

3.2

Variable Measurement

The variables observed in our study consisted of three categories: The dependent, independent, and control variables. The dependent variable of this study is sustainability. The dependent which is measured using two proxies consisting of sustainability disclosures (denotes Corporate Social Responsibility (disclosure) Index (CSRI)) and sustainability expenditures (denotes LnExpend). The independent variable in this study is board diversity. Board diversity is measured using eight dimensions: Board size, gender (denotes Women), ethnicity (denotes Nation), age, independence, level of education, country of education, and CEO duality. The control variables are company size (denotes LnSize), and performance variable, measured by return on assets (ROA). The measurement of the variables is presented in Table 1.

3.3

Data

The process of collecting data for this research was carried out in two stages. First, we collect board diversity and control variable data from sample companies. Second, we collect information on the extent to which these companies follow sustainability practices, which includes the level of disclosure in their sustainability reports and the amount spent on sustainability activities. The data come from two main sources: Financial reports and sustainability reports. Board diversity and control variable data are drawn from financial reports, whereas sustainability disclosure and sustainability

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spending data are collected from sustainability reports. We perform content analysis manually on sustainability reports to extract sustainability disclosure data based on 83 items of the 2016 GRI standard consisting of economic (GRI-200), environmental (GRI-300), social (GRI-400) and governance factors. The Global Reporting Initiative (GRI) standard is a globally accepted framework for measuring global corporate disclosures on economic, environmental, and social performance (Chen & Bouvain, 2014). As such, GRI is considered the most relevant institution for sustainability reporting (Moneva et al., 2006). Ultimately, the GRI standard has become the world’s leading framework used by researchers in measuring corporate sustainability disclosures (e.g., in Fuente et al., 2017; Juwita & Honggowati, 2022; Zuraida et al., 2018).

3.4

Data Analysis

This study examines the relationship between board diversity and sustainability from the perspective of both sustainability disclosure and sustainability spending. These relationships were tested using descriptive statistics, Pearson correlations, and multiple linear regressions. The relationship between board diversity and sustainability disclosure (CSRI) is analyzed in Model (1), and the relationship between board diversity and sustainability expenditure (Expend) is analyzed in Model (2). CSRI = a þ β1 BoardSize1 þ β2 Women2 þ β3 Nation3 þ β4 Age4 þ β5 IndDirector5 þ β6 EduLevel6 þ β7 EduCountry7 þ β8 Size8 þ β9 ROE9 þ ε

ð1Þ

Expend = a þ β1 BoardSize1 þ β2 Women2 þ β3 Nation3 þ β4 Age4 þ β5 IndDirector5 þ β6 EduLevel6 þ β7 EduCountry7 þ β8 Size8 þ β9 ROE9 þ ε

ð 2Þ

Before running the regression models, in addition to the Mahalanobis Distance test, we also performed several classical assumption tests. We used a graphical plot for the normality test. For multicollinearity diagnostics, we used the Pearson correlation test and variance inflation factor test, and no multicollinearity was detected. We also used robust regression command in Stata to control for heteroscedasticity.

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Table 2 The statistical description of the sample data Variable CSRI BoardSize Women Nation Age IndDirector EduLevel EduCountry CEODuality LnSize ROA Expend

N 239 239 239 239 239 239 239 239 239 239 239 191

Minimum 0 6 0 0 0 .0000 .1111 .0833 .0000 13.6135 –.1993 0

Maximum 66 23 .4285 .7272 .0666 .5 1 1 .0000 20.9832 .2860 599,717

Mean 17.4895 14.4853 .1260 .1551 .0019 .2072 .6125 .6077 .0000 17.9838 .0459 56,761.23

Std. Deviation 15.9228 3.5121 .1008 .1989 .0105 .0668 .2194 .2121 .0000 1.2970 .0632 102,720.8

This table illustrates the descriptive statistics of variables. All variables are defined in Table 1 Source: Own work

4 Results and Discussions 4.1

Descriptive Statistics

Table 2 shows the descriptive statistics for the observed variables. It shows descriptive statistics for the board diversity variable. The mean score of the board size is 14.48 with a standard deviation of 3.51, and the range of board members is from 6 to 23 members in one company. The literature suggests that there is no universal consensus on what is a good size for the board of directors. However, it is recognized that many board members can be ineffective because each board member cannot participate optimally. Therefore, other studies suggest that small is better for board sizes. The literature also states that the average board size is between 9.2 members, ranging from 3 to 31 people in one company (Farnham, 2022). Thus, the sample companies are still within reasonable limits regarding board size. This result is in line with the expectation that the sample companies included in the OJK 50 best company index that have been screened based on excellence in corporate governance have a good level of diversity in their board composition. Furthermore, Table 2 shows the mean values for the other board diversity dimensions: gender, nation, age, independent director, education level, and country of education. The mean value for each dimension is significantly low, with each score below 1%. This shows that not all aspects of governance have been implemented in the sample companies, even though they have been included in the OJK version of best companies. Table 2 also shows that there is no CEO duality in the sample companies, which means that there is no CEO who also serves as chairman of the board of directors. This is not surprising because Indonesian regulation on good corporate governance

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prohibits CEO duality. Since CEO duality data are not available, we excluded this variable from further analysis. The literature is not clear about the merits and demerits of CEO duality. Some have commented that CEO duality can be a mediator between management and the board of directors, thereby minimizing information asymmetry. Agency theory may support firms to do so to reduce agency costs associated with the separation of ownership and control. However, others have pointed out that CEO duality can create a fundamental conflict of interest. Agency theory can resist combining two roles in one person, which gives the CEO too much power and reduces the board of directors’ power. This argument suggests that while there is some reason to believe in the effectiveness of CEO duality, there is a potential risk of abuse of power to the detriment of stakeholders’ interests. Therefore, the absence of CEO duality in the sample companies indicates good corporate governance. Table 2 also shows the descriptive statistics for sustainability disclosure. The average score of sustainability disclosure as measured by the GRI standards is 17.4895, with a standard deviation of 15.9228. This shows that sustainability disclosure in the sample companies is still relatively low, with an average of 17.49 points from 83 points recorded in the GRI standards. This score is quite low compared to that reported by Juwita and Honggowati (2022) before the pandemic was 0.317 and during the pandemic was 0.385. Furthermore, Yarram and Adapa (2021) reported significantly higher sustainability disclosures of 42.5%. We also run a separate descriptive statistical test for sustainability spending; we obtain an average value of 56,761.23 (in millions of Rupiah) and further investigate which company discloses information about sustainability spending. We find that 48 observations (20% of the total sample) from 16 companies (42% of the total sample) did not report sustainability expenditures. In addition, we investigate these companies in terms of their sustainability disclosures, and we find that, on average, their level of disclosure was 14.2708, which is only slightly below the full sample average of 17.4895 points. This indicates that the level of disclosure about expenditures is not in line with the level of disclosure about sustainable activities. It seems that most companies tend to disclose their sustainability activities, but the amount of Rupiah that they have spent on these activities was still relatively small, so they did not disclose it. This shows that some of the disclosures are still descriptive elaborations in nature. As such, they may deliberately choose to engage in activities that do not impact expenses or they may simply use sustainability disclosures for “cosmetic” purposes rather than giving away a portion of their company’s revenue.

4.2

Pearson Correlations

The results of the Pearson correlation test are reported in Table 3. The table shows that CSRI has a positive and significant correlation with CSR spending, nationality and educational level of directors and; negative and significant correlation with

CSRI 1.0000 0.4123*** (0.0000) 0.0410 (0.5285) –0.2285*** (0.0004) 0.1337* (0.0390) –0.1651*** (0.0106) –0.0516* (0.0190) 0.1666** (0.0099) 0.1111 (0.0864) 0.0659 (0.3100) 0.2363*** (0.0002)

0.1071 (0.1404) –0.3886*** (0.0000) –0.2006** (0.0054) –0.2482*** (0.0005) 0.0738 (0.3104) 0.3704*** (0.0000) –0.0378 (0.6039) 0.2506*** (0.0005) 0.3331*** (0.0000)

1.0000

LnExpend

0.0180 (0.7820) 0.2637*** (0.0000) 0.1493* (0.0209) 0.0750 (0.2479) 0.0927 (0.1532) 0.2015** (0.0017) 0.6476*** (0.0000) –0.1831** (0.0045)

1.0000

Board size

0.1713** (0.0080) 0.0405 (0.5329) 0.0991 (0.1267) –0.1528* (0.0181) 0.1543* (0.0170) 0.0091 (0.8888) –0.2804*** (0.0000)

1.0000

Women

0.0701 (0.2807) –0.0278 (0.6695) –0.1715** (0.0079) 0.5759*** (0.0000) –0.0989 (0.1272) –0.1251 (0.0533)

1.0000

Nation

–0.0079 (0.9031) –0.2192*** (0.0006) –0.1301* (0.0446) –0.0883 (0.1736) –0.1103 (0.0888)

1.0000

Age

Note: This table reports Pearson correlations between variables. All variables are defined in Table 1 *p < 0.05, **p < 0.01, ***p < 0.001 Source: Own work

ROA

Edu country LnSize

Ind director Edu level

Age

Nation

Women

BoardSize

Stats CSRI LnExpend

Table 3 Pearson’s correlation

–0.0079 (0.9031) 0.0029 (0.9643) 0.1346* (0.0376) –0.0557 (0.3915)

1.0000

Ind director

0.2053** (0.0014) 0.1443* (0.0257) –0.0237 (0.7157)

1.0000

Edu Level

0.0212 (0.7442) –0.1115 (0.0854)

1.0000

Edu country

–0.1794** (0.0054)

1.0000

LnSize

1.0000

ROA

16 Z. Zuraida and S. Musnadi

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gender, age, and independent directors. Meanwhile, sustainability spending has a negative and significant correlation with gender, nationality, and age of directors. This mixed relationship is consistent with previous research conducted by Juwita and Honggowati (2022). Furthermore, the table shows that all correlation values are within a reasonable range or under 0.80 except for the correlation between company size and gender of the board of directors of 0.8888 but this figure is not significant, indicating that multicollinearity issues are not detected in this study (Hartmann & Carmenate, 2020). This is reinforced by VIFs results, which are below the acceptable limit of 10 for all variables (Hartmann & Carmenate, 2020).

4.3

Regression Results

Table 4 shows the results of two regression models to examine the relationship between board diversity and the two proxies of the dependent variables in this study, namely, sustainability disclosure and sustainability expenditure. We use eight dimensions to measure board diversity. However, because CEO duality was not found in the sample companies of this study, we regressed only seven proxies from Table 4 Regression results

Variable BoardSize Women Nation Age IndDirector EduLevel EduCountry LnSize ROA N Intercept F (9, 229) F(9, 181) R-Square Adjusted R-Square RMSE Mean VIF

Predicted sign + + + + + + + + +

Model 1 CSRI1 Estimates ( p-value) –.4297 (0.262) –25.7601* (0.011) 23.0033*** (0.001) –136.5990* (0.025) –35.6616** (0.008) 13.0563* (0.012) –2.7892 (0.688) 2.2644* (0.017) 56.4748** (0.004) 239 –18.5780 (0.207) 8.1400*** (0.0000) 0.2003 16.88% 14.5160 1.58

Model 2 Expend2 Estimates ( p-value) .0234 (0.592) –3.9254*** (0.000) .0659 (0.911) –17.3719 (0.102) 1.4122 (0.516) 1.6304** (0.002) –.2447 (0.635) .2594** (0.042) 7.3852** (0.004) 191 4.0842* (0.035) 13.77*** (0.000) 0.3848 35.41% 1.2568 1.71

Model 1: To test hypothesis 1; Model 2 to test hypothesis 2. p value in parentheses, *p < 0.05, **p < 0.01, ***p < 0.001 Source: Own work

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board diversity, namely, the board size, gender, ethnicity, age, independence, level of education, and country of education, with two control variables, namely size and ROA. Model 1 in Table 4 shows that only the nationality and education level of the board of directors have a positive and significant relationship with sustainability disclosure. In contrast, the gender, age, and independent directors have a negative and significant relationship with sustainability disclosure. In model 2, only board education level has a positive relationship with sustainability spending, whereas gender has a negative relationship with sustainability spending. In addition, in both models, Size and ROA, as our control variables, show a positive and significant relationship with sustainability disclosure and sustainability spending. The two models do not show a significant relationship between the other dimensions of board diversity and the dependent variable proxies. The results of this study is generally mixed and in accordance with previous studies such as Shamil et al. (2014). The results also contradict predictions provided by agency theory which supports a positive relationship between board diversity and sustainability. The inconclusive research results may be affected by the limited disclosures made by the company in the diversity of directors and in sustainability, including disclosures for sustainability expenditures. Low disclosure affects the effectiveness of disclosure and any material effect on the relationship between corporate governance and sustainability. This leads to our policy recommendations for more excellent corporate governance and sustainability disclosure.

5 Conclusions We find that the companies included in our sample have demonstrated a relatively good level of governance, as indicated by the level of diversity of their directors (Board diversity). Despite the excellent level of board diversity, sustainability disclosure in sustainability reports, as measured by GRI standards, is still relatively low. Moreover, the disclosure on sustainability expenditure is much lower. The results of the study show that nationality and level of education have a positive relationship whereas gender, age, and independent directors have a negative relationship with sustainability disclosure. In addition, board education level has a positive relationship but gender has a negative relationship with sustainability spending. Meanwhile, Size and ROA have a positive relationship with sustainability disclosure and sustainability spending. This study has three limitations. The use of content analysis requires several interpretations from the researcher, thus subject to increased human error. Future researchers can improve the quality of research data by using databases for automated data collection methods. Second, this study focuses on the OJK 50 best companies that have advantages in corporate governance; therefore, the results of this study may not represent companies with weaknesses in corporate governance.

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Research with broader sample will provide future researchers with more generalizable research results. Third, our research only focuses on eight dimensions of board diversity. As Zaid et al. (2020) suggested, to explore the strong implications of board diversity on sustainability, multiple measures are required to provide accurate results. Therefore, future research could use broader dimensions of diversity, such as CEO reputation, CEO expertise, and CEO domination. Acknowledgment The authors thank Lembaga Penelitian dan Pengabdian kepada Masyarakat, Universitas Syiah Kuala, for funding this research project. The authors also thank Prof. Mehtab Eklund and other participants of the 38th EBES Conference for their insightful comments and suggestions on the initial version of this paper. The authors further thank the reviewer of our conference proceeding paper for his/her constructive comments.

References Adeniyi, S. I., & Fadipe, A. O. (2018). Effect of board diversity on sustainability reporting in Nigeria: A study of beverage manufacturing firms. Indonesian Journal of Corporate Social Responsibility Environmental Management, 1, 43–50. Ahmed, A. (2016). Women on corporate boards: Determinants and consequence. Doctoral, Griffith University. Anazonwu, H. O., Egbunike, F. C., & Gunardi, A. (2018). Corporate board diversity and sustainability reporting: A study of selected listed manufacturing firms in Nigeria. Indonesian Journal of Sustainability Accounting Management, 2, 65–78. Ararat, M., Aksu, M. H., & Tansel Cetin, A. (2010). The impact of board diversity on boards’ monitoring intensity and firm performance: Evidence from the Istanbul Stock Exchange. In The second Asian roundtable on corporate governance. Bear, S., Rahman, N., & Post, C. (2010). The impact of board diversity and gender composition on corporate social responsibility and firm reputation. Journal of Business Ethics, 97, 207–221. Beji, R., Yousfi, O., Loukil, N., & Omri, A. (2020). Board diversity and corporate social responsibility: Empirical evidence from France. Journal of Business Ethics, 173, 133–155. Bing, N. S., & Amran, A. (2017). The role of board diversity on materiality disclosure in sustainability reporting. Global Business Management Research, 9, 96–109. Biswas Pallab, K., Mansi, M., & Pandey, R. (2018). Board composition, sustainability committee and corporate social and environmental performance in Australia. Pacific Accounting Review, 30, 517–540. Buallay, A., Hamdan, R., Barone, E., & Hamdan, A. (2022). Increasing female participation on boards: Effects on sustainability reporting. International Journal of Finance & Economics, 27, 111–124. Chen, S., & Bouvain, P. (2014). Adoption of the Global Reporting Initiative by FT500 firms: Overcoming the liability of foreignness. In Y. Temouri & C. Jones (Eds.), International business and institutions after the financial crisis. Springer. Djulic, K., & Kuzman, T. (2013). Women on corporate boards in Bosnia and Herzegovina, FYR Macedonia, and Serbia. World Bank. Fajri, M. M. P. (2016). Peran Penting KPK dalam Implementasi GCG. Annual Report.id [Online]. Available http://annualreport.id/dialog/mohamad-fajri-mp-peran-penting-kpk-dalamimplementasi-gcg. Farnham, K. (2022). Board size: Can smaller boards make a more significant impact? Diligent [Online], Strategy & Leadership. Diligent.

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Margaretha, F., & Isnaini, R. (2014). Board diversity and gender composition on corporate social responsibility and firm reputation in Indonesia. Jurnal Manajemen dan Kewirausahaan, 16, 1–8. Mcgrath, J. E., Berdahl, J. L., & Arrow, H. (1995). Traits, expectations, culture, and clout: The dynamics of diversity in work groups. In S. E. Jackson & M. N. Ruderman (Eds.), Diversity in work teams: Research paradigms for a changing workplace. American Psychological Association. Mirza, S. S., Majeed, M. A., & Ahsan, T. (2020). Board gender diversity, competitive pressure and investment efficiency in Chinese private firms. Eurasian Business Review, 10, 417–440. Moneva, J. M., Archel, P., & Correa, C. (2006). GRI and the camouflaging of corporate unsustainability. Accounting Forum, 30, 121–137. Elsevier. Moses, E., Che-Ahmad, A., & Abdulmalik, S. O. (2020). Board governance mechanisms and sustainability reporting quality: A theoretical framework. Cogent Business & Management, 7, 1771075. Naciti, V. (2019). Corporate governance and board of directors: The effect of a board composition on firm sustainability performance. Journal of Cleaner Production, 237, 117727. Naciti, V., Cesaroni, F., & Pulejo, L. (2021). Corporate governance and sustainability: A review of the existing literature. Journal of Management Governance, 1–20. https://doi.org/10.1007/ s10997-020-09554-6 Pritchard, J. (2018). The mortgage crisis explained: What caused the mortgage crisis? The Balance [Online]. Available: https://www.thebalance.com/mortgage-crisis-overview-315684. Pucheta-Martínez, M. C., & Gallego-Álvarez, I. (2019). An International approach of the relationship between board attributes and the disclosure of corporate social responsibility issues. Corporate Social Responsibility and Environmental Management, 26, 612–627. Saidu, M., Gold, N., & Aifuwa, H. O. (2020). Board diversity and sustainability reporting: Evidence from industrial goods firms. Journal of Varna University of Economics, 64, 377–398. Segal, T. (2018, September 20). Enron scandal: The fall of a wall street darling. Investopedia [Online]. Available: https://www.investopedia.com/updates/enron-scandal-summary/. Shamil, M. M., Shaikh, J. M., Ho, P.-L., & Krishnan, A. (2014). The influence of board characteristics on sustainability reporting: Empirical evidence from Sri Lankan firms. Asian Review of Accounting, 22(2), 78–97. Sustainability. (2018). Sustainability: Can our society endure? Available: https://sustainability.com/ sustainability/. Tran, M. (2010). WorldCom accounting scandal. The Guardian [Online]. Williams, R. J. (2003). Women on corporate boards of directors and their influence on corporate philanthropy. Journal of Business Ethics, 42, 1–10. Yarram, S. R., & Adapa, S. (2021). Board gender diversity and corporate social responsibility: Is there a case for critical mass? Journal of Cleaner Production, 278, 123319. Zahid, M., Rahman, H. U., Ali, W., Khan, M., Alharthi, M., Qureshi, M. I., & Jan, A. (2020). Boardroom gender diversity: Implications for corporate sustainability disclosures in Malaysia. Journal of Cleaner Production, 244, 118683. Zaid, M. A., Wang, M., Adib, M., Sahyouni, A., & Abuhijleh, S. T. (2020). Boardroom nationality and gender diversity: Implications for corporate sustainability performance. Journal of Cleaner Production, 251, 119652. Zuraida, Z., Houqe, M. N., & Van Zijl, T. (2018). Value relevance of environmental, social and governance disclosure. In S. Boubaker, D. Cummings, & D. Nguyen (Eds.), Research handbook of finance and sustainability. Edward Elgar.

The Impact of Audit Committee Composition on Corporate Risk Disclosure in Emerging Countries Musa Uba Adamu and Irina Ivashkovskaya

Abstract The study examines corporate risk disclosure and the audit committee’s effect on corporate risk disclosure in emerging African economies. The study analyzes 42 financial and non-financial firms listed on the Nigerian and Johannesburg stock exchanges. We use manual content analysis to ascertain the risk information reported by firms. Besides, we use the regression method to ascertain the relationship between audit committee composition and corporate risk disclosures. According to the findings, operational risks are more frequently disclosed by firms than strategic and environmental risks. However, the greater visibility of non-monetary risk information, as well as historical and positive news, has diminished the importance of risk information, as monetary, future, and negative news are increasingly important for stakeholders’ decision-making. Moreover, the results indicate that the audit committee size is negatively connected with corporate risk disclosure, implying that risk disclosure practices decrease as people in the audit committee increases. Additionally, an audit committee composes of a greater number of independent directors, as well as an audit committee chairs by an independent director, tends to encourage firms to disclose additional risk information. In contrast, the risk disclosure policies are unaffected by the presence of non-executive members on the committee or the frequency with which members meet. Keywords Content analysis · Audit committee · Risk management disclosure · Corporate governance · Corporate risk disclosure

1 Introduction Corporate managers are required by national regulation to prepare financial statements and distribute them to their shareholders for informed decision-making. The accuracy of the information must be validated by an independent auditor, who is in M. U. Adamu (✉) · I. Ivashkovskaya School of Finance, National Research University Higher School of Economics, Moscow, Russia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_2

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charge for reviewing the financial statement information and expressing his professional judgment on the status of the business’s affairs (Adamu & Ivashkovskaya, 2022). The purpose of getting the professionals’ view is to guarantee that the financial statements are of a high quality, which will instill confidence in investors (Adamu, 2021). Nonetheless, stakeholders (e.g. Shareholders, financial analysts) have raised substantial concerns about the quality of corporate reporting in recent decades, as well as the auditor’s opinion included in the corporate reporting. The primary reason that stakeholders began to distrust the quality of corporate reporting was the large number of corporate manager scandals, which ultimately result in the bankruptcy of various organizations, including high-profile ones worldwide (Rajab & Handley-Schachler, 2009). In response to the issue, the Institute of Chartered Accountants in England and Wales (ICAEW) published a first discussion paper in 1998 which underlined the necessity of corporate risk disclosure and recommended its inclusion in corporate reporting (Grassa et al., 2020). Moreover, the 2008–2009 financial crises, which resulted in a global economic slowdown, have prompted many stakeholders to boost their advocacy for better corporate governance and risk disclosure practices (Al-maghzom et al., 2016). It is widely believed that prosperous corporate governance cannot be achieved unless the business has a robust internal control structure and satisfactory corporate transparency. Therefore, corporate managers are urged to implement a robust internal control system and be open about their risk and risk management practices in order to achieve effective corporate governance (Vergauwen et al. (2009). Furthermore, it is critical to recognize that, in recent years, corporate risk disclosure has evolved into one of the fundamental aspects of business risks (Linsley et al., 2006), as the release of supplementary information promotes greater investor confidence and company transparency. Likewise, the adoption of IFRS 7, BASEL II, and corporate governance reform by authorities has all played a larger role in increasing corporate risk transparency (Al-maghzom et al., 2016). May be that is why Ivashkovskaya and Nadezhda (2009) also say that an effective corporate governance framework is the engine of economic recovery. Meanwhile, a growing number of academics from a variety of countries have expressed an interest in risk disclosure research (e.g., Adamu, 2013, 2021; Adamu & Ivashkovskaya, 2022; Al-maghzom et al., 2016; Barakat & Hussainey, 2013; Deumes & Knechel, 2008; Elamer et al., 2017; Elghaffar et al., 2019; Khlif & Hussainey, 2016; Neifar & Jarboui, 2018; Rajab & Handley-Schachler, 2009; Solomon et al., 2000). So, they looked into risk disclosure procedures around the world and the factors that influence them. Besides, the importance of risk disclosure cannot be overstated, as previous research has established a link between risk disclosure and high propensity to cut agency costs that may come from the asymmetry of risk information (Rajab & Handley-Schachler, 2009). In addition, a company that discloses its risk profile is more likely to develop efficient risk management systems (ICAEW, 2002), enhance its brand (Rajab & Handley-Schachler, 2009), and safeguard investors (Linsley & Shrives, 2006), all of which will help to regain stakeholders’ trust. Furthermore, the literature has found a number of variables that determine the degree to which risk disclosure practices are implemented. Numerous corporate governance characteristics, such as audit quality, board

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structure, and ownership composition, were found to be significant in describing the amount to which corporations report their risk. Nonetheless, the audit committee functions as a monitoring tool under the corporate governance code, and firms who use it often pay less agency cost as a result of improved disclosure quality (Forker, 1992). Despite this justification, little study has been done to look at how audit committee characteristics relate to company transparency (Albitar, 2015). Forker (1992) contends that the qualities of an audit committee might enhance voluntary corporate disclosure procedures. The aforementioned assertion inspired various researchers (such as Adamu, 2022; Al-maghzom et al., 2016) to investigate the impact of audit committee factors on risk disclosure, and their findings suggest that the audit committee is a factor influencing the risk disclosed by enterprises. The audit committee variables, however, have not been thoroughly examined in developing African countries. Even if a recent study by Adamu (2022) has been conducted, it is solely applicable to the Nigerian banking industry. However, this study aims to increase the sample size by incorporating non-financial organizations. This is congruent with Kang & Gray (2019). A different result could be reached by increasing the sample size and conducting a cross-country analysis. This study contributes to the governance and risk disclosure literature by evaluating risk disclosure practices in emerging economies in Africa, particularly Nigeria and South Africa, during a 5-year period (from 2014 to 2018). In terms of the research’s real-world consequences, we pursue to evaluate the risk disclosure practices of listed firms in Nigeria and South Africa, as well as the influence of audit committee characteristics on corporate risk disclosure. It looks like it would be beneficial for policymakers, regulators, company reporting preparers, and consumers. The report encourages regulators to enhance corporate risk disclosure transparency by guaranteeing rigorous adherence to sound corporate governance principles via auditing committee systems. The paper is structured as follows: The first portion has an introduction; the second section contains pertinent literature and hypotheses. The third section discusses the sample, the data, and the variables’ measurement. We discuss the results in the fourth section. The fifth section includes a conclusion, a limitation, and a recommendation for future research.

2 Literature Review 2.1

Risk and Risk Disclosure

While technical innovation and the emergence of globalization have aided in the growth of numerous firms throughout the world in recent decades, they have also increased the risk exposure of whole corporate settings (Adamu, 2021). This risk exposure does not have to be systematic, but can also be unsystematic (Adamu & Ivashkovskaya, 2021). As a result, numerous high-profile enterprises apparently failed in a variety of jurisdictions during the late 1990s and early 2000s (Rajab & Handley-Schachler, 2009). Corporate failures were not unrelated to managers who

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were embroiled in significant corporate financial scandals (Adamu, 2013). Numerous stakeholders were alarmed by these instances, and as a result, accounting associations and shareholders forced businesses to publish risk information (ICEAW). To address such instances, authorities have implemented stringent corporate governance policies (Ivashkovskaya & Nadezhda, 2009). Besides, the onset of the global financial crisis in 2007/2008 shook investors’ and other stakeholders’ confidence, intensifying the campaign for business risk disclosure. Regulators have strengthened corporate governance policies in response to stakeholders’ demand. Moreover, the standard-setters released IFRS 7, which would permit companies to publish the risk related to financial instruments. Regulators’ new policies have prompted numerous scholars to study firms’ annual reports, interim reports, and prospectuses in order to ascertain corporate risk disclosure and its determinants (Adamu, 2021). These researches were undertaken in both developed and underdeveloped countries. Risk information disclosure is crucial because it enables stakeholders to comprehend the nature of the company’s risk, its extent, and the risk management procedures in place. Meanwhile, because risk disclosure is necessary for informed decision-making, it is useful to understand how stakeholders define risk. For instance, the occurrence of a negative event was connected to several stakeholders’ earlier perceptions of risk (Linsley & Shrives, 2006). However, this notion was deemed obsolete in the modern era, as a corporation’s opportunities and prospects are also considered to be part of the company’s risk (Rajab & HandleySchachler, 2009). Risk Disclosure can only be understood and deemed adequate “if the reader is informed of any opportunity or prospect, or of any hazard, danger, harm, threat, or exposure, that has already impacted or may impact the company, or of the management of any such opportunity, prospect, hazard, danger, harm, threat, or exposure” (Linsley & Shrives, 2006). Readers can better grasp the firm’s risk profile and the corporate executives’ risk management approach with the disclosure of this kind of information. Despite a variety of compelling reasons for corporate risk disclosure, many company executives remain hesitant to share their risk data (Adamu, 2021). This is not unrelated to the negative consequences of corporate risk disclosure. Numerous countries appear to have no regulations governing the annual report’s disclosure of risk information. As a result, enterprises’ risk communication is inconsistent and ambiguous (Lajili et al., 2012). Moreover, the framework suggested by the Institute of Chartered Accountants in England and Wales in 1999 was used by the bulk of earlier academics to develop the risk disclosure index. Typically, researchers adjust the framework to account for the country’s features (culture, religion, environment, etc.), as well as its norms and regulations. Additionally, researchers now frequently employ the content analysis method when studying narratives from annual reports of companies. Despite the subjective nature of the risk information collection phase, content analysis continues to be the best method for risk disclosure research (Vandemaele et al., 2009). This technique has made it possible for academics to study the information disclosure practices of firms in corporate reporting. However, earlier research from different nations has highlighted their concerns regarding how businesses provide information about risks. For instance, the majority of earlier studies (e.g., Adamu &

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Ivashkovskaya, 2021; Linsley & Shrives, 2006) reported that organizations often communicated positive news far more often than negative news. This current practice appears to contravene the interests of many stakeholders, as the publication of unfavorable news is more likely to influence their decision. Furthermore, earlier studies (Adamu, 2013, 2021) identify temporal horizon biases in which forwardlooking (future) risk disclosure is considerably outweighed by backward-looking (past) risk information. Future data is more important since stakeholders may use it to forecast the performance of the business. Likewise, the importance of the information may be extremely high, especially if stakeholders have access to quantitative (monetary) risk information. Nonetheless, the vast majority of risk information provided in corporate reporting is non-monetary in nature (Adamu, 2021; Lajili, 2009; Lajili et al., 2012; Rajab & Handley-Schachler, 2009). In light of the aforementioned claim, many researchers have come to the conclusion that the existing risk disclosure practice is insufficient to satisfy the demands of stakeholders.

2.2

Audit Committee Regulations in Nigeria

The fundamental legal framework for conducting business in Nigeria is established by the Company and Allied Matters Act (CAMA), which was passed in 2004 (Emeh & Ebimobowei, 2013). It is the key piece of legislation that regulates publicly traded firms’ financial reporting. For instance, the primary criteria for financial reporting by enterprises are outlined in part X1-Financial Statements and Audit. Financial statements are covered by sections 331–356 of CAMA 2004, while audits are covered by sections 357–369 (CAMA, 1990; Okolie, 2014). In addition to the CAMA, corporate reporting must abide by additional regulations such the International Accounting Standard (IAS), International Financial Reporting Standards (IFRS), and the local Accounting standard created by professional organizations in Nigeria (Adamu, 2022). The consistency and comparability of financial reporting are the main concerns of accounting standards. Accounting standards compliance was persuasive prior to the declaration of CAMA 1990, which is now a statute under Nigeria’s civilian government. However, after the implementation of CAMA 1990, businesses are now required to report financial information (Edogbanya & Kamardin, 2014). In addition, the Financial Reporting Council of Nigeria (formerly NASB) is tasked with promoting increased financial transparency in Nigerian businesses that complies with global best practices (Ekumankama & Uche, 2009). In order for firms to report on their financial performance, the Nigeria Accounting Standard Board (NASB) was founded in 1982 with the responsibility of creating and issuing accounting standards. However, following the adoption of IFRS by numerous businesses worldwide, the Nigerian government ordered all businesses to follow suit as of January 1, 2012. The Nigerian government’s motivation for the implementation of IFRS is to raise the standard of financial reporting so that stakeholders may utilize it to make decisions (Adamu, 2021). As a result, the national standard-setters changed their name from NASB to Financial Reporting Council of Nigeria (Edogbanya &

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Kamardin, 2014). Besides, corporate governance rules established by Nigerian authorities have also contributed to the advancement of the corporate reporting environment. Meanwhile, in 2003, the Nigerian Securities and Exchange Commission (SEC) published a code of best corporate governance standards, and Section 11 (a) of that code requires the creation of an audit committee for publicly traded companies in Nigeria (Miko & Kamardin, 2015). Similarly, Section 12(a) of the 2003 SEC law limits the number of executive directors to one and mandates that the audit committee’s membership must consist mostly of non-executive directors (Securities and Exchange Commission and Corporate Affairs Commission, 2003). A postconsolidation code of best practices was similarly published by the apex bank (CBN) in April 2006 in response to the 2005 banking consolidation in Nigeria. All banks are required under Section 5.3.12 of the Code to establish an audit committee as a permanent committee of their board of directors (Miko & Kamardin, 2015). It is noteworthy to remember that, in accordance with Section 8.1.4 of the code, ordinary shareholders representatives and non-executive directors must be chosen at the annual general meeting in order to serve on the committee (Adamu, 2022). Nonetheless, the maximum size of the committee has not been mentioned by the code.

2.3

Audit Committee Regulations in South Africa

The formation of audit committee to oversee the certain corporate activities in South African has been regulated. For example, the law mandates the formation of an audit committee for all South African listed firms, in accordance with King III’s report on the code of governance (Sellami & Fendri, 2017). The committee’s responsibility is to identify and manage financial risk in order to ensure the integrity of company reporting and financial controls. This is reflected in Section 71, Chapter 3 of the Company Act, which was implemented in 2008 and mandates public companies and state-owned enterprises to create an audit committee in order to raise accountability and transparency standards (Sellami & Fendri, 2017). Likewise, given the relevance of the Kings III report, the Johannesburg Stock Exchange has made it mandatory for all listed companies to comply. It is important to note that the aforementioned committee is a board of director’s subcommittee tasked with monitoring financial reporting and associated activities on the board’s behalf. According to the Act, the audit committee must be appointed by shareholders at each annual general meeting and must consist of at least three directors. The Act also outlines the responsibilities of audit committees, from which they must create a report that will be included in the annual financial statements and will describe how the audit committee carried out its tasks. Marx and Voogt (2010) summarize the audit committee roles as follows: One, making comments on the internal financial control, accounting procedures, and financial statements in any way the committee considers suitable. Secondly, the committee is also responsible for receiving and effectively addressing any issues or complaints about accounting processes, internal audit, financial controls, accounting records, and reporting, whether from within or

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outside the organization. Besides, Sellami and Fendri (2017) argue that, in order to strengthen corporate governance in South Africa through audit committee, King III makes the following recommendation: One, the audit committee is responsible for managing financial and non-financial risks that may affect the accuracy of the company’s external reporting. Two, the committee needs to look at how internal financial controls are designed and implemented, and three, the committee needs to assess how well the company’s finance department and chief financial officer are performing. Similarly, it is widely known that the Kings reports on corporate governance were periodically updated. The release of King IV on corporate governance also suggests that the audit committee be given more responsibilities. Thus, the audit committee must make specific disclosures: one, disclosure of how the audit committee evaluates and addresses material issues in corporate reporting; and two, whether the audit committee is satisfied with the auditor’s independence. Besides, it will be more fascinating if the committee members have knowledge of and expertise in financial reporting, internal audits, external auditing, and the industry in which the company operates (Marx & Voogt, 2010). In addition, according to Regulation 42 of the Act, at least one-third of the members of a company’s audit committee must have training or expertise in any of the following fields: finance, accounting, business, public affairs, law, corporate governance, or human resource management.

2.4

Theoretical Background

The relevant theoretical frameworks for examining corporate risk disclosure include agency and institutional theories (Lajili et al., 2020). As a result, corporate managers must be appointed by the company’s shareholders in accordance with the legislation so that they can regularly supervise the company’s business (Adamu, 2022). By designating shareholders as the principal and corporate management as the agents, these guidelines have justified the principal–agent relationship. The agency theory has provided a thorough explanation of how the connection between the agent and the principal will work. Conflicts of interest are unavoidable in practically any organizational environment, since each stakeholder group seeks to preserve its own interests. It is crucial to keep in mind that managers may promote pointless agency costs by hiding relevant information from stakeholders such as investors. In order to avoid potential agency costs brought on by information asymmetry, corporate executives are regularly required to communicate pertinent risk information. According to Lajili et al. (2020), giving sufficient information serves as a monitoring tool since it helps stakeholders align management incentives with the goal of maximizing firm value while lowering the cost of capital. Likewise, it may be possible to understand disparities in risk disclosure across national boundaries, legal systems, and institutional contexts by using institutional theory and sociopolitical research (Lajili et al., 2020). Institutional theory, which emphasizes the significance of identifying factors that influence organizational behavior, can be used to explain social pressures to comply with the standard and the quest of

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building or maintaining legitimacy (e.g., risk disclosure practice). The Central Bank of Nigeria (CBN)’s requirements, for instance, which require all publicly traded banks in Nigeria to compile their corporate reporting on a consistent schedule (December 31st), are one such example. Another item to think about is IFRS adoption. The implementation of IFRS and standardized calendars has increased stakeholder confidence in Nigeria.

2.5

Audit Committee and Risk Disclosure: Prior Studies

The audit committee of a business is a vital corporate governance measure that is responsible for ensuring that the company maintains an appropriate level of internal control and risk management. Given the audit committee’s oversight of risk management within the business, several researchers feel it may influence the risk information disclosed by the corporations. Their impact on risk disclosure, on the other hand, is significantly dependent on the audit committee’s composition. As a result, various academics felt forced to study the connection between risk disclosure and audit committee composition. Vandemaele et al. (2009), for example, investigated the risk committee’s (and risk manager’s) influence on risk information given by firms in 2006. The results of the content analysis and regression analysis reveal that there is no association between corporate risk disclosure and presence of a risk manager/committee. This illustrates that the existence of risk manager or an independent risk committee in an organization does not obligate corporate executives to give extra risk disclosure. This finding contradicts with that of Seta and Setyaningrum (2018), who studied the risk committee’s effect on the volume of risk information disclosed by Indonesian companies. After conducting a content analysis of 365 annual reports from various firms for the year 2015 and employing the regression method, the outcome suggests a statistically significant positive coefficient. This illustrates that including a risk committee generally results in an increase in the amount of risk information given by organizations. In a related study, Al-maghzom et al. (2016) examined how the structure of the audit committee affected the disclosure of risk in 12 publicly traded Saudi Arabian banks. They conducted a content study of 60 annual reports published between 2009 and 2013. The regression results indicate that there is a positive relationship between audit committee meetings and firm risk disclosure. This result supported the premise that increasing the frequency of audit meetings would encourage business executives to improve their risk disclosure practice. Nevertheless, the size of the independent directors and audit committee are not statistically significant. This was justified on the grounds that the committee’s size or the addition of an independent member to the audit committee would have no effect on how firms present risk information. Moreover, Ishtiaq et al. (2017) investigate the annual reports of 85 Pakistani listed companies for the years 2011–2016 in a related study. The results show that the audit committee has an impact on how a company discloses its risk. Additionally, the result demonstrates that the volume of risk information grows proportionately as

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meeting frequency increases. In a related study, Viljoen et al. (2019) investigate the influence of audit committee traits on firm risk disclosure in South Africa. After conducting a content analysis of 40 annual reports of the top companies listed on the JSE in 2011 and doing a regression analysis, the authors conclude that a risk officer and audit committee meetings influence firms to increase their risk disclosure. However, the sizes of the audit committee, the independent director’s independence, and his experience on the audit committee have no statistically significant impact on the amount of risk information disclosed by corporations.

2.6 2.6.1

Hypothesis Development Audit Committee Size

It is important to know that the audit committee is in charge of overseeing and making sure the company has a functioning internal control system. As a result, it has long been regarded as one of the most effective corporate governance tools. How committees are formed is one of the more recent research topics in risk disclosure. Furthermore, Forker (1992) asserted that audit committees can serve as a qualitycontrol tool for corporate disclosures, potentially lowering total agency costs. Firms that have an audit committee, a larger audit committee, and a composition of individuals with diverse knowledge bases are more likely to voluntarily share useful information. One of the elements affecting corporate transparency, according to a recent review of the literature, is the size of the audit committee. For instance, results from earlier studies (Achmad et al., 2017; Alshirah et al., 2021; Lawati et al., 2021) have established positive association between the audit committee size and the amount of risk disclosed by the company. In contrast, the other studies (Abdullah et al., 2017; Mukhibad et al., 2020) argue that audit committee size does not influence corporation to improve their risk disclosure practice. In light of the above inconsistent empirical conclusions, the following hypothesis was advanced based on agency theory prediction: H1: The size of the audit committee is positively connected with the disclosure of company risk.

2.6.2

Independent Member on the Audit Committee

Audit committees are being emphasized as a tool for reducing agency expenses and promoting corporate transparency (Forker, 1992). To guarantee excellent internal control and governance, businesses must establish an audit committee (Adamu, 2022). Additionally, the committee’s existence significantly affects how companies disclose information (Ho & Wong, 2001). The committee shall be held responsible for the delegation of authority provided to them by the board of directors and has a

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duty to safeguard the interests of the shareholders at the moment. The users of corporate reporting may benefit from enhanced business transparency if the audit committee’s critical members are independent directors empowered to control the amount of information withheld. As long as independent directors are involved, the audit committee is frequently self-contained. Agency theory states that due to the audit committee’s obligation to take investors’ interests into account when performing its tasks, the committee’s independence from senior management has a larger impact on addressing concerns about information asymmetry (Al-maghzom et al., 2016). The role of independent members in increasing corporate disclosure, on the other hand, has generated conflicting results in earlier empirical study. For example, erstwhile studies (Abdullah et al., 2017; Oliveira et al., 2011) observed a positive association between risk disclosure and the independence of the audit committee, while the other study (Lawati et al., 2021) reported an inverse relationship between the two variables. By contrast, Viljoen et al. (2019) discovered a non-significant correlation. However, the following hypothesis is presented in accordance with agency theory: H2: The presence of an independent director on the audit committee correlates positively with the disclosure of firm risks.

2.6.3

The Independent Chairperson of the Audit Committee

It is impossible to overstate the importance of the audit committee in maintaining effective internal control systems within the company (Al-maghzom et al., 2016). Despite the fact that the committee is a part of the corporate governance system, it has more of an impact on how firms communicate their information to different stakeholders (Ho & Wong, 2001). Despite the fact that the board of directors appoints the committee’s members, the committee’s level of independence from management is upheld by the way it is organized. The committee with a larger number of non-executive directors tends to be independent, which could have an impact on how much information a company reports about its risk (Adamu, 2022). Additionally, an audit committee with a committed chairperson tends to be more independent from needless management interference. Hence, users of corporate reporting typically benefit from increased firm openness since committee independence has the potential to restrict the quantity of information hidden (Ho & Wong, 2001). As the committee is obligated to take investors’ interests into account when performing its duties, agency theory claims that the audit committee’s independence from top management has a higher effect on reducing information asymmetry issues. It is crucial to realize that the committee’s primary responsibility is to guarantee that risk management is efficient, internal controls are in place, and corporate reporting is accurate (Al-maghzom et al., 2016). Nevertheless, earlier studies on the relationship between corporate risk disclosure and audit committee independence has yielded conflicting results. For example, Oliveira et al. (2011) found a positive association

The Impact of Audit Committee Composition on Corporate Risk Disclosure. . .

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between risk disclosure and the presence of an independent audit committee chairperson. As a result, the following theory is advanced: H3: Businesses disclose more risk information when the audit committee is chaired by an independent person.

2.6.4

Non-executive Member of the Audit Committee

The existence of an audit committee has a considerable impact on the extent to which corporate transparency is practiced (Adamu, 2022). Nonetheless, it is important to consider the committee’s makeup since if the business selects senior non-executive members, they will use their influence to change information that has been withheld in order to improve corporate transparency (Ho & Wong, 2001). Additionally, experts (Adamu, 2022; Al-maghzom et al., 2016) urged businesses to expand the size of their audit committees in order to strengthen their risk disclosure procedures. This method may assist in mitigating any information asymmetry caused by information concerns. Throughout this section, the term “independent” refers to the committee’s non-executive directors. Prior empirical research, however, has produced inconsistent findings regarding audit committee independence and corporate disclosure. Oliveira et al. (2011), for example, found a link between audit committee independence and risk disclosure. As a result, the following is the hypothesis: H4: A non-executive member of the audit committee has a positive correlation with the amount of risk disclosure.

2.6.5

Audit Committee Meetings

The boardroom meeting is frequently viewed as the setting for major company strategic decisions. In order to design an efficient internal control system and risk management strategy, the board must create an audit committee in accordance with corporate governance rules. The audit committee’s membership must include non-executive members capable of mitigating management’s undue influence during meetings. The literature reveals that directors exercise oversight over businesses by varying the level of corporate transparency in accordance with the frequency of the committee’s meetings (Allegrini & Greco, 2013). As Cheng and Courtenay (2006) found, businesses can reduce their risk of fraud by holding regular meetings, hence the value of regular meetings cannot be overstated. An earlier investigation (Al-maghzom et al., 2016) revealed a positive linear connection between the frequency of audit committee meetings and the disclosure of business risks. Therefore, it is claimed as follows: H5: Risk disclosure and the frequency of audit committee meetings are positively correlated.

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3 Methodology 3.1

Sample and Data Collection

The sample contains 42 companies from the Nigerian Stock Exchange and the Johannesburg Stock Exchange. Initially, the study chose all of the listed banks in Nigeria and South Africa. However, we then realized that several of the banks do not have data relating to our independent variables in the Bloomberg data stream. As a result, the researchers limit their sample to companies who have complete data. Besides, companies in the financial and non-financial sectors have been chosen. Nonetheless, it is widely believed that the methods used by financial organizations to prepare their income statement and statement of financial position differ considerably from those used by non-financial companies. As a result, Linsley and Shrives (2006) suggest that the financial sector should be evaluated separately since it has different risks, rules, and regulations. Nevertheless, Ibrahim et al. (2022) argue otherwise as there is no prior research has been done to proposed index or model to quantify risk disclosure in financial institutions. Despite the preceding reasoning, we randomly selected non-financial enterprises from both nations and included them in the sample (see Appendix). The inclusion of financial sector and non-financial sector into the study sample is consistent with past-risk disclosure studies (Kang & Gray, 2019). The rationale behind this, one, the study is not meant to use any figure to estimate the company’s risk exposure or to assess compliance with specific rules and regulations. Nevertheless, the goal is to investigate and observe how corporations disclosed risk in their corporate reporting. In addition, the disparities in income statements and statements of financial position between the financial and non-financial sectors will have no effect on our analysis because the study focuses solely on the narrative sections of annual reports (e.g., Chairman Statement, MD Review, and notes to the account). From 2014 to 2018, we evaluated 5 years’ worth of annual reports. The independent variable data came from the Bloomberg data stream, whereas the dependent variable data came from 42 sample companies’ annual reports, which were retrieved from their websites. Additionally, the research is comparable to prior work in that it employed manual content analysis on all narrative sections of the sample firms’ annual reports, including notes to the accounts. Moreover, the data were analyzed using two unique methods. To begin, we conducted a content analysis to establish the manner in which organizations report risk. Regardless of the number of words, pages, or paragraphs used to code risk information, we chose to follow earlier research (Adamu, 2013; Linsley & Shrives, 2006) that counted the quantity of risk sentences disclosed in corporate reporting. The advantage of this sentence-based method is that it enables us to categorize risk information accordingly. To begin the coding process, we used a checklist that had been employed in earlier investigations (Rajab & Handley-Schachler, 2009). According to the checklist, there are four different quality levels of risk disclosure. The first one categorizes risk as operational, strategic, or environmental. As a result,

The Impact of Audit Committee Composition on Corporate Risk Disclosure. . .

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Table 1 Measurement of the variables Variables RD SAC IDAC ICPAC NEDAC AC_Meetings

Measurement Log of total number of risk sentences Members of the Audit Committee as a whole Number of Independent members in AC 1 if Chairman of audit committee exist, and 0 otherwise Number of Non-executives in the audit committee Number of audit committee meetings

Source Annual report Bloomberg Bloomberg Bloomberg Bloomberg Bloomberg

Source: Authors’ compilation

every piece of risk information that is reported must fall into one of these three risk categories. The second quality element in the checklist focuses on the time-horizon, which enables us to categorize risk data as past, future, or non-time. The third quality variable on the checklist may enable us to categorize risk data as monetary (quantitative) or non-monetary (qualitative). The fourth quality characteristic is the tone of the information, which enables us to categorize risk information as good news, bad news, or neutral. Additionally, it is widely accepted that a sentence-based method has an element of subjectivity. So, we used a decision rule that was similar to the one used in earlier studies (Adamu, 2013; Linsley & Shrives, 2006; Rajab & HandleySchachler, 2009) in order to reduce the risk of bias. Moreover, regression analysis is the second analytic approach. A regression analysis was performed to determine the relationship between risk disclosure (the dependent variable) and the audit committee’s structure (independent variables). Audit committee meetings, non-executive directors, the size of the audit committee, independent members, and the audit committee chairperson are all employed as independent variables. In order to assess the link between our risk disclosure and its drivers, we created a model based on an improved model from Adamu and Ivashkovskaya (2022) and that of Viljoen et al. (2019). Thus: RD = β0it þ β1it ðSACÞ þ β2it ðIDACÞ þ β3it ðICPACÞ þ β4it ðNEDACÞ þ β5it ðAC MeetingsÞ þ αit þ eit

ð1Þ

where RD denotes risk disclosure; SAC denotes the audit committee’s size; the audit committee’s independent director is referred to as IDAC; ICPAC denotes the audit committee’s independent chairperson; NEDAC denotes non-executive director in the audit committee; AC_Meetings denotes the audit committee’s meetings; e is the random error term, while i and t reflect a firm-specific effect and the year-specific effects, respectively. Moreover, to begin our examination of the relationship between audit committee membership and risk disclosure, it is necessary to outline the processes employed to obtain data for our dependent and independent variables. Our variables, measures, and their sources are summarized in Table 1.

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4 Result and Discussion 4.1

Content Analysis and Descriptive Statistics

The descriptive statistics, content analysis outcome, the regression findings, and the diagnostic tests are all summarized and discussed in this section. Before examining the effect of audit committee composition, we began with the analysis of corporate risk disclosure behavior in emerging African countries. The descriptive statistics of the content analysis are presented in Table 2. According to the findings, the average total risk disclosure in corporate reporting is 2054.71. The maximum risk sentence reported by firms in annual reports amounted to 3585, and there is no firm that disclosed less than 388 risk sentences. This demonstrates the degree to which businesses are committed to the risk disclosure procedures that have been promoted by numerous stakeholders for more than a decade. The total risk disclosure is break down into environmental, operational, and strategic risk disclosure. According to the findings, operational risk disclosure dominates strategic and environmental risk disclosure as their mean amounted to 960, 361, and 734, respectively. We also evaluate the quality of risk information published in corporate reporting in which bad news, forward-looking information, and monetary risk disclosure are expected to be higher for the information to be more relevant to various stakeholders. However, the findings presented in Table 2 show the 235 bad news which is substantially less than 670 sentences related to good news. This is not the best practice expected by risk disclosure advocates and other users of corporate reporting. Despite the fact that positive and negative incidences are considered as risk, but many users of corporate reporting considered negative information as the most relevant risk disclosure. The finding is consistent with Adamu (2013). Moreover, Table 2 shows the average of 269 quantitative risk, while 1786 is qualitative risk information. This is indicating that most of the risk information reported by firms Table 2 Content analysis result Variable Total risk disclosure Environmental Risk Operational Risk Strategic Risk Quantitative Risk Qualitative Risk Good news Bad news Neutral news Future Risk info Past-Risk information Non-time information Source: Authors computation

Obs. 210 210 210 210 210 210 210 210 210 210 210 210

Mean 2054.71 734.267 960.757 361.124 269.905 1786.243 670.081 235.114 1150.952 361.124 792.262 902.771

Std. dev. 761.33 294.141 405.814 138.598 101.161 688.817 283.268 103.617 435.7 138.598 383.797 319.102

Min 388 88 142 74 60 253 81 63 197 74 99 124

Max 3585 1501 1860 973 710 3201 1389 467 2355 973 1778 1667

The Impact of Audit Committee Composition on Corporate Risk Disclosure. . . Table 3 Descriptive statistics

Variable RD SAC IDAC ICPAC NEDAC AC_Meetings

Obs. 210 209 151 146 160 202

Mean 7.542 4.598 3.179 0.959 4.037 4.916

Std. dev. 0.449 1.421 1.362 0.199 1.202 2.576

37 Min 5.961 2 0 0 2 1

Max 8.185 9 8 1 8 32

Source: Authors computation

could not be translated into monetary term. Certainly, this has reduced its relevance to many stakeholders. The finding is consistent with Adamu (2013). Meanwhile, time-horizon is also one of the criteria used to assess the quality of risk information, on which the future information is considered as the most valuable in decisionmaking process. According to our results, the average future risk amounted to 361 which is substantially below 792 historical risk information. The higher frequency of past-risk information more than forward-looking is in line with the results reported by Adamu and Ivashkovskaya (2021). Besides, the summary statistics provide the mean, standard deviation, minimum and maximum number of variables utilized in the analysis, as well as the total number of observations included in the study. The findings are shown in Table 3, where the mean for risk disclosure is equal to 7.542 and the standard deviation is equal to 0.449. In addition, the company disclosed the minimal and maximum risk information as 5.961 and 8.185, respectively. Moreover, it is important to present the means for our independent variables, where the values for the audit committee size and the independent chairperson’s value were 4.598 and 0.959, respectively. Non-executive directors have a mean score of 4.037. Furthermore, the mean for audit committee meetings is 4.916, while the average for the committee’s independent members is 3.179. The study investigates the effect of the makeup of the audit committee on company risk disclosure. Numerous diagnostic tests were run to ensure that our data fits the model we developed. It would be beneficial to have a firm grasp on the relationships between our variables. This will aid us in establishing whether our variables are exogenous or endogenous, as well as their multicollinearity, heteroskedasticity, and other characteristics.

4.2

Correlation

Pearson’s correlation coefficients are shown in Table 4 of this part to shed light on the linearity (or lack thereof) of the variables in the study. To identify relevant factors, correlations were analyzed at a 5% level of significance. Risk disclosure appears to be related to the size of the audit committee, audit committee independent chairperson, and the participation of an independent director on the audit committee,

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Table 4 Correlations Variables 1. RD 2. SAC 3. IDAC 4. ICPAC 5. NEDAC 6. AC_Meetings

1

2

3

4

5

6

1.000 –0.401* 0.325* 0.176* –0.046 0.125

1.000 0.211* 0.013 0.885* –0.021

1.000 0.317* 0.420* 0.239*

1.000 0.113 0.038

1.000 0.085

1.000

Source: Authors’ Compilation

according to the findings. However, our correlation results indicate that the other variables (non-executive directors on the audit committee and audit committee meetings) are not associated with risk disclosure. Meanwhile, to verify that the Ordinary Least Square (OLS) was more appropriate, we used the correlation coefficients to signify the independent variables’ connection locations. The procedure could assist us in determining whether or not our data met the multicollinearity assumption. The outcome shows that independent directors (0.885) and non-executive directors (0.211) are both positively correlated with the size of the audit committee. Furthermore, the existence of independent audit committee chairpersons is significantly and positively associated with non-executive director (0.420), audit committee meetings (0.239), and independent member (0.317). Nonetheless, with the exception of the audit committee’s size and non-executive members, the linearity between our explanatory factors is much less than the 0.80 limit. However, because non-executive members and the audit committee’s size are greater than 0.8, we must either eliminate one of the two factors or take the interaction of the two. However, consistent with earlier research, we addressed multicollinearity difficulties by considering the interaction of the impacted factors. Similarly, we calculated a variance inflation factor (VIF) to show the validity of the multicollinearity assumption. The model had problems with multicollinearity, as shown by the VIF results, which supported our pairwise correlation findings. In order to determine if our error term’s variance is homoscedastic, we also ran the Breusch–Pagan LM test. The p-value is equal to 0.0008 and the chi-square value is 11.32. Our error term is not homoscedastic since it has a p-value less than 5%. As a result, we solved the problem of heteroskedasticity in our model by incorporating the white standard error.

4.3

Regression

We used random effect regression analysis to determine the audit committee’s effect on risk disclosure practices. Risk disclosure, a dependent variable, was regressed against five audit committee characteristics, and the results are shown in Table 5 of this section. Non-executive directors, independent members, meetings, the size, and

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Table 5 Random effect regression results RD SAC IDAC IAC NEDAC AC_Meetings Constant Observations Overall R-squared R-squared between R-squared within Chi-square Prob > chi2

Coefficient –0.069* 0.03** 0.295** 0.044 0.003 7.399*** 142 0.201 0.177 0.035 19.327 0.002

Std. err. 0.039 0.014 0.116 0.035 0.003 0.109

t-value –1.78 2.19 2.55 1.24 1.20 67.63

p-value 0.076 0.029 0.011 0.214 0.23 0.000

*p < 0.1, **p < 0.05, ***p < 0.01 Source: Authors’ compilation

the independent chairman of the committee are the five audit committee variables that were used. Following regression analysis, the total p-value was 0.002, which is statistically significant at the 5% level of significance, and the chi-squire value was 19.327. Moreover, the R-squared-within and R-squared-between have a value of 0.035 and 0.177, respectively. Besides, the overall R-squared value was 0.201, suggesting that the explanatory factors in the model explained 20.1% of the variation in risk disclosure. Meanwhile, an examination of the independent members reveals a statistically significant positive coefficient at the 5% level of significance. This demonstrates that increasing the number of independent members on the audit committee tends to convince corporations to report higher-risk information. This argument adds considerable validity to H2, which predicts that independent audit committee members have a large influence on risk disclosure. The outcome is consistent with previous research (Oliveira et al., 2011). The audit committee has a track record of successfully fulfilling its duties, thus the chairperson’s presence is also essential. So long as a head and vice-chair are chosen, the audit committee seems to be self-sufficient. The chairperson is regarded as the risk committee’s chairman if the body is tasked with managing risks. Table 5 illustrates the relationship between the audit committee’s chairman and risk disclosure practices. At the 5% level of significance, the findings reveal a statistically significant positive coefficient. This finding establishes the significance of H3. Similarly, our conclusion corroborates empirical research conducted previously (Viljoen et al., 2019). Additionally, the results in Table 5 demonstrate that the audit committee’s size has a statistically significant effect on risk disclosure. This is compelling evidence that expanding the audit committee’s size has a major impact on the risk information that corporations provide. As a result, H1 was accepted because it anticipated a link between risk disclosure and the size of the audit committee. This conclusion corroborates earlier research findings (Al-maghzom et al., 2016; Alshirah et al.,

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2021; Lawati et al., 2021). Nonetheless, the remaining explanatory variables in the model (Audit committee meetings and non-executive director in the audit committee) are not statistically significant. The frequency of the audit committee meetings is crucial because it is during these meetings that crucial concerns are thoroughly discussed. The audit committee was established to make sure the company has strong internal control systems and efficient risk management practices. Although the regression results in Table 5 show a positive coefficient, they are not statistically significant at that level of significance since the p-value is higher than that threshold of 5%. As a result, we reject H5, which would imply a positive link between risk disclosure and audit committee meetings. This finding backs up recent research in non-African emerging markets (Alshirah et al., 2021; Handoko & Probohudono, 2021; Rahayu et al., 2022), although it was in direct opposition to other prior research (Viljoen et al., 2019). Similarly, the non-executive director coefficient reported in Table 5 appears negligible. In spite of the fact that non-executive directors were obliged to be a part of the audit committee under corporate governance standards, the results show that this requirement has no impact on how corporations communicate risk information. H4 does not appear to have statistical support, and hence is dismissed.

5 Conclusions The study assessed the risk disclosure behavior in African emerging countries as well as the effect of audit committee composition on the quantity of risk information released by firms. Operational risk disclosure appears to be the most frequent type of risk information released by firms, more frequently than strategic and environmental risk disclosure. In addition, past-risk information, non-monetary, and good news appear to be more common in corporate reporting than monetary, bad news, and forward-looking information. This practice has reduced the relevance of corporate reporting to many stakeholders. Moreover, the model was proven to be significant after studying the link between audit committee traits and the volume of risk disclosures made by businesses in rising African nations. The size of the audit committee is inversely related with corporate risk disclosure, which is explaining that any increase in audit committee member tend to influence organization to reduce the risk information. However, the inclusion of an independent member, and the appointment of an independent chairperson all contribute to firms reporting greater risk information. Nevertheless, neither the frequency of audit committee meetings nor the number of non-executive members had a substantial effect on the maximum risk to publish. To summarize, the current practice of risk disclosure is insufficient to address the needs of a large number of stakeholders. However, the authorities need to make sure that companies have the right number of people on their audit committees, which could improve their risk disclosures. Moreover, the study is comparable to prior research in which the risk disclosure coding technique was highlighted as a major

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limitation. Manual content analysis, it is widely accepted, contains an element of subjectivity when it comes to classifying pertinent risk information. However, by adopting previous research’s decision guidelines prior to coding any risk information, the study’s element of subjectivity was significantly decreased. Nonetheless, due to the dearth of data related to our independent variable on the Bloomberg data stream, we were forced to narrow our attention to organizations that possessed pertinent data. Consequently, we must accept this as another constraint that compelled us to cut our sample size. Furthermore, because manual content analysis requires time and is labor-intensive, we limit our investigation to a 5-year period. Five years is not considered excessively short, particularly in risk disclosure studies. While several prior studies were limited to 1, 2, or 3 years, extending the study to more than 5 years is also encouraged in future studies. Besides, all of the constraints revealed in this study can serve as a springboard for future research, particularly in African emerging markets. Following the coronavirus outbreak, future studies should also look at risk disclosure practices in emerging African countries. This will help determine whether or not COVID-19 has changed risk disclosure practices in these countries.

Appendix 1: Risk Disclosure Categories Operational Risk Operational risk is the likelihood that losses will occur in the company’s core business operations. It includes things like product failure, internal control and risk management policies, infrastructure risk, liquidity and cash flow, project failure, operational disruption, operational problem, employment practices and workplace safety, and environment risk (risks arising from the impact of businesses’ operations on the environment).

Environmental Risk Environmental risk comprises disclosure due to the following factors, which are fundamentally beyond the organization’s control: • Political risk; • Economic risk (such as interest rate, currency risk, price and commodity risk, inflation, taxation, and credit risk); Regulation and legislation; Social risk; Regulation and legislation; Industry sources (such as competition, potential entrants, suppliers, substitutes, strategic partners); Customers (such as changes in demand, changes in client requirements and client preferences); Climate and catastrophic.

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Strategic Risks Strategic risks are brought on by a company’s participation in a particular industry and are associated with its long-term business goals and strategy. Research and development, product markets, intellectual property rights, alliances, joint ventures, growth management, derivatives, investment, and technology are just a few of the strategic risks.

Appendix 2: Decision Criteria 1. The definition below must be taken into account while coding risk disclosure sentence. Thus, according to Linsey and Shrive, “if the reader is informed of any opportunity or prospect, as well as any risk, danger, harm, threat, or exposure, that has already had an impact on the company or that may have an impact in the future, or of the management of any such opportunity, prospect, risk, harm, threat, or exposure.” 2. Phrases referencing uncertainty are also regarded as risk disclosure. 3. The disclosures must be explicit, not implied, before any language is coded as a risk. 4. The risk disclosures need to be organized according to the risk categories listed in Appendix 1. 5. General policy directives pertaining to internal control and risk management systems, employee health and safety, corporate governance, and other topics are to be labeled as “non-monetary/neutral/non-time”. 6. Risk management policies that are general in nature and do not include money or specific dates or times are classified as non-monetary, neutral, or non-time. 7. Financial risk disclosures are risk disclosures that either explicitly state the financial impact of a risk or give the reader sufficient details to ascertain the risk’s financial impact. 8. If a statement can be categorized in more than one way, the information will be put into the category that is most prominent in the phrase. 9. Occasionally, tables may be used to present risk information. In this situation, One line here equals one sentence 10. Making a disclosure more than once is not uncommon. A risk disclosure sentence must then be recorded for any repeated disclosures. 11. A vague disclosure is not one that should be documented as a risk disclosure.

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Linsley, P. M., Shrives, P. J., & Crumpton, I. (2006). Risk disclosure: An exploratory study of UK and Canadian banks. Journal of Banking Regulation, 7(3/4), 268–282. https://doi.org/10.1057/ palgrave.jbr.2350032 Marx, B., & Voogt, T. (2010). Audit committee responsibilities vis-á-vis internal audit: How well do Top 40 FTSE/JSE listed companies shape up? Meditari Accounting Research, 18(1), 17–32. https://doi.org/10.1108/10222529201000002 Miko, N. U., & Kamardin, H. (2015). Corporate governance and financial reporting quality in Nigeria: Evidence from pre- and post-code 2011. International Journal of Emerging Science and Engineering, 4(2). Mukhibad, H., Nurkhin, A., & Rohman, A. (2020). Corporate governance mechanism and risk disclosure by Islamic banks in Indonesia. Banks and Bank Systems, 15(1), 1–10. https://doi.org/ 10.21511/bbs.15(1).2020.01 Neifar, S., & Jarboui, A. (2018). Corporate governance and operational risk voluntary disclosure: Evidence from Islamic banks. Research in International Business and Finance, 46, 43–54. https://doi.org/10.1016/j.ribaf.2017.09.006 Okolie, J. U. (2014). Corporate governance and audit committee in Nigeria. Journal of Policy and Development Studies, 9(1), 226–233. https://www.arabianjbmr.com/pdfs/JPDS_VOL_9_1/15. pdf Oliveira, J., Rodrigues, L. L., & Craig, R. (2011). Voluntary risk reporting to enhance institutional and organizational legitimacy: Evidence from Portuguese banks. Journal of Financial Regulation and Compliance, 19, 271–289. https://doi.org/10.1108/13581981111147892 Rahayu, I., Ardi, D. S., & Hamdani, R. (2022). Risk management disclosure and their effect on banking firms value in Indonesia. Humanities and Social science Letters, 10(2), 139–148. https://doi.org/10.18488/73.v10i2.2959 Rajab, B., & Handley-Schachler, M. (2009). Corporate risk disclosure by UK firms: Trends and determinants. World Review of Entrepreneurship Management and Sustainable Development, 5(3), 224–243. https://doi.org/10.1504/WREMSD.2009.026801 Securities and Exchange Commission and Corporate Affairs Commission. (2003). Report of the Committee on Corporate Governance of Public Companies in Nigeria (Lagos, Securities and Exchange Commission and Corporate Affairs Commission). Sellami, Y. M., & Fendri, H. B. (2017). The effect of audit committee characteristics on compliance with IFRS for related party disclosures: Evidence from South Africa. Managerial Auditing Journal, 32(6), 603–626. https://doi.org/10.1108/MAJ-06-2016-1395 Seta, A. T., & Setyaningrum, D. (2018). Corporate governance and risk disclosure: Indonesian evidence. In Advances in economics, business and management research, 6th international accounting conference (Vol. 55, pp. 37–41). Retrieved from http://creativecommons.org/ licenses/by-nc/4.0/ Solomon, J. F., Solomon, A., Norton, S. D., & Joseph, N. L. (2000). A conceptual framework for corporate risk disclosure emerging from the agenda for corporate governance reform. British Accounting Review, 32, 447–478. https://doi.org/10.1006/bare.2000.0145 Vandemaele, S., Vergauwen, P., & Michiels, A. (2009). Management risk reporting practices and their determinants. Vergauwen, P., Dao, M., & Brüggen, A. (2009). Determinants of intellectual capital disclosure: Evidence from Australia. Management Decision, 47(2), 233–245. https://doi.org/10.1108/ 00251740910938894 Viljoen, C., Bruwer, B. W., & Enslin, Z. (2019). Determinants of enhanced risk disclosure of JSE top 40 companies: The board risk committee composition, frequency of meetings and the chief risk officer. Southern African Business Review, 20, 208–235. https://doi.org/10.25159/19988125/6050

Part II

Eurasian Business Perspectives: Entrepreneurship

The Dynamic Capability and Ambidexterity in the Early-Stage Startups: A Hierarchical Component Model Approach Prio Utomo and Florentina Kurniasari

Abstract This study investigates the implication of startup ambidexterity toward competitive advantage. It integrates a model that combines the dynamic capability and ambidexterity concept through organizational learning, innovation, and adaptation. This research is a cross-sectional quantitative research. There are 121 startup founders and co-founders participated in the survey distributed through questionnaires at a regional event in Indonesia. The Hierarchical Component Model (HCM) was established using Partial Least Square – Structural Equation Model (PLS-SEM), which analyzes the high-order and low-order construct through measurement and structural model evaluation. The study has two main results; first, it shows that startups have more exploration behavior than exploitation at the early stage of startup development by measurement model evaluation. The second result shows that startup ambidexterity has a positive and significant effect on competitive advantage. This study integrates dynamic capability and ambidexterity through learning ambidexterity, innovation ambidexterity, and alignment/adaptation and seeks to understand its implication for competitive advantage. The study also stresses the importance of future research from methodological and substance to reconcile the dynamic capability and organization ambidexterity. Keywords Ambidexterity · Competitive advantage · Hierarchical component model · Startup · Early-stage · PLS

1 Introduction The ambidexterity and dynamic capability literature have highly contributed to strategic management, explaining how an organization acquires and maintains its competitive advantage toward sustainable performance (Popadiuk et al., 2018). P. Utomo (*) · F. Kurniasari Department of Technology Management, Universitas Multimedia Nusantara, Tangerang, Indonesia e-mail: [email protected]; fl[email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_3

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Despite much literature and research conducted on both ambidexterity and dynamic capability, the relationship between dynamic capability and ambidexterity has not yet been sufficiently examined (Jurksiene & Pundziene, 2016; van Lieshout et al., 2021). The previous study focused on several only focused on the macro-level relationship between organization ambidexterity and dynamic capability (O’Reilly & Tushman, 2008). Their proposition suggests ambidexterity should function as a dynamic capability. There are three capabilities required to succeed at ambidexterity: sensing the opportunity and threats, having the right decision and execution, and orchestrating organizational assets by recombining, reconfiguring, and restructuring them as the market and technology change. It is also said that ambidexterity lies in the senior leader’s capability to orchestrate complex assets as part of tacit knowledge and long-term commitments but do not have to be repeatable. At the same time, the dynamic capability must be repeatable on the ability of senior leadership to deal with the contradiction associated with the exploration and exploitation. Zimmermann and Birkinshaw (2016) focus on the micro-level aspect mentioned that the lack of integration between both disciplines happened due to different views of organizational environmental dynamism and their focus. Dynamic capabilities can be achieved through meta and high-order capabilities and focus on the resources and strategies, while ambidexterity focuses on the organizational context and arrangement, e.g. firm-level, group-level, and individual-level. Despite the difference in concepts, the previous research focuses on large enterprises that have reached a certain maturity. The study on both concepts in smaller-sized enterprises is still limited (Andrade et al., 2022). There is a different study focusing on organization size. The large enterprise focuses on the dynamic capability and ambidexterity to deal with market changes and innovation to sustainable competitive advantage (Frigotto et al., 2014), while the smaller company investigates the role of resource availability in a smaller company to exploit and explore (Voss & Voss, 2013). The empirical research that integrates both concepts is still also limited. Most of the studies on determining and evaluating the determinants and antecedence that might cause one to attain the dynamic capabilities or ambidexterity separately, and not many integrate both in one research. This study tries to fill the gap by answering capabilities in learning, innovating, and contextualizing capability that tries to answer what would be three research questions: first is how the operational capabilities contribute to the exploitation and exploration capabilities empirically; second, does the proposed ambidexterity model related to the competitive advantage? By studying the respondent in the most extreme condition, which is in the early stage, the Startup focuses on product development, building its customer base, and establishing a solid cash flow with a high failure rate (Salamzadeh & Kesim, 2015; Dellermann et al., 2018). The study hopes can capture the interactions between operational capabilities in exploitative and explorative modes. The study will thus contribute to several critical disciplines. First, this study provides a new perspective on integrating and reconciling the ambidexterity and dynamic capability literature through operational capabilities. Second, the study

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provides empirical insight on how the operational capabilities relate to the organization’s ambidexterity. Third, the notion that reconciliation between those concepts needs to be bounded in the organization’s development. This paper first begins with discussion on the state of the background and the relationship between two major disciplines of organization dualism: organizational ambidexterity and dynamic capability, and how both relate to the competitive advantage. In addition, on the literature review, the development and the need of integration were discussed, and the gap was identified, and the integration model was proposed. Furthermore, the model tested using the method and data gathered to test its relationship. And in the conclusion, the result was presented and used to develop the conclusion.

2 Literature Review 2.1

Organizational Ambidexterity and Dynamic Capability

The theory of dynamic capability has its origins in the resource-based view of the firm that mentioned the competitive advantage of a company came from the Valuable, Rare, Inimitable, and Non-Substitutable with a purpose to generate abnormal returns (Barney, 1991). As the business environment changes fast, the resources focus as the ground for competitive advantage becomes irrelevant. Based on this condition, Teece (2007) suggests that organization needs the ability to create new or reconfigure their resources toward their competitiveness, and it becomes vast research attention (Zimmermann & Birkinshaw, 2016). The study on understanding the dynamic capability antecedences is growing; the recent one by Bitencourt et al. (2020) mentions five antecedences of the dynamic capability: resources (both tangible and intangible), knowledge management and learning, alliances, entrepreneurial environment, and dynamism. The resources have a vital role as they are needed to understand and create sustainable competitive advantages. Knowledge management and learning focus on organizations’ learning to respond to market change by promoting new ideas. The research in organizational ambidexterity started from the seminal work of March (1991) that argues that two conflicting corporate activities compete for a scarce resource called exploitation and exploration in the learning activities. The exploitation relates to activities such as refinements, choice, production, efficiencies, selection, implementation, and execution, whereas exploration refers to search, variation, risk-taking, experimentation, play, flexibility, discovery, and innovation. The knowledge required for exploitation is the existing knowledge, skills, and capabilities while the exploration requires new knowledge from existing knowledge within or outside the organization. The outcomes expected from the exploitation are more on optimizing current systems, designs, markets, and business models. At the same time, exploration seeks a new design, new market, and new business model. The relationship between both concepts of exploitation and exploration is varied;

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several researchers mentioned they have a complementary relationship, no relationship, and also reinforce each other (Guisado-Gonzáles et al., 2017). The reconciliation between two disciplines was discussed in several contexts: organizational learning, technological innovation, organization adaptation, strategic management, and corporate design. Since the respondent in this research is Startup, strategic management and corporate structure are not considered since the short longevity of the Startup and the casual or flat organization design.

2.2

Competitive Advantage

The competitive advantage topic starts with why one company is more profitable than the other. There are at least four theories where the competitive advantage comes from: first, the competitive advantage can come from organizational position (Porter, 1985); second, the competitive advantage can be acquired by having Valuable, Rare, Inimitable, and Non-Substitutable as part of Resource-Based View (Barney, 1991); third is based on Market-Based View (MBV) where understanding the market becomes a critical aspect of competitive advantage (Day, 1994); and last due to temporal competitive advantage can be attained through interactive actionreaction based on the action-based view (Madhok & Marques, 2014; Lee, 2017). The competitive advantage is the firm’s capability to meet customer needs by supplying products and services valued more than the competitor with considerable profit Chikan, (2008). This capability consists of the capability to create, manufacture, and sell their product, including acquiring and retaining customers. In the existing literature, design and product development capabilities are associated with new product development. Still, marketing and customer acquisition and retention capabilities are associated with marketing capability and business model. Startups, or new ventures, are defined by their entrepreneurial conduct, fueled by millennials’ desire to provide value to their customers. They seek to add value to society by producing novel products, services, processes, or platforms within a sustainable and replicable business model. In comparison to giant corporations, startups focus on producing a minimal viable product through iterative and experimentation with user and customer interaction (Frederiksen & Brem, 2017) that focuses on solving customer problems. This research proposed frameworks based on the above theory, as depicted in Fig. 1.

3 Data and Methodology This research is a cross-sectional quantitative research that used High-Order Construct Partial Least Square (PLS-SEM) analysis approach to deal with a complex model. The non-probability purpose sampling design was used to gather feedback

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Fig. 1 Research Framework. Source: Teece (2007)

from the respondent. There are 121 founders and co-founders of early-stage Startups responded to the onsite survey during Tech in Asia event in Jakarta Indonesia back in 2018. The early-stage Startup is defined as the stage where the Startup begins to generate ideas and build products, targeting the market and offering its products. The questionnaire was developed by integrating indicators from several previous ambidexterity studies in innovation, learning, and adaptability in addition to indicator of competitive advantage. The analysis procedure follows Hair et al. (2022) which comprises measurement and structural model evaluation processes. The analysis began by using Repeated Indicator approach to analyze the complex model as it will minimize the parameters bias in the higher-order construct measurement model relationship to support the hypothesis testing between ambidexterity and competitive advantage. In addition, the analysis was using Mode A setting to evaluate reflective-reflective model of firstand second-order construct analysis. The Model B setting was used to evaluate the reflective-formative of second- and third-order construct analysis. Furthermore, the measurement model evaluation for reflective-reflective model will interpret the factor loadings, convergent validity, internal consistency reliability, and discriminant validity metrics, while reflective-formative higher-order construct will analyze

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weights and assess convergent validity, collinearity, and the significance and relevance of the weight; and finally, the structural model evaluation follows standard evaluation model evaluation.

4 Result and Discussion 4.1

Measurement Model Evaluation

In the first measurement model evaluation (Table 1) there are no indicators were removed since all of the load factors within are above 0.708, with several indicators falling between 0.4–0.7 that still can be retained since they do not influence the average variance extracted (AVE), where all latent variable AVE value was over 0.5 (Hair et al., 2022). The construct internal consistency and reliability were evaluated using composite reliability (CR) and Cronbach’s alpha (CA) indicators. The results showed that both CA and CR are consistent and reliable since their value exceeded the value 0.7. The discriminant validity was assessed by Heterotrait-Monotrait Ratio of Correlations (HTMT) confidence interval. The construct does not include value one except for exploitative and explorative learning. In the second-order measurement model evaluation (Table 2), the factor load also exceeded the 0.708 threshold except for the AVE since the measurement bias was due to repeated indicator measurement. The Internal consistency and reliability that measure using CR and CA also exceed the threshold value of 0.7. The third-order measurement model evaluation uses a formative approach where the result shows that there is no collinearity between the formative variables with Variance Inflaction Factor (VIF) below 5. Significant and relevance of weight show that explorative has higher and more significant weight while the weight of exploitation is significant (Table 3).

4.2

Structural Model Evaluation

The structural model evaluation was conducted to understand the significance of the ambidexterity construct with the competitive advantage. The result (Table 4) shows that the organization’s ambidexterity has a positive and significant competitive advantage.

Competitive advantage

Adaptability

Explorative innovation

Explorative learning

Alignment

Exploitative innovation

Latent Variable Exploitative learning

Indicator LXT1 LXT2 LXT3 IXR1 IXR2 IXR3 ALIGN1 ALIGN2 ALIGN3 LXR1 LXR2 LXR3 IXR1 IXR2 IXR3 ADAPT1 ADAPT2 ADAPT3 CA1 CA2 CA3 CA4

>0.708 0.651 0.843 0.766 0.733 0.859 0.720 0.809 0.810 0.796 0.691 0.802 0.836 0.801 0.743 0.852 0.776 0.874 0.841 0.938 0.812 0.687 0.645

>0.50 0.424 0.711 0.587 0.537 0.738 0.518 0.654 0.656 0.634 0.477 0.643 0.699 0.642 0.552 0.726 0.602 0.764 0.707 0.880 0.659 0.472 0.416

Convergent validity Indicator Loading reliability

Table 1 First-order measurement model evaluation (reflective)

0.648

0.691

0.640

0.607

0.648

0.598

0.5 0.574

AVE

0.858

0.870

0.842

0.607

0.847

0.816

0.60–0.90 0.800

0.806

0.775

0.717

0.673

0.729

0.664

0.60–0.90 0.626

Internal consistency and reliability Composite Cronbach’s reliability Alpha

Yes

Yes

Yes

No

Yes

Yes

(continued)

Discriminant validity The HTMT confidence interval does not include 1 No

The Dynamic Capability and Ambidexterity in the Early-Stage Startups:. . . 55

Indicator

0.5

>0.708

>0.50

AVE

Convergent validity Indicator Loading reliability

Source: Authors Evaluation Result using Smart PLS

Latent Variable

Table 1 (continued)

0.60–0.90

0.60–0.90

Internal consistency and reliability Composite Cronbach’s reliability Alpha Discriminant validity The HTMT confidence interval does not include 1

56 P. Utomo and F. Kurniasari

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Table 2 Second-order measurement model evaluation (reflective)

Latent variable Exploitation

Exploration

Indicator Exploitative learning Exploitative innovation Alignment Explorative learning Exploitative innovation Flexibility

Convergent validity Indicator Loading reliability >0.708 >0.50 0.784 0.615 0.766

0.587

0.715 0.845

0.511 0.714

0.810

0.656

0.794

0.630

AVE 0.5 0.343

Internal consistency and reliability Composite Cronbach’s reliability Alpha 0.60–0.90 0.60–0.90 0.823 0.757

0.430

0.871

0.833

Source: Authors Evaluation Result using Smart PLS Table 3 Significant and relevance of weight

Exploitation!Ambidexterity Explorative!Ambidexterity

Original sample (O) 0.471 0.594

Sample mean (M) 0.493 0.554

Standard deviation (STDEV) 0.255 0.255

T statistics (| O/STDEV|) 1.848 2.329

P values 0.07* 0.02**

Standard deviation (STDEV) 0.065

T statistics (| O/STDEV|) 8.936

P values 0

* α ¼ 0.1 ** α ¼ 0.05 Source: Authors Evaluation Result using Smart PLS Table 4 Significant test

Organization ambidexterity!Competitive advantage

Original sample (O) 0.578

Sample mean (M) 0.631

Source: Author Evaluation Result using Smart PLS

4.3

Discussion

The main objective of this study is to develop and validate an operationalized model of organization ambidexterity that comprises exploitation and exploration activities. The hierarchical component model uses low-order and high-order models to build the complexity of the organization ambidexterity construct. There are three ambidexterity domains used in this research: organizational learning, technological innovation, and organization adaptation, divided into two conflicting activities on exploitation and exploration (Fig. 1). A survey yielded 100 completed and useable

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questionnaires to run the analysis. There are three orders of measurement model evaluation analysis on the proposed model using the reflective-formative PLS model. In the first-order analysis, seven constructs have gone through measurement evaluation. They are exploitative and explorative learning, exploitative and explorative innovation, alignment, and adaptation. The result shows that only exploitative and explorative learning does not have a determinant validity. The study argued that the respondents failed to separate between exploitative and explorative learning due to the functional structure’s informality. The contextual ambidexterity might be more relevant to the Startup as the learning activities happen concurrently within an organizational unit (Zhao et al., 2020). A reflective approach and construct validity and reliability analysis were also conducted in the second-order analysis. All measures were within the threshold expected for the AVE. As mentioned earlier, it is caused by the repeated indicator approach to analyzing the high-order model. The third-order analysis was conducted in the formative approach, where exploitation and exploration form the ambidexterity. The significant weight analysis shows that the exploitation is not substantial in higher-level confident levels, where the exploration activities are significant. The early-stage Startup is looking for a new sustainable business model (Ries, 2011), which has become part of its exploration (Sinha, 2015). Although the exploitation activity was not substantial it has influence to the exploration persistency as the exploration persistency is influenced by the effect of exploitation and knowledge accumulation (Shibata et al., 2022) that driven by the startup dynamic culture (Utomo & Budiastuti, 2019). The significant test between the organization ambidexterity and the competitive advantage shows a significant relationship between both constructs. The tension between exploitation and exploration might be challenging to attain a competitive advantage. The strategic ability driven by top management becomes an essential aspect of building the capability to change direction as competition increases, and the business environment becomes uncertain (Clauss et al., 2021). As the early-stage Startup looking for a new form of business, agility, and adaptation become an essential aspect of the company’s sustainability.

5 Conclusion, Limitations, and Future Research 5.1

Conclusion

This study has several purposes. First, this study aims to develop and validate the operationalized model of organization ambidexterity, which comprises exploitation and exploration activities with several capabilities; second, the study investigated the relationship between ambidexterity and competitive advantage. The model uses learning, innovation, and alignment/adaptability capability as the exploitation and exploration activities. The result indicates that these three domains can represent the

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exploitation and exploration that form the organizational ambidexterity shown by the measurement model evaluation using repeated indicator analysis. The hypothesis testing shows that organization ambidexterity has a positive and significant effect on competitive advantage. The contribution of this study will provide additional insight into the reconciliation between the complexity of dynamic capability and organization ambidexterity in the lower operationalized capability by integrating learning, innovation, and adaptability capability into one model and its relationship with a competitive advantage.

5.2

Research Limitation

There are three research limitations in this study; the first limitation was the respondents’ quality and quantity. There is a potential bias in answering questions as the respondents are inexperienced new businesspeople. The respondent also finds it challenging to reach out as they are nomadic and do not have any dedicated workplace. The second limitation is the evaluation method and reporting of the hierarchical construct model using a repeated indicator approach. Though there are many references on evaluation and reporting on high-order construct evaluation, there are very limited that evaluate and report up to third-order construct. The third limitation is the capability selected as the operationalized capability in exploitation and exploration activities determined based on the seize, sense, and reconfigure.

5.3

Future Research

Several future research opportunities are found in this study from methodological and discipline substances. First, comparative estimation fitness between several estimates approaches such as extended repeated indicator, disjoint two-stage, and embedded two-stage approaches. The second future research opportunity is leveraging different capabilities relevant to reflect exploitation and exploration activities based on various dynamic capability theoretical ground perspectives.

References Andrade, J., Franco, M., & Mendes, L. (2022). Facilitating and inhibiting effects of organisational ambidexterity in SME: An analysis centred on SME characteristics. Journal of the Knowledge Economy, 14, 35. https://doi.org/10.1007/s13132-021-00831-9 Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99–120. https://doi.org/10.1177/014920639101700108

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The Impact of Institutional Framework On Entrepreneurship in OECD Members Countries Ante Zdilar

Abstract Achieving high-quality institutional framework is one of the basic assumptions for achieving a higher degree of welfare state. In addition to the existence of the “game rules” in the market that is equally valuable to all economic agents, it is also a condition without which a higher degree of entrepreneurial activity in the economy cannot be achieved. The aim of this research is to examine which determinants of the institutional framework are crucial for increasing entrepreneurial activity in 24 OECD member countries in the period 2010–2018. The results of Hausman test indicate that a panel with fixed effects is a suitable method for this research. The results of the investigation suggest that Political Rights, Civil Liberties, Freedom of Expression and Belief, Functioning of Government and Rule of Law are important determinants of entrepreneurial activity in observed countries. Furthermore, policies aimed at increasing media freedom and freedom of expressing personal views increase the New business density in OECD member countries. Moreover, when making certain institutional changes, policy makers should be aware of the time gap between the moment of making a decision and its reflection in the economy. Introduction of certain institutional decision may have completely different repercussions in the economy after some period of time. Consequently, particular institutional decisions may require some changes in a longer period after their adoption. Keywords Panel · Institutional framework · OECD · Freedom House

1 Introduction Encouraging entrepreneurship can be considered as one of the main goals of governments around the world. Greater entrepreneurial activity does not only lead to an increase of the employment rate but also to an increase of overall productivity A. Zdilar (✉) The Department of Economics and Business, University of Dubrovnik, Dubrovnik, Croatia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_4

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in the economy. As a result of the previous, higher added value, higher wages and ultimately a higher standard of living are created. Throughout the history, the concept and definition of entrepreneurship has changed both due to the emergence of new businesses and due to the separation of ownership from management. One of the first definitions of a modern entrepreneurship includes a definition of Knight (1921), who under the notion of entrepreneurship implies a profit made from uncertainty and risk. Furthermore, Schumpeter (1934) argued that entrepreneurship includes a wider range of activities—from creating new products and new services to finding new sources of raw materials, new production methods, new markets, and new forms of organization. In the paper from 1959, Cole defined entrepreneurship as gathering activities that lead to the emergence of a profit-oriented organization. On the other hand, Casson (1982) argued that entrepreneurship encompasses all decisions and judgments related to the coordination of scarce resources. However, all definitions above contain at least one of the four components listed: innovation, resource gathering, creating a new business, and the ability to make a profit with some degree of risk and uncertainty. Accordingly, the following question arises: is there a way in which the government impacts one of these components and how prevalent that impact should be? Under normal circumstances, most economic policy makers would probably argue that this degree of government interference should be as small as possible, as the market and the private sector will resolve main economic problems on their own. The crisis caused by the COVID-19 pandemic has partly refuted such claims and once again demonstrated the importance of the state in the economic environment. Governments should certainly establish an effective and flexible institutional framework that will ensure the equality of game rules for all entrepreneurs in the market. Its flexibility allows the government to change the mentioned “rules” in the market in a very short time and to adapt them to the new circumstances (e.g., the COVID-19 crisis). However, it should be kept in mind that measuring effect of formal institutions is not an easy task and it is most often a subject to a certain degree of subjectivism. Frequently, the impact of institutions is measured as the subjective impression of a certain group of people, which can be very heterogeneous. Additionally, different authors use different databases when trying to approximate the effects of formal institutions. The most frequently used databases are the Fraser Institute dataset (Bénassy-Quéré et al., 2007; Nyström, 2008; Bjørnskov & Foss, 2008, 2013; Dau & Cuervo-Cazurra, 2014; Bosma et al., 2018; Boudreaux & Nikolaev, 2018), the Worldwide Governance Indicators dataset (Daude & Stein, 2007; Klapper et al., 2007; Munemo, 2014; Dau & Cuervo-Cazurra, 2014; Aparicio et al., 2015; Farla et al., 2016; Peres et al., 2018; Silve & Plekhanov, 2018; Tomelin et al., 2018; Ghura et al., 2019; Chambers & Munemo, 2019; Agostino et al., 2019), and Freedom House dataset (Dawson, 1998; Djankov et al., 2002; Dollar & Kraay, 2003; Nasreen et al., 2015; Autio & Fu, 2014; Ando, 2015; Raza et al., 2018). In their research, different authors investigate the effects of the institutional framework on different economic variables. Bosma et al. (2018) in their paper show that institutions have a positive effect on economic growth through entrepreneurial activity. On the other hand, Slesman et al. (2021) point out a similar effect of

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institutions on stimulating domestic entrepreneurship through foreign direct investment. Additionally, Silve and Plekhanov (2018) point out that institutions represent an important channel through which innovations affect economic growth in the long term, while Gwartney et al. (2004) claim that countries, whose policies and institutions are consistent with economic freedom, grow faster. Nevertheless, in order to encourage employment and economic growth through entrepreneurship, economic policy makers must determine which components of the institutional framework are deficient (Estrin et al., 2013). Taking into account all of the above, this paper highlights two main goals: (a) to determine which components of the institutional framework have a decisive influence on entrepreneurial activity approximated by the new businesses density; (b) to investigate whether there is a time lag from the moment of a certain change within the institutional framework to the moment of its reflection on the economy. New business density is taken as a measure of entrepreneurship because it does not depend directly on the size of the country and its population. The investigation shows that Political Rights, Civil Liberties, Functioning of Government, and Freedom of Expression and Belief are the main components that significantly affect entrepreneurial activity within the 24 OECD countries. Additionally, there is evidence of a three-year lag between changes within the institutional framework and their reflections on the economy. The rest of this paper is structured as follows. The next section gives literature review. Description of the data are presented in Sect. 3. The specification of the model is presented in Sect. 4, followed by results in Sect. 5. Conclusion is given in Sect. 6.

2 Literature Review In the last two decades, different authors have studied the impact of formal institutions on different economic variables (GDP per capita, employment rate, investments, etc.). In this section, the literature review will be focused on those authors who placed some of the entrepreneurial variables at the center of their analysis. The paper of Desai et al. (2003) points out that higher fairness and higher protection of property rights are two key components for increasing the rate of entry. Nevertheless, the results suggest that this effect is somewhat more robust for Eastern Europe than it is for Western Europe. Furthermore, in correspondence with Desai et al. (2003) using logit and multiple logit models, (Aidis et al., 2007) also found a significant effect of property rights on the possibility that a person is involved in start-up activity. On the other hand, the Aidis et al. (2007) also argue that the level of economic development as well as the possibility of private sector financing are variables that significantly affect the start-up activities of individuals. In their research, van Stel et al. (2007) use the nascent entrepreneurship rate and the young business entrepreneurship rate as a measure of entrepreneurship. The results of their research suggest that there is no significant relationship between the mentioned entrepreneurship measures used and the administrative framework such as time, cost, and the number of procedures required to start a business. However, their

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results have certain limitations. First of all, the time period used was too short (2000–2005 and 2002–2005) which can affect the research results. Secondly, only one method (Ordinary Least Squares—OLS) was used in the study. Using a panel analysis on a sample of 84 countries, Klapper et al. (2007) claimed that the entry rate is significantly associated with better governance (an average of six Worldwide Governance Indicators). On the other hand, variables such as the number of procedures, the level of development, and access to finance significantly affect the entry rate per capita. Additionally, business density is statistically related to governance and easier barriers to entry. Furthermore, Chambers and Munemo (2019) used three different approaches in their work on a sample of 118 countries: cross-section, panel analysis, and panel analysis with differences. The analysis revealed that Voice & Accountability, Regulatory Quality, Political Stability, and Startup Procedures are significantly related to new business density. Moreover, as a measure of entrepreneurship, Dau and Cuervo-Cazurra (2014) used the ratio between a number of newly registered businesses and the working-age population (formal entrepreneurship) as well as the ratio between a number of newly unregistered businesses and the working-age population (informal entrepreneurship). The results of analysis point out several conclusions. Firstly, economic liberalization has a positive effect on both formal and informal entrepreneurship. Secondly, the size of government has a positive effect on formal entrepreneurship and a negative effect on informal entrepreneurship. Lastly, the intensity of the previous effect is much stronger regarding the informal entrepreneurship when compared to the formal one. Similar to the previous authors, Autio and Fu (2014) also used formal and informal entrepreneurship as measures of entrepreneurial activity. In their paper, the authors concluded that political and economic institutions have a significant impact on both formal and informal entrepreneurship. In contrast to the above, Bosma et al. (2018) used total entrepreneurial activity (hereinafter TEA) as a measure of entrepreneurial activity. The authors seek an answer to the following question: what are the factors that decisively impact the entrepreneurship leading to the increased economic growth? The results of the research suggest that financial stability as well as ability and knowledge to start a business is statistically the most robust variable affecting TEA. Furthermore, when using the Heritage Foundation database on a sample of 63 countries, Fuentelsaz et al. (2015) revealed that different institutional indicators positively affect TEA opportunity, while for TEA necessity they did not find that link.1 Bjørnskov and Foss (2008) showed that government size is negatively associated while access to money is positively associated with the entrepreneurial activity measured through TEA. However, there are certain variations here as well depending on whether the TEA is motivated by opportunity or necessity. The share of government in total spending is negatively related to TEA opportunity, while government spending, subsidies, and transfers are negatively related to TEA necessity.

1

TEA opportunity means that total entrepreneurial activity is motivated by opportunities and TEA necessity that total entrepreneurial activity is motivated by necessities.

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Furthermore, Agostino et al. (2019) in their paper tested the hypothesis that the entrepreneurial entry rate may be affected by the quality of local institutions in the Italian provinces. Founding the support for the mentioned hypothesis, the authors highlighted the rule of law and the regulatory quality as the two most significant variables from local institutions. However, there were certain differences between regions, rule of law had a greater influence in the southern provinces while the regulator quality had a greater influence in the developed central-northern provinces. On the other hand, in their research Brieger et al. (2020) used multilevel mixedeffects linear regressions on a sample of over 15,000 entrepreneurs in 45 countries. They showed that formal institutions approximated by economic, social, and political freedom had a positive impact on the social value creation of entrepreneurs. Slesman et al. (2021) investigated the spillover effects of foreign direct investment on domestic entrepreneurship through institutions. Using System Generalized Method of Moments on panel data for 97 countries, the analysis revealed that foreign direct investment has a negative effect on domestic entrepreneurship at belowthreshold levels of institutional capacity, and a positive effect at above threshold levels of institutional capacity. The paper of Audretsch et al. (2022) points out four main variables which have a significant impact on shaping latent and emergent entrepreneurship: corruption, social relationships, property rights, and government size. Additionally, the authors revealed that entrepreneurs have less aspiration to own or start businesses in those countries with a higher degree of corruption. In the contrast to above, the research of Makhdoom et al. (2021) shows that formal and informal institutions had a negative impact on entry into formal entrepreneurship. However, one of the main issues of their paper was the fact that they used only the ordinary least square method which significantly reduces the robustness of their analysis. Furthermore, Boudreaux and Nikolaev (2018) used multilevel logistic regression on a sample of 45 countries to examine how economic institutions affect opportunity-motivated entrepreneurship. The investigation revealed that economic freedom (a proxy for economic institutions) had positive significant effect in all six models related to opportunitymotivated entrepreneurship. During the literature review, several gaps have been identified. First of all, most of the mentioned authors ignore the time gap between the exact time of a certain change in the institutional framework and the effect of that change in the economy.2 The majority of research conducted on this issue ignore this time gap and assume that the effect is momentary and that it does not happen with a certain time lag. In this paper, the time gap will be incorporated into the models by using all institutional variables with a three-year lag. Secondly, the introduction of lags will eliminate the potential endogeneity problem—a better institutional framework increases

2 The only authors who take this into account to some extent are Chambers and Munemo (2019). Their research took data for the new business density in the period 2007–2012 and for the independent variables in the period 2001–2006. In that way, they attempted to examine the longterm effects of the independent variables on entrepreneurship.

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entrepreneurial activity and higher entrepreneurial activity improves the institutional framework. Finally, the studies mentioned above use mostly institutional variables from databases such as the Heritage Foundation, Worldwide Governance Indicators, Doing Business, and Fraser Institute while variables from Freedom House were less represented.3

3 Description of the Data 3.1

Dependent Variable

There are different types of variables that are used to approximate the entrepreneurial activity. Due to the longer availability of data for the large number of OECD member countries, the New business density variable from the World Bank database will be used in this paper. According to the official definition of the World Bank (2020), it measures the number of newly registered limited liability corporations per calendar year, normalized by working-age population. Table 1 shows that the top 5 countries with the largest new business density in 2018 are Australia, Denmark, Chile, Luxembourg, and the United Kingdom. On the other hand, the top 5 countries with the lowest score in 2018 are Japan, Austria, Mexico, Germany, and Poland. Surprisingly, Germany is at the bottom of this list.

3.2

Independent Variables

A number of scientific studies (Fuentelsaz et al., 2015; Boudreaux & Nikolaev, 2018; Agostino et al., 2019; Slesman et al., 2021; Audretsch et al., 2022) indicate the existence of a positive relationship between the institutional framework and entrepreneurship in the economy. However, in this paper, we will follow the example of authors (Autio & Fu, 2014; Raza et al., 2018; Brieger et al., 2020) who used the Freedom House database. As it can be seen from Tables 2 and 3, in this paper, we will use nine different institutional variables each consisting of several different questions. Each question can take a value from 0 to 4.4 Political Rights consisted of ten different questions, with three questions related to the variable Electoral Process, three to the variable Functioning of Government, and four to the variable Political Pluralism and Participation. In other words, Political Rights consisted of Electoral Process, Functioning of Government followed by Political Pluralism and Participation, where its maximum value is 40 points (see Tables 2 and 3).

3

The authors mentioned in Sect. 1 mostly examined the effect of Freedom House institutional variables on GDP growth rate or GDP per capita. 4 The questions relating to each variable can be seen in the Appendix of this paper (Table 6).

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Table 1 New business density in OECD members countries Country Australia Austria Chile Czech Republic Denmark Finland France Germany Hungary Iceland Ireland Israel Italy Japan Luxembourg Mexico Netherlands Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom

Year 2010 10.79 0.62 3.79 3.05 5.19 3.49 3.89 1.37 6.41 7.55 4.44 3.43 2.25 0.09 12.69 0.62 3.04 4.30 0.67 4.06 4.51 3.83 2.50 5.67 9.69

2011 11.18 0.57 4.93 2.89 5.07 3.60 3.96 1.35 7.60 8.04 4.63 3.29 2.07 0.11 5.39 0.60 3.19 4.96 0.71 4.57 4.89 4.07 2.66 7.16 10.93

2012 12.13 0.54 5.72 2.97 4.74 3.54 3.80 1.31 4.73 8.41 4.47 2.92 1.95 0.14 14.01 0.41 4.38 7.72 0.92 4.01 5.15 4.38 2.75 6.39 11.84

2013 14.64 0.61 7.06 3.09 4.68 3.54 3.79 1.29 4.15 9.03 4.99 2.87 2.14 0.19 15.80 0.55 6.25 7.85 1.12 4.67 5.63 4.34 2.97 6.37 12.53

2014 14.84 0.74 8.05 3.42 7.14 3.43 3.96 1.27 3.63 9.70 5.74 3.07 2.34 0.26 17.55 0.55 5.81 7.65 1.13 4.68 3.12 4.46 3.01 6.83 14.00

2015 15.36 0.63 8.32 3.70 8.31 3.64 4.12 1.30 3.14 10.88 6.25 3.25 2.56 0.29 16.75 0.58 5.83 7.83 1.53 5.06 2.72 4.00 3.05 7.37 14.56

2016 15.47 0.61 8.77 3.98 9.18 3.93 4.52 1.31 3.36 12.27 6.70 3.40 2.63 0.31 15.30 0.56 6.06 8.13 1.66 5.02 4.72 3.13 3.26 7.99 15.75

2017 15.57 0.63 9.35 4.49 9.87 4.02 4.76 1.33 3.47 11.33 7.14 3.27 2.86 0.36 15.94 1.01 6.18 8.65 1.50 5.63 5.24 3.34 3.05 7.72 14.94

2018 14.47 0.65 10.31 4.39 10.01 4.29 4.84 1.35 3.74 9.88 7.13 3.27 2.96 0.39 17.20 1.00 6.42 8.62 1.44 6.49 5.25 3.09 3.07 7.18 15.65

Source: World Bank

On the other hand, the variable Civil Liberties consisted of fifteen different questions, with four relating to Freedom of Expression, three to Organizational Rights, four to Rule of Law, and four to Personal Autonomy and Individual Rights. In other words, Civil Liberties consisted of four variables mentioned above with a maximum value of 60 points (see Tables 2 and 3). There are two main reasons why the main indices (Political Rights and Civil Liberties) and the variable of which these indices consist are used in this research. First of all, a literature gap was observed since authors in the empirical research (Dollar & Kraay, 2003; Autio & Fu, 2014; Nasreen et al., 2015; Raza et al., 2018; Brieger et al., 2020;) frequently use only two major indices from the Freedom House database. Secondly, extending the analysis to the level of variables contributes to the robustness of the results and reduce the degree of subjectivism. The latter is important since subjectivism exists to some extent when different institutional variables are measured.

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Table 2 Variables, measures, and sources of data Variables Measure Control variables dctps Domestic credit to private sector (% of GDP) ser Self-employed, total (% of total employment) (modeled ILO estimate) export Exports of goods and services (% of GDP) fdi Foreign direct investment, net inflows (% of GDP) pop Population (in millions) tnr Total natural resources rents (% of GDP) hc Human capital index ir Long-term interest rate tax Corporate income tax rate Institutional variables prfh3 Political rights epfh3 Electoral process pppfh3 Political pluralism and participation fogfh3 Functioning of government clfh3 Civil liberties febfh3 Freedom of expression and belief aorfh3 Associational and organizational rights rolfh3 Rule of law parfh3 Personal autonomy and individual rights

Source World Bank World Bank World Bank World Bank World Bank World Bank Penn World Table OECD OECD Freedom House Freedom House Freedom House Freedom House Freedom House Freedom House Freedom House Freedom House Freedom House

Source: systematization of the author

Furthermore, as reported in Table 4, there is a high degree of correlation between selected institutional variables. To avoid the potential problem of multicollinearity the variables will be added to the regression one by one.

4 Method of Analysis The empirical part of the paper will investigate the impact of different institutional variables of Freedom House on the New business density in 24 OECD member countries in the period 2010–2018. The research will be conducted with a fixed effects panel. Due to the robustness of the results, all model variants will be made with the cluster option. Introducing the cluster option in panel analysis the standard errors become robust to disturbances such as heteroscedasticity and autocorrelation (Hoechle, 2007). The general model which will be used is as follows: Y it = α1 þ B1 X it þ B2 H it þ B3 Z it - 3 þ εit

ð1Þ

The Impact of Institutional Framework On Entrepreneurship in OECD. . . Table 3 Descriptive statistics

Variable nbd dctps ser export fdi hc pop ir tax tnr prfh3 epfh3 pppfh3 fogfh3 clfh3 febfh3 aorfh3 rolfh3 parfh3 N = 24

Obs 216 216 216 216 216 216 216 216 216 216 216 216 216 216 216 216 216 216 216

Mean 5.298 98.753 15.42 53.124 3.943 3.331 31.11 2.883 23.876 1.33 38.306 11.88 15.324 11.102 55.625 15.356 11.718 14.005 14.546

Std. Dev. 4.222 40.3 5.963 37.207 11.242 0.329 36.645 2.07 5.151 2.47 2.249 0.523 0.816 1.165 4.912 1.033 0.84 2.158 1.452

71 Min 0.088 23.329 6.46 14.545 -40.33 2.361 0.32 -0.177 9 0 28 9 12 7 36 12 8 6 10

Max 17.553 193.04 33.65 221.197 73.533 3.849 128.542 10.547 44.429 12.112 40 12 16 12 60 16 12 16 16

Source: Author’s calculation

The dependent variable Yit refers to the New business density. The vector of variables Xit refers to the control variables in the model and includes: Domestic credit to private sector (dctps), Self-employed (ser), Exports of goods and services (export), Foreign direct investment (fdi), and Human capital index (hc). Vector of variables Hit considers additional independent variables that could have a potential effect on New business density. It is expected that Population (pop) and Total natural resources rents (tnr) have a positive effect while Corporate income tax rate (tax) and Long-term interest rate (ir) have a negative effect on New business density. The Zit 3 vector refers to the institutional variables of interest defined in the section above. To avoid the potential endogeneity problem key variables of interest will be lagged for three years. Furthermore, the variables will be added to the regression one by one to avoid the potential problem of multicollinearity. Finally, due to structural fractures, dummy variables for years are included in all models.

5 Results The results of all estimated models are presented in Table 5. The p-values of the Hausman test (0.0000) indicate that a panel with fixed effects is a suitable method compared to a panel with random effects. Similar to the previous one, p-values of the

1.000 -0.359 -0.274 -0.003 0.008 0.046 -0.210 0.323 0.084 0.585 0.366 0.504 0.612 0.474 0.252 0.195 0.580 0.448

(1) dctps (2) ser (3) export (4) fdi (5) hc (6) pop (7) ir (8) tax (9) tnr (10) prfh3 (11) epfh3 (12) pppfh3 (13) fogfh3 (14) clfh3 (15) febfh3 (16) aorfh3 (17) rolfh3 (18) parfh3

1.000 -0.265 -0.006 -0.579 0.340 0.532 0.150 0.262 -0.549 -0.533 -0.509 -0.464 -0.513 -0.179 -0.468 -0.607 -0.436

(2)

1.000 0.358 0.165 -0.421 -0.160 -0.366 -0.227 0.100 0.083 0.189 0.023 0.208 0.236 0.209 0.132 0.216

(3)

1.000 -0.054 -0.105 -0.034 0.025 0.011 0.150 0.128 0.105 0.159 0.114 0.112 0.070 0.089 0.133

(4)

Source: Author’s calculation Method of Analysis

(1)

Variables

Table 4 Matrix of correlations

1.000 -0.163 -0.551 -0.422 -0.068 0.245 0.373 0.142 0.206 0.160 -0.065 0.269 0.227 0.094

(5)

1.000 -0.012 0.316 -0.018 -0.508 -0.456 -0.497 -0.428 -0.527 -0.539 -0.649 -0.395 -0.434

(6)

1.000 0.052 0.271 -0.313 -0.323 -0.261 -0.278 -0.308 -0.028 -0.222 -0.398 -0.303

(7)

1.000 0.157 -0.139 -0.157 -0.191 -0.063 -0.144 -0.097 -0.258 -0.122 -0.089

(8)

1.000 -0.057 -0.178 -0.150 0.076 -0.022 0.127 -0.163 -0.063 0.024

(9)

1.000 0.826 0.891 0.936 0.918 0.661 0.801 0.903 0.827

(10)

1.000 0.669 0.676 0.703 0.424 0.779 0.721 0.552

(11)

1.000 0.718 0.852 0.662 0.737 0.804 0.787

(12)

1.000 0.859 0.623 0.681 0.857 0.797

(13)

1.000 0.823 0.808 0.925 0.954

(14)

1.000 0.652 0.588 0.821

(15)

1.000 0.673 0.691

(16)

1.000 0.836

(17)

1.000

(18)

72 A. Zdilar

epfh3

prfh3

tnr

tax

ir

pop

hc

fdi

export

ser

dctps

_cons

(1) nbd FE -25.211 (15.629) -0.019** (0.008) 0.359*** (0.123) 0.059*** (0.02) -0.004 (0.009) 2.789 (3.425) 0.009 (0.123) 0.197* (0.115) -0.012 (0.046) -0.494*** (0.109) 0.402** (0.173)

Table 5 Fixed effects panel

0.566

(2) nbd FE -7.72 (12.374) -0.02** (0.008) 0.417*** (0.12) 0.064*** (0.02) -0.002 (0.009) 0.866 (3.354) -0.102 (0.107) 0.218* (0.115) -0.009 (0.046) -0.521*** (0.109)

(3) nbd FE -8.825 (16.309) -0.018** (0.008) 0.403*** (0.125) 0.061*** (0.021) 0.002 (0.009) 1.663 (3.524) -0.102 (0.124) 0.234** (0.115) 0.005 (0.046) -0.503*** (0.112)

(4) nbd FE -10.514 (12.655) -0.022*** (0.008) 0.396*** (0.121) 0.065*** (0.02) -0.002 (0.009) 2.024 (3.4) -0.083 (0.108) 0.209* (0.115) -0.002 (0.045) -0.512*** (0.109)

(5) nbd FE -34.562** (15.498) -0.022*** (0.008) 0.464*** (0.118) 0.053*** (0.02) 0.001 (0.009) 3.435 (3.377) 0.045 (0.117) 0.166 (0.114) -0.01 (0.045) -0.529*** (0.107)

(6) nbd FE -21.797 (13.634) -0.025*** (0.008) 0.463*** (0.119) 0.058*** (0.02) 0.003 (0.009) 3.431 (3.427) -0.031 (0.109) 0.21* (0.113) -0.011 (0.045) -0.513*** (0.107)

(7) nbd FE -14.209 (15.175) -0.02** (0.008) 0.459*** (0.122) 0.055** (0.022) 0.001 (0.009) 2.584 (3.567) -0.078 (0.115) 0.201* (0.118) 0.01 (0.045) -0.519*** (0.109)

(8) nbd FE -5.887 (11.579) -0.02** (0.008) 0.418*** (0.119) 0.066*** (0.02) 0.001 (0.009) 0.476 (3.336) -0.118 (0.102) 0.206* (0.115) -0.005 (0.045) -0.539*** (0.109)

(continued)

(9) nbd FE -2.69 (14.373) -0.02** (0.008) 0.431*** (0.121) 0.064*** (0.021) 0.002 (0.009) 0.979 (3.389) -0.14 (0.114) 0.235** (0.116) 0.005 (0.046) -0.519*** (0.11)

The Impact of Institutional Framework On Entrepreneurship in OECD. . . 73

Observations Number of countries Time Dummies R-sq: within R-sq: between R-sq: overall F test Prob>F Pesaran test Hausman test

parfh3

rolfh3

aorfh3

febfh3

clfh3

fogfh3

pppfh3

Table 5 (continued)

216 24 Yes 0.4402 0.0001 0.0044 7.60 0.0000 1.0918 1990.72

(1)

216 24 Yes 0.4308 0.0403 0.0483 7.32 0.0000 0.5302 963.31

(2) (0.364)

216 24 Yes 0.4248 0.0515 0.0598 7.14 0.0000 0.7498 355.93

0.323 (0.429)

(3)

216 24 Yes 0.4340 0.0514 0.0610 7.41 0.0000 0.8877 110.23

0.476* (0.258)

(4)

216 24 Yes 0.4549 0.0097 0.0012 8.07 0.0000 1.3823 51.76

0.367*** (0.115)

(5)

216 24 Yes 0.4476 0.0035 0.0097 7.83 0.0000 1.1228 49.67

0.655*** (0.235)

(6)

216 24 Yes 0.4294 0.0267 0.0357 7.28 0.0000 0.6493 611.69

0.519 (0.369)

(7)

216 24 Yes 0.4387 0.0744 0.0817 7.56 0.0000 0.7358 945.71

0.458** (0.207)

(8)

0.126 (0.419) 216 24 Yes 0.4232 0.0628 0.0687 7.09 0.0000 0.6432 2871.28

(9)

74 A. Zdilar

Source: Author’s calculation

Prob>chi2 0.0000 0.0000 Cluster option Yes Yes Standard errors are in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1

0.0000 Yes

0.0000 Yes

0.0000 Yes

0.0000 Yes

0.0000 Yes

0.0000 Yes

0.0000 Yes

The Impact of Institutional Framework On Entrepreneurship in OECD. . . 75

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A. Zdilar

F test (0.0000) indicate the appropriate specification of all models while the values of the Pesaran test show that there is no evidence of the presence of serial correlation of the residuals. Additionally, all models include time dummies with values of R-sq.: within from 0.42 to 0.44. Self-employment rate, Export, Domestic credit to the private sector, Long-term interest rate, and Total natural resource rents are statistically significant control variables in all models. However, only the Self-employment rate and Export are with an expected sign, while the other mentioned variables have signs opposite that expected. Political Rights, Functioning of Government, Civil Liberties, Freedom of Expression and Belief and Rule of Law are significant variables of interest. Moreover, Civil Liberties and Freedom of Expression and Belief are significant at the 1% level, Political Rights and Rule of Law are at the 5% level while Functioning of Government is significant at the 10% level. Additionally, all variables of interest have expected signs. The results of the analysis are in correspondence with Autio and Fu (2014), Nasreen et al. (2015), Raza et al. (2018), and Brieger et al. (2020). In their research, the mentioned authors mainly used two variables that proved to be significant in this research as well: Political Rights and Civil Liberties. However, there are also certain differences in relation to the mentioned authors. Due to the robustness of the results in our analysis, we include other variables of which the Political Rights and Civil Liberties variables consist. The investigation points out Functioning of Government, Freedom of Expression and Belief and Rule of Law, as significant components of the institutional framework, which have an impact on entrepreneurial activity within 24 OECD countries. In the end, the three-year lag of the key independent variables of interest reveals the existence of a time lag between certain changes within the institutional framework and their reflections on the economy.

6 Conclusion Policy makers should focus their actions on those policies that mostly encourage entrepreneurship and thus economic growth. Measures aimed at encouraging Selfemployment and Export certainly fall into that category. Through the Functioning of Government policy makers should raise the degree of openness and transparency which automatically decline the degree of corruption in the country. On the other hand, the Rule of Law indicates the importance of a fair and independent juridical system. Additionally, policies aimed at increasing media freedom and freedom of expressing personal views increase the New business density in OECD member countries. Moreover, when making certain institutional changes, policy makers should be aware of the time gap between the moment of making a decision and its reflection in the economy. Introduction of certain institutional decision may have completely different repercussions in the economy after some period of time.

The Impact of Institutional Framework On Entrepreneurship in OECD. . .

77

Consequently, particular institutional decisions may require some changes in a longer period after their adoption. The research conducted in this paper has several limitations that need to be considered. First of all, measuring the institutional variables contains a certain amount of subjectivism that needs to be taken into account before interpreting economic results. Secondly, due to the lack of data, the analysis was conducted on a relatively small sample (24 countries) and time period (2010–2018) which may affect the results of the research. As the third, this paper uses a three-year lag for all institutional variables, even though there is the possibility that certain institutional changes in the economy are reflected at a different pace or through a shorter or longer time period. At the last, the entrepreneurship in this paper is approximated only by New business density, while other possible measures are not taken into account. Following previous discussion, the suggestion for future research is to include other variables that can be used as a proxy of the entrepreneurship. Additionally, analysis could be done at enterprise level using more complex statistical methods.

Appendix Table 6 The questions related to each variable Label Variables Political Rights epfh3 Electoral process

pppfh3

Political Pluralism and Participation

fogfh3

Functioning of Government

The questions of which the variable consists 1. Was the current head of government or other chief national authority elected through free and fair elections? 2. Were the current national legislative representatives elected through free and fair elections? 3. Are the electoral laws and framework fair, and are they implemented impartially by the relevant election management bodies? 1. Do the people have the right to organize in different political parties or other competitive political groupings of their choice, and is the system free of undue obstacles to the rise and fall of these competing parties or groupings? 2. Is there a realistic opportunity for the opposition to increase its support or gain power through elections? 3. Are the people’s political choices free from domination by forces that are external to the political sphere, or by political forces that employ extrapolitical means? 4. Do various segments of the population (including ethnic, racial, religious, gender, LGBT+, and other relevant groups) have full political rights and electoral opportunities? 1. Do the freely elected head of government and national legislative representatives determine the policies of the government? 2. Are safeguards against official corruption strong and (continued)

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Table 6 (continued) Label

Variables

The questions of which the variable consists effective? 3. Does the government operate with openness and transparency?

Civil Liberties febfh3 Freedom of Expression and Belief

aorfh3

Associational and Organizational Rights

rolfh3

Rule of Law

parfh3

Personal Autonomy and Individual Rights

1. Are there free and independent media? 2. Are individuals free to practice and express their religious faith or nonbelief in public and private? 3. Is there academic freedom, and is the educational system free from extensive political indoctrination? 4. Are individuals free to express their personal views on political or other sensitive topics without fear of surveillance or retribution? 1. Is there freedom of assembly? 2. Is there freedom for nongovernmental organizations, particularly those that are engaged in human rights- and governance-related work? 3. Is there freedom for trade unions and similar professional or labor organizations? 1. Is there an independent judiciary? 2. Does due process prevail in civil and criminal matters? 3. Is there protection from the illegitimate use of physical force and freedom from war and insurgencies? 4. Do laws, policies, and practices guarantee equal treatment of various segments of the population? 1. Do individuals enjoy freedom of movement, including the ability to change their place of residence, employment, or education? 2. Are individuals able to exercise the right to own property and establish private businesses without undue interference from state or nonstate actors? 3. Do individuals enjoy personal social freedoms, including choice of marriage partner and size of family, protection from domestic violence, and control over appearance? 4. Do individuals enjoy equality of opportunity and freedom from economic exploitation?

Source: Freedom House

References Agostino, M., Nifo, A., Trivieri, F., & Vecchione, G. (2019). Rule of law and regulatory quality as drivers of entrepreneurship. Regional Studies, 54(6), 814–826. Aidis, R., Estrin, S., & Mickiewicz, T. (2007). Entrepreneurship, institutions and the level of development. Tiger Working Paper Series, 103, pp. 1–47. Ando, M. (2015). Dreams of urbanization: Quantitative case studies on the local impacts of nuclear power facilities using the synthetic control method. Journal of Urban Economics, 85, 68–85.

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Aparicio, S., Urbano, D., & Audretsch, D. (2015). Institutional factors, opportunity entrepreneurship and economic growth: Panel data evidence. Technological Forecasting and Social Change, 102, 1–17. Audretsch, D. B., Belitski, M., Caiazza, R., & Desai, S. (2022). The role of institutions in latent and emergent entrepreneurship. Technological Forecasting and Social Change, 175, 121356. Autio, E., & Fu, K. (2014). Economic and political institutions and entry into formal and informal entrepreneurship. Asia Pacific Journal of Management, 32(1), 67–94. Bénassy-Quéré, A., Coupet, M., & Mayer, T. (2007). Institutional determinants of foreign direct investment. The World Economy, 30(5), 764–782. Bjørnskov, C., & Foss, N. (2008). Economic freedom and entrepreneurial activity: Some crosscountry evidence. Public Choice, 134(3–4), 307–328. Bjørnskov, C., & Foss, N. (2013). How strategic entrepreneurship and the institutional context drive economic growth. Strategic Entrepreneurship Journal., 7(1), 50–69. Bosma, N., Content, J., Sanders, M., & Stam, E. (2018). Institutions, entrepreneurship, and economic growth in Europe. Small Business Economics, 51(2), 483–499. Boudreaux, C. J., & Nikolaev, B. (2018). Capital is not enough: Opportunity entrepreneurship and formal institutions. Small Business Economics, 53(3), 709–738. Brieger, S. A., Baro, A., Criaco, G., & Terjesen, S. (2020). Entrepreneurs’ age, institutions, and social value creation goals: A multi-country study. Small Business Economics, 57(1), 425–453. Casson, M. (1982). The entrepreneur: Aa economic theory. Barnes & Noble Books. Chambers, D., & Munemo, J. (2019). Regulations, institutional quality and entrepreneurship. Journal of Regulatory Economics, 55(1), 44–66. Cole, A. H. (1959). Business enterprise in its social setting (Vol. 27, p. 198). Harvard University Press. Dau, L. A., & Cuervo-Cazurra, A. (2014). To formalize or not to formalize: Entrepreneurship and pro-market institutions. Journal of Business Venturing, 29(5), 668–686. Daude, C., & Stein, E. (2007). The quality of institutions and foreign direct investment. Economics and Politics, 19(3), 317–344. Dawson, J. W. (1998). Institutions, investment, and growth: New cross-country and panel data evidence. Economic Inquiry, 36(4), 603–619. Desai, M., Gompers, P., & Lerner, J. (2003). Institutions, capital constraints and entrepreneurial firm dynamics: Evidence from Europe. National Bureau of Economic Research, 1–51. Djankov, S., La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (2002). The regulation of entry. The Quarterly Journal of Economics., 117(1), 1–37. Dollar, D., & Kraay, A. (2003). Institutions, trade, and growth. Journal of Monetary Economics., 50(1), 133–162. Estrin, S., Korosteleva, J., & Mickiewicz, T. (2013). Which institutions encourage entrepreneurial growth aspirations? Journal of Business Venturing, 28(4), 564–580. Farla, K., de Crombrugghe, D., & Verspangen, B. (2016). Institutions, foreign direct investment, and domestic investment: Crowding out or crowding in? World Development, 88, 1–9. Fuentelsaz, L., González, C., Maícas, J. P., & Montero, J. (2015). How different formal institutions affect opportunity and necessity entrepreneurship. Business Research Quarterly, 18(4), 246–258. Ghura, H., Harraf, A., & Hamdan, A. (2019). Entrepreneurship in emerging economies: The role of corruption and rule of law. 14th European Conference on Innovation and Entrepreneurship ECIE 2019, pp. 1–9. Gwartney, J., Holcombe, R., & Lawson, R. (2004). Economic freedom, institutional quality, and cross-country differences in income and growth. Cato Journal, 24(3), 205–233. Hoechle, D. (2007). Robust standard errors for panel regressions with cross-sectional dependence. The Stata Journal, 7(3), 281–312. Klapper, L., Amit, R., Guillen, M. F., & Quesada, J. M. (2007). Entrepreneurship and firm formation across countries. Policy Research Working Paper: World Bank, pp. 1–38. Knight, F. H. (1921). Risk, uncertainty and profit. Houghton Mifflin Company.

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Makhdoom, H. U. R., Li, C., Asim, S., & Murad, M. (2021). Evaluating the determinants of formal entrepreneurship: An institutional asymmetry perspective. Journal of Developmental Entrepreneurship, 26(1), 1–22. Munemo, J. (2014). Business start-up regulations and the complementarity between foreign and domestic investment. Review of World Economics, 150(4), 745–761. Nasreen, S., Anwar, S., & Waqar, M. Q. (2015). Institutions, investment and economic growth: A cross - country and panel data study. The Singapore Economic Review, 60(04), 1–19. Nyström, K. (2008). The institutions of economic freedom and entrepreneurship: Evidence from panel data. Public Choice, 136(3–4), 269–282. Peres, M., Ameer, W., & Xu, H. (2018). The impact of institutional quality on foreign direct investment inflows: Evidence for developed and developing countries. Economic Research, 31(1), 626–644. Raza, A., Muffatto, M., & Saeed, S. (2018). The influence of formal institutions on the relationship between entrepreneurial readiness and entrepreneurial behaviour: A cross-country analysis. Journal of Small Business and Enterprise Development, 26(1), 133–157. Schumpeter, J. A. (1934). The theory of economic development. Harvard University Press. Silve, F., & Plekhanov, A. (2018). Institutions, innovation and growth: Evidence from industry data. The Economics of Transition, 26(3), 335–362. Slesman, L., Abubakar, Y. A., & Mitra, J. (2021). Foreign direct investment and entrepreneurship: Does the role of institutions matter? International Business Review, 30(4), 1–28. Tomelin, J., Amal, M., Hein, N., & Carpes Dani, A. (2018). Foreign direct investment in the G-20: To what extent do institutions matter? RAUSP Management Journal, 53(3), 404–421. Van Stel, A., Storey, D. J., & Thurik, A. R. (2007). The effect of business regulations on nascent and young business entrepreneurship. Small Business Economics, 28(2–3), 171–186. World Bank. (2020). Doing Business 2020. World Bank.

Part III

Eurasian Business Perspectives: Management Information System

Combining Robotic Process Automation with Artificial Intelligence: Applications, Terminology, Benefits, and Challenges Lewin Schaudt and Dennis Schlegel

Abstract The relevance of Robotic Process Automation (RPA) has increased over the last few years. Combining RPA with Artificial Intelligence (AI) can further enhance the business value of the technology. The aim of this research was to analyze applications, terminology, benefits, and challenges of combining the two technologies. A total of 60 articles were analyzed in a systematic literature review to evaluate the aforementioned areas. The results show that by adding AI, RPA applications can be used in more complex contexts, it is possible to minimize the human factor during the development process, and AI-based decision-making can be integrated into RPA routines. This paper also presents a current overview of the used terminology. Moreover, it shows that by integrating AI, some unseen challenges in RPA projects can emerge, but also a lot of new benefits will come along with it. Based on the outcome, it is concluded that the topic offers a lot of potential, but further research and development is required. The result of this study help researches to gain an overview of the state-of-the-art in combining RPA and AI. Keywords Robotic Process Automation · Artificial Intelligence · Intelligent Robotic Process Automation · Cognitive Automation · Intelligent Automation

1 Introduction Robotic Process Automation is a term for software tools that “operate on the user interface of other computer systems in the way a human would do.” (van der Aalst et al., 2018, p. 269). The well-known technological research and consulting firm Gartner Inc. estimates the current volume of the RPA market at approx. two billion USD and predicts that the RPA market will further grow substantially over the next few years (Gartner Inc., 2021). Plain RPA tools are usually limited to automating L. Schaudt · D. Schlegel (✉) School of Informatics, Reutlingen University, Reutlingen, Germany e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_5

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processes with a high level of repetition and low level of variation. Forrester predicted already in 2019 that the convergence of Artificial Intelligence (AI) and RPA would increase the number of processes that are automatable (Forrester Research Inc., 2018). This direction can also be observed in practice, as leading RPA vendors have equipped their robots with AI functionalities in the last few years (Ribeiro et al., 2021). While the relevance of combining the two technologies is very high, only fragmented knowledge is available in the academic sphere. Authors that have previously conducted research on combining RPA with AI usually focus on one specific orchestration of both technologies, leading to knowledge of only a single combination possibility of RPA and AI amongst others. An overview of possible applications, benefits, and challenges would help to accumulate knowledge in this area much faster. No such overview was presented by any researcher yet, to the best of our knowledge. Recently, more and more researchers (Ng et al., 2021; van de Weerd et al., 2021) have started to examine possible use cases and to evaluate costs and values of combining AI and RPA. However, the understanding of the used terminology varies amongst the research papers. What is missing is a clear distinction between those terms. The main purpose of this study has been to analyze and synthesize prior research in order to provide an overview of the current state of research about the combination of RPA and AI. In order to address this aim, a systematic literature review (Webster & Watson, 2002) has been conducted based on specific, predefined research questions that will be presented in the research design section of this paper. The paper is structured as follows. In Sect. 2, the background of RPA and AI is explained, before the research design is explained in Sect. 3. Subsequently, the results are presented in Sect. 4. Finally, we conclude by summarizing the results and discussing implications of our research, as well as, limitations and future research opportunities.

2 Background 2.1

Robotic Process Automation

van der Aalst et al. (2018) describe Robotic Process Automation as “an umbrella term for tools that operate on the user interface of other computer systems in the way a human would do.” RPA tools can be used to develop and implement software scripts to automate certain actions of a task or process. A script that is created for a specific task is also called a “robot.” In the literature, various benefits of RPA are mentioned, such as the 24/7 availability of robots (Santos et al., 2020). Bots are also faster than humans, as they do not depend on the user’s speed on a computer. Since RPA routines are strictly rule-based, they are also less error-prone than humans. RPA vendors are also seeing advantages for the human workers as RPA is made for

Combining Robotic Process Automation with Artificial Intelligence:. . .

85

“boring” and repetitive tasks and thus relieves employees from doing these tasks. This could lead to higher motivation and a higher focus on value adding tasks. Since RPA is working in a rule-based way, the structure of a task has to be representable with unambiguous rules (Asatiani & Penttinen, 2016). Moreover, processes with stable user interfaces are better suited for automation with RPA as RPA works on an interface level. If the interfaces are not changing frequently, an automation with RPA is even more economically feasible (Slaby & Fersht, 2012). However, RPA has clear limitations. Tasks that have cognitive requirements cannot be automated with RPA alone, because raw RPA lacks analytical and creative skills (Asatiani & Penttinen, 2016).

2.2

Artificial Intelligence

Artificial Intelligence (AI) can be defined as “the ability of a machine to think like a human being, in order to perform a particular task, without being explicitly programmed.” (Silaparasetty, 2020). The vision of AI is to replicate and simulate the cognitive abilities of humans. This vision can be also described as Artificial General Intelligence (AGI) which was not achieved yet. If the application of an AI is limited to a singular task and goal-oriented it can be classified as Artificial Narrow Intelligence (ANI). ANI is the only sort of AI that has been realized to date (Strelkova, 2017). Fields of applications of today’s AI include credit card fraud detection (Benson Edwin Raj & Annie Portia, 2011), speech recognition of voice assistants (Hennebert, 2009), and self-driving cars (Rao & Frtunikj, 2018). Unlike RPA, an AI does not obtain their intelligence from pre-programmed rules, but rather is dependent on the observations from former interactions. The AI learns from old data and reveals underlying information via specific algorithms. The phenomenon of how AI learns is also known as Machine Learning (ML) (Joshi, 2020).

3 Research Design A systematic literature review (SLR) was performed to answer four predefined research questions (RQ). A SLR is a common method to examine multiple sources about a predefined goal (Stamm & Schwarb, 1995). An SLR aims to answer a specific question or to summarize the current state of the art (Conn et al., 2003). The performed SLR is based on the basic ideas presented by Webster and Watson (2002). This SLR has analyzed articles based on the following four research questions:

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• RQ1 What applications are possible by combining RPA with AI? • RQ2: What terms have been used to describe the combination of RPA and AI and how do they differentiate from each other? • RQ3: What are the benefits of combining RPA and AI? • RQ4: What challenges occur in projects that combine RPA with AI? As a literature database, Google Scholar and Web of Science were used. Google Scholar was used since it also contains gray literature. Gray literature can be defined as follows: “gray literature is produced on all levels of government, academics, business and industry in print and electronic formats, but which is not controlled by commercial publishers, i.e., where publishing is not the primary activity of the producing body” (Schöpfel & Farace, 2010, p. 2030). For this work, gray literature is essential, as latest work in the domain often depends on practice-oriented work by consulting firms and RPA software vendors. Because of the specific characteristics of each database different search strings were used. Since Google Scholar’s filter functionalities are limited, the search string was only applied to the title, in order to avoid too many results: ((“Artificial Intelligence” OR “AI” OR “Intelligent” OR “Smart” OR “Cognitive”) AND (“Robotic Process Automation” OR “RPA” OR “Process Automation”)) The search in Web of Science was not limited to certain fields, but applied to the complete article. For that reason, the search string was slightly different. The term RPA was not included because that acronym also has different meanings in different fields (e.g., medicine). The search string for Web of Science was defined as follows: ((“Artificial Intelligence” OR “AI” OR “Intelligent” OR “Smart” OR “Cognitive”) AND (“Robotic Process Automation” OR “Process Automation”)) Furthermore, a backward search was conducted in order to identify additional relevant papers by reviewing the citations of relevant articles (Webster & Watson, 2002). Figure 1 outlines the article selection process in several steps. A total of 152 relevant articles were found within Google Scholar and a total of 433 articles from Web of Science. Of these 585 articles, 43 duplicates were identified, leaving a total of 542 articles left. The articles were then examined based on their titles, abstracts, and full texts in several iterations in order to decide whether they should be part of the SLR. An article was included in the SLR if it met all of the following criteria: 1. Provides insight for at least one of the RQs 2. Is written in English or German 3. Is accessible for the authors Gray literature was included if the publisher was either an RPA vendor, RPA consultant, or a well-known blog or magazine for computer or information science, in order to ensure a sufficient quality of the information. Other SLRs have not been included into this SLR as a source. 60 relevant articles were included in the final analysis.

Combining Robotic Process Automation with Artificial Intelligence:. . .

87

Fig. 1 Article selection process. Source: own work

4 Results 4.1

Applications

Out of 60 Articles, 46 described at least one possible application. We have categorized the possible applications of combining RPA with AI into three categories that were developed inductively based on the concepts that emerged from the literature: (1) Enhancing RPA bots by using ML/AI, (2) AI controlling an RPA routine, and (3) using AI to develop RPA bots 1. The first application is to enhance RPA bots with more sophisticated functionalities using the ML/AI technology. Above all, the most mentioned enhancement is the use of unstructured data. One weakness of pure RPA is that it is only possible to process structured data. With the help of AI, robots are now capable of extracting information from unstructured data or converting unstructured data into structured data (Rizk et al., 2020; Reddy et al., 2019; Lasso & Herrera, 2019a; Cheng et al., 2019; S. C. Lin et al., 2018; Beerbaum, 2021; Kobayashi et al., 2019; Epiance Software Pvt. Ltd., 2017; McKinsey, 2017; Koch & Wildner, 2020; Viehhauser, 2020; Yatskiv et al., 2020; Anagoste, 2018; Ribeiro et al., 2021; Deloitte, 2016b; Moiseeva, 2020; Williams & Allen, 2017; Martens, 2018; Cutura, 2021; Capgemini, 2020; Ephesoft, 2018; Bingler, 2019; AIMdek

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Technologies, 2018; Martínez-Rojas et al., 2020; Gentsch, 2017). Moreover, using AI/ML as a function in a routine can also enhance the RPA robots with cognitive abilities. Robots are now able to perform cognitive tasks (Polak et al., 2020). Cognitive skills extend the range of automation abilities (Deloitte, 2016b) and make RPA even more human-like (Williams & Allen, 2017). Robots with cognitive enhancements are also capable of interacting with users (Bingler, 2019). These applications are not restricted to a single area of AI. Some areas of AI are well researched and can already be included into RPA routines. For example, AI for Optical Character Recognition (OCR) is already used widely as an RPA function. OCR is used to automatically analyze handwritten notes or printed text from a scanned document. The fact that those kinds of unstructured data are part of a lot of business processes is one reason why this aspect has already been very well researched (Bingler, 2019; Bremmer, 2019; Gould, 2018; Safar, 2019; Ephesoft, 2018; Ling et al., 2020; Cfb Bots, 2018; Kirchmer & Franz, 2019; Palanivel & Joseph, 2020; Ribeiro et al., 2021; Anagoste, 2018; Yatskiv et al., 2020; Rizun et al., 2019; Viehhauser, 2020; Galer, 2020; Koch & Wildner, 2020; S. C. Lin et al., 2018; Cheng et al., 2019; Yara Rizk et al., 2020). Another area that has already been intensely discussed is Natural Language Processing (NLP). NLP deals with the processing of natural language to provide machines with information and statements of spoken or written words. Similar to OCR, NLP is also a big part of business processes and has also been deeply investigated (Beerbaum, 2021; Kobayashi et al., 2019; Zou et al., 2020; Mohanty & Vyas, 2018; Lasso-Rodriguez & Winkler, 2020; Viehhauser, 2020; Deloitte, 2016b; Moiseeva, 2020; Moiseeva et al., 2020; Cutura, 2021; Ling et al., 2020; Ephesoft, 2018; Bingler, 2019; Bremmer, 2019; Safar, 2019; Deloitte, 2016a; AIMdek Technologies, 2018; Galer, 2020). Image Recognition (IR) deals with the processing of images. Processing means that AIs in this field are used to classify pictures or find objects in images. IR is not as common to combine with RPA as NLP or OCR, but it is also relatively well explored (S. C. Lin et al., 2018; Mohanty & Vyas, 2018; McKinsey, 2017; Koch & Wildner, 2020; Viehhauser, 2020; Rizun et al., 2019; Martins et al., 2020; Deloitte, 2016a). While OCR, NLP, and IR mainly focus on filtering and analyzing unstructured data, it is also possible to include AIs with a focus on interactions with users of RPA routines directly. Research has already included techniques for Facial Recognition (FR). With FR techniques it is possible to verify the person that is sitting in front of the machine or recognize faces on images (Kobayashi et al., 2019; Koch & Wildner, 2020). Lasso and Herrera went a step further by adding a capturing of facial expressions (Lasso & Herrera, 2019a; Lasso & Herrera, 2019b). While using images for FR, Voice Recognition (VR) is using a recording for similar approaches. While some articles just mention the possibility to include VR (Cutura, 2021; Koch & Wildner, 2020; Mohanty & Vyas, 2018; Viehhauser, 2020), Kobayashi et al. (2019) proposed an RPA routine where VR is used for personal identification. Eye Tracking (ET) is only used by Lasso and Herrera (2019b) and it extracts the information where the RPA user is looking at.

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2. Regarding the application where AI is controlling an RPA routine, one possible application is a self-healing or retraining software robot (Williams & Allen, 2017; Mohanty & Vyas, 2018). Chakraborti et al. (2020) suggest retraining the RPA routines based on monitoring. The routines could be redesigned, for example, by interaction with human workers and/or a feedback loop where the AI is asking specific questions to solve the occurred problem (Martens, 2018; Tuttle, 2019; Bingler, 2019; Palanivel & Joseph, 2020). Having the RPA robot find ways to fix errors, it should be possible to have a robot restructure its routine and optimize it. Williams and Allen (2017) also mention that possibility. Unfortunately, they are not further elaborating on that application. Raw RPA bots are based on predefined rules. By integrating AI, it is possible to involve AI as a decision maker. In this way, a RPA robot is enabled to make more complex decisions (Mohanty & Vyas, 2018; Agostinelli et al. 2020, b; Kirchmer & Franz, 2019; Chakraborti et al., 2020; Agostinelli et al. 2020, b). With deep learning algorithms, it is possible to learn from human decisions (McKinsey, 2017; Polak et al., 2020). This leads to almost completely autonomous decision-making (Williams & Allen, 2017; Tuttle, 2019; Bingler, 2019; Met et al., 2020). Replacing a ruleset with an ML model makes it possible to memorize even less-used patterns (Beerbaum, 2021; Kirchmer & Franz, 2019). It is even possible to handle exceptions that would be too complicated to implement in a raw RPA routine. AI brings also the ability to make decisions based on unstructured input (Yatskiv et al., 2020) and it is not necessary that the basis of the decision is structured, as AI can handle unstructured data without preprocessing it and transforming it to structured data. 3. The third category is that AI is used to design RPA robots (Chakraborti et al., 2020). At first, an AI could be used to analyze event logs of processes (Agostinelli et al. 2020, b; Martínez-Rojas et al., 2020; Epiance Software Pvt. Ltd., 2017). In this case, it is not necessary that the processes are provided by a human worker. Because of the nature of RPA, it is also possible to derive logs of RPA routines (Gao et al., 2019; Williams & Allen, 2017; Koch & Wildner, 2020). The recorded events are then analyzed with process mining algorithms to find suitable candidates for developing RPA routines. The result can then be used to decide if a process is a good candidate for an RPA implementation (Agostinelli et al. 2020, b; Geyer-Klingenberg et al., 2018). In a second step, an RPA routine is implemented with the result of the process mining algorithm as a template (Ferreira et al., 2020; Williams & Allen, 2017; AIMdek Technologies, 2018; Agostinelli et al., 2019; Anagoste, 2018; Agostinelli et al. 2020, b). A complete description of this process from analyzing logs, finding automation candidates, implementing the routines, and monitoring and testing them can be found in the respective literature sources (Geyer-Klingenberg et al., 2018; Agostinelli et al. 2020, b; Chakraborti et al., 2020). Another possible way to create a RPA robot is the use of a demo video (Ferreira et al., 2020; Agostinelli et al., 2019). Instead of logs that are used to derive the process, an AI is trained to develop RPA robots from a video that shows the steps of a process. It is also possible to use a textual description instead of a video to build an RPA routine (Ferreira et al., 2020).

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Terminology

36 out of the used 60 articles provided information regarding RQ2. 17 different terms were used, while only 4 of them were used by more than two different papers. The term “Intelligent Process Automation” was used in 17 articles and is by far the most used term. However, it has to be noted that this term is used for a variety of different definitions. The definitions or descriptions from the articles contained the following parts: • AI is implementing RPA (Gao et al., 2019; Ferreira et al., 2020; Agostinelli et al. 2020, b) • Using AI to improve RPA (Reddy et al., 2019) • Identifying automation opportunities (Chakraborti et al., 2020) • Monitoring robot performance (Chakraborti et al., 2020) • RPA with self-learning capabilities (Mohanty & Vyas, 2018; Koch & Wildner, 2020; Ferreira et al., 2020; Ravindranath & Bhaskar, 2020; Williams & Allen, 2017) • RPA using AI/ML (Williams & Allen, 2017) • Optimizing RPA routines with AI (Williams & Allen, 2017) • ML-based decision-making (Williams & Allen, 2017) • Adding cognitive skills to robot (Tuttle, 2019) The term “Cognitive Robotic Process Automation” was used in 11 separate articles and used more consistently in the literature. The definitions are mostly related to RPA using AI/ML to gather unstructured data or decisions being made by ML models. Only one author (Sobczak, 2021) is using that term to refer to a robot with self-learning capabilities. The following listing is presenting the sub-components that were used within the articles: • Using AI to improve RPA (Beerbaum, 2021; Koch & Wildner, 2020) • Using AI/ML to use unstructured data (Gupta et al., 2019; Sobczak, 2021; Deloitte, 2016b; Cutura, 2021; Bingler, 2019; Safar, 2019; AIMdek Technologies, 2018; Martínez-Rojas et al., 2020) • ML-based decision-making (Sobczak, 2021; Deloitte, 2016b; Met et al., 2020; Cutura, 2021; Bingler, 2019; AIMdek Technologies, 2018) • RPA with self-learning capabilities (Sobczak, 2021) • Adding cognitive skills to robot (Bingler, 2019) With a usage of 7 times, the term “Intelligent Robotic Process Automation” was used less frequently than the terms described before and largely overlaps with the term “Cognitive Robotic Process Automation” regarding the detailed content. In total, 4 articles used the term “Intelligent Automation.” The definition in all cases was relatively superficial. The three sub-components, “Using AI to improve RPA” (Beerbaum, 2021; Koch & Wildner, 2020), “RPA using AI/ML” (Cfb Bots, 2018; Bingler, 2019), and “Combination of RPA and AI” (Cutura, 2021). The following terms are the terms that were not in frequent use to describe combinations of RPA

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and AI: RPA with Cognitive Technology (Deloitte, 2016b), Smart Process Automation (Beerbaum, 2021), RPA third Generation (Vajgel et al., 2021; Koch & Wildner, 2020), intelligent, interactive, and self-learning RPA (Epiance Software Pvt. Ltd., 2017), RPA 2.0 (Chakraborti et al., 2020), Cognitive Automation (Koch & Wildner, 2020; Safar, 2019), Cognitive Process Automation (Koch & Wildner, 2020; Safar, 2019), Autonomous RPA (Gupta et al., 2019), Cognitive Robotics (PWC, 2017), Complex Process Automation (Williams & Allen, 2017), Smart RPA (Kirchmer & Franz, 2019), and True Cognitive RPA (Gould, 2018).

4.3

Benefits

RQ3 focuses on the benefits of combining RPA and AI. 21 of the 60 articles included information about benefits. The results are summarized in Table 1. Combining RPA and AI brings the possibility of automation of even more complex processes. One reason is the chance to involve AI decision-making (Chakraborti et al., 2020). Another reason is that RPA is able to perform more complex tasks if it is enhanced with AI/ML (Mohanty & Vyas, 2018; Williams & Allen, 2017; Kirchmer & Franz, 2019; Capgemini, 2020). RPA and AI are also capable of handling processes that need cognitive skills (Moiseeva et al., 2020), as well as handling complex data structures (Safar, 2019). RPA routines are also able to handle even complex exceptions in a process (Deloitte, 2016b). Adding AI to RPA can help to minimize human-dependent work in the RPA lifecycle (Chakraborti et al., 2020). Thus, during the execution of a RPA routine, only minimal human intervention is needed (Cfb Bots, 2018). Having an AI in control of RPA, no human worker is needed for handling errors or feedback either, as the RPA robot can be set up to process feedback automatically (Tuttle, 2019). If the routine has AI support for their self-learning, no RPA developer is needed to adjust the procedure (Gao et al., 2019). One advantage of erasing the human factor out of a process is that it is less vulnerable for fraud and that there is less human contact with sensitive data (Reddy et al., 2019; Ravindranath & Bhaskar, 2020; Peterson & George, 2017). Using ML for decision-making in an RPA routine can lead to better decisionmaking. According to some authors, well-trained ML models make better decisions than humans (Cfb Bots, 2018). AI and RPA can reduce the mistakes made by a human which leads to a higher effectiveness of a process (Yoon, 2020; Deloitte, 2016b). Unlike in traditional RPA where only structure data is applicable, the use of unstructured data is enabled by AI (Gotthardt et al., 2020; Capgemini, 2020; Vajgel et al., 2021; Viehhauser, 2020; Capgemini, 2020). With AI it is possible to convert unstructured data into structured data (Mohanty & Vyas, 2018) or to directly make decisions based on unstructured data (Yatskiv et al., 2020). Combining RPA with AI increases the efficiency of RPA. It is more efficient because a process that includes AI runs faster, as more complex tasks can be

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Table 1 Concept matrix for benefits More complex automation McKinsey (2017) Capgemini (2020) Cfb Bots (2018) Chakraborti et al. (2020) Deloitte (2016b) Galer (2020) Gao et al. (2019) Gotthardt et al. (2020) Kirchmer and Franz (2019) Mohanty and Vyas (2018) Moiseeva et al. (2020) Peterson and George (2017) Ravindranath and Bhaskar (2020) Reddy et al. (2019) Safar (2019) Tuttle (2019) Vajgel et al. (2021) Viehhauser (2020) Williams and Allen (2017) Yatskiv et al. (2020) Yoon (2020)

Minimize humandependent work

Make better decisions

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●

Use unstructured data

Increase efficiency ●

● ● ●

●

●

● ● ● ○

● ●

●

● ● ● ● ● ●

● ● ●

●

● ● ●

(●) directly mentioned, (○) indirectly mentioned Source: own work

automated (McKinsey, 2017; Galer, 2020). As larger parts of the business process are automated, the complete process is faster. It also improves the interaction between humans workers and machines (Williams & Allen, 2017).

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Challenges

With regard to challenges that need be considered when using AI to enhance RPA, 15 articles were identified to shed light on this question. As can be seen in Table 2, different challenges can arise. Adding AI to RPA also adds some challenges related to AI in general. One challenge is the need of good quality data. If an AI is trained with data of bad quality, it is very likely that the quality of the AI is also bad (Lasso & Herrera, 2019a; Martens, 2018; Kirchmer & Franz, 2019). Another challenge is the consideration of AI ethics (Mendling et al., 2018). Beerbaum (2021) describes possible compliance challenges that can occur during the development of the combination of RPA and AI, due to strong legal regulation of some processes. In general projects that contain the combination of RPA and AI have higher costs (Cfb Bots, 2018). One reason is that building an AI is more costly than pure RPA because of necessary preparation of training data (Chakraborti et al., 2020). An AI that has to be set up from scratch is affecting the project with additional costs (Lasso Table 2 Concept matrix for challenges General AI challenges Agostinelli et al. (2019) Agostinelli et al. (2020, b) Beerbaum (2021) Cfb Bots (2018) Chakraborti et al. (2020) Deloitte (2016b) Ferreira et al. (2020) Gotthardt et al. (2020) Kirchmer and Franz (2019) Koch and Wildner (2020) Lasso and Herrera (2019a) Martens (2018) Mendling et al. (2018) Peterson and George (2017) Vajgel et al. (2021)

Higher project complexity

Higher costs

RPA tool readiness ●

Trust

● ● ●

● ●

● ●

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● ● ● ● ● ●

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● ● ● ●

(●) directly mentioned, (○) indirectly mentioned Source: own work

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& Herrera, 2019a). Also maintaining AI is relatively expensive. If the requirements or the process is changing, and the AI has to be retrained, the cost-intensive process of rebuilding an AI has to be done (Chakraborti et al., 2020). Because two technologies are in use, the project complexity is higher than in usual RPA projects (Cfb Bots, 2018). Although AI has already been a part in many projects for a longer time now, AI skills are still relatively rare, also among RPA developers. These skills can only be acquired with experience which needs time to build (Deloitte, 2016b). Also, the orchestration of RPA robots with AI and other RPA routines is very complex (Chakraborti et al., 2020). One reason why the orchestrating of RPA robots is still very complex is the capability of RPA tools to include AI. Many of the RPA tools do not have the needed AI functionalities yet (Ferreira et al., 2020; Agostinelli et al., 2019; Agostinelli et al. 2020, b; Martens, 2018; Peterson & George, 2017). Both technologies, RPA and ML, suffer from low trust and acceptance by many business users (Chakraborti et al., 2020; Mendling et al., 2018). The addition of AI brings a new risk of losing money and reputation if the project fails. Moreover, the general trust in results of AI models is very low (Chakraborti et al., 2020). Also the complexity of the technologies is deterring the trust in both technologies (Mendling et al., 2018).

5 Conclusion This review shows that the addition of RPA with AI can be used to develop more complex and independent robots that are able to use unstructured data or make decisions in the process. Additionally, AI can be used in the development process of RPA bots. In general, it can be said that combining RPA with AI enhances the toolset of traditional RPA and enables it to realize more complex automation, but on the other hand, the implementation of such projects gets more complex. One of the issues that emerges from our findings is that no consistent terminology is used to describe these types of bots. Instead, various buzzwords including “intelligent” and “cognitive” are combined in different ways by different authors to describe similar or diverging concepts. In order to advance the field, more conceptual research should be conducted to organize and structure the different concepts and terms. Moreover, it has to be noted that in some prior research, the boundary between vision and reality is not always clear as of today. Our research contributes to knowledge by providing a first attempt to structure the field by explicitly differentiating between three different categories of applications. Furthermore, we provide a starting point for further in-depth analyses of the terminology used in the field and a reconciliation of different definitions. Despite that our work will hopefully be useful for further research, our study clearly has some limitations. Due to the nature of our research design, we might have adopted bias and imperfections from the previous studies. In a similar vein, analyzing previously published papers does not include a feasibility check of some of the concepts that

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were mentioned, so that further research should be done to investigate the technical and economic feasibility of combining RPA with AI.

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Google Analytics Best Practices in Slovak and Czech Online Business Miroslav Reiter and Andrej Miklosik

Abstract This paper describes some of the typical mistakes in web analytics in various organizations. It identifies the most common errors in Google Analytics accounts. We have also compiled a list of best practices for using Google Analytics by online businesses. The basis of our research was an audit of data from Google Analytics accounts of selected Slovak and Czech companies that sell their products and provide services through e-shops. The qualitative research was conducted through personal, semi-structured interviews with two web analytics experts who are Google-certified trainers. Web analytics, measurement, and Google Analytics are of great importance to any type of organization regardless of its size. That is why we attempted to highlight the most common mistakes and compile a list of recommendations applicable to most e-shops. Individual errors are categorized according to the group of problems and their severity. Web analytics and measurement are key to the continuous improvement of products and services not only in online business. If an organization is to improve, it needs the most accurate information about traffic, users, and conversions such as purchases, sign-ups, video and content viewing, and more. The main aim of this paper is to provide a list of specific analytics recommendations for any of the websites and e-shops in Google Analytics to support online business. Keywords Best practices · Business · Google Analytics · Online Marketing · E-shops · Web Analytics

1 Introduction Google Analytics is one of the most popular online analytical tools available, with a market share above 55% (W3Techs.com, 2021). It can assist managers of e-commerce organizations to make sense of the big data about the behavior of M. Reiter (✉) · A. Miklosik Faculty of Management, Comenius University in Bratislava, Bratislava, Slovak Republic e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_6

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their users to help them survive and grow in the highly competitive local or global markets (Kucharcikova et al., 2018). Although many businesses are struggling with the optimal implementation and use of Google Analytics (Patel, 2022), academic research that covers verified best practices and the identification of the most common implementation mistakes is scarce. Conducting the research presented in this paper has been motivated by the existence of this research gap. The main aim of this paper is to provide a list of specific recommendations for managers of websites and e-commerce solutions concerning the effective use of Google Analytics for online businesses. Using qualitative research methods of semi-structured interviews and structured qualitative analysis (SQA), we identify the most common errors in Google Analytics accounts. The findings include the identification of the most fundamental issue, which was the incorrectly deployed tracking code for Google Analytics. Other 25 issues were identified and categorized by their area and severity. Based on these issues, we have created a list of 10 recommendations that can be used by any e-shop operating in any segment. We have compiled the list with regard to the diversity of knowledge levels and seniority of companies. The existing literature dealing with the best practices of Google Analytics implementation and the identification and categorization of errors is limited. There are some academic papers available addressing certain aspects of web analytics, e.g. ROI (Silva et al., 2020), website performance analysis (Domazet & Simovic, 2020), or website traffic (Semeradova & Weinlich, 2020). Industry white papers or blog posts focusing on Google Analytics errors can be found, e.g. Harnish (2021) or Patel (2022), however, these often lack academic rigor and methodology details. This paper contributes to the literature by providing structured, business practiceverified insights into the implementation and set-up processes of Google Analytics that can result in enhanced performance of organizations. The paper is structured as follows: Following the Introduction section, the Background section summarizes in three subchapters the current state of the investigated phenomena. Next, Data and Methodology section describes the research approach, research sample, and details regarding data processing and analytics. Findings are presented in Chapter “The Impact of Institutional Framework on Entrepreneurship in OECD Members Countries”, followed by Discussion and Conclusion sections.

2 Background 2.1

Consumer Behavior in the Online Environment

Over the past decade, e-commerce has been growing steadily with consumers spending more money on online purchases every year. The COVID-19 pandemic has only accelerated this development when various restrictions prohibited people from shopping in physical retail stores. According to the latest Statista.com data,

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Fig. 1 Retail e-commerce sales worldwide from 2014 to 2024. Source: Chevalier (2021) 1 * represents forecasts of sales in billion U.S. dollars in Fig. 1

retail e-commerce sales worldwide have grown from 1336 billion USD in 2014 to more than 4280 USD in 2020. Further steady growth is projected for years 2021 to 2024 (Fig. 1). In this situation, it is an absolute necessity for organizations to analyze the behavior of the users of their websites and mobile applications. There is so much to understand about consumers that can be used to serve them better and tailor marketing programs to support the success of e-commerce organizations. Some of this knowledge includes: • Understanding how consumers find information and how the consumer decisionmaking process develops over time (Miklosik et al., 2020a). • Analyzing the effectiveness of various marketing communication channels, both offline and online (Korenkova et al., 2020; Krizanova et al., 2019; Miklosik, Starchon, & Evans, 2020b). • Evaluating the effects of scarcity on online consumer behavior (Goldsmith et al., 2020; Hamilton et al., 2019; Mou & Shin, 2018; Wu et al., 2012). • Getting the right data for the optimization of conversion rates based on user paths, funnel analysis, user behavior on a particular web page, etc. (Pavan, 2018; Strzelecki, 2019; Troisi et al., 2020). Analytical software tools such as Google Analytics can provide inputs and data for all the above decisions. The challenge is to make sense of the vast amount of data generated by such tools, configure them correctly, and have the knowledge to extract the right data for a particular decision.

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Making Sense of Big Data in e-Commerce Organizations

Big data has been part of marketing analytics since the dawn of web analytics. Technology enables us to capture every single action of a website visitor, resulting in vast amounts of data. Big data and technologies such as machine learning, enabling marketers to analyze big data, are the driving force behind digital transformation in marketing (Miklosik & Evans, 2020), resulting in data-driven marketing and marketing organizations (Johnson et al., 2019). On the one hand, having access to big data related to online consumer behavior presents an enormous opportunity for marketers. Just a decade ago, it was so difficult to get insights into how consumers decide, what is important to them, what kind of information they require to choose a particular brand or make the purchase, and which type of communication is more effective for a certain consumer segment, etc. This has all changed and this data is readily available for analysts and marketers to be used at any time. On the other hand, these huge data sets can provide big challenges as it can be difficult to navigate such loads of different data to make sense of them (Reiter & Miklosik, 2020). Cleansing, processing, and analyzing such large datasets create significant challenges for marketing organizations (Jabbar et al., 2020). In relation to big data, the theory has started to distinguish between Big Data Analytics (BDA) and Classical Marketing Analytics (CMA) (Saidali et al., 2019). The analysis is shifting toward broadening the understanding of online retailing across various electronic channels and e-channel touchpoints (e.g., websites and mobile shopping apps) from a consumer perspective (Wagner et al., 2020). Various analytical tools are widely adopted in marketing assisting marketers in deciphering customer journeys and tailoring their marketing strategies (Miklosik et al., 2019). Google Analytics is one such tool which enables website administrators to track and analyze behavior of their users to make qualified decisions.

2.3

Opportunities and Challenges of Web Analytics

Efficient web analytics is the core of a working e-business model. Boufenneche et al. (2022) prepared an overview of web analytics tools for e-commerce. They focus on the identification of tools for web analytics that are utilized not just for monitoring and measuring website traffic but also for examining business activity (Boufenneche et al., 2022). The work of Kumar and Ayodeji (2022) discusses the different categories of web analytics, along with their implementation and management perspective concerning online retail in India. Önder and Berbekova (2022) examined the use of web analytics by European destination management organizations. They conclude that many organizations use web analytics data for website quality assurance, but that some are also using them to drive marketing program. Using web analytics data for business intelligence was identified as a research gap (Önder & Berbekova, 2022). The study by Chitkara and

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Mahmood (2020) focuses on web analytics as the catalyst for the success of startup businesses that can prevent them from failing. Google Analytics is a dominant online analytical tool with its market share above 55% (W3Techs.com , 2021). Launched in 2005 by Google, one of the main drivers of its wide acceptance was it is free of charge. Its features have evolved in time with GA4 being the current platform and its fourth iteration, combining both website and application analytics (Google, 2021). Although the data has mostly been presented as generalized and anonymized, Google has always captured individual user’s IP address to determine their approximate location. Quite recently, there has been a way of extracting detailed information on individual user behavior based on their IP address. This, also in relation to the implementation of GDPR (General Data Protection Regulation) (Strzelecki & Rizun, 2020), has naturally further fuelled privacy concerns that were discussed in relation to Google Analytics and similar online analytical tools. In response to this, Google removed the access to viewing visitor IP addresses in Google Analytics completely in 2020 (Aube, 2021). Google Analytics has been widely used by marketers for: 1. Examining website traffic (Semeradova & Weinlich, 2020) 2. Measuring and comparing website performance (Domazet & Simovic, 2020) 3. Determining the right KPIs for measuring marketing and business performance (Domazet & Simovic, 2020) 4. Analyzing and optimizing the Return on Investment (ROI) for various communication channels (Silva et al., 2020) 5. Designing and developing mobile applications (Jabbar et al., 2020) The literature dealing with implementation of best practices and identification and categorization of errors is rather limited. There are some practical studies available. For example, Harnish (Harnish, 2021) identified 15 common Google Analytics errors. Patel (2022) categorizes Google Analytics errors into campaign errors, code errors, tag manager errors, eCommerce errors, AdWords & Analytics Linking Errors, or filter errors. The data sources and research approach of such practical studies are often unclear and not rigorous. The documentation from Google (Google, 2022) deals mostly with technical/code errors.

3 Data and Methodology Qualitative research was conducted through personal, semi-structured interviews with two web analytics experts who are also Google-certified trainers. The experts were selected based on their certification and experience. Only highly knowledgeable people with extensive experience can become Google trainers. Each EU country only has one Google-certified trainer for each of the main Google Apps, such as Google Ads, Google Analytics, Google Tag Manager, etc. Both chosen trainers were

106 Table 1 Research sample companies for data analysis

M. Reiter and A. Miklosik Period Count of companies Count of companies with shop Average revenue Count of segments

30.09.2020–29.09.2021 32 29 €108,732 12

Source: own work

certified for more than five years and also accredited by the Ministry of Education, Science, Research, and Sport of the Slovak Republic. Both authors prepared the interview structure and questions. The interviews were conducted between 09/2020 and 09/2021 via Google Meet and Skype, were audio recorded, and subsequently transcribed. The reason for choosing this type of research and research sample was to get expert insights into what the current best practices in web analytics are and what the typical mistakes of e-commerce organizations look like in this regard. Following this, an analysis of user behavior data from Google Analytics of selected Slovak and Czech e-commerce companies was performed to verify the previously identified issues, confirm them, and identify new ones. The reasons behind the selection of businesses from Slovakia and the Czech Republic in the research sample are twofold. Firstly, there is no academic paper addressing the issues of web analytics and specifically Google Analytics implementation in these countries. Secondly, the authors of this study could use their relationships with some of these companies to access these sensible and confidential analytics data. Structured qualitative analysis (SQA) was used to analyze online consumer behavior data. SQA has proved to be efficient in providing multiple relationships to represent different sizes of data with different characteristics to create consistent empirical results (Huarng & Yu, 2020). Companies that sell their products and provide services through e-shops have been selected for the research sample. These companies took part in the educational program Google Business Academy and were interested in deeper cooperation in regard to their Google Analytics account. 32 Google Analytics profiles were analyzed (Table 1). 91% of the analyzed websites do have an e-shop. The following segments were involved: advertising services, construction, cosmetics, health, education, fitness, furniture, health, IT services, pet shop, taxi, and translations. Author 1 of this study had full access to the anonymized user data based on his business relationships with these companies. Based on the analysis, the list of the most common mistakes in using and configuring Google Analytics for e-commerce analytics was compiled.

4 Findings In Google Analytics, we can evaluate and analyze more than 200 different parameters. We have decided to evaluate the key parameters according to the recommendations of Google-certified trainers from the interviews. We categorized the common

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account issues into 2 groups, namely 1) errors caused by incorrect or insufficient settings and 2) analytical (interpretation) errors. This was done to simplify the taxonomy from Patel (2022) and combine it with the one from Google (2022). In Fig. 2, we can see that the issues from category 1 (Google Analytics settings) are prevalent. An important step in Google Analytics is to set up your accounts correctly from the beginning. This avoids unnecessary mistakes and problems. We further categorized the individual problems according to their level of severity (Fig. 3). Based on structured qualitative analysis, we have compiled Table 2 with the most common account issues. Based on the issues, we have created a list of top 10 recommendations in Table 3 that can be used for any e-shop and segment. We have compiled the list with regard to the diversity of knowledge levels and seniority of companies.

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Fig. 2 Google Analytics issue categorization. Source: own work 14

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Fig. 3 Google Analytics severity issue categorization. Source: own work

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Table 2 Most common issues at Google Analytics N 1 2

Categorization Settings Settings

3 4

Settings Settings

5 6 7 8 9 10

Settings Settings Settings Settings Settings Settings

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Settings

12 13 14

Settings Settings Settings

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Analytics

16 17

Analytics Analytics

18

Analytics

19

Analytics

20

Analytics

21 22

Analytics Analytics

23 24 25

Analytics Analytics Analytics

26

Analytics

Source: own work

Issue Google Analytics Tracking Code (GATC) incorrectly deployed Acceptance protection terms for shared data and data processing terms No backup email to access the GA account No or incorrect connection to Google Ads and Google Search Console Incorrect basic settings for country, currency, and time zone No internal search query parameters are used Bots and spiders filtering is not active No custom filters are used GA account has only 1 view User and event data retention is set to expire after 26–38 months Session and campaign timeout has not been checked and set up according to the content and use of the website Organic search sources have not been added The referral exclusion list remained empty Data collection for Google signals and ads personalization settings are disabled Not using custom and recommended segments from the Google Analytics Solutions Gallery No or insufficiently described annotations Not using custom and recommended audiences from the Google Analytics Solutions Gallery Not using custom and recommended goals from the Google Analytics Solutions Gallery Not using custom and recommended dashboard from the Google Analytics Solutions Gallery Not using custom and recommended reports from the Google Analytics Solutions Gallery No content grouping settings Disabled ecommerce set-up and enabled enhanced ecommerce reporting at the e-shop website Unset checkout labeling Regular checking and audit of the account Not using URL and campaign UTM (Urchin Tracking Module) tagging No monetary values for goals and thus, no page value analysis is available and ROI for different marketing channels cannot be calculated

Severity High High High Medium High Medium Medium Medium High High Medium Low Medium Low Medium Low Medium High Medium Medium Low High Medium High Medium High

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Table 3 Recommendations for Google Analytics N 1 2 3 4 5 6 7 8 9 10

Recommendation Use Google Tag Manager to connect to Google Analytics Link Google Analytics to Google Ads and Search Console and vice versa User and event data retention set to Do not automatically expire Set your time zone, country, and currency correctly Set up a contact person and company information about data processing Deploy recommended segments, dashboards, audiences, reports from the Google Analytics Solutions Gallery Start tagging your URLs via UTM parameters Create at least 3 views as recommended by Avinash Kaushik (Kaushik, 2009) and Googlecertified trainers Filter bots, spam, employees, and irrelevant traffic From the beginning set goals according to company KPIs and set their monetary value correctly

Source: own work

5 Discussion The 26 most common issues with Google Analytics were categorized into two groups: i) errors caused by incorrect or insufficient settings and ii) analytical (interpretation) errors and also by their severity (low, medium, high). This was done to simplify the taxonomy from Patel (2022) and combine it with the one from Google (2022). The most fundamental issue was the incorrectly deployed tracking code for Google Analytics. This has been identified as one of the most common issues in other work, too (Harnish, 2021; Patel, 2022). The third most common serious issue with high severity was the absence of a backup email to access the Google Analytics account. We also found that almost 30% of the accounts had an incorrectly set time zone and the companies from Slovakia/Czech Republic (GMT + 01:00) use the Pacific time zone. We further identified that as many as 79% of the accounts did not have a proper Administrative Matters related to the Data Processing Addendum and violated the GDPR General Data Protection Regulation. We recommend that this needs to be fixed as soon as possible through the Google Marketing Platform—this recommendation is in line with the findings of Strzelecki and Rizun (2020) who confirmed that it is closely related to consumers’ security and trust they put in online retailers. Addressing all the issues from Table 2 should become the top priority for managers of e-commerce platforms. They should start with the issues with the highest severity and then continue with those of medium and low impact. Following this, they should review the ten recommendations from Table 3 which are connected to the most common issues but can be considered as an extension of the necessary fixes. The first recommendation, for example, suggest the use of Google Tag Manager to deploy the Google Analytics code. There are other options of how to

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deploy the code correctly, however, Google Tag Manager can also integrate other code snippets that need to be inserted into the e-shop’s HTML code. One of the other recommendations (number 8) suggests that at least three Google Analytics views are created. This came as the result from the two in-depth interviews with the Googlecertified trainers and is also in line with the recommendation of Kaushik (2009). The research presented in this paper has confirmed that businesses have a basic awareness of Google Analytics. They typically deploy it and then do not utilize its potential for marketing analytics. This confirms the findings of Önder and Berbekova (2022) who concluded that only some companies are actively using web analytics to drive their marketing programs. For medium and large e-shops, we recorded the least number of errors and noticed active and regular work with their Google Analytics account. Although Chitkara and Mahmood (2020) suggest that it is essential for smaller companies and startups to utilize the potential of web analytics for their business growth, our research has found out that sadly the most deficiencies in the set-up and use of Google Analytics were found with small businesses. We encourage businesses to learn about web analytics and how to use Google Analytics. Also, in line with Boufenneche et al. (2022) to use Google analytics regularly to make informed decisions. Annual audits help identify issues in account settings and analytics.

6 Conclusion We believe our findings can be used as a checklist for better future deployments and more efficient use of Google Analytics. We also assume that businesses will focus on the top 10 recommendations and use them to correctly set up their Google Analytics accounts. More reliable data in Google Analytics will enable companies to identify consumer preferences when buying Slovak and Czech products more accurately. We used the free version of Google Analytics (Universal Analytics), which does not guarantee data reliability and can record a maximum of ten million hits per month. For better reliability and interpretation of results, it would be appropriate to repeat the analysis with Google Analytics Premium, when hits are limited to the limit of 1 trillion and quality guarantee is provided through the Service Level Agreement. We only analyzed Google Analytics accounts of Slovak and Czech e-shops. Our results cannot be compared to previous studies because such studies do not exist to our best of knowledge. Therefore, our results can be used as benchmarks for future research and the methodology can be applied by researchers gaining insights from other segments and countries. Acknowledgments This paper/publication originated as the result of working on the grant scheme VEGA (S.G.A.) 1/0737/20 Consumer literacy and intergenerational changes in consumer preferences when purchasing Slovak products.

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Part IV

Eurasian Business Perspectives: Sustainability

Product Sustainability in Spatial Competition with Consumer Environmental Awareness Hamid Hamoudi and Carmen Aviles-Palacios

Abstract Sustainable management of natural stock resources can be promoted through government intervention. This paper analyzes the policies that authorities should use to promote the sustainable use of these resources and the effects on market functioning. A model of differentiated products with environmental regulations and environmentally conscious consumers is applied—the Hotelling’s linear city model. The analysis is conducted from two perspectives: business and society. In the first case, a theoretical regulatory agency establishes the sustainability characteristics and then the level of awareness, and then, privately managed firms compete over characteristics and prices. In the second case, decision-making lies exclusively within the government, with a public manager and an environmental regulator. With both approaches, the sustainable management of natural resources and the promotion of the bioeconomy are optimized when consumer awareness corresponds to consumer preferences. In the business approach, this outcome can be achieved when the authorities have a minimum capacity to raise awareness among the population. Environmental regulation improves competitiveness by reducing prices and product differentiation. This article contributes to the environmental economics and industrial management literature by providing a framework to investigate the effects of awareness campaigns. It is shown that consumer awareness is the key to improving sustainability. Keywords Spatial competition · Environmental awareness · Sustainability · Regulation

H. Hamoudi Dpto. Fundamentos de Análisis Económico, Universidad Rey Juan Carlos-URJC, Madrid, Spain e-mail: [email protected] C. Aviles-Palacios (✉) Dpto. Ingeniería de Organización, Administración de Empresas y Estadística, E.T.S.I. Montes, Forestal y del Medio Natural. Universidad Politécnica de Madrid, Madrid, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_7

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1 Introduction The economic strategies promoted by governments and international institutions as 2030 Agenda-SDG; new Green Deal, Missions Europe, or the sustainable finance strategy and their taxonomy look forward more sustainable companies. In this framework, firms must balance profit maximization systems and sustainability under a triple balance view—economic, environmental, and social—into their competitive production strategies. This global achievement is not only a matter of governments and companies, but also consumers should act as drivers to promote the necessary change. So, it would be necessary to regulate the sustainability of production considering what consumers prefer, focusing on product differentiation through prices and their inherent sustainability by applying the Hotelling model (1929). It is considered that a regulator, in its defense of social interests and of environmental protection, along with its ability to provide material resources, should promote production based on a sustainability characteristic (SC). In this case, it would use both classic mechanisms of environmental protection or awareness campaigns. The classic mechanisms (i.e., taxes or fines) are particularly unpopular in the business world and pose several drawbacks such as the difficulty of defining the harm caused individually and not always leading to the expected outcomes, which has prompted authorities to propose other more innovative instruments. The awareness campaigns generate and develop environmental consciousness (consumer environmental awareness—CEA) and are highly successful in the short term. CEA can be maintained in the medium and long term through educational policies and environmental programs; energy and infrastructure policies, support for renewable energies or green infrastructure; or industrial policies that promote circular economy processes. CEA affects both consumption and production: the change in consumers’ green purchasing behavior causes changes in supply, so firms must adapt their pricing and differentiation strategies to sustainable consumers’ new demands (Kaufman, 2014; Hsu et al., 2017; He & Deng, 2020) This work aims to optimize both the decisions such as a regulatory authority to favor sustainability and the strategies of firms to differentiate their products and prices, considering a structure of consumer preferences determined simultaneously by the price, the level of product differentiation, and the ecological harm perceived by the consumer. For this scenario, a regulated market in the Hotelling’s linear city model is considered. Two approaches are proposed: (1) private-sector management and (2) public-sector business management. In both approaches, the regulatory authorities propose an SC of production, related to an optimal use, linked to the management of natural stock resources such as forestry or fisheries. This strategy will be supported by a budget limited campaign to raise awareness about the harm caused by irresponsible production or consumption. Considering the specificities of the modeled environmental authority and both strategic variables that affect the behavior

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of companies in the market and consumer choices constitute the novelty of this approach. The model is developed as a dynamic game in which the decision-making of the agents is sequential: (1) the SC, (2) the awareness campaign, (3) business characteristics, and (4) prices. It will be dynamic because the SC defined by the regulator is affected by the awareness campaign as well as by the level of the product characteristics and the prices set by firms. The resolution is performed by the backward induction method to identify the optimal strategies in terms of prices, characteristics, level of awareness campaign, and SC. Under this approach, promoting campaigns to inform and increase awareness among consumers is the key element to improve sustainability both in public and private managed companies. This guideline adds reasons to the debate among permissive or restrictive policies. Beyond economic policy, the regulator’s political instruments that have been analyzed, i.e., a sustainability characteristic or awareness campaign to increase CEA, are complementary. To suggest or impose characteristics of sustainable production would be ineffective without awareness publicized by the authority so that consumers know the harm associated with consuming products that are not managed sustainably, and vice versa. With this work we have been able to determine the optimal preventive policies that environmental authorities must carry out to achieve responsible consumer behavior that in a certain way induces and forces companies to follow the suggestions of the authorities. This work is organized as follows. Section 2 provides a review of the literature regarding sustainability of natural resources and environmental policies in imperfect competition. Section 3 describes the model. Section 4 analyzes the market from a business perspective and explains the equilibrium in terms of prices and characteristics, the environmental regulator’s optimal consumer awareness, and the optimal sustainability characteristics. Section 5 examines a social perspective of the model and explains how the public manager defines the optimal characteristics of the firms, and how the regulatory authority determines the level of awareness and the sustainability characteristics, respectively. Conclusions, limitations of the study, and future directions of research are presented in Sect. 6.

2 Overview 2.1

The Sustainability of Natural Resources

The adequate use and consumption of renewable natural resources should be the basis of sustainable development (McCormick & Kautto, 2013; Marini et al., 2022). So, the United Nations calls for a review of the consumption and production models of industrialized states in accordance with the Paris Agreement and the Sustainable Development Goals (OECD, 2016). This alignment constitutes the basis of the bioeconomy (Georgescu-Roegen, 1977) and is produced through striking a balance between economic and social development and environmental protection.

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Bioeconomy involves developing new products and services adapted to the principles and flows of the circular economy (Martinez de Arano et al., 2018), and involves adopting lifestyles and patterns of sustainable consumption and production to allow a natural regeneration at the same time as a responsible use. Therefore, to protect renewable natural resources due to their unique characteristics (Hepburn, 2010) is needed a state intervention and bioeconomic management systems. In short, natural resources must be protected, promoting the sustainability of both the process and the product, and raising consumer awareness (Sanz-Hernández et al., 2019): (1) Governments promote sustainable management through forestry or fishing plans operations and responsible consumption habits through ecolabels (PEFC or FSC, for forestry, or MSC for fisheries); (2) Industry would incorporate ecolabels that indicate the inherent sustainability of production. Doing so, consumer can easily identify whether the products were sustainably obtained and exert a pull effect by directly impacting the competitive strategies of firms (Clemenz, 2010).

2.2

Environmental Policies in Imperfectly Competitive Markets

This work is framed within the literature on environmental policies in the context of imperfect competition. Several studies have considered regulatory instruments based on taxation or subsidization and/or the enforcement of a product standard and/or an environmentally conscious consumer policy. Others have analyzed the interaction between environmental policies and the environmental behavior of firms or consumers using different approaches. Indicatively, Marini et al. (2022), considering a differentiated duopoly, study how the supply side of greening affects the way firms choose their prices and products and the resulting consequences for the overall level of pollution. They find that greening does not necessarily lead to a better environmental outcome, as it gives green firms greater market power which they use to charge higher prices. However, it can be used to effectively complement more traditional policy instruments, such as a minimum environmental standard. Ambec and De Donder (2022) analyze an economy with two types of citizens, named neutral and green, who consume a whole unit of a polluting good and where green firms differentiate products according to their environmental quality. They contrast two ways of public intervention: an environmental quality standard and a pollution tax. First, they consider an arbitrary pollution target. For any given level of pollution, emission taxes turn out to be less cost-effective than an emission standard because taxes always induce a higher wedge between the environmental qualities of products. On the contrary, consumers prefer taxes to standards when the intensity of the warm glow is not too great. Arguedas and Rousseau (2021) analyze the behavior of a monopolist and a duopolist with four different approaches, (1) an initial laissez-faire approach, (2) a policy of environmental awareness of consumers, (3) imposition of a product standard, and (4) application of technology subsidy. They conclude that a

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policy based on consumer education will induce the monopolist and duopolists to increase the energy efficiency of the product and to charge a higher price. As for the imposition of a product standard, they find that such a policy can counteract the negative effects of displacement of consumers’ intrinsic motivation in a monopoly environment, although this counteracting effect is less powerful under duopoly. However, a subsidy does not provide such a support system and the full effect of exclusion will be visible. Using a setting much closer to ours, He and Deng (2020) in a horizontal product differentiation model á la Hotelling (1929) examine how consumer environmental awareness (CEA) can affect green consumption decisions in different and confusing ways. To explain the reasons for these divergences, they divide CEA into two main components: the subjective effect and the social effect. They show that the subjective effect of CEA increases the price and profit level of firms and increases the environmental friendliness of products. Meanwhile, the social effect of CEA reduces the price and profit level of firms and reduces the difference in the environmental quality of products. On the other hand, Espínola-Arredondo and Zhao (2012) analyze Hotelling’s linear city model with two types of consumers, green and brown, where the final products of two firms are symmetric except for their environmental impact. In their efficiency comparison they find that, in a context of horizontal product differentiation, environmental regulation produces higher social welfare than the absence of policy. Clemenz (2010) researches the impact of ecolabels on emissions reduction in a market with horizontal product differentiation á la Hotelling and á la Salop (1979), finding that the reduction method makes a difference to the effectiveness and efficiency of ecolabels. Eriksson (2004) and Conrad (2005) analyze price competition and product differentiation with organic consumers. However, they do not endogenize environmental regulation with a political economy approach which is the fundamental difference with our research. Different approaches with government strategies that employ CEA as environmental policy have also been analyzed. Van der Made and Schoonbeek (2009) assume persuasive awareness-raising operations that aggravate consumers’ environmental concerns. Sartzetakis et al. (2012) examine, in a dynamic framework, the role of environmental damage information related to the consumption of certain products as a policy instrument that complements environmental taxes. In their model, the advertising campaign helps to reduce the information asymmetry between the population and the business world. Kaufman (2014) proposes a dynamic learning model to investigate the degree of effectiveness between financial incentives and informative advertising campaigns in encouraging green purchasing. Mantovani and Vergari (2017) compare two policy instruments that can be adopted to curb carbon emissions: (1) a conventional pollution tax and (2) an environmental campaign that raises consumers’ awareness of the relative impact of their consumption choices. They show that the relative effectiveness of the two policy instruments depends critically on consumers’ initial concern for the environment. Although this list is not exhaustive, and besides the similarities and complementarities between the contributions mentioned and ours, the novelty of this paper lies on the specificities of the environmental authority modeled. In particular, examining

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the role of an environmental authority based on two strategic variables, a baseline environmental characteristic and a budget to promote it has not been considered previously in literature. Both variables affect firms’ market behavior and consumers’ choices.

3 The Model A duopoly market structure differentiated á la Hotelling is considered in which an environmental authority proposes a characteristic of SC and is supported by an awareness campaign. The campaign delivers messages about the harm that can result from inappropriate behavior so that each individual can act accordingly. This concept is supported by Schwartz’s theory of basic values (1970, 1977), revised by Turaga et al. (2010), according to which the activation of personal moral norms influences environmental behavior. However, in this case, a person’s behavior is not interpreted as bad or good public awareness, but rather as a social fear regarding the environmental harm that ensures if certain behaviors are not corrected (Conrad, 2005; Mantovani et al., 2016; or Mantovani & Vergari, 2017). The market is represented by an interval I = [0, 1],1 in which there is a continuum of uniformly distributed consumers, two firms producing the same good, and a social planner that promotes a SC of the product with an awareness campaign of the good qualities. It is assumed that each consumer x 2 [0, 1] has an income k sufficient to acquire only one unit of good. Let x1 2 [0, 1], x2 2 [0, 1] represent the characteristics of the products that firms sell at price pi, i = 1, 2, such that x1 < x2. The SC of the regulator is defined by c 2 [0, 1] and is supported by a level of consumer awareness campaign given by γ 2 ½0, γ , where γ is a positive real number that represents the maximum budget available for the campaign. Unlike Conrad (2005), where the products are labeled in increasing order with respect to environmental quality, in the present model, the ordering of the characteristics defined by x1 < x2 does not mean that firm 1 is less concerned with sustainability than firm 2 is. He and Deng (2020) consider that social expectations generally do not set environmental quality at the highest level and that it is an average of the characteristics offered in the market, in this work, the concept of the economy of sustainability advanced by Baumgärtnerm and Quaas (2010a, 2010b) is considered. Thus, the sustainability of the products is established by competent authorities according to environmental, social, and economic criteria, and according to that criterion, the sustainability characteristic c considered here is set by the regulator. It is assumed that the awareness campaign γ has a direct impact on the population and aims for the consumer to perceive the campaign as a warning of environmental

1

The intervalI represents a range of authorized production characteristics.

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harm by consuming a good with a characteristic different from the SC c, so the campaign is considered CEA. Following Hamoudi and Aviles (2015) and He and Deng (2020), the social effect of CEA is defined as a quadratic linear function Γ ðxi , c, γ Þ = γ ðc - xi Þ2 , i = 1, 2

ð1Þ

which is the loss of utility for the consumer. Therefore, the clearer the information on SC and the incidence of sustainable management of natural stock resources is, the greater the value of γ is, the greater the impact on the population is, and the greater the social effect of the CEA is. In contrast, when γ → 0, the level of awareness campaign is low, and the social effect of the CEA is minimal, so the population does not switch to more sustainable consumption patterns. For γ = 0, the version by D’Aspremont et al. (1979) is re-established, and similarly to that case, it is assumed that a consumer x buying the characteristic xi incurs a cost given by T ðx, xi , t Þ = t ðx - xi Þ2

ð2Þ

i = 1, 2, where t > 0 measures consumer preference. Now, considering income k, price pi, the social effect of CEA Γ(xi, c, γ) and the cost T(xi, x, t), the utility of consumer x when acquiring characteristic xi, is defined as uðx, xi Þ = k - ½pi þ T ðx, xi , t Þ þ Γ ðxi , c, γÞ

ð3Þ

i = 1, 2. The location x of the consumer who is indifferent in terms of buying from one of the two firms is obtained by equating the utility functions: ð4Þ

uðx, x1 Þ = uðx, x2 Þ and its expression is given by x½ðp1 , p2 Þ, ðx1 , x2 Þ, ðc, γÞ = x0 þ

γ t

ð x 2 þ x1 Þ -c 2

ð5Þ

where x0 =

ðx þ x1 Þ p2 - p 1 þ 2 2 2t ðx2 - x1 Þ

ð6Þ

represents the indifferent consumer in a market lacking environmental consciousness (γ = 0) (D’Aspremont et al., 1979). Knowing that consumers x < x buy from firm 1, while consumers x > x choose firm 2, assuming 0 < x < 1, the demand functions for firms 1 and 2 are expressed as follows:

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Q½ ðp1 , p2 Þ, ðx1 , x2 Þ, ðc, γÞ1 = x ½ ðp1 , p2 Þ, ðx1 , x2 Þ, ðc, γÞ = x0 þ

ð x2 þ x1 Þ -c 2

γ t

ð7Þ

Q½ ðp1 , p2 Þ, ðx1 , x2 Þ, ðc, γÞ2 = 1 - x ½ ðp1 , p2 Þ, ðx1 , x2 Þ, ðc, γÞ = ð1 - x0 Þ -

γ t

ðx2 þ x1 Þ -c 2

ð8Þ

The demands have two terms: (1) demand of a market without a regulator and (2) the influence of the CEA(γ), and the SC(c) on the market share. Note that if the firms are not located symmetrically with respect to SC,(x2 + x1) - 2c ≠ 0,2 one of them will be closer to c, that is, more concerned with the sustainable management of natural resources than its rival will be and will be in greater demand. Therefore, a priori, firms are interested in demonstrating appropriate environmental responsibility. However, if the firms are located symmetrically with respect to c(x2 + x1) 2c = 0,3 the environmental variables (c, γ) will not distort the market because demands (7) and (8) are equal to the unregulated case γ = 0. Without loss of generality, production costs are assumed to equal zero. When firms are privately managed, they compete among themselves with respect to characteristics and prices, and the objective function of each firm is given by the profit Bi = pi Qi

ð9Þ

i = 1,2. If the firms have a common public manager, their only objective will be to determine the optimal characteristics, considering the same price for the two firms and considering the environmental effects on consumers. Each firm’s objective function is social welfare W, usually defined as the sum of consumers’ surplus EC and producers’ surplus EP, whose expressions are x

EC = 0

1

uðx, x1 Þdx þ

uðx, x2 Þdx - ðB1 þ B2 Þ, EP = B1 þ B2 :

x

From the expressions EC and EP, it follows that the profits B1 + B2 represent only a monetary transfer from consumers to corporations. Substituting u(x, xi) and i = 1, 2, by expression (3), the welfare function is written as W = K - (CT + ΦT), where

1Þ If ðx2 þ x1 Þ - 2 c > 0 , c < ðx2 þx 2 , then x > x0 , and firm 1 is closer to the regulator and will experience greater demand, and vice versa for the opposite case. 3 It would be a sound strategy to relax competition in the case of state-owned intervention so that the two firms can agree on or promote a monopoly.

2

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1

1. K is the income of all consumers, K = 0 kdx 2. CT is the cost of all consumers when buying the characteristics x

CT ðp1 , p2 , x1 , x2 , t, γ, cÞ =

1

T ðx, x1 , t Þdx þ

0

T ðx, x2 , t Þdx

x

and 3. ΦT is the social effect of the CEA for all consumers. x

ΦT ðp1 , p2 , x1 , x2 , t, γ, cÞ =

1

Γ ðx1 , γ, cÞdx þ

0

Γ ðx2 , γ, cÞdx

x

Substituting T(x, xi, t), and Γ(x, xi, t), i = 1, 2, respectively, with expressions (1) and (2), we have: C T ðp1 , p2 , x1 , x2 , t, γ, cÞ = t x ðx2 - x1 Þ½x - ðx2 þ x1 Þ - x2 ð1 - x2 Þ þ ΦT ðp1 , p2 , x1 , x2 , t, γ, cÞ = γ x ðx2 - x1 Þ½2c - ðx2 þ x1 Þ þ ðx2 - cÞ2

1 3

ð10Þ ð11Þ

therefore, the welfare function is given as W ð x1 , x 2 Þ = K t 3 ð12Þ

- ðx2 - x1 ÞðxÞ½ð2γc þ txÞ - ðt þ γ Þðx2 þ x1 Þ - tx2 ð1 - x2 Þ þ γ ðx2 - cÞ2 þ

However, it is considered that environmental regulation is conducted by a public manager inexperienced in business management and whose only concern is environmental protection. The aim is thus to determine the optimal SC(c) and the level of awareness (γ) maximizing the social effect of the CEA of all consumers ΦT given by expression (10) to activate those personal values that motivate consumers to engage in pro-environmental behavior. The purpose of the model analysis is to determine the optimal behaviors of both the managers, private and public, of the firms and the environmental regulatory authority. The study will be treated from two perspectives: business and social. The solution in both cases is obtained by backward induction. (a) The business approach develops as a game in four stages because there is interaction between the two firms that interact with the environmental regulator. Firstly, the regulator chooses the characteristic c; in a second stage, it decides the

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level of awareness γ; in a third stage, the firms set their production characteristics (x1, x2); and in the final stage, they choose the prices ( p1, p2). (b) The social approach considers a game in three stages. In this case, the following elements will be determined sequentially: the environmental regulator first chooses the sustainability characteristic c and then the level of awareness γ; and finally, the public manager of each firm chooses the firm’s production characteristics (x1, x2). These two configurations are developed as a sequential game that lends the model a dynamic aspect. Indeed, the sustainability characteristic c is affected by the level of the awareness campaign γ, which in turn is influenced by given factors, such as the characteristics of the products and prices set by the firms. In the end, the social effect function of the CEAΦT resulting from the environmental regulator integrates the behaviors of producers and consumers. Thus, the problem posed is generically dynamic.

4 Optimal Strategies from a Business Perspective Strategic interaction is considered as a sequential game between regulating authorities and firms. The environmental regulator first chooses the sustainability characteristic (c) of the good and then the level of awareness γ. Subsequently, firms decide the characteristics of their products (x1, x2) and then the prices ( p1, p2). Using the typical backward induction method, the game is solved. In Sect. 4.1, the optimal pricing strategies are determined. In Sect. 4.2, the optimal characteristics are established, considering the equilibrium prices. In Sect. 4.3, knowing the previous decisions of the firms, the regulator specifies the optimal awareness level. In Sect. 4.4, given the optimal strategies obtained, the characteristics of sustainable production are defined.

4.1

Optimal Prices

Each firm maximizes its profit with respect to its price, given the sustainability characteristic(c), the awareness level (γ) set by the regulator, the production characteristics (x1, x2), and assuming the rival’s price is fixed. Considering expressions (6) and (7) of x0 and Qi, now using expression (9) of the profit function of each firm, we obtain the following: B1 ðp1 , p2 Þ = p1

p2 - p1 ðt þ γ Þðx2 þ x1 Þ γc þ 2tr 2t t

ð13Þ

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B2 ðp1 , p2 Þ = p2

p 1 - p 2 ð t þ γ Þ ðx 2 þ x 1 Þ γc þ 12t 2tr t

125

ð14Þ

From the first-order conditions, the optimal prices are determined as follows: pE1 =

ð x2 - x1 Þ ½2ðt - γ cÞ þ ðt þ γ Þðx2 þ x1 Þ 3

ð15Þ

pE2 =

ð x2 - x1 Þ ½2ð2t þ γ cÞ - ðt þ γ Þðx2 þ x1 Þ 3

ð16Þ

γ γ x2 þx1 t 2t Assuming the following: ðtþγ Þ c - ðtþγ Þ ≤ 2 ≤ ðtþγ Þ c þ ðtþγ Þ (0 < xC < 1) The optimal prices of (15) and (16) can be divided into two terms:

pE1 = pE0 1 þ

ðx2 - x1 Þγ ½ðx2 þ x1 Þ - 2c 3

ð17Þ

pE2 = pE0 2 -

ðx2 - x1 Þγ ½ðx2 þ x1 Þ - 2c 3

ð18Þ

where pE0 1 =

ðx2 - x1 Þt ½ 2 þ ð x2 þ x 1 Þ  3

ð19Þ

pE0 2 =

ðx2 - x1 Þt ½4 - ðx2 þ x1 Þ 3

ð20Þ

E0 The first terms pE0 1 , p2 in expressions (17) and (18) correspond to the equilibrium prices of the unregulated model, and the second terms show the effects of the sustainability characteristic (c) and the level of publicity(γ). Therefore, as for the indifferent consumer x0 , regulation distorts equilibrium prices as long as (x2 + x1) 2c = 0. Note that if (x2 + x1) - 2c > 0, that is, firm 1 has a characteristic closer to SC compared with firm 2, firm 1’s optimal price pE1 is greater than the equilibrium price E E0 without regulation pE0 1 , whereas for firm 2, the optimal price is lower p2 < p2 . In the case of (x2 + x1) - 2c < 0, the effect is the opposite. The demands at the equilibrium prices are as follows:

γ ½ðx þ x1 Þ - 2c 6t 2 γ QE2 = QE0 2 - 6t ½ðx2 þ x1 Þ - 2c

QE1 = QEO 1 þ

where

ð21Þ ð22Þ

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1 QE0 1 = 6 ½ 2 þ ð x2 þ x 1 Þ  1 QE0 2 = 6 ½4 - ðx2 þ x1 Þ

ð23Þ ð24Þ

E0 As with the prices, the demands QE1 , QE2 have two components: (1) QE0 1 , Q2 correspond to the demand functions of the equilibrium prices of the unregulated model and (2) a second term shows the effects of the sustainability characteristic (c) and the level of awareness. The formulation of profits at equilibrium prices is achieved by substituting (21) and (23) into (13) and (14), respectively, and is summarized in the following expressions:

BE1 ðx1 , x2 Þ =

ð x2 - x1 Þ ft ½2 þ ðx2 þ x1 Þ þ γ ½ðx2 þ x1 Þ - 2cÞg2 18t

ð25Þ

BE2 ðx1 , x2 Þ =

ðx2 - x1 Þ ft ½4 - ðx2 þ x1 Þ - γ ½ðx2 þ x1 Þ - 2cÞg2 18t

ð26Þ

The profits can also be expressed in two terms: (1) profits without regulation and (2) effects of regulation. In any case, the distortion of the results with respect to the solution of D’Aspremont et al. (1979) is clear. Based on the above, the firm that more closely follows the recommendations of the environmental regulator asks a higher price and yet still obtains greater demand and profit, which means that both the awareness campaign (γ) and the CEA lead consumers to value the sustainability characteristic of the product more than its price, which benefits the sustainable firm. Considering the equilibrium prices pE1 , pE1 , the optimal characteristic of each firm will be determined next. Therefore, the following problem is posed: Max BE1 ðx1 , x2 Þ, Max BE2 ðx1 , x2 Þ x2 2½0, 1

x1 2½0, 1

4.2

Optimal Characteristics of the Firms

Considering equilibrium prices pE1 , pE1 , the optimal characteristics of each firm is determined by first maximizing the profit functions for x1 2 R and x2 2 R:

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MaxBE1 ðx1 , x2 Þ, MaxBE2 ðx1 , x2 Þ, x1 2R

x2 2R

whose only interior solution4 is given by x1 = x2 =

1 γ ð4c þ 1Þ þ 4 4ð t þ γ Þ

5 γ ð4c - 5Þ þ 4 4ð t þ γ Þ

ð27Þ ð28Þ

Knowing that a pair of characteristics xE1 , xE2 will be a Nash equilibrium when, simultaneously, xE1 maximizes BE1 x1 , xE2 in [0, 1], and xE2 maximizes BE2 xE1 , x2 in [0, 1]. Therefore, x1 , x2 would not always represent an equilibrium because they do not belong to [0, 1] for any t,c and γ. Based on expressions (27) and (28) of x1 , x2 , several situations in which equilibrium can be reached are analyzed. Given that for any positive t, c and γ, x1 ≤ 1 and 0 ≤ x2 is always satisfied, the alternatives to consider for the equilibrium calculation are as follows: (i). First case: γ ≤ Min

t t 4ð1 - cÞ , 4c

given by x1 ≤ 0, x2 ≥ 1

(ii). Second case: Presents two alternatives: • •

t t 4ð1 - cÞ ≤ γ ≤ 4c t t 4c ≤ γ ≤ 4ð1 - cÞ

(iii). Third case: Max

if c 2 0, if c 2

1 2

1 2,1

t t 4ð1 - cÞ , 4c

given by x1 ≤ 0, 0 ≤ x2 ≤ 1 given by 0 ≤ x1 ≤ 1, x2 ≥ 1: ≤ γ given by

0 ≤ x1 ≤ 1 , 0 ≤ x2 ≤ 1

Given the symmetry of c and (1 - c) with respect to (1/2), the analysis of the three previous cases, (i), (ii), and (iii), are restricted to c 2 0, 12 . The previous cases are formulated as ðS1Þ : γ ≤

t t t , ðS2Þ : ≤ γ, ðS3Þ : ≤γ 4c 4ð 1 - c Þ 4ð 1 - c Þ

Results in cases (I) and (II) are not valid, so only the case (III) is considered as an interior solution.

4 For γ = 0 and/or c = 0 (unregulated market), one obtains x1 = ð- 1=4Þ, x2 = ð5=4Þ. This solution is the identical to the outcome obtained by Lambertini (1994) and Tabuchi and Thisse (1995), when companies can be located over the course of the range (-1, +1). This possibility is not feasible in this model.

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Third Result: Interior Solution (S3)

Finally, starting from (S3), 0 ≤ γ ≤ γ , and assuming γ ≥ 4ct , the following result can be stated: Proposition 1 For any c 2 0,

1 t , t > 0, and ≤ γ ≤ γ 2 4c

ð29Þ

there is a unique equilibrium for characteristics given by the interior solution:  xE3 1 = x1 = -

1 γ ð4c þ 1Þ E3 5 γ ð4c - 5Þ , x = x2 = þ þ 4 4 4ð t þ γ Þ 4ð t þ γ Þ 1

ð30Þ

Observations (OP1.): E3 (OP1.) 1. The firms are located on one side of the regulator xE3 1 < c < x2 . The E3 differentiation between the characteristics xE3 2 - x1 = ð3t=2ðt þ γ ÞÞ depends on only the CEA γ; as the level of awareness campaign γ increases, production differentiation decreases. (OP1.) 2. The demands are equal and independent of γ and c, and the prices are identical but depend on only γ. As γ increases, both prices and profits decrease: E3 pE3 1 = p2 =

3t 2 1 3t 2 E3 E3 E3 , QE3 1 = Q2 = 2 y B1 = B2 = 2ð t þ γ Þ 4ð t þ γ Þ

The impact of CEA γ is highly advantageous for consumers because it decreases prices and intensifies competition. E3 is reached for γ ≥ 4ct and c 2 0, 12 , or (OP1.) 3. Given that equilibrium xE3 1 , x2 alternatively, 4tγ ≤ c ≤ 12, and given that this condition is true if and only if γ ≥ 2t , the regulator is granted a maximum level of awareness campaign γ greater than or equal to half of the level of consumer preference t. The previous results show that the different optimal characteristics found depend on the relationship between the level of the awareness campaign γ and the sustainability characteristic c, the maximum awareness capacity γ of the regulator, and the level of consumer preference t. However, not all results are equally effective in terms of sustainability. The effectiveness depends on the trade-off between the upper limit of the level of awareness γ and the proportion of the parameter t and the SC(c). The E3 only solution that improves the management of natural resources is xE3 1 , x2 because it simultaneously increases the sustainability of the optimal characteristics of the two firms. For this reason, to determine optimal policies γ and c of the E3 regulator, only the equilibrium is considered xE3 1 , x2 .

Product Sustainability in Spatial Competition with Consumer. . .

4.3

129

Optimal Level of the Awareness Campaign

E3 E3 Given the optimal strategies xE3 and pE3 in terms of characteristics and 1 , x2 1 , p2 prices, the optimal strategies of environmental regulation are examined below, first by determining the CEA γ. The regulator has as an objective function, the function of E3 with (27) social effects ΦTE(t, γ, c), given by expression (10). Substituting xE3 1 , x2 and (28), the following function is obtained:

ΦT E ðt, γ, cÞ =

γt 2 9 þ 4ð2c - 1Þ2 ðt þ γ Þ2

ð31Þ

Proposition 2 For any c2

1 1 , , t > 0, and γ ≥ t 4 2

ð32Þ

the optimal level of awareness is reached when γO E3 = t:

ð33Þ

Observations (OP2.): (OP2.) 1. The level of the optimal awareness campaign of the regulator is equal to the level of consumer preference. The prices and profits O E3 O pE3 1 t, γ E3 = p2 t, γ E3 =

3t 3γ O 3t 3γ O O E3 O t, γ t, γ = B = = E3 , BE3 = E3 , 1 E3 2 E3 4 4 4 4

are independent of the sustainability variable SC(c) and paradoxically increase as the level of awareness γ O E3 increases; however, they remain lower in an unregulated context. (OP2.) 2. The optimal characteristics of the firms are independent of the level of E3 E3 awareness γ O E3 and are given by x1 = ðc=2Þ - ð1=8Þ and x21 = ðc=2Þ þ ð5=8Þ, where the first terms show the effect of SC(c) on the locations, and differentiation is E3 constant xE3 2 - x1 = 3=4 . (OP2.) 3. To enforce its policy, the regulator must have an awareness capacity γ greater than or equal to the preference level t of consumers.

4.4

Sustainability Characteristic Chosen by the Regulator

The regulator will determine the optimal sustainable production by considering the previous results. Therefore, substituting γ with t in expression (31), the function of the social effects of the CEA is

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ΦET ðt, cÞ =

t 64

9 þ 4 ð2c - 1Þ2

ð34Þ

Using the first-order condition, we obtain the following: Proposition 3 For t > 0 and γ ≥ t, the optimal sustainability characteristic is given by cO E3 =

1 2

ð35Þ

Observations (OP3): E3 (OP3.) 1. The optimal characteristics are xE3 1 = 1=8 and x2 = 7=8. Firms are E3 E3 located symmetrically with respect to the extremes, x1 þ x2 = 1. However, the optimal sustainability characteristic SC cO E3 does not affect competition between firms because prices and demand do not depend on it. (OP3.) 2. The optimal sustainability characteristic cO E3 is at the midpoint of the market, which corresponds to the average characteristic of the good. This result coincides with the equilibrium when firms compete only in characteristics and without regulation. (OP3.) 3. The functions corresponding to the social effects of CEA and firm profits, respectively, are as follows: ΦE3 T ðt Þ =

3t E3 3t , B ðt Þ = BE3 2 ðt Þ = 4 4 1

The variation in the two functions with respect to t is similar: the higher the value of t is, the greater the profits for firms and the environmental awareness for consumers are because the environmental optimum is reached for γ O S = t. This case is intriguing for enacting changes in the production model, when an optimal level of awareness and an optimal sustainability characteristic are chosen, as what is truly being stimulated is a change in the behavior of firms and consumers. All the results obtained are similar when the range of SC(c) is c 2 12 , 1 .

5 Optimal Strategies from a Social Perspective Earlier, it was shown how, from a business perspective, the optimal production characteristics chosen by firms differ from the SC characteristic (c) chosen by the environmental regulator. Therefore, it is insightful to address this problem from a social perspective, assuming the public management of firms, because among their business priorities will be maximizing social welfare. We will examine what happens when the public manager establishes the optimal production characteristics of the firms and how the firms are affected by the choice of the optimal sustainability

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characteristic c and the level of awareness γ. The product is offered at the same price by the two firms, so, a sequential game is defined as follows: the environmental regulator chooses: (1) the optimal sustainability characteristic c, (2) the optimal awareness campaign γ, and (3) the characteristics xS , xS2 . To resolve this case, the backward induction method is used.

5.1

Optimal Characteristics of the Firms

Assuming that the prices are identical for both firms p1 = p2, market shares are defined by the indifferent consumer, whose expression is xS =

ðt þ γ Þðx2 þ x1 Þ γc 2t t

ð36Þ

where the condition 0 ≤ xS ≤ 1 here is presented as 2γc þ 2t 2γc ≤ ðx 2 þ x 1 Þ ≤ ðt þ γ Þ ðt þ γ Þ

ð37Þ

The objective function W(x1, x2) of the social planner is given by expression (12), substituting x with xS . Using this function, the optimal choice of characteristics is obtained: Proposition 4 For any t > 0, γ 2 ½0, γ  and 0 < c < 1, there are unique optimal characteristics, and xS1 2 ½0, 1, xS2 2 ½0, 1 given by xS1 =

1 γ ð4c - 1Þ þ 4 4ð t þ γ Þ

ð38Þ

xS2 =

3 γ ð4c - 3Þ þ 4 4ð t þ γ Þ

ð39Þ

Observations (OP4.): (OP4.) 1. In the case that γ = 0, the results are identical to the solution obtained by the planner in the classic model without environmental regulation (Tirole, 1988). (OP4.) 2. Unlike the previous case (business approach), here there is no condition on the optimal characteristics, as its existence does not depend on the level of the awareness campaign γ or its maximum capacity γ . In this case, the ideological bias of the regulator, his or her will, and the priority given to environmental policies are the factors that will influence and define the actions of firms. (OP4.) 3. If the SC c = (1/4), the characteristic of firm 1 is optimal in terms of sustainability for the regulator because xS1 = c = ð1=4Þ; however, the characteristic

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of firm 2 does not reach the desired sustainability xS2 = inverse effect is obtained. Now, if

3 4

-

γ 2ðtþγ Þ.

For c = (3/4), the

• 0 < c ≤ 14 , firms are located to the right of the regulator c ≤ xS1 < xS2 , • 14 ≤ c ≤ 34 , firms are located within the regulator xS1 ≤ c ≤ xS2 , and • (3/4) ≤ c < 1, firms are located to the left of the regulator xS1 < xS2 ≤ c . (OP4.) 4. Comparing the optimal characteristics with those of the business approach, it is verified that they are closer to c xE1 ≤ xS1 and xE2 ≥ xS2 , and differ2t entiation is reduced: xS2 - xS1 = t=2ðt þ γ Þ. Consider xS1 - xE1 = 4 ðtþγ Þ > 2t xS2 - xE2 = 4 ð-tþγ Þ 0, γ ≥ t, and 0 < c < 1, the optimal level of awareness is given by γO S = t,

ð42Þ

Observations (OP5): (OP5) 1. Similar to proposition 5, the optimal strategy γ O S is reached for a value is independent of the sustainequal to consumers’ tastes, and the optimal level γ O S and c. ability characteristic SC (c), so there is no trade-off between γ O S (OP5.) 2. The optimal characteristics given by xS1 = ðc=2Þ þ ð1=8Þ, xS2 = ðc=2Þ þ ð3=8Þ are closer to the sustainability characteristic SC (c) than in the private E3 approach, and the differentiation between the two firms is lower xE3 2 - x1 = 1=4 . The joint action of public management with environmental regulation improves the sustainability of production.

5.3

Optimal Characteristics

Considering the optimal production characteristics chosen by the social planner and the level of awareness set by the environmental authority, the optimal sustainability production will be determined. Substituting γ by t in expression (41), we obtain the following: ΦST ðt, cÞ = ðt=64Þ 1 þ 4 ð2c - 1Þ2

ð43Þ

a function of the social effects of the CEA from which the following result is easily determined: Proposition 6 Given the optimal characteristics xS1 , xS2 , the optimal level of awareness γ O S = t, and any t > 0, the optimal sustainability characteristic is cO S =

1 : 2

ð44Þ

Observations (OP6): (OP6) 1. As in the private approach, the optimal SC cO S is given by the average with (1/2), the perfect market equilibrium is defined characteristic. Substituting cO S by

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cO S =

1 O 3 , γ = t, xS1 = , and xS2 = 5=8, 2 S 8

ð45Þ

The optimal characteristics xS1 , xS2 are symmetrical with respect to the extremes and S S with respect to cO S (x1 þ x2 = 1). (OP6) 2. The objective function of the social planner not concerned with sustainability is summarized as WS(t, 1/2) = K + (59/64)t, while the function of the social effects of the CEA of the environmental authority corresponds to ΦST ðt, 1=2Þ = ðt=64Þ. The variation in the two functions with respect to t is identical, so the interpretation of the outcome is similar to that of the private approach. O (OP6) 3. Despite optimal sustainability policies, the SC cO S and the CEA γ S are similar under both approaches. Comparing them, it is verified that ΦET ð1=2Þ > ΦST ð1=2Þ, thus confirming improved sustainability with public management, as noted in observation (OP.4) 4 of proposition 4. In both cases, from a social and environmental viewpoint, sustainability is noticeably improved compared to an unregulated market. In the private sectorstyle regulated market, leaving firms to choose the sustainability characteristics, it is found that firms are less engaged. If one wants to maximize the sustainable management of natural stock resources, a social approach is necessary because the sustainability of production improves substantially. Thus, it is reasonable to believe that publicly managed forests or fisheries would allow firms to align their strategies with sustainable behaviors, maximizing the achievement of sustainable development sought in a bioeconomy.

6 Discussion This paper introduces an environmental regulation in a horizontally differentiated market à la Hotelling, where customers are also concerned with the environmental aspects of the product they consume. We consider consumers with heterogeneous environmental awareness as opposed to vertically differentiated models where CEA is homogeneous, i.e. there is unanimity in the environmental quality classification of goods. CEA is commonly considered in the literature to be composed of subjective norms and/or social norms and has been found to be an important factor in explaining consumer behavior in the field of green consumption. Among others, He and Deng (2020) building on Hotelling’s standard model, formalize a model of price competition and unregulated product differentiation that incorporates the subjective and social effects of CEA on consumers’ utility. In our contribution, utility incorporates both, the consumer’s stance toward the environmental characteristics of the product equivalent to the subjective component of CEA considered in He and Deng (2020) and, the awareness campaign promoting the product proposed by the environmental regulator equivalent to the social component of CEA. Analytically, the consumer utility function of the two models has the same structure. Other

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authors examine differentiated markets with environmental regulation where consumer behavior is described by the concept of green consumption, which consists of dividing consumers into two groups, green and neutral, and an environmental regulator. As examples, consider Mantovani and Vergari (2017) in a vertical differentiation model and Espínola-Arredondo and Zhao (2012) in Hotelling’s horizontal differentiation model. The formulation of our model makes a simplifying assumption to make the analysis tractable. In particular, production costs such as regulatory costs are assumed to be zero.

6.1

The Role of Regulation

In this research, the regulator designs an awareness campaign to boost consumers’ environmental awareness according to its own criteria. For this purpose, he/she is based on an environmental characteristic, the regulator’s own assessment of the environmental quality of the product, and a budget to promote it. It is assumed that consumers internalize the message of the product campaign by incorporating it into their utility. The aim is to induce firms to sell a product in line with the regulator’s recommendation. While the closest approach of regulation to our model is that of Mantovani and Vergari (2017) and Espínola-Arredondo and Zhao (2012), however, their objectives are different. In our model the regulator’s purpose is to determine the optimal environmental standard of the product in question and the level of the optimal awareness campaign that supports it. In Mantovani and Vergari (2017), the regulator’s purpose is to determine an optimal tax or an optimal environmental campaign with firms competing on features and prices and then comparing the two policies. In the case of Espínola-Arredondo and Zhao (2012), the only regulatory instrument considered is a conventional tax and firms compete only on prices. Our analysis is based on a four-stage non-cooperative game, while Mantovani and Vergari (2017) in three stages Espínola-Arredondo and Zhao (2012) in two stages.

6.2

Results

Despite the similarity in the structure of the consumer utility between our model and He and Deng’s (2020), the methodology and objectives of our contribution are different because of the role of regulator. The objective is to determine optimal regulatory policies and their effect on the environmental sustainability of product characteristics and, where appropriate, on price competition. The study is carried out from two perspectives: private management and public management of the firms. The results are more efficient with respect to an unregulated market with non-environmentally conscious consumers. In the first scenario, the regulator’s optimal decision on a sustainable characteristic is weighted in the sense that it sets a median characteristic while the level of the optimal awareness campaign that

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supports it is equal to the consumer’s taste level. This leads to firms narrowing their differentiation in the environmentally sustainable characteristics of their products and moving closer to the regulator’s indications. However, this result is feasible if the environmental authorities have a minimum capacity to raise public awareness. The public authorities should, at least, have the means to achieve a free market environmental regulation. Under the second approach, the optimal strategies of the environmental regulator are similar to those of the first one, but the firms’ optimal strategies are greatly improved in terms of efficiency because they are managed by a public administrator with social objectives. Moreover, in this scenario, the environmental regulator does not need to have a minimum capacity to disseminate awareness campaigns in order to carry out its optimal policy. The policy instruments, either the indication of a benchmark environmental characteristic or the awareness campaign, are indispensable to one another. Promoting a sustainability characteristic by the regulator will have no effect on consumption and production behavior if it is not supported by an advertising campaign and vice versa.

7 Conclusion Bioeconomy pursues the sustainable use of natural resources so that efficiencies are gained in productive processes, based on the principles of sustainable management of the natural resources so that their regeneration is favored while being consumed responsibly. Within this framework, at least three actors support sustainable markets: (1) consumers who can demand sustainable production, (2) production firms that offer goods and services identified as sustainably produced, and (3) regulators that must promote a balance between market performance and natural conservation. The determinants of these attitudes are essentially based on competitive strategies such as prices and product differentiation. By introducing an environmental authority in a Hotelling’s model of product differentiation, we aim to find regulatory instruments that contribute to making firms more sustainable. Specifically, ex ante deterrence mechanisms for excessive or scarce use of renewable resources are examined. The model studies government intervention based on the proposal of a sustainable product supported by awareness campaigns to affect CEA. The objective is to determine optimal policies and their effects on the sustainability of product characteristics and, where appropriate, on price competition. It is considered that an increase in CEA can exert a positive influence on the business environment that in turn will improve sustainability. The analysis is performed from two management perspectives: business and social. First, the regulator establishes a characteristic of sustainability, centered on usage levels of a natural resource to subsequently establish the level of awareness. With this regulatory framework, firms compete on characteristics and prices. From the second perspective, regulatory authorities choose SC first and then the level of awareness. Subsequently, the public managers of the firms will choose the production characteristics.

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In terms of sustainability, the outcomes are more efficient in a regulated market compared to an unregulated market. From a business perspective, the optimal decision of the regulator is based on determining a characteristic of sustainable production that is weighted, complemented by policies aimed at increasing CEA through campaigns that show the harm caused by non-responsible consumption. These campaigns should reach a level like the preferences shown by consumers. This situation leads to firms narrowing their differentiation in characteristics and brings them closer to what the regulator indicates, thus breaking with the wellknown result of maximum differentiation, with a desirable collateral effect for consumers because prices are lower. This result is possible only if the authorities have a minimum capacity to communicate environmental awareness to the population, without which the improvement in sustainability would not be feasible, in this sense, the consumer acts as a sustainability driver. In this situation, the government impetus is key. If there is no clear pro-environmental political will, it will be difficult to achieve environmental regulation of the free market. The key elements for improving sustainability are campaigns to inform and raise awareness among consumers. From both perspectives, the outcomes in sustainability and awareness are similar. The biggest difference occurs in the optimal characterization of the product. Contrary to the business approach, to enforce environmental policies, the regulator does not require a minimum capacity to disseminate awareness campaigns; it only requires pro-environmental political will. In this sense, the consumer does not exert a pull effect on the market, but it is the authority that adopts this role. How much sustainability improves depends on the maximum possible threshold of awareness. Given the results, it seems more advisable for production decisions to be made by state authorities. This guideline adds an additional ingredient to the age-old debate concerning the desirability of permissive policies (private production decisions) or restrictive policies (state production decisions). Beyond economic policy, the regulator’s political instruments that have been analyzed, i.e., a sustainability characteristic or awareness campaign to increase CEA, are complementary. To suggest or impose characteristics of sustainable production would be ineffective without awareness publicized by the authority so that consumers know the harm associated with consuming products that are not managed sustainably, and vice versa. In the fishing and forestry industries, these results make sense. The aim is to achieve an adequate use of both fisheries and forest products so as not to cause environmental collapses by either overexploitation that prevents regeneration or underutilization, which can cause the ecosystem economic and cultural harm. The analysis performed from the perspective of both public and private management of firms is apt, given that these are assets whose ownership may fall into private hands, as well as public management, for example, in the case of communal or public forests. The authority can suggest or impose, depending on the degree of regulation, that firms extract these natural resources according to a defined rate which satisfies a sustainability characteristic. Performing only this action the transition to more

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sustainable production would occur in the medium or long term. However, if consumers value sustainability in the management of these resources to the point of preferring to buy them, the necessary change from the firm’s supplies will occur in the short or medium term. The consumer can identify these sustainable products through ecolabeling, which must be complemented with awareness of the harm caused by unsustainable management. The increase in CEA is achieved through awareness campaigns. In any of the options analyzed, this campaign is essential to accelerate change and improve the sustainability of the system. Currently, these dynamics are occurring, in which products are managed in a sustainable manner that is also certified as such. These products may not have a different price than those that are not identified as sustainable, but this mere fact helps ensure a growing market. Various action options have been described by the competent authority assuming ideal conditions: the cost of production is similar for firms; the consumer presents a certain CEA and is responsible for their actions; and the authority does not consider the imposition of fines. Given that these conditions are ideal, future lines of research are opened, including, among others, (1) analyzing the influence of other intervention mechanisms as fines; (2) considering consumers without prior environmental awareness; (3) exploring the need for firms to incur additional costs to implement sustainable management models, as well as considering awareness campaign costs; and (4) including regeneration rates for each product type. All these enquiries can, a priori, modify the results achieved. This article contributes to the environmental economics and industrial management literatures by providing a framework to investigate the levels of awareness campaigns that regulators should conduct and the role they play in where firms locate themselves in the market. Funding Action financed by the Community of Madrid within the framework of the Multi-year Agreement with the Universidad Politécnica de Madrid in the line of action Program of Excellence for University Teaching Staff-Echegaray Grant

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Is the Green Economy the Key Factor in Reducing Urban Pollution in Romania? Alin-Cristian Maricuț and Giani-Ionel Grădinaru

Abstract The study aims to give an answer to the question “Is green economy the key factor in the process of reducing urban pollution in Romania?”. In this respect, the paper focuses on finding the nature of relationship between green economy and urban pollution in territorial profile. Hence, in order to achieve the main scope of the study, it was selected and collected data for variables which characterizes both concepts, green economy and urban pollution. Data allow a spatial statistical analysis due to its disaggregations in territorial profile (cities, development regions), being collected in order to assure completeness and comparability of data. Spatial statistical analysis among Romanian development regions and cities was conducted by using appropriate statistical methods, like: descriptive statistics, Random Forest Model, and K-Means Clustering Method. Results of study show differences in territorial profile from the perspective of relationship between green economy and urban pollution. These differences could be removed by using an integrated plan which aims to deploy green economy in multisectoral profile. Keywords Circular economy · Pollution · Urban development · Air quality · Sustainable cities

1 Introduction In the context of the process of increasing the degree of urbanization at global level, it is necessary to analyse the pollution in the urban environment, as well as to identify the factors that could lead to the reduction of pollution. Starting from this idea, the paper aims to bring to the fore the association relationship between the green economy and the level of pollution in the urban environment. This association will be analysed in territorial profile at the level of development regions and cities in A.-C. Maricuț (✉) · G.-I. Grădinaru Department of Statistics and Econometrics, The Bucharest University of Economics Studies, Bucharest, Romania e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_8

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Romania. The main advantage of analysing the relationship between the two concepts in the territorial profile is the inclusion in the analysis of the territorial diversity of Romania, as well as of the specificity of the local public administration. Gozalo et al. (2013) state that urban traffic is the main source of pollution, especially in major cities. Guttikunda et al. (2014) identified waste management as a key factor in determining urban air quality. A third important factor, which has a major impact on pollution, is represented by the urban development in territorial profile (Huang & Du, 2018). Identifying these three main factors that have a major impact on air quality, the originality of the work is represented by the integrated analysis of the main three pollution factors in the urban environment. Based on the results of this research, the directions for improving air quality can be identified by establishing trade-offs among these three urban pollution main factors (MartinezBravo et al., 2019). Through the diversity and complexity of the statistical methods used in this study, the paper benefits from the advantage of an innovative approach, which allows an X-ray on the association relationship between the green economy and the level of pollution at the level of the development regions of Romania. In addition, based on the clustering method used (K-Means Clustering Algorithm), groups of citiesmunicipalities can be identified so that the subsequent strategy for reducing pollution is carried out not only at the level of development regions and at a higher level of specificity in the territorial profile, city-municipality. Finally, based on the Random Forest method, valuable information is provided at the level of influence on pollution by key aspects of the green economy (waste recycling rate, urban ecosystems, urban transport). Based on the influence score on the level of pollution within the development regions, a plan can be made aimed at reducing the level of pollution at the level of the cities-municipalities within each development region. By identifying the causes and the impact they generate on air quality, strategies for reducing pollution will be developed in close correlation with the impact score of the variables associated with the concept of green economy. This innovative approach, realized at the level of Romania, is distinguished by the originality of identifying the causal relationship between the green economy and the level of pollution in the territorial profile. Based on the methodological approach of this study, statistical studies can be easily carried out to identify the best methods to reduce pollution, taking into account the significant differences in territorial profile existing at the level of each country. In this study, it was shown that there are structural differences in territorial profile in the association relationship between the green economy and the level of pollution. Romania’s territorial specificity plays a defining role in establishing the causal relationship between the green economy and the level of pollution. The main factor of influence on pollution is represented by urban transport, waste recycling and urban ecosystems being secondary problems in the process of reducing pollution in the urban environment. However, the process of reducing pollution and increasing air quality must take into account all 3 components of the green economy, especially from the perspective of the low recycling rate of waste in some cities-municipalities in Romania, within each cluster predominating the low values of this indicator, able

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to substantially reduce the pollution associated with improper management of waste generated by the population and economic operators. In order to be able to answer the fundamental question of the paper “Is the green economy the key factor for reducing pollution from the urban environment?” the following structure was considered for the unitary and coherent presentation of ideas: Introduction, Literature Review, Data & Methodology, Results & Discussions, Conclusions, Bibliography.

2 Literature Review Pollution is one of the most important features of our society. The economic progress came along with a big cost, rising of pollution, especially in urban areas. Starting from that, UNECE and its state members have developed a Sustainable Development Strategy. The strategy aims to achieve 17 main goals which have direct impact on 3 dimensions: social, economic, and environment (UNECE, 2020). Hence, one of the most important targets of strategy is Goal number 11 “Make cities and human settlements inclusive, safe, resilient and sustainable” which is focused on sustainable transport and sustainable cities & human settlements. The causes of pollution are multiple and not being able to talk about a single cause that would significantly influence urban pollution at the expense of other factors. In fact, it can be said that there is a whole causal relationship between various economic and social factors, which is reflected by increased pollution. This was one of the valid points that guided the European institutions towards an integrated policy that could face the new challenges associated with the lack of circularity in the economy. Consequently, the circular economy was added as a key module of the sustainable development agenda to the European Deal. In this way, it widens the role of the existing action plans to assess the major changes in the production and consumption behaviour of businesses and consumers, respectively, by extending the life of products and making people responsible for how products are used and recycled (European Commission, 2022). Recent studies have identified other domains related to the circular economy by exploring digital solutions that can add value to economic activity. Torfgård et al. (2021) recalled the digitization process that involves the use of new technologies to adapt traditional sectors to environmental requirements. One of the major factors in pollution is urban traffic, especially when it comes to European capitals (Gozalo et al., 2013). Another determining factor with a direct impact on the increase in pollution level is the management of waste, particularly plastic waste. The management of plastic waste is one of the key factors when it comes to the concept of sustainable development. Therefore, in order to prevent waste management from being mismanaged, action must be taken in a number of ways, such as increasing the recycling rate, the rate of re-use, developing innovation in this area, and testing for substitution of plastic products/goods (Kumar et al., 2021). Since 1979 and continuing to the time being, many researchers have studied the possible solutions offered by the circular economy to environmental problems. Therefore, Georgescu-Roegen (1979) established the existence of a link with the

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physical and biological sciences in the form of bioeconomy and presented its implications in economics. Through the assumption that everything is a source for something else, Segerson et al. (1991) coordinated the development of a circular model of the economy and its correlation with the environment through several factors: resource supply, waste management and utility. Specifically, Kardung et al. (2019) investigated the connection between circular economy, bioeconomy, green economy, and sustainable development. Even so, a waste management policy that may seem efficient may become ineffective if its transport produces more greenhouse gases than they actually produce. In other words, it is extremely important to re-join a cost–benefit analysis in terms of waste transport and waste management (Fan et al., 2021). An experimental study carried out in Sri Lanka shows that waste management has a direct impact on pollution through soil quality. Thus, it is important to conduct a policy of polarization of the way in which waste is managed, both by citizens and business (Beckanov & Mirzabaev, 2018). Another factor that has a direct impact on evolution of pollution is urban ecosystems. According to Gomez and Barton (2013), conserving and restoring ecosystem services in urban areas can reduce the ecological footprints and the ecological debts of cities while enhancing resilience, health, and quality of life for their inhabitants. That is why there is an acute need to develop a plan for the management and governance of green infrastructure in urban areas (Peleg et al., 2016). Cortinovis and Geneletti (2018) also state that the development of urban ecosystems is essential for the promotion of sustainable cities. Identifying these three factors with an immediate impact on pollution, the most possible solution, especially in terms of sustainability, is the transition to green economy. It is demonstrated that the high level of poverty, exposure to a polluting air, and the increase in CO2 have a negative effect on sustainable development, while the increase in R&D capacity and the use of renewable energy have a positive effect on the development of sustainable settlements (Skvarciany et al., 2021). Moreover, the concept of sustainable development should not be considered by consumers alone, an important role in the revolution of the economic models used is also played by the business environment (Pakhomova et al., 2017). Starting from this idea, there are a number of limitations regarding the transition to green economy, for example, Campbell-Jhonston et al. (2019) on the basis of an experimental study on the main subjects of the cities of the Hague, Amsterdam, and Utrecht noted that there is a need for a series of policies adopted at local level, such as: the adoption of an urban planning plan that envisages the reduction of pollution, the implementation of a waste management system, and the development of a culture for recycling and re-use of resources. Williams (2019) also confirms the idea that the transition of the urban environment towards green economy implies a multisectoral development, following well-defined guidelines.

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3 Data & Methodology Most of the data (Waste Recovery rate, Green space per capita, Cars per 1000 inhabitants) were provided by the National Institute of Statistics from Romania at the disaggregation level of the city-municipality of Romania. However, there is enough controversy about the amount of waste collected at municipal level. At the level of all cities in Romania, the waste collection service is outsourced, with at least one or two operators for each city. For this reason, the reporting of the amount of waste collected and the amount of its re-use is uncertain. Therefore, in order to ensure the relevance of the data, only those city-municipalities whose values were reliable were selected for the analysis, eliminating outlier values. In this study, the pollution component is represented by the average rate of persons exposed to PM 2.5 particles. These data have been provided by OECD. Unfortunately, the level of de-aggregation available at the moment is only at the level of the 8 Romanian development regions. The analysis is based on descriptive statistics, but also on the establishment of a clustering of the city in the three variables (Waste Recovery rate, Green space per capita, Cars per 1000 inhabitants). For clustering, the K-means clustering algorithm was used, an unsupervised learning method (Sinaga & Yang, 2020). This algorithm is based on an iterative approach. In the first instance, it is considered that each statistical unit in the sample represents a distinct cluster. Then, based on the Euclidean distances, other clusters are formed based on centroid values. This process is repeated until the cluster composition remains unchanged for two iterations in a row, with the ultimate aim of ensuring that the individuals inside the cluster are as homogeneous as possible and that the clusters are as heterogeneous as possible (James et al., 2013). Then, in order to observe which is the most important factor for the time being in classifying municipalities, it has been used a Random Forest Model Classification. According to James et al. (2013), random forests provide an improvement over bagged trees by way of a random small tweak that decorrelates the trees. As in bagging, it builds a number forest of decision trees on bootstrapped training samples. But when building these decision trees, each time a split in a tree is considered, a random sample of m predictors is chosen as split candidates from the full set of p predictors. The split is allowed to use only one of those m predictors. A fresh sample of m predictors is taken at each split, and typically we choose m ≈ √p that is, the number of predictors considered at each split is approximately equal to the square root of the total number of predictors (Scornet et al., 2015). The main difference between bagging and random forests is the choice of predictor subset size m. For instance, if a random forest is built using m = p, then this amounts simply to bagging. Random forests using m = √p leads to a reduction in both test error and OOB error over bagging. Using a small value of m in building a random forest will typically be helpful when we have a large number of correlated predictors (Scornet et al., 2015). Clusters have also been analysed in terms of representing the 8 development regions within each cluster. Thus, a descriptive analysis of the regions could be

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carried out on the basis of the 3 variables, which played a defining role in the composition of clusters. Exposure to PM 2.5 particles was analysed by reference to the composition of clusters, in order to identify the association between the working variables and the pollution component.

4 Results and Discussions A first step in the process of identifying structural differences between the 66 municipalities analysed in this paper is the interpretation of the indicators of descriptive statistics. The rate of re-use of waste is one of the most important catalysts for the predominant use of the circular economy. However, the quantification process of reused waste is a time-consuming process, and it is difficult to measure with an accuracy of 100% because of the dynamic nature of the waste collection. On average, the rate of re-use of waste for the Romanian municipalities is 18.2%. At first sight, the average rate of re-use seems to be a decent one. However, if this rate is analysed together with other indicators (Kurtosis, Skewness and coefficient of variation), will see there are a lot of gaps among Romanian cities. In addition, the coefficient of variation higher than 1 indicates that there is a strong variation in the middle of the series (Table 1). The air quality inside urban settings can also be influenced by the green space, capable of producing oxygen per capita. On average, the green space allocated to a person living in one of the Romanian municipalities. The smoothing coefficient identifies the presence of pronounced queues of the distribution of values, which is also confirmed by the asymmetry coefficient, which indicates that there is an asymmetry pronounced to the right, predominating the low values of the series (Table 1). Furthermore, the coefficient of variation shows that the mean is not representative of the sample in the analysis. In short, it can be said that there is a quite high variation at the level of Romanian municipalities, the value of this indicator being proportional to local strategy and politics.

Table 1 Descriptive statistics Indicator Mean Kurtosis Skewness Coefficient of variance Source: Own work

Waste recovery rate 18.2% 0.68 1.35 1.38

Green space per capita 21.30 21.85 4.01 0.76

Cars per 1000 inhabitants 569 3.90 -1.20 0.28

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Transport is one of the most important issues when dealing with pollution. On average, there are 569 cars per 1000 inhabitants. In practice, it can be stated that there is a car in less than 2 persons, this average being representative of the selected sample. The distribution of the series tends towards normal distribution, with the value of the Kurtosis coefficient being very close to a mesokurtic distribution. However, the series has negative, pronounced asymmetry, which means that the high values prevail in the series (Table 1). Given the coefficient of variation value, the 66 units are homogeneous in terms of the registered cars, it can be identified that the number of registered cars is not necessarily the catalyst for pollution, but rather its characteristics and the type of fuel used. Starting from the fact that the population analysed seems to be heterogeneous from the perspective of the 3 variables analysed so far, it is interesting to see how they are distributed on the basis of the k-means method, where k = 3.

4.1

Means of Clusters

Further, the 3 clusters resulting from the application of the k-means clustering algorithm will be analysed. The first cluster is composed by 4 cities, the second by 49 and the last by 13. The first cluster is characterized by low values of the 3 variables, opening up the opportunity to discuss the level of development of the 4 cities that make up the cluster. On the basis of these results, it can be assumed that the 4 cities are cities that have not defined their local strategic framework in order to integrate circular economy. The cluster with the number two has higher values than the first cluster, which is also the cluster with the most cities inside. These cities have a relatively high rate of re-use of waste above the average of this variable, which means that strategies are in place at local level in these cities to implement the circular economy, designed to substantially reduce the amount of pollutants caused by the misuse of the collected waste. The 13 cities making up this cluster have higher values for the Green space per capita and the Number of vehicles per 1000 inhabitants (Table 2). On the other hand, as far as the Waste Recovery rate is concerned, the value of 13% is very close to that of the first cluster. Thus, it can be deduced the idea that this cluster is made up of cities with a high economic potential, but unfortunately they have not developed a strategy for the management of the collected waste. Table 2 Mean of variables on each cluster Variable Waste recovery rate Green space per capita Cars per 1000 inhabitants Source: Own work

Cluster 1 12% 16.37 98.52

Cluster 2 20% 20 553

Cluster 3 13% 27 770

148 Table 3 Distribution of municipalities in Regions

A.-C. Maricuț and G.-I. Grădinaru Region Centre North-East North-West South-Muntenia South-East South-West Oltenia West

Cluster 1 0% 25% 0% 50% 0% 0% 25%

Cluster 2 20% 14% 16% 14% 12% 8% 14%

Cluster 3 38% 0% 15% 8% 8% 15% 15%

Source: own work

However, in order to be able to have an overall idea, it is important to see the component of each cluster, and to be able to identify differences in development regions, the rate of each region of appearance within each cluster will be included. It can be seen that in this first cluster, there are 4 cities that come from only 3 development regions, of which the South-Muntenia region is represented by 2 cities. In short, it can be said that there are a number of outliers specific to these 3 regions (Table 3). As regards the second cluster, distribution seems more uniform at the level of the development regions, but there are two exceptions, the Centre region which is best represented in the cluster and the South-Muntenia region, which is poorly represented. The Centre region seems to have the most coherent strategy for the implementation of the circular economy, whereas the South-Muntenia region appears to be the region that does not have a coherent plan for waste management. Air quality is one of the most important components of the quality of life of citizens living in urban areas. The average rate of persons exposed to PM 2.5 particles is an important indicator in the measurement of pollution. At the level of 2019, the region of Bucharest–Ilfov poses the greatest risk in terms of pollution of the population. This is because of the density of the resident population in Bucharest, but also because of the intense economic activity in Ilfov County. Unfortunately, the Ilfov county does not have a city-municipality in its composition, and the reporting on the collection of waste from Bucharest is a bad one, because the collection services are outsourced to the local councils of the sector municipalities, and there are many operators dealing with this issue (Fig. 1). If it is talking about the other regions, the differences are not very large. The regions of Centre and West appear to be the best in terms of public exposure to pollution. In other words, by reference to the descriptive analysis of clusters, it can be said that maintaining and restoring urban ecosystems and increasing the use of the circular economy are two important areas for reducing urban pollution. Thus, it is very important and interesting, in the same time, which is the most important predictors in clustering data. For that matter, it has been used Random Forest Model classification. The classification error of the model has been around 7.58%, which means that the model is reliable. The classification error is maximum for the cities that make up the first cluster. However, it can be noted that this classification model performs at the best

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25.00 20.40 20.00 16.20

16.00

15.00

14.50

14.30

13.70

12.20

11.90

10.00 5.00 0.00

Fig. 1 Mean Population Exposure to PM2.5 (Micrograms/m3)—Air Pollution. Source: authors own study Table 4 Confusion matrix

Cluster 1 2 3

1 0 0 0

2 4 49 1

3 0 0 12

Error of classification 100% 0% 7.7%

Source: own work

parameters (Table 4) for the cities that form clusters 2 (error rate = 0%) and 3 (error rate = 7.7%). To further understand of these differences in classification, the importance of the predictors in the classification of cities should be analysed. It is obvious that the determining factor in the classification process of these cities is the number of Cars per 1000 inhabitants. However, it is interesting that, on the basis of this classification, the cities in the first cluster are considered to be part of the second cluster, which means that they have particular cases for which this classification model does not perform at its best. As it was presented earlier, these cities have low values for all 3 variables, which leads to a lack of vision in the administrative profile. Based on this assumption, it can be stated that cluster 2 is made up of cities with both low and high values for variables (Waste recovery rate and Green space per capita), the determining factor being the value of the Cars per 1000 inhabitants (Table 5) which confirms the idea of Gozalo et al. (2013). Moreover, the emissions from Cars per 10,000 inhabitants could cancel the benefits from circular economy, as Fan et al. (2021) also sustain. Perhaps the main factor which differentiates the score of the 3 predictors is the form of the distribution of the themselves, the only one of them aiming for a normal distribution is the number of Cars per 1000 inhabitants. In this respect, the

150 Table 5 Importance of predictors

A.-C. Maricuț and G.-I. Grădinaru Predictor Waste recovery rate Green space per capita Cars per 1000 inhabitants

Score 3.94 4.64 17.65

Source: Own work

differences among cities come from different amount of emissions which are similar with the idea promoted by Skvarciany et al. (2021). The two other variables (Waste recovery rate & Green space per capita) show asymmetric distributions, which are characterized by the existence of a lot of extreme values, which confirms the idea of Campbell-Jhonston et al. (2019) that state there is needed to adopt an urban plan that envisages the reduction of pollution, the implementation of a waste management system, and the development of a culture for recycling and re-use of resources. In other words, many of these cities are close to the minimum and others close to the maximum which confirms the idea of Campbell-Jhonston et al. (2019) that state there is needed to adopt an urban plan that envisages the reduction of pollution, the implementation of a waste management system, and the development of a culture for recycling and re-use of resources. Hence, in order to improve air quality and to reduce air pollution, central and local administrations should focus on a multisectoral development, following well-defined guidelines, as stated by Williams (2019).

5 Conclusion There are significant differences in the distribution of development regions per cluster. While the Regions of Center and West seem more oriented towards a circular economy and the maintenance and rehabilitation of urban ecosystems, the Region of South-Muntenia, South-East, and North-East represent 3 regions whose economic development is not based on the circular economy and its benefits. By linking the analysis of clustering to the territorial analysis of citizens’ exposure to PM 2.5 particles, it can be argued that there is more or less a combination of waste management, urban ecosystems, urban traffic, and the pollution component (population exposed to PM 2.5 particles). Therefore, the traffic is a major factor in urban pollution, similar with Gozalo et al. (2013) assumption. There is also sufficient evidence in line with the idea promoted by Kumar et al. (2021) that the circular economy has shown its positive effect in reducing pollution. However, in order to significantly reduce pollution, local councils need to focus on sustainable measures based on three-dimensional multisectoral impact analysis (social, environmental, economic), similar to Williams’ assumption (2019). Apart from local government, the Romanian Government can play a defining role in increasing the use of the circular economy. The government can propose a set of measures to encourage business to invest and use the circular economy.

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In conclusion, the answer to fundamental question “Is green economy the key factor in reducing pollution of urban areas?” is yes, but it should take into account that it is mandatory to develop a comprehensive strategy oriented to urban transport, waste management, and urban ecosystems. Unfortunately, the work is limited to the study of the 66 cities for which the selected data are consistent. Moreover, one negative aspect of the approach in this work is represented by the areas of the cities in Bucharest and Ilfov. Ilfov is an atypical county, which does not have municipalities, while Bucharest is divided into 6 administrative territorial areas. Thus, at Bucharest level, the 6 local councils are responsible for waste management. Each of the 6 either manages waste through its own apparatus or outsources it, resulting in the impossibility of providing relevant statistics with a high degree of accuracy as regards waste management. Thus, for a future study, it is intended to update the methodology for calculating the recycling rate at the level of each city in Romania. Such an analysis would increase the relevance of the results and lead to the formulation of specific policies according to the territorial-administrative profile.

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Huang, Z., & Du, X. (2018). Urban land expansion and air pollution: Evidence from China. Journal of Urban Planning and Development, 144(4), 05018017. https://doi.org/10.1061/(ASCE)UP. 1943-5444.0000476 James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). K means clustering. In An introduction to statistical learning with Applications in R (pp. 386–390). Springer. https://doi.org/10.1007/9781-4614-7138-7 Kardung, M., Costenoble, O., Dammer, L., Delahaye, R., Lovric, M., van Leeuwen, M. G. A., M’Barek, R., van Meijl, H., Piotrowski, S., Ronzon, T. B., Verhoog, A. D., Verkerk, H., Vrachioli, M., Wesseler, J. H. H., & Zhu, B. X. (2019). D1.1: Framework for measuring the size and development of the bioeconomy. Kumar, R., Verma, A., Shome, A., Sinha, R., Sinha, S., Jha, P., et al. (2021). Impacts of plastic pollution on ecosystem services, sustainable development goals, and need to focus on circular economy and policy interventions. Sustainability, 13(17), 9963. https://doi.org/10.3390/ su13179963 Martinez-Bravo, M., Martinez-del-Rio, J., & Antolin-Lopez, R. (2019). Trade-offs among urban sustainability, pollution and livability in European cities. Journal of Cleaner Production, 224, 651–660. https://doi.org/10.1016/j.jclepro.2019.03.110 Pakhomova, N., Richter, K., & Vetrova, M. (2017). Transition to circular economy and closed-loop supply chains as driver of sustainable development. Vestnik Sankt-Peterburgskogo Universiteta-Ekonomika-St Petersburg University Journal Of Economic Studies, 33(2), 244–268. https://doi.org/10.21638/11701/spbu05.2017.203 Peleg, K., Hamstead, Z., Haase, D., McPhearson, T., Frantsezkaki, N., Andersson, E., & Kabisch, N. (2016). Key insights for the future of urban ecosystem services research. Ecology and Society, 21(2), Article 29. Scornet, E., Biau, G., & Vert, J.-P. (2015). Consistency of random forests. Annals of Statistics, 43(4), 1716–1741. https://doi.org/10.1214/15-AOS1321 Segerson, K., Pearce, D., & Turner, R. (1991). Economics of natural resources and the environment. Land Economics, 67(2), 272. Sinaga, K., & Yang, M.-S. (2020). Unsupervised K-means clustering Algorithm. IEEE Access, 8, 80716–80727. https://doi.org/10.1109/ACCESS.2020.2988796 Skvarciany, V., Lapinskaiate, I., & Volskyte, G. (2021). Circular economy as assistance for sustainable development in OECD countries. Oeconomia Copernicana, 12(1), 11–34. https:// doi.org/10.24136/oc.2021.001 Torfgård, L., Bhatia, R., Blomquist, M., Tunberg, M., Ekegren, K., Roos, A., Korsvik, T. R., Gustafsson, I., & Nordström, I. (2021). Redefining digital bioeconomy reviewing how the digital transformation affects gender inequalities in the Nordic bioeconomy. Semantic Scholar. UNECE. (2020). Sustainable Development. Retrieved October 7, 2020, from Sustainable Development: https://sustainabledevelopment.un.org/topics/ nationalsustainabledevelopmentstrategies Williams, J. (2019). Circular cities. Urban Studies, 56(13), 2746–2762. https://doi.org/10.1177/ 0042098018806133

Part V

Eurasian Economic Perspectives: Agricultural Economics

Rural Development in the Context of Agricultural Models: Evidence from Bulgaria Julia Doitchinova and Albena Miteva

Abstract This article analyzes the changes in the agricultural sector over a ten-year period and their impacts on rural areas. Based on the derived characteristics of the Southern and Northern models of agriculture in the various statistical regions, several hypotheses about their effects are tested. The drawn conclusions are based on the analysis of statistical information and qualitative assessments of more than 160 experts from all areas of the country. The predominant part of the analysis reveals the results of the implemented measures from the Program for Development of Rural Areas of Bulgaria (2014–2020) and the attitudes of farmers to apply agroecological practices, to make a transition to organic farming, to make direct sales from their farms, and to diversify the activity, etc. It is concluded that the implementation of the Common Agricultural Policy is the cause of the accelerated adverse changes in the socio-economic characteristics of the regions with predominant Northern agriculture. As a result we observe a relatively faster decrease in population, deterioration of qualitative educational and age characteristics and of ecological footprint. These trends are relatively less pronounced in the South-Central and South-West regions, where the importance of small family farms is significantly greater. Keywords Rural development · Structural change · Agricultural models · Regional differences

1 Introduction The processes of change in rural areas have economic, social, political, and environmental dimensions. According to some authors (Schiller et al., 2015), there is a consensus on the content of these transformations in the direction of shifting the J. Doitchinova (✉) · A. Miteva Department of Natural Resources Economics, University of National and World Economy, Sofia, Bulgaria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_9

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structure of economic activities by changing the role of agriculture. The polarization of agricultural structures continues to increase, accompanied by the growing importance of rural migration. On this basis, we notice a change in the role and function of land, an increased role of tourism, as well as a growing importance of links between rural and urban areas. Along with the structures, functional changes are observed, such as increased multifunctionality and diversification, which leads to “move away from food and fibre production as the sole focus of farming” (Copus et al., 2011, p. iii) and to differences of regional diversification patterns (Weltin et al., 2017). Regional development scientists underline that the qualities of soil resources, natural-climatic state, proximity to markets (for resources and products), agrarian policy, and other specific characteristics provoked considerable differences in applied agricultural practices between regions (Van Hecke, 1995; Wilson, 2001). They also refer to differences in the intensity of production, the elaborated products, and the application of innovations. As farm types and farming systems are in harmony with local conditions this has created clear differences between regions (Mettepenningen et al., 2011). Our motivation for writing this paper is that although in recent decades, due to globalization, the Common Agricultural Policy, technological development and modernization of agriculture, traditional regional differences in agriculture have decreased, local agricultural systems are transforming at different speeds and impacting rural development differently. The research question and main contribution of the conducted research helps to assess the changes in farming patterns in Bulgaria and their impact on the development of rural areas. The emphasis is placed on the directions for the development of European agriculture, stimulated by the Common Agricultural Policy during the program period 2014–2020. The impacts of farming patterns in the northern and southern regions of the country are evaluated. In this context, the aim of the article is to analyze and evaluate the differences in the development of rural areas in Bulgaria in the transforming models of agriculture in the regions of the country. The first part of the report provides a literature review of the research on changes in rural areas and the established models of agriculture in them. The second part contains a description of the used methods. On this basis, the following sections analyze the changes in rural areas, the transformations in agriculture and the experts assessment of the importance of agriculture for rural areas and the trends in the changes in agricultural models. The last part presents the trends and conclusions on what are the impacts of agricultural models on rural development.

2 Literature Review In the agrarian economic literature, rural development is seen by a number of authors as a search for a new model for agricultural development (van der Ploeg et al., 2002; Van der Ploeg & Roep, 2003). A model of agriculture in which the rural area will develop sustainably. The multi-level and multifaceted nature of rural development leads to a paradigm shift in terms of the modernization of agriculture (van der Ploeg

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et al., 2002). This change includes the transformation of monofunctional, specialized agricultural holdings, characteristic of the modernization of agriculture, into multifunctional enterprises. It is emphasized that agriculture is often just one element of agricultural household activities, and that agricultural households can apply different models of resource use and income generation (Doitchinova & Stoyanova, 2020). More and more researchers are coming together to understand the new role of agriculture. Its goal is not maximum production, but to improve the condition and to develop rural areas, to achieve environmental and social justice. Lamine (2015) points out the perspective of territorial agro-food systems as an opportunity to search for new solutions to agricultural, food, and environmental problems. This approach accentuates on the two-way relationship between agriculture and the environment and the economic, social, and ecological dimensions of the transition to organic farming systems. Despite the declining importance of agriculture for the economy, the quantitative and qualitative importance of family farming is significant. According to some authors (Samberg et al., 2016), it generates income for approximately one third of the world’s population. The Food and Agriculture Organization (FAO) estimates that about 53% of all agricultural land is used by family farms (Graeub et al., 2016; Lowder et al., 2016). A number of studies show that due to the higher labor intensity on family farms, they employ a much larger number of people per unit of agricultural land compared to larger capital-intensive agricultural units. Family farmers are more efficient and productive in terms of resources per unit of agricultural land than in corporate agriculture (Lowder et al., 2016; Van der Ploeg, 2008, 2017). Under these conditions, family farming has the potential to expand employment in agriculture and the rural economy (Milone & Ventura, 2010). This also determines the importance of family farming in terms of global livelihoods and well-being in rural areas. For these reasons, it is extremely important for rural development whether family farms and agriculture are stable and sustainable and whether new, innovative forms of resource use can be created, creating different interactions between social and natural resources in rural areas (Van der Ploeg, 2013, 2008; Snipstall, 2015; Woods, 2014; Suess-Reyes & Fuetsch, 2016). Rural residents who are farmers are a diverse group of owners and users of agricultural land. They have different motivation and commitment to agricultural production and different market participation. Their individual choices have an impact on the regional economy, on the use of natural resources, on the landscape, on the potential for collective action (Doitchinova, 2022) on maintaining a “sense of place.” In practice, the totality of individual farmer choices largely determines the regional farming models and systems. The common agricultural policy also has a major impact on agricultural structures, production models and systems. The significant reduction in the number of animals leads to a reduction in the use of pastures, to a reduction in the production and use of manure, to an increase in the seasonality of labor and others. As a result, unemployment and emigration are rising, employment chances are declining, and

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higher subsidies are needed. This leads to a spiral of disinvestment in services and other rural infrastructure, and to further migration to urban centers (Giller et al., 2021).

3 Data and Methods To achieve the goal of the paper, statistical methods are complemented and combined with expert assessment of changes in farming patterns and their impact on rural areas. Тhe authors analyze the changes in the population in rural areas, the importance of agriculture in them, and changes in production and organizational structures in agriculture. The impact of agriculture and its changes in rural areas is assessed on the basis of statistical information on demographic change, agricultural statistics, and a survey. The obtained results are used to differentiate the rural areas in Bulgaria depending on the formed models of agriculture in them, with the emphasis on family farming. The survey was carried out in February–April 2021 on the territory of the country. It was conducted using a survey form that was sent to the regional and municipal structures of the Ministry of Agriculture and Agricultural Advisory Services. The survey card includes a wide range of questions to assess the systems and models of agriculture, the ongoing transformations in them and their impact on demographic processes, incomes, and the environment in rural areas. For the purposes of this report, part of the questions are used to assess the impact of agriculture on the economic development of the regions, their social characteristics and environmental changes. The emphasis is placed on the transformations that were stimulated by the Common Agricultural Policy during the previous program period in the direction of the transition to sustainable agriculture (such as organic farming, shortening of value chains, direct sales, implementation of agro-ecological schemes, diversification of agricultural holdings, etc.) From the 280 survey cards sent to the 28 regions of the country, 163 experts from all regions participated in the study, who expressed their opinion using a 5-point positive evaluation scale.

4 Changes in Rural Areas and Transformations in Agriculture In the years of our country’s membership in the EU, the processes of depopulation of the population are deepening. In ten years, the population has decreased by 7.8%, with significant regional differences. This decrease is largest in the North-West and North Central regions, by 18.2% and 15.2%, respectively.

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Depopulation processes are even faster in rural areas of the country—ranging from 8.1% to 20.1% (2020 compared to 2010). The comparison between the regions shows significant differences. In two of the regions—North-West and North-Central the decrease is extremely large—20.1% and 16.1%. In the Southwest and South Central region—the decrease is just over 8% (Fig. 1). At the same time, the agricultural sector is declining in importance for the Bulgarian economy. In 2019, the importance of the agricultural sector by regions ranges from 5.5 (for the South Central region) to 9.6% (for the North-West region) for 2019 (Table 1). The data show the most significant relative share of the national production of the South Central region—20.76%, followed by the Northeast and Northwest regions— 16.9% and 16.54%, respectively. Most numerous are the agricultural holdings in the South Central region—42.7 thousand, which are 32.21% of all farms. Next follows the South Central region with 18.95% of agricultural holdings. More than half (51.16%) of the farms in the country function in the two southern regions of the country, the majority of which are familyrun and are a source of income for their households. At the same time, their decrease compared to the previous census of agricultural holdings (2010) is slower compared to the three northern regions of the country.The comparison between the formed average sizes of used agricultural lands by one farm shows significant differences between the regions in Bulgaria (Fig. 2). Compared to the North-West region of the country, the average sizes of farms in the South-West and South-Central regions are 4.14 and 4.2 times smaller, respectively. At the same time, compared to the data from the previous census of agricultural holdings (2010), the average size increased at different speeds—significantly faster in the North-West and North-Central region the first two northern regions and relatively slower in the South-West, South-Central, and Northeast regions. Differences in production specialization are the reason why the low average size is combined with the use of significantly more labor. The information in the figure

South Central

91.9

South-West

91.8

South-East

88.7

North-East

88.9

North Central

83.9

North-West

79.9 72

74

76

78

80

82

84

86

88

90

92

94

Fig. 1 Change in population in rural areas in percents (2020 compared to 2010). Source: National Statistics Institute, 2021

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Table 1 Importance of agriculture—regional differences Areas and indicators NorthWest North Central NorthEast SouthEast SouthWesta South Central

Share of agriculture in the Gross Value Added (GVA) of the region (%) 9.6

Share of the region in the national production (%) 16.54

Number of agricultural holding 15,234

Structure of agricultural holding (%) 11.48

8.1

16.07

14,883

11.21

6.15

16.9

16,349

12.32

5.13

15.2

18,355

13.83

6.24

14.46

25,154

18.95

5.5

20.76

42,745

32.21

Sources: National Statistics Institute, (2021), Ministry of Agriculture, Food and Forestry (2021) Without Sofia region (city of Sofia)

a

60

54.7 48.8

50

46.7 40.8

40 30 20

13.2

13

10

0

North-West North Central North-East

South-East South-West South Central

Fig. 2 Average sizes of the used agricultural lands by regions. Source: Ministry of Agriculture, Food and Forestry (2021)

shows that almost half of the sector’s employment is concentrated in the two Southern regions. Those working in agriculture in the South Central region of the country are 32.76% of all employed in the sector, followed by 16.42% in the SouthWest region (Fig. 3). The production of vegetables, fruits, and grapes is developed in these areas, as well as the majority of the livestock production is concentrated— mainly the breeding of cattle, sheep, and goats. At the other pole are the three Northern regions of the country, which employ between 11.06% (Northwest region) and 12.86% (Northeast region). They are dominated by the production of cereals and oilseeds, and in some micro-regions

Rural Development in the Context of Agricultural Models: Evidence. . . Fig. 3 Structure of the employed in agriculture by regions of the country. Source: Ministry of Agriculture, Food and Forestry (2021)

161 11.06

32.26

12.38

12.86

16.42

15.02

North-West

North Central

North-East

South-East

South-West

South Central

the specialization of production has led to the cultivation of a very limited number of crops with all the resulting adverse effects on soil fertility and the environment. Traditional farming systems are associated with a high degree of mechanization of work processes and the use of a small number of mechanics. The latter intensifies the migration of the population from rural areas. Evidence of the differences between the production structures by regions is the relative share of the used agricultural land, which is used by the family farms (Fig. 4). In the regions characterized by the Northern model of agriculture, they range below 30% (between 21.44% in Northeast region and 29.31% in Southeast region), while in the Southwest and South Central region the agricultural areas cultivated by family farms are 64.72% and 44.83%, respectively. The presented data and comparisons between the average sizes of agricultural used land, the number of holdings and others show significant differences between two of the southern regions (Southwest and South Central regions) and the three northern regions (North Central, Northwestern, and Northeastern regions). The three main processes of agricultural modernization discussed by Ilbery and Maye (2010) are drivers of agricultural development in Northern Bulgaria. The priority is intensification (through mechanization, use of chemicals, change of plant varieties and animal breeds), followed by specialization of farms and concentration of production. These processes led to the formation of large specialized farms with high productivity based on the application of high mechanization and automation of production. Of the greatest importance for production are the established farms with the status of sole proprietorships, commercial companies, and cooperatives, which are the main tenants of land in rural areas and often the only employers in rural areas. These characteristics confirm the results of previous research (Doitchinova et al., 2018) and prove that the formed structures can be referred to as representatives of the Northern European model of agriculture.

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70.00

64.72

60.00 44.83

50.00 40.00 30.00

25.87

23.90

Northwest

North Central

29.31 21.44

20.00 10.00 0.00 Northeast

Southeast

South central

Southwest

Fig. 4 Relative share of used agricultural land from holdings of individuals (%). Source: Ministry of Agriculture, Food and Forestry (2021)

In the southern regions of the country are located the majority of agricultural holdings with significantly lower amounts of used agricultural land and developed animal husbandry. The established traditional production specialization is logically connected with the predominance of family farms, with a lower use of hired labor, with less leased land and with a lower degree of mechanization of the production process. And these traits are classic characteristics of the majority of agricultural holdings in Southern Europe.

5 Expert Assessment of the Importance of Agriculture for Rural Areas and the Trends in Changes in Agricultural Models Regardless of the statistics on the importance of agriculture, in the survey conducted in 2021, the importance of agriculture was highly rated by the experts. The formed estimates of the experts range from 4.54 for the South-East region to 3.91 for the North-West region (Fig. 5). The estimates for the possibilities of agriculture to be a source of income are significantly lower. Here the range is from 3.85 in the Southeast region to 3.13 in the Northwest. The limits of the estimates for the jobs created by the agricultural sector are similar—3.92 and 3.22, respectively. The positive impact of agriculture on the environment is rated highest by the experts in the North Central (3.89) and South Central (3.88) regions. South-East and South-West regions follow them. Expert ratings are between 2.92 (Northeast) and 2.78 (Northwest region). The problems of the shortage of irrigated areas and of workers are shared by the respondents in all regions of the country. Irrigated land shortage scores are above

Rural Development in the Context of Agricultural Models: Evidence. . . 5.00 4.33

4.50 4.00 3.50 3.00

3.91 3.22 3.13 2.78

3.89 3.72 3.72

163

4.54

4.25 3.74 3.67

3.85 3.92 3.38

4.04 3.88 3.72 3.40

2.92

4.19 3.38 3.28

3.25

2.50 2.00 1.50 1.00

0.50 0.00

Northwest

North Central

Northeast

Southeast

South central

Southwest

Importance of agriculture for the region Agriculture is a source of income Agriculture creates jobs Agriculture has a positive impact on the environment

Fig. 5 Importance and impacts of agriculture on rural development. Source: Own study

4 for all areas, and labor shortage is highest in the South-East (4.95) and lowest in the North-East (3.66) regions. The surveyed experts positively assess the changes that have occurred during the years of our country’s membership in the European Union. The tendency to increase farms selling directly is more highly evaluated (by more than 0.5) in the Southern regions of the country (Table 2). In comparative terms, the growth of organic producers in the North Central region is the highest. The lowest rating was formed in the North-West region—only 1.82. According to experts, there is an increase in farms with agro-ecological practices in the South and North Central regions. Of the studied transformation trends of agricultural holdings, the diversification of their activity was rated the lowest. Scores above 3 were only received in the South Central and South West regions. These are the areas with the highest population density, where the largest cities, resorts for mountain tourism and sites for historical and cultural tourism in Bulgaria are located. In the three regions of Northern Bulgaria, the experts indicated estimates ranging from 2.09 (Northwest) to 2.29 (Northeast).

6 Discussion Research on the impacts of farming patterns on rural development has attempted to answer the question of whether these changes positively affect the characteristics of rural areas. The results of the expert evaluations show that the northern and southern

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Table 2 Changes and trends in the development of agricultural holdings

Regions NorthWest North Central NorthEast SouthEast South Central South West

The number of Holdings with direct sales increases 3.0

Holdings implementing agri-environmental schemes increases 1.95

Organic holdings increases 1.82

Holdings which diversify their activity increases 2.09

3.0

3.42

3.31

2.22

3.09

2.29

3.09

2.29

3.73

3.34

3.04

2.84

3.72

3.69

3.0

3.5

3.55

3.0

3.1

3.0

Source: Own results

models of agriculture have a different impact on rural areas, on a number of their characteristics and development opportunities. The opinion of the interviewed experts is that the impacts of the Southern model of agriculture are more favorable. In the areas dominated by this model, the population in rural areas decreases more slowly, migration processes are less pronounced, farms decrease more slowly. The processes of shortening the value chains, of increasing the number of farms that carry out direct realization of the production, of expanding the biological production and diversification of agricultural holdings are also highly valued there. These changes together with changes in regional product structures lead to achieving not only higher economic results from the use of resources, but also more favorable social and environmental impacts (Doitchinova et al., 2017). Last but not least, the predominance of family farms in the southern regions of the country creates prerequisites for sustainable development of the regions. The detailed analysis of the statistical data and the results of the survey show more favorable indicators and higher evaluations from the interviewed experts from areas with large consumption centers (cities and resorts) and transport possibilities. The Common Agricultural Policy during the last two program periods actively influences the development and consolidation of the northern model of agriculture. The statistical data and the results of the survey show rapid changes in the average size of the agricultural used land, reduction in the number of agricultural holdings and in intensification of production. According to the surveyed experts, there is significantly less interest in direct sales, in the implementation of agro-ecological schemes, in the development of organic farming, and diversification of the economic activity of agricultural holdings. The applied technologies, the higher degree of applied chemicalization of production, the deterioration of the condition of the soils, and other environmental characteristics are the reasons for the lower

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assessments of the respondents on the social impact of the applied model of agriculture and its impact on the environment in rural areas. Further development of the study could include expanding and deepening this analysis at district and municipal level, as well as exploring farmers’ intentions for future changes in production patterns and their impact on rural development. On this basis, guidelines for the development of Bulgaria’s national agrarian policy can be developed. Acknowledgements The paper uses results from project DH 15/8–2017 financed by Bulgarian Research Fund.

References Copus, A., Courtney, P., Dax T., Meredith D., Noguera J., Talbot H., & Shucksmith M. (2011). EDORA final report. Parts A, B and C, Luxembourg. Available online at: http://www.espon.eu/ export/sites/default/Documents/Projects/AppliedResearch/EDORA/EDORA_Final_Report_ Parts_A_and_B.pdf. Doitchinova, J. (2022). Local action groups as motivators of local dеvеlopmеnt in rural arеas of Bulgaria, in Sustainable agriculture and rural development ii, Thematic proceeding, Institute of agricultural economics, Belgrade – Serbia, 265–272. Doitchinova, J., Harizanova, H., & Miteva, A. (2017). Product restructuring of Bulgarian agriculture - dilemmas and strategic directions. In Strategies for the Agri-food sector and rural areas – Dilemmas of development”, Instytut Ekonomiki Rolnictwa i Gospodarki Żywnościowej – Państwowy Instytut Badawczy, 2017 Poland, 222–235. Doitchinova, J., Kanchev, I., Terzyiska, R., & Todorova, K. (2018). Socio-economic and environmental parameters and results of rural development under the CAP: The case of Bulgaria. In The common agricultural policy of the European Union – The present and the future. EU Member States point of view (pp. 247–259). Institute of Agricultural and Food Economics - National research Institute. Doitchinova, J., & Stoyanova, Z. (2020). How different farming patterns are changing rural areas. European agriculture and the new CAP 2021-2027: Challenges and opportunities, рp. 92–100. Giller, K., Delaune, T., Silva, J. V., Descheemaeker, K., van de Ven, G., Schut, A., van Wijk, M., Hammond, J., Hochman, Z., Taulya, G., Chikowo, R., Narayanan, S., Kishore, A., Bresciani, F., Teixeira, H., Andersson, J., & van Ittersum, M. (2021). The future of farming: Who will produce our food? Food Security, 13, 1073–1099. Graeub, B., Chappell, M., Wittman, H., Ledermann, S., Kerr, R., & Gemmill-Herren, B. (2016). The state of family farms in the world. World Development, 87, 1–15. Ilbery, B., & Maye, D. (2010). Agricultural restructuring and changing food networks in the UK. In N. Coe & A. Jones (Eds.), The economic geography of the UK (pp. 166–180). Sage. Lamine, C. (2015). Sustainability and resilience in Agrifood systems: Reconnecting agriculture, food and the environment. Sociologia Ruralis, 55(1), 41–61. Lowder, S., Skoet, J., & Raney, T. (2016). The number, size, and distribution of farms, smallholder farms, and family farms worldwide. World Development, 87, 16–29. Mettepenningen, E., Messely, L., Schuermans, N., Cappon, R., Vandermeulen, V., Van Huylenbroeck, G., Dessein, J., Van Hecke, E., Leinfelder, H., Bourgeois, M., Laurijssen, T., Bryon, J., Lauwers, L., Allaert, G., & Jourez, M. (2011). Multifunctionality and local identity as paradigms for a sustainable and competitive agriculture. Koninklijke vlaamse academie van belgie voor wetenschappen en kunsten.

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Milone, P., & Ventura, F. (2010). Networking the rural, the future of green regions in Europe. Royal Van Gorcum. Ministry of Agriculture, Food and Forestry, Census of agricultural holdings, 2021. National Statistics Institute, Population by statistical regions, 2021. Samberg, L., Gerber, J., Ramankutty, N., Herrero, M., & West, P. (2016). Subnational distribution of average farm size and smallholder contributions to global food production. Environmental Research Letters, 11, 124010. Schiller, S., Peter, S., & Kühn, S. (2015). Agricultural and Rural Transformation in Europe. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH. Snipstall, B. (2015). Repeasantization, agroecology, and the tactics of food sovereignty. Canadian Food Studies, 2, 164–173. Suess-Reyes, J., & Fuetsch, E. (2016). The future of family farming: A literature review on innovative, sustainable and succession-oriented strategies. The Journal of Peasant Studies, 47(Part A), 117–140. Van der Ploeg, J. D. (2008). The new peasantries. Struggles for autonomy and sustainability in an era of empire and globalization. Earthscan. Van der Ploeg, J. D. (2013). Peasants and the art of farming. A Chayanovian Manifesto. Fernwood Publishers. Van der Ploeg, J. D. (2017). Differentiation: Old controversies, new insights. The Journal of Peasant Studies, 45(2), 1–36. Van der Ploeg, J. D., Long, A., & Banks, J. (2002). Living Countrysides. Rural development processes in Europe: The state of the art. Elsevier. Van der Ploeg, J. D., & Roep, D. (2003). Multifunctionality and rural development: The actual situation in Europe. In G. van Huylenbroeck & G. Durand (Eds.), Multifunctional agriculture. A new paradigm for European agriculture and rural development (pp. 37–54). Ashgate. Van Hecke, E. (1995). Structure des exploitations agricoles, Enquête 1987. Analyse cartografique des résultats. Weltin, M., Zasada, I., Franke, C., Piorr, A., Raggi, M., & Viaggi, D. (2017). Analysing behavioural differences of farm households: An example of income diversification strategies based on European farm survey data. Land Use Policy, 62, 172–184. Wilson, G. A. (2001). From productivism to post-productivism . . . and back again? Exploring the (un)changed natural and mental landscapes of European agriculture. Transactions of the Institute of British Geographers, 26(1), 77–102. Woods, M. (2014). Family farming in the global countryside. Anthropol. Noteb., 20, 31–48.

Risk Management in Agriculture: Lesson from Bulgaria Hristina Harizanova-Bartos

and Zornitsa Stoyanova

Abstract Agriculture is a traditional sector in Bulgaria. The relevance of the challenges faced by the agricultural sector and the achievement of sustainability leads to a practical need to minimize the risks of the activity. The aim of the article is to evaluate the risk management mechanisms in Bulgaria and on this basis to be prepared conclusions about the most applicable for the Bulgarian agricultural holdings and to be given recommendation for improvement of risk management. In 2020, a survey was conducted among 50 farmers by prepared special questionnaire to study the risk. The contributions are related to proposals for improvement of risk management processes in Bulgarian agriculture. Our findings demonstrate that many risk-reduction mechanisms are not applied, and that there is a considerable disparity in risk-reduction mechanisms used between different agricultural specializations and according to the economic size. Greater attention should be paid to the specifics of activities in different agricultural sub-sectors and to manage those which will reduce the economic losses from the activity. Keywords Agriculture · Risk management · Bulgaria · Policy · Farm

1 Introduction Risk management in agriculture has an important role for the sustainable development of the sector. Without correct chosen measurements being implemented in the daily activities or neglecting the possible negative outcome, it may lead to a lower income for the farmers. The motivation to reveal which measures in Bulgaria are recognizable by farmers and whether they apply or tend to apply them results in research and examination of this issue. Another important question is, as well, should risk management be a general strategy or should it be developed for each H. Harizanova-Bartos · Z. Stoyanova (✉) Economics of Natural Resources Department, University of National and World Economy, Sofia, Bulgaria e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_10

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activity in the farm? In addition, appropriate changes should be proposed to improve the process of implementing and monitoring the measures. The aim of the article is to evaluate the risk management mechanisms in Bulgaria and on this basis to be prepared conclusions about the most applicable for the Bulgarian agricultural holdings and to be given recommendation for improvement of risk management. Both in Bulgaria and in other countries often, the risks that arise cannot be overcome, and therefore an option is sought to transfer them or to minimize them. Some of these risks are catastrophic (hail, floods, fires) and lead to huge losses. This in turn affects the price and quality of the product, and hence its demand to meet the nutritional needs of the population. These statements justify the importance of risk management. For successful risk management in the agricultural sector, each farmer should answer a few questions, which will help the owner to understand which risks are important for him, what their effects are, and whether the cost of their management is sufficiently achievable. On the one hand, government programs insist on the use of yield insurance, but on the other hand, this publication will also raise the question of why not everyone does it. As well, some authors (Bielza et al., 2007; Skees, 2003; Andreeva, 2021) stress that there are existing even further technicalities like reinsurance, but usually farmers insure only hail and fire. As the government’s involvement in insurance increases, more comprehensive coverage is provided by the insurance system, making possible the insuring of agricultural systemic risks. Bielza et al. (2007) consider that in Europe, the main risk-sharing tools for risk management in agriculture are calamity funds, regional mutual schemes, and insurance. Calamities funds are usually regulated by the government. The instruments related to insurance are mainly divided into categories as yield insurance, whole-farm yield insurance, revenue insurance, income insurance, index insurance, area-yield insurance, area-revenue insurance, and indirectindex insurance. In the developing systems, the bonus/malus system is obtained in most countries, but it still depends on different levels of risk, the frequency of the events, type of risk, sensitivity of the crops, the number of risks covered (single-risk, combined-risk, yield insurance), etc. (Diaz-Caneja et al., 2009). According to Chrastinová et al. (2016), risks in agriculture are managed by different types of instruments, but the main risk management tool is insurance provided by private insurance companies (Andreeva, 2022). Risk management in the Nordic countries, for example, is based on limiting the damage from emerging climate risks, which are also most often borne by the insurer (Koundouri et al., 2009). In 2016, insurance based on indices was introduced to cover the loss due to the load on soils and pastures. The acceptance of these instruments on the market shows that farmers really need such products and are willing to pay for them (Sinabell, et al., 2016). With regard to risk management in agriculture, Bachev (2013) often focuses on technical instruments and their capacity both for the prevention of risk events and for overcoming risk and recovering from the various risks. Other authors (Severinia et al., 2019) state that farms, although aware of the risk of their activities and have the necessary information about possible adverse events, do not take any strategy because they do not have the necessary financial resources to redirect and to deal with the problem. This attitude is characteristic of small farms, due to their inability

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to withstand financial losses associated with risk (Kahan, 2013). If farmers are not risk oriented, they are neutral with respect to all proposed strategies to reduce agricultural risk (Antón & Giner, 2005). Due to the high cost of insurance in Bulgaria, farmers frequently seek strategies other than insurance, and the most applicable measurement is diversification (Nikolova & Linkova, 2011). The described diversification is in two directions only: horizontal and vertical. The authors stress that the risk can be minimized by, for example, creating added value into the production. The diversification of agricultural products and activities is viewed as a strategy to receive extra income and to create additional employment (Doytchinova et al., 2017). There is evidence that in family farms, off-farm income is more stable and secure than on-farm income (OECD, 2003). The weak point is that the price of the investment is not measured and not risk-free, so even if it is a very attractive idea for diversification, it should be considered very carefully. Furthermore, they stress that the optimum diversification can be reached by mixing agriculture and mostly by adding livestock. However, in theory, it is possible, but in practice, there are numerous requirements that the farmer must meet, and the risk is frequently greater. In addition, there is a difference in approach between small and big farmers. Small farmers limit the risk on their farms using traditional, time-tested methods. This contrasts with the opinion of farm owners, who share the view that they are innovative and easily accept innovations in the agricultural sector (Dimitrova, 2019). Harizanova-Bartos and Dimitrova (2018) based on the analysis of the variety of literature sources consider that the degree of readiness and continuity of innovations is different according to the type of agricultural subsector. The risk stages should be measured for the best strategy. Some other measures carried out by the farmers to manage their risk exposure are sought by the other authors (Barnett & Coble, 2009). They commonly diversify across commodities or geographic locations. Farm households also manage risk by producing crops that generate multiple harvests over a single growing season, securing off-farm employment, or investing in off-farm assets. Other risk management strategies include using risk-reducing inputs such as irrigation, forward pricing, savings, and maintaining credit reserves, implementation of ecoinnovation (Stoyanova et al., 2022). Thus, for a better understanding of risk, it should be identified and the likelihood and assessed impact, which should reflect an appropriate response. Taking measures and actions to respond to identified and assessed risks is a very important stage in risk management. The government structure as well as various types of risk response is prepared (Ministry of Agriculture and Food, 2018). However, some risks can be reduced and there are several strategies to improve their ability to withstand adverse business conditions (Miller et al., 2004). The following response options are possible: limitation, transfer, tolerance, and eliminate the risk. When selecting appropriate actions/responses, account is taken of the requirement that their costs do not exceed the expected benefits (Harizanova-Bartos et al., 2021). The structure of the article includes: (1) literature review of risk management from the previous research; (2) Defining and evaluation of measures and mechanisms applicable in Bulgaria according to the farm size, type of production, and risk

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management strategies; (3) Analysis of relationship between type of production and government support; (4) Conclusions and recommendations for improvement of risk management in Bulgarian agricultural sector. The study is based on own research.

2 Data and Methodology An analysis based on the questionnaire is prepared, which will aid in understanding the potential outcomes of risk management in the agricultural sector. Bulgaria’s agricultural sector consists of crop productions and animal breeding, and each of these sub-sectors includes productions traditional to the country. The outputs of their activity have been converted into economic units, allowing the economic significance of each to be determined. In order to be able to group the farms in Bulgaria and propose analysis and conclusions is used the economic size which is transformed in production volume of the farms. This method allows the comparison between various types of production, taking into account the volume of production as well as the costs of production. The survey areas were chosen based on the territorial approach, which means that the farms were in areas where current production is most prevalent. Respondents in these areas were chosen randomly. Table 1 shows the structure of farm distribution in Bulgaria according to main production. The main reason for choosing this method is the opportunity to study farmers from these important Bulgarian productions, revealing their attitude toward risk management. Since it is impossible to survey all farms in the country, a sample is used by allowing us to draw conclusions for the entire agricultural sector. The total number of surveyed farmers is 50, and the holdings follow the structure shown in Table 1. The survey was conducted via phone, according to health restriction in this period in the country. Table 1 Ratio of specialized farms by economical size Production Cereals, oilseeds, and protein crops Dairy cattle Other field crops Sheep, goats, and other grazing animals Pigs, birds, and rabbits Vegetables, flowers, and mushrooms Orchard Vines Cattle with meat direction Cattle with dairy and meat direction Mixed perennials Source: own calculations, based on MAF (2020)

% of the economical size in the sector 42 17 12.6 9 6 4.8 3 2 1.8 1.5 0.3

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Aside from the sectoral analysis presented in the article, differences in risk management will be tried to seek in relation to the size of the holdings. A variety of policies support the Bulgarian agricultural sector, including those aimed at reducing risk or damage from risky events. The size of the holdings is frequently essential, and the most common division used by the ministry is small, medium, and large holdings. The Ministry of Agriculture divides Bulgarian farms into three major groups based on economic size, using a standard production volume. The first category includes farms with a standard production volume of less than 8000 euros (small farms), between 8001 and 20,000 euros (medium farms), and over 20,000 euros (large farms). The farms are divided into three sizes: 15 large farms, 15 medium farms, and 20 small farms. The results of the various risk management mechanisms are presented in the tables in the analytical section, both for the three groups of farms and for the types of production (Doytchinova et al., 2017).

3 Results of the Survey 3.1

Relationship Between Farm Size, Type of Produce, and Risk Management Strategies

According to the methodology used for the paper, a survey of Bulgarian farmers was done, and part of that survey included an assessment of risk management tools. According to the economic size of the farms, the results are decomposed to the groups. Table 2 provides comprehensive data by farmer groups and implemented mechanisms. Risk-reduction mechanisms were explored in each group based on economic size. The numbers represent the percentage of farmers who responded positively to the examined mechanisms. Except for Diversification with other non-farm activities, all of the proposed mechanisms are used by the large farms in the sample (over 20,000 euros in production volume). All farmers in the group use the mechanisms for implementing pest and disease prevention and control measures (e.g., strict hygiene rules) and using market information and weather forecasts when planning farm activities for the next season. The mechanism for improving cost flexibility (e.g., renting land instead of buying, signing fixed-term employment contracts instead of permanent ones) is used by 85% of large farms, and they are the only ones who use it compared with the other two groups studied. 70% of respondents (large farms) are saving money for difficult years. Despite the fact that only 40% of the farmers insure their production, this is the highest percentage of the three groups. A small percentage of respondents work outside the farm, and a small percentage collaborate with other farms for economies of scale. For a more in-depth analysis, the applied mechanisms were divided based on the type of production. The answers are presented in Table 3.

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Table 2 Application of risk management mechanisms according to size of the farm, %

Application of mechanisms Creating financial savings for difficult years Keeping loans low to protect against financial risks Investment in technologies (e.g., irrigated facilities or hail nets) to control environmental risks Introduce measures to prevent and control pests and diseases (e.g., strict hygiene rules) Use of market information and weather forecast when planning the activities on the farm for the next season Diversification with other on-farm activities (e.g., agritourism, on-farm sales, environmental protection, or renewable energy) Diversification of production (e.g., mixed livestock and crop production or combination of several crops or animals) Improving cost flexibility (e.g., renting land instead of buying, concluding fixed-term employment contracts instead of permanent ones) Finding a job off the farm (independent of the farmer or family member) Cooperation with other farmers for supply of raw materials and production (e.g., purchase of seeds, preparations, shared use of machinery, exchange of land) Learning and sharing experiences on agricultural challenges (e.g., farmers’ groups, consultants, farmers’ trainings) Insurances

Size under 8000 25 85 10

8001–20,000 40 50 80

Over 20,000 70 40 60

75

75

100

100

100

100

0

25

0

50

40

15

0

0

85

90

35

15

30

60

15

15

40

15

15

15

40

Source: own data

Creating financial savings for difficult years is a typical mechanism for farms with sheep, goats, and other grazing animals, which is used by 60% of farmers. Less than 50% of all the farms with other types of production use this mechanism. Keeping loans low to protect against financial risks is one of the most ineffective risk management mechanisms in the Bulgarian farms (6 out of 11 produces do not use it). It is used by 25% of farms that grow fruit trees, which is the highest percentage. The most common mechanisms for reducing risk are the use of market information and weather forecasts when planning farm activities for the next season and improving cost flexibility (e.g., renting land instead of buying, concluding fixed-term employment contracts instead of permanent ones). The use of insurance to reduce risk is a research interest. Pig and vegetable producers do not insure their output, whereas grain and cattle producers do (50% of sampled farms).

Application of mechanisms Creating financial savings for difficult years Keeping loans low to protect against financial risks Investment in technologies to control environmental risks Introduce measures to prevent and control pests and diseases (e.g., strict hygiene rules) Use of market information and weather forecast when planning the activities on the farm for the next season Diversification with other on-farm activities (e.g., agritourism, on-farm sales, environmental protection, or renewable energy) Diversification of production (e.g., mixed livestock and crop production or

Dairy cattle 45

0

0

70

100

0

25

Cereals, oilseeds and protein crops 25

0

25

40

100

0

25

15

0

75

70

25

0

Other field crops 25

40

15

100

70

25

0

Sheep, goats and other grazing animals 60

60

0

100

80

15

0

Pigs, birds and rabbits 40

40

15

40

25

10

15

Vegetables, flowers and mushrooms 15

Table 3 Application of risk management mechanisms according to type of production, %

15

15

70

40

40

25

Orchard 25

0

0

100

25

60

15

Vines 25

25

0

40

40

30

15

Cattle with meat direction 40

40

0

40

15

25

15

Cattle with dairy and meat direction 25

(continued)

50

0

100

15

0

0

Mixed perennials 25

Risk Management in Agriculture: Lesson from Bulgaria 173

Source: own data

combination of several crops or animals) Improving cost flexibility (e.g., renting land instead of buying, concluding fixedterm employment contracts instead of permanent ones) Finding a job off the farm (independent of the farmer or family member) Cooperation with other farmers for supply of raw materials and production (e.g., purchase of seeds, preparations, shared use of machinery, exchange of land) Learning and sharing experiences on agricultural challenges (e.g., farmers’ groups, consultants, farmers’ trainings) Insurances

Application of mechanisms

Table 3 (continued)

100

0

0

15

50

0

0

15

50

Dairy cattle

100

Cereals, oilseeds and protein crops

50

15

0

0

100

Other field crops

15

40

40

15

25

Sheep, goats and other grazing animals

0

15

15

0

0

Pigs, birds and rabbits

0

30

40

40

15

Vegetables, flowers and mushrooms

15

15

40

40

75

Orchard

25

15

15

15

45

Vines

40

0

15

15

40

Cattle with meat direction

40

0

0

15

60

Cattle with dairy and meat direction

15

0

15

15

60

Mixed perennials

174 H. Harizanova-Bartos and Z. Stoyanova

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3.2

175

Relationship Between Type of Production and Government Support

According to Table 4, a risk event occurred in each of the sub-sectors, and some of them also received state support. As shown in the table, the grain sector received government assistance for lost yields until some sub-sectors did not. The majority of the cases, in the respondents’ opinions, have a moderate to low level of impact on occurred risk. Furthermore, price risk and climate risk were associated with the most frequent risks. Representatives of the grain industry identified a labor shortage as the risk that is most likely to arise when it is most needed. According to the data, a sizable part of the farms under study that experienced a risk used state assistance. The state has provided financial assistance to all farmers who produce cereals, oilseeds, and protein crops. The sector for sheep, goats, and other grazing animals is on second place with 75% as a receiver of government support. The other activities were not covered by state funding. The needs of all agricultural activities should be taken into consideration by the state, taking into account the size and specialization of the farm. The research confirms that the risk management plan has to be improved. Respondents consider that the government

Table 4 Risk occurrence and government support

Production Cereals, oilseeds, and protein crops Dairy cattle Other field crops Sheep, goats, and other grazing animals Pigs, birds, and rabbits Vegetables, flowers, and mushrooms Orchard Vines Cattle with meat direction Cattle with dairy and meat direction Mixed perennials Source: own data

Has a risky event occurred in the last 5 years? YES—% 35

Level of impact—selfevaluation 1 low till 5 catastrophic 4

Did you get government support—YES—% 100

15 40 60

2 3.5 2.8

25 75 75

40

3

15

25

4.50

0

30 30 15

4 1.8 2.25

0 15 0

15

4

0

25

3.25

15

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should provide more support to agrarian structures that have experienced risky events, as the state intervention in these farms is insufficient, unsatisfactory, and it is necessary to work toward increasing support.

4 Conclusion and Discussions Farmers in Bulgaria regularly look for alternatives to insurance due to the high cost of insurance and the most useful measurement is diversity. However, literature review points out that the horizontal and vertical directions of the stated diversification are included. The risk might be reduced, for instance, by adding value to the output. It is believed that diversifying agricultural outputs and activities is a good way to increase employment and revenue. The findings show that in order to reduce the risk associated with their agricultural activity, small farms (with a production output of up to 8000 euros) mostly adopt techniques connected to the usage of weather monitoring systems. According to the results, small farms neither diversify with additional on-farm operations nor use methods for boosting cost flexibility (such as renting land instead of purchasing it or signing fixed-term employment contracts rather than long-term ones) (e.g., agritourism, on-farm sales, environmental protection, or renewable energy). Just 15% of respondents reported that their farm employed insurance or training programs to reduce risk, which indicates that most farms do not. Mediumsized farms (with a production volume of between 8001 and 20,000 euros) use the indicated risk management measures more extensively than small farms do. All of the suggested mechanisms—aside from diversification with other non-farm activities—are utilized by the major farms in the sample (over 20,000 euros in production volume). All of the farmers in the group follow the procedures for putting pest and disease prevention and control measures into effect (such as stringent cleanliness standards), as well as using market data and weather forecasts to plan farm operations for the upcoming season. Only big farms, out of the other two groups under study, use the mechanism for increasing cost flexibility (e.g., renting land instead of purchasing it, signing fixed-term employment contracts rather than long-term ones). 60% of farmers who have farms with sheep, goats, and other grazing animals utilize a common strategy to save money for tough years. This mechanism is used by less than half of all farmers with different product specialization. One of the most unsuccessful risk management practices in Bulgarian farms is keeping loan amounts low to guard against financial concerns (6 out of 11 produces do not use it). The largest percentage of farms that cultivate fruit trees that utilize it is 25%. Using market data and weather forecasts when planning farm operations for the upcoming season and increasing cost flexibility (e.g., renting land instead of purchasing it, signing fixed-term employment contracts) are the two most popular mechanisms. According to the responses, the majority of cases have a moderate to low level of risk event impact. Furthermore, climate risk and price risk were identified as the most

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common threats. Representatives from the grain sector indicated a labor shortage as the most likely risk occurrence during the periods when the working force is most needed. The results show that many of the farms under examination that were at danger utilized governmental support. For the agricultural industry to expand sustainably, risk management is crucial. It may result in a decreased revenue for the farmers if the proper chosen metrics are not implemented in the daily operations or if the potential negative impact is ignored. Literature review is also confirmed by the results from the survey. The field of risk management in agriculture is still underdeveloped, as seen by the low outcomes of farmers’ risk measurement implementation. The government’s assistance should not be the exclusive means of resolving the sector’s challenges. Future research recommendations could be related to risk management at sub-sectoral level, because different sub-sectors in agriculture meet different risks and risk events that are specific for each of them. Therefore, risk management needs to be prepared individually for each agricultural subsector, taking into account its specifics. Acknowledgment The findings are part of scientific the project DN 15/8/2017, titled “Sustainable multinational rural areas: reconsidering agricultural models and systems with increased demands and limited resources” funded by Bulgarian research fund.

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Doytchinova, J., Miteva, A., Harizanova-Bartos, H., Stoyanova, Z. et al. (2017). Scientific project DN 15/8 2017 sustainable multinational rural areas: Reconsidering agricultural models and systems with increased demands and limited resources, funded by the Bulgarian research fund. Sofia. UNWE. Harizanova-Bartos, H., & Dimitrova, A. (2018). Perspectives and barriers in the implementation of innovations in Bulgarian agriculture, Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development Vol. 18, Issue 4, 2018, PRINT ISSN 2284-7995, (pp. 143–149), http://managementjournal.usamv.ro/pdf/vol.18_4/Art21.pdf. E-ISSN 2285-3952. Harizanova-Bartos, H., Stoyanova, Z., Petkova, I., Harizanova-Metodieva, H., Metodiev, N., Dimitrova, A., & Sheitanov, P. (2021). Integrated risk management approach in agricultural sector, Publishing complex—UNWE, ISBN 978-619-232-537-4. Kahan, D. (2013). Managing risk in farming. FAO. Koundouri, P., Laukkanen, M., & Myyra, S. (2009). The effects of EU agricultural policy changes on farmers’ risk attitudes. European Review of Agricultural Economics, 36(1), 53–77. MAF. (2020). Census of agriculture in Bulgaria. Miller, A., Dobbins, C., Pritchett, J., Boehlje, M., & Ehmke, C. (2004). Risk management for farmers. Staff Paper, 4–11. Nikolova, M., & Linkova, M. (2011). Risk diversification in the agricultural sector in Bulgaria. Economic Interferences, 13(29), 305–320. OECD. (2003). Farm household income: Issues and policy responses. Paris. Severinia, S., Biaginia, L., & Finger, R. (2019). Modeling agricultural risk management policies– the implementation of the income stabilization tool in Italy. Journal of Policy Modeling, 41(1), 140–155. Stoyanova, Z., Dojchinova, J., Todorova, K., Pejcheva, M., Dineva, V., & Blagoev, A. (2022). Ecoinnovations for the provision of agro-ecosystem services by farms, Publishing Complex— UNWE. Skees, J. (2003). Risk management challenges in rural financial markets: Blending risk management innovations with rural finance. Paving the Way Forward for Rural Finance. (pp. 1–31). Sinabell, F, Heinschink, K, & Url T. (2016). An index-based margin insurance for agriculture—the example of wheat production in Austria. Risk in the food economy—theory and practice. (pp. 53–63). Jachranka: IAFE-NRI. 150–160.

Part VI

Eurasian Economic Perspectives: Banking

Impact of Merger Announcements on Stock Price of Participating Banks Tamy Al-Binali

Abstract Mergers and Acquisitions are one of the most effective approaches toward horizontal growth and expansion of businesses especially in the banking sector. Merger events come hand in hand with a plethora of information that affects the stock price behavior of participating firms. Although the impact of such events has been widely studied in developed economies, not many studies have been conducted in the Gulf region or, more specifically, in Qatar. This study explores the effect of merger announcements by Masraf Al-Rayan (Bank) and Khaliji Bank by using the event study method for two different events, i.e. the announcement regarding entering merger negotiations and the announcement regarding the finalization of the merger deal. Market Model has been used to calculate the abnormal returns and cumulative abnormal returns for examining the effects of the merger announcement on stock prices of both banks. The results present mixed findings as to the behavior of the shareholders and the stock price performance of both the banks after the merger announcements. The study reflects that stocks of Masraf Al-Rayan have performed negatively after the merger announcements. In contrast, stocks of Khaliji Bank showed significantly positive performance after event 1 and remained almost insensitive after event 2. Keywords M&A · Bank mergers · Event study · Islamic Banks · Qatar Banks

1 Introduction Last two decades have seen significant surge in corporate restructuring and strategic groupings such as merger and acquisitions (M&A) in the banking industry around the world (Abbas et al., 2014). M&A fast growth in recent years demands study to analyze what motivates organizations to step into M&A activity and how it affects their performance pre and post M&A activity (Andrade et al., 2001). Announcement T. Al-Binali (✉) College of Islamic Finance, Hamad Bin Khalifa University, Doha, Qatar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_11

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of an M&A transaction comes with substantial volume of information regarding the M&A deal, which is vital to evaluate the stock market reaction to the said announcement. Theoretically, M&A creates synergies, produces and explores economies of scale, increases operations in novel markets, products, and services (financial and terrestrial expansion), increase in client list, broadening of activities, and reduction in both cost and risk, consequently leading to improved financial and operational performance (Ma et al., 2011). Mall and Gupta (2019) believe that the effects of M&A announcements on the stock markets can predict future success or failure of the merger transaction as the stock yields related to the announcement may characterize stockholders’ expectations of M&A benefits. Stock market reactions to M&A announcements not only help envisage M&A viability; but also help to understand the short-term effects of instantaneous trading prospects that they create. The most statistically dependable evidence on whether M&A creates stockholders’ wealth can be established by conducting event studies. Duso et al. (2010) advocate that event studies are built on the central idea that stock prices represent the discounted value of organizations’ future stream of gains. Therefore, the change in the share price of companies observed due to the response of stock market toward the announcement of M&A may be considered as a measure of the additional profits that they are expected to bring as a result of M&A. Numerous empirical studies rely on event study methodology to analyze the effects of an M&A event. Although plethora of research is available that has studied the performance of bank stocks after as a result of M&A activity, empirical studies around the globe have provided mixed results as to the positive impact of M&A for the participating banks and their respective investors/shareholders and as such there is a conflict in results produced by several empirical studies which makes it difficult to deduce a common result regarding impacts of M&A deals on stock prices. Further, there is a lack of such empirical studies regarding M&A activities in Qatar, especially in the banking industry resulting in a research gap. Therefore, this study is an attempt to fill this gap and is intended to help to understand the performance of banks stocks during M&A events. Being the first of its kind in Qatar, this study shall not only contribute to the existing studies around the globe but also serve as a bench mark for more such research opportunities within Qatar and the Gulf region. There has been a series of M&A activities, especially in the Islamic banking sector globally, regionally, and most recently in Qatar. The successful merger between Barwa Bank and International Bank of Qatar (IBQ) in 2019, the first bank merger, and the largest M&A transaction across all business sectors in Qatar, opened gates for more such ventures. The next big announcement came in June 2020 when Masraf Al-Rayan (MAR) and Khaliji Bank (KCB) announced that they have entered into negotiations regarding a potential merger. In January 2021, both banks announced that a merger deal has been agreed upon. This study, which is the first of its kind in Qatar, is a humble attempt to objectively analyze the impact of merger announcements on participating banks’ stock prices, i.e. MAR and KCB. The effect on stock price of MAR and KCB will be analyzed for two distinct events:

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• Event 1 would be the official date when both banks announced that they have entered initial negotiations regarding a potential merger, i.e. June 30, 2020. • Event 2 would be the date on which the formal announcement of the merger agreement was made, i.e. January 07, 2021. The objective of this study is to establish the behavior of the stock market and analyze the trends of the share prices of both banks and the impact the merger announcements had on the investors/shareholders in the events mentioned above. This current section of the paper presents a brief background and context of the study. The rest of the paper is divided into four more sections. Section 2 gives a brief description of the reviewed literature. Section 3 describes the sample and the methodology used. Section 4 presents the empirical analysis and results, and finally, Sect. 5 concludes the paper along with the limitations of this study.

2 Literature Review Around the globe, the performance of acquiring firms has been widely studied through empirical methods. Similarly, the primary objective of this paper is to empirically evaluate the impact of M&A announcements on shareholders of both banks. Accordingly, the literature review is largely attentive on research conducted around the impact of M&A on stockholders’ wealth. In the developed economies, several studies have been undertaken to determine how the stock markets respond toward M&A announcements and to assess whether the stockholders see these announcements as positive or negative news for the banks. Zhang et al. (2016) observed that difference between the outcomes of various empirical studies is due to geographical specialization of acquiring banks. Berger et al. (1999) found that increased transaction cost in developed countries and involvement of high-risk premium in developing countries are the reasons behind negative CAR from integration deals. However, not many studies in this area have been undertaken in developing economies. The results may vary significantly, mainly because developing countries share peculiar characteristics such as the supremacy of state-owned financial institutions and M&A transactions motivated by policy initiatives and not the market drivers, which are not commonly found in mature market economies. However, M&A deals between larger banks have also been undertaken by the necessity for strategic repositioning and accumulation (Asimakopoulos & Athanasoglou, 2009), especially in a volatile and dicey macro-economic situation. Palmucci and Caruso (2008) went further to suggest that incentives concerning the bidder’s decisions may not always originate from efficient institutional or economic rationale but from stemming individual gains for the management of the acquiring bank. Theoretically, M&As may have positive or negative bearings on shareholders’ wealth. Goddard et al. (2012) studied 132 merger events from 1998 to 2009 in Asian

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and Latin American counties. The results of their study suggested that M&As have had a positive effect on the shareholders’ wealth for the acquired firms, and no loss was born by the shareholders of the acquiring firm. Conversely, Soongswang (2011) noticed negative returns for the acquiring organization, although the impact on shareholders’ wealth for target organizations was found to be positive. Likewise, Chavaltanpipat et al. (1999) also reported an impact on shareholders’ wealth for the acquirer organization and positive abnormal returns for the acquired organization at the M&A announcement. Guest et al. (2010) concluded that the impact of M&A on the share returns was negative after the event. Their study suggests that the stock returns of the acquiring firm on event day and during the event window were negative after the acquisition of the target organization. The cumulative average abnormal returns for acquirers were found to be negative, whereas the same was positive for the target firms (Cummins & Weiss, 2004). Pessanha (2016) also concluded that the announcement of M&A transaction and profitability are inversely related. Bruner (2002) determined that in spite of a glut of research work on the consequences of M&A activity, the pragmatic evidence on yields to the stockholders of the acquiring entity is not definite, and these varied results render the deductions regarding the acquiring entitys’ performance more complex. Adegboyega and Dele (2014) stated that stockholder’s wealth reduces in the post-merger situation because of increase in the non-performing assets of merged banks, that results in reduced profits and minimal or no dividend to stockholders. Many other researchers also concluded the same, such as Imala (2005) who found low or nil gains to stockholders of acquirer banks. Likewise, Kim and Finkelstein (2009) were of the opinion that M&A deals results in negative cumulative average returns for acquirer banks because of the surge in managing costs (Greve, 1999) and increased premium that managers have to pay to produce operational efficacy (Chronopoulos et al., 2013). Vecchia and Etges (2021) conducted a study of events concerning the stock price using the efficient markets theory and assessed the impact of the M&A announcement on the performance of the acquiring businesses. They observed significant abnormal returns in two mergers involving only acquired and bank acquirers in the period under study. Gupta et al. (2021) studied the impact of post M&As on value creation for 64 Indian firms from 2012 to 2018. The objective of their paper was to assess the synergy effects and value creation post M&A activity and to analyze the impact of insulated synergy on the M&A performance of acquiring firms. Their study suggested that mergers create positive value acquiring firms after the merger. They further concluded that lagged synergy influences future synergies positively. Houston et al. (2001) studied the 64 most significant bank mergers transactions in the US from years 1985 to 1996. They used organizational estimations of projected cost savings and revenue growth. They concluded that merger transactions that took place in the 1990s have resulted in positive revaluations of the joint value of acquiring and the target entities. However, they noticed negative and statistically substantial returns for the bidder banks. Their results were aligned with the concept that bank mergers are aimed for synergistic motives and are not the market share increase.

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Cybo-Ottone et al. (2000) conducted a similar study to analyze 54 M&A deals between 1988 and 1997, covering 13 banking markets across the EU, including the renowned Swiss financial institutions. Their study found a positive and substantial growth in stock market price at the deal announcement for the average merger. Their conclusion contradicted with most empirical studies undertaken in the US banking industry, which concluded that no evidence of value creation effects has been ascertained. They believed that the difference in results is due to different structures and regulations adopted by the EU banking markets. They further concluded that the returns to acquired bank stockholders are expressively positive, but the bidder firms’ returns are negative using a bank sector index. Another study that was conducted by Scholtens and Wit (2004) examined the short-term effects of bank mergers on shareholders’ wealth for the acquiring and target banks in the US and European markets. Wealth was calculated based on performance with respect to the market and sector-specific benchmarks. They found substantial variances in the effects on stockholder wealth regarding the US and European bank mergers; however, instances where market reactions do not significantly differ, were also noted. Positive cumulative abnormal returns were noted for both the target and the bidder in the European bank mergers. In contrast, positive returns were observed for the target firms only in the US. They concluded that the total impact of merger transactions was positive in the European banking industry and neutral in the US market. Cybo-Ottone (2000) suggested that the difference in the results regarding the impact of bank mergers on shareholders’ wealth was primarily due to the different structures and regulations of the US and the EU banking markets. On the other hand, similar studies in developing countries presented slightly different results. Basu et al. (2004) studied more than 100 banks from Argentina to ascertain the effects of bank consolidation on the performance of bank stocks between December 1995 and December 2000. The results demonstrated a positive and significant effect of bank mergers on performance; as a result, bank returns increase, and insolvency risk is reduced. The study specified a bank return generating process including several macro-economic and bank-specific risk factors. Anand et al. (2008) conducted a study titled “Impact of merger announcement on shareholder wealth: evidence from Indian private sector banks.” They took a sample of five mergers from the Indian banking market from 1999 to 2005 to analyze the returns to shareholders resulting from the merger announcement using the event study methodology. Their study concluded that merger announcements for the sample studied have a positive and significant impact on shareholders’ wealth for bidder and target banks. However, it is essential to note that no comprehensive study for analyzing the impact of merger announcements on the wealth of shareholders and the stock market’s response has been carried out in the Gulf region and, more specifically, in Qatar. This paper makes a humble attempt in this regard.

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3 Research Methodology This event study explores the behavior of the Qatar Stock Exchange (QSE), the trends in the share price of MAR and KCB, and the impact that merger announcements had on the investors/shareholders for two specific events, i.e. the announcement on June 30, 2020, that MAR and KCB have entered initial negotiations regarding a potential merger and the formal announcement on January 07, 2021, that the merger agreement has been finalized. Market Model (MacKinlay, 1997) has been used to measure the effect of merger announcements on the share price of both banks. This method estimates the expected stock returns of the merging banks on the day of the event and several days prior to and after the event (i.e., during the event window). After that, the method deducts these “Expected Returns (ERs)” from the “actual returns” to receive “Abnormal Returns (ARs)” attributed to the event. The Cumulative Abnormal Returns (CARs) are obtained by summing all abnormal returns in the event window of the study. Event day (referred to as T0 in this study) is the day on which the announcement regarding the M&A activity was made. The event day is essential to determine the impact of merger transactions on the performance of the bank stocks. It is also worth noting that information about potential merger announcements may have leaked to the market even before the event day. In such instances, abnormal returns may occur even before the event day. Therefore, to overcome this lacuna, it is vital that accurate information regarding stock price movements be collected from the market, and the calculations for abnormal returns include this possibility. The period for which the ARs and CARs are measured for any stock is called the event window. There is no standard duration of the event window. Different studies have used a varied number of days as event windows to calculate the returns of the stock prices by using the event study methodology. The shortest event window used by event studies has been 3 days (comprising the event day, 1 day before, and 1 day after the event), although longer window days of 60 days before and 750 days after the events have also been used in other studies. Researchers have argued the pros and cons of using shorter and longer windows. One such argument is that a short window helps study the increase or decrease in share prices due to the event under study. Longer windows may also include the changes in stock prices due to other economic factors. However, those favoring longer event windows argue that shorter windows may not give complete information as the information regarding merger announcements may not reach the public immediately or that the information may have leaked to the market before the announcement, resulting in stock price changes even before the event day. In order to overcome these drawbacks, this study explores an event window of 31 days. With T0 as the event day, 15 days preceding T0, i.e., T-15 up to T 0, and 15 days preceding the event day, i.e. T0 up to +T15. The reason for choosing a 31 day window (+15 to -15) is the fact that QSE is a developing market and is not very sensitive to M&A information in the long term and a mid-sized event window would be best fit for this study.

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Another significant component of the Market Model is to calculate the Expected Returns (ERs). The time period for calculating the ERs is called the estimation window, which is the selected number of days before the event window. The fundamental principle behind the calculation of ERs and the estimation window is to study the behavior of stock price movements of the target and bidder banks when there is no merger information in the market. This study used the estimation window 90 days before the event window. Actual return is the actual gain or loss an investor experiences on an investment or in a portfolio. It is also referred to as the internal rate of return (IRR). Actual returns have been calculated using the following formula: Ri, t =

Closing Price of stock - Opening Price of Stock Opening Price of Stock

ð1Þ

Where • Ri,t is the actual return of bank stock i at time t (in days). Expected return is the projected return on an investment based on historic performance combined with predicted market trends. For the purpose of this study, expected return has been calculated by using the following equation: ERi, t = α þ βRm, t

ð2Þ

Where • ERi,t is the expected return of bank stock i at time t (in days). • α and β are the Market Model parameters for the bank stock. • Rm,t is the return of market portfolio at time t (in days). Ordinary Least Square (OLS) regression method is generally used to calculate α and β for the estimation window (90 days before the event window). The expected market returns (Rm,t) are calculated by assuming the bank stock returns in normal market, had the M&A event not occurred. α and β are calculated separately for both events under this study. The market model builds on the actual returns of a reference market (QSE in this case) and the correlation of the bank’s stock with the reference market. The abnormal return on a distinct day within the event window represents the difference between the actual stock return (Ri,t) on that day and the expected return (ERi,t), which, as described above, is predicted based on two inputs; the typical relationship between the firm’s stock and its reference index (expressed by the α and β parameters), and the actual reference market’s return (Rm,t). Thus, the abnormal return is calculated as follows: ARi, t = Ri, t - ðα þ βRm, t Þ Where

ð3Þ

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• • • •

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ARi,t is the abnormal return for bank stock i at time t (in days). Ri,t is the actual return for bank stock i at time t (in days). α and β are the market model parameters for the bank stock. Rm,t is the return of market portfolio at time t (in days).

To calculate the total impact of an event over a particular time period (referred to as the event window), individual ARs are added to create a CAR. The Equation below formally shows this practice. As stated above, the event window for this study has been taken as 31 days, starting from day -15 until day +15, which gives us a CAR of 31 days. However, shorter CARs such as 5 days, 11 days, or 15 days can also be gauged from the same datasheet. t2

CARðt1,t2Þ =

ARit

ð4Þ

ðt = t1Þ

Where • CAR (t1,t2) is the aggregate abnormal returns from day x to day y. • ARi,t is the abnormal returns of bank stock at time t (in days). The ARs and CARs have been calculated for MAR and KCB separately for both the events in context of this study.

4 Data Analysis and Findings The ARs and CARs are the parameters to gauge the effects of an event on the stockholders’ wealth before and after the announcement of M & M&As. Three distinct data sets were used in this study to calculate ARs and CARs regarding both events under discussion. The first data set was the historical share price for MAR, the second was the historical share price for KCB, and the third was the historical share index for the QSE for calculating the market returns. Using the equations illustrated above, actual returns, ERs, ARs, and CARs were calculated, and results have been presented in Tables 2 and 3. A significant part of these calculations was to derive the α and β parameters for the bank stocks under study. Table 1 presents the α and β of MAR and KCB for both events. Table 2 shows the results of ARs and CARs during the 31 days event window for the first event, i.e. when both banks announced that they had entered initial negotiations regarding a potential merger on June 30, 2020. The result displays a negative effect on ARs and CARs for most of the event window for MAR except for 5 days starting from T - 3 up to T + 1 with a CAR of 0.726%. 1.240%, 0.178%, and 0.219%, respectively. This trend indicates two things: i.) the shareholders/investors did not perceive much gains for MAR after the potential merger and ii.) there was some information leak in the market before the official announcement date, which

Impact of Merger Announcements on Stock Price of Participating Banks Table 1 Event-wise α and β

Event Event 1 Event 2

MAR α 0.0012 0.0021

189

β 0.3472 0.3297

KCB α -0.0340 -0.0256

β 0.3092 0.0648

CAR -0.308% -0.911% -1.103% -1.531% 0.351% 0.011% 0.290% 0.225% -0.169% -0.933% -0.844% -1.109% -0.511% 0.726% 1.240% 0.178% 0.219% -1.108% -2.507% -2.539% -3.247% -3.579% -4.179% -4.405% -4.682% -6.325% -6.711% -7.234% -7.497% -7.195% -5.963%

KCB AR 1.605% 1.119% 1.907% 1.196% 1.383% 2.618% 2.070% 1.366% 0.802% 0.954% 0.706% 2.397% 0.974% 0.957% 1.372% 0.743% 0.693% 0.339% -2.085% 0.738% -1.043% -0.082% 2.442% 0.077% 0.838% 0.674% 0.513% 1.052% 0.851% 0.574% 0.636%

CAR 1.605% 2.724% 4.631% 5.827% 7.210% 9.828% 11.898% 13.264% 14.066% 15.020% 15.727% 18.123% 19.097% 20.054% 21.427% 22.169% 22.863% 23.201% 21.116% 21.855% 20.811% 20.729% 23.172% 23.248% 24.086% 24.760% 25.273% 26.325% 27.176% 27.751% 28.387%

Source: Own work

Table 2 ARs and CARs for event 1

Time T - 15 T - 14 T - 13 T - 12 T - 11 T - 10 T-9 T-8 T-7 T-6 T-5 T-4 T-3 T-2 T-1 T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T 10 T 11 T 12 T 13 T 14 T 15

MAR AR -0.308% -0.604% -0.192% -0.427% 1.882% -0.341% 0.279% -0.064% -0.395% -0.764% 0.089% -0.264% 0.598% 1.237% 0.515% -1.062% 0.041% -1.327% -1.399% -0.032% -0.708% -0.332% -0.600% -0.226% -0.277% -1.643% -0.386% -0.523% -0.264% 0.302% 1.232%

Source: Own work

190 Table 3 ARs and CARs for event 2

T. Al-Binali Time T - 15 T - 14 T - 13 T - 12 T - 11 T - 10 T-9 T-8 T-7 T-6 T-5 T-4 T-3 T-2 T-1 T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T 10 T 11 T 12 T 13 T 14 T 15

MAR AR -0.334% 1.048% -1.034% 0.374% -0.031% 0.315% 0.071% -0.533% -0.033% 0.449% -0.388% 0.003% -0.707% -0.228% -1.184% 0.576% -1.137% 0.078% -0.181% 0.181% -0.426% -0.553% -0.326% -1.503% -1.336% -0.985% 0.180% 1.144% 0.501% 0.057% -0.917%

CAR -0.334% 0.715% -0.319% 0.055% 0.024% 0.339% 0.411% -0.122% -0.155% 0.293% -0.094% -0.091% -0.799% -1.026% -2.210% -1.634% -2.771% -2.694% -2.875% -2.694% -3.120% -3.673% -4.000% -5.503% -6.839% -7.824% -7.644% -6.500% -5.998% -5.941% -6.858%

KCB AR -0.633% -0.086% 1.038% 0.241% -0.303% 1.293% -0.822% -0.430% -0.554% -0.134% 0.965% 3.041% 0.542% -0.779% 5.600% 2.350% -4.437% 2.522% -0.432% -0.035% 0.025% 0.423% 0.263% 0.232% -1.373% -2.552% -1.119% 1.034% -2.024% -0.532% -3.514%

CAR -0.633% -0.719% 0.319% 0.560% 0.257% 1.550% 0.728% 0.297% -0.257% -0.391% 0.574% 3.615% 4.157% 3.378% 8.979% 11.329% 6.891% 9.413% 8.982% 8.946% 8.971% 9.394% 9.658% 9.890% 8.517% 5.965% 4.846% 5.880% 3.855% 3.324% -0.191%

Source: Own work

led to the positive performance 2 days before the event day, especially on day T - 1, which shows an AR of 1.237%. As for KCB, the results positively affect ARs and CARs for almost the entire period during the event window. 31-day CAR for KCB is 28.387%, reflecting a significant gain for the shareholders/investors during the event window and the market perception that the potential merger is a significant opportunity for future gains. KCB results also support the fact that there was some information leak in the market before the official announcement date, which is evident from the AR of 2.397%, 0.974%, 0.957%, and 1.372% on days T - 4 through T - 1.

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35.000% 30.000% 25.000% 20.000% 15.000% 10.000% 5.000% 0.000% -5.000%

-15-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-10.000% Event 1 - CAR MAR

Event 1 - CAR KCB

Fig. 1 Event 1 CAR. Source: Own work

Figure 1 is the graphical representation of the CARs of MAR and KCB during the 31 days event window for the first event. The results of ARs and CARs during the 31 days event window for the second event, i.e., when both banks formally announced that they have agreed upon a merger deal, are presented in Table 3. Similar to event 1, the result shows a negative effect on ARs and CARs for MAR with a 31-day CAR of -6.858%. This reinforces the assumption that the shareholders/investors did not perceive many gains for MAR after the merger. As for KCB, the results positively affect ARs and CARs before and until the event day. However, the ARs and CARs started declining gradually after the event day and ended with a 31-day CAR of -0.191%. This indicates that the investors initially responded well to the merger announcement but subsequently turned insensitive to the merger event. It would be safe to construe that the phenomenon of M&A has mostly adversely affected the shareholders’ wealth (stock returns) for MAR and positively affected the shareholders’ wealth (stock returns) for KCB. These results are similar to findings of Greene and Watts (1996) and are inconsistent with conclusions of the studies conducted by Neely (1987), Siems (1996), and Becher (2000). These results imply that any news regarding M&A activity in banking sector affects the stock returns negatively for the acquiring bank (MAR). This conclusion is also concurring with the findings of Pessanha (2016), Kamau (2016), and Louhichi (2008). The CAR graphs illustrate that there is consistent increase in the CAR during the two event windows for the acquired bank (KCB). Similar results were found by French and Roll (1986), Jayaraman et al. (1991), Bharath and Wu (2005), Mall and Gupta (2019) in their studies. Figure 2 presents the graphical illustration of the CARs of MAR and KCB during the 31 days event window for the second event.

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15.000% 10.000% 5.000% 0.000% -15-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 -5.000% -10.000% Event 2 - CAR MAR

Event 2 - CAR KCB

Fig. 2 Event 2 CAR. Source: Own work

5 Conclusion and Limitations This study analyzed the effects of merger announcements on stock returns for shareholders of MAR and KCB. The objective of this study was to establish the behavior of the stock market and analyze the trends of the share prices of both banks and the impact the merger announcements had on the investors/shareholders for two different events. The empirical results suggest mixed trend of ARs and CARs during the time period of events under study. The results reflect that stocks of MAR have performed negatively after the M&A announcements, with CARs of -5.963% and -6.858% after both events, respectively. This indicates that investors/shareholders of MAR do not envisage positive gains from this merger activity. Conversely, stocks of KCB showed significantly positive performance after event 1 with a CAR of 28.387%. However, after event 2 the market remained almost insensitive to the M&A activity w.r.t KCB share price, which is visible with a CAR of almost 0 (-0.191%). This performance makes sense when we correlate this with the fact that MAR is a much larger bank than KCB and was already performing equivalent to the market (α 0.0012), so the investors/shareholders do not foresee further gains. However, as KCB was performing below the market (α -0.0340), its investors/shareholders took the M&A announcement as an opportunity for future gains. Like all empirical studies, the assumptions used in this event study have certain limitations. For example, stock prices may not fully and immediately reflect all information due to market inefficiency. Similarly, variations in estimation and event periods are commonly found in event studies as precise estimation periods are not easy to determine. The length of the estimation period is subject to a tradeoff between improved estimation accuracy and potential parameter shifts. Moreover, the estimation period is challenging to control for other confounding effects if researchers select long test periods or long event windows. Another significant factor is that not all stocks trade every day, and thin trading over the estimation and event period may not fully reflect the behavior of the market.

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Impact of Macroprudentiality on Customer Protection of Banking Services: The Case of the Republic of Moldova Cociug Victoria and Turcan-Munteanu Natalia

Abstract Banks provide services that are indispensable to customers’ confidence in their financial stability. The protection of customers of banking services also includes maintaining the financial stability of the provider of such services. Macro-prudential supervision aims to maintain this stability and from this perspective contributes to the protection of consumers of banking services. The study is based on the analysis of theoretical concepts regarding the role of macroprudentiality and the possibility of its extension on consumer protection. The practical detailing is carried out on the case of the Republic of Moldova. The conclusions reflect the need to coordinate the supervisory function and the consumer protection function at the central bank level in the case of countries where the level of trust and financial inclusion of the population is low. This article aims to initiate a discussion about the relationship between the protection of consumers of banking services and macroprudentiality, because the subject of the connection between macroprudentiality and the behavior of consumers of banking services is not discussed as much. Keywords Macro-prudential policy · Central bank · Consumer protection · Financial education

1 Introduction Analyzing the specialized literature, we tried to understand if the protection of the consumer of banking services is on the agenda of the Central Bank. How can and does the Central Bank play an important role in consumer protection of banking C. Victoria (✉) Department of Finance, The Academy of Economic Studies of Moldova, Chisinau, Republic of Moldova e-mail: [email protected] T.-M. Natalia The Academy of Economic Studies of Moldova, Chisinau, Republic of Moldova © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_12

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services in countries where consumer protection legislation is general and does not include the specifics of financial services? Can the Central Bank protect consumers of banking services by applying macro-prudential tools? We are aware that in a country with a well-developed legislative framework in the field of consumer protection of banking services, this subject is not a pressing one, but in the Republic of Moldova, where 40% according to report of the National Bank of Moldova (NBM, 2021) of bank card holders prefer to use them to withdraw cash from fear of losing money from their accounts in case of financial instability of the banks, the involvement of macro-prudential instruments would be a solution. We will develop this research hypothesis based on the experience of the banking system of the Republic of Moldova, as one being in the process of development, with harmful experience of bankruptcy of 2/3 of its banks during the last 30 years of existence and a confrontation with population’s mistrust in payment banking services and the consumer’s strong attachment to cash. We consider the Republic of Moldova an eloquent example, as in any country with a banking system in the process of formation and consolidation, the problems of increasing banking services consumer protection by strengthening the stability of the banking system with the involvement of macro-prudential policy are vital. The process of elaborating and institutionalizing banking regulations, especially prudential ones, has been a long one and they have essentially been reactions of the authorities and the private sector to resonant bank failures and/or, in particular, to financial crises (banking) of scope, reflecting the vision that existed at that time on their causes and possible ways of solving them (Ohler et al., 2020). It can therefore be considered that banking regulation has naturally developed as a set of reactions set out in the form of institutional rules or arrangements designed to prevent similar crisis situations from occurring in the future, but the financial stability was and still is responsible, in the first place, for protection of depositors’ interests and rights. From a practical point of view, macro-prudential policy is usually treated as part of banking regulation (Hanson et al., 2011). However, the effects of applying the macro-prudential policy instruments extend beyond the banking sector. They also play a prominent role in the real sector, of non-financial corporations, but especially of the population, which are consuming the products and services it offers, and should therefore ensure their protection. However, the evolution of prudentiality had as an objective to maintain the stability of financial institutions, without taking into consideration their customers’ interests. Until recently, consumer protection played a secondary role in the introduced regulatory and supervisory practices, but today it lays the fundamentals for public confidence in banking products and services provided by banks (Lane, 2019). In the financial services market, consumer confidence supports long-term financial stability and innovation, making consumer protection one of the main objectives of regulators, in addition to financial stability. The regulators have thus strengthened the regulatory framework regarding the protection of the consumer of financial services, but at the local level certain actions are needed to make workable the mechanism for the financial services consumer protection.

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Central Banks are beginning to play an increasingly active role in protecting consumers of financial services (Lane, 2017). This is primarily due to the mandate to promote financial stability. This theme is approached from the perspective of the need to involve central banks in increasing the protection of the consumer of banking services, a similar function of the regulator, without making the connection with the application of macro-prudential instruments (Chakrabarty, 2011; Duke, 2009; Bakani, 2015). The relationship between the stability of the financial system and the protection of the banking services consumer is somewhat intuitive, but it has not been studied in sufficient detail. There are researches on the impact of macro-prudential policy and access to credit or deposits (ex. Borio, Drehmann 2009; Sutt et al., 2011; Cociug, Postolache, 2020) but these are only banking products. Unfortunately, there is currently no research focused on the impact of macroprudential regulations on the protection of consumers of banking services, such as payment services. It is clear that the stability of the banking system is a prerequisite for protecting the interests of the consumer of services offered by this system, but this is not enough. We believe that it is necessary to incorporate in the macro-prudential policy separate instruments that would guarantee the protection of banking services consumers. This article is structured as follows: the first part is the theoretical basis of macroprudentiality and the protection of the banking services consumer, the second part refers to the mechanism of interaction between macro-prudential policy and the protection of the banking services consumer, and the third part will describe the experience of the Republic of Moldova in this field. At the end, conclusions and recommendations will be formulated.

2 Defining the Macro-prudential Policy The first way to define macro-prudence by stating the set objectives is often used by international regulators, which in such a way can form the prerequisites for developing its strategies for the application of macro-prudential instruments. Thus, in the European Central Bank’s Macroprudential Policy Strategies, (ECB, 2016) macroprudentiality is stated as the policy aimed at maintaining financial stability by preventing the excessive risk accumulation, resulting from external factors and market failures, in order to smooth the financial cycle (time dimension), increasing the resilience of the financial sector and limiting the effects of contagion (transversal dimension), encouraging a system-wide perspective on financial regulation to create the right set of incentives for market participants (structural dimension). The second approach to macroprudentiality is to define it by contrasting it with micro prudentiality and to compare the aims of each of them. Thus, Hanson et al. (2011) distinguish micro- and macro-prudential policies through the final purpose. Micro-prudential policies aim at “preventing the costly failure of individual financial institutions.” In the context of a macro perspective, financial regulation seeks to

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maintain overall financial stability, thus recognizing the overall balancing effects of individual failures. In the same vein, Galati and Moessner (2010) consider that macro-prudential policy is designed to reduce systemic risk, defined as the risk of disrupting the activities of the financial system that will have serious consequences for the real economy. Therefore, macro-prudential policy is a useful complement to microprudential policy: the former focuses on the stability of the overall financial system, while the latter seeks to ensure the solvency of individual financial institutions. A compilation of both ideas—by declaring counteracting systemic risk as the ultimate goal, but also by comparing it with micro-prudence is also encountered in central bank approaches. Thus, in the Bank of England (2009), macro-prudence is defined as an approach to financial regulation that seeks to reduce the risk and macroeconomic costs of financial instability and as “an ingredient needed to fill the gap between macroeconomic policy and traditional micro-prudential regulation of financial institutions.” However, the most succinct, unbiased, and clearest definition is given by the Cambridge Business English Dictionary, which specifies that macroprudentiality is the term used to describe the laws, rules, and conditions for banks and financial organizations that are intended to protect the entire financial system from risks. This approach can be extended from the perspective of understanding that the operational activity of each bank depends on the trust of its customers. The volume of sales of its services depends on the confidence of customers in the financial stability of the bank, and reducing their exposure to risk and avoiding bankruptcy in this regard directly correlates with the protection of consumers, who can be sure that they will not lose their balances in their settlement accounts. Thus, the purpose of macroprudential policy, which is defined as the protection of the financial system against risks, can be extended by the protection of the rights of its customers, especially protection against financial losses caused by bank failure. In this context, several functions of macroprudentiality can be deduced (Clement, 2010): social, financial, macroeconomic, fiscal and ensuring optimal market conditions for banks’ activity. Of all these functions, only the social one manifests itself through the implementation of its function of protecting the rights of consumers of financial products/services and has a dual nature: on the one hand, this function is ensured by macroprudentiality as a component of banking regulation, in the aspect its global (including, and perhaps even more emphasized, microprudentiality), and on the other hand, being treated as a policy, macroprudentiality has its unique place in the macroeconomic management system, ensuring a close connection between various components of the inter-hierarchical relations of the economy. The problem of achieving its objectives is a current one for macro-prudential policy, because, in the process of evolution, it took over and developed the instruments of micro-prudential regulation, which, applied separately, contribute to the accumulation of risks and, consequently, to the increase of losses resulting from the formation of assets of an inadequate quality at the level of individual banks. Currently, it is necessary to develop the own macro-prudential instruments in

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combination with those previously applied at the micro level, and this combination must not lead to an overregulation harmful to the macroeconomic evolution.

3 Contribution of Macro-Prudential Policy to Reconfiguring Banking Services Consumer Protection The banking system contributes to the economic well-being of the consumer and the development of the economy by increasing financial inclusion, financial intermediation, reducing risks and barriers in the financial system, and strengthening confidence in the use of banking products and services. In this respect, the protection of the banking services consumer is based on fundamental principles such as: reciprocity, trust, impartiality, non-discrimination, competence and professionalism, respect for the law and the rules of professional ethics, integrity, confidentiality, and protection of personal data (Campbell et al., 2016). The protection of the consumer of financial-banking services aims at creating means of preventive counseling, so that situations of financial risk or inability to pay of individuals are treated with more concern by all involved institutions (Levesque, 2010). Central banks are beginning to play an increasingly active role in protecting consumers of financial services (Duke, 2009). This is primarily due to the mandate to promote financial stability. The actions of central banks in the field of consumer protection in the financial market have an indirect impact on macro-prudential policy—by identifying and managing systemic risk, in particular systemic economic imbalances. Central banks, as well as autonomous public trust institutions, are particularly interested in maintaining price stability and financial stability, promoting sustainable growth, thus directly affecting the protection of financial market participants, including consumers of financial products and services (Lane, 2017). The protection of banking services consumers is based on several mechanisms, which, being implemented, provide a guarantee of the rights of customers of financial institutions: 1. The separation of responsibilities for the supervision of the rights of the banking services consumer and their transmission to a specialized body (Reifner & ClercRenaud, 2011). Consumer supervisory authorities are usually endowed with the obligation to protect the rights of consumers of all products and services on the market in general. In the case of banking services, it is another approach, as the failure to realize the right to banking service leads to major losses not only for the individual consumer, but also for society by losing confidence in the financial system. The premise of integrating the function of consumer protection into macro-prudential policy is also dictated by the fact that this function is often assigned to the central bank, which is the guarantor of the financial system stability by applying macro-prudential instruments. For example, in 11 of the 18 EU’ member states where central banks have prudential supervision, the

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establishment of financial services consumer protection departments has been legislated. 2. Consumer education. A consumer who possesses financial literacy is a protected consumer who knows how to adequately protect himself in situations where he feels his rights have been violated when using a financial product or service. The role of financial literacy is growing for the protection of consumer rights (Aziz, 2011, Chakrabarty, 2012). Researches show (ex. Rutledge, 2010) that even under full disclosure, many consumers of financial services are unable to make appropriate and reasonable decisions, and the level of personal finance management skills is generally and directly related to the level of knowledge of the underlying financial categories. Proper management of personal finances by consumers should be considered a key factor in helping to prevent over-indebtedness and financial exclusion (Cociug, Turcan-Munteanu, 2021). 3. Consumer information. If they were better informed and more knowledgeable about their rights, consumers could have more confidence in the banking system (Campbell et al., 2016). At the same time, one of the problems for consumers is the information asymmetry, but also the degree of complexity of banking products. The consumer does not know for sure the specifics of the banking service, its risks and benefits for oneself, and later, he can accuse the financial institution of abuse in offering it. In order to avoid such a situation, there is a need for full transparency regarding the product’s costs, its potential effects and benefits, as well as the possible risks. In this context, the macroprudentiality contributes to the protection of financial consumer rights through obligation of ensuring information publication by financial entities and rising their activity transparency. 4. Ensuring that consumers’ rights to the sale of consumer goods and related guarantees are respected through the existence of a legal framework to protect their interests. These measures would offer the possibility to make proper decisions, to use payment services with maximum safety and efficiency and to rise the general level of consumer protection in the bank and non-bank financial system. The European Systemic Risk Board (ESRB, 2013) states that “The ultimate objective of macro-prudential policy is to contribute to the safeguard of the stability of the financial system as a whole, including by strengthening the resilience of the financial system and decreasing the build-up of systemic risks, thereby ensuring a sustainable contribution of the financial sector to economic growth” (p. C 170/1). Thus, the overall objective of macro-prudence is to ensure the full functioning of financial markets. Regardless of their mandate, the specifics and structure of macro-prudential institutions, their functional area of responsibility includes the development of a strategy, methodological support and implementation of macro-prudential policy, as well as close interaction with other financial system regulators in ensuring the continuity of financial markets evolution, the rights of financial products and services consumers protection, and the prudential supervision of individual banks. By applying macro-prudential policy instruments, a central bank will be able to ensure that individual banks promote appropriate behavior not only in the process of

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shaping the portfolio of products and services, but also in relation to its consumers, reducing the risk of bankruptcy and, therefore, the loss of customer deposits. Respecting the rights of banking products consumers also implies an important dose of information and financial culture, which indisputably only central banks, as macro-prudential supervisors, can ensure.

4 Contribution of Macro-Prudential Policy to the Protection of the Banking Services Consumer in the Republic of Moldova Banking system of the Republic of Moldova has been marked by transition through different models of prudential supervision. The application of macroprudential supervision instruments at the global level was mainly conditioned by the economic crisis from 2007 to 2009. In the Republic of Moldova, these instruments were also consolidated after a crisis and financial instability generated by the bankruptcy of 3 banks in 2015. Until the adoption of the Basel III international regulatory framework, which introduced macroprudential instruments at the legislative level, banking supervision in the Republic of Moldova consisted of a set of microprudential instruments that served as the basic foundation that the reform of prudential supervision supplemented with a macro view (Cociug, Postolache, 2021). Till the adoption of the Basel III international regulatory framework, which introduced macroprudential instruments on a legislative level, banking supervision has formed a set of prudential instruments which served as a basic foundation and prudential supervision reform completed them with a macro vision. On an institutional aspect, in Republic of Moldova, macro-prudential politics is formed according to the model of fractional supervision, where the determining role in the National Financial Stability Committee is played by the National Bank of Moldova (Law on the National Bank of Moldova no.548-XIII, 1995) and the other key entities involved directly or indirectly in regard to banking supervision and financial stability are Deposit Guarantee Fund, Ministry of Finance, Ministry of Economy, National Commission for Financial Markets (Cociug, 2022). Based on its supervisory responsibilities, the National Bank of Moldova pursues, in essence, the ability of banks, which are subject to supervision, to properly manage the amounts attracted as deposits or other repayable funds, which are the sources of financing loans to the population and companies, so that these institutions can honor on time and in full the payment obligations incumbent on them in connection with the attracted sources. These powers are closely linked to the objectives set by the Law on the National Bank of Moldova, 1995 to ensure and maintain financial stability and the protection of depositors, which is a category of banking services consumers. Consumer protection in financial services can be assured from generic legal texts (such as civil law codes); indirectly from laws or voluntary standards aimed at market regulation; from specific rules, statutory or voluntary, aimed at protecting

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the consumer in general or the consumers of financial services (FS) in particular (Consumers International: The World Federation of Consumer Groups, 2013). In the first case it is a “soft law,” the regulation is general, and the financial specifics of banking services are somewhat neglected. This is the case of the Republic of Moldova, which does not have a clear legislation in the protection of the consumer of banking services. In this case, it is necessary to involve the Central Bank through direct regulations, the so-called hard law. Examples for “hard law” can be found in the EU member states through the transposition of Directives, (Consumer and mortgage credit, Markets in Financial Instruments Directive (MIFID), distance selling of FS, etc.) (Consumers International: The World Federation of Consumer Groups, 2013). These regulations are part of macro-prudential instruments. In this case, they come to supplement the consumer protection legislation of banking services. We believe that this should also be implemented in the Republic of Moldova, to ensure a fair relationship between banks and consumers of banking products. Regarding the competencies of financial services customers protection in the Republic of Moldova, they are clearly established, at the level of law, being in charge of a separate authority, namely the Agency for Consumer Protection and Market Surveillance. The exercise of these powers involves the intervention, when required by law, in the contractual relations concluded by consumers with professional creditors. At the same time, the involvement of the NBM in the consumer– creditor relationship, under current conditions, is outside the legal framework. In this regard, the NBM can, however, contribute to the protection of banking services consumers by maintaining the financial stability of the banking system, monitoring the risk of banks in relation to the risks assumed, and the correctness of setting the types of services provided to its financial capabilities in relation to own funds requirements. Unfortunately, high indebtedness, especially for low-income people corroborated with low confidence in the banking system, leads in some cases to an unbalanced regulatory trend toward over-protecting consumers through overapplication of macro-prudential instruments. Although such an approach may give the illusion of settling problems, there are enough side effects, both immediate and medium and long term, respectively: disruption of the functioning of the credit market and harming the interests of bona fide creditors, which can even lead to decisions to relocate investments, but also to the negative influence of customers by slowing down innovation in the field, restricting the supply of banking services or stimulating a behavior characterized by a low responsibility on the part of the client. However, Republic of Moldova lacks a clear regulating and supervision mechanism for normative acts which state the financial rights and obligations in the financial customer protection field. Without alternative dispute resolution mechanism for financial consumers and also taking into consideration the local payment services providers’ low level of business ethics, there is a persistent risk of violation of financial consumer’s rights, even in conditions of financial service development and improvement of local financial market. Consumer protection measures are not clear and don’t reflect a unique approach for supervising existing rules which regulate financial consumer’s rights. The supervision could be performed either by

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type of financial service offered, or by the specific institution types which offer financial services. The financial consumer protection regulatory framework, both from a role perspective and from direct obligations imposed is erratic and segmented. Another problem that needs to be addressed in the field of banking services consumer protection is inclusion and low financial education. Unfortunately, the Republic of Moldova is still at the bottom of the rankings, in terms of financial education, inclusion, and financial intermediation (Cociug, 2022). In this regard, it is absolutely necessary to eliminate the precarious level of financial education of the financial products consumers, which translates into the acute need to inform and educate consumers about the implications of the decisions taken in this matter. According to The National Bank of Moldova’s press releases, the institution is involved in promoting and supporting financial education, including through a series of educational programs that it conducts among the public, developing projects addressed to target groups, developing partnerships with higher education institutions, by organizing training workshops for the young, elaborating consumer’s Guides. All of these activities ensure a success in coexistence of interest from financial market participants by allowing them to develop their own educational programs, but also to ensure an adequate control regarding protection of financial consumer’s rights. Beyond the specific issues covered by consumer protection legislation regarding the need for creditors to conduct contractual relations with consumers on an honest, transparent, and professional basis, banks and other financial service providers need to step up their efforts to educate their customers financially. In addition, ensuring a stimulating competitive environment, while reducing the effects of direct or indirect contagion on other institutions in the banking system, is a requirement that must be seen as a priority. Maintaining a healthy banking system also implies the existence of complementary mechanisms to macro-prudential regulations, such as market discipline. In a free, competitive economy, customers and creditors must themselves protect their interests by taking action to discipline the bank, or in other words, to influence its behavior in a way leading to sustainable financial stability. All of these deficiencies in financial consumer protection have direct consequences in stagnation of financial market development and also are decreasing the level of trust of the consumer in financial services and products. The direction of macro-prudential development in the Republic of Moldova is to adjust, after a comprehensive preliminary assessment, the way of regulating the non-banking financial sector, which is under-regulated and able to create regulatory arbitrage with substantial negative consequences on the financial stability of the Republic of Moldova. These are effects which are necessary to be eliminated through evaluation of the state of play and also by development of new regulatory framework in financial consumer protection, ensuring rather a market discipline than a prudential or conduct regulation, even if the possible consequences of maintaining the current condition are of a macro-prudential nature.

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5 Conclusion The problem of protecting consumers of banking services comes from the need to provide information about the qualities of the financial service before, during, and after the provision of the service. The transparency of banking activity, ensured by macroprudentiality, comes to reduce this informational asymmetry and in this sense, there is a correlation between consumer protection and macroprudentiality. At the same time, as long as the protection of the consumers of banking services is not expressly given in the legislation of a country, that is, the central money has this protective function through soft power. Therefore, in order to improve the situation with consumer protection of banking services in the Republic of Moldova, NBM should focus on increasing its role in promoting the collective and individual interests of financial services consumers. It can be done not only by the development of macro-prudential regulations, but also by emphasizing financial education and financial inclusion, regulation and behavioral supervision of financial market actors, sufficiently and consistently applying the rules for the protection of financial services consumers, not only the development of these regulations. And through macroprudential policy instruments, the NBM should ensure market transparency and safe rules for the banks’ activity, so that, by maintaining the stability of the financial system, it protects consumers of banking services from losing money from their accounts. In this case, macroprudentiality will develop a strong culture of compliance and trust, with financial market players acting in the interests of the consumer and creating consistent rules designed to be effective and enforceable. We intend to continue to follow this topic by studying the correlation between financial stability, induced by the implementation of the macro-prudential policy and the protection of the consumer’s right to banking services. Unfortunately, since macro-prudential instruments were implemented in the Republic of Moldova only in 2019, data series are insufficient to make a more extensive analysis at present. Acknowledgments The article is part of the research project “Optimizing the effects of monetary policy on economic growth by correlating it with macroprudential supervision” registered in the State Register of projects in the field of science and innovation with code 20.00208.1908.12. The editing was financed from the sources allocated by contract no. 13/80-II of 04.01.2021.

References Aziz, Z. A. (2011). Consumer protection and financial education. Forum on Consumer Protection and Financial Education. Kuala Lumpur, 5 April. From https://www.bis.org/review/r11040 5c.pdf. Bakani, L. M. (2015). The importance of financial consumer protection. Keynote address at the CEFI Equity Workshop Smart Campaign Client Protection Principles Training. Port Moresby, 21 October. From https://www.bis.org/review/r160118b.htm. Bank of England. (2009). The role of macroprudential policy. Bank of England Discussion Paper. November. ISSN 1754–4262.

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Borio, C., & Drehmann, C. (2009, March). Assessing the risk of banking crises – revisited. BIS Quarterly Review. www.bis.org/publ/qtrpdf/r_qt0903e.pdf Campbell, J. Y., Jackson, H. E., & Brigitte, C. (2016). Madrian and Peter Tufano. “Consumer financial protection”. Journal of Economic Perspectives, 25(1), 91–114. Chakrabarty, K. (2012). Financial literacy and consumer protection. BIS central bankers' speech. Washington DC, 22 April. From https://www.bis.org/review/r120425b.pdf. Chakrabarty, K. C. (2011). Convergence of mobile banking, financial inclusion and consumer protection-trend: Remarks by Dr K C Chakrabarty, Deputy Governor of the Reserve Bank of India. Financial consumer protection network (FINCONET) meeting, OECD Headquarter. Paris, 9 November. From https://www.bis.org/review/r111114d.pdf. Clement, P. (2010). The term “Macroprudential”: Origins and evolution. SSRN scholarly paper. Rochester. NY: Social Science Research Network. From https://papers.ssrn.com/abstract=1561 624. Cociug, V. (2022). Macroprudențialitatea—de la teorie la politică. Ch.: Artpoligraf. 215 p. ISBN 978-9975-3395-6-8. Cociug, V., & Turcan-Munteanu, N. (2021). Low financial inclusion as an educational gaps result: The Republic of Moldova case. SHS Web Conf, 97, 01027. https://doi.org/10.1051/shsconf/ 20219701027. From https://www.shs-conferences.org/component/makeref/?task=show& type=html&doi=10.1051/shsconf/20219701027 Cociug, V., & Postolache, V. (2020). The role of macroprudential policy instruments in the development of the banking sector of the Republic of Moldova. Competitivitate şi Inovare în Economia Cunoaşterii (pp. 584–592). Ediţia a 22-a, 25–26 septembrie. Chișinău: ASEM. e-ISBN 978-9975-75-985-4. Cociug, V., & Postolache, V. (2021). Macroprudential policy of the Bank of Republic of Moldova in the context of pandemic crisis. The 4th international conference on Covid-19 studies (pp. 519–526). April 17-19, İstanbul, Turkey. ISBN: 978-625-7720-35-9. Consumers International: The World Federation of Consumer Groups. (2013). In search of good practices in financial consumer protection. February. From http://www.consumersinternational. org/media/2264/in-search-of-good-practices-in-financial-consumer-protection.pdf. Duke, E. A. (2009). Consumer protection. Testimony. July 16. Washington, DC: Subcommittee on Domestic Monetary Policy and Technology, Committee on Financial Services, U.S. House of Representatives. From https://www.federalreserve.gov/newsevents/testimony/duke20090716a. htm. ESRB. (2013). Recommendation of the European Systemic Risk Board of 4 April 2013 on intermediate objectives and instruments of macro-prudential policy (2013/1). Official Journal of the European Union, 2013/C 170/01, 1–19. European Central Bank. (2016). Macroprudential policy strategy. European Central Bank. Retrieved 20 December, 2021, from https://www.ecb.europa.eu/ecb/tasks/stability/strategy/ html/index.en.html. Galati, G., & Moessner R. (2010). Macroprudential policy—a literature review. De Nederlandsche Bank Working Paper No. 267. Retrieved December 20, 2021, from https://ssrn.com/abstract=1 950093; https://doi.org/10.2139/ssrn.1950093. Hanson, S., Kashyap, A., & Stein, J. (2011). A macroprudential approach to financial regulation. Journal of Economic Perspectives, 25(1), 3–28. Lane, P. R. (2017). The role of financial regulation in protecting consumers. Speech by Mr Philip R Lane, Governor of the Central Bank of Ireland. Cork, Cork: University College, 23 February 2017. From https://www.bis.org/review/r170310b.pdf. Lane, P. R. (2019). 2019–2021 strategic plan focuses on consumer protection, resilience and Brexit. BIS central bankers' speech. 26 March, Dublin. From https://www.bis.org/review/r1 90326f.pdf. Lege cu privire la Banca Națională a Moldovei. (1995). no. 548. Monitorul Oficial al Republicii Moldova, Nr. 56–57, art. 624. Retrieved January 9, 2022, from https://www.legis.md/cautare/ getResults?doc_id=66524&lang=ro.

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Levesque, B. (2010). Addressing financial consumer protection deficiencies in the post crisis era. OECD DAF/CMF, 6. From https://studylib.net/doc/5906005/addressing-financial-consumerprotection. National Bank of Moldova. (2021). Annual report 2021, from www.bnm.md/files/RA_2021_1.pdf. Ohler, C., Amtenbrink, F., Herrmann, C., & Repasi, R. (Eds.). (2020). The EU law of economic and monetary union. New York., Retrieved November 7, 2022. https://doi.org/10.1093/oso/ 9780198793748.003.0045 Reifner, U., & Clerc-Renaud, S. (2011). Financial supervision in the EU a consumer perspective. BEUC: The European consumers’ organisation. Hamburg: Institut für Finanzdienstleistungen e.V. Rödingsmarkt. From https://www.beuc.eu/sites/default/files/ publications/2011-00396-01-e.pdf. Rutledge, S. L. (2010). Consumer protection and financial literacy : lessons from nine country studies. Policy Research working paper, no. WPS 5326 Washington, DC: World Bank Group. http://documents.worldbank.org/curated/en/676251468233092150/Consumer-protection-andfinancial-literacy-lessons-from-nine-country-studies. Sutt, A., Korju, H., & Siibak, K. (2011). The role of macro-prudential policies in the boom and adjustment phase of the credit cycle in Estonia. World Bank Policy Research Working Paper 5835.

Part VII

Eurasian Economic Perspectives: Empirical Studies on Finance and Economics

The Lack of Public Health Spending and Economic Growth in Russia: A Regional Aspect Olga Demidova, Elena Kayasheva, and Artem Demyanenko

Abstract This essay investigates the influence of an increase of government healthcare expenditures on regional economic growth in Russia. This particular fiscal policy measure can significantly stimulate recovery of the regional economy after the crisis caused by COVID-19 pandemic. Studies have shown that an increase in healthcare expenditures stimulates an increase of gross domestic product through several channels. First, it improves the quality of the labor force that can lead to an increase of labor productivity. Secondly, an increase in the productivity and size of the labor force leads to consumption extension and then to firms’ income growth, so there is a multiplication effect. Including the presupposition that the relationship between healthcare expenditures and economic growth may be non-linear we formed the hypothesis of the existence of the optimal share of health expenditure in gross regional product that maximizes the impact on regional economic growth. Focusing on data from 2005 to 2018, we used the spatial Durbin model to show that this optimal share is 5.9% with an inclusion of spatial effects and 6.4% without them, outlining the importance of considering the interconnection between Russian regions. The regional statistical analysis showed the failure to reach the recommended share by most Russian regions, which can be viewed as a possibility of the future economic growth stimulation if there is an increase of governmental spending on healthcare. Keywords Russian regions · Healthcare · Government expenditures · Economic growth · Spatial effects

O. Demidova (✉) · A. Demyanenko Department of Applied Economics, HSE University, Moscow, Russian Federation e-mail: [email protected]; [email protected] E. Kayasheva Department of Theoretical Economics, HSE University, Moscow, Russian Federation e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_13

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1 Introduction In the present economic context, when the development of countries on different continents is interdependent and subject to external shocks, when borders are open to the flow of goods, resources and production technologies, public health issues have also become global. The COVID-19 pandemic was a new or long-forgotten challenge for humanity, but it stressed the importance of investing in the quality of people’s lives: in education and science—to develop innovative technologies and their more rapid implementation into mass production, and in health—to create favorable living conditions and facilitate the development of the population. However, it is not only in the periods of crises that healthcare expenditures matter. Direct economic loss in CEE countries due to illness of employees and disability of the workforce amounted to 252 billion euro in pre-COVID 2018 and these figures do not take into account the estimated lost income tax revenue (PWC, 2021). Preventive, timely and sufficient healthcare support can help to lessen the negative influence of listed factors and foster growth of an economy at a time. Since the 1990s, economists have focused on the impact of human capital development on economic growth, including knowledge, skills and experience owned by the labor together with physical capital as key factors into the growth models (Lucas, 1988; Romer, 1990 cited in Kurt, 2015). Health expenditures together with expenditures on education and science can be regarded as a direct factor in the formation of human capital through increased productivity and life expectancy and therefore it can stimulate economic growth of a country (LópezCasasnovas et al., 2005). Some researchers state that all kinds of expenditures on education and health raise the level of human capital and make a positive contribution to economic growth (Kurt, 2015); others see these expenditures as a threat to governmental budget (Sood et al., 2007). In our research we suggested that there can be an optimal level of healthcare expenditures that will stimulate economic development but prevent from overfinancing of this sector. This corresponds to the ideas of Wang who investigated preventive healthcare financing cuts during economic downturns in Taiwan (Wang, 2015) and (Sirag et al., 2016) research based on data from 97 developed and developing countries. Optimal level of expenditures is obtained based on assumption of non-linear relation between economic growth and healthcare expenditures. The main criteria for assessing the effectiveness of healthcare expenditures can be their impact on the region’s life expectancy and economic growth. In this work, we focus on the economic consequences and estimate the contribution of healthcare spending to economic growth in 80 Russian regions using the spatial Durbin model with normally row-standardized first-order contiguity weights matrix. The aim of our study was to determine the average optimal share of health and sport expenditures in GRP so that the average economic growth rate of the regions was the highest.

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Similar research has been conducted for US, China and OECD countries (Wang, 2015; Wang et al., 2016), and our work intends to be a contribution to a research of the non-monotonic relationship between health expenditure and economic growth for Russian regions. Data analysis showed that in the period 2005–2018 this share was 5.9%, that is significantly higher than the actual values. We believe that our economic evaluations of the relation between public health spending and economic growth might contribute to the discussion of problems regarding government policy decisions. We consider government expenditures on healthcare to be one of the key fiscal policy measures which can stimulate the recovery of the economy in the post-COVID period. In the next section, we provide a brief overview of the main literature on the impact of healthcare expenditures on economic growth. Afterwards, we describe the current state of healthcare financing in Russia. In the fourth section, we describe the econometrics approach used in the research and provide our data sources. In the fifth section, we demonstrate the estimation results and discuss their interpretation. The final section contains some concluding remarks.

2 Literature Review and Hypothesis Development The increased interest in the analysis of the relationship between healthcare expenditures and economic growth is, on the one hand, a continuation of studies assessing the effectiveness of various public expenditures within the government fiscal policy, and, on the other hand, shifts the focus to human capital development in order to achieve sustained economic growth. Growth models consider health as a factor that influences the quality of labor force and exogenously (Solow, 1956) or endogenously (Romer, 1990; cited in Kurt, 2015; Aghion et al., 2010) contribute to the growth of a gross domestic product as an indicator of economic growth of a country. Researchers compare the impact of health indicators in developed and developing countries (Bhargava et al., 2001), proving the necessity of reducing health shocks as 50% of divergence in economic growth here is attributed to ill-health and low life expectancy in less developed countries (World Health Organization, 2005). Positive impact of healthcare expenditures on economic growth has been highlighted in the works of Kleiman (1974), Newhouse (1977), Wang (2011) and Penghui et al. (2022). However, some studies consider healthcare expenditures to be a consumer commodity (or unproductive expenditures) rather than an investment commodity that diverts resources from other sectors of an economy and therefore hinders it from development (Barro, 1990; Kurt, 2015). There are several approaches in the literature of explaining the impact of health expenditure on economic growth. The first is based on the positive impact of the health promotion system on life expectancy and further on the total income of the economy through the production function (Bloom et al., 2001; Lorentzen et al.,

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2008; Kurt, 2015). The second investigates the cost of economic recovery and the mitigation of negative effects during epidemics (Gallup & Sachs, 2000; Dixon et al., 2002). The third approach views investment in health as investment in human capital (Chang and Ying, 2006; Untura & Kaneva, 2018). The positive impact of healthcare expenditures on total income can be explained by the following factors: increased health expenditure can contribute to greater social protection of the population, improving physical health, productivity and economic inclusion. Increases in public expenditure can have a multiplier effect on aggregate demand in an economy: increases in productivity and labor force lead to higher personal income and consumer spending and, subsequently, higher revenue for firms which is distributed to households and further encourages spending. Increased healthcare spending is therefore seen as a part of stimulating public policies. Studies have shown that the relationship between health expenditure and economic growth depends on the country’s level of economic development, and in lowor high-income countries health expenditure may not effectively enhance growth (Wang, 2011). It was estimated that in Turkey, for example, a 1% increase in per-capita health expenditure leads to a 0.434% increase in the per-capita gross domestic product (Atilgan et al., 2017) and in Iran only weak causal relationship was found (Mehrara & Musai, 2011). A review of the time effects (Ashraf et al., 2008; Boussalem et al., 2014) revealed the positive impact of healthcare spending on long-term growth, but the lack of it in the short term, that can be explained by the spread over time of the cost of restoring and maintaining the health of the population and the resulting economic impact. However, if spending on healthcare has increased life expectancy in the country, both short- and long-term effects are positive (Bloom et al., 2001). In the long run, expenditure on health may even have a greater impact on economic development compared to education expenditures (Mayer et al., 2000). The existence of an interrelationship between economic growth and healthcare costs makes it difficult to analyze cause-effects (Strauss & Thomas, 1998; Elmi & Sadeghi, 2012) and requires further clarification. Since healthcare expenditures often compete with other social expenditures in the national budget, it is the increase in total income that will allow more funds to be directed toward financing this sector (Ulumbekova et al., 2019). It is important to determine the necessary share of investment in healthcare (Chang & Ying, 2006) in order to avoid an unnecessary waste of budgetary resources. It is also worth to mention studies concerning econometric modeling of economic growth and convergence in Russian regions. Russian Federation units vary dramatically in terms of many social and economic indicators, thus there are a few papers which investigate those problem. For example, the study (Lugovoy et al., 2007) provided a thorough discussion on gross regional product convergence, besides authors claimed that introduction of spatial effects into the model helps to eliminate the possible bias in the parameter estimates. Another related research (Kholodilin et al., 2012) revealed that overall speed of income convergence in Russian regions is slow. But spatial econometrics technique used in the study allowed to emphasize that convergence in clusters of high-income regions is stronger than in low-income ones.

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Probably one of the latest takes on regional economic growth was introduced in Demidova and Ivanova (2016). In that study the authors demonstrated that region’s susceptibility to spillover effects can depend directly on various features of the region, for instance urbanization, size, population density. Finally, Kolomak (2010), also showed that spillover effects, which might also be considered as externalities affecting economic growth, not only just differ across Russian Federation units, but appear to be positive in western regions and negative in eastern ones. Therefore, results of the recent studies dedicated to economic growth and convergence in Russian regions imply that it is necessary to account for spatial effects in the model and, moreover, estimates of such effects are very likely to vary from region to region. All these papers based on Russian regional data did not consider influence of healthcare expenditures economic growth, while they can provide direct impact through the previously discussed channels. Besides, budget expenditures on healthcare in a particular federation unit might indirectly affect the neighboring regions, for example due to networking and technology exchange between bordering regions. The stimulating effect of increased public expenditure on health varies depending on the initial share of this expenditure in the total income of a country or region. In this case, the financing of large-scale investments in the industry using public funds is reasonable, but the competitiveness of budget expenditures obliges comparisons of the different uses of these funds in order to identify priorities. A study of the Organization for Economic Co-operation and Development (OECD) member countries during the period of 1980–1998, indicated an excessive distribution of the budget in favor of healthcare in most of the sample countries (Chang & Ying, 2006). The implementation of healthcare reforms and the reduction of healthcare budgets due to a lack of funds in the years of crisis have changed the situation, and the analysis of data for 1990–2009 has shown another result. It showed a lack of investment in this sector: with the optimal share of health spending in these countries at 7.55% of GDP, while the actual figures reached only 5.48% of GDP (Wang, 2015). In our work we investigated the impact of the increase of a share of public expenditure on healthcare in the GRP of Russian regions on the rate of economic growth of the regions. We assume that the dependence of the rate of economic growth of the Russian regions on the share of spending on health and sport is non-linear. The aim of the study was to identify the optimal average share of public expenditure in GRP at which the average growth rate of the regions would be the highest. The definition of this value is important in order to avoid the unnecessary expenditure of budgetary resources under one item at the expense of other types of expenditures aimed at the development of human potential. Therefore, we formulate the first hypothesis to be tested in the research: Hypothesis 1. There is an average optimum share of public expenditures on health and sport in GRP which has the greatest positive impact on the average growth rate of GRP.

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According to National Medical Chamber (Novye izvestia, August 25th 2020) and former Finance Minister, Alexei Kudrin (RBK, April 21st 2020), the share of healthcare expenditure in Russia’s GDP should increase from today’s 3–3.5% to 5–7%. Even before the epidemiological situation worsened, the President’s message to the Federal Assembly on March 1, 2018 (Russian Government, 2018) called for the allocation of 4–5% of GDP per year for the development of the healthcare system in 2019–2024. In this connection, the analysis of Russian regional data to confirm or refuse empirically these hypotheses is important. Thus, we formulate the second hypothesis to be tested in the research: Hypothesis 2. The actual share of public expenditures on health and sport in GRP is less that the estimated optimum level in majority of Russian regions.

3 Trends and Patterns of Healthcare Expenditure in the Russian Federation Healthcare funding in Russia is mixed. Most of the expenditure is financed by the state, with private expenditures accounting for about 35% in 2018 (Ulumbekova et al., 2019). State funding comes from three sources: The Compulsory Health Insurance Fund, to which the employer contributes 5.1% of the wage fund (Law 326-FL, 2010), and the regions remit funds for the non-working population, and federal and regional budgets. At present, the financing of healthcare expenditures is carried out in accordance with the Strategy for the Development of Healthcare in the Russian Federation (Russian Government, 2019), within the framework of the implementation of the national project “Healthcare,” that aims at reducing the mortality of the population, improving the quality of medical services, ensuring that all citizens are covered by preventive medical examinations and increasing life expectancy in Russia to 78 years by 2024, and 80 years by 2030. Government expenditure on healthcare in Russia is 1.5–2.5 times lower than in the EU, and 2–4 times higher per capita (Ulumbekova et al., 2019). The relative share of public expenditure in the country’s gross domestic product (GDP) in 2018 was 3.2% (3.6% in 2016). In comparison, in countries such as Denmark, Germany, Belgium and Japan, the proportion exceeds 9% (Statista Research Department cited in Ulumbekova et al., 2019). The World Health System Performance Rating, compiled by Bloomberg, which takes into account the share of healthcare expenditure in GDP per capita and life expectancy, ranked Russia last in 2018. Figure 1 shows that the share of GRP allocated to healthcare on average by region has been declining since 2012. In 2017, the sharp contraction in spending was due to a general lack of funds, while budget revenues declined due to the fall in oil prices. On average, regional budget healthcare financing decreased by almost 43%, and in some regions, it fell by two to three times.

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0.045

Share of healthcare expenditures in GRP

0.04 0.035 0.03 0.025 0.02 0.015 0.01 0.005 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year Mean

Median

Mode

Fig. 1 Dynamics of regional share of expenditures of the consolidated budget on healthcare, physical education and sports in GRP, 2005–2018. Source: Authors’ calculations based on the Russian Federal State Statistic Service data

In about half of the Russian regions less than 1% of GRP is allocated to healthcare. The Smolensk, Astrakhan and Saratov Regions and the Yamal-Nenets Autonomous Region are particularly affected by the funding problem, as the share there is barely over 0.5%.

4 Methodology 4.1

Data and Variables

To test the hypothesis, panel data from 80 Russia regions, published by the Federal State Statistics Service annually from 2005 to 2018, are used. Growth of GRP per , where i is a number of capita is used as a dependent variable grp growth = YYitþ1 it the region and t is the observed year. The panel has N = 80 objects and T = 14 time moments. In this study, we consider 6 main categories of expenditures of the consolidated budgets of the Russian Federation units: shexp_health is the share of expenditures on healthcare, physical education and sports in GRP (this is the main variable of

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interest), X1 = shexp_gov is the share of expenditures on the government issues1 in GRP, X2 = shexp_econ is the share of expenditures on the national economy2 in GRP, X3 = shexp_housing is the share of expenditures on housing3 in GRP, X4 = shexp_edu is the share of expenditures on education in GRP, X5 = shexp_socpol is the share of expenditures on social policy in GRP. Naturally, economic growth is influenced by other factors in addition to government spending. Investment is one of the key drivers of economic growth (Solow, 1956), therefore, one of the explanatory variables is X6 = invgdp—the ratio of fixed investment to GRP. Cities are centers of economic activity with significant flows of labor. Both, the presence of industrial enterprises and access to a high level of education ensure high levels of labor productivity. Several studies have shown (Henderson, 2003; Friedmann, 2006) that urbanization has a positive effect on economic growth. A similar effect is expected for Russian regions, that is why we use X7 = urbansharebig—the share of the population in cities with more than 500,000 citizens. The degree of openness of the economy can also affect economic growth (Ivanova & Kamenskikh, 2011). In order to describe the openness of the economy, we use the variable X8 = import_grp—the share of imports in GRP. The openness of the economy is also characterized by exports, but the corresponding coefficient was not statistically significant in any of the preliminary versions of the model, thus it was decided to exclude this factor. It is a common practice to take into account the structure of the regional economy (Nakamura & Steinsson, 2014). In Essletzbichler (2007), Shediac et al. (2008), the authors showed that economic diversification increases production. As an indicator of the degree of diversification of the region’s economy, the Herfindahl-Hirschman index (X9 = HH), was used. Its values range from 0 to 1, a higher number corresponds to a less diversified economy. We consider the variable X10 = higheduc—the proportion of people with higher education as a feature of human capital. Qualified workers have higher labor productivity, which should positively influence economic growth (Maddison, 1991; Lutz & Samir, 2011). In addition, X11 = road—the density of roads in the region is included as a control variable. Such a factor reflects the development of infrastructure in the region (Tscherbanin, 2011), it can also affect the openness of the economy and labor mobility to some extent. By following the concept of conditional convergence (Barro & Sala-i-Martin, 1992), we included X12 = gdppercapppp—GRP per capita in 2005 base year prices, adjusted by purchasing power parity as another explanatory variable.

1

These expenditures are mainly used to sustain functioning of local authorities and courts. These expenditures are used to support particular enterprises and sectors of the region’s economy. 3 These expenditures are aimed to sustain housing and communal services like central heating, water supply, etc. 2

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A favorable investment climate in the region contributes to economic growth, which is why we include the variable X13 = IR—the investment risk index calculated by the RA Expert agency. The indicator takes into account many factors, for example, the financial reliability of potential counterparties, the debt burden of the authorities, the quality of the regional budget, the unemployment rate, the level of environmental pollution, etc. X14 = banks—the aggregate index of the region’s banking services is considered as variable characterizing the state of the banking system. It is calculated annually by the Bank of Russia as the geometric mean of three different components of the banking system: the institutional provision of banking services, the financial provision of banking services and the state of savings. Descriptive statistics for the variables are shown in Table 1. All variables demonstrate significant variation between the minimum and maximum values, with the standard deviation being comparable to the mean of the variables. These facts confirm the strong heterogeneity and imbalances in the development of Russian regions. In theory, government spending affects economic growth, but the volume of spending can vary depending on the trend of GRP. It is difficult to unambiguously determine the direction of the channel of influence, hence, endogeneity might be present in the model. To avoid the problem of inconsistency of the estimates caused by endogeneity, all values of the regressors in the model are used with a lag of 1 year. In the next section, we describe the model where all the variables are included.

4.2

Model

Our main hypothesis assumes the existence of an optimal level of healthcare expenditures. Therefore, after reaching it, a further increase in expenditures in this category does not result in the maximum positive effect on economic growth. Thus, not only the share of expenditures on healthcare (shexp_health), but also its square (shexp_health2) is included in the model. Spatial Durbin model (SDM) is common econometric technique used for studying regional aspects of fiscal policy in general (Almasi et al., 2022; Huang et al., 2022), government expenditures on healthcare (Song et al., 2019; Zhang et al., 2020) and non-linear relations between dependent and explanatory variables (Zhang et al., 2022). Thus, to test our hypothesis, we use SDM: ~tθ Y t = α þ γ t þ δ1 shexp healtht þ δ2 shexp health2t þ ρWY t þ X t β þ W X þ εt

ð1Þ

where Yt = (Y1t, . . ., YNt)′ is a vector of GRP growth values in N regions at time t (t = 1, . . ., T ), Xt = (X1, . . ., X14) is a matrix of independent variables described in ~ t = shexp health, shexp health2 , X 1 , . . . , X 14 is a matrix of the previous section, X

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Table 1 Descriptive statistics for variables (from 2005 to 2018) Variable grp_growth shexp_gov

shexp_econ

shexp_housing

shexp_edu

shexp_health

shexp_health2

shexp_socpol

grppercapppp

urbansharebig invgdp highed HH road import_grp banks IR

Full variable name GRP growth Share of expenditures of the consolidated budget of the region on the government issues in GRP Share of expenditures of the consolidated budget of the region on the national economy in GRP Share of expenditures of the consolidated budget of the region on housing in GRP Share of expenditures of the consolidated budget of the region on education in GRP Share of expenditures of the consolidated budget of the region on healthcare, physical education and sports in GRP Square of the share of expenditures of the consolidated budget of the region on healthcare, physical education and sports in GRP Share of expenditures of the consolidated budget of the region on social policy in GRP Gross regional product per capita adjusted for purchasing power parity Share of urban population (largest cities only) Share of investments in GRP Share of population with university education Herfindahl-Hirschman index Density of roads Share of imports in GRP Security of the banking system Investment risk index

Source: Authors’ calculations

Mean 1.027 0.017

Standard deviation 0.054 0.013

Minimum value 0.795 0.004

Maximum value 1.270 0.218

0.036

0.023

0.004

0.229

0.023

0.023

0.004

0.383

0.055

0.027

0.016

0.224

0.031

0.016

0.005

0.142

0.001

0.002

2.610–5

0.020

0.033

0.018

0.005

0.171

149,687

166,736

19,320

1,619,000

0.172

0.225

0

1

0.271

0.099

0.108

1.080

0.270

0.058

0.125

0.5

0.155

0.074

0.091

0.627

192.62 0.098 0.823

275.78 0.118 0.196

0.80 0 0.150

2468 1.015 1.770

0.275

0.194

0

1

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independent variables with elements corresponding to the share of healthcare expenditures included, α = (α1, . . ., αN)′ is a vector of fixed effects, γ t = (γ 1, . . ., γ T)′ is a vector of time effects, W is a spatial weights matrix, WYt and WXt are spatial lags of the dependent and independent variables, εt = (ε1t, . . ., εNt)′ are the error terms at time t, δ1 and δ2 are the estimated coefficients, β, θ and ρ are vectors of the estimated parameters. Spatial econometrics models assume that the dependent variable of a particular region is influenced not only by its own set of regressors, but also by the values of the dependent variable and the regressors of other regions. Such an assumption can indeed be justified in our case. For instance, a large allocation of funds to a given region for the realization of government programs (so-called national projects), can result in additional subcontracts with the region’s neighbors, which will have a positive effect on economic growth of a given region. The opposite situation is also possible: strong economic growth in one region can stimulate an influx of labor to this region from neighboring ones, slowing the economic growth of neighboring regions. In our research, the lack of considering such interactions might lead to a bias in the estimate of the optimal share of healthcare expenditures in GRP. Comfortable operations with spatial effects are usually achieved by using a spatial lag which is directly related to the spatial weights matrix W. W is necessary to set the weights for the influence from all regions on a particular one. In this essay, we use a normally row-standardized first-order contiguity weights matrix. Note that there is an endogenous term Wy on the right-hand side of the equation of model (1), which must be taken into account when choosing an estimation technique. Equation (1) after simple transformations can be reduced to Eq. (2), Yt ~ t θ þ εt = ðI - ρW Þ - 1 α þ γ t þ δ1 shexp health þ δ2 shexphealth 2 þ X t β þ W X ð2Þ where I is the identity matrix. Model (2) was estimated by using the maximum likelihood method which is provided in STATA’s xsmle package. To conclude the section, we note that the structure of Eq. (1) assumes almost the most general form of spatial econometric models. When certain conditions are met, the spatial Durbin model can be reduced to two other, simpler, models: the spatial autoregressive model (SAR), which contains only the spatial lag of the dependent variable, and the spatial error model (SEM), which contains a spatial effect in the regression error term. LeSage and Pace (2009) showed that in order to choose between SDM, SAR and SEM models, it is sufficient to carry out two tests. The first one is the Wald test which tests the joint significance of the spatial lags of the independent variables (H0 : θ = 0). The second one is the Wald test for assessing a non-linear constraint (H0 : θ + βρ = 0). If such an expression is true then SDM can be reduced to SEM (Elhorst, 2014). The test results are presented in Table 2 of the next section, and they confirm that in our case the Durbin model (SDM) is a better choice

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Table 2 Estimation results of the spatial Durbin model for Russian regions (dependent variable— growth of gross regional product) Regressors shexp_gov shexp_econ shexp_housing shexp_edu shexp_health shexp_health2 shexp_socpol grppercapppp urbansharebig Invgdp Highed HH Road import_grp Banks IR Time effects ρ

Β 0.282 (0.274) 0.0037 (0.196) 0.156 (0.164) 0.290 (0.243) 1.520b (0.576) -12.89b (4.847) -0.296 (0.285) 1.47e-07c (8.68e-08) 0.027 (0.020) -0.0266 (0.0249) 0.1517c (0.0767) -0.1564c (0.0795) -6.95e-05 (4.29e-05) 0.0476c (0.0203) 0.0191 (0.0217) 0.0016 (0.0228) Yes 0.337b (0.027)

θ -1.567a (0.835) -0.042 (0.280) 0.682 (0.514) -0.578 (0.578) -0.797 (0.935) 7.531 (7.837) -0.085 (0.395) 2.27e-07 (2.08e-07) -0.077 (0.074) -0.0484 (0.0427) -0.091 (0.165) -0.038 (0.169) 4.36e-05 (5.78e-05) -0.0137 (0.0388) 0.0203 (0.0591) 0.0158 (0.0354)

Tests Test name The Wald test for significance of θ (H0 : θ = 0) The Wald test for constraint (H0 : θ + βρ = 0)

Direct effect 0.172 (0.275) -0.006 (0.193) 0.231 (0.151) 0.229 (0.255) 1.499b (0.585) -12.53c (4.918) -0.322 (0.287) 1.63e-07c (8.32e-08) 0.0235 (0.0224) -0.0301 (0.0259) 0.1455a (0.0802) -0.157c (0.080) -7.47e-05a (4.23e-05) 0.0478c (0.0219) 0.0223 (0.0238) 0.0034 (0.0219)

Indirect effect -2.001a (1.327) 0.013 (0.378) 0.947 (0.675) -0.743 (0.892) -0.318 (1.290) 4.115 (10.88) -0.313 (0.611) -2.55e-07 (2.81e-07) -0.093 (0.107) -0.074 (0.063) -0.0485 (0.2290) -0.122 (0.237) 9.08e-05 (7.49e-05) 5.92e-04 (0.0546) 0.0509 (0.0818) 0.0256 (0.0483)

Total effect -1.829 (1.132) 0.007 (0.427) 1.178a (0.686) -0.514 (1.055) 1.182 (1.586) -8.411 (13.043) -0.634 (0.776) 9.24e-08 (2.92e-07) -0.069 (0.121) -0.104 (0.077) 0.097 (0.266) -0.279 (0.255) -1.66e-04 (8.84e-05) 0.0484 (0.0648) 0.073 (0.092) 0.0290 (0.0581)

Test statistic value 30.07c 23.02

Note: Standard errors are in parentheses. Significance levels: a 10%; b 1%; c 5%. All independent variables are used with lag of 1 year Source: Authors’ calculations

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than SAR. In the next section, the results obtained are presented and their interpretation is given.

5 Results First of all, it is necessary to outline some features of the estimation result interpretation in the spatial Durbin model. Estimates of the coefficients of the explanatory variables obtained from conventional linear regression models for panel data give a clear impact on the dependent variable since the value of a coefficient is a partial derivative of the function with respect to the corresponding argument. One might consider a common fixed effects model as a trivial example (3), where all notations are preserved. After differentiating Y t = α þ X t β þ εt

ð3Þ

with respect to any given independent variable Xk one gets: ∂Y i ∂Y i = βk , =0 ∂X ki ∂X kj

ð4Þ

i.e. a change in factor Xk in any region i equally affects their own Yi value without affecting other regions. In the case of the spatial Durbin model, the situation is somewhat more complicated: the coefficients themselves are no longer directly interpretable. After differentiation with respect to any of the independent variables, in contrast to the usual regression equations, the result is no longer a single constant. By analogy with examples (3) and (4), let us select one of the regressors, and denote it by Xk, further, we differentiate (2) by Xk: ∂Y = ðI - ρW Þ - 1 ðβk I þ Wθk Þ ∂X k

ð5Þ

Taking into account that all diagonal elements of the first-order contiguity weights matrix W are equal to 0, it is possible to rewrite the right side of expression (5). Diagonal elements of the matrix (βkI + Wθk) are simply equal to θk, and off-diagonal ones are products of spatial weights wij and a coefficient θk (6): ðI - ρW Þ - 1 ðβI þ WθÞ = ðI - ρW Þ - 1

βk w21 θk ⋮ wN1 θk

w12 θk βk wN2 θk

⋯

w1N θk

⋱ ⋯

βk

=S

ð6Þ

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By analogy with Arbia (2014), let us denote the matrix obtained in (6) as S, with elements sij, where i, j = 1, . . ., N. Each element sij indicates the change of the dependent variable (in our case, GRP growth) in region i when the value of a given explanatory variable Xk also changes in region j. This effect of the regressors on the dependent variable in the spatial Durbin model is called the marginal effect, and it is usually divided into 2 categories: 1. direct effect (elements of the matrix S with i = j), i. e. the reaction of the dependent variable of a given region with index i to a change in the explanatory variable in the same region with index i; 2. indirect effect, that is also called the spillover-effect (elements of the matrix S with i ≠ j), i. e. the reaction of the dependent variable of a given region with index i to a change in the explanatory variable in another region with index j. Thus, we get N elements of the matrix S, corresponding to direct effects, and (N2 - N ) elements, corresponding to indirect effects, which is a fairly large amount of information. For a more compact and clear interpretation of the marginal effects, the concept of average effects proposed by (LeSage & Pace, 2009) is used. The average direct effect (ADI) is the average of the direct effects described earlier or the average of the diagonal elements of the matrix S: ADE =

tr ðSÞ N

ð7Þ

The average indirect effect (AIE) is a specific averaging of the indirect effects: AIE =

N i=1

N j = 1 sij

- N tr ðSÞ

N

ð8Þ

The average total effect (ATE) is the sum of the average direct and average indirect effects: ATE =

N i=1

N j = 1 sij

N

ð9Þ

To put it less formally, the average direct effect is a response of the dependent variable of a given object to a change in the explanatory variable in the same object, and the average indirect effect is the response of the dependent variable of a given object to a change in the explanatory variable in all other objects. Further, under the direct, indirect and total effects, we mean precisely their “average” definitions. Estimation results for the spatial Durbin model are illustrated in Table 2. The estimate of direct effect from an increase in the share of healthcare expenditures in GRP is positive for the linear term (shexp_health) and negative for the quadratic term (shexp_health2).

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Share of healthcare expenditures in GRP

Mean in 2018

Median in 2018

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Mode in 2018

0.045 0.04 0.035 0.03 0.025 0.02 0.015 0.01 0.005 0

Fig. 2 Regional share of expenditures of the consolidated budget on healthcare, physical education and sports in GRP in 2018 (by region). Source: Authors’ calculations based on the Russian Federal State Statistic Service data

To find the optimal level of expenditures, we focus on the direct effect from the change in variable shexp_health in particular: grpgrowth = - 12:53  shexphealth 2 þ 1:499  shexphealth þ . . .

ð10Þ

The graph of such a function is an inverse U-shaped parabola. It satisfies a necessary and sufficient condition for the existence of an extremum of a function, which in this case is a maximum point. Differentiating (10) by shexp_health and equating it to 0, allows us to determine that the maximum value of GRP growth is achieved with the share of healthcare expenditures in GRP equal to 5.9%, that is, further increases in the share of expenditures will have a smaller impact on economic growth. We also computed the 95% confidence interval for the point estimate by utilizing a series of non-linear Wald tests, which provided us with the following result: [4.36%; 7.43%]. Estimates of the coefficients obtained after excluding all the spatial effects from the main model (1) resulted in an optimal level of the share of healthcare expenditures in GRP of approximately 6.4%, while the corresponding 95% confidence interval is [4.5%; 8.3%]. That is, the lack of consideration of spatial interactions causes a bias in the estimation of the parameters. Based on our previous discussion on current state healthcare financing of Russian regions in Sect. 3 (Figs. 1and 2), we can conclude that current budget expenditures in this field fail to reach the computed optimal level. Thus, we provided an empirical evidence for our hypothesis.

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We would like to make a small remark regarding the model choice to conclude this section. The result of the Wald test for a non-linear constraint (H0 : θ + βρ = 0) shown in Table 2 tells us that the null hypothesis cannot be rejected, so technically, the spatial error model (SEM) should be preferable since the respective p-value for test statistics is 0.11. SEM yields a slightly higher estimate of the optimal level of healthcare expenditures of roughly 6.1%, which still lies in the previously computed confidence interval for SDM. However, it is also possible that our assumption regarding quadratic relation between economic growth and government expenditures might be true in case of other expenditures categories in addition to healthcare. There are definitely some concerns about including all possible square terms in the model at once due to multicollinearity. We estimated a series of SDMs with different combinations of government expenditures square terms and performed Wald test for significance of corresponding coefficients. Expenditures on education together with expenditures on healthcare turned out to be the only categories that provide a robust significant impact on economic growth. Estimation results for model with the square of shexp_edu variable are demonstrated in Appendix 1 and yield an optimal share of government expenditures on healthcare of approximately 6.3%, which still lies inside the confidence interval of SDM estimate. Finally, we computed the direct effects on grp_growth from shexp_health increase for all regions in 2018 which can be found in Appendix 2. The largest effect is observed in federation units with the smallest share of healthcare expenditures in GRP, for example in Yamal-Nenets autonomous region, Orenburg region, Saratov region and Astrakhan region while the smallest effect is found in regions with the largest share of healthcare expenditures, for instance in Republic of Altay, Chukotka Autonomous region and Magadan region. The main estimation results of our research are in line with other studies, which as well utilize SDM (Song et al., 2019; Zhang et al., 2020; Penghui et al., 2022). The direct effect for healthcare expenditures is positive and significant, but unfortunately, we cannot directly compare the magnitude of the effect to the studies by Song et al. (2019), Zhang et al. (2020), and Penghui et al. (2022) since the authors used log transformation of the variables in their models, but it is worth mentioning that all those papers were based on data prior to 2018. Our model confirms non-linear relation between government expenditures on healthcare and GRP growth, which was also found in papers by Wang (2015) for the panel of OECD countries and Wang et al. (2016) for the US data. Our estimation for optimal level of expenditures is 5.9% of GRP, which is slightly lower than the optimal level for OECD countries that makes up 7.55% of GDP (Wang, 2015). The optimal level values obtained in (Wang et al., 2016) cannot be directly compared to our findings due to log model specification, nevertheless, the authors showed that the actual ratio of healthcare expenditures to GDP is two times lower than the estimated optimum. In comparison, healthcare systems in Russian regions appear to have a bigger lack of funding, since on average the government expenditures need to be raised by 3–3.5 times in order to achieve the estimated optimal level.

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According to Appendix 2, the direct effect value ranges approximately between 1.01 and 2.04. This evidence demonstrates the regional heterogeneity of the effect. Similar findings we obtained in the case of Spanish healthcare system by Palència et al. (2013) and Zhang et al. (2020) for different Chinese regions. In addition, our direct effect estimations allow us to determine regions that would benefit the most out of their budget restructuring. For example, a raise in share of healthcare expenditures in GRP by 1 percentage point will result in GRP growth increase of 2 percentage points. In contrast, expenditure gain for Republic of Altay would lead to a GRP growth increase of only 1 percentage point, which is two times lower. All the referenced papers demonstrated that expenditures on healthcare have a positive impact on economic growth in many other regions and countries. Our analysis confirms that government investments in healthcare can be a great source of economic growth for Russian regions, because for most of them current level of healthcare expenditures is dramatically lower than the estimated optimum. We also found evidence for significant spillover effects for Russian regions, which has already been confirmed for China in Song et al. (2019), Zhang et al. ( 2020), Penghui et al. (2022).

6 Conclusion The research has shown that an increase in the share of public spending on health and sports in GRP can stimulate economic growth in the region. The competitive distribution of the budget funds among various branches of social support makes it particularly important to identify the optimal share of each line of expenditure, at which the positive impact on GRP growth will be maximized. By assuming a non-linear relationship between the share of healthcare expenditure and economic growth, the average optimal share can be found on a historical basis. In our work, we showed that for 2005–2018 the average optimal share of public expenditure on healthcare and sport in the GRP was 5.9%. Exceeding this threshold may serve as an incentive for regional authorities to reallocate budgetary resources in favor of other social support measures for the population, a lag justifies a possible way to accelerate the economic growth of the region while increasing spending on health and sports. An analysis of regional data showed that regions differ greatly in the amount of spending on health and sports and in its share in GRP. In most regions, this proportion has not exceeded 3% in the last 15 years and has declined since 2012. The problem is most acute in the Smolensk, Astrakhan, Saratov and Yamal-Nenets Autonomous Regions, where the share of healthcare and sports expenditure in GRP in 2018 barely exceeded 0.5%. The leading regions include the Altai Republic (4.1%), the Magadan Region (3.1%) and the Chukchi Autonomous Region (3.1%). Worldwide consequences of COVID-19 pandemic have shown that countries should invest enough resources for healthcare development to withstand possible future threats. Our research allowed to identify the regions where such investments will lead to the most significant GRP growth. The assumption of the spatial

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interdependence between regions has made it possible to take into account the impact of health investment in one region on its neighbors through a spatial weight matrix. The sharing of knowledge and skills between regions and the joint implementation of national projects can lead to higher economic growth. The average optimal share of health and sport expenditure in GRP can then be reduced from 6.4% to 5.9%, respectively, justifying the need to consider the impact of spatial effects. In this study, the aim was to determine the average optimal share of healthcare and sport expenditure in GRP for all Russian regions, but one should not forget about the great differentiation of regions and the changing epidemiological situation in recent years. In this regard, the aim of further research may be to calculate the optimal share of expenditures at the level of individual entities and to identify regional factors that increase the efficiency of these expenditures (Tables 3 and 4).

Appendix 1

Table 3 Estimation results of the spatial Durbin model for Russian regions with the square term for expenditures on education included (dependent variable—growth of gross regional product) Regressors shexp_gov shexp_econ shexp_housing shexp_edu shexp_edu2 shexp_health shexp_health2 shexp_socpol grppercapppp urbansharebig invgdp

β 0.146 (0.272) -0.096 (0.174) 0.239 (0.182) 1.005b (0.336) -6.426b (2.259) 1.274c (0.563) -10.069c (4.741) -0.572c (0.253) -1.71e-07c (8.69e-08) 0.027 (0.018) -0.0317 (0.0242)

Θ -1.379a (0.804) -0.062 (0.274) 0.471 (0.477) -0.119 (0.573) -1.142 (2.045) -0.993 (0.932) 6.283 (7.387) -0.051 (0.387) 2.48e-07 (2.01e-07) -0.106 (0.076) -0.0494 (0.0427)

Direct effect 0.052 (0.276) -0.114 (0.171) 0.300a (0.151) 1.007b (0.356) -6.649b (2.208) 1.238c (0.574) -9.823c (4.885) -0.604c (0.258) 1.58e-07c (7.93e-08) 0.0199 (0.0209) -0.0356 (0.0249)

Indirect effect -1.760 (1.111) -0.14 (0.362) 0.712 (0.630) 0.297 (0.856) -4.708 (3.165) -0.714 (1.389) 3.416 (10.849) -0.368 (0.558) -2.97e-07 (2.77e-07) -0.140 (0.114) -0.077 (0.060)

Total effect -1.708 (1.155) -0.255 (0.405) 1.012 (0.672) 1.304 (1.111) -11.357b (4.413) 0.524 (1.658) -6.407 (13.098) -0.972 (0.685) 1.21e-07 (2.81e-07) -0.120 (0.128) -0.112 (0.073) (continued)

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Table 3 (continued) Regressors highed HH road import_grp banks IR ρ

β 0.1580c (0.0779) -0.1652c (0.0780) -6.60e-05a (3.95e-05) 0.0582b (0.0206) 0.0197 (0.0217) -0.0048 (0.0229) 0.329b (0.025)

Θ -0.109 (0.157) -0.034 (0.168) 4.35e-05 (5.18e-05) -0.0196 (0.0388) 0.0263 (0.0584) 0.001 (0.034)

Direct effect 0.1522a (0.0813) -0.166c (0.079) -7.21e-05a (4.08e-05) 0.0585b (0.0214) 0.0223 (0.0243) -0.0047 (0.0219)

Indirect effect -0.0880 (0.2177) -0.113 (0.216) 8.97e-05 (7.05e-05) -0.0019 (0.0552) 0.0487 (0.0842) -0.0023 (0.0484)

Total effect 0.064 (0.254) -0.279 (0.237) -1.62e-04a (8.54e-05) 0.0565 (0.0649) 0.071 (0.096) -0.0069 (0.0576)

Note: Standard errors are in parentheses. Significance levels: a 10%; b 1%; c 5%. Constants and fixed effects are omitted in order to save space. All independent variables are used with lag of 1 year Source: Authors’ calculations

Appendix 2

Table 4 Direct effects values in 2018 by region Region Belgorod region Bryansk region Vladimir region Voronezh region Ivanovo region Kaluga region Kostroma region Kursk region Lipetsk region Orel region Ryazan region Smolensk region Tambov region Tver region Tula region Yaroslavl region Moscow+MoscDist Republic of Karelia

Direct effect in 2018 1.92261693 1.83445268 1.765845224 1.882305971 1.789321606 1.816633329 1.862220931 1.889542594 1.976081543 1.826822835 1.826039908 1.883991639 1.976431377 1.85264819 1.847837921 1.852905444 1.818571672 1.854576436 (continued)

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Table 4 (continued) Region Republic of Komi Arkhangelsk region-NAO Nenets Autonomous Okrug Vologda region Leningrad region Murmansk region Novgorod region Pskov region Saint-Petersburg Republic of Adygea Republic of Kalmykia Krasnodar Territory Astrakhan region Volgograd region Rostov region Republic of Dagestan Republic of Ingushetia Republic of Kabardino-Balkaria Republic of Karachaevo-Cherkessia Republic of Northen Osetia—Alania Stavropol Territory Republic of Bashkortostan Republic of Marii El Republic of Mordovia Republic of Tatarstan Republic of Udmurtia Republic of Chuvashia Perm territory Kirov region Nizhny Novgorod region Orenburg region Penza region Samara region Saratov region Ulyanovsk region Kurgan region Sverdlovsk region Tumen region-AO Khanty-Mansi Autonomous Area—Yugra Yamal-Nenets autonomous region Chelyabinsk region Republic of Altay

Direct effect in 2018 1.900001752 1.828951107 2.018462271 1.918489754 1.760856159 1.871778096 1.924986003 1.760328118 1.647608505 1.736950336 1.858555663 1.81970799 1.978448836 1.933749398 1.915348365 1.94328677 1.707814915 1.644767047 1.455730161 1.434624049 1.830706322 1.895815108 1.957103918 1.803293984 1.947094863 1.980180945 1.806281179 1.947534025 1.975042324 1.927631953 2.003840251 1.813252234 1.97677769 2.00051342 1.892845435 1.841891062 1.881611952 1.858030631 1.955147312 2.04325096 1.908397131 1.011575169 (continued)

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Table 4 (continued) Region Republic of Buryatia Republic of Tyva Republic of Khakassia Altay Territory Zabaykalsky Territory Krasnoyarsk Territory Irkutsk region Kemerovo region Novosibirsk region Omsk region Tomsk region Republic of Sakha (Yakutia) Kamchatka territory Primorsky Territory Khabarovsk Territory Amur region Magadan region Sakhalin region Jewish autonomous area Chukotka Autonomous Okrug

Direct effect in 2018 1.398187501 1.330510866 1.657903603 1.674053837 1.833777591 1.936613979 1.854764378 1.854989209 1.946621778 1.909598347 1.941762784 1.765458636 1.396930273 1.819927723 1.754769121 1.731108895 1.240657531 1.731555268 1.682252308 1.230391165

Source: Authors’ calculations

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Analysing the Asymmetric Effect of Oil Price Shock on Inflationary at the Aggregate and Disaggregated Levels in Malaysia Wong Hock Tsen, Kasim Mansur, and Cheong Jia Qi

Abstract Oil price shock creates uncertainty about future oil price movement, making complicated for policymakers to protect economic welfare. A sharp increase in the price of oil raises production costs, which raises consumer prices and thus leads to inflation. High inflation can reduce consumer surplus and economic welfare in an economy. This study examines the asymmetric effect of oil price shock on inflationary at aggregate and disaggregated levels in Malaysia using an openeconomy structural vector autoregressive (VAR) model. The impulse response function analysis reveals that the impact of an oil price shock on aggregate price, food index and transport and communication index is large and positive, regardless of whether the shock is positive or negative. The responses for other consumer price index (CPI) sub-groups differed in response to positive and negative oil price shocks, confirming the existence of an asymmetric effect of disaggregated price levels. The asymmetric impact of real oil price changes (both positive and negative) on the transport and communication index is the largest and most persistent among the CPI sub-groups as confirmed by forecasting error variance decomposition. In addition, asymmetric changes in real oil price exert inflationary pressure on aggregate and disaggregated price levels in Malaysia. Keywords Oil price · Asymmetric · Disaggregated price · Malaysia

1 Introduction Protecting economic welfare is somewhat challenging due to economic uncertainty in the global economy. Oil price fluctuation is typically among the identified sources that inevitably generate uncertainty concerning future oil price movement (Bernanke, 1983; Hamilton, 1996). The fluctuations in oil price can have W. H. Tsen (✉) · K. Mansur · C. J. Qi Faculty of Business, Economics and Accountancy, University Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_14

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implications for economic welfare around the globe. For instance, a sharp increase in oil price raises production costs, which raises consumer prices and thus leads to inflation (Li & Guo, 2021). As a result, high inflation reduces consumer surplus and therefore reduces economic welfare in an economy (Husaini et al., 2019). A loss of consumer surplus can be used to indicate a decrease in economic welfare, which reflects a society’s high cost of living (Mankiw, 2017). Oil price fluctuations have been a major source of concern for policymakers all over the world. In Malaysia, the central bank’s reaction in setting monetary policy to ensure price stability in the economy reflects this concern. Inflation is expected to average higher in 2021, primarily due to higher global oil prices, despite the benchmark interest rate (OPR) having been cut three times in the first nine months of 2020, from 3.00% to 1.75%, to cushion the economic slowdown from the COVID-19 pandemic (Bank Negara Malaysia, 2021). Therefore, if the central bank responds by raising the interest rate, it has implications for the economic recovery plan in Malaysia. Tightening monetary policy in response to a positive oil price shock can deteriorate output and in the worst-case scenario, lead to a recession (Bernanke et al., 1997). In this regard, it appears that determining the magnitude of the oil price shock on consumer prices in Malaysia is necessary to effectively implement an inflationtargeting monetary policy framework, as well as to help alleviate the welfare effects of inflation and contain inflationary pressure. In Malaysia, empirical studies on oil price have primarily focused on the aggregate inflationary effect of oil price shock. While studies on the relative price impacts of macroeconomic policy shock have been conducted, there have been a limited number of studies on the asymmetric inflationary effect of oil price at disaggregated levels. This study provides information to help stabilise prices at both aggregated and disaggregated levels. Given that inflation is generally regarded as the underlying cause of rising living costs, policymakers and consumers would benefit from understanding how aggregate and disaggregated prices react to oil price shock. If the inflationary effect of oil price shock varies across sub-group prices and are more intense on high-energy-intensive products, Malaysia’s government should promote service-oriented economies and diverse types of energy consumption. Furthermore, the central bank of Malaysia modifies to its monetary policy implementation. From the standpoint of the consumers, this study can serve as a guide in managing expenditures. For instance, consumers can reduce spending that is sensitive to oil price fluctuation and transition to more energy-efficient technology. To add new value to the current literature, this study employs an open-economy structural VAR (SVAR) model to discover the inflationary effect of oil price at aggregate and disaggregated levels. Cushman and Zha (1997) stated that SVAR models are not only reliable but also provide valid results, particularly for small open economies. Moreover, SVAR could analyse the net impacts of an unanticipated change (shock) in one or more variables on other variables in the system (Lütkepohl, 2005). This study improves studies by Ibrahim and Said (2012) and Ibrahim (2015) by extending the analysis to nine sub-groups of consumer price index (CPI) and accounting for asymmetric inflationary effect of oil price changes. Prior studies on

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the inflationary effects of oil price, for example, Ibrahim and Said (2012), Ibrahim (2015), and Husaini and Lean (2021) neglected the combination of fiscal and monetary policy in examining oil price shock at disaggregated levels. Thus, this study improves their studies by incorporating fiscal and monetary policy variables in an open-economy SVAR model given the fact that both policies are important in determining the equilibrium of prices (Sargent & Wallace, 1981; Leeper, 1991; Woodford, 1995; Sims, 1994, 2011).

2 Literature Review Numerous studies have been conducted on the inflationary effect of oil price shock and found inconclusive results (Furuoka et al., 2007; Saudi et al., 2018; Saudi & Tsen, 2019). For instance, Hamilton (1983) among the early study examined the effect of oil price shock on the US macroeconomy, including price levels and found that an increase in oil price would reduce output growth after 3–4 quarters, with a recovery beginning after 6–7 quarters. Hamilton (1983) concluded that oil price shock was one of the factors that contributed to economic recession of the United States (US) following the Second World War. Rotemberg and Woodford (1996) discovered the higher real price of oil leads the general price level to increase. Similarly, Bernanke et al. (1997) discovered that oil price shock slows economic growth while raising inflation in the US. Hooker (1999) claimed that significant oil price effects on macroeconomy are indirect through inflation and interest rate. Later, an increasing number of studies have supported the inflationary effect of oil price shock, for example, Chang and Wong (2003) for Singapore, Cologni and Manera (2008) for some G-7 countries, Basnet and Upadhyaya (2015) for ASEAN countries and Zakaria et al. (2021) for South Asian countries. Nonetheless, many scholars argue that the impact of oil prices on inflation is declining or refuse to admit the inflationary effect of oil prices (Tsen, 2010). For instance, Hooker (2002) stated that oil price pass-through to core inflation has been largely absent in the US since 1981. Similarly, Barsky and Kilian (2002) discovered that oil price does not provide a plausible explanation for the sustained inflation seen in both the GDP deflator and the CPI. Barsky and Kilian (2004) extended the analysis, claiming that there is no convincing empirical evidence that oil price shock is associated with higher inflation rate in the GDP deflator. Later, the decline of oil price pass-through to inflation consistent with a growing literature, including van den Noord and André (2007) for the US and the euro area, Chen (2009) for 19 industrialised countries, Jongwanich and Park (2011) for developing Asia, Conflitti and Luciani (2019) for the US and the euro area and Jiranyakul (2021) for most 9 Asia and the Pacific countries. Previous empirical studies have concentrated primarily on the inflationary effect of oil price at the aggregate level. More study is required, with a special emphasis on disaggregated prices. Focusing on disaggregated pricing should be more informative and lead to more policy-relevant conclusions, as postulated by Baffes (2007). Baffes

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(2007) found large variations in the pass-through across commodities. For instance, a 10% increase in oil price corresponds to a 1.8% rise in food prices. Chen et al. (2010) demonstrated that crude oil price has a significant influence on the three agricultural prices, consists of world corn, soybean and wheat prices. Baumeister and Kilian (2014) examined oil price shock on the US food prices and found a 1% real oil price shock tends to be followed by a persistent and statistically significant increase in the real price of corn that peaks at 0.5% 1 year later. In the case of Malaysia, the effort to examine the inflationary effect of oil price at disaggregated levels is still lacking. Among them were Ibrahim and Said (2012), Ibrahim (2015), and Husaini and Lean (2021). Ibrahim and Said (2012) focused on the inflationary effect of oil price on four sub-groups of CPI that consists of food price index, rent, fuel and power price index, transportation and communication price index and the medical care and health price index. The results show that food price inflation, rent, fuel and power price inflation and transportation and communication price inflation are all significantly affected by oil price changes in the short run, whereas the medical care and health price index is neutral to oil price changes. In the extended study, Ibrahim (2015) focused only on food price inflation and found that changes in the oil price increase are significantly related to the food price inflation. Husaini and Lean (2021) examined disaggregation price inflation, namely, CPI and producer price index (PPI) and discovered that the impact of oil price increase on PPI is greater than CPI in Malaysia.

3 Methodology 3.1

Data

This study uses quarterly data from quarter 1, 1980 to quarter 3, 2020. This period is selected because Malaysia has gone through major transformations, such as the early 1990s shift in monetary policy strategy and had encountered economic and financial crises, such as the 1997–1998 ASEAN financial crisis, the 2008 global financial crisis and the 2020 economic crisis due to COVID-19 pandemic. This study divides the variables into two blocks: the first block consists of foreign variables, while the second block consists of domestic variables. Data are collected from various issues of the Monthly Statistical Bulletin of the central bank of Malaysia, Energy Information Administration, Federal Reserve Economic Data and the International Financial Statistics online database.

3.2

Variables

This study divided the variables into two blocks: the first block consists of foreign variables, while the second block consists of domestic variables. The foreign block

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includes two variables: world oil price and foreign national income. Oil price is based on real crude oil imported acquisition cost as suggested by Alquist et al. (2013). To account for asymmetric effect of oil price shock, specifically positive and negative changes in oil price, this study applies Jiménez-Rodríguez and Sánchez (2005) principle: pþ t =

t

max ðΔpk , 0Þ

ð1Þ

min ðΔpk , 0Þ

ð2Þ

k=1

and pt- =

t k=1

The foreign national income is represented by the US industrial production index (IPI). The domestic block includes five variables: domestic national income, inflation, government expenditure, government taxes and interest rates. The IPI is used to proxy domestic national income. The IPI was selected because it comprises the three main indices in the economy (Karim & Karim, 2014). The fiscal policy instruments are government expenditure and government taxes. Government expenditure comprises the sum of operating expenditure and development expenditure whereas government taxes included the sum of tax revenue, non-tax revenue and non-revenue receipts. The CPI measures the inflation and the interbank overnight rate (IBOR) chosen as the interest rate. The central bank of Malaysia has introduced a new interest rate framework in April 2004, declaring the Overnight Policy Rate (OPR) to be the primary indicator of monetary policy. As a result, because OPR data became available only in 2004, the IBOR used to represent the stance of monetary policy. Previous studies for example Domac (1999), Ibrahim (2005), Umezaki (2007), Karim and Karim (2014) and Raghavan and Athanasopoulos (2018) have used IBOR as monetary policy stance in their study. For sub-group of CPI, this study selects nine sub-groups that consist of (1) Food; (2) Beverages and tobacco; (3) Clothing and footwear; (4) Gross rent, fuel and power; (5) Furniture, furnishings and household equipment and operation; (6) Medical care and health expenses; (7) Transport and communication; (8) Recreation, entertainment, education and cultural services; (9) Miscellaneous goods and services. This study denotes the nine sub-groups as LCPI1, LCPI2, LCPI3, LCPI4, LCPI5, LCPI6, LCPI7, LCPI8 and LCPI9, respectively. All variables are transformed by taking logarithms except the interest rate as this variable measurement in percentage. Additionally, three dummies were added to represent the following event: the 1997–1998 Asian Financial Crisis (quarter 3,1997—quarter 4,1998), the 2008 Global Financial Crisis (quarter 3, 2007—quarter 1, 2009) and the effects of

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economic recession triggered by COVID-19 pandemic (quarter 4, 2019—quarter 3, 2020).

3.3

SVAR Model

The inflationary effect of oil price shock can be explained using SVAR model as follows: Ayt = v þ CðLÞyt - p þ εt

ð3Þ

where A is a rectangular matrix that describes the simultaneous structural relationship between variables, yt is (n × 1) vector of variables included in a system, v is (n × 1) a vector of deterministic variables (constants and dummy variables), C(L) is (n × n) square matrix polynomial in the lag operator L and εt is (n × 1) vector of structural error that satisfies the conditions Eðεt Þ = 0 and E εt ε0t = I n is n × n of the identity matrix. Equation (3) cannot be estimated using the OLS because there is a lag effect for the dependent variable. This problem, however, can be solved by converting Eq. (3) to the reduced form representation by multiplying A-1 shown below: yt = A - 1 v þ A - 1 C 1 L þ C 1 L2 þ . . . þ C k Lk yt þ A - 1 εt or yt =

þ 0

yt þ μ t

ð4Þ

1

where ∏0 = A-1v, ∏1yt = A-1(C1L + C1L2 + . . . + CkLk)yt and μt = A-1εt. In Eq. (4), the value of μtis a residual reduced from VAR that meets the conditions E(μt) = 0 and E μt μ0t = μ is a positive and symmetric matrix that can be estimated from the data. Given that the residual from reduced-form VAR (μt) and the structural error (εt) have the relationship μt = A-1εt or Aμt = εt, the variance-covariance matrix to capture this relationship is as follows: E μt μ0t = A - 1 εt A - 10 εt 0 = A - 1 Eðεt εt 0 ÞA - 10 = A-1

ε

A - 10

Analysing the Asymmetric Effect of Oil Price Shock on Inflationary at. . .

μ

239

= A - 1 A - 10

ð5Þ

The variance-covariance matrix (∑μ) has n(n + 1)/2 different elements. The number of these elements represents the maximum number of identifiable parameters in matrix A where n represents the number of endogenous variables in the SVAR system. However, matrix A contains n2 parameters, which exceeds the maximum number of parameters required by the SVAR system. As a result, the SVAR system faces identification problems. The order condition introduced by Rothenberg (1971) can be used to solve the identification problems in the SVAR system. Order condition is a standard criterion for resolving SVAR system identification problems (Lutkepohl & Kratzig, 2004). This condition states that the zero restrictions in matrix A must be determined subject to (n2 - n)/2. After resolving the identification problem, the SVAR model can be estimated using maximum likelihood. The short-run zero restrictions on A matrix are imposed in this study, as shown in Eq. (6) in compact matrix form. The short-run zero restrictions can be used to generate valid impulse responses as proposed by Christiano et al. (2006). This study uses recursive identification to determine the contemporaneous causal relationships among the endogenous variables. Using this approach, the variables are ordered in a specific way, imposing some structure on the computation of the impulse response functions (IRFs).

A

1 α21 α31 α41 α51 α61 α71

0 1 α32 α42 α52 α62 α72

0 0 1 α43 α53 α63 α73

0 0 0 1 α54 α64 α74

0 0 0 0 1 α65 α75

0 0 0 0 0 1 α76

0 0 0 0 0 0 1

μ

LOP,t μLIPIUS t

μLTRt LGEt μμLIPIM μLCPI μR t t t

=

εLOPt εLIPIUS t εLTRt LGEt εεεLIPIM t LCPI ε t

ð6Þ

Rt

Based on Eq. (6), the variables are orders as follows, that is, LOP is oil price (the first letter L denotes logarithm), LIPIUS represents the US national income, LTR represents domestic government tax revenues, LGE represents domestic government expenditure, LIPIM represents domestic national income, LCPI is domestic consumer price index and R is domestic monetary policy. The results could be sensitive to variable orderings, hence theoretical considerations are used in this study (Bernanke, 1986). For example, the foreign variables (LOP, LIPIUS) are placed ahead of the domestic variables (LTR, LGE, LIPIM, LCPI, R) and are regarded as fully exogenous to the domestic variables. This indicates that the domestic variables respond to the foreign variables contemporaneously, but not otherwise. Prior literature on SVAR model has guided the order in which the foreign block leads the domestic block such as Cushman and Zha (1997), Brischetto and Voss (1999), Kim and Roubini (2000), and Zaidi et al. (2016).

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Table 1 ADF test result Variables LOP LIPIUS LGE LTR LIPIM LCPI R

Level Constant -2.3007(0) -1.9910(1) -0.3740(8) -1.4047(1) -0.3352(0) -3.8086(0)a -2.6707(2)c

Constant and trend -2.4920(0) -1.2046(1) -2.0094(2) -2.1971(1) -2.2380(0) -2.2958(0) -3.6776(2)b

First difference Constant -12.9717(0)a -8.8187(0)a -11.2333(2)a -17.6220(0)a -13.1798(0)a -8.9271(0)a -8.7769(1)a

Constant and trend -12.9392(0)a -9.0248(0)a -11.1884(2)a -17.6418(0)a -13.1584(0)a -9.3877(0)a -8.7455(1)a

Notes: (a) indicates significance at the 1% level, (b) significant at the 5% level, (c) significant at the 10% level. For the constants, the τ (tau) -statistic values were -3.47, -2.87 and -2.57 for the 1%, 5% and 10% significance levels, respectively. The τ (tau) -statistic values for constants with time trends were 4.01, 3.43 and 3.14 for significance levels of 1%, 5% and 10%, respectively. Figures in parentheses () represent the optimal lag as determined by the Schwarz Info Criterion (SIC) Source: Authors’ calculations

Furthermore, this study assumes that the Malaysian government formulates budget planning based on government tax revenue. Thus, it is assumed that government tax revenue shock has a positive impact on government spending. This means that an increase in government tax revenue can increase the country’s source of income and thus allow the government to increase government spending. Several studies, including Tang et al. (2013) and Hong (2016) supported this assumption. Domestic income can affect both consumer price index and domestic monetary policy. The domestic monetary variable (interest rate) is arranged last, in accordance with the literature on the lag effects of monetary policy. The assumption that interest rates react immediately to fiscal policy is consistent with previous empirical studies (Fatas & Mihov, 2001; Perotti, 2002; Hong, 2016). Perotti (2002), for example, stated that interest rates can respond to fiscal policy shocks in the same quarter. In this study, all variables are treated as jointly determined; no a priori assumptions regarding the exogeneity of any of the variables in the system are made.

4 Results and Discussion This section focuses on the responses of aggregate and disaggregated prices to oil price increase and oil price decrease. The ADF test is reported in Table 1. The results show that except for the consumer price index and the domestic monetary policy that are stationary in level, the remaining variables are differenced-stationary. We do not show the results of ADF test for disaggregated price levels to converse space. In short, this study discovers that some of the disaggregated price levels are stationary in level.

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Sims (1980) and Sims et al. (1990) stated that the objective of a VAR model is to determine the interrelationships between variables, not to estimate the parameters. Hence, they advise against differencing and recommend that variables in model VAR be in level, even if the variables have a unit root. Furthermore, even if the variables contain unit roots, a VAR model at the level can be estimated, thereby neglecting potential cointegration restrictions. This is commonly used in SVAR modelling to avoid imposing too many restrictions (Lutkepohl & Kratzig, 2004). Based on their recommendations, the SVAR model is specified in levels. Previous empirical studies have estimated a VAR in levels, even the model contains some unit root series (Sims, 1992; Kim & Roubini, 2000; Zaidi et al., 2016; Nguyen et al., 2019). In determining the optimal lag length, this study uses several model selection criteria. When a maximum lag order of Pmax = 4 is used, LR suggests p = 3, AIC and FPE suggest p = 2, whereas SBC and HQC choose p = 1 for aggregate price model and some disaggregated price model estimations. However, when the results of the autocorrelation LM test are considered, a lag order of 1 and 2 fail to reject the null hypothesis of autocorrelation, except lags order of 3 for aggregate price model. Therefore, we use 3 lags for our VAR estimation. Meanwhile, estimates from the VAR companion matrix revealed that the eigenvalues are less than one. If the eigenvalue is less than one, the VAR ( p) process is said to be stable (Lütkepohl, 2005). Similarly, for disaggregated price models, the lag length is chosen when the lag order up to h does not suffer from autocorrelation problem.1 Figures 1 and 2 depict the results of the impulse response function for the aggregate and disaggregated prices to positive and negative oil price shocks. The solid line represents estimated responses and the two dashed lines represent confidence intervals.2 The upper and lower bound lines can be used to determine the significance of the impulse response function. Specifically, if the upper and lower bound lines move in the same direction, either positive or negative, the resulting response is significant. The resulting response, on the other hand, is insignificant if the upper and lower bound lines move in opposite directions. Each figure illustrates the effect of a one-standard-deviation shock to the oil price. Figure 1 displays the response of the aggregate and disaggregated prices to a positive change in the real oil price shock. The response of aggregate price (CPI) is positive after second quarter and lasted till the end of the period (24 quarters). This demonstrates that a one-unit standard deviation increase in oil price causes an increase in aggregate CPI. The result highlighted that oil price shock causes

1

Since we estimated the asymmetric effect of real oil price changes (both positive and negative) for disaggregated price levels separately, the optimal lag-lengths are chosen if the model does not suffer from autocorrelation. Using positive real oil price changes, we discover that lags of 4 for CPI8 and lags of 3 for the remaining CPI sub-groups are free from autocorrelation. Using negative real oil price changes, we find that lags of 3 for the CPI, CPI3, CPI5 and CPI7 and lags of 6 for the remaining CPI sub-groups do not suffer from autocorrelation. (These results are available from the authors upon request.) 2 The confidence intervals are obtained from 1000 Monte Carlo simulations.

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Fig. 1 Impulse response function to RLOP+. Source: Authors’ calculation

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Fig. 2 Impulse response function to RLOP-. Source: Authors’ calculation

inflationary pressure in the Malaysian economy, as suggested by the concept of costpush inflation (Parkin, 2019). Khan and Ahmed (2011) also showed that the inflation

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remained positive till the end of period following positive oil price shock in developing countries. For disaggregated price levels, this study found that the effects of positive oil price shock are varied across CPI sub-groups, suggesting the heterogeneity of the effects of positive oil price changes. In particular, this study found a positive response for CPI sub-groups in LCPI 1) Food; LCPI 3) Clothing and footwear; LCPI 5) Furniture, furnishings and household equipment and operation; LCPI 6) Medical care and health expenses; LCPI 7) Transport and communication; LCPI 8) Recreation, entertainment, education and cultural services; and LCPI 9) Miscellaneous goods and services. Food index raises immediately and remains positive for the remaining period. As Malaysia has transformed from a commodity-based economy to an industrial-based economy, thus become a net food importer, Malaysia becomes vulnerable to global food price fluctuations (Ibrahim and Said, 2012). As postulated by Tyner (2010), oil price and food prices have become closely linked where food prices rise in tandem with the price of oil. Oil price affects food prices through increased transportation costs. This is consistent with the finding that the transport and communication index raises following positive oil price changes. Transport and communication index shot up immediately and lasted till the end of the period. It seems that positive oil price changes have permanent effect on transport and communication index. As transportation is heavily dependent on the oil price, change in the oil price directly influences this price index (Baffes, 2007). For the remaining CPI indexes, positive oil price change affects these CPI sub-groups through the production costs. In contract, the CPI sub-groups for 2) Beverages and tobacco; and 4) Gross rent, fuel and power, however response negatively to positive real oil price changes. Figure 2 depicts the responses of aggregate and disaggregated price levels to negative changes in real oil price. The aggregate price response to a declining real oil price was positive and lasted until the end of the period. For CPI sub-groups, this study finds a positive response in 1) Food; and 7) Transportation and communication, while the other responds negatively following a decrease in real oil price. Food index and transport and communication index immediately increase and remain positive for the remainder of the period. Based on the findings above, the impact of an oil price shock on aggregate prices, food index and transport and communication index is large and positive, regardless of whether the shock is positive or negative. This is in line with the findings of Khan and Ahmed (2011), who claimed that inflation responds symmetrically to changes in oil price. The responses for other CPI sub-groups differed in response to positive and negative oil price shocks, confirming the existence of an asymmetric effect of disaggregated price levels. Our findings are consistent with the evidence of an asymmetric effect found by Husaini and Lean (2021), despite the fact that this study focuses on disaggregated price inflation, namely, CPI and PPI. Table 2 shows the forecasting error variance decomposition of aggregate and disaggregated prices in order to determine the proportion of the movement generated by positive and negative oil price shocks. Panel A depicts the allocation of positive oil price changes to the CPI and CPI sub-groups. The impact of real oil price changes

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Table 2 Variance decomposition of positive and negative oil price shocks LCPI

LCPI 1

LCPI 2

Quarter Panel A: Positive oil price shock 1 1.62 0.29 0.11 4 3.34 1.15 0.23 8 4.94 2.40 0.12 12 5.62 3.48 0.13 16 6.17 4.33 0.36 20 6.64 4.98 0.86 24 7.04 5.46 1.53 Panel B: Negative oil price shock 1 4.65 4.92 0.79 4 14.90 6.91 1.22 8 11.05 7.13 3.43 12 8.72 7.72 2.94 16 8.72 7.87 2.53 20 6.33 7.77 2.46 24 5.70 7.52 2.39

LCPI 3

LCPI 4

LCPI 5

LCPI 6

LCPI 7

LCPI 8

LCPI 9

0.61 3.81 5.87 6.41 6.29 5.84 5.29

2.41 0.56 0.45 0.90 1.48 2.00 2.42

0.07 0.75 1.21 1.38 1.47 1.55 1.62

0.17 0.33 0.27 0.46 0.79 1.16 1.51

1.23 5.34 6.86 8.08 8.95 9.57 10.00

0.04 2.42 2.76 2.77 2.74 2.71 2.69

2.24 4.28 4.64 4.55 4.56 4.66 4.80

1.36 4.24 3.44 3.15 3.06 2.95 2.78

6.30 1.32 1.14 3.52 6.11 8.17 9.21

0.22 1.26 1.55 1.65 1.71 1.77 1.83

2.76 5.13 3.76 2.59 2.25 2.46 2.81

18.69 28.64 31.43 30.85 29.90 29.11 28.54

0.39 0.71 3.06 3.04 2.43 1.90 1.57

0.24 11.92 13.03 9.35 6.61 4.89 3.78

Source: Authors’ calculation

on the transport and communication index (CPI7) is the largest and most persistent among the CPI sub-groups. Positive real oil price changes contributed 1.23% in the first quarter and increased to 10% over a 24-quarter horizon. This result confirms the earlier presented IRFs finding. The impact of positive real oil price changes on other CPI sub-groups has also increased over time, indicating that the impact is persistent over the longer term. These findings imply that positive changes in real oil price exert inflationary pressure on aggregate and disaggregated price levels in Malaysia. In the case of negative oil price shock (Panel B), the impact of real oil price changes on CPI sub-groups triggered inflationary pressure. The transport and communication index (CPI7) demonstrates the larger and more persistent impact of oil price changes once again. The impact of a negative real oil price shock on the CPI7 is 18.69% in the first quarter, peaks in the eighth quarter and then gradually declines to 28.54% in the 24th quarter. The impact of a negative oil price on other CPI sub-groups increased as the time horizon increases.

5 Conclusion This study has estimated an open-economy SVAR model for Malaysian economy aimed at examining the asymmetric inflationary effect of oil price shock at aggregate and disaggregated levels using quarterly data over the period quarter 1, 1980 to

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quarter 3, 2020. Several conclusions can be drawn from the empirical finding. First, impulse response function analysis reveals that the impact of an oil price shock on aggregate prices, food index and transport and communication index is large and positive, regardless of whether the shock is positive or negative. Second, the responses for other CPI sub-groups differed in response to positive and negative oil price shocks, confirming the existence of an asymmetric effect of disaggregated price levels. Third, forecasting error variance decomposition reveals that the asymmetric impact of real oil price changes (both positive and negative) on the transport and communication index (LCPI7) is the largest and most persistent among the CPI sub-groups. In addition, asymmetric changes in real oil price exert inflationary pressure on aggregate and disaggregated price levels in Malaysia. These findings have several implications for the implementation of government policy and managing consumer spending. First, since the response of disaggregated price levels to asymmetric oil price shock varies, monetary authority has to design appropriate policy in curbing inflationary consequences of oil price. For example, monetary authority has to ensure a low inflation environment. According to Taylor (2000), regimes with lower inflation appear to have less persistent costs and lower pricing power of firms, reducing the degree of pass-through of oil price shock. Other measures the government has to take include fostering more service-oriented economies and a wider range of energy consumption, given the results that negative real oil price changes induce negative responses in most of the CPI sub-groups. Second, to reduce the cost of living caused by the oil price, consumers should manage their spending properly and utilise more energy-efficient technologies. Acknowledgement The authors gratefully acknowledge the financial assistance provided by the Ministry of Higher Education, Malaysia through Fundamental Research Grant Scheme (FRG05382020) and Shariff Umar bin Shariff Abd. Kadir for his excellent research assistance.

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Qatari Real Estate Market and Its Response to Shocks Alanoud Hamad Fetais

Abstract This study analyzes the real estate market in Qatar and its response to local and international shocks and crises. Shocks that are addressed in the study are of a different nature, starting from the global financial crisis and the decrease in oil prices, moving to the Gulf Crisis, and recently the COVID-19 pandemic. To assess the situation, the paper begins by examining the factors that affect local and international investors’ decisions in this market. In a second step, the study explores the Qatari government’s role in the real estate sector through diverse initiatives and legislations. It is found out that this role supports the real estate sector, especially in crisis periods, and presents recommendations for a sustainable real estate market. This study addresses the Qatari real estate sector by using the real estate price index (REPI) issued by Qatar Central Bank (QCB) and covering the period from April 2006 to August 2021. Furthermore, it highlights the sold properties by the Ministry of Justice for the same period. Analysis of the data reveals that the global financial crisis had the strongest impact on the Qatari real estate sector, exceeding the effects engendered by the political crises, the decrease in oil prices, and the COVID-19 pandemic during the studied period. Keywords Qatar · Real estate · Price index · COVID-19 · Global financial crisis

1 Introduction In the past two decades, the global real estate market, similar to other global economic sectors, has experienced fundamental booms (Anghel & Hristea, 2015). Since the last decade, however, and due to the 2008 financial crisis, many changes were imposed on many economic and social sectors, and a new geographical real estate map, which looked different from the one that existed in 2007, was reshaped (Anghel & Hristea, 2015). Even though this crisis originated as a bubble burst in the A. H. Fetais (✉) College of Islamic Studies, Hamad Bin Khalifa University, Doha, Qatar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_15

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housing sector in the United States, its effects were reverberated worldwide. Among the countries whose real estate sector was affected is Qatar. Although Qatar’s economy depends on hydrocarbon resources, the main drivers of this economy are finance and real estate. As with other sectors, the real estate sector was affected by the 2008 financial meltdown, and as a result, real estate prices witnessed a sharp decrease. This led to measures being taken to preserve all sectors, including the real estate one (Qatar Central Bank, 2011). Despite being one of the fastest-growing sectors in Qatar, real estate sector has been subjected to several challenges, in addition to 2008 crisis, including the drops in oil prices in 2014 and 2016 (Benlagha, 2020). Since June 2014, oil prices have dropped sharply after a period of stability at approximately US $105 per barrel. Prices have remained low, dropping to US $26.19 per barrel on February 11, 2016 (Benlagha, 2020). Another political crisis that the country went through and that affected the real estate sector to a great extent is the 2017 blockade known as the Qatar diplomatic crisis or the Gulf Crisis of 2017. The blockade began in 2017 and lasted until January 2021 and has significantly affected the Qatari economy. Another crisis that hit this sector hard was the COVID-19 crisis, which began in 2020 and is still ongoing. The COVID-19 crisis has led to turbulence in global real estate markets, which caused a collapse in markets (Kauko, 2020). Certainly, Qatar has become affected by the consequences of the COVID-19 pandemic. Despite the conditions and effects of the blockade, geopolitical risks, and the COVID-19 pandemic that continue to affect the global economy, the real estate sector in Qatar is expected to recover because of Qatari hydrocarbon projects, infrastructure projects, and preparation for the 2022 World Cup (Oxford Business Group, 2020). From 2010 until a decrease in oil prices between 2014 and 2016, Qatar experienced a more than 5% increase in its population, as a result of an influx of expatriates (Oxford Business Group, 2019). This led to increases in real estate movement in the Qatari real estate market. According to the HSBC Expat Explorer 2020 Survey, Qatar ranked first for expats to work and live in the Middle East and the sixth in the world for the same reason (Property Finder, 2020). Supported by infrastructure projects, population growth and economic output have saturated investors with positive sentiments regarding the real estate sector (Al Refai et al., 2021). The studied resources have illustrated to us that the Qatari government encourages investment in the real estate sector through initiatives and legislations that encourage local and foreign investment. “Real Estate Ownership Law for Foreigners” is the latest legislation, and it has been put into practice since October 1, 2020. Through this, the government determined nine areas for non-Qatari to own properties and 16 areas for usufruct. This legislation grants permanent residency benefits with the benefits of healthcare services, education services, and investment opportunities if the value of the property is more than the US $1 million and grants the residency if the property costs not less than US $200,000. Due to these government initiatives, Qatar obtained the first international ease of real estate ownership registration according to the 2020 World Bank’s Ease of Doing Business 2020 Report. Moreover, the Ministry of Justice in Qatar signed a memorandum of understanding in October 2018 with the UK’s Royal Institution of

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Chartered Surveyors to improve market transparency and investor confidence by regulating the real estate sector in accordance with international standards (Oxford Business Group, 2020). Through its National Vision 2030, Qatar aims to move from an economy based on gas and oil to one based on knowledge and sustainable development (Ministry of Municipality and Environment, 2016), hence the country has recently started to lend more attention to sectors other than the hydrocarbon ones. Since there is a lack of studies on the Qatari real estate market and its response to shocks, this present study examines the Qatari real estate market, the issues that affect it, the government’s role, and how to keep the market stable. Additionally, this present study explores the importance of the real estate sector in the growth and stability of the Qatari economy, as a model that can be emulated in other countries. The procedures, and the legislation that encourages Qataris and non-Qataris to invest in this sector are also discussed. Based on data analyses and interpretation of the current market statistics, the study aims to benefit policymakers, local and international investors, and those interested in the real estate sector in Qatar. This study also fills the gap in the existing literature by analyzing the Qatari real estate market, focusing on the shock periods and how the new legislation affects the market and investors’ decisions. Finally, the study examines how the market deals with the circumstances to ensure market stability. The remainder of this paper is organized as follows. Section 2 reviews the related literature. Section 3 describes the real estate market in Qatar and its development since the 1950s. Section 4 presents the crisis and shocks that affected the Qatari real estate market and how it functions during crises. Finally, concluding remarks are provided in Sect. 5.

2 Literature Review Real estate development projects in Middle Eastern countries are important tools for economic growth. As a result of high oil revenues and real estate market activity, the Gulf Cooperation Council countries (GCC) achieved high growth rates from 1999 to 2008 (Bagaeen, 2014). Also, the Global Building report, issued in 2015, indicated that the real estate sector construction will be 13.5% of the world’s total domestic income by 2025 (Anghel & Hristea, 2015). The literature includes several works on the Qatar economy regarding oil prices, stock markets, financial markets, and banks. However, there is a lack of studies on the Qatari real estate sector and shocks impacting the market. This section discusses the previous literature from two perspectives: the first one relates to the market and legislation, while the second one relates to the region and global shocks and how they affect investors’ decisions and the real estate market. There is no doubt that legislation, along with economic, political, and social conditions affects the real estate markets significantly. Anghel and Hristea (2015)

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found in their study that unlike the other markets, the real estate market does not selforganize but is organized through the intervention of the state or the authorities. Furthermore, these markets are affected by social and economic phenomena such as unemployment and salary increase. In contrast, Kauko (2020) argued that real estate is not sensitive to outside influence because real estate agents are risk averse. In this regard, some government programs may affect the real estate market. For example the program that allows international investors to obtain citizenship by investing in Turkey can affect real estate prices. It encouraged foreigners to purchase real estate in order to apply for Turkish citizenship. As a result, thousands of foreigners, particularly from the Middle East and Asia have bought houses in Istanbul. Therefore, the program has eventually contributed to an upsurge in house prices of the districts of Istanbul (Aysan et al., 2021). In addition to social and economic conditions, political stability is a significant determinant of real estate investment (Al Refai et al. 2021). Salem and Baum (2016) investigated the real estate sector for selected MENA markets (Turkey, Tunisia, Morocco, Algeria, Egypt, Saudi Arabia, Qatar, and Emirates) from 2003 until 2009. They found that political stability is the reason these countries attract more real estate investments than other countries in the MENA region. After 2009; Arab Revolutions, MENA politicians may know that political risk in their countries affects their countries’ real estate market. Taking Qatar as a case study (Al Refai et al., 2021) investigated the dynamics of the relationship between real estate and stock markets in an energy-based economy. They found that oil price fluctuations and political tensions in the Gulf led to negative investments. This certainly affects the real estate sector. The oil price decline in countries such as Qatar will reduce real estate demand due to the decline in jobs and investments. While Saudi Arabia lifted its blockade on Qatar on January 5, 2021, along with the COVID-19 vaccines to end the global pandemic, this could propel the real estate market performance. They concluded that there is a need for monetary and economic measures to support real estate markets during crises. Many research studies examined and still examine the COVID-19 pandemic effects. Rikshpun et al. (2020) examined the effects of the COVID-19 pandemic and social unrest on real estate markets in the United States. They found that many states have implemented strict guidelines regarding the coronavirus pandemic, these guidelines led many to flee due to economic repercussions. In addition, each city witnessed its own unique situation, and not all cities experienced a drop in their prices. Hu et al. (2021) used the daily hedonic housing price index for five Australian state capital cities, noting a negative correlation between previous COVID-19 cases and daily housing returns. They concluded that due to its recent emergence, little is known about its impact on real estate. While limited attention has been paid to how the COVID-19 pandemic is affecting real estate markets, containment policies by the government may have powerful and important impacts on the economy. Some studies examine investors’ decisions. Anghel and Hristea (2015) analyzed the situation of the real estate market in several countries to identify the characteristics that affect investors’ decisions and found that the markets now differ from those that existed until 2007. They found that “Asian Tigers” seek to dominate the

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real estate market, and countries such as India, Chile, China, and Qatar are the major player. According to the same study, the financial crisis created imbalances in all areas of daily life and indicated that the real estate market, just like all global economic sectors, has undergone important transformations. As a result of the global financial crisis, Qatar Central Bank (QCB) adopted a “real estate price index” to assess developments in the sector and to promote financial stability (Qatar Central Bank, 2011). Geltner (2015) demonstrated that the development of real estate price indexing and data sources, especially investment ones, has provided important results regarding real estate pricing and price dynamics, which are a large part of the national wealth and capital markets because they are major investment assets. While there is a lack of research regarding the Qatari real estate market, this study fills the gap in the literature by analyzing the market, focusing on the shock period and how the new legislation affects the market and investors’ decisions. It examines how the market will deal with circumstances to ensure the stability of the market and how to keep the indicators at appropriate levels. Findings may help policymakers to evaluate and improve regulations to enhance the real estate sector and increase economic diversification, especially after shocks such as blockades and COVID-19. Investors may also benefit from these findings in terms of investment decisions.

3 Qatari Real Estate Market Before the discovery of oil, Qatar depended on fishing and pearling. These economic activities were the main drivers in developing Doha and contributed to people settling along the eastern coastline. Before the 1950s, Qatar’s eastern coastline remained unchanged. But after the 1950s, due to the increase in oil revenues, development and urban expansion occurred through the growth along the eastern coastline and around the city center (Pollalis & Ardalan, 2018). Strategic land-use planning was undertaken in Qatar for nearly 40 years. The first strategic plan, the Qatar Development Plan, was adopted in 1972, followed by a series of plans over the subsequent 20 years. Qatar National Vision (QNV) 2030 aims to change Qatar into a country that can maintain its development and provide a high quality of life for generations. QNV 2030 is based on four pillars of sustainable development: economic, human, social, and environmental development. Based on this vision, the Ministry of Municipality and Environment established a national development strategy in 2014, as a basis for forming and organizing all planning and development processes in Qatar. This strategy is known as the “Qatar National Development Framework 2032.” The plan adopted an integrated system for urban requirements with the best practices to ensure sustainable growth. This will drive urbanization and development in Qatar during the next 20 years (Ministry of Municipality and Environment, 2016). During the last few years, Qatar’s economy has been affected by several shocks, such as a trade blockade in 2017, the COVID-19 pandemic in March 2019, and the

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unexpected oil price decline in early 2020. To mitigate the economic costs of these shocks, Qatar has encouraged investment in non-oil sectors, such as the real estate sector, by pursuing economic diversification policies.

3.1

Government Efforts in Real Estate Sustainability

The real estate sector in Qatar is one of the fastest-growing sectors, ranking second after power resources. In terms of economic and financial importance, the real estate credit accounts reached 38% of the domestic credit of local banks (Planning and Statistics Authority, 2020). The government plays the most important role in the sustainability of the market through the application of real estate legislation and government measures. The Qatari government encourages investment in this sector through initiatives and legislation that encourages local and foreign investment. In Doha 2004, the government issued a law allowing foreign ownership of real estate (Wiedmann et al., 2012). This event was the start of the flourishing of real estate and concentrated government projects in the country. Law No. 16 of 2018 was issued regarding the non-Qatari ownership of the real estate. Cabinet Resolution No. (28) of 2020, which was applied on October 1, 2020, is the latest legislation. The Foreign Real Estate Ownership Law outlines nine areas in which non-Qataris can own real estate and 16 areas for usufruct. The areas for ownership are the Pearl, West Bay (Legtaifiya), Al Khor Resort Project, Al Dafna, Onaiza, Al Qassar, Lusail, Al Khuraij, and Jabal Thuaileb. The usufruct areas include Msheireb, Fereej Abdulaziz, Al Doha Al Jadeedah, Old Al Ghanim, Al Rufaa, Old Al Hitmi, Al Slata, Rawdat Al Khail, Bin Mahmoud, Najma, Umm Ghwailina, Al Mansoura, Bin Derham, AlKulaifat, Alsadd, New Al Mirqab, Al Nasar, and Doha International Airport Area. When a non-Qatari buys a property with a value not less than US$ 200,000, the owner of the property can obtain a residence permit without the need for a sponsor, as long as the period of residence is not less than 90 days per year, whether the stay is continuous or intermittent. When a non-Qatari purchases a property with a value not less than US $1 million, they will receive a permanent residence card with privileges granted for healthcare services, education services, and investment, as long as the period of residence is not less than 90 days per year, whether the stay is continuous or intermittent (MOJ Non-Qatari ownership and use of real estate, 2021). Ministry of Justice initiatives, encourages and simplifies real estate procedures, therefore, encourages local and foreign investors. These initiatives decrease the implementing service time, streamlining procedures, and provides several digital services, since the government has moved to digital portals and applications as explained below. (MOJ Services, 2021a, 2021b, 2021c) • A page on their website for foreign investors explaining ownership legislation in Arabic and English. They also opened two offices for foreign investors in the

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Pearl City in October 2020 and in Lusail City in April 2021 to use services through a single channel. Investors can obtain the title deed within 1 h. • Real estate registration services specialize in real estate registration and real estate brokerage. To simplify these processes, the real estate registration department digitally provides it. The registration system in Qatar is considered one of the best internationally, as the ownership process does not take more than 10 min. According to the Ease of Doing Business Report issued by the World Bank for the year 2020, Qatar ranked first in the world in the ease of registering real estate ownership. • SAK—an official application, has been adopted by the Ministry of Justice, for electronic services for real estate registration and documentation in Qatar. The aim is to simplify services and provide them to investors, individuals, and government institutions quickly and with a high level of privacy. This application won first place for the best government project in the Arab world in the category of informatics in Salem Al Sabah Informatics Award in Kuwait 2018. • Al-Mothamin application—Appraiser App—an application aims to assist those wishing to sell or buy real estate by knowing the approximate prices of these properties. This application also allows investors to know the location of the property and displays a graph. That shows the price per square foot, in addition to the ability to share opinions and observations.

3.2

Economic Growth and Real Estate

Owing to its oil and gas reserves, the Qatari economy has witnessed significant growth over the last few decades. The report by the World Economic Forum on Global Competitiveness 2020 classified Qatar’s economy among the top 20 fastestgrowing economies in the world. The high GDP growth rates, together with the expected influx of the expatriate population and efforts to diversify the economy are the primary drivers of growth in the real estate sector. While global economic growth could affect the growth of the country, the strong fundamentals in place in Qatar keep the economy resilient. This allows investors to view Qatar as a safe location for investment in times of slow growth in other parts of the world. Thus, investments are growing in Qatar, in spite of slow growth in the rest of the world. Investors are always seeking confidence, safety, and stability. Qatar was able to attain all of these aforementioned prerequisites as it was ranked as one of the safest countries in 2020 and second in 2021. Doha scored 87.96 points out of the 431 cities in the safety index and 12.04 in Numbeo’s Crime Index by City 2021. According to the HSBC Expat Explorer 2020, the oldest survey of expats in the world, Qatar is one of the top 10 places to work and live for expats, ranking first in the Middle East and sixth globally (Property Finder, 2020).

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Supply and Demand Dynamics

The demand for Qatari real estate has witnessed an increase during the last decades due to the ongoing migration of expatriates to this country. This has led to an increase in population and foreign ownership legislation. According to the census results, population growth rose from 744 thousand to 1699 thousand from 2004 to 2010 by 128.4% (Planning and Statistics Authority, 2016). The latest census in 2020, indicates that Qatar’s population increased by 67% in 2020 compared to 2010. Qatar’s population growth during the Census years (1986–2015) is illustrated in Fig. 1. Qatar began hosting sports events in the 2000s, which led to an increase in housing demand to accommodate the high population number. In addition, the announcement in 2010 that Qatar would host the World Cup in 2022 made the real estate market even more active. The real estate trading index reached a total value of 31 billion Qatari riyals for 5117 real estate deals in 2020, while the total deals amounted to 22.7 billion Qatari riyals for 3783 real estate deals in 2019 (MOJ Real Estate Bulletin, 2021). The total residential supply reached 300,550 units during the first half of 2020. This period also witnessed the completion of 2250 apartments and 700 villas; 2000 in Lusail, Pearl, and West Bay, while 157 apartments were in Msheireb Downtown (ValuStrat, 2020). In addition, the residential supply in 2021 is estimated to be about 8200 housing units, about 80% of which are concentrated in Lusail, Pearl, and West Bay. It is expected that the expatriate population in the second half of 2021 will increase after lifting restrictions. It will positively affect rent stabilization by the end of 2021 (ValuStrat, 2021b).

3000 2,405

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0 1986

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Fig. 1 Qatar’s population growth during the Census years (1986–2015). Source: Planning and Statistics Authority, 2021

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The Supreme Committee for Delivery and Legacy, the entity that is responsible for developing sports facilities projects for the 2022 World Cup, asked real estate apartment owners to register their desire to rent their properties during the World Cup period, as well as projects under construction that will be ready in 2021, which may help and support the real estate sector.

3.4

Qatari Banks and Mortgage Loans

The Qatari banking sector is the main driver of the real estate sector. The banking sector plays a prime role in pumping billions into the real estate market. The real estate sector benefits from the financial facilities provided by Qatari banks. The total of local loans and credit facilities provided by banks to the real estate sectors and contractors in the private sector amounted to approximately 190.4 billion riyals in December 2020 (Lusail, 2021). This increase in the volume of credit facilities granted to the real estate and contracting sectors confirms that real estate construction operations continue to grow. Also, the law of ownership for foreigners and increasing the areas of ownership and use will help increase real estate loans, which may have a positive impact on the banking sector and the real estate sector significantly. In light of the real estate and stock prices increase, and the increase in speculative activities in these two sectors in Qatar during the last period, Qatar Central Bank (QCB) has taken some precautionary measures to maintain a balance between the country’s development needs and financial stability. These measures were also followed to control the real estate finance risks and reduce them to the lowest level. The QCB had made some adjustments by the end of January 2011. These adjustments are: The total financing granted does not exceed 100% of the bank’s Tier 1 capital. The real estate financing for individual clients against their salaries and the real estate financing for other clients should not exceed the maximum total categories. The financing granted to the customer is 60% of the value of the real estate guarantee and the repayment period should not exceed 15 years, including the grace period (Qatar Central Bank, 2021). The change in the real estate business credit has a medium positive relationship with the rates of change in real estate values and rents. It indicates that the real estate market is witnessing self-corrections of price imbalances, whether at the level of rents or property values. Subjecting the real estate market to the forces of supply and demand will be a challenge for credit providers. This will require commercial banks that grant mortgages to adapt to these changes (The Planning and Statistics Authority, 2020). The International Monetary Fund statement in 2019 illustrated the importance of continuing to monitor the banking sector’s assets in light of the decline in real estate prices. The role of the central bank is to consider introducing additional indicators such as vacancy rates to assess development in the real estate sector, with an

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emphasis on risk-based supervision. This would help to detect the weaknesses point in the real estate sector at an early stage (IMF, 2019).

4 Qatari Real Estate Market and Shocks This section highlights the Qatari real estate sector using the real estate price index (REPI). This index is based on composite prices for land, villas, and residential buildings covering the period from 2006 to August 2021. It also highlights the daily transactions of real estate trading volume, at the Department of Real Estate Registration at the Ministry of Justice, including vacant lands, houses, residential buildings, commercial buildings, apartment complexes, and so on from 2006 to August 2021, according to the municipality, location, space, and price. The methodology of this study relies on the analysis of real estate trading volumes and REPI during the studied period. It also examines the periods of shocks, such as the global financial crises, Qatar blockade, COVID-19 pandemic, the announcement of World Cup 2022, and if the new legislation has affected the trading volume and REPI indicator. This section studies properties sold in Qatar by type, over three periods, from April 2006 to August 2021, including two specific types. The first is buildings and apartment complexes, while the second is commercial buildings. The study periods are divided into three periods, the first period relates to before and after the global financial crisis, while the second includes oil price drops and the Gulf Crisis in 2014 and 2017; and finally, the fourth period 2018–2021 includes the blockade in addition to the COVID-19 pandemic.

4.1

Before and After the Global Financial Crisis

The Qatari real estate market witnessed a remarkable recovery before the global financial crisis of 2006–2007. There was a significant increase in the real estate trading movement, which reached 4017 deals during the period from April to December 2006, and continued to rise, reaching 9004 deals in 2007. This increase was due to several factors, the most important of which are population growth, an increase in the level of per capita income, and an increase in public spending (Qatar Central Bank, 2011). Like several other global financial markets, the Qatari market has been affected by the global economic meltdown as a drop in real estate sales has been observed since August 2008. The analysis in Fig. 2 illustrates that while there was a boom in traded real estate between 2006 and 2007, there was a sharp decrease in August 2008; only 284 deals were made. In the aftermath of the global financial crisis, given the exposure of traditional banks to the risks of the real estate sector and the potential fluctuations in the price level, which could significantly affect their financial positions, the QCB adopted the

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10000 9000

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Fig. 2 Number of traded real estate from April 2006 to August 2021. Source: Calculations conducted by the author, based on Ministry of Justice data, 2021 160 140 120 100 80 60 40 20 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 commercial building

Building and apartment Complex

Fig. 3 Real estate sold by type, over three periods, from April 2006 to August 2021. Source: Calculations conducted by the author, based on Ministry of Justice data, 2021

REPI to assess developments in the real estate sector and to promote financial stability. The QCB chose 2009–2010 as the base year, and this index is updated quarterly and is based on the composite of the house, villa, land, and residential building prices registered at the Ministry of Justice (Qatar Central Bank, 2011). The financial crisis affected investors in the Qatari real estate market. To examine this effect, this study examined two types of real estate used by investors: buildings and apartment complexes, and commercial buildings. The analysis in Fig. 3 shows that the demand for both types of real estate decreased after the 2008 financial crisis. According to Table 1, the index volatility was high during the period under consideration. Prices in the real estate sector have undergone several fluctuations since 2006. Before the 2008 financial crisis, this sector witnessed rapid growth, supported by the boom in the domestic economy. In the aftermath of the crisis, the REPI witnessed a sharp decrease. As illustrated in Fig. 4, the REPI increased by 20%

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Table 1 Descriptive statistics for the real estate price index from April 2006 until June 2021

Statistical tool Mean Standard deviation Minimum Maximum

Real estate price index 187.36 70.85 66.86 311.44

Source: Calculations conducted by the author, based on Qatar Central Bank data, 2021 350.00 300.00 250.00 200.00 150.00 100.00 50.00

Apr-06 Oct-06 Apr-07 Oct-07 Apr-08 Oct-08 Apr-09 Oct-09 Apr-10 Oct-10 Apr-11 Oct-11 Apr-12 Oct-12 Apr-13 Oct-13 Apr-14 Oct-14 Apr-15 Oct-15 Apr-16 Oct-16 Apr-17 Oct-17 Apr-18 Oct-18 Apr-19 Oct-19 Apr-20 Oct-20 April

0.00

Fig. 4 Real estate price index from April 2006 until June 2021. Source: Qatar Central Bank, 2021

by the end of 2011 (Qatar Central Bank, 2011) and by 5.7% by the end of 2012 (Qatar Central Bank, 2012) compared to 2010. This increase after December 2010 stems from the positive sentiment of winning the bid to host FIFA 2022. According to the QCB, the real estate sector has witnessed steady growth since 2009; during the period from December 2010 to December 2013, it experienced a growth of 52.6%.

4.2

Oil Prices Drop and Gulf Crisis 2014–2017

This period includes several shocks that affected the Qatari real estate market and investors, including the drop in oil prices in 2014 and 2016, as well as the Gulf Crisis that occurred in March 2014. In 2014, the economy grew, and the population also increased because of new projects and infrastructure projects, which made Qatar the fastest-growing economy in the region, leading to an increase in house and land prices. Therefore, the REPI increased by 34.7% by the end of 2014 (Qatar Central Bank, 2014). The REPI continued to rise and hit its highest value in November 2015. During the third quarter of 2015 and the second quarter of 2016, the index fluctuated and continued to rise.

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The third quarter of 2017 witnessed a temporary decline in the index due to the blockade of Qatar. But it rose in the fourth quarter, in line with macroeconomic improvements (Qatar Central Bank, 2017). As illustrated in Fig. 3, according to the stable levels of traded real estate, the Gulf Crisis that began in March 2014 and ended in November of the same year had no impact on investors’ decisions. No decrease was observed in the traded real estate. According to the International Monetary Fund Report (IMF, 2019) in May 2019, Qatar’s economy was successfully adjusted to the shocks of lower oil prices and diplomatic disputes in Gulf countries. The Qatari economy absorbed the crisis of the drop in oil prices during the period 2014–2016. It was also able to adapt to the Gulf Crisis due to financial policy and the large safety margins.

4.3

Gulf Crisis in Addition to the Corona Epidemic During the Period 2018–2021

This period includes several shocks that affected the Qatari real estate market and investors. The first is the continuation of the Gulf Crisis and the blockade from 2017 to 2021. The second is the COVID-19 pandemic, which had a huge impact on Qatar’s economy. While the REPI rose in the first quarter of 2018, it decreased in May and June. By the end of 2018, the index decreased by 2.6% compared with December 2017 (Qatar Central Bank, 2018). As illustrated in Fig. 4, between 2019 and 2021, the REPI fell to its lowest level since 2015 because of a severe drop in the demand for real estate and investment. According to data from the QCB, the REPI reached 229.2 points in the third quarter of 2019. The index fell to 225.8 points in the last quarter of 2019, declining by 0.7% (Qatar Central Bank, 2019). This behavior is explained by the fact that the real estate market in Qatar was witnessing a high supply compared to declining demand for purchase, in conjunction with fluctuating liquidity availability in the local markets, and a weak work and investment environment, as one of the consequences of the blockade. Finally, by comparing 2020 with 2019 and during the COVID-19 pandemic, the index decreased by 8.7% (Qatar Central Bank, 2020a), while it increased by 0.9% in June 2021 compared with the same period in 2020. Figure 3 indicates that the demand for building and apartment complexes increased slightly after 2018. In 2020, the demand for real estate continued to fall, and the major demand was for residential properties. The volume of transactions to buy residential houses decreased by 26.2% in 2020 compared to 2019, and the value of these transactions is about 2.6 billion riyals. In the first half of 2020, approximately 2250 apartments and 700 villas were added to the market, bringing the total stock to 300,550 units. Extra 7250 residential units were anticipated to be finalized in the second half of 2020, bringing the total supply to nearly 308,000 units by the end of 2020 (ValuStrat, 2020) In 2021, residential sales continued to fall and dominate the real estate market. Despite this decrease in demand, supply continues to increase.

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Transaction volumes for homes decreased by 3.3% on a quarterly basis but were up 52.8% compared to the same period in 2020 (ValuStrat, 2021a). According to ValuStrat (2021b), the number of arrivals is expected to increase in the second half of 2021 after the gradual lifting of restrictions regarding COVID-19 pandemic in Qatar. This will lead to higher demand, which will bring about more stable rents by the end of 2021. Referring to Fig. 2, the number of traded real estate in 2021 is increasing considering the existing data up to August 2021. Despite the challenges imposed by the coronavirus pandemic, the blockade on Qatar, in addition to the drop in oil prices, the Qatari economy was able to adapt to these shocks. Economic facilities, incentives, and government support contributed to improving the performance of all sectors. In regard to the COVID-19 pandemic, Qatar was the least economically affected Gulf country (Qatar Central Bank, 2020b), even though the pandemic has led to a severe contraction in economic activities in the region. The real estate sector in Qatar has been able to deal with these effects, especially as the demand continued to grow as a result of the many major events that the country will host in the coming years. The government made some amendments to economic legislation, which made it more flexible and open during this critical period. According to the World Bank’s Ease of Doing Business 2020 Report, Qatar has achieved its first position internationally in terms of ease of real estate ownership registration and was among the top 20 countries in 2019. This led to an increase in local and foreign investors’ confidence in the Qatari real estate sector. The Qatari government supports the local economy and confronts shocks. For example, the government’s financial and economic stimulus package was announced in March 2021, with 75 billion riyals to support the Qatari economy to face the coronavirus pandemic. Also, exemption from rents for logistical areas, and small and medium industries for a period of 6 months. In addition to exempting the small and medium industries sector from electricity and water fees. Moreover, establishing mechanisms by the QCB to postpone loan installments and private sector obligations with a grace period of 6 months. Qatar issued Law No. 28 of 2020 regarding foreign ownership of the real estate, which increases local and foreign investors’ confidence and strengthens the economy and real estate sector. The aim of issuing this legislation is to put Qatar on the map for real estate investment and make it an investment destination in the region. Seeking to maximize returns from the non-oil sectors within the framework of economic diversification, Cabinet Resolution No. (10) of 2020 formed a committee concerned with developing the real estate sector. This is an important step, especially in this period, where the sector faces several challenges. The current period requires working according to a specific and clear strategy that suits the local and global markets and reassures investors. Finally, the committee concerned with real estate sector development strategy undertakes several tasks, including proposing the strategy and the necessary policies and plans for its implementation, developing a governance system for the sector. Proposing means to support and develop the sector. In addition, the committee is concerned with the necessary controls for investing in the sector, proposing controls and conditions to encourage and attract investment, and proposing the legislative tools necessary to

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implement the development strategy. This committee is for 6 months, starting in April 2020, and it was extended for another 6 months.

5 Conclusion Before Qatar hosted the World cup of 2022, there were many opinions and discussions about the future of the real estate sector in Qatar. While some argued that the vision is unclear, others argued that this sector will boom. When Qatar hosted the Asian Games in 2006, this helped the real estate sector to flourish as investments increased by 100–150% compared to 2004. The real estate sector did not collapse, but it remained balanced. In addition, the government now plays an essential role in this balance, encouraging investment through initiatives and legislation to develop this sector. For example, Cabinet Resolution No. (28) of 2020, issued on 30/8/2020, allows non-Qataris to own property in 9 areas and usufruct in 16 areas. Cabinet Decision No. (10) of 2020 established the Strategy Committee for the Real Estate Development Committee. These decisions will revive the real estate sector. In addition, Qatar has a strong record of hosting international sporting events. It has ambitions to become a global leader in sports and bring the world together through sustainable sports development. For example, the request to host the AFC Asian Cup 2027, FINA World Championships 2023, Asian Games 2030, Qatar Exxon Mobile Open-Tennis since 1993, and WTA Qatar Open-Tennis since 2001. Qatar provides economic initiatives, incentives, and organizes major sporting events, which leads to local and foreign investors’ confidence and benefits investors, the economy, and the real estate sector. Hosting more than 1.5 million visitors to Qatar during the 2022 World Cup will revive the economy as a whole, as well as the real estate sector, both during and after this period. Qatar has a good infrastructure, a strong economy, and a rational policy that has helped it overcome economic and political crises. It also encourages and welcomes foreign investments. All these reasons send reassuring messages that the Qatari economy and the real estate sector are working according to well-thought-out plans and are being evaluated to remain at appropriate levels. The Qatari real estate sector faced several challenges during previous years, such as the financial crisis of 2008, the blockade in 2017, oil price changes, COVID-19 pandemic, and preparation for hosting the 2022 World Cup during these challenges. The Qatari government encourages investment in this sector through legislation and encourages local and foreign investment. Also, providing them with many benefits, such as healthcare services, education services, and investment opportunities. Since 2001, the sector has achieved qualitative leaps and benefited from government expenditures, and per capita GDP income. This leads people to purchase and invest in real estate. This study examined the impact of shocks in the Qatari real estate market by analyzing two types of data. The REPI issued by the QCB from 2006 to August 2021, and the property sales volume issued by the Ministry of Justice for the same

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period. The results reveal that the impact of the global financial crisis on the Qatari real estate sector is much stronger than that of the other shocks. In addition, government initiatives and legislation stabilize the market, especially during the blockade, COVID-19 pandemic. However, future research should consider reviewing and evaluating initiatives and legislations that encourage local and foreign investment, as well as assessing the situation regarding the 2022 World Cup to achieve a sustainable real estate market in line with the Qatar National Vision.

References Al Refai, H., Eissa, M., & Zeitun, R. (2021). The dynamics of the relationship between real estate and stock markets in an energy-based economy: The case of Qatar. The Journal of Economic Asymmetries, 23, e00200. Anghel, I., & Hristea, A. (2015). Some considerations regarding the international real estate market—present and future predictions. Procedia Economics and Finance, 32, 1442–1452. https://doi.org/10.1016/S2212-5671(15)01520-8 Aysan, A., Genc, I., & Gündüz, L. (2021). Buying citizenship: A boon to district-level house prices in Istanbul. SSRN Electronic Journal. Bagaeen, S. (2014). Saudi Arabia, Bahrain, United Arab Emirates and Qatar: Middle Eastern complexity and contradiction. In G. Squires & E. Heurkens (Eds.), International approaches to real estate development (1st ed.). Routledge. Benlagha, N. (2020). Stock market dependence in crisis periods: Evidence from oil price shocks and the Qatar blockade. Research in International Business and Finance, 54, 101285. https://doi. org/10.1016/j.ribaf.2020.101285 Geltner, D. (2015). Real estate price indices and price dynamics: An overview from an investments perspective. Annual Review of Financial Economics, 7(1), 84–85. https://doi.org/10.1146/ annurev-financial-111914-041850 Hu, M., Lee, A., & Zou, D. (2021). COVID-19 and housing prices: Australian evidence with daily hedonic returns. Finance Research Letters, 43, 101960. https://doi.org/10.1016/j.frl.2021. 101960 International Monetary Fund. (2019). IMF executive board concludes 2019 article IV consultation with Qatar. [online] Available at: https://www.imf.org/en/News/Articles/2019/06/03/pr191 92-qatar-imf-executive-board-concludes-2019-article-iv-consultation-with-qatar [Accessed 24 November 2021]. Kauko, T. (2020). Real estate appraisal in the aftermath of the coronavirus pandemic. International Journal of Real Estate Studies, [online] Available at: http://www.utm.my/intrest [Accessed 23 November 2021]. Lusail (2021). Credit facilities for the real estate and contracting sector. [online] p.24. Available at: https://www.alasmakhrealestate.com/Content/Articles/Jan_2021/24-01-2021/lusail%20newspa per.pdf [Accessed 24 November 2021]. Ministry of Justice. (2021a). MOJ services. [online] Available at: https://www.moj.gov.qa/en/ Pages/Services.aspx [Accessed 24 November 2021]. Ministry of Justice. (2021b). Non-Qatari ownership and use of real estate. [online] Available at: https://www.moj.gov.qa/en/Departments/Pages/nqo.aspx [Accessed 24 November 2021]. Ministry of Justice. (2021c). Real Estate Bulletin. [online] Available at: https://www.moj.gov.qa/ en/MediaCenter/Pages/RealEstateNewsletter.aspx [Accessed 4 December 2021]. Ministry of Municipality and Environment. (2016). Qatar National Development Framework 2032 (pp. 7–19). Urban Planning Department.

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Oxford Business Group. (2019). The report: Qatar 2019 accessed from https://oxfordbusiness. group.com/qatar-2019. Oxford Business Group. (2020). Qatar’s real estate market shows signs of recovery. [online] Available at: https://oxfordbusinessgroup.com/overview/foundation-growth-market-showssigns-recovery-after-several-years-instability [Accessed 24 November 2021]. Planning and Statistics Authority. (2016). Analysis of the results of population, housing and establishments census 2015. Doha. Planning and Statistics Authority. (2020). Qatar Economic Outlook 2020–2022. vol 12. (p. 46). Pollalis, S., & Ardalan, N. (2018). Gulf sustainable urbanism—past (Vol. I). Hamad Bin Khalifa University Press. Property Finder. (2020). Trends. Qatar Real Estate Market Report. Qatar Central Bank. (2011). Financial stability review (pp. 84–85). Financial Stability and Statistics Department. Qatar Central Bank. (2012). Financial stability review (pp. 82–83). Financial Stability and Statistics Department. Qatar Central Bank. (2014). Financial stability review (pp. 75–76). Financial Stability and Statistics Department. Qatar Central Bank. (2017). Financial stability review (p. 33). Financial Stability & Statistics Department. Qatar Central Bank. (2018). Financial stability review (p. 35). Financial Stability & Statistics Department. Qatar Central Bank. (2019). Financial stability review (pp. 34–35). Financial Stability & Statistics Department. Qatar Central Bank. (2020a). Financial stability review (pp. 82–83). Financial Stability and Statistics Department. Qatar Central Bank. (2020b). The forty fourth annual report (Vol. 44, p. 141). Financial System Stability & Statistics Sector. Qatar Central Bank. (2021). Overview. [online] Available at: http://www.qcb.gov.qa/english/ financialstability/pages/overview.aspx [Accessed 24 November 2021]. Rikshpun, A., Bardales, A., Goroshnik, B., & Morano, T. (2020). 2020 & real estate: The effects of COVID-19 and social unrest on real estate markets. International Socioeconomics Laboratory, 2(2), 1–20. https://doi.org/10.5281/zenodo.4285636 Salem, M., & Baum, A. (2016). Determinants of foreign direct real estate investment in selected MENA countries. Journal of Property Investment & Finance, 34(2), 116–142. https://doi.org/ 10.1108/JPIF-06-2015-0042 ValuStrat. (2020). Qatar Real Estate Market 2020 Review 1st Quarter, (pp. 1–28). ValuStrat. (2021a). Qatar Real Estate Market 2021 Review, (pp. 1–12). ValuStrat. (2021b). Qatar Review 2020–2021 Outlook, (pp. 1–13). Wiedmann, F., Salama, A., & Thierstein, A. (2012). Urban evolution of the City of Doha: An investigation into the impact of economic transformations on urban structures. METU Journal of the Faculty of Architecture, 29(2), 35–61. https://doi.org/10.4305/METU.JFA.2012.2.2

Part VIII

Eurasian Economic Perspectives: Investment

Investor Sentiment and Efficiency of the Cryptocurrency Market: The Case of the Crypto Fear & Greed Index Blanka Łęt , Konrad Sobański and Katarzyna Włosik

, Wojciech Świder

,

Abstract This paper aims to simulate cryptocurrency investment strategies using the Crypto Fear & Greed Index and verify whether they outperform passive buy-andhold investment. Consequently, the study assesses the efficiency of the cryptocurrency market, since no investment strategy generates higher returns than the passive one in an efficient market. The Crypto Fear & Greed Index measures market sentiment using several factors, including price volatility, market volume and momentum, posts on social media, Google Trends data, and Bitcoin’s dominance of the market. The performance assessment is based on two measures: the Sharpe ratio and the mean return. The research concentrates on the 20 largest non-stable cryptocurrencies and uses daily time series for the period starting on 1 February 2018 and ending on 31 December 2021. The findings of the heteroscedasticity and autocorrelation robust testing procedure have both practical and theoretical implications. They indicate that contrarian strategies based on the Crypto Fear & Greed Index may generate significantly better results than the passive investment in the cryptocurrency market. This, in turn, indicates that generally, the cryptocurrency market is still inefficient. Nevertheless, the results show that cryptocurrencies should

B. Łęt Department of Applied Mathematics, Poznań University of Economics and Business, Poznań, Poland e-mail: [email protected] K. Sobański (✉) Department of International Finance, Poznań University of Economics and Business, Poznań, Poland e-mail: [email protected] W. Świder Department of Public Finance, Poznań University of Economics and Business, Poznań, Poland e-mail: [email protected] K. Włosik Department of Investment and Financial Markets, Poznań University of Economics and Business, Poznań, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_16

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not be treated as a set of homogeneous coins, as the performance of strategies can vary from one to another. Keywords Cryptocurrency · Market efficiency · Market sentiment · Investment performance · Sharpe ratio · Ledoit and Wolf test

1 Introduction In recent years, cryptocurrencies have begun to be perceived as new investment vehicles, attracting the interest of investors and researchers. One of the challenges of investing in the cryptocurrency market is the determination of their fundamental value. Detzel et al. (2021) find that the determinants of intrinsic value are not clear. Chen and Hafner (2019) note that due to limited knowledge in this area, it is not possible to promptly correct sentiment-driven mispricing and revert the cryptocurrency price to its fundamental value. Therefore, the importance of investor sentiment in this market seems to be high. One of its measures is the Crypto Fear & Greed Index, which is provided by Alternative.me. It aims to reflect market sentiment using several different components—price volatility, current volume and market momentum, posts on various hashtags for each coin in social media, Google Trends data, and also Bitcoin’s dominance of the market. Values range from 0 (indicating extreme fear) to 100 (indicating extreme greed) (Alternative.me, 2022). This measure has been used by researchers to determine its relationship with returns on Bitcoin and its volatility (Güler, 2021; Mokni et al., 2022). In this paper, the analysis goes one step further and the performance of strategies based on investor sentiment is tested. The main goal of the analysis is therefore to verify whether active investment strategies based on the Crypto Fear & Greed Index (FGI) outperform the buy-and-hold strategy in the cryptocurrency market. The study focuses on the 20 largest non-stable cryptocurrencies: Bitcoin, Ethereum, BNB, XRP, Cardano, Dogecoin, Litecoin, Chainlink, Tron, Bitcoin Cash, Ethereum Classic, Stellar, Decentraland, Monero, Theta Network, WAVES, EOS, Zcash, MIOTA, and Maker. This group was selected based on market capitalization and data availability. The analysis uses daily time series for the period February 2018– December 2021 from databases provided by Glassnode, Alternative.me, and CoinMarketCap. The tested strategy assumes that the contrarian investor buys the cryptocurrency when the Crypto Fear & Greed Index is characterized by low values, and sells it when the values are high, indicating that the market is due for a correction. As a robustness check, six levels of market entry and six levels of market exit are examined. The performance evaluation of the active strategy is based on the mean annualized logarithmic return and the Sharpe ratio. To compare the active strategy with the passive one, the testing procedure described by Ledoit and Wolf (2018) is applied. In most cases, the strategy based on the Crypto Fear & Greed Index outperforms the passive investment approach, indicating the lack of efficiency of the cryptocurrency market.

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The paper contributes to the literature in several ways. First, by verifying the usefulness of a synthetic and aggregated measure of investor sentiment in the cryptocurrency market—the Crypto Fear & Greed Index—in designing profitable investment strategies. To the best of the authors’ knowledge, this paper is the first to present such an analysis, since the results of Güler (2021) and Mokni et al. (2022), focused on FGI features, do not contain similar inferences. Second, by assessing the efficiency of this market based on the performance of an active strategy designed using an aggregate measure of investor sentiment in the cryptocurrency market. A similar approach is used by Gerritsen et al. (2020), Resta et al. (2020), and Hudson and Urquhart (2021). However, they apply technical analysis and assess the market efficiency based on a relatively small sample of cryptocurrencies. In the study of Zhang et al. (2018) a broader market picture is concerned, yet their analysis ends with data for January 2018. Since then, the cryptocurrency market has grown and evolved, hence conducting the current research for the 20 largest cryptocurrencies seems to be justified and necessary. The rest of the paper is structured as follows. Section 2 reviews the related literature. Section 3 presents the data and methodology, whereas Sect. 4 depicts the results. Finally, Sect. 5 concludes.

2 Literature Review Many studies indicate that the cryptocurrency market is inefficient (e.g., Al-Yahyaee et al., 2018; Zhang et al., 2018). However, some also argue that Bitcoin market inefficiency has decreased over time (Sensoy, 2019; Urquhart, 2016; Vidal-Tomás and Ibañez, 2018). Despite indications that the efficiency of the cryptocurrency market is increasing, still some investment strategies—especially those based on technical analysis—seem to outperform the general market (Detzel et al., 2021; Gerritsen et al., 2020; Resta et al., 2020). It is worth noting, however, that Noda (2020) indicates that the degree of efficiency of the cryptocurrency market changes with time, which supports the adaptive market hypothesis. Similarly, López-Martín et al. (2021) also address this hypothesis as they find that for Stellar, Monero, and Ripple, periods of efficiency and inefficiency alternate. In contrast, in the case of Bitcoin, Ethereum, and Litecoin, they find that these markets become more efficient. Moreover, Wei (2018), and Brauneis and Mestel (2018) argue that efficiency is positively related to liquidity. Researchers also find that the COVID-19 pandemic affected the efficiency of the cryptocurrency market, but come to different conclusions about the direction of change. Assaf et al. (2022) detect a change in the long-range dependence for most of the cryptocurrencies analyzed. Their results show a downward trend in persistence after the 2017 bubble and then a strong decrease after the outbreak of the COVID-19 pandemic. The results of Mnif et al. (2020) indicate a positive impact of COVID-19 on the efficiency of the cryptocurrency market, while Naeem et al. (2021a) argue that the pandemic outbreak affected it negatively. Kakinaka and Umeno (2022) suggest

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that after the outbreak of the COVID-19 pandemic, the efficiency of major cryptocurrency markets increased in the long run but decreased in the short run. The presence of market inefficiency causes researchers and investors to look for tools that give an advantage over the market. One of these tools might be the Crypto Fear & Greed Index, reflecting investor sentiment in the cryptocurrency market. It is estimated based on several factors: volatility, market momentum/volume, social media dominance and internet trends (e.g., the number of search queries). Most of these categories have attracted the interest of researchers in the cryptocurrency market. Numerous studies address the topic of market sentiment in the context of cryptocurrencies. Lopez-Cebarcos et al. (2019) investigate the impact of investor sentiment, S&P 500 index returns, and VIX index returns on Bitcoin return volatility. The results indicate that these factors affect Bitcoin volatility during stable periods. Mokni et al. (2022) analyze relationships between Bitcoin and investor sentiment as measured by the Crypto Fear & Greed Index. They observe a significant positive relationship between investor sentiment and Bitcoin returns. What is interesting, according to this study there is no causality running from investor sentiment to Bitcoin prices. Güler (2021) documents a positive impact of investor sentiment on Bitcoin returns. The author shows that it can be attributed to the fear of missing out (FOMO) behavior of speculative and irrational investors. Naeem et al. (2021b) study the impact of investor sentiment on the returns of six major cryptocurrencies. Two factors are analyzed: the FEARS index and the Twitter happiness sentiment index. The happiness sentiment index seems to significantly predict returns of selected cryptocurrencies. The prediction of the FEARS index is less precise and short term. The FEARS index has been constructed by Da et al. (2015) to measure investor sentiment. It is calculated by aggregating the number of Internet search queries related to household concerns (e.g., “recession,” “unemployment,” and “bankruptcy”). Considering volatility, Fang et al. (2020) report that news is crucial for the longterm volatility of cryptocurrencies. Moreover, volatility is raised by abnormal events such as a pandemic, war, etc. Aysan et al. (2019) detect significant predictive power of the Geopolitical Risk (GPR) index for both Bitcoin returns and its volatility. Another component of the Crypto Fear & Greed Index is market momentum and volume. The momentum strategy has been intensively studied in equity markets. According to this strategy, investors buy companies that have been gaining recently (Jegadeesh & Titman, 1993). Significant momentum benefits have been documented in equity markets (Rouwenhorst, 1998), commodities (Miffre & Rallis, 2007) and other markets like bonds, currencies, etc. (Zaremba et al., 2019). This anomaly has also been studied in the cryptocurrency market. Grobys and Sapkota (2019), based on a set of 143 cryptocurrencies in a sample spanning 2014–2018, do not confirm significant momentum payoffs. Some authors document a momentum effect in short time horizons (Liu et al., 2022; Tzouvanas et al., 2020). Dobrynskaya (2021) identifies a positive momentum (sample of 2000 largest cryptocurrencies) in short time horizons up to 2–4 weeks and a significant reversal in longer horizons. Kozlowski et al. (2021) indicate next-day reversals for 200 cryptocurrencies. Caporale and Plastun (2020), investigating the Bitcoin, Ethereum, and Litecoin

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markets, show strong momentum on days with abnormal returns and the day after. What is interesting, in the context of portfolio management Tzouvanas et al. (2020) show that the momentum effect is present in the cryptocurrency market. They also identify cross-correlations of weekly returns between the momentum portfolio of cryptocurrencies and traditional assets are unlike the correlations of returns among traditional assets. Consequently, it could be useful in diversifying an investment portfolio. Social media also impact the cryptocurrency market. During the pandemic, many market analysts, investors, and economists exchanged views on Twitter. This medium has grown a lot within this time (Hutchinson, 2020). French (2021) claims that Twitter information has a large impact on cryptocurrency markets in the aftermath of the outbreak of the COVID-19 pandemic. Shen et al. (2019) note that the number of tweets is a significant driver of trading volume and realized volatility in the Bitcoin market. A relatively large amount of research has been done to analyze Google search trends. One of the earliest studies in this field is conducted by Kristoufek (2013). He shows that Google Trends search query (Bitcoin) was strongly correlated with the upward trend in this cryptocurrency in 2013. After a strong appreciation, the price of Bitcoin stagnated and Google queries fell significantly. This observation may suggest that Google queries for Bitcoin are elevated during periods of strong price movements. Google Trends is a useful tool to track the activity of Internet users. It can be used to forecast economic indicators (Vicente et al., 2015) or even to predict political outcomes (Mavragani & Tsagarakis, 2016). Urquhart (2018) shows that realized volatility, volume, and returns influence the future search for the term “Bitcoin.” Using a weekly dataset from 2013 to 2017 Nasir et al. (2019) indicate that a high frequency of Google searches results in positive returns and a surge in Bitcoin trading volume. The study reveals that shocks to search numbers have a positive effect that persists for at least a week. Aslanidis et al. (2022) find a bidirectional flow of information between Google Trends attention and cryptocurrency returns for up to 6 days.

3 Methodology 3.1

The Crypto Fear and Greed Index Description

The Crypto Fear & Greed Index aggregates information related to investor sentiment in the cryptocurrency market. It is estimated by Alternative.me and serves as a proxy for the emotional state of the investors in this market. The index is based on several factors, for which data come from differentiated sources.1 It has been used by Güler

1

Description of the indicator is based on Alternative.me (2022).

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(2021) and Mokni et al. (2022) as a proxy of cryptocurrency market sentiment to determine its relationship to Bitcoin returns and volatility. The Volatility component contributes to the index with a weight of 25%. It is measured by the current volatility and maximum drawdowns of Bitcoin compared to the corresponding average values over the last 30 days and 90 days. An unusual rise in volatility is interpreted as a sign of a fearful market. The Market Momentum/ Volume component (25%) is approximated by the current market volume and momentum (compared to averages over the last 30/90 days) and these two values are stacked together. A high buying volume in a positive market on a daily basis is a sign that the market participants are too greedy. Another factor is related to social media (15%). For each coin, posts containing various hashtags and published on Twitter are aggregated and counted. Then the speed and number of interactions they receive over specific time frames are calculated. A high interaction rate results in an increase in public interest in a particular coin and corresponds to the behavior of a greedy market. The next factor considered in the index is dominance (10%). It is based on Bitcoin’s share of the capitalization of the entire cryptocurrency market. A rise in Bitcoin’s dominance and a decrease in speculative investments in alt-coins is interpreted as a sign of fear since Bitcoin is treated as the safe haven for alt-coins. The index also includes trends (10%), as measured by Google Trends data for various Bitcoin-related search queries. The change of search volume as well as other currently popular searches connected with the “Bitcoin” query is taken into account. The Surveys component (15%), which was included at the beginning of the index construction, is estimated based on a large public polling platform, including several questions related to respondent’s opinion about the cryptocurrency market. Figure 1 depicts the Crypto Fear & Greed Index in the analyzed period.

3.2

Specification of the Active and Passive Strategies

The study uses daily price data sourced from the Glassnode and CoinMarketCap databases for the period starting on 1 February 2018 and ending on 31 December 2021. The group of analyzed cryptocurrencies consists of the 20 largest non-stable coins: Bitcoin (BTC), Ethereum (ETH), BNB, XRP, Cardano (ADA), Dogecoin (DOGE), Litecoin (LTC), Chainlink (LINK), Tron (TRX), Bitcoin Cash (BCH), Ethereum Classic (ETC), Stellar (XLM), Decentraland (MANA), Monero (XMR), Theta Network (THETA), WAVES, EOS, Zcash (ZEC), MIOTA and Maker (MKR). The selection is based on market capitalization and data availability. In the paper, the active investment is a contrarian-type strategy. Moments of investor anxiety, accompanied by low values of the Crypto Fear & Greed Index (FGI), are treated as investment opportunities. In contrast, a high value of FGI is interpreted as a sign that investors are getting “greedy” and the market is due for a correction. Hence, the strategy using this index is based on the following assumptions:

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IN

OUT

FGI

Fig. 1 The Crypto Fear & Greed Index in the period February 2018–December 2021. Source: Own compilation based on the Alternative.me data. Note: IN—points where entry levels in the active strategy are triggered, OUT—points where exit levels in the active strategy are triggered. FGI—the Crypto Fear & Greed Index

• Open a long position whenever the FGI index is sufficiently low. In the study, six levels of entry are considered (FGI ≤ 20, FGI ≤ 21, FGI ≤ 22, FGI ≤ 23, FGI ≤ 24, FGI ≤ 25), • Close the long position whenever the FGI index is sufficiently high. In the study six levels of exit are considered (FGI ≥ 80, FGI ≥ 79, FGI ≥ 78, FGI ≥ 77, FGI ≥ 76, FGI ≥ 75). As a passive investment, the popular buy-and-hold strategy is applied. In this case, there are no strict rules that would indicate the date on which the investment should start. Hence, for the passive strategy, the study assumes that a passive investor buys a cryptocurrency on a random day t and sells it on a day t + 365. The active strategies used in this analysis are based on selected trading rules. Such a methodological approach has been used in previous studies. For instance, Gerritsen et al. (2020) and Resta et al. (2020) employ trading rules that rely upon technical analysis and apply them to the Bitcoin market, while Hudson and Urquhart (2021)— to the Bitcoin, Litecoin, Ripple, and Ethereum markets. Moreover, in these papers, the buy-and-hold strategy serves as a benchmark to compare the performance of active strategies. However, to the best of the author’s knowledge, no previous study has used trading rules with respect to the Crypto Fear & Greed Index.

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3.3

Calculating the Performance Measures

The study uses the annualized log returns calculated using the following formula: Ri =

P 1 ln Sell , PBuy T

ð1Þ

where T is the time of investment in years and PSell, PBuy relate to the prices of a cryptocurrency when an investor should close or open a position, according to the rules for the strategies described in Sect. 3.2. Mean return acts as a natural performance measure of an investment and is calculated as: μ=

1 N

N

Ri ,

ð2Þ

i=1

where N equals the number of returns calculated for the active strategy. The second performance measure—the Sharpe ratio for a strategy is calculated as: SR =

μ , σ

ð3Þ

where σ is the standard deviation of returns calculated using the formula:

σ=

1 N

N

ðR i - μ Þ2 :

ð4Þ

i=1

The study randomly chooses N passive returns using uniform sampling without replacement and proceeds with a one-sided test for differences in the performance measures. It repeats this step 500 times and calculates the mean p-value. The research applies the asymptotic HAC inference described in the next subsection.

3.4

Hypothesis Testing for the Performance of Two Strategies

Ledoit and Wolf (2018) describe the testing procedure for the performance of two strategies that is robust to the time series effects in the returns. Two investment strategies A and B, whose raw returns at the time t are rt, A and rt, B, respectively can be compared and the significance of the difference between selected performance measures can be tested. The study considers the following null and alternative hypotheses:

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H 0 : Δ = 0, vs:H 1 : Δ > 0 or H 1 : Δ < 0

ð5Þ

Δ = θ A - θB

ð6Þ

where

and θ is a performance measure, for example, the mean return or Sharpe ratio. Ledoit and Wolf (2018) consider performance measures, that can be expressed as the smooth function of a population moments υðmÞ = E r m t , i.e. as a function h(υ(1), . . ., υ(M )), where M ≥ 1 denotes the maximum order of population moments needed to calculate the value of θ. Specifically, if a performance measure is the mean return, then M = 1 and θ = h(υ(1)),where h(a) = a. However, in the case of the Sharpe ratio, M = 2 and θ = h(υ(1), υ(2)), where hða, bÞ = pb a- a2 . Then Δ = θA ð1Þ

ðM Þ

θB = h(υA) - h(υB) = f(υ), where υ0X = υX , . . . , υX

, υ0 = υ0A , υ0B

and

f : ℝ → ℝ is also a smooth function. The estimator of Δ is given by: 2M

ð1Þ

ðM Þ

Δ = θA - θB = h υA , . . . , υA

ð1Þ

ðM Þ

- h υB , . . . , υB

= f ðυÞ,

ð7Þ

ðmÞ

where υX the mth sample moment of the observed returns from the strategy X: ðmÞ

υX =

1 T

T t=1

rm t,X :

ð8Þ

If the following relation holds: p

T ðυ - υÞ d → N ð0, ΨÞ,

ð9Þ

where Ψ is a symmetric positive definite matrix of dimension 2M × 2M and d → denotes the convergence in distribution, the delta method implies: p

T Δ-Δ

d

→ N 0, ∇0 f ðυÞΨ∇f ðυÞ ,

ð10Þ

where ∇f(υ) is a gradient of f(υ). An asymptotic standard error s Δ for Δ equals:

s Δ =

∇0 f ðυÞΨ∇f ðυÞ , T

where Ψ is a consistent estimator of Ψ and ∇′f(υ) = (∇h′(υA), - ∇ h′(υB)). The gradient ∇h(υ) is calculated as:

ð11Þ

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1. ∇h(a) = 1, if a performance measure θ is a mean return, 2. ∇0 hða, bÞ =

b ðb - a2 Þ1:5

,-

1 a 2 ðb - a2 Þ1:5

, if a performance measure θ is the Sharpe

ratio. Ledoit and Wolf (2008, 2018) propose in their papers two alternate solutions: HAC inference and bootstrap inference to properly test the hypothesis H0 : Δ = 0 using the methodology that is robust against fat tails or time-series effects. The first one, applied in this paper, is similar to the method of Jobson and Korkie (1981). However, Ledoit and Wolf (2008, 2018) suggest using a delta method using heteroscedasticity and autocorrelation robust (HAC) kernel estimation.2 The second method is based on the construction of a studentized time series bootstrap confidence interval for the difference in the performance measures and is recommended for small samples.

4 Empirical Results 4.1

Descriptive Statistics

Table 1 presents descriptive statistics of daily log returns on cryptocurrencies. Cryptocurrency prices are usually quite volatile. For the daily log returns on cryptocurrencies included in the study, returns on Bitcoin (BTC) have the lowest standard deviation in the analyzed period. The most volatile cryptocurrencies are Dogecoin (DOGE), Decentraland (MANA), and Theta Network (THETA). The last columns of Table 1 contain Pearson’s and Spearman’s correlation estimates between daily log returns for a given coin and BTC. The high correlation values for Ethereum (ETH), Litecoin (LTC), Bitcoin Cash (BCH), and Monero (XRM) indicate that there is a high level of connectedness between respective coin and Bitcoin. On the other hand, Dogecoin, Decentraland, and Theta Network are the least correlated coins with BTC.

4.2

Testing Results

Table 2 presents the estimates of the difference between mean annualized logarithmic returns from the active and passive investment strategies and testing results. The results indicate that strategies in the cryptocurrency market based on the Crypto Fear & Greed Index may perform significantly better than the passive investment when the assessment is based on the mean return. However, these results are not uniform across all cryptocurrencies analyzed and there are exceptions to this general 2

In the empirical part of this study, a prewhitened Parzen kernel is applied.

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Table 1 Descriptive statistics and correlation with BTC of daily log returns Coin BTC ETH BNB XRP ADA DOGE LTC LINK BCH TRX ETC XLM MANA XMR THETA WAVES EOS ZEC MIOTA MKR

Mean 0.001 0.001 0.003 0.000 0.000 0.002 0.000 0.003 -0.001 0.000 0.000 0.000 0.002 0.000 0.002 0.001 -0.001 -0.001 0.000 0.000

Std. dev. 0.040 0.052 0.057 0.061 0.064 0.080 0.054 0.070 0.063 0.060 0.061 0.059 0.077 0.054 0.076 0.065 0.064 0.060 0.064 0.062

Skewness -1.30 -1.16 -0.12 0.08 -0.60 5.68 -0.62 -0.34 -0.30 -0.38 -0.13 0.58 1.35 -1.24 -0.07 0.13 -0.27 -0.71 -0.60 -1.04

Excess kurtosis 17.58 12.25 14.55 13.52 8.17 99.00 8.10 7.74 12.20 7.32 9.38 11.37 20.96 13.66 7.20 8.25 9.52 7.68 8.17 26.63

Pearson’s corr. with BTC 1.00 0.82 0.67 0.62 0.69 0.45 0.80 0.61 0.77 0.69 0.67 0.67 0.54 0.77 0.55 0.57 0.72 0.70 0.69 0.66

Spearman’s corr. with BTC 1.00 0.80 0.66 0.69 0.63 0.63 0.78 0.56 0.78 0.67 0.68 0.66 0.54 0.73 0.50 0.55 0.71 0.69 0.63 0.62

Source: Own compilation based on the Alternative.me, Glassnode, and CoinMarketCap data

observation. The largest average advantage of active strategies over passive ones among all cryptocurrencies and different entry and exit levels is observed for investment in Cardano (ADA). In the case of 10 cryptocurrencies: Ethereum (ETH), XRP, Cardano (ADA), Litecoin (LTC), Bitcoin Cash (BCH), Tron (TRX), Stellar (XLM), Monero (XMR), EOS and ZCash (ZEC), the active strategy outperformed the passive approach regardless of the adopted entry or exit level. Therefore, for these cryptocurrencies, the market is found to be inefficient. Most of these coins, in particular ETH, LTC, and BCH, are highly correlated with BTC (see Table 1). On the other hand, the passive investment in Dogecoin (DOGE) and Decentraland (MANA), on average, provides relatively better results, although the advantage is not statistically significant. These cryptocurrencies have the highest volatility and the lowest correlation with BTC (see Table 1). Consequently, a low level of connectedness with the BTC market is related to the poor performance of the strategy designed based on the Crypto Fear & Greed Index that measures overall sentiment in the cryptocurrency market. On average, the greatest outperformance occurs when a trader enters the market at a Crypto Fear & Greed Index level of 21 and closes the long position at an exit level of 79. Table 3 presents the estimates of the difference between Sharpe ratios of annualized logarithmic returns on the active and passive strategies. The table also depicts testing results. Considering the risk-adjusted measure of investment performance,

20/80 Diff. of means 0.497 0.662 0.376 0.554 0.841 -0.694 0.895 0.900 0.591 0.603 0.109 0.548 -0.287 0.431 0.117 0.321 0.720 0.886 0.581 0.325

pvalue 0.079 0.035 0.305 0.071 0.013 0.199 0.066 0.001 0.062 0.054 0.326 0.017 0.192 0.044 0.387 0.267 0.002 0.000 0.007 0.126

21/79 Diff. of means 0.586 0.775 0.646 0.917 1.349 -0.458 1.020 1.094 0.774 0.710 0.375 0.521 -0.045 0.725 0.322 0.584 0.947 1.102 0.678 0.671 p-value 0.179 0.007 0.157 0.007 0.000 0.244 0.057 0.001 0.050 0.022 0.097 0.005 0.393 0.021 0.179 0.096 0.008 0.90 RFI > 0.90 IFI > 0.90

0.98 0.97 0.89 0.96 0.98

Good fit Good fit Marginal fit Good fit Good fit

Source: Researcher’s Data Analysis using LISREL 8.80 (2021) Table 2 R-squared and adjusted R-squared Variables Propensity for regret Propensity for overconfidence Income level

R-squared 0.025 0.461 0.032

Source: Researcher’s Data Analysis using LISREL 8.80 (2021)

Adjusted R-squared 0.013 0.434 0.021

The Key Determinants of Financial Risk Tolerance Among Gen-Z Investors:. . .

0.26 0.29 0.27 0.23

0.53 0.49 0.58

RE1 RE2 RE3

RE4

OC1 OC2 OC3

0.60 0.69 0.63 0.58

0.87 0.82 0.88

PRE

0.553 POC 0.628

0.31 0.34 0.43 0.41 0.42

IL1 IL2 IL3 IL4 IL5

0.70 0.73 0.81 0.78 0.79

295

FRT

0.82 0.86 0.77 0.92

RT1 RT2 RT3

RT4

0.51 0.54 0.48 0.57

0.607 IL

Fig. 2 Structural path diagram (standardized). Source: Researcher’s Data Analysis using LISREL 8.80 (2021)

5.85 5.96 5.87 5.78

6.87 6.58 6.99

RE1 RE2 RE3

RE4

OC1 OC2 OC3

6.31 6.89 5.99 6.08

8.37 8.08 8.82

PRE

9.991 POC 12.731

5.88 5.98 6.48 6.00 6.09

IL1 IL2 IL3 IL4 IL5

7.46 7.57 7.81 7.63 7.78

FRT

8.64 8.76 8.55 8.98

RT1 RT2 RT3 RT4

8.64 8.78 7.90 8.92

10.890 IL

Fig. 3 Structural path diagram (t-value). Source: Researcher’s Data Analysis using LISREL 8.80 (2021)

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Table 3 Result of hypotheses testing Hypothesis H1 H2 H3

Variable PRE → FRT POC → FRT IL → FRT

Coefficient standard 0.553 0.628 0.607

t-value 9.991 12.731 10.890

Statistical conclusion Data supported H1 Data supported H2 Data supported H3

Source: Researcher’s Data Analysis using LISREL 8.80 (2021)

might have made a better preference if they went for the riskier option (Rahman, 2020). People with higher propensity for regret will likely be in a constant state of doubt, which keeps them wondering if they would have gained more return had they chosen a riskier option, as it is popularly believed in our society that higher risk will yield higher return. The result of this research showed that the propensity for regret was affected by the degree of financial information. Adequate information and data can be used for the investor in making the financial investment wisely. Therefore, it would reduce the risk of failure in making financial decisions. Investor propensity for overconfidence has a positive effect on developing financial risk tolerance, with the t-value of 12.731 and 62.8% effect value. This finding supports the previous research of Rahman (2020), which found that financial risk tolerance was influenced positively by the propensity for overconfidence. This result of hypothesis testing showed Gen-Z stock investors would be able to take some financial risk tolerance if they felt that they acquired more financial knowledge. This finding also supported the previous study of Kurniasari et al. (2021) who also mentioned that financial institutions in collaboration with government should increase the financial literacy among the Gen-Z since more knowledgeable stock investors means they are confident in making financial decisions and they feel that they make better decisions than others. Overconfidence leads to overestimation of one's knowledge or abilities in making financial decisions. The success in financial decision also depends on other external factors, such as luck and good timing. Income level has a positive effect toward financial risk tolerance, with the t-value of 10.890 and 60.7% effect value. This result is consistent with the research findings of Hanna et al. (2018) and Laurinaityte (2018), which found that investor level of income has a good relationship with the level of financial risk tolerance. The finding also supports the research conducted by who explained that, using their income, the Gen-Z generation were interested in investing in the stock market to get higher return. Previous research proposed that individuals with higher income have more resources to invest in high return (which are usually higher risk) investments (Hanna et al., 2018). In addition, the high income investors would have more financial funds to handle the losses of risky investment since they have adequate funding to recover from losses. The research finding showed that the majority of Gen-Z investors had a range income between 3 million rupiah up to 4.8 million rupiah. This range of age was referred to as the working segments age who had just graduated from the college or university.

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Since all the Gen-Z investors are unmarried and still living with their parents, they have more flexibility in managing the income and allocating the funding in some financial investments.

6 Conclusion and Recommendation Propensity for overconfidence had the highest effect in influencing financial risk tolerance among Gen-Z stock investors in Indonesia. Meanwhile, all the variables, such as propensity for regret and income level, also had positive impact in developing financial risk tolerance among Gen-Z stock investors. The Gen-Z stock investors must read all the related financial information before making any risky investment. The financial ecosystem should provide open access information. There should be more coordination among the related financial institutions, including Indonesia Financial Authority Services, Indonesia Stock Exchange, Securities Companies and Universities to educate the Gen-Z investors to achieve financial literacy among the young generations. The study has a limitation because of only collecting investors’ data with the following characteristics: stock investors in the Gen-Z range age with unmarried status; working and domiciled in surrounding Jakarta areas. Future research should collect more data from respondents who domicile outside Jakarta areas to give more generalization of the study. This study was able to show the value of R-squared = 0.862. It means that even though propensity for regret, propensity for overconfidence and income level had a positive impact in financial risk tolerance among Gen-Z stock investors with 86.2% effect, there might be other indicators that could be considered in influencing financial risk tolerance, such as government policy, social influence and customer knowledge. Acknowledgments The author would like to thank Universitas Multimedia Nusantara who has given support in this research.

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CNN Indonesia. (2021, April 10). Tingkat Literasi dan Inklusi Keuangan Meningkat. Retrieved from https://www.cnnindonesia.com/ekonomi/20201203123254-78-577515/tingkat-literasidan-inklusi-keuangan-meningkat. Cooper, D. (2014). Business research methods. McGraw Hill. Durand, R. N. (2013). Overconfidence, overreaction and personality. Review of Behavioral Finance., 5(2), 104–133. Financial Services Authority. (2020). Report on Indonesia literacy index and financial inclusion index. Jakarta. Hair, J. B., Jr. (2014). Multivariate data analysis. Pearson Education Limited. Hanna, S. D., Kim, K. T., & Zhang, L. (2018). Factors related to the risk tolerance of households in China and the United States: Implications for the future of financial markets in China. Financial Services Review, 27(3), 279–302. Harvey, M., Burke, J., Serido, C. J., & Cities, T. (2018). What drives financial overconfidence among young adults? Consumer Interests Annual, 64, 1–3. Hatak, I., & Snellman, K. (2017). The influence of anticipated regret on business start-up behaviour. International Small Business Journal, 35(3), 349–360. Indonesia Central Securities Depository. (2021). Statistik Pasar Modal Indonesia. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292. Kurniasari, F., Gunardi, A., Putri, F., & Firmansyah, A. (2021). The role of financial technology to increase financial inclusion in Indonesia. International Journal of Data and Network Science, 5 (3), 391–400. Laurinaityte, N. (2018). Household financial risk tolerance in Europe. Available at: https://papers. ssrn.com/sol3/papers.cfm?abstract_id=3175881 Lee, S. T., & Hanna, S. D. (2022). What, me worry? Financial knowledge overconfidence and the perception of emergency fund needs. Journal of Financial Counseling and Planning, 33(1), 140–155. Leon, F. M., & Aprilia, A. (2018). Study on financial risk towards individual investors as strategy to improve urban community empowerment. https://doi.org/10.1088/1755-1315/106/1/012097 Lucarelli, C., & Brighetti, G. (2011). Risk tolerance in financial decision making. Palgarave Macmillan. Malhotra, N. (2018). Marketing research: An applied orientation (7th ed.). Pearson. Pan, C. H., & Statman, M. (2012). Questionnaires of risk tolerance, regret, overconfidence, and other investor propensities. Journal of Investment Consulting, 13(1), 54–63. Rabbani, A., O’Neill, B., Lawrence, F., & Grable, J. (2018). The investment risk tolerance assessment: A resource for extension educators. The Journal of Extension, 56(7). Rahman, M. (2020). Propensity toward financial risk tolerance: an analysis using behavioural factors. Review of Behavioral Finance, 12(3), 259–281. https://doi.org/10.1108/RBF-012019-0002 Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach (7th ed.). Wiley. Sim, C. W., Heuse, S., Weigel, D., & Kendel, F. (2020). If only I could turn back time—Regret in bereaved parents. Pediatric blood & cancer, 67(6), e28265. Sivaramakrishnan, S., Srivastava, M., & Rastogi, A. (2017). Attitudinal factors, financial literacy, and stock market participation. International journal of bank marketing, 35(5), 818–841. Zeelenberg, M. (2018). Anticipated regret: A prospective emotion about the future past. In A. T. Sevincer, G. Oettingen, & P. M. Gollwitzer (Eds.), The psychology of thinking about the future (pp. 276–295). Guilford Press. Zeeshan, A., Ssttar, A., Babar, S., Iqbal, T., & Basit, A. (2021). Impact of demographic factors on investment risk tolerance. International Journal of Business and Economic Affairs, 6(2), 97– 105. https://doi.org/10.24088/IJBEA-2021-62004

Profiling the Victims of Ponzi Schemes: The Role of Financial Literacy Abdur Rafik, Dwipraptono Agus Harjito, Bagus Panuntun, and Anisa Rahmadani

Abstract In many countries, including Indonesia, the number of victims of investment fraud is still vast, even though prevention efforts from juridical and non-juridical aspects have been carried out. Generally, victims fall into this investment trap because they are attracted by the promise of return far exceeding the average market return. This study attempts to profile the victims of investment fraud by analysing whether or not the financial literacy of the victims is lower than the financial literacy of those who are non-victims. The survey approach was chosen, and questionnaires were distributed conveniently online and offline to those who had experienced and never experienced investment fraud. In total, 267 respondents were collected, of which 80 were the victims of Ponzi, and the remaining 187 were non-victims. The data was then analysed using regression to capture the difference in financial literacy between victims and non-victims. The results found that the victims of Ponzi had lower levels of financial literacy than non-victims. Alternative testing using the vulnerability level to investment fraud as an independent variable also confirmed that those with high exposure to investment scams tend to have lower financial literacy than those with low exposure. This finding confirms the importance of financial literacy as an initial instrument to prevent victims of investment fraud. Keywords Ponzi · Investment fraud · Financial literacy · Investment scams · Exposure to scams

A. Rafik (✉) · D. A. Harjito · B. Panuntun · A. Rahmadani Department of Management, Faculty of Business and Economics, Universitas Islam Indonesia, Yogyakarta, Indonesia e-mail: abdurrafi[email protected]; [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_18

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1 Introduction Investment fraud is still common in developed and developing countries (Jacobs & Schain, 2011; Nash et al. 2017; Fei et al., 2021; Kasim et al., 2020). The victims also have diverse demographic profiles, ranging from those with low education and old age to those with high education and young age (Obamuyi et al. 2018; Fei et al., 2021; Huang et al., 2021). Many countries drafted regulations and prosecutions for these crimes, but the perpetrators and victims are still there and increasing from time to time. Indonesia is one of several countries that formed a Task Force for Handling Alleged Unlawful Actions in the Field of Public Funds Collection and Investment Management on January 1, 2016. Of course, the aim is to prevent the development of fraudulent activities under the guise of investment and provide a framework for law enforcement if the fraud occurs. Unfortunately, even though it has been more than 6 years since this Task Force was formed, investment fraud is still rife in Indonesia. This fraudulent activity recently appeared in binary options in the cryptocurrency market, where the perpetrators and victims mostly came from the younger generation. The most common form of investment fraud is Ponzi schemes. Ponzi schemes can be interpreted as a fraudulent investment mode that provides investors with profits from their own money or from money paid by subsequent investors (Fei et al., 2021), which means that investors’ returns do not come from business activities or real investments. Data released by the Investment Alert Task Force of the Indonesian Financial Service Authority in 2021 reveals that in the last decade (2011–2020), the total loss caused by Ponzi schemes has reached IDR.114.9 trillion. Of the total losses, IDR.9.7 trillion occurred in 2016–2017, with 1,314,600 victims, and IDR.11.4 trillion in 2018–2020 (Kunjana, 2021). Poor investment decisions are often associated with low financial literacy in the finance literature. Some researchers have confirmed a positive relationship between financial literacy and the quality of financial decision-making (van Rooij et al., 2011; Jappelli & Padula, 2013; Klapper et al., 2013; Calcagno & Monticone, 2015; Astuti & Trinugroho, 2016; Scholz, 2016; Mohd Padil et al., 2022). Mohd Padil et al. (2022), through their initial research on students, even proved that briefing that was carried out from an early age to students would be able to reduce the level of individual vulnerability to becoming victims of investment fraud. Interestingly, some early researchers related to Ponzi schemes found that the profile of victims of investment fraud was not concentrated in the lower class with low education but also among those with a relatively good level of education (Jacobs & Schain, 2011; Wilkins et al., 2012; Ullah et al., 2020). Ullah et al. (2020) found that most Ponzi victims had no experience in investing, which means that their knowledge of financial management is also likely to be low. According to Sutherland (1949), Ponzi schemes are destructive to the social fabric because they break trust and impair social morale. Ponzi also endangers society’s integrity (Edelhertz & Rogovin, 1980) and tarnishes the banking industry’s

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reputation for safety and soundness (Gogozan, 2009). Furthermore, the credibility of regulators and political authorities can be harmed since they failed to detect and stop such fraudulent actions swiftly (Fei et al., 2021). Given the consequences of a Ponzi scheme’s aftermath and the difficulty of detecting it, many researchers believe that evidence-based prevention of Ponzi schemes is preferable to treatment (Jarvis, 2000). However, empirical studies on Ponzi are sparse, and most articles on the subject are journalistic. So far, not too many researchers have examined the relevant factors that cause someone to get involved in Ponzi, let alone involving the victims directly as the unit of analysis. It could be because the victims of this crime may be reluctant to become involved in providing further information due to regret aversion bias. This study, involving victims of Ponzi as part of the unit of analysis, attempts to profile the victims of Ponzi schemes in Indonesia by analysing how their financial literacy is compared to non-victims. The findings of this study will contribute to the personal finance topic concerning how financial literacy is related to an individual’s risk of being exposed to investment fraud. So, augmented programmes for prevention can be well designed.

2 Literature Review and Hypothesis Development Historically, the term “Ponzi scheme” was born in the United States after Charles Ponzi created a financial scheme that offered consumers an unusually high rate of return with the motto “double the money within three months” in 1920 (Cortés et al., 2016). This payment scheme can be fulfilled because this scheme operates with a pyramid structure where capital deposits from old investors are used to pay high returns to new investors. So, in Ponzi, investors’ returns are not from business profits (Lewis, 2012). Usually, this scheme will only be crowded on the surface after the liquidity demand from investors exceeds the ability of the Ponzi operator to attract new investors (Drew & Drew, 2010). Several theories can be used to explain why people tend to be the hotbeds of Ponzi scams. Durkheim’s theory of anomie proposes that crime is a normal and inherent property of civilisations undergoing fast social change. It is caused by the collapse of social norms and standards (Durkheim, 2014). Based on Durkheim’s thesis, Merton (1968) hypothesised that some stable social situations may also contribute to greater crime levels. He focuses on America’s cultural values, which overemphasise material success while advocating that everyone can realise their dream, even if some people, particularly those from lower socioeconomic backgrounds, may be ill-equipped to do so and thus find it hard to achieve their goals. It puts high pressure on the absence of legitimate resources, which drives people to commit crimes to fulfil their desires (Schoepfer & Piquero, 2006). After Merton (1968), Messner and Rosenfeld (2001) proposed institutional anomie theory (IAT), arguing that the dark side of the American Dream encourages members of society to pursue their ends by any means necessary, creating a cultural environment that is

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highly conducive to criminal behaviour. According to IAT, the high crime rate in America is due to a flaw in the country’s institutional framework (Messner & Richard, 2001). The idea specifically stated that when economic values predominate in society and noneconomic social institutions supposed to govern society members operate ineffectively, this imbalance contributes to the emergence of anomie and greater crime levels. While Durkheim (2014), Merton (1968), and Messner and Rosenfeld (2001) focus on social change and social structure to explain anomie and crime in societies, Greenspan (2009) describes why people can be scammed using the theory of gullibility. According to this theory, the vulnerability to being deceived is a sub-type of foolishness constructed from four main dimensions: situation, cognition, personality, and emotion. Greenspan (2009) defines foolish behaviour as an act in which a person continues socially or physically risky behaviour despite red flags or unresolved questions that should be the source of concern for the actor. According to him, this foolish act is triggered by high social and situational pressures, low cognitive capabilities, trusting personalities, and uncontrollable emotions for instant riches. Like Messner and Richard (2001), Greenspan (2009) suggests that situational pressure can foster an environment that encourages people to acquire wealth and reputation by whatever means necessary. Despite various institutional and regulatory reinforcement, Ponzi victims continue to grow from time to time. Therefore, Jarvis (2000) contends that prevention of Ponzi schemes is better than cure. However, prevention is similarly challenging to do. Although all Ponzi schemes eventually fail, with operators frequently acquiring notoriety, they appear to have an enduring attraction. As a result, Jarvis (2000) admits that such methods are unlikely to disappear shortly and that people will continue to be fooled. Many researchers, such as Tennant (2011) and Kasim et al. (2020), advocate for wide financial literacy programmes, among other things, to educate the public as investors. Therefore, increasing literacy on relevant financial information is believed to improve an individual’s cognitive ability, enhancing the quality of financial decision-making in the future. In the personal finance literature, financial literacy and financial management have long been strongly believed to be critical factors in determining the quality of decision-making (Bönte & Filipiak, 2012; Jappelli & Padula, 2013; Calcagno & Monticone, 2015; Kramer, 2016; Scholz, 2016; Chu et al., 2017). Financial literacy can be interpreted as a person’s multidimensional knowledge of financial concepts. According to Scholz (2016), financial literacy represents a person’s ability to understand, and analyse financial choices, plan for the future and respond appropriately to financial events. Shen et al. (2016) concluded that the financial literacy gap would create information asymmetry, providing opportunities for disputes and fraud. Mohd Padil et al. (2022), through their research on students, found that providing the proper knowledge of financial management and goals from the beginning will increase individual awareness of the potential for investment fraud. Therefore, we hypothesise that;

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H1: Financial literacy of the Ponzi victims is lower than that of the non-victims of Ponzi.

3 Methods This study was conducted from 2019 to 2020 in Yogyakarta, and designed using a survey to those who have ever and never been victims of Ponzi schemes. The sampling technique is purposive sampling with the minimum criteria for respondents currently using financial products and services from financial institutions. The questionnaire was administered both online and offline to the targeted respondents. Two-hundred sixty-seven respondents have been collected with the profile descriptions in Table 2. Of the 267, 187 are non-victims, and the remaining 80 are victims of Ponzi. In measuring financial literacy, we follow the approach adopted by van Rooij et al. (2011), Shen et al. (2016), and Hamurcu and Hamurcu (2021). They divided the measurement of financial literacy into basic financial literacy and advanced financial literacy. There is no standardised measurement to measure financial literacy in the finance literature. However, according to Huston (2010), disclosure of financial literacy must accommodate basic knowledge related to finance (such as the time value of money), knowledge of credit and loans, knowledge of savings, knowledge of investment, and knowledge of insurance. To measure financial literacy, we adopted the indicators from Shen et al. (2016) and made minor modifications to suit the Indonesian context for some indicators. Each indicator is a true/false question, where we denote 1 for the correct answers and 0 otherwise. To score basic and advanced financial literacy, we sum up the answers of all indicators in basic and advanced financial literacy (see Table 1). Meanwhile, we use a single item to measure the experience of being a Ponzi victim and individual exposure to investment scams. For the former, we asked respondents to respond to a question, “have you ever been a victim of Ponzi schemes (investment scam)? (answers: Yes/never)”. The answer “yes” is labelled 1, and the answer “no” is labelled 0. For the latter, we asked them to answer a question “If you are offered an investment product with a fixed return of 25% per month, what will be your decision? (answers: a. It is favourable, and I will invest my money in it; b. I will gather and analyse complete information on whether the offer is rational before deciding to invest; c. I will completely ignore the offer; and d. I will see what my relative or my neighbour did. If they could generate an offered return by buying the product, I would buy it too)”. “B” and “c” answers represent low exposure to investment scams, “d” represents medium exposure, and “a” denotes high-level exposure. Details of the variables and their measurement scale can be seen in Table 1. We used objective measurements for all variables. We then analysed the data using regression with a dummy variable with the shift in the intercept. The experience of being scammed and the vulnerability level to investment scams served as independent variables, and financial literacy served as

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Table 1 Variables and measurements Variables Dependent variables Basic financial literacy (a) Money management and saving (b) Credit and loan management (c) Financial and investment planning (d) Insurance and retirement planning Advanced financial literacy (a) Money management and saving (b) Credit and loan management (c) Financial and investment planning (d) Insurance and retirement planning Independent variables Experience of being scammed by a Ponzi operator Vulnerability level to investment scams

Measurement

Number of items

Objective (true/ false questions)

3 5

Scale

Nominal (categorical). 1 for the correct answer and 0 for the wrong answer

3 7

3 7

Nominal (categorical). 1 for the correct answer and 0 for the wrong answer

5 4

Objective

1

Nominal (categorical). 1 for victims and 0 for non-victims

Objective

1

Ordinal (categorical). 0 for low, 1 for medium, and 2 for high exposure

Source: Own work, based on Shen et al. (2016)

the dependent variables. Suppose it is true as many researchers found that financial literacy is an essential determinant of quality financial decision-making. In that case, the level of financial literacy of those who are victims of Ponzi and have high exposure to investment scams will be lower than those who are non-victims and have low exposure to investment fraud.

4 Results and Discussion Table 2 provides the profiles of respondents involved in this study. The table shows that most respondents are male (144), 20–30 years old, have at least a senior high school education, are single, and have an income of less than IDR.40 million per year. Most respondents are young people with relatively low educational backgrounds and probably students. The distribution of data, primarily young people,

Profiling the Victims of Ponzi Schemes: The Role of Financial Literacy Table 2 Demographic profile of respondents

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Variables Frequency % Gender (n = 267, missing = 0) Male 144 53.93 Female 123 46.07 Age (n = 267, missing = 0) 20–30 196 73.41 >30–40 36 13.48 >40–50 18 6.74 >50 17 6.37 Education (n = 267, missing = 0) Senior high school 148 55.43 Diploma 11 4.12 Undergraduate 75 28.09 Post graduate 29 10.86 Doctoral 4 1.50 Income (n = 267 missing = 0) 100 million 25 9.36 Marital status (n = 267, missing = 0) Single 202 75.66 Married 65 24.34 Experience of being victims (n = 267, missing = 0) Victims 80 29.96 Non-victims 187 70.04 Exposure to investment scams (n = 267, missing = 0) Low exposure 231 86.51 Medium exposure 14 5.24 High exposure 22 8.23 Source: Own work, based on the output of analysis data

is in line with the profile of investment fraud in Indonesia, which has recently targeted young people, including students. Of the 267 respondents, 29.96% (80) are victims of Ponzi schemes, and the remaining 70.04% (187) are non-victims. Meanwhile, most respondents (86.51%) have a low vulnerability to investment fraud. Table 3 provides descriptive statistics of variables. The basic financial literacy of respondents is higher (mean = 12.45) than the advanced financial literacy (8.06). Meanwhile, the mean score of experience of being victims is 0.30, indicating that the number of non-victims is higher than that of victims (see the frequency in Table 2). Exposure to investment scams has a mean score of 0.22, meaning that those with lower exposure tend to dominate the data compared to those with medium and high exposure (low exposure = 231, medium exposure = 14, and high exposure = 22).

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Table 3 Descriptive statistics of variables Basic financial literacy Advanced financial literacy Experience of being victims Exposure to investment scams

N 267 267 267 267

Minimum 6 2 0 0

Maximum 17 17 1 2

Mean 12.45 8.06 0.30 0.22

Std. deviation 2.303 2.780 0.459 0.580

Source: Own work, based on the output of analysis data Table 4 Hypothesis testing results

Model 1

Model 2

Variables Intercept Experience of being victims Exposure to scams Gender Age Education Income Marital status R2 N Intercept Medium exposure High exposure Gender Age Education Income Marital status R2 N

Basic financial literacy Beta Sig. 0.000 12.135a 0.008 -0.744a 0.001 -0.774a 0.166 0.519 -0.063 0.813 0.221 0.150 0.581c 0.015 0.377 0.460 0.222 267 0.000 12.024a -1.978a 0.001 0.004 -1.357a 0.096 0.713 -0.127 0.635 0.219 0.155 0.006 0.659a 0.187 0.714 0.214 267

Advanced financial literacy Beta Sig. 7.211a 0.000 -0.605b 0.063 -0.664c 0.010 0.168 0.573 0.295 0.335 0.000 0.766a 0.384 0.163 -0.076 0.897 0.289 267 7.105a 0.000 -1.370c 0.040 -1.248c 0.021 0.124 0.680 0.248 0.419 0.000 0.764a 0.439 0.112 -0.215 0.715 0.283 267

Source: Own work, based on the output of data analysis Note: a, b, and c indicate that coefficients are significant at alpha 10%, 5%, and 1%, respectively

Table 4 presents the results of hypothesis testing. The test results show that all regression coefficients of the variable “experience of being victims” and “exposure to scams” are significant for basic and advanced financial literacy. The negative sign on the coefficients of “experience of being victims” indicates that the financial literacy of victims is lower than those who are non-victims. Meanwhile, the negative sign of “exposure to scams” suggests that the higher the exposure, the lower the financial literacy. This finding is corroborated by model 2. In model 2, our independent variable is the individual’s vulnerability to investment scams. The data for this variable is ordinal, where 0, 1, and 2 indicate low exposure, medium exposure, and high exposure, respectively. The test results in Table 3 show that the regression coefficients for “medium exposure” and “high

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exposure” are both negative and significant. It means that those with high exposure to investment scams have a lower level of financial literacy than those with medium and low exposure. The magnitude of the literacy decline that is represented by the coefficient decline indicates that the higher the exposure, the lower the financial literacy. In the analysis, we also control the model with demographic variables. Among the variables, only income and education impact the variation of financial literacy. The higher the education, the higher the advanced financial literacy, while the higher the income, the higher the basic financial literacy. This empirical evidence supports our hypothesis and confirms the findings of some researchers, such as Shen et al. (2016), Mohd Padil et al. (2022). They found a negative relationship between financial literacy and potential disputes in financial management. Specifically, Mohd Padil et al. (2022) confirm that if financial literacy, particularly concerning budgeting, is appropriately given to students, their awareness of potential investment scams will increase. If they can easily identify the attributes and characteristics of investment scams, the level of vulnerability and the potential to be deceived by Ponzi operators will be low. The findings of several researchers such as Jacobs and Schain (2011) and Jacobs and Schain (2011), Ullah et al. (2020) identified that many of the victims of investment scams came from those with a good educational background. In that case, it can be assumed that a high level of education may not always be positively related to a high level of financial literacy. Further, Ullah et al. (2020) found that those with a high educational background who were later caught in investment fraud generally had less experience in investing. In addition, behavioural finance research acknowledges typically that a person’s cognitive abilities are not always positively related to the quality of decision-making (West et al., 2012). It is because, in the decision-making process, cognitive biases are often involved. Moreover, according to the theory of gullibility, a foolish act can be influenced by the combination of situation, cognition, personality, and emotion.

5 Conclusion and Implication In general, our findings prove that financial literacy has a crucial role in determining the quality of investment decision-making. The victims of Ponzi and those with high exposure to investment scams have a lower financial literacy than non-victims and those with low exposure to investment scams. This finding brings a strong message to policymakers. Financial literacy programmes in the community must continue to be intensified. The programmes should prioritise efforts to provide a comprehensive understanding of public about budgeting and how the level of return and risk should ideally be related to investment products. In the literature, many possibilities can be used to explain why someone can become a victim of Ponzi. Greenspan (2009), with his theory of gullibility, states that individual foolish actions are at least influenced by four main dimensions: situation,

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cognition, personality, and emotion. Our initial research only highlighted the relationship between cognitive aspects represented by financial literacy and one’s involvement in Ponzi schemes. The subsequent studies can simultaneously examine other aspects’ effects on individual exposure to being trapped in investment fraud. Acknowledgement The Directorate of Research and Community Service, Ministry of Education, Culture, Research, and Technology of Indonesia, supported this research.

References Astuti, P. H., & Trinugroho, I. (2016). Financial literacy and engagement in banking. Journal of Economics and Economic Education Research, 17(1), 1–6. Bönte, W., & Filipiak, U. (2012). Financial literacy, information flows, and caste affiliation: Empirical evidence from India. Journal of Banking and Finance, 36(12), 3399–3414. https:// doi.org/10.1016/j.jbankfin.2012.07.028 Calcagno, R., & Monticone, C. (2015). Financial literacy and the demand for financial advice. Journal of Banking and Finance, 50, 363–380. https://doi.org/10.1016/j.jbankfin.2014.03.013 Chu, Z., Wang, Z., Xiao, J. J., & Zhang, W. (2017). Financial literacy, portfolio choice and financial well-being. Social Indicators Research, 132(2), 799–820. https://doi.org/10.1007/s11205-0161309-2 Cortés, D., Santamaría, J., & Vargas, J. F. (2016). Economic shocks and crime: Evidence from the crash of Ponzi schemes. Journal of Economic Behavior and Organization, 131, 263–275. https://doi.org/10.1016/j.jebo.2016.07.024 Drew, J. M., & Drew, M. E. (2010). The identification of Ponzi schemes: Can a picture tell a thousand frauds? Griffith Law Review, 19(1), 51–70. https://doi.org/10.1080/10854668.2010. 10854668 Durkheim, E. (2014). In S. Lukes & W. D. Halls (Eds.), The division of labor in society (Free Press trade paperback ed.). Free Press. Edelhertz, H., & Rogovin, C. (1980). A national strategy for containing white-collar crime. Free Press. Fei, L., Shi, H., Sun, X., Liu, J., Shi, H., & Zhu, Y. (2021). The profile of Ponzi scheme victims in China and the characteristics of their decision-making process. Deviant Behavior, 42(12), 1596– 1609. https://doi.org/10.1080/01639625.2020.1768639 Gogozan, A. (2009). Marketing fraud: Pyramid schemes in Eastern Europe. Marketing From Information to Decision, 2, 211–224. Greenspan, S. (2009). Fooled by Ponzi: How Bernard Madoff made off with my money, or why even an expert on gullibility can get gulled. Skeptic (Altadena, CA), 14(4), 20. Hamurcu, C., & Hamurcu, H. D. (2021). Can financial literacy overconfidence be predicted by narcissistic tendencies? Review of Behavioral Finance, 13(4), 438–449. https://doi.org/10.1108/ RBF-05-2020-0113 Huang, L., Li, O. Z., Lin, Y., Xu, C., & Xu, H. (2021). Gender and age-based investor affinities in a Ponzi scheme. Humanities and Social Sciences. Communications, 8(1). https://doi.org/10.1057/ s41599-021-00733-w Huston, S. J. (2010). Measuring financial literacy. Journal of Consumer Affairs, 44(2), 296–316. https://doi.org/10.1111/j.1745-6606.2010.01170.x Jacobs, P., & Schain, L. (2011). The never ending attraction of the Ponzi scheme. Journal of Comprehensive Research, 9, 40–46. Jappelli, T., & Padula, M. (2013). Investment in financial literacy and saving decisions. Journal of Banking and Finance, 37(8), 2779–2792. https://doi.org/10.1016/j.jbankfin.2013.03.019

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Jarvis, C. (2000). The rise and fall of the pyramid schemes in Albania. IMF Staff Papers, 47(1), 1–29. Kasim, E. S., Md Zina, N., Mohd Padil, H., & Omar, N. (2020). Ponzi schemes and its prevention: Insights from Malaysia. Management & Accounting Review, 19(3), 89–118. https://doi.org/10. 24191/mar.v19i3.1381 Klapper, L., Lusardi, A., & Panos, G. A. (2013). Financial literacy and its consequences: Evidence from Russia during the financial crisis. Journal of Banking and Finance, 37(10), 3904–3923. https://doi.org/10.1016/j.jbankfin.2013.07.014 Kramer, M. M. (2016). Financial literacy, confidence and financial advice seeking. Journal of Economic Behavior and Organization, 131, 198–217. https://doi.org/10.1016/j.jebo.2016. 08.016 Lewis, M. K. (2012). New dogs, old tricks. Why do Ponzi schemes succeed? Accounting Forum, 36(4), 294–309. https://doi.org/10.1016/j.accfor.2011.11.002 Merton, R. K. (1968). Social theory and social structure (enl. ed.). Free Press. Messner, S. F., & Rosenfeld, R. (2001). In R. Rosenfeld (Ed.), Crime and the American dream (2nd ed.). Wadsworth. Nash, R., Bouchard, M., & Malm, A. (2017). Social networks as predictors of the harm suffered by victims of a large-scale Ponzi scheme. Canadian Journal of Criminology and Criminal Justice, 59(1), 26–62. https://doi.org/10.3138/cjccj.2014.E16 Obamuyi, T. M., et al. (2018). Factors influencing Ponzi scheme participation in Nigeria. Advances in Social Sciences Research Journal, 5(5), 429–444. https://doi.org/10.14738/assrj.55.4547 Kunjana, G. (ed.) (2021) 2011–2020, Investasi Ilegal Rugikan Masyarakat RP 114,9 Triliun, investor.id. Available at: https://investor.id/finance/244597/20112020-investasi-ilegal-rugikanmasyarakat-rp-1149-triliun (Accessed 13 November 2022). Mohd Padil, H., Kasim, E. S., Muda, S., Ismail, N., & Md Zin, N. (2022). Financial literacy and awareness of investment scams among university students. Journal of Financial Crime, 29(1), 355–367. https://doi.org/10.1108/JFC-01-2021-0012 van Rooij, M., Lusardi, A., & Alessie, R. (2011). Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449–472. https://doi.org/10.1016/j.jfineco.2011. 03.006 Schoepfer, A., & Piquero, N. L. (2006). Exploring white-collar crime and the American dream: A partial test of institutional anomie theory. Journal of Criminal Justice, 34(3), 227–235. https:// doi.org/10.1016/j.jcrimjus.2006.03.008 Scholz, R. W. (2016). In T. Harrison (Ed.), Financial literacy and the limits of financial decisionmaking, decision making under uncertainty: Cognitive decision research, social interaction, development and epistemology. Springer. https://doi.org/10.1007/978-3-319-30886-9 Shen, C. H., Lin, S. J., Tang, D. P., & Hsiao, Y. J. (2016). The relationship between financial disputes and financial literacy. Pacific Basin Finance Journal, 36, 46–65. https://doi.org/10. 1016/j.pacfin.2015.11.002 Sutherland, E. H. (1949). White collar crime. Dryden Press. Tennant, D. (2011). Why do people risk exposure to Ponzi schemes? Econometric evidence from Jamaica. Journal of International Financial Markets, Institutions and Money, 21(3), 328–346. https://doi.org/10.1016/j.intfin.2010.11.003 Ullah, I., Ahmad, W., & Ali, A. (2020). Determinants of investment decision in a Ponzi scheme: Investors' perspective on the Modaraba scam. Journal of Financial Crime. https://doi.org/10. 1108/JFC-02-2020-0027 West, R. F., Meserve, R. J., & Stanovich, K. E. (2012). Cognitive sophistication does not attenuate the bias blind spot. Journal of Personality and Social Psychology, 103(3), 506–519. https://doi. org/10.1037/a0028857 Wilkins, A. M., Acuff, W. W., & Hermanson, D. R. (2012). Understanding a Ponzi scheme: Victims’ perspectives. Journal of Forensic & Investigative Accounting, 4(1), 1–19.

Part IX

Eurasian Economic Perspectives: Public Economics

Chosen Central European Countries Compared in Measuring Efficiency of General Government Expenditures Petr Makovský and František Hřebík

Abstract The primary objective is to compare the efficiency of general government expenditures in the data sample of the chosen Central European countries. This analysis would be useful in the evaluation of the European Union Presidency expanses planned to be made in the second half of the year 2022 by the Czech government. The final decision of the Czech government will be made on the analysis of the data sample of chosen Central European countries. Motivation is due to a research gap in comparison of the Presidency that has been held so far. It is not clear which activities and costs are necessary and which are done only to increase the reputation of a particular country. Methodologically, we used a comparison based on the macroeconomic analysis approach on the data sample of the selected countries’ secondary data from the particular national accounts system. In general, government expenditure information is obtained from this system. The less known alternative information system is the general government part of the National Account System, harmonized according to the European System of Accounts (ESA 2010). Keywords Value added · Non-financial sectoral accounts · EU Presidency · Macroeconomic analysis

1 Introduction The primary aim is to compare the efficiency of general government expenditures in the data sample of chosen Central European countries and then to evaluate the expanses of the European Union Presidency planned to be made in the second half of 2022 by the Czech government. Motivation is due to a research gap compared to the Presidency that has been held so far. We have not found related research on this

P. Makovský · F. Hřebík (✉) Masaryk Institute of Advanced Studies, Czech Technical University Prague, Prague, Czechia e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_19

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topic among the EU countries. Moreover, that is why the Czech government initialized the research team to fill this gap to prepare for the Presidency. It is not clear which activities and costs are necessary and which are done only to increase the reputation of a particular country. Methodologically, we used a comparison based on the macroeconomic analysis approach on the data sample of the selected countries. Secondary data is from the particular national accounts system. In this system, based on the accrual principle, we see the formation and consumption of income. From the alternative system we can gain exhaustive information about the evolution of the main aggregates. These aggregates are the value added to the general government (gross or net value added), the disposable income of the general government, and collective and individual general government consumption. Moreover, we describe the aggregates of evolution of the general government national accounts for chosen countries in the period 2009–2020. These countries are the Czech Republic, Slovakia, Slovenia, Hungary, Austria, Estonia, and Portugal. The data sources are the particular national statistics offices. The outputs are harmonized according to the ESA 2010 system, so they are all comparable. The Eurostat database does not provide this kind of information. The main goal of the research was to examine the efficiency of expenditures in the Presidency of the EU organized in the particular country in the last 10 years. We provide not only the order (ranking) of the studied countries (Presidency expenditures/gross value added), but also the analysis of efficiency in a particular country. Motivation appears due to a research gap in comparison of the Presidency that has been held so far. It is not clear which activities and costs are necessary and which are done only to increase the reputation of a particular country. The aim of the paper is to prepare additional material for the Czech government at the time the Presidency was planned. In the second half of 2022, the presidency is running, so we will see the final comparison of planned variables and the real situation. The main finding is that the costs as planned are undervalued compared with the previous presidencies and ambitions. We have to supplement the data from the national database because the Eurostat data field is not fully filled with data. The following indicators are objective for analysis: gross and net value added of the public sector, net disposable income of the public sector, final consumption expenditure of the public sector, and expenditure on final collective consumption. Indicators are compared in terms of the evolution in the given period. The output of the research itself will assess the development of public sector spending between the Great Financial Crisis in 2009 and the pandemic crisis in 2020. Conclusions on public sector spending in selected EU countries will help highlight trends that emerged in the process of addressing the impact of the Great Financial Crisis (2009). These conclusions are used to address the impact of the current pandemic crisis in 2020–2021. There is also a lack of similar studies due to the fact that data envelope analysis (DEA) is used frequently but rarely (Lábaj et al., 2013; Zaja et al., 2019) with data from national accounts. By rarely it is meant that authors take only certain part of the national accounts, such as data on foreign direct investments or only data of

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consumption regarding the environment or data analysing data of only one sector in the economy. Generalized DEAs in which countries are considered decision-making units can be considered exceptional. This paper partly fills the gap. This paper is organized into two main parts. First, understanding of data sources with brief literature review that is intervened with data description and methodology. Second, the results of the DEA analysis are presented and discussed. The conclusion takes the form of remarks that are twofold: (1) towards the budgetary strategies and decision-making of the countries and (2) towards the methodology and data collection. The structure of the paper is the introduction (aim, contribution, main findings), research gap, brief review of the literature, data and methodology, model, discussion, and concluding remarks.

2 Brief Review of the Literature The primary account for the general government sector is the production account, showing, at the resource side, general government output. The output is distributed (used) for intermediate consumption and gross value added. If we deduce the consumption of gross fixed capital, we obtain the net value added of the general government sector. In 2016, the gross value added of the general government represented approximately 15% of the gross value added of the national economy (do not identify with the value added of GDP). The structure of national accounting system applied in this paper is generally agreed (Hronová et al. (2009) and Hronová, Hindls (2000) or Moulton and van de Ven (2018)). The net value added enters as input the income account as a source. Here, the entire volume is intended to pay the salaries of government employees. It is always necessary to note that the government sector does not operate on a profit basis. Another public sector account in national accounts is the primary income distribution account. The resources of the general government sector are mainly taxing on production and imports reduced by subsidies, property income, and especially retained earnings from the ownership of public non-financial corporations. The main uses of these resources are interest paid on public sector debt. The account is offset by the balance of primary income. The balance of primary income is the source in the secondary distribution of the income account. An important source is the net social contributions of employees and employers, and current taxes on income and wealth. These resources are used for social benefits other than kind social transfers and other current transfers. The balance of the account is net disposable income. The use of the disposable sector account shows the structure of general government consumption. These are mainly expenditures on final consumption (to illustrate, we use data of the Czech Republic in 2016, CZK 917 billion), expenditures on individual consumption of CZK 485 billion (2016), and expenditures on collective consumption of CZK 432 billion (2016). The balance of the account will create net savings for the general government sector. Other important national accounts of the general government are the redistribution of

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income in “usage” (output type in the input-output dimension) (we get net adjusted disposable income), the change in the net worth account (we get changes in net worth due to savings and capital transfers), and the Acquisition of non-financial assets (we get net loans (+)/loans (-)). The balance sheet shows the assets and resources of the general government sector in the final balance sheet (Pošta, 2018). The structure of these sub-accounts is also harmonized as described in Lequiller and Blades (2014). The second potential source of data is government financial statistics (GFS). GFS does not use the accrual principle. Differences are described in Afonso and Sousa (2012). The GFS provides information with a shorter time delay as it captures economic transactions during the period in which cash flows. The basic structure is as follows: (a) The statement of operations shows income and expenses, including net investments in non-financial assets, net acquisitions of financial assets, and changes in liabilities. The net operating balance can be understood as an indicator that measures the sustainability of financial management of the general government sector as described by Rojíček (2016). From the point of view of national accounts, the indicator is identifiable with the indicator of net savings, (a) Statement of resources and use of funds, which is used to assess general government liquidity, (b) Statement of other economic flows, which explains changes in values or changes in the physical volumes of assets or liabilities due to unexpected external influences (destruction). Following is further discussed in Rojíček (2020), Banker et al. (1984), Charnes et al. (1978), or Koľveková et al. (2019).

3 Data and Methodology In our research, we focus on the public sector. It should be noted that the required structure is also available for other sectors of the economy. Although at first glance it would seem that according to the ESA methodology, everything will be available on EUROSTAT, this is not the case. It was necessary to filter national accounts in the online statistical databases of selected countries. All the data are in robust files, which are not possible to present in this essay. The database is available upon request. The countries for analysis were chosen according to the discussion among researchers cooperating on the project, which is mentioned in acknowledgement. But the final decision was made by the contracting authority of the Czech Republic General Government Office.

3.1

Data

The basic filter (method) for obtaining data is noted as subsections of the database in order:

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0.26 0.25 Hungary

0.24

Estonia

0.23

Slovakia 0.22

Czechia (general government)

0.21

Austria

0.2

Slovenia Portugal

0.19 0.18 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Fig. 1 General government expenditures per GVA of the general government. Source: Eurostat input data

National Accounts (Macroeconomics): Sectoral Accounts—Non-financial Sectoral Accounts The original data source was attached as Appendix no. 1. This was removed due to publishing problems. If anyone needs the original data ask us for them directly via mail. The selection of data for Hungary and Portugal was obtained after missing data were found so that the database was complete and provided valid results. For the analysis, we chose a time series of annual values for the last 10 years (2009–2019). Estonia offers the best user-friendly statistical portal in terms of national accounts (clear, very intuitive, and corresponding to the ESA methodology). Slovakia and Austria provide data in a user-sophisticated way. For Hungary and the Czech Republic, we evaluated that the statistical offices offer very confusing portals for the database of national accounts; after a certain search, we have obtained all the required data. However, for the search for foreign clients, current IT knowledge is very inconvenient, lengthy, and inadequate (clarity of the environment). Portugal offers two official statistical portals; however, on each of them, we find incomplete data from national accounts; in the end, we get all the required values except for the time series of net disposable income. In Slovenia, we find practically everything except the net disposable income of the public sector (we obtained gross); unfortunately, there is no output from the production account. The ESA methodology is certainly not followed for free online access. The second sheet sets aside GDP at current prices for selected countries and the gross value added of the public sector, this time from Eurostat (including government expenditure on education and health). Finally, we emphasize that GDP for the whole economy is gross value added plus taxes on production and imports minus subsidies. All data are expressed as current prices for the given year in the national currency (or euro). It is checked according to the ESA2010 code that all items from national accounts are fully cross-sectional comparable for selected countries. The connections are displayed in Fig. 1. Tables 1 and 2 include the original data sources.

Source: Eurostat input data

GDP and main aggregates—selected international annual data [naida_10_gdp] Unit Current prices, million units of national currency NA_ITEM Gross domestic product at market prices GEO/TIME 2009 2010 2011 2012 Czechia 3,954,320 3,992,870 4,062,323 4,088,912 Estonia 14,212 14,861 16,827 18,051 Hungary 26,458,264 27,431,270 28,501,501 28,920,370 Austria 288,044 295,897 310,129 318,653 Portugal 175,416 179,611 176,096 168,296 Slovenia 36,255 36,364 37,059 36,253 Slovakia 64,096 68,189 71,305 73,576 Data extracted on 24/01/2021 12:11:33 from [ESTAT] National accounts indicator (ESA 2010) Value added, gross GEO/TIME 2009 2010 2011 2012 Czechia 3,578,059 3,613,528 3,668,903 3,677,512 15,791 Estonia 12,346 13,017 14,774 Hungary 22,485,632 23,278,960 24,248,459 24,333,154 Austria 256,671 263,634 276,404 283,548 Portugal 155,547 157,971 154,128 147,215 Slovenia 31,725 31,694 32,266 31,475 Slovakia 57,994 61,755 64,178 66,867 2014 4,345,766 20,180 32,742,178 333,146 173,054 37,634 76,270

2014 3,930,576 17,614 27,656,184 297,230 151,136 32,532 68,857

2013 4,142,811 19,033 30,290,920 323,910 170,492 36,454 74,449

2013 3,713,015 16,691 25,571,146 288,624 149,802 31,509 67,359

2015 4,165,174 18,042 29,431,865 307,038 156,517 33,592 71,786

2015 4,625,378 20,782 34,937,313 344,269 179,713 38,853 79,768

Table 1 Gross domestic product, gross value added (current prices, million units of national currency)

2016 4,314,719 18,929 30,675,148 318,953 161,993 35,030 73,022

2016 4,796,873 21,932 36,167,453 357,608 186,490 40,443 81,052

2017 4,592,620 20,700 33,285,449 329,396 169,642 37,372 75,781

2017 5,110,743 23,858 39,233,430 369,341 195,947 43,009 84,532

2018 4,875,019 22,565 36,620,653 344,339 177,466 39,946 80,217

2018 5,409,665 25,938 43,347,041 385,362 205,184 45,863 89,506

2019 5,189,666 24,461 40,270,141 355,359 184,531 42,343 83,986

2019 5,748,805 28,112 47,513,912 397,575 213,301 48,393 93,865

318 P. Makovský and F. Hřebík

Source: Eurostat input data

Classification of economic activities—NACE Rev. 2 Unit of measure National accounts indicator (ESA 2010) TIME 2009 GEO (Labels) Czechia 537,604 Estonia 2158 Hungary 4,031,165 Austria 45,818 Portugal 34,128 Slovenia 5464 Slovakia 8523

2011

532,798 2205 4,033,535 47,981 32,131 5698 8701

2010

537,697 2097 4,046,892 46,997 33,989 5638 8832

543,721 2293 4,125,592 49,382 29,250 5631 8964

2012 553,795 2485 4,349,922 50,262 30,606 5447 9345

2013

Current prices, million units of national currency Value added, gross

578,641 2656 4,662,389 51,624 30,099 5390 9055

2014 599,287 2871 4,948,643 53,715 30,574 5469 9441

2015

626,143 3039 5,374,436 55,927 31,401 5806 10,581

2016

Public administration, defense, education, human health, and social work activities

Table 2 Value added from public administration, defence, education, human health, and social work activities

670,028 3270 5,748,635 57,637 32,695 6087 11,022

2017

734,579 3561 6,195,160 59,649 33,919 6416 11,824

2018

799,881 3883 6,688,143 61,680 35,564 6915 13,081

2019

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At this point, we want to highlight a very important trend that appeared in Central Europe before the GFC 2009. We have meant the FDI direct investment favouritism of the general government using the tax vacancies and the other advantages for the international companies. Now a few years after, we observe the criticism on the financial outflows from national economies in Central Europe and many other negative effects. Fiscal problems appeared during the 2020–2021 pandemic crisis. Originally, FDI support was argued as follows. The support of foreign direct investment from the general government in the particular economy has become a very used tool in fiscal policy. General governments in Central Europe even created a special office to entice foreign companies. The competition was very hard. Even other economies promote the rule as “no taxes ever”. In terms of the national economy, it has become certainly relevant whether such government expenditures are used effectively and, moreover, objectively. To attract foreign businesses, general administrations in Central Europe even established a separate office. The contest was really challenging. Even other economies advocate for the “no taxes ever” principle. It is now important to consider how effectively and impartially such government expenditures are employed in the context of the whole economy. The solutions to these problems, which became crucial during the economic downturn from 2008 to 2013, were strengthened by these patterns. The European Union has governance shortcomings, which policymakers wanted to address. After the pandemics in 2020 and the subsequent War in Ukraine, current difficulties and widening budget deficits result in a similar problem with budget spending.

3.2

Methodology

A few years ago, Makovský (2013) had created the model of efficiency of the comparison of the Central European countries from the point of view of data envelope analysis (DEA). This model is based on mathematical linear programming. This approach solves the problem of optimization of the criteria function with the restrictions. Makovský (2013) compared the efficiency of government expenditures for investments in Central European economies (CEE) in the five chosen states. Data envelope analysis (DEA) is a quantitative method that uses the analysis of the efficiency subset among the set of production units. Each chosen unit of this set must be characterized by identical inputs and outputs. First, there is a need for recognization of the so-called efficiency border (the subset of efficient units) and, naturally, the subset of inefficient units. This is done using the Euclidean linear distance from the intersection of the axes. We also assume a preference of averages to extremes, but this is also general. The units that provide the same amount of output while using less of one of the inputs make up the effective subset. There is an option to define a dual explanation for this problem. For the model with one output with two inputs, we use a graphical solution. Here, the x-axis represents the division of the first input/output, and so the y-axis represents another input ratio. The efficient subset consists of those units at

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which they are closer according to Euclidean metrics to the x-axis or to the y-axis or combination. An analogous procedure may be used for a system with n inputs and m outputs (also using weights).

4 Model of Government Support in Central Europe The Central European data sample is analysed as if there are homogeneous units with only two features (two inputs and one output). Standard production microeconomic systems, in which we have many factories with identical products using two major single inputs (labour force and capital stock). The presented idea leads us to the comparison in Central Europe using the effectiveness of public procurement. Each economy (also government) is imagined to produce the FDI intensity index (output) with the participation of public procurement (first input) and with the amount of capital endowed labour (second output). The results of the input analysis for the comparison Makovský (2013) are that in 2006 the efficiency set was created with Poland, Hungary, and the Czech Republic, in 2007 it was Poland and Slovakia, in 2008 these were Hungary and Slovakia, in 2009 these were Poland and Hungary, in 2010 the efficiency set was created with the Czech Republic and Hungary, and finally in 2011 it was only Hungary. Due to its differences from other countries, Hungary appears to be an outlier and should be excluded from the comparison. Subsequently, Poland and the Czech Republic dominated Slovakia and Slovenia in terms of public procurement efficiency and endowment of labour capital in specific economies from 2006 to 2011. The DEA methodology leads us to the situation where some national economies are more successful in generating the FDI intensity index than others. The efficiency subset varied more widely between 2006 and 2011. The time horizon 2006, 2011 was specified by an initially high rate of economic growth, then unfortunately crisis and recession, and then “hopefully” stabilization of economic activity. The model system had assumed that the output of the FDI intensity index is produced with the involvement of only two inputs (public procurement and capital endowment of labour). Public procurement has been lagged for 1 year. Hungary achieved the best results in this analysis. This economy was able to generate an FDI intensity index with a lower value of both inputs. The Czech Republic also achieved excellent results in Central Europe. We also see the dependence due to the size of the economy and its degree of openness. We see a very interesting situation between Poland and Slovakia.

5 Discussion The basic building block of the comparison of the general government sector in selected EU countries is the government final consumption expenditures from the national accounts databases of individual countries, as organized by national

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statistical offices. Harmonization is ensured by the national common methodology of the ESA2010 accounts. In the Introduction, we described the disadvantages of the intuitive description of the development and dynamics of nominal quantities over time. It is mainly a very different inflation in individual countries but in individual years, but also a very different economic power (economic level) among different countries. The values of government consumption for final consumption are related to gross domestic product, even better to the gross value added in a given year. We get a dimensionless number, a percentage. We can already compare these percentages in any way (cross-sectionally within a sample country or through a time series of development in one country). Remember that an effective comparative analysis never relies solely on relative quantities; rather, the conclusions must take into account the values of absolute economic quantities and levels (in our country, the absolute values of government final consumption expenditure, which were mentioned in the second subheading of the brief literature review we used as an example, are absolute values). First, we want to document that when government consumption spending increases by a percentage in a more developed country, it is always a more dramatic shift than in a country that is not so developed (and which would also increase spending by only 1%). Second, the gross value added is the GDP adjusted for taxes on production and imports. In addition, we add subsidies. It is obvious that the tax systems in different EU countries are different (indirect taxes on production, import taxes, but also subsidies on production and imports are different). However, in the context of the economic cycle, the GVA is falling or accelerating more strongly than the GDP. In the words of the classic, GDP is well “automatically stabilized thanks to subsidies and taxes”. The GDP used for the metrics does not show large declines due to economic recessions as in the case of GVA. Let us go to the final comparison of the final consumption expenditure of the general government with GDP. We see that there are very similar developments in the countries in our sample. The share of final government consumption expenditure has declined in all countries surveyed in the last 10 years. This is a decrease from 21% to 19.5%. In terms of gross value added, this is an even more significant decline. In the situation after 2009, when national governments had to massively support the economy with their spending in view of the great financial crisis and subsequent developments, governments were in a very difficult position. We observe the largest decrease in Slovakia, up to a value below 17% (in terms of GDP). On the contrary, in the case of the Czech Republic, there has been a growth of 20% since 2017. In 2009 it was 21.5% and between 2015 and 2017 it was 19%.Please note that Slovakia is not the worst in terms of gross value added (GVA); in contrast, its situation is improving in terms of government final consumption expenditure relative to gross value added. Portugal has the worst situation and trend. The main clear macroeconomic trends in selected EU countries were confirmed with the data presented by us. We have also shown the indisputable development of the public sector in selected countries over the last 10 years (since 2009). In 2009, not only was the EU country hit by a major global financial crisis, in 2020 it was a health crisis (pandemic,

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coronavirus). Let us look at the basic development in our selected countries. In all countries of the analysed sample (except Slovenia), the share of final consumption expenditures in the gross value added of the given economy decreases. This is the first section of the time series (2009–2012). For Portugal, the final consumption expenditure to gross value added decreases throughout the observation. In the Czech Republic, the decline persists until 2015 (2016). The Slovak turning point occurs in 2012; then the value of the indicator increases. However, in Slovakia, the indicator was the lowest in 2009. In 2019, we do not get a higher share of the final consumption expenditure in GVA. Let us state that in 2019, none of the countries in the analysed sample reached the value of the final public sector consumption expenditure at the level of 2009. In other words, the increase in public expenditure after the 2009 crisis was not strong enough to maintain a stable value of the share of public sector final consumption expenditure in relation to GVA. GVA decreased, but public spending decreased more. If any of the readers does not see such a conclusion, it is necessary to realize that in our conclusions, we consider the effects of inflation. Nominally, both GVA and GDP grew, of course, but in real terms we did not reach the level of 2009 (consumption of general government per GVA of general government) in any country, with the exception of Slovakia, which had the lowest value of the indicator at the beginning of the time series. Now, we move on to the specific development. After 2009, the Czech Republic underwent a second recession in 2012–2013. For this reason, the recovery of economic activity and thus the recovery of the observed share of the final consumption expenditure to the GVA later (in 2016, 17). In Austria, we see a steady decline in the indicator at low rates, which documents a more robust and resilient government sector in the case of a more developed economy. In Estonia, in a small dynamic economy, we observe a large decline but even faster growth of the indicator (bottom in 2011). In the case of Slovenia, another example of a small economy, in contrast, we observe an increase in the indicator from 2009 to 2012 and then only a steady decline. Such a development testifies to a well-grasped economic policy at the beginning of the crisis; however, we see even greater stagnation and decline in the longer term, as generous fiscal policy usually ends after a maximum of 3 years. We view such concerns for developed countries in the context of the 2020 economic impact of the coronavirus crisis. In Hungary, we observe a stable decline in the indicator (however, consistently the largest value of the final government consumption expenditure on GVA in the entire time series). In Portugal, we are seeing a steady decline in the final consumption expenditure indicator of the general government throughout the period under review. It is a developed peripheral economy of the original EU. There is no turnaround in general government final consumption expenditure relative to GDP, which we dare to claim due to the existence of the euro and the impossibility of “drawing” the productive effects of moderate inflation. This is a very interesting phenomenon because Portugal opposed the policy of cuts in the EU when others did it, and even there the Socialist Party has won the elections and their prime minister leads the government in Portugal. Unfortunately, from Portugal’s point of view,

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modern crises are exacerbating the structural shortcomings of economies, regardless of the level of activation of aggregate demand by public consumption (this is only a short-term effect). In conclusion, we do not observe in any of the final countries the level of public sector consumption expenditure on GVA as it was in 2009. This is a very worrying finding given the optimistic development of GDP in most developed countries after 2014. Of course, better weak growth than nothing; however, 2009 is still an unattainable goal in real terms in terms of general government final consumption expenditure from the system of national accounts. We are building such a view until 2019, the year 2020 is already a different story (coronavirus). In the context of the findings mentioned, we do not strengthen optimism for the future. We must constantly remember that the period 2020 to today is absolutely unsuitable for analysis, as 2020 represents a disruption to global pandemics. Furthermore, the Russian attack on Ukraine (2/24/2022) complicates the conclusion of the present effects. We will surely unexpectedly observe an increase in inflation and decelerating economic growth in the short term. In the long period, we will see increasing defence expenditures of the general government within the EU countries. We focus further on the detailed specifics of individual countries. In Austria, it is a strongly service-oriented economy (not seeing services as traditionally mostly consumed by households, but rather financial services, consulting, and IT services). The economy is very badly hit by the pandemic due to the strong impact on tourism (as are, of course, all European countries). The economy experienced a very high rate of inflation (the highest in the Eurozone, but lower than in the Czech Republic). In the case of Estonia, we observe a strong link to FDI (financial account of the balance of payments). In this country, it is mainly about services (also in the Czech Republic there was the largest share in services, finance, consulting at the FI balance of payments). In this context, Estonia is highly vulnerable in these areas, as has been Ireland in the past. There is a well-known and successful e-Government in Estonia, but also special economic measures for reinvestment (prevention of repatriation of profits in the form of dividends). The absorption of the economy appears to be a significant problem, with millions of people leaving the Baltic states. The government is betting on reducing administrative barriers in business. Estonia is in the orbit of Sweden and Finland, in terms of economic influence and economic cycle influences. In the Czech Republic, the last decade has been characterized by a change of government under the dominance of President Miloš Zeman and Prime Minister Andrej Babiš. As one of the few EU countries, the Czech Republic suffered two crises after 2009. The second crisis of 2012–2013 was caused by undercutting the economy by insufficient government spending. In the last 5 years (2015–2020), the economy has achieved interesting economic growth, and the government can afford to stimulate the economy (increase in wages of workers in the state sector, growth of old age pensions). These economic crises and the subsequent changes in political representation in the Czech Republic due to corruption are fully described in Dvořáková V. (2020). The real economy itself is driven by inflation, which from

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an ex post perspective appears to be a very successful scenario. The long-term strategy of the government adviser Jaroslav Ungermann, who has been drawing attention to inflationary effects in an economy with low unemployment and an undervalued currency due to purchasing power parity, has been drawing attention for several decades that necessary reforms (not even the publicly mentioned reform of the pension system and the healthcare system) are not taking place (nor are they being prepared and discussed) as described in Čadil et al. (2011). We discuss more reforms of state administration and self-government, digitization, education, preparation of transport, technical and scientific infrastructure, and other important reforms by Civelek et al. (2019). Such important changes would significantly raise the potential output around which even in recessions an oscillating positive real GDP would be realized. We described the main idea that the Czech economy is a very well-growing economy, which, however, gives the impression, where the decline may be even worse. However, the Czech economy is driven by industries and exports with all possible problems (diesel gate, green deal, distance from coal mining, etc.). The positive feature of the low Czech economy is the long-term unemployment rate, the high participation rate in the labour market, and the stable financial sector. The negative depends on the excessive dependence on foreign trade when a large part of the added value of exports is formed outside the Czech Republic. At the Portuguese level, there is a clear long-term divergence from the core of the EU. The general trend is due to the peripheral position of this insufficiently structurally strong economy. Moreover, the industrial revolution, which has spread to several other European countries and created more advanced and richer societies, has historically arrived in Portugal with great delay. Since the early 1960s, Portugal has tried to start a period of strong economic growth and modernization of structures linked to economic liberalization and wider international integration. The state-ofthe-art level of the economy was definitely not started until full integration into the European economy in 1986. The country adopted the euro in 1999. Despite great pressure from full European integration, national GDP per capita was around 80% of the EU27 average. In the early 1970s, relatively satisfactory growth rates (sometimes double-digit) accelerated Portugal to 56% of the ES12 average by 1973. This economic expansion continued until the middle of the 1970s, when the 1973 oil crisis and the political instability that began in 1974 caused the country to enter a democratic transition in 1976. The IMF assisted Portugal with a strong integration of structural and cohesion funding implemented in the EU after decades of economic misery, during which Portugal underwent two bailouts. The described phenomenon was related to the positive growth and intensive development of many major Portuguese export companies in the new period of severe economic crisis. Portugal is salivating in the EU against the so-called cut-off policy.

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6 Conclusions The remarks as outcomes of the document are twofold: (1) towards the budgetary strategies and decision-making of the countries and (2) towards the methodology and data collection. . After examining spending between the Great Financial Crisis in 2009 and the pandemic crisis in 2020 in seven countries: – We agree that PL, SK, HU are below average efficiency, similar as (Zaja et al., 2019), – The crisis meant an increase in public debt as this will require a solution among suggested could be further regulation of expenses or strengthening of fiscal rules (Reuter, 2019). Conclusions on public sector spending in selected EU countries will help highlight trends that emerged in the process of addressing the impact of the Great Depression. These conclusions can be used to address the impact of the current pandemic crisis in 2020–2021. We also set proofs that the Czech Presidency planned economic expanses are undervalued in comparison with the other Presidencies. The study that has been provided reveals that, over the past ten years, the consumption of general government per GVA of general government dropped in each of the nations in our data sample. This means that the public sector is still less robust. Moreover, many newly appearing problems are solvable only using an efficient and robust public sector. We mean pandemics, but not only. There are new challenges in the twenty-first century. At the end of the paper, we see that there are many limitations. The Eurostat database has not provided the necessary data from national accounts. But they should have done so according to their own rules. National databases provide this type of information, but only some of the data. First, it is necessary to look at the data options in the chosen countries and then begin the research. We do not see future research options. This research task was entered into with the previous Czech government. Second, now in the Czech Republic, there is a new government. One of their first steps was to increase the budget for the Czech Republic EU Presidency, as they see the greater importance of communicating intensively to other EU countries, the Russian attack on Ukraine moves out the situations in the EU countries far from their economic, social, and political equilibrium that we can hope for. Acknowledgements The essay is organized as one of many outputs of the project devoted to ‘Evaluation and recommendations based on budgetary strategies of previous presidencies’ (with the code TIRDUVCR932MT04. The research is entitled ‘Financial aspects of CZ PRES 2022).

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References Afonso, A., & Sousa, R. M. (2012). The macroeconomic effects of fiscal policy. Applied Economics, 44(34), 4439–4454. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. Civelek, M., Ključnikov, A., Krajčík, V., & Žufan, J. (2019). The importance of discount rate and trustfulness of a local currency for the development of local tourism. Journal of Tourism and Services, 10(19), 77–92. Čadil, J., Pavelka, T., Kaňková, E., & Vorlíček, J. (2011). Odhad nákladů nezaměstnanosti z pohledu veřejných rozpočtů [Estimation of unemployment costs from the point of view of public budgets]. Politická ekonomie, 59(5), 618–637. Dvořáková, V. (2020). O prostoru korupčních příležitostí. Kdy, kde a jak se vytváří v České republice [on the space of corruption opportunities. When, where and how it is created in The Czech Republic]. SLON. Hronová, S., Fischer, J., Hindls, R., & Sixta, J. (2009). Národní účetnictví. Nástroj popisu globální ekonomiky. [National accounting. Global economy description tool]. Nakladatelství CH Beck. Hronová, S., & Hindls, R. (2000). Národní účetnictví: koncept a analýzy [National accounting: concept and analysis]. Nakladatelství CH Beck. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. Koľveková, G., Liptáková, E., & Šebová, M. (2019). Measurement of efficiency in cultural institutions. International Days of Statistics and Economics. https://doi.org/10.18267/pr.2019. los.186.74 Lábaj, M., Luptáčik, M., & Nežinský, E. (2013). Data envelopment analysis for measuring of economic growth in terms of welfare beyond GDP (p. 19). University of Economics in Bratislava, Department of Economic Policy., c Policy Working Paper Series, No. 2. Lequiller, F., & Blades, D. (2014). Understanding national accounts. OECD Publishing. Makovský, P. (2013). Government support for investments in Central Europe. In Innovation management and company sustainability. Moulton, B., & van de Ven, P. (2018). Addressing the challenges of globalization in national accounts. The challenges of globalization in the measurement of national accounts. University of Chicago Press. Pošta, V. (2018). Makroekonomická analýza na příkladu ČR [Macroeconomic analysis on the example of The Czech Republic]. Nakladatelství CH Beck. Reuter, W. H. (2019). When and why do countries break their national fiscal rules? European Journal of Political Economy, 57, 125–141. https://doi.org/10.1016/j.ejpoleco.2018.08.010 Rojíček, M. (2020). Průvodce Světem Národních Účtů [Guide to the World of National Accounts]. Politická ekonomie, 1, 113–117. Rojíček, M. (2016). Makroekonomická analýza-teorie a praxe [Macroeconomic analysis-theory and practice]. Grada Publishing. Zaja, M., Kordic, G., & Kedzo, M. (2019). The analysis of contextual variables affecting the efficiency of fiscal rules in the EU. Croatian Operational Research Review, 10(1), 153–164. https://doi.org/10.17535/crorr.2019.0014

A Contribution to the International Trade Theory Truong Hong Trinh and Tran Thi Ngoc Duy

Abstract This paper seeks a rigorous microfoudation that is the key to explaining market behaviour and general equilibrium mechanism. From this microfoundation, home margin and foreign markup are formulated upon demand and cost structures between the home country and the foreign country. The trade gain rules are then developed for the comparative advantage analysis in international trade. The zero trade gains rules occur in countries with no international trade. The non-zero trade gain rule implies that both the home and foreign countries benefit from international trade. The necessary condition for a home country to export is a relatively high home margin over other exporting countries. However, the sufficient condition for a home country to be accepted import by a foreign country is that the home country has a relatively high foreign markup over other exporting countries. These trade gain rules also provide implications for domestic trade with product differentiation and cost efficiency at the industry and firm levels. Since the demand structure is connected with the cost structure between the home and foreign markets, trade flows and gains can be analysed at the country, industry, and firm levels. The paper contributes to the international trade theory that provides the theoretical foundation for empirical research on international trade and trade policy. Keywords International trade · General equilibrium · Microfoundation · Home margin · Foreign markup

T. H. Trinh (✉) Faculty of Finance, The University of Danang – University of Economics, Danang, Vietnam e-mail: [email protected] T. T. N. Duy Faculty of International Business, The University of Danang – University of Economics, Danang, Vietnam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_20

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1 Introduction International trade and trade policy are always the central themes in economic literature. In recent years, the developments in the trade theory and their applications for trade policy have led to changing views on the relative importance of factors influencing trade and trade patterns and on the role of trade in economic development (Wangwe, 2003). International trade issues generally pose three types of questions for economists: (1) explanations of trade flows between at least two nations, (2) the nature and extent of gains and losses to an economy, and (3) the effects of trade policies on an economy (Morgan & Katsikeas, 1997). Currently, international trade theory requires a theoretical foundation for theoretical and empirical researches to explain international trade issues in the real-world economy. Classical trade theory starts with the theory of mercantilism from the sixteenth century to the eighteenth century, which encourages exports and discourages imports through tax policies and trade regulations to gain a trade surplus (Smith, 1776). According to mercantilism, countries worldwide cannot become rich simultaneously via international trade. Thus, the theory of mercantilism had a major drawback in neglecting free trade. The theory of absolute advantage (Smith, 1776) provides an absolute advantage principle to gain from international trade. A foreign country can supply a commodity cheaper than making it in the home country. The absolute advantage of a home country over a foreign country is based on inherited resources or low production costs. However, the theory of absolute advantage cannot explain how a country without absolute advantage in all products would incentivize international trade (Hunt & Arnett, 2004). The theory of comparative advantage (Ricardo, 1891) argued that a country gains from international trade by exporting a commodity with the greatest comparative advantage in productivity and importing a commodity with the least comparative advantage. However, the theory of comparative advantage does not specify how to combine production factors or the availability of technology to maximize production efficiency. The factor proportion theory (Heckscher, 1919; Ohlin, 1933) extends the concept of economic advantage by considering the endowment and cost of production factors. According to this theory, countries will intend to produce and export commodities that intensively use relatively abundant factors of production. However, the Leontief paradox (Leontief, 1953) reveals that countries can successfully export commodities that use less abundant resources. In addition, the classical trade theories could not adequately address current trade issues like internationalization patterns or Intra-industry trade in the global trade context (Sen, 2010). To better understand the global trade issues, the classical trade theories from the country perspective began to shift to the modern trade theories in explaining the trade issues from the firm perspective. Linder (1961) proposed the country similarity theory to explain the concept of intra-industry trade. Customers in countries with the same or similar stage of development would have similar preferences. Meantime, the product life cycle theory (Vernon, 1966; Wells, 1968) provided a useful framework for explaining and predicting global trade patterns in which technological innovation

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and market expansion are critical issues in international trade patterns. Balassa (1965) proposed indices of export performance and export-import ratios as trade indicators presenting the “markedness” of comparative advantage that is likely to differ from country to country. Grubel and Lloyd (1975) provided empirical evidence on significant trade among countries with similar technologies and endowments. In recent literature, new trade theory emphasizes economies of scale in creating a cost advantage with increased product output (Krugman, 1979; Krugman, 1980) and product differentiation (Helpman, 1981). According to the new trade theory, a country’s comparative advantage decreases as the similarity of production factors across countries increases (Krugman, 1980; Helpman & Krugman, 1985). The Dixit-Stiglitz model attempted to synthesize the classical and new trade theories in general equilibrium models with both market structure and foreign trade (Dixit & Stiglitz, 1977). Helpman and Krugman (1987) expanded the Dixit-Stiglitz models with inter-industry and intra-industry trade. Later, Melitz (2003) extended Krugman’s (1980) trade model that incorporates firm-level productivity differences. Melitz’s model reveals that the more productive firms enter the export market, but some less productive firms continue to produce only for the domestic market (Melitz, 2003). Since international trade theory is closely associated with foreign direct investment (FDI) theory, recent researchers have focused on studying trade and investment patterns at the firm level. The firm’s decision to invest in foreign markets relies on certain capacities not shared by competitors in foreign countries (Hymer, 1970), specific attractions of its home country compared with resource implications, and advantages of locating in other countries (Dunning, 1980). The firm’s direct operations are toward controlling activities in intermediate markets and gaining lower costs on transactions in internationalization strategies (Buckley, 1989). Foreign direct investments (FDIs) were motivated into foreign markets and international trade due to higher profitability and low-interest financing options (Faeth, 2009). However, the analysis of supply and demand in domestic and international markets is still limited in explaining the equilibrium mechanism and comparative advantages in international trade models. The contribution of international trade to economic growth and welfare has not been identified in the general equilibrium models. More importantly, integrating international trade models in the general equilibrium framework remains a major unresolved challenge. These limitations stem from underlying assumptions and a lack of rigorous microfoundations in the earlier theoretical models (Lucas, 1976; Stiglitz, 2018; Storm, 2021; Trinh, 2022a). Microfoundation is the key to understanding the relationship between market demand and market supply (Trinh, 2020, 2021), the interrelation between commodity market and resource market (Trinh, 2022a), and the integration between the home market and foreign market. The general equilibrium approach with rigorous microfoundations is the base to identify international trade contributes to economic growth and economic welfare. Then, trade indicators of home margin and foreign markup are formulated for comparative advantage analysis in international trade. In the next section, microfoundation is developed to explain the general equilibrium mechanism. Since the demand structure is connected with the cost structure between the home and foreign markets, macroeconomic issues are concerned with market

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behaviour and economic surplus in trade patterns. Section 3 defines home margin and foreign markup as the key to formulating trade gain rules and explaining international trade behaviour in the global economy. A numerical example is presented in Sect. 4 as the theoretical illustration explaining international trade behaviours in terms of comparative advantages and trade gains. The last section is summarized with the main findings and concluding remarks.

2 General Equilibrium Approach The general equilibrium approach considers the relationships and interactions among economic agents (Households, Enterprises, Government, ROW) in markets (Commodity, Resource, Finance). Figure 1 illustrates the circular flow between economic agents through markets. These inflows and outflows are the basis for determining economic agents’ economic surpluses and macroeconomic balances, including saving-investment balance, government balance, and trade balance. In the real economy, the circular flow includes fund flows from commodity production, financial investment, and capital transfer activities. Since GDP reflects a country’s production value in a year, Fig. 1 illustrates only the fund flows in the economy’s production activities. The arrows coming into each economic agent represent the fund inflows, and the arrows from each agent represent the fund outflows. Each economic agent’s economic surplus (or net saving) is the difference between inflow and outflow. If the economic saving is positive, there will be a fund

KC, LC

Resource Markets

KC, LC CS > 0

FS > 0

Enterprises FS

Households CS

I

Government GS G

C FS < 0

GS > 0

T

CS < 0

GDP

Commodity Markets E

GS < 0

M

ROW NX

NX > 0

NX < 0

Fig. 1 Circular flow of the economy. Source: Authors’ own study

Financial Markets

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flow to financial markets (representing economic surplus and deposits in financial markets). If the economic saving is negative (indicating economic deficits and loans in financial markets), there will be a fund flow from financial markets. The GDP formula under the expenditure approach includes household spending (C), government expenditure (G), capital investment (I ), and net export (NX). GDP = C þ G þ I þ NX

ð1Þ

The GDP formula under the income approach includes capital compensation (KC), labour compensation (LC), net operating surplus (FP), and net tax and subsidy (T ). GDP = KC þ LC þ FP þ T

ð2Þ

Households’ economic surplus (CS) is the difference between income from capital and labour compensations (KC + LC) and household spending (C). CS = KC þ LC - C

ð3Þ

Enterprises’ economic surplus (FS) is the difference between net operating surplus (FP) and capital investment (I ). The net operating surplus (FP) is the difference between GDP and operating costs (capital and labour compensations, and net tax and subsidy). FP = GDP - KC - LC - T

ð4Þ

FS = GDP - KC - LC - T - I

ð5Þ

The saving-investment balance is the sum of households’ economic surplus (CS) and enterprises’ economic surplus (FS). The government’s economic surplus (GS) is the difference between net tax and subsidy (T ) and government expenditure (G). Therefore, the government balance is also the government’s economic surplus (GS). GS = T - G

ð6Þ

Total economic surplus (TS) includes households’ economic surplus (CS), enterprises’ economic surplus (FS), and the government’s economic surplus (GS). TS = CS þ FS þ GS = GDP - C - G - I

ð7Þ

From Eqs. (1) and (7), the total economic surplus (TS) is rewritten as follows:

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TS = CS þ FS þ GS = NX

ð8Þ

The net export value (NX) is the difference between the export value (E) and the import value (M) of the economy. Therefore, the trade balance is also the net export value (NX). Since the total economic surplus (TS) is equal to the net export value (NX), the net export value (NX) is an important component in production activities affecting the gross domestic product (GDP) and total economic surplus (or economic welfare) in the economy. Economists explain the behaviour of the economic agents in the markets through the general equilibrium mechanism. Theories of a representative agent and rational choice are the underlying assumptions for the general equilibrium theory (Trinh, 2022a). In the general equilibrium theory, microfoundation is a theoretical basis to explain the relationship between supply and demand in the market, and the interrelationship between the commodity market and the resource market in the economy. Aggregate commodity demand (AD) is a function that represents the relationship between the average commodity price ( pAD) and quantity demanded (Q) of a country in a year (Trinh, 2022a, 2022b). The GDP function under the expenditure approach represents the components of the aggregate commodity demand of a country as follows: GDP = pAD × Q = pC × QC þ pG × QG þ pI × QI þ pNX × QNX

ð9Þ

Q = QC þ QG þ QI þ QNX

ð10Þ

pAD =

pC × QC þ pG × QG þ pI × QI þ pNX × QNX Q

ð11Þ

The aggregate resource demand (RD) is a function that represents the relationship between the average resource price (wRD) and the quantity demanded (Q) of a country in a year (Trinh, 2022a, 2022b). Since input factors determine resource demand to produce commodities in the economy, the relationship between total revenue in the resource market (TRR) and the total cost in the commodity market (TCA) is expressed under the market-clearance condition as follows: TRR = wRD × Q = KC þ LC þ T = K × wK þ L × wL þ T = TCA

wRD =

K × wK þ L × wL þ T Q

ð12Þ

ð13Þ

From Eq. (12), the marginal commodity cost (MCA) is determined upon resource demand (wRD) as follows:

A Contribution to the International Trade Theory

MCA = wRD þ w0RD ðQÞ × Q

335

ð14Þ

The aggregate commodity supply function ( pAS) is determined by the relationship between aggregate commodity demand ( pAD) and marginal commodity cost (MCA) as follows: pAS = MCA - p0AD ðQÞ × Q

ð15Þ

Substituting MCA from Eq. (14) into Eq. (15), the aggregate commodity supply function ( pAS) is rewritten as follows: pAS = wRD þ w0RD ðQÞ - p0AD ðQÞ × Q

ð16Þ

Since total resource cost (TCR) is determined from aggregate commodity demand (TRA), the marginal resource cost (MCR) is determined as follows: TCR = TRA = pAD × Q

ð17Þ

) MCR = pAD þ p0AD ðQÞ × Q

ð18Þ

The aggregate resource supply function (wRS) is determined by the relationship between the aggregate resource demand (wRD) and the marginal resource cost (MCR) as follows: wRS = MCR - w0RD ðQÞ × Q

ð19Þ

Substituting MCR from Eq. (18) into Eq. (19), the aggregate resource supply function (wRS) is rewritten as follows: wRS = pAD þ p0AD ðQÞ - w0RD ðQÞ × Q

ð20Þ

The commodity market is in equilibrium at the quantity (QA) where marginal commodity revenue (MRA) equals marginal commodity cost (MCA) (Trinh, 2021). At this equilibrium quantity, the aggregate commodity demand (AD) will intersect with the aggregate commodity supply (AS). The resource market is in equilibrium at the quantity (QR) where marginal resource revenue (MRR) equals marginal resource cost (Trinh, 2021). Figure 2 illustrates the interrelationship between the commodity market and the resource market. The change in aggregate commodity demand (AD) affects not only the aggregate commodity supply (AS) but also the aggregate resource supply (RS). Similarly, the change in the aggregate resource demand (RD) affects both the aggregate resource supply (RS) and the aggregate commodity supply (AS). The economy achieves the general equilibrium status at the equilibrium quantity (QE) where the equilibrium

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P

Commodity market

A

AS EA

MCA

pE K B

AD Q A = QE

W

Q

MRA

Resource market C

RS ER

MCR

wE L D

RD Q R = QE

MRR

Q

Fig. 2 General equilibrium of the economy. Source: Authors’ own study

quantity in the commodity market (QA) equals the equilibrium quantity in the resource market (QR) (Trinh, 2022a), as illustrated in Fig. 2. While market equilibrium is the basis for determining economic agents’ economic surplus (economic welfare) (Trinh, 2019), the general equilibrium mechanism also explains individual behaviour and cooperative mechanism of the economic agents in the economy.

3 International Trade Rules International trade theory defines the necessary and sufficient conditions for a country to gain a competitive advantage in exchanging commodities with foreign countries. The international trade models explain the trade gain rules and how to

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improve competitive advantage in international trade. As international trade is considered in the economy, the total commodity value ( pAD × Q) includes the domestic commodity value ( pDM × Q) and net export commodity value ( pNX × Q) as the following formula: pAD × Q = pC × QC þ pG × QG þ pI × QI þ pNX × QNX

ð21Þ

I × QI þpNX × QNX where Q = QC + QG + QI + QNX and pAD = pC × QC þpG × QG þp Q The domestic commodity value ( pDM × Q) includes household spending ( pC × QC), government expenditure ( pG × QG), and enterprise investment ( pI × QI) rewritten as follows:

pDM × Q = pC × QC þ pG × QG þ pI × QI

ð22Þ

where Q = QC + QG + QI + QNX and pDM = pC × QC þpGQ× QG þpI × QI The following formula (αA) estimates the ratio of the domestic commodity demand and the aggregate commodity demand. αA =

pC × Q C þ p G × Q G þ pI × Q I p = DM pC × QC þ pG × QG þ pI × QI þ pNX × QNX pAD

ð23Þ

From Eq. (23), the relation between the domestic commodity demand function ( pDM) and the aggregate commodity demand function ( pAD), as illustrated in Fig. 3, is expressed as follows: pDM = αA × pAD

ð24Þ

Since the domestic commodity demand is an input to the resource market, the total domestic commodity revenue is also total cost in the resource market (TCR) under the market-clearance condition as follows: TCR = PDM × Q = αA × pAD × Q

ð25Þ

) MCR = αA × p0AD × Q þ αA × pAD

ð26Þ

The following formula shows that the aggregate resource supply function (wRS) relies on aggregate resource demand (wRD) and the marginal resource cost (MCR). wRS = MCR - w0RD × Q

ð27Þ

From Eqs. (26) and (27), the aggregate resource supply function (wRS) is rewritten as follows:

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wRS = αA × pAD þ αA × p0AD - w0RD × Q

ð28Þ

Similarly, the total resource value (wRD × Q) includes the domestic resource value (wD × QD)and net export value (wX × QX) in the resource market as in the following formula. wRD × Q = wD × QD þ wX × QX

ð29Þ

X × QX where Q = QD + QX and wRD = wD × QD þw Q The domestic resource demand function (wDM) is computed from the domestic resource value (wD × QD) as follows.

wDM × Q = wD × QD

ð30Þ

where Q = QD + QX and wDM = wD Q× QD The following formula (αR) estimates the ratio of domestic resource demand and the aggregate resource demand. αR =

wD × QD w = DM wD × QD þ wX × QX wRD

ð31Þ

From Eq. (31), the relation between the adjusted domestic resource demand (wDM) and the aggregate resource demand (wRD), as illustrated in Fig. 3, is expressed as follows: wDM = αR × wRD

ð32Þ

Since the domestic resource demand is an input to the commodity market, the total domestic resource revenue is also total cost in the commodity market (TCA) under the market-clearance condition as follows: TCA = wDM × Q = αR × wRD × Q

ð33Þ

) MCA = αR × w0RD × Q þ αR × wRD

ð34Þ

The aggregate commodity supply function ( pAS) relies on aggregate commodity demand ( pAD) and the marginal commodity cost (MCA) as in the following formula. pAS = MCA - p0AD × Q

ð35Þ

From Eqs. (34) and (35), the aggregate commodity supply function ( pAS) is rewritten as follows:

A Contribution to the International Trade Theory P

339 W

Home commodity demand

Home resource demand

wRD0

pAD0

wDM0

pDM0 pDM=ADMH

pAD=ADH

wDM=RDMH Q

Q0

Q0

wRD=RDH

Q

Fig. 3 Structure of home demands. Source: Authors’ own study

pAS = αR × wRD þ αR × w0RD - p0AD × Q

ð36Þ

A country will export commodity only when αA < 1 and will export resource when αR < 1. By setting: θA =

pADh - pDMh p = 1 - DMh = 1 - αA pADh pADh

ð37Þ

θR =

wRDh - wDMh w = 1 - DMh = 1 - αR wRDh wRDh

ð38Þ

where θA is the home margin of the commodity demand, and θR is the home margin of the resource demand. Home margin is a ratio that presents the percentage of the contribution of the net export value of commodity (or resource) to the total value of commodity (or resource) in the country. The positive value of the home margin (θA > 0 or θR > 0) indicates a home country has a positive net export value of commodity (or resource). However, the foreign countries importing a home country’s commodity (or resource) depends on the structure of foreign demand for the commodity (or resource). Under the free trade and the same demand sizes, the foreign domestic demand has the same price level as the home aggregate demand as illustrated in Fig. 4. Figure 4 illustrates the structure of foreign demands for commodity and resource. For the structure of the foreign demands, the demand ratio of commodity (βA) and the demand ratio of resource (βR) between the home country and foreign country are estimated as follows:

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Foreign commodity demand

pADf0

wRDf0

pDMf0

wDMf0

Foreign resource demand

pADf=ADF

wDMf=RDH

PDMf=ADH Q

Q0

Q0

wRDf=RDF

Q

Fig. 4 Structure of foreign demands. Source: Authors’ own study

βA =

pADf w and βR = RDf pADh wRDh

ð39Þ

The foreign markup, an essential condition for the commodity and resource of a home country to be imported from foreign countries, is defined as follows: γA =

pADf - pADh = βA - 1 pADh

ð40Þ

γR =

wRDf - wRDh = βR - 1 wRDh

ð41Þ

The condition for the commodity of a home country is imported from foreign countries when the foreign markup of the commodity (γ A > 0) is positive. Similarly, the condition for the resource of a home country is imported from the foreign countries when the foreign markup of the resource (γ R > 0) is positive. Since home margin and foreign markup are directly measured upon economic and trade data at the country and industry levels, these indicators may be used in the empirical trade analysis. Formulating home margin and foreign markup provides a better understanding of demand and supply structure in commodity and resource markets. Then, the trade gain rules are developed for trade advantage analysis for domestic and international trade. If countries have the same foreign markup of the commodity (or resource), then the country with a higher home margin of the commodity (or resource) will have the cost advantage over other exporting countries. Similarly, if countries have the same home margin of the commodity (or resource), then the country with a higher foreign markup of the commodity (or resource) will have the price advantage over other exporting countries. While the home margin of

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the commodity affects the consumption costs of the resource, the home margin of the resource affects the production costs of the commodity. The linkage between the commodity and resource markets through the general equilibrium mechanism allows a comprehensive analysis of the demand and cost structures. The analysis of demand structure implies product differentiation and innovation to promote domestic and international trade. The cost structure analysis suggests that technology innovation and economies of scale improve the home margin and economic surplus. By linking the commodity and resource markets, capital and labour flows between countries are expanded in analysing trade patterns and internationalization strategies.

4 Numerical Examples This section assumes a hypothetical economy with given demand and supply functions. The numerical examples are to illustrate rules and conditions to gain a comparative advantage in international trade. The first example is to conduct the zero trade gain rule in the closed economy. The hypothetical economy with no international trade assumes the aggregate commodity demand (pAD) and the aggregate resource demand (wRD) as follows: pAD = 40 - 0:2Q

ð42Þ

wRD = 30 - 0:1Q

ð43Þ

The aggregate commodity supply function ( pAS) and the aggregate resource supply function (wRS) are determined as follows: pAS = wRD þ w0RD - p0AD × Q = 30 - 0:1Q þ ð- 0:1 þ 0:2ÞQ = 30

ð44Þ

wRS = pAD þ p0AD - w0RD × Q = 40 - 0:2Q þ ð- 0:2 þ 0:1ÞQ = 40 - 0:3Q ð45Þ The commodity market equilibrium at the point EA0( p0, QA0) and the resource market equilibrium at the point ER0(w0, QR0) are computed as follows: QA0 : pAD = pAS ) 40 - 0:2QA0 = 30 ) QA0 = 50; p0 = 30 QR0 : wRD = wRS ) 30 - 0:1QR0 = 40 - 0:3QR0 ) QR0 = 50; w0 = 25

ð46Þ ð47Þ

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Fig. 5 General equilibrium of the hypothetical economy. Source: Authors’ own study

P

Commodity market

40 EA0 30

AS0

AD0

W

Q

200

50 Resource market

40

30

ER0

25

RS0 50

133.3

RD0 300 Q

Since QA0 = QR0 = Q0, the economy is in the general equilibrium status at the quantity (Q0 = 50), and total economic surplus is maximized, as illustrated in Fig. 5. As a result, the economic surplus in the commodity market (TSA) and the economic surplus in the resource market (TSR) are determined as follows: TSA =

ð40 - 30Þ × 50 ð30 - 40Þ × 50 = 250 and TSR = = - 250 2 2

ð48Þ

The total economic surplus (TS) is the sum of the economic surplus in the commodity market (TSA) and the economic surplus in the resource market (TSR) as follows: TS = TSA þ TSR = 250 - 250 = 0

ð49Þ

If a home country without international trade reaches general equilibrium status, the home economy’s total economic surplus (economic welfare) will be zero. According to the zero trade gain rule, the commodity market’s economic surplus will offset the resource market’s economic deficit and vice versa. The question is whether international trade contributes to economic growth and economic welfare. The second example is to conduct the non-zero trade gain rule. Assuming that the home country has αA = 0.8 and αR = 1, thus the home margin of commodity and

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343

W

Home commodity market

Home resource market

40 EAh

ASH

30

32 30 28.33

ERh

ADH 50

200

RSH Q

16.67

145.45

RDH 300 Q

Fig. 6 Market equilibrium in the home country. Source: Authors’ own study

home margin of resource are θA = 1 - αA = 0.2 and θR = 1 - αR = 0, respectively. The foreign country has βA = 1.5 and βR = 1.2, thus, the foreign markup of commodity and the foreign markup of resource are θA = βA - 1 = 0.5 and θR = βR 1 = 0.2, respectively. The aggregate commodity demand ( pADh) and the aggregate resource demand (wRDh) of the home country are rewritten as follows: pADh = 40 - 0:2Q

ð50Þ

wRDh = 30 - 0:1Q

ð51Þ

The aggregate commodity supply function ( pASh) and the aggregate resource supply function (wRSh) under the international trade condition are determined as follows: pASh = αR × wRDh þ αR × w0RDh - p0ADh × Q = 30 - 0:1Q þ ð- 0:1 þ 0:2ÞQ = 30 wRSh = αA × pADh þ αA × p0ADh - w0RDh × Q = 0:8 × ð40 - 0:2QÞ þ ð0:8 × ð - 0:2Þ þ 0:1Þ × Q = 32 - 0:22Q

ð52Þ

ð53Þ

Figure 6 illustrates the market equilibrium of home commodity at the point EAh( ph, QAh) and the market equilibrium of home resource at the point ERh(wh, QRh) that are computed as follows:

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Fig. 7 General equilibrium in the home country. Source: Authors’ own study

70 62 EH 40

ADH+RDH 100

233.3

ASH+RSH 281.8 Q

QAh : pADh = pASh ) 40 - 0:2QAh = 30 ) QAh = 50; ph = 30

ð54Þ

QRh : wRDh = wRSh ) 30 - 0:1QRh = 32 - 0:22QRh ) QRh = 16:67; wh = 28:33

ð55Þ

The general equilibrium of the home country, as illustrated in Fig. 7, occurs at the quantity (QEh) determined as follows: QEh : pADh þ wRDh = pASh þ wRSh ) 70 - 0:3QEh = 62 - 0:22QEh ) QEh = 100; ph þ wh = 40

ð56Þ

At the general equilibrium quantity (QEh = 100), the total economic surplus of the home country (TSh) is computed as follows: TSh =

ð70 - 62Þ × 100 = 400 2

ð57Þ

When the home country has trade activities with a foreign country, that leads to an increase in the total economic surplus (economic welfare) only when αA < 1 or θA > 0. However, trade activities rely on the structure of foreign demand, which includes the demand structure of price and quantity. This example assumes that the quantity ratio of commodity and resource demand between foreign and home countries equals 1 as illustrated in Fig. 8. Therefore, the price ratio of foreign commodity demand to domestic commodity demand is assumed with βA = 1.5, which will correspond to foreign markup is γ A = 0.5. Similarly, the price ratio of foreign resource demand to domestic resource demand is assumed with βR = 1.2, corresponding to foreign markup is γ R = 0.2.

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The aggregate commodity demand ( pADf) and the aggregate resource demand (wRDf) of the foreign country are computed as follows: pADf = βA × pADh = 60 - 0:3Q

ð58Þ

wRDf = βR × wRDh = 36 - 0:12Q

ð59Þ

The aggregate commodity supply function ( pASf) and the aggregate resource supply function (wRSf) of the foreign country under the international trade condition are determined as follows: pASf = wRDh þ w0RDh - p0ADf × Q = 30 - 0:1Q þ ð - 0:1 þ 0:3ÞQ = 30 þ 0:1Q wRSf = pADh þ p0ADh - w0RDf × Q = 40 - 0:2Q þ ð - 0:2 þ 0:12Þ × Q = 40 - 0:28Q

ð60Þ

ð61Þ

The market equilibrium of foreign commodity at the point EAf( pf, QAf) and the market equilibrium of foreign resource at the point ERf(wf, QRf), as illustrated in Fig. 8, are computed as follows: QAf : pADf = pASf ) 60 - 0:3QAf = 30 þ 0:1QAf ) QAf = 75; pf = 37:5

ð62Þ

QRf : wRDf = wRSf ) 36 - 0:12QRf = 40 - 0:28QRf ) QRf = 25; wf = 33

ð63Þ

The general equilibrium of the foreign country occurs at the quantity (QEf) is determined as follows: QEf : pADf þ wRDf = pASf þ wRSf ) 96 - 0:42QEf = 70 - 0:18QEf ) QEf = 108:33; pf þ wf = 50:5

ð64Þ

At the general equilibrium quantity (QEf = 108.33), the total economic surplus of the foreign country (TSf), as illustrated in Fig. 9, is computed as follows: TSf =

ð96 - 70Þ × 108:33 = 1408:33 2

ð65Þ

The above numerical examples reveal that home margin and foreign markup affect market equilibrium and economic surplus. The zero trade gain rule indicates that a home country without international trade (zero home margin) will receive zero economic surplus (zero economic welfare). Meanwhile, the non-zero trade gain rule

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Foreign commodity market

60

Foreign resource market

ASF 40 EAf

37.5

36

30

33

ERf

ADF 75

200

RSF Q

25

142.86

RDF 300 Q

Fig. 8 Market equilibrium in the foreign country. Source: Authors’ own study Fig. 9 General equilibrium in the foreign country. Source: Authors’ own study

P,W 96 70 EF 50.5

ADF+RDF 108.33

228.6

ASF+RSF 388.9 Q

states that the conditions for the home country to gain trade advantages only when both home margin and foreign markup are positive. This difference in market structure reflects differences in resource income and production cost for each country. Moreover, the non-zero trade gain rule provides implications for product innovation and production productivity to gain domestic and international trade advantages that contribute to economic growth and economic welfare.

5 Conclusion This paper approaches the general equilibrium framework with the microfoundation for international trade theory. The general equilibrium approach is crucial to connect aggregate demand with aggregate cost in the markets between home and foreign

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countries. Then, trade flows and advantages can be analysed through trade instruments and policies at industry and country levels. Microfoundation is the base to conduct market behaviour and economic surplus in international trade through the interdependence of demand and supply in the markets. In addition, the interrelation between the commodity and resource markets is crucial to analyse strategic behaviours in the global trade patterns at the firm levels. The general equilibrium theory reveals that production activities in the closed economy lead to economic growth with zero economic surplus (or zero trade gain). The zero trade gain rule means that the commodity market’s economic surplus will offset the resource market’s economic deficits and vice versa. The non-zero trade gain rule implies that both the home and foreign countries benefit from international trade. In the free trade condition, a home country with the same demand and low cost (or high demand and same cost) over foreign countries can get benefits (economic surplus) from international trade. The findings of the trade gain rules are relevant to the classical trade theory that encourages the net exports to gain trade surplus. This paper defines home margin and foreign markup in international trade that are formulated from the demand structure and cost structure between the home country and foreign country. Both home margin and foreign markup are trade indicators that present the contribution of net exports to the economy. While home margin stands for comparative advantage indicator for the export considerations, foreign markup stands for comparative advantage indicator for the import considerations. The research reveals that the high home margin and high foreign markup are critical conditions for international trade. While the necessary condition for a home country to export is a relatively high home margin over other exporting countries, the sufficient condition for a home country to be imported by a foreign country is a relatively high foreign markup over other exporting countries. Since home margin and foreign markup are formulated upon demand and cost structures, these trade indicators provide a better understanding of the Leontief paradox and the new trade theory. Moreover, these trade indicators and trade gain rules are incorporated into the general equilibrium framework, the international trade models address the global trade issues at the industry and firm levels. This paper contributes to the international trade theory with the trade advantage indicators and trade gain rules that provide key implications for international trade policies at the country, industry, and firm levels. However, this research has some limitations that suggest further researches: (1) the demand structure must examine both the price and quantity dimensions. While the price structure provides trade advantage indicators from home margin and foreign markup, the quantity structure provides trade performance indicators from home and foreign demand sizes; (2) the trade gain rules need to expand with trade regulations and exchange rate changes that affect the market structures and trade advantages between the home and foreign countries; (3) although international trade theory extends the classical trade theories and expands with modern trade patterns, the general equilibrium modelling needs to undertake economic data to analyse comparative advantages and deliver trade policies for economic growth; and (4) the market structure analysis is essential to

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identify trade patterns and agreements that significantly affect the industrial organization in the real-world economy. Acknowledgements This research are funded by the Ministry of Education and Training (Vietnam) under project number B2020-DNA-12, and the University of Danang - University of Economics for University-level research project with the grant number of T2023-04-29.

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Part X

Eurasian Economic Perspectives: Tourism

COVID-19: How Do Companies in the Tourism Sector React? The Case of Riccione Stefania Vignini

Abstract The COVID-19 crisis is a new challenge for companies and the issue of business continuity plays a central role in the process of drafting financial statements, as the assessments of the presence of business continuity conditions are strong critical issues in light of the uncertainties of the timing as well as the methods of exit from the current health emergency. The pandemic crisis has been reflected in a deterioration of employment conditions and in a contraction of the turnover of the tourism sector more marked than the other sectors. This contribution analyzes companies that seem to be most affected by the pandemic crisis, operating in the Riccione area. The methodology followed is eminently qualitative-exploratory based on the administration of 95 questionnaires, aimed at knowing which strategies have been implemented by the hotels to adapt to the crisis. Our findings highlight how hotels adapt their facilities to environmental change within a short period of time and what they expect in terms of reduction of the operating result. The work presents elements of novelty, as the first empirical study in managing the impact of COVID-19 (after one year from the beginning of the pandemic) on businesses belonging to the Italian tourism sector. Keywords COVID-19 · Crisis · Business crisis · Financial crisis · Pandemic crisis · Tourism companies and hospitality

1 Introduction In December 2019, a novel coronavirus disease was first reported in Wuhan, China, the origin of the virus. This isolated virus was officially named coronavirus disease 2019 (COVID-19) by WHO (2020) and over 76 million people across 222 countries have been infected as of December 2020 (WHO, 2020).

S. Vignini (✉) Department of Management, University of Bologna, Bologna, Italy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_21

353

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As regards COVID-19, existing studies are mostly descriptive (Anderson et al., 2020; Ugur & Akbıyık, 2020; Metaxas & Folinas, 2020; and Sigala, 2020 for a preliminary survey) or focus on a particular segment of the tourism industry (Wen et al., 2020), such as short-term rental (Anderson et al., 2020; Hu & Lee, 2020; Kraus et al., 2020; Zielinski & Botero, 2020; Guglielminetti et al., 2021; Li et al., 2022). Part of the literature is concerned with the devastating effects on people’s behavior scaremongering in communications. Indeed according to Fan et al. (2018) behaviors, sometimes excessive, are very much influenced by the communication of information from social media. Italy as well as countries imposing drastic measures to restrict movement in the population (Germany) were heavily exposed to the crisis with large case numbers causing dramatic newspaper headlines (Gössling et al., 2020; Guglielminetti et al., 2021; Rieger & Wang, 2022). Strategies adopted with the aim of reducing the devastating effect of COVID-19 have generated the temporary closure of many hotels and lowered, considerably, the demand for businesses. This study analyzes the possible impact of COVID-19 on hospitality. A local case in Riccione, an important city specializing in tourism, is introduced as an example of a hospitality and tourism provider. General suggestions on how to respond to COVID-19 and such crises are provided. More importantly, this study attempts to elicit more attention from academics and practitioners on the operation of tourism sector. Hospitality businesses are expected to make important changes to their operations in the COVID-19 business environment with the ultimate purpose of ensuring the health of customers and employees (Fan et al., 2018; Gössling et al., 2020; Rieger & Wang, 2022). Preliminary findings suggest that reopening the sit-down restaurants and easing travel restrictions will not bring customers back immediately (Gursoy et al., 2020; Gursoy & Chi, 2020; Zielinski & Botero, 2020). It is very important to say that the hospitality sector has a quite BEP (Break Even Point) due to the presence of high operating costs, the survival of many accommodation facilities depends on the increase in demand for products/services. A lot of literature deals with the behavior of tourists as a result of the pandemic. This work becomes interesting because it looks at the behavior that accommodation facilities are putting in place to face the pandemic. This paper contributes to the literature on how the outbreak of contagious diseases affects tourism flows. In particular, focuses on the behavior of accommodation facilities in the face of the restrictions imposed. It is a qualitative study conducted through the distribution of questionnaires and interviews to businesses in Riccione city. Indeed this paper aims to see how the companies in the tourism sector (in an important Italian city) reacted to the first year of the pandemic crisis. In Sect. 2 the framework used in the research is presented. Section 3 shows the methodology developed. Section 4 presents the first results. Finally, Sect. 5 includes some brief conclusions.

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2 Theoretical Foundation and Framework This section is dedicated to the concept of crisis. It necessarily starts from the definition of crisis in general and then in its declinations up to the pandemic crisis.

2.1

A Definition of Crisis

The etymology of the word crisis comes from the Greek krisis (choice), from krino (to distinguish). The concept of crisis in historiography is attributed to Hazard in his main work of 1935: “The Crisis of the European Mind.” From the course of an illness to the life of a government, from the turmoil in the face of certain problems to a cyclical pathology of the economic order, the crisis fills our discourses and is part of our lives. It represents a difficult, hard, unpleasant moment but what its wise etymology tells us is that the crisis is nothing more than a moment of choice, of strong decision. In an attempt to define the term crisis, we cannot fail to refer to Hermann who in 1972 showed his concern that there was no common meaning in the literature to the word “crisis.” A crisis is often regarded as a turning point for better or worse, an unstable time when a major change is made—either one with an undesirable outcome or one with a highly positive outcome. It is characterized by uncertainty (Fink, 1986) and risk (Coombs, 2007) and can be represented by three features: high threat, an element of threat, and short decision time (Hermann, 1972; Perrow, 1984; Kash & Darling, 1998).

2.2

Natural and Socio-Technical Disasters and Business Crises

From the perspective of organizational management, literature sustains that natural and socio-technical disasters differ from business crises for two motives (Richardson, 1994; Baekkeskov & Robin, 2014). Disasters can be natural or man-made events, or either, involve dangers of injury and loss of people. Business crises are purely man-made events (Wang, 2009) and impair the quality of personal, social, and work lives of people. However, disasters can be the starting point for a business crisis (Lagadec, 1993: Shaluf et al., 2003). This one has the characteristics of low frequency and deep consequence that can significantly impede organizations from continuing to act successfully,1 as an “unexpected and destructive event with high

1

An example of business crises is the collapse of Barings Bank (Sheaffer et al., 1998).

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levels of initial uncertainty that develops negative perceptions” (Bundy & Pfarrer, 2015, p. 347). Generally, crisis management frameworks (Smith, 1990; Darling, 1994; Kash & Darling, 1998; Chong, 2004; Mitroff, 2004; Koksal & Ozgul, 2007; Wang, 2009; Raithel & Hock, 2020) can be distinguished into those that focus on why crises occur (operation-oriented frameworks) and those that dwell on how crises impact companies (process-oriented frameworks). Operation-oriented frameworks focus on crises that result from the regular operations of organizations (Dutton, 1986; Salter, 1997; Ratnasingam, 2007) while process-oriented frameworks focus on how organizations can remove weaknesses due to the crisis (Darling, 1994; Richardson, 1994; Chong, 2004).

2.3

Financial Crisis

A financial crisis can have multiple causes. Generally, a crisis can occur if institutions or assets are overvalued due to irrational investor behavior (see among the others De Bonis et al., 1999; Allen & Snyder, 2009). When a crisis arises, the real economy is adversely affected and companies undergo important change (Lagadec, 1993; McMullan, 1997). There are negative impacts on employment, production, and purchasing power. Businesses, and also governments, become fundamentally unable to meet their obligations and the risk of insolvency becomes apparent.2

2.4

Pandemic Crisis

The global pandemic from the novel coronavirus (COVID-19) has caused numerous victims and also has led to serious limitations on daily private and working life. In an attempt to define a pandemic crisis COVID-19 pandemic can show four prevalent effects: 1. Health epidemic. There is no doubt that the total number of infected people and the total number of deaths (constantly updated) highlight a situation that is nothing short of critical for the various hospitals around the world (Canyon, 2013; Ferguson et al., 2020; Hopkins, 2020); 2. Political-bureaucratic epidemic: the behaviors of the different countries are different (Anderson et al., 2020). The least common multiple is however a rain of emergency decrees, implementing rules, ordinances, and circulars. The political theory of disaster management usually postulates that democratic governments When an organization is fundamentally solvent but temporarily unable to meet its financial obligations, then the notion of “illiquidity” is often used, but in practice insolvency and illiquidity are difficult concepts to distinguish (Allen & Snyder, 2009). 2

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will help victims of disasters (Gasper & Reeves, 2011; Reeves, 2011; Morrow et al., 2008; Boin et al., 2005, 2008; Diamond, 2008; Mesquita et al., 2003; Sheaffer & Mano-Negrin, 2003; Sen, 2009). Government responsiveness in times of crisis is normally linked to the fact that governments will go to any extent to avoid a humanitarian catastrophe (Rieger & Wang, 2022). 3. Psychological epidemic: There is a revival of interest in psychological (Kindleberger, 1989) and social constructs, including what Keynes (1936) called “animal spirits” and other attitudes that may occur if people have a limited cognitive basis for fully rational decision-making. In these circumstances, they rely on certainly less rational social conventions and other psychological factors (Allen & Snyder, 2009). The market will alternate waves of optimism and pessimism. This circumstance seems obvious in a moment where there is no solid basis for a reasonable calculation (Keynes, 1936). 4. Economic-financial epidemic. The latter is of particular interest to our work which intends to focus on the tourism sector of a particularly important area from the Italian seaside tourism point of view because demand in companies such as restaurants, and tourism has almost cancelled. During COVID-19 crisis stock markets have crashed remarkably (Baker et al., 2020; Baum et al., 2020; Del Rio-Chanona et al., 2020), with economists consistently forecasting tough economic recessions (Baldwin & Weder di Mauro, 2020; McKibbin & Fernando, 2020; He et al., 2022; Plzáková & Smeral, 2022). The governments of various countries have imposed strong restrictions to protect health and social distancing. Many businesses have been closed in many countries, generating supply and demand problems (Del Rio-Chanona et al., 2020; Rieger & Wang, 2022). Governments implement measures such as interim financing, and fiscal stimulus packages to support firms (Stein et al., 2020; Rieger & Wang, 2022). Companies in reaction to these changes take congruous measures to adapt their corporate behavior, as consumers modify their consumption behaviors (GordonWilson, 2022). Inevitably it is fundamental that companies employ a strategic approach to crisis management (Darling, 1994; Kash & Darling, 1998; Jaques, 2010). In so doing, important advantages can be obtained by minimizing the possibility of crises in presence of controllable operations of the organization and with a proactive attitude toward situations that can have negative effects on companies (Lagadec, 1993; Pollard & Hotho, 2006). Much of the literature on damage containment is not contemporary (Hermann, 1972; Perrow, 1984; Fink, 1986; Mitroff et al., 1988; Shrivastava et al., 1988; Roberts, 1990; Mitroff & Pearson, 1993; Lagadec, 1993; Pearson & Mitroff, 1993; Weick, 1993; Pearce & Michael, 1997; Roux-Dufort & Metias, 1999), however, it remains very relevant. Currently, few models of organizational assessment can be found (Brown et al., 2001; Fink, 2002; Levinson, 2002). Part of the literature focuses on the search for models to be used ex-ante with respect to crises (Levinson, 2002) and the other part of the literature is related to the human impact of crises (Myer et al., 2007; Paton, 2003). The best-known general measures include

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reducing costs and investment (Bruton et al., 2003), cutting production, working more with equity capital, entering foreign markets, improving efficiency, and re-structuring debt (Pearce & Robbins, 1993; Beaver & Ross, 1999; Zehir, 2005). Nowadays, the pandemic crisis is affecting the health of people and governments are implementing strong measures to save people‘s lives but these measures are literally threatening the survival of companies in all sectors around the world. In their recent work, Wenzel et al. (2020) suggest four strategic crisis responses and we like to use them as a reference framework for our analysis because we believe it can be useful even in a pandemic crisis. The responses are in detail: Retrenchment: a generally observable strategic response to a crisis (Bruton et al., 2003). This measure refers to “reductions in costs, assets, products, and product lines” (Pearce & Robbins, 1993, p. 614). These measures have the effect to reduce the scope of a firm’s business activities. This first point mainly highlights the problem of the impact of COVID on the hospitality workforce (Baum et al., 2020). Persevering: Relates to measures aimed, in presence of a crisis, at sustaining a firm’s business activities and aims at preserving the initial situation by trying, at the same time, to mitigate the negative effects of the crisis. As Stieglitz et al. (2016) indicate, such a measure can be an effective strategic response to a crisis. Even if it is true that crises can have disastrous effects on businesses and people, however, it is also true that they can open up an opportunity space for strategic resumption because they allow you to do everything that was previously unthinkable (Roy et al., 2018; Rosenbloom, 2000;). Wenzel et al. (2020) precis that innovating (the third measure) is a strategy with sustainable effects that can strengthen the company in the future vision. By exit, the last measure, literature relates to the discontinuation of a firm’s business activities in response to a crisis (Argyres et al., 2015; Xia et al., 2016). An exit may be the last measure when the other responses fail and when the company believes that no other response can help it survive the crisis (Wenzel et al., 2020). But new resources can be freed up by a successful commercial exit and new opportunities could be generated. Indeed according to Ren et al. (2019) exit can lead to strategic renewal with a new company.

3 Data and Methodology We conducted qualitative expert interviews (Kvale, 1983; Neergaard & Ulhøi, 2007) and obtained an understanding (Outhwaite, 1975) of tourism sector firms’ reactions to the COVID-19 crisis. We distributed questionnaires during the lockdown period. Precisely we administered questionnaires and contacted the structures by phone from the period of January to May 2020 to 95 hotels operating in Riccione. In particular, a series of in-depth interviews were conducted to elicit opinions from experts in the tourism industry. The interviewees were asked to watch various discussion programs regarding COVID-19 and its economic impact, specifically in the tourism industry.

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The verbatim transcripts of the interviews were compiled and analyzed for qualitative data analysis. The choice of Riccione is due to the presence of numerous accommodation businesses specializing in seaside tourism. Riccione is located, geographically, on the Romagna Riviera, close to Rimini to the north and Misano Adriatico to the south. It is one of the most famous Italian seaside towns and among the main landmarks of the Riviera. It has been called the Green Pearl of the Adriatic for its tree-lined avenues, parks, and marvelous gardens. The history of the city of Riccione is quite young (Lombardi, 2002), although traces of the first settlements dated to the second century B.C. and is identified with the birth of tourism in the mid-80s: from a rural seaside village, dedicated to agriculture with about 1800 inhabitants, in a few decades it becomes a tourist resort known in Italy and Europe, and reported in all the most important tourist guides of the time as a seaside town “of Italy” (Passarelli, 2001). The companies to which the questionnaire will be sent were selected thanks to an extraction carried out through the AIDA database, using the ATECO code no. 55 Accommodation services. From the extraction, 95 hospitality companies emerged, of which 10 are currently undergoing a liquidation procedure. Of the remaining 85, it was possible to contact only 35 companies because the other structures, being mostly family-run, did not publish an email address. The responses received are 20. We contacted the same accommodation facilities at two different times (March– April 2020) and also at a later time (October–November 2020) to closely follow their forecasts mentioned during the first administration of the questionnaires. This second interview was motivated by the most truthful possible research of the data offered by the hotels a few months after the lockdown.

4 Findings Initially, companies were asked if they opened their business even in this particular year and if they did so as soon as the legislation allowed or waited for the situation to stabilize further. In reference to the first question, we noticed that almost all the companies have decided to reopen. This element appears to be in contrast to what has been observed in the literature (Bartik et al., 2020) and the percentage could even reach almost 100% if we add the other 14 companies that are open but, even after telephone contact, they did not want to answer the questionnaire. The second question about the timing of the reopening is due to the fact that, at a national level, as reported by the mass media, many companies in the sector have decided not to start the activity due to the strong regulatory limitations and the uncertainty of arrivals from foreign countries or to delay the opening because back with the renovations blocked in the months of March and April 2020. The lockdown was not applied contemporarily but was applied originally in some Northern cities and, since March, in the whole country. Lockdown represented a very severe measure because it included a stay-at-home order, the shutdown of all

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Fewer services offered, swimming pool, sauna, etcetera Reducon of the number of employees Increase of the prices Eliminang the internal catering External catering Other 0

2

4

6

8

10

12

Fig. 1 Strategies of modification of accommodation facilities (Figure 1 shows how hotels have modified the offer) Source: Our elaboration Table 1 Choices of accommodation facilities Questions formulated to the structures Have you decided to reopen your hotel in 2020? Have you immediately reopened after the lockdown? Have you modified your offer to the customer?

Positive answers 95% 53% 79%

Negative answers 5% 47% 21%

Source: Our elaboration

non-essential economic business, and other important restrictions to mobility (Della et al., 2021; He et al., 2022). Following statistical data in 2020 foreign guests in Italy were 39 million overall (about 60% less than the preceding year).3 Subsequently, the companies were asked if COVID-19 led to a change in the accommodation proposal and in what terms: renunciation of internal catering, reduction of employees, collaboration with external catering companies, elimination of some services such as the swimming pool, sauna, etc., price increases (like presented in Fig. 1: Strategies of modification of accommodation facilities). This element seems very near to the literature and in particular shows important elements of similarity with Gössling et al. (2020). This question was formulated with the support of both the information circulating by mass media at that time in Italy and with the help of the accommodation facilities themselves. In connection with the last row of Table 1 (Have you modified your offer to the customer?), 79% of the accommodation facilities have changed their offer and have tried to meet all the needs of customers and employees. All the 3

Bank of Italy Survey on International Tourism (BISIT, henceforth), which was established in the mid-’90s to gather data for the compilation of the “travel” item in the current account of the Italian balance of payments.

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Fig. 2 Percentage of reduction of the profit (Figure 2 shows this question to the hotels: If you estimate a reduction of your economic result in 2021 due to COVID 19, in which range of reduction?). Source: Our elaboration

strategies offered are part of the first three strategies envisaged by our principal framework. The last question of the questionnaire (if you estimate a reduction of your profit) wanted to investigate the economic impact (in terms of profit reduction) that hospitality businesses have had or expect to have from the pandemic. In fact, it was asked whether, in the case of non-owned companies, they could benefit from a rent reduction, if they decided to increase prices and to what extent, and if they expect a reduction in the economic result and in what terms (Fig. 2). Since the questionnaires were administered during the months of March/April 2020, during the months of October/November of the same year, we returned to request questionnaires precisely to understand what had changed after the summer months and especially with reference to the upcoming closures established by the government for the entire remaining part of the year. Obviously, the structures were very worried about the future. All the hotels involved in the research said they were particularly satisfied with the summer season but at the same time strongly worried about the upcoming closure scheduled by the government. At that point, the question of the expected drop in turnover was reformulated. The last income-related question that hotels expect was consequential to the three strategies adopted by hotels. It seemed correct to us to have their own perception of the current situation. So results show that all the strategies (with the exception of the exit strategy which deserves a few more words) are practiced in some way. Important in this regard is the first graph (Fig. 1: Strategies of modification of accommodation facilities) which implies a combination of different coping mechanisms: persevering, innovation, and retrenchment. The hotels decide to reopen anyway and undertake to modify their accommodation structure to face the crisis. This highlights a sector that is very ready to overcome the pandemic crisis.

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S. Vignini 40% 35% 30% 25% 20% 15% 11% 10% 5% 0% 1 21 - 30%

31 - 40%

41 - 50%

51 - 60%

More than 60 %

Fig. 3 Percentage of reduction of the profit (Figure 3 presents the same question as Fig. 2 after the second administration of the questionnaires). Source: Our elaboration after the second administration of the questionnaires

However, Fig. 3 (Percentage of reduction of the profit) is very important because it shows an estimate of the reduction in operating income that hotels expect for 2021. The results are certainly critical, during the first administration of questionnaires given that 58% expect a reduction of the economic result between 31 and 40% followed by 21% who expect a reduction of more than 40%. We must add that this last graph (Fig. 3: Percentage of reduction of the profit), which seemed worrying with the first administration of the questionnaires, after the second administration, showed a percentage of higher concentration around 51–60%. This figure (Percentage of reduction of the profit) should be studied in the long term because we believe that if it were to persist for other years, it would certainly generate an increase in the exit strategy and it would threaten going concern. It must be said that, even if the hotels in the Riccione area are rather specialized in seaside tourism, as affirmed above, for several years now, they continue to remain open all year round to accommodate conferences, fairs, and congresses of various types and these events are practically stopped from the pandemic. To date, the facilities are very worried about the future and are awaiting refreshments from the government and are not sure they want to reopen their facilities in 2022. These are the words that all hotels said during the interviews carried out toward the end of 2021. The exit strategy, during the first administration of the questionnaires, did not seem to be adopted by companies, also considering the 14 companies that have decided to reopen but were not inclined to fill out the questionnaire.

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The situation, on the other hand, appears very different after the second administration of the questionnaires, especially after the words of the various hoteliers. As we expected it, therefore, seems that the exit strategy may have a certain diffusion. It is clear that to be even safer we should wait until the year 2022 ends to understand the exact percentage of hotels that have then decided to exit the market. The result indicates that government-sponsored loans are crucial to the survival of the tourism industry and this conclusion is very similar to other studies (i.e. Yeh, 2021). Indeed countries that depend on tourism throughout the year, according to literature, will be the most affected by the industry slowdown (Zielinski & Botero, 2020) and empirical studies confirm this notion (Gössling et al., 2020; Li et al., 2022).

5 Conclusions Our essay shows the first empirical research in a famous Italian city specializing in seaside tourism. Our findings (based on a study of 20 hotels) highlight how these companies cope with a lockdown situation. Considering our framework, we can affirm that compared to the four strategies we used as the main framework of our paper, three strategies were followed, and, in particular, the second remains the most used. Hotels have currently decided to resist the pandemic by taking all possible measures to promote safe tourism. It is true, however, that after the second administration of the questionnaires, the hotels seemed more concerned and oriented mainly toward obtaining important aid from the government. This is to avoid exiting the market and therefore avoid adopting the fourth strategy. There is also the problem of changes in consumer behavior and consequently in travel demand. There are many factors that influence behavior (personal economic well-being and related disposable income, changes in costs and consumption capacities, and health risks as a result of pandemic restrictions). For example, McKinsey and Company (2020) suggest, using qualitative analysis, that consumer optimism will not always be the same but greater at the beginning/end of the pandemic and will also change between countries. An example illustrating these aspects can be the SARS outbreak of 2003 which generated growth in tourism in Asia when the perceived threat subsided (McKercher & Chon, 2004). It would be appropriate to act as a longitudinal analysis to know the strategic responses of the tourism sector to the pandemic. In fact, it is our intention to use subsequent questionnaires during the year 2022 to try to definitively understand the trend of the company’s situation in the last three years. Our exploratory study only shows initial findings and this is the limit of this paper. In addition, the scope of the study may be limited to a certain area in a particular time frame. However, we think that it can be a preliminary study that provides direction for future studies to do a comparative evaluation.

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Analysis of Social Capital in Aragon’s Tourism Cluster: A Social Network Resources Perspective on Twitter Natalia Sánchez-Arrieta, Ferran Sabate, Antonio Cañabate, and Umair Tehami

Abstract This paper explores social capital in the sustainable tourism cluster of Aragon in Spain, based on the interactions that its member companies have made on Twitter. Social capital was determined from the resource approach calculating the structural, cognitive and relational capital present in the network. To do this, on the one hand, information was collected on the interactions, represented by mentions in the period between May-2020 and May-2021. On the other hand, the characteristics of the network structure were identified by adopting the framework of social network analysis from a static perspective. According to the criteria of the analysis, the results indicate at the node level, the existence of network members with roles associated with the resource approach to social capital. At the global level, it is observed that, even if their members have a certain level of communication, there are a large number of them who are not connected to all the others through a short path length. Their low density and moderate accessibility denote that the minimum number of ties is used to connect with other members. This reveals a network structure of mentions that is not fully interconnected. Likewise, we observe a network that has a coherent structure of subgroups, with links within them that are not very dense. These general characteristics of the network are particularly useful for understanding how mention interactions can influence the fostering of participation, prestige and other resources that generate social capital.

N. Sánchez-Arrieta (✉) Department of Management (OE), Universitat Politècnica de Catalunya, BarcelonaTech (UPC), Barcelona, Spain Programme “Colombia Científica” - Component: “Pasaporte a la Ciencia”, Instituto Colombiano de Crédito Educativo y Estudios Técnicos en el Exterior (ICETEX), Bogotá D.C., Colombia e-mail: [email protected] F. Sabate · A. Cañabate · U. Tehami Department of Management (OE), Universitat Politècnica de Catalunya, BarcelonaTech (UPC), Barcelona, Spain e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_22

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Keywords Social capital · Social Network Analysis · Social Networking Site · Twitter · Social Resources · Tourism

1 Introduction In Spain, since 2006, there have been policies aimed at promoting and supporting cooperative relationships between actors in a sector. These are initiatives, under the programme of Associations of Innovative Enterprises (AEI), which have been proposed for the institutions that make up a cluster motivated to promote collaborative exchange and to seek joint benefits of an innovative nature (Ministerio de Industria Comercio y Turismo, 2021). Increasingly, companies are involved in different clusters or associations depending on their objectives and corporate activities, where their social ties are lately being managed through virtual platforms. That is, companies seek to form networks of useful contacts that effectively drive their activities, that enable the mobilisation of material or symbolic resources and, in turn, promote social capital benefits. In this sense, there are empirical studies that have analysed clusters as a means of generating social capital in business networks. However, due to the multidimensionality of the term, the literature has found a lack of consensus for assessing social capital in virtual platforms reflected in the need to clearly define the focus of the analysis according to its conceptualisation, the context of the study and the measurement perspective (Etxabe, 2018; Sánchez-Arrieta et al., 2021; Young, 2014). To address this, this paper has considered social capital from its resource approach, analysed in the context of a cluster in the tourism sector and under the perspective of social networks. Although there is evidence of studies that have analysed the context of tourism in virtual platforms, they are still in their infancy and, in general, are focused on the user-consumer’s point of view and their engagement in the generation of content in virtual platforms (Wang et al., 2017). In contrast to that group of research, this paper welcomes an approach from the user-business point of view to examine the roles of their contacts that generate social capital and that enable them to improve the competitiveness of each user and the group as a whole. This paper aims to evaluate the social capital of a tourism cluster in the community of Aragon, Spain, from a social network perspective when the associated companies interact on the Twitter platform. In this way, this work offers a tool for measuring the resource approach to social capital from the framework of social network analysis from a static perspective and using the information available on the Twitter platform. For this study, information was collected on the interactions represented by mentions that the companies associated with the cluster have made during the exchange of Tweets over one year. According to the criteria of the analysis, the results indicate at the node level, the existence of network members with roles associated with the resource focus of social capital. At the global level, significant values are observed for general network characteristics (i.e., density, short path length, diameter, etc.) that are particularly

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useful for understanding how mention interactions can influence the fostering of resources that generate social capital (i.e., engagement, prestige, etc.). This paper is structured as follows: in section two, the definition of social capital, the approach taken and its characteristics are presented. This section also explains the operational description of measuring social capital from a social network perspective. Section three details the data source and the context of the research. Section four presents the methodology: the procedure and criteria adopted to meet the objective. Section five develops the discussion of the results obtained. Section six draws some conclusions about the process followed to measure social capital in the cluster under study.

2 Theoretical Background 2.1

Social Capital

According to Bourdieu (1986), social capital is defined as “the aggregate of actual or potential resources which are linked to an individual through the possession of a durable network based on good relations” (p. 21). This possession refers to participation in the network where a user relates to others he knows directly or to others who know those he knows. The importance of these connections lies in the fact that they are the infrastructure through which resources are exchanged to achieve mutual benefits, generating social capital (Greve & Salaff, 2003). In the corporate context, each company has its own resources that it often considers necessary to complement with the resources of its contacts, enabling it to achieve positive results by investing in the relationships between its social networks (Burt, 2001; Lee & Hallak, 2020; Lin, 2001). This suggests that companies that form network structures based on connections with diverse user profiles can access resources from different parts of the network in a more agile and rapid way. They can even offer diverse approaches, experiences and ideas to neighbouring users. In the context of the tourism sector, social capital has become a tool to respond to the different social processes that lead to the analysis of tourism as an agent of change (Park et al., 2012; Ramírez Hernández et al., 2018a). Although there is literature on the relationship between social capital and tourism, it is largely concerned with their conceptual relationship, the recognition of their scope and common elements that identify them as a phenomenon that impacts social issues. A small group of this research also contemplates the definition of the roles and types of relationships between network users engaged in different tourism activities (Hall & Lew, 2009; Zhao et al., 2011). However, to date, there is no evidence of a generalised way of measuring social capital in this sector. In fact, the instruments that prevail in the studies conducted so far are interviews and questionnaires (Ramírez Hernández et al., 2018b). To fill this gap, in this paper we have considered tourism as a sector for analysis and

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Table 1 Variables of social capital dimensions Dimension Structural Cognitive

Variables* Social connectivity Exchange by affinity Social cohesion

Relational

Engagement Social influence Popularity Prestige

Description of variables Ability to be socially close, to bond, to a member in the network Affinities are identified and shared through the exchange of language and narratives among members of a social network Ability to keep members of a social network together by sharing common standards, values, ideas and beliefs Actively contributing interventions that facilitate greater communication The extent to which a member directly or indirectly affects the thoughts, feelings and actions of others Measure to evaluate the behaviour of a member about others in a network A measure of the relationship between members

Referencesa Riedl et al. (2013) Rehm et al. (2017) Moody and White (2003) Kornbluh (2019) Tanase et al. (2015) Garcia et al. (2017) Zheng et al. (2019)

* Variables are supported by Sánchez-Arrieta et al. (2021) Source: Own elaboration a Main references that support the description of the variables adopted in this work

measurement of social capital from a social network perspective when companies in a cluster interact on the Twitter platform.

2.2

Social Capital Approach

Due to the multidimensional nature of social capital, there are several approaches to its analysis and measurement in the literature. The variety of group studies focuses on analysing different contexts, on the role of users, on the elements and characteristics of the types of relationships (Woolcock & Narayan, 2000). Among the most recognised and studied approaches are the linkage and resource approaches (Putnam, 2000; Tsai & Ghoshal, 1998). The approach adopted in this paper is based on the types of resources that are transferred in a social network. The best-known contribution to this approach is Nahapiet and Ghoshal’s (1998) proposal, which focuses on identifying ways of presenting the various resources that are owned, acquired or mobilised in a social network. These authors considered representing social capital with three interrelated dimensions: the structural dimension that refers to patterns of connection; the cognitive dimension that refers to shared interests and interpretations; and, the relational dimension that constitutes the types of relationships developed from the interactions nested in the network structure. Each of these dimensions has been evaluated with various characteristic variables as can be seen in the contribution by Sánchez-Arrieta et al. (2021). Based on this work, Table 1 presents the variables that

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we have considered to be distinctive in the context of the research, as well as the points that characterise them.

2.3

Social Network Analysis

According to Annen (2003), a social network is a set of users and patterns of resource exchange between those users that are represented by nodes (users) and edges (links between users as the infrastructure of resource exchange). Analysing a social network involves understanding the nature of the users, their behaviour and their relationships by mapping and statistically evaluating the interactions. This is possible with a quantitative research method called Social Network Analysis (SNA), which makes it possible to observe the behaviour of a social network at the node level (individual) and at the global level (network patterns). The SNA involves a qualitative and quantitative analysis of the network, which is nowadays performed with computer tools such as UCINET, NodeXL, Gephi, etc. (Bastian et al., 2009; Borgatti & Foster, 2003). With these tools, interactions are visually represented using a graph allowing us to understand the connections between network users. Also, network indicators are evaluated based on the statistical analysis of the graph. There is evidence, on the one hand, that indicators of network properties characterise social capital (Granovetter, 1973; Lin et al., 1981; Recuero et al., 2019). And, on the other hand, that access to and mobilisation of resources within a social network can be determined by different network characteristics or indicators, from a structural point of view. Also, the data used can be from social networking sites (SNS) as they are virtual platforms where resources are shared, links and interactions between users are promoted (i.e., Brooks et al., 2011; Lesser et al., 2016). However, due to the diversity in the conceptualisation of social capital, there is a wide variety of proposed network indicators for measurement in SNS platforms (Norbutas & Corten, 2018). In Tables 2 and 3, we present the set of indicators that we consider to characterise the network and quantify its dimensions at the node and global levels.

2.4

Social Capital Description from Social Network Perspective

In this section, we present the framework that describes social capital from the perspective of social networks together with the measurement indicators (Tables 2 and 3). It is a framework that consists of two parts (Fig. 1), one in which social capital is described at the global level based on the variables of the structural and cognitive dimensions denoting the configuration of the network. The other part describes social capital at the node level based on the variables that show the relationships, roles and behaviour of users in the network.

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Table 2 Framework describing social capital from a social network perspective at network level Social capital resources Structural dimension

Cognitive dimension

Variables Social connectivity

Exchange by affinity

Social cohesion

Key features Structure that allows for greater diffusion and interchange of resources (high) or that presents greater diversity for resource management (low) Access to distant resources (reachability). To know how far apart the two farthest nodes are Resources embedded in a network are mobilised in subgroups where intragroup connections are dense and inter-group connections are weak. Inclusion of members in specific subgroups for the exchange of ideas and resources on a specific topic Identify the quality of interactions in terms of the percentage of subgroups that are highly connected Level of interconnection of the network

Identify how many pairs of members interact in a network

Network indicators Network density

Reference valuesa Ranges from 0 (no ties at all) to 1 (all possible ties in a network are present).

Diameter

≥0

Modularity

Ranges from 0 (random network) to 1 (the partition is better).

Cluster

≥0

Transitivity

Ranges from 0 (no ties among neighbours) to 1 (node’s neighbours are fully connected).

Average clustering coefficient

Ranges from 0 (there are no triangles complete in the network) to 1 (all triangles are completed) ≥0

Average weighted degree

Source: Own elaboration a The reference values are supported by Bastian et al. (2009), Cherven (2015)

First, we have identified the key components of the variables characterising the structural distribution and dimensions of social capital focusing on the resource approach. We highlighted those elements that can be analysed with network properties. And then we determined the network indicators that interrelate with the social capital variables in the context of the research.

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Table 3 Framework describing social capital from a social network perspective at node level Social capital resources Structural dimension

Cognitive dimension

Variables Social connectivity

Exchange by affinity

Social cohesion

Relational dimension

Engagement

Popularity

Social influence

Key features How valuable is the information stored in a node?

Network indicators Hubs score

What is the quality of that node’s links?

Authority score

Relative level of clustering in terms of the influence it has on its own neighbourhood to form a complete sub-network. Frequency with which a network node interacts with others. Distance a node needs to reach everyone else. How social proximity is the node?

Clustering coefficient

Number of nodes with whom a specific node links concerning all possible relationships Are there critical points that interrupt the exchange of resources?

Out-degree centrality

How many nodes are linked to a specific node by having adequate resources? How many steps are necessary for other nodes to connect with a particular node?

In-degree centrality

Node with increased opportunity for transmission and transfer of resources

Betweenness centrality

Degree centrality Closeness centrality

Bridging centrality

Eccentricity centrality

Eigenvector centrality

Reference valuesa Ranges from 0 (node that has no ties to authoritative nodes) to 1 (node that has ties) Ranges from 0 (node that has not been connected by Hubs nodes) to 1 (node that has been connected) Ranges from 0 (no ties among neighbours and they are pendants to the node) to 1 (node’s neighbours are fully connected) ≥0

Ranges from 0 (nodes closest to the rest of the nodes) to 1 (nodes distant to the rest of the nodes) ≥0

Ranges from 0 (peripheral nodes) to 1 (bridging node located at an important position) ≥0

Low values: Nodes are more central, they require fewer steps. High values: Least central nodes Ranges from 0 (nodes with non-privileged positions) to 1 (intermediate nodes) Ranges from 0 (node has no ties with (continued)

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Table 3 (continued) Social capital resources

Variables

Prestige

Key features Is the node connected to highly connected neighbours?

Network indicators

How many nodes point directly and indirectly to a specific node?

Proximity prestige

Which fraction of the network can reach a specific node?

Domain prestige

Reference valuesa important neighbours) to 1 (node’s neighbours are also influential) Ranges from 0 (no other nodes are directly and indirectly linked to a specific node) to 1 (all other nodes are directly and indirectly linked to a specific node) Ranges from 0 (low fraction of nodes that are directly or indirectly pointing to a specific node) to 1 (high fraction)

Source: Own elaboration The reference values are supported by Bastian et al. (2009), Cherven (2013, 2015)

a

Fig. 1 Description of social capital at global and node level. Source: Own elaboration

3 Data Source In this study, it was decided to work with the micro-blogging platform, Twitter, as a source of information to determine the social capital in the cluster. Mainly for three reasons: (a) Twitter is a real-time SNS in which information can be disseminated that can influence consumer attitudes and behaviour; (b) because it has been considered an important tool in electronic word-of-mouth communication being more effective, that is, than marketing (Jansen et al., 2009); (c) because of the thousands of scientific publications that have relied on Twitter in different disciplines (Williams et al., 2013).

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Aragon’s Sustainable Tourism Cluster

In order to evaluate the social capital from the interactions that take place on Twitter, on this occasion it was decided to analyse the tourism sector of a Spanish region. Aragon was selected, which is a region located in the northeast of Spain comprising the provinces of Huesca, Zaragoza and Teruel. Aragon is defined by its diversity derived from its natural contrasts. In such a small space it offers different landscapes, climates, and ecosystems, as well as desert spaces and authentic orchards that surprise and attract visitors. In the centre is the Ebro Valley, which is barely more than 100 metres above sea level, to the north the Pyrenees with heights of more than 3000 metres, such as the Aneto (3404 metres), to the west and south the Sierra Ibérica, with the Moncayo (2314 metres) as its summit. Aragon’s diversity is also defined by the culture and traditions of its towns and cities, which invite you to contemplate the vestiges of the civilisations that settled on the Iberian Peninsula. Thus, tourism is a strategic sector with great implications in the growth of wealth, employment and population fixation in the community of Aragon. According to statistical information from the government of Aragon, tourism in Aragon accounts for nearly 8% of GDP and has been growing steadily since 2015, contributing to approximately 10% of employment in the Autonomous Community. In fact, it has also reached the highest overall figure in the last decade and in the entire history of Aragon, which is why it is considered a wealth-generating tool (Aragón, 2020). Nowadays, Aragon has a strong group of companies promoting the transformation of urban tourism into sustainable tourism that has formed a non-profit association called the Sustainable Tourism Cluster of Aragon (TSAC). TSAC is mainly made up of 31 companies in the sector and entities linked to it. Among its objectives are: to improve the individual competitiveness of each company by facilitating and encouraging cooperation between its members; and to create a meeting and training platform that allows the development of common initiatives in the tourism sector, guaranteeing compliance with the objectives of sustainable development. Measuring the social capital of TSAC with Twitter is of interest because it is feasible that companies related to this association use the SNS platform to promote their commercial activities. In fact, as of October 2021, 67.7% of cluster members have a Twitter profile suggesting that the way they interact on the platform could represent the pool of resources that can be mobilised through networking. In total, we have worked with 22 Twitter user profiles including the TSAC cluster profile.

4 Methodology A three-stage method was followed to determine the social capital of the TSAC cluster on the Twitter platform:

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Data Collection

The information was extracted about the followers and followees of the user object with the software Social Capital Twitter Data eXtractor (SCTDX)1 software. According to the interest of this study, it was decided to select the following criteria for data extraction: (1) the type of Twitter access configuration was academic; (2) the network based on mentions which is a meaningful representation of the concurrent interactions in a social network; (3) the network of mentions was weighted; (4) the capture was carried out in the period between May 2020 and May 2021. As a result of the execution, a dataset organised in a matrix was obtained and saved in a .csv file.

4.2

Data Analysis

Once the .csv file with the data was obtained, it was exported to an SNA software tool to proceed with the construction and analysis of the network. In this work, Gephi was chosen because it is a software widely used to analyse different types of networks due to its functionalities and attributes. First, Gephi was used to build the weighted mention network with its corresponding connections. Then the network graph was explored using distribution algorithms based on the principles of repulsion and attraction (i.e., Fruchterman-Reingold, ForceAtlas 2, etc.). Finally, statistical analysis was performed to determine the properties of the network.

4.3

Measuring Social Capital

Once the network indicators are determined, we calculate and analyse the social capital at the global and node level based on the framework derived from the social structure and interactions in the SNS platform (Tables 2 and 3). In order to visualise the combination of these metrics, in their mutual effort relationship, we have used operators with the Gephi software filters. That is, we made queries based on the fusion of conditions and attributes that we have selected as equivalent to the variable we are studying.

1

For further information: https://github.com/umairtehami/Twitter-Social-Capital

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Fig. 2 Network of Mentions Generated for May 2020 to May 2021 after Applying the Yifan Hu Layout. Source: Own elaboration

5 Results and Discussion 5.1

Shaping of the Network of Mentions

The data we obtained from Twitter was subjected to an exploration process in Gephi software to generate the graphical representation and identify the key properties of the TSAC cluster’s network of mentions for the period May 2020 to May 2021 (Fig. 2). The network design has been carried out using the distribution algorithm proposed by Hu (2005), which describes that links create attractive forces between nodes. That is, the closer the nodes are, the more weighted the edges are, and the more distance there is between the nodes, the weaker or less weighted the link is. In the context of the study, the TSAC cluster mention network has 5813 nodes and 7437 edges, indicating that a node has less than two ties on average. In Fig. 2, the larger nodes are the members associated with the TSAC cluster and the smaller nodes are

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the users who mention or are mentioned by those associated with the cluster. And each edge is an interaction between a pair of nodes, which is represented by the mentions that have been made during the exchange of tweets in the analysis period. The figure shows a network connected in a single component with no fragments or isolated pockets of activity where interactions between users are concentrated in the main component. In the graph, a network is observed with possible sub-clusters present indicating that users associated to the TSAC cluster mention and/or are mentioned by associated and non-associated users. In the network, nodes can be observed that are linked to more distant nodes, suggesting the possibility of collaboration between sub-clusters and possibly holding the network together by facilitating the transfer of resources.

5.2

Social Capital at Network Level

Based on the framework proposed in Sect. 2.4, we have calculated social capital at the global level from a social network perspective, the results of which are presented in Table 4. At the global level, we have considered that social capital focuses on patterns of connection and shared interests among users. This is described in its resource approach with structural dimension and cognitive dimension of social capital.

5.2.1

Structural Dimension

We consider describing the structural dimension of social capital with the variable “social connectivity”, which is suggested as a measure of the quality of the network that offers social capital benefits equivalent to the behaviour and relationship between network members. In structural terms, high connectivity is identified as a relative number of connected users who can transfer or access each other’s resources; as well as, a level of bonding representing the quality of ties between subgroups. In this study, the measure of social connectivity at the global level is Table 4 Social Capital of the TSAC Cluster at the Network Level Social capital resources Structural dimension

Variables Social connectivity

Cognitive dimension

Exchange by affinity Social cohesion

Source: Own elaboration

Network indicators Network density Diameter Modularity Cluster Transitivity Average clustering coefficient Average degree Average weighted degree

Value 0.0001 7.0 0.32 9.0 1463 0.065 1.3 6.04

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based on the links represented by the mentions received or sent between TSAC cluster network users (partners and non-partners). The information provided by the graph indicates that the network has low connectivity (Table 4). On the one hand, its low density confirms that some users associated to the cluster have links to others who are not connected to any other by mentions (i.e., the nodes @GobAragon, @camarazaragoza). On the other hand, the diameter is indicating the moderate accessibility that users have to each other when they are mentioned. The fact that the network has a low density and a moderate diameter suggests that minimum number of links is being used to connect all users. Also, the modularity of the TSAC cluster mention network at 0.32 indicates the presence of sub-clusters and the existence of links within and between sub-clusters, without specifying details of the relationship.

5.2.2

Cognitive Dimension

To assess the cognitive dimension of social capital, at a global level, the variables Exchange by affinity and social cohesion have been considered. The measure of Exchange by affinity, at the global level, has been analysed by identifying the sub-clusters and knowing whether the possible connected triads in the network are Table 5 Distribution of associated and non-associated members of TSAC cluster Subcluster 1 2

Network members (% of total) 64.82 9.29

Non-associated members (#) 3767 537

3

8.31

474

4 5

5.06 3.72

293 213

6

3.66

211

7 8 9

3.30 1.79 0.05

191 103 2

Source: Own elaboration

Twitter profile associated members @GobAragon @Camarazaragoza @FBasilioParaiso @Ceste_zgz @TsacAragon @Delsat_drones @ARPAemc @Culturandorra @CajaRuralAragon @Ochardinet @ChentaPirineo @Arinobalneario @Esnepi @ZaragozaTurismo @TuHuesca @Geoventur @Villanuaturismo @DPTeruel @CampingEstanca @Ilunionhotels @FCQorg @hospederiarueda

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strong or not (Lőrincz et al., 2019). In the network of TSAC cluster mentions, nine sub-clusters were identified, observing that at least one TSAC cluster member initiates or promotes resource transfer (Table 5). It should be noted that edge weights were considered in the distribution. Moreover, only 1463 complete triangles are observed in the network of mentions, 6.5% of all possible triangles. This indicates that neighbours do not often mention each other, showing that the link between neighbours is weaker than between pairs of nodes and that transitivity is not strong. To measure social cohesion, we have considered analysing the probability that a node’s neighbours are connected to each other, as well as the relative number of links per network member. In the network of mentions of the TSAC cluster, a low volume of relationships is evident, where the result of the network indicator indicates that on average the number of links per node is between one and two with an approximate weight of six (Table 4). It is also evident that the subgroups of the mention network are not very strong, which is reflected in the fact that one of the 15 possible triads are completed within the network.

5.3

Social Capital at Node Level

Based on the framework proposed in Sect. 2.4, we calculated the social capital at the node level from a social network perspective, the results of which are presented in Tables 6 and 7. At the node level, we considered that social capital can be described by the structural, cognitive and relational dimensions of social capital.

5.3.1

Structural Dimension

To define the structural dimension of social capital we have considered the social connectivity variable, which can be measured with the Hits algorithm using two metrics: Hubs score (nodes that have mentioned more times other members of the network) and Authority score (nodes that have received more mentions from other users). It can be said that approximately 72% of the total number of users in the TSAC cluster mention network have high Hubs and Authority scores. Of which, only eleven are users associated to the cluster (Fig. 3). Some with the characteristic of being Hubs connecting with others that are highly informative and others with the characteristic of nodes receiving valuable information.

5.3.2

Cognitive Dimension

The cognitive dimension has been defined by the variable’s affinity exchange and social cohesion. The former was measured with two network indicators and the latter with one indicator (Table 6). Using the filters in Gephi we were able to identify the group of users who have relative cognitive social capital. Thus, we find that 724 nodes are connected while maintaining significant cohesion (Fig. 4).

Source: Own elaboration a Value is weighted

Username of TSAC members on Twitter @Zaragozaturismo @Villanuaturismo @Tuhuesca @Tsacaragon @OChardinet @Ilunionhotels @Hospederiarueda @Gobaragon @Geoventur @FCQorg @Fbasilioparaiso @Esnepi @DPTeruel @Delsat_drones @Culturandorra @Chentapirineo @Ceste_zgz @Campingestanca @camarazaragoza @Cajaruralaragon @Arpaemc @Arinobalneario

Total followers 30,189 2021 4887 481 411 11,952 247 104,416 798 2944 244 800 4711 207 318 108 1317 167 14,034 1494 479 765

TSAC followers 15 4 6 8 1 2 3 18 2 2 3 5 7 0 3 2 2 2 11 3 2 2

Followees total 2144 672 1287 1085 923 3062 186 486 728 48 352 714 502 92 133 218 504 533 3141 586 588 221

Followees TSAC 5 5 5 18 7 0 5 3 3 0 6 6 2 2 3 2 3 5 6 7 3 5

Table 6 Structural and cognitive social capital of the TSAC cluster at node level

Authority 0.0259 0.0041 0.0140 0.0048 0.0008 0.0052 0.0002 0.9966 0.0008 0.0085 0.0051 0.0020 0.0450 0.0043 0.0005 0.0003 0.0076 0.0008 0.0513 0.0273 0.0073 0.0016

Hubs 0.0178 0.0161 0.0162 0.0197 0.0001 0.0001 0.0000 0.0000 0.0000 0.0000 0.0169 0.0000 0.0165 0.0000 0.0000 0.0000 0.0156 0.0000 0.0184 0.0191 0.0155 0.0000

Social connectivity

Structural dimension Exchange by affinity Clustering Degree coefficient centralitya 0.0034 1616 0.0115 396 0.0055 569 0.0139 532 0.0641 27 0.0007 590 0.0000 3 0.0001 24,653 0.0000 6 0.0000 183 0.0316 395 0.0000 24 0.0046 1657 0.0060 64 0.0000 3 0.0000 1 0.0253 106 0.0278 14 0.0022 2788 0.0043 1646 0.0167 148 0.0833 15

Cognitive dimension Social cohesion Closeness centrality 0.4503 0.3542 0.3751 0.5247 0.3460 0.3100 0.0000 0.0000 0.0000 0.0000 0.3968 0.0000 0.3810 0.2165 1.0000 0.0000 0.2976 0.2802 0.4802 0.5023 0.2491 0.0000

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Value is weighted Source: Own elaboration

a

Username of TSAC members on Twitter @Zaragozaturismo @Villanuaturismo @Tuhuesca @Tsacaragon @OChardinet @Ilunionhotels @Hospederiarueda @Gobaragon @Geoventur @FCQorg @Fbasilioparaiso @Esnepi @DPTeruel @Delsat_drones @Culturandorra @Chentapirineo @Ceste_zgz @Campingestanca @Camarazaragoza @Cajaruralaragon @Arpaemc @Arinobalneario

Engagement Out-degree centralitya 367 294 187 452 18 186 0 0 0 0 277 0 457 1 1 0 38 5 1623 1013 45 0 Bridging centrality 0.000000 0.000000 0.000000 0.000000 0.000003 0.000000 0.000000 0.000000 0.000000 0.000000 0.000001 0.000000 0.000000 0.000001 0.000010 0.000000 0.000001 0.000004 0.000000 0.000000 0.000001 0.000000

Popularity In-degree centralitya 275 37 115 37 6 167 3 4168 5 124 30 17 264 31 2 1 45 5 392 209 58 9

Table 7 Relational social capital of the TSAC cluster at node level Eccentricity centrality 4 4 3 3 4 5 0 0 0 0 4 0 3 5 1 0 5 4 4 3 6 0

Social influence Betweenness centrality 0.0124 0.0030 0.0059 0.0077 0.0004 0.0074 0.0000 0.0000 0.0000 0.0000 0.0019 0.0000 0.0116 0.0007 0.0000 0.0000 0.0018 0.0002 0.0224 0.0145 0.0019 0.0000 Eigenvector centrality 0.0708 0.0092 0.0284 0.0128 0.0018 0.0417 0.0007 1.0000 0.0013 0.0296 0.0104 0.0041 0.0667 0.0078 0.0007 0.0005 0.0141 0.0014 0.1017 0.0563 0.0148 0.0026

Prestige Proximity prestige 0.1041 0.0710 0.0746 0.0852 0.0706 0.0883 0.0005 0.7184 0.0673 0.0804 0.0861 0.0619 0.0932 0.0721 0.0626 0.0625 0.0872 0.0626 0.1096 0.1018 0.0791 0.0711 Domain prestige 0.2335 0.2335 0.2335 0.2335 0.2335 0.2335 0.0005 0.8613 0.2342 0.2529 0.2335 0.2361 0.2335 0.2335 0.2338 0.2337 0.2335 0.2335 0.2335 0.2335 0.2335 0.2345

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Fig. 3 TSAC Cluster Network Members with Connectivity: (a) dark grey coloured nodes have higher Authority scores; (b) black coloured nodes have higher Hubs scores. Node size indicates the high score. Source: Own elaboration

Fig. 4 Members of Mention Network of the TSAC with Cognitive Social Capital: (a) Associate and non-associated members; (b) Associate members. Node size indicates a low closeness centrality score and the colours of the nodes indicate the different sub-clusters. Source: Own elaboration

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Among them, 17 users associated with the TSAC cluster are present, ten of them with closeness centrality values close to zero (i.e., @FCQorg) suggesting that they have very short paths to the other members of the network. In other words, by mentioning or being mentioned more times by others, they are possibly achieving greater proximity, accessibility and opportunity to share resources.

5.3.3

Relational Dimension

The relational dimension has been defined with the variables of engagement, popularity, social influence and prestige (Table 7). The engagement variable suggests the level of interaction that members have in the network by identifying users who are likely to actively disseminate network resources, those who participate and establish connections. Using Gephi filters that manage to combine indicators by specifying engagement as the only data attribute (Table 7), we were able to identify that 105 members have high engagement and participation in the network (Fig. 5). Of these, only nine are users associated with the TSAC cluster. Although they are considered as those who have high involvement in capturing the attention of a large number of users, they are not those who facilitate the transfer of resources in an intermediate position. On the contrary, some non-associated members are those who participate in the network by disseminating relevant resources to TSAC cluster members as mediators (i.e., @aragontv). The popularity variable indicates that users are naturally conversation Hubs, those users who are considered credible, experienced and supportive, who have been mentioned by others and who can get information to many quickly. To measure this variable, we combined the equivalent indicators with the Gephi filters allowing us to identify 27 members who are considered to have relative popularity. Of these, 14 are users associated with the TSAC cluster identified as having received the most mentions and being close to all the others (Fig. 6).

Fig. 5 Members of Mention Network of the TSAC with Engagement: (a) Associate and non-associated members; (b) Associate members. Node size indicates a high out-degree centrality score and black nodes indicate a high bridging centrality score. Source: Own elaboration

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Fig. 6 Members of Mention Network of the TSAC with Popularity: (a) Associate and non-associated members; (b) Associate members. Node size indicates a high in-degree centrality score and black nodes indicate a high eccentricity centrality score. Source: Own elaboration

Fig. 7 Members of Mention Network of the TSAC with Social Influence: (a) Associate and non-associated members; (b) Associate members. Node size indicates a high betweenness centrality score and black nodes indicate a high eigenvector centrality score. Source: Own elaboration

The social influence variable indicates the presence of members in the network who are in a better position to find and share resources. To measure social influence, we consider the number of times a network member appears in the shortest paths between others (associated and not associated with the TSAC cluster) when they have been mentioned. We include the likelihood that members have links to reputable neighbours. From the combination of the indicators, we have identified 61 members who are considered to have relative social influence. Of these, 16 are members associated with the TSAC cluster (i.e., @Arpaemc) identified as those members likely to promote information exchange by being connected to other members recognised as important for their quality and quantity of resources (Fig. 7).

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Fig. 8 Members of Mention Network of the TSAC with Prestige: (a) Associate and non-associated members; (b) Associate members. Node size indicates high proximity prestige score and black nodes indicate a high domain prestige score. Source: Own elaboration

The prestige variable indicates the degree of recognition a member receives in the network related to the ability to convey trust, opportunities and support. To measure prestige, proximity prestige and domain prestige metrics were adopted. In the case of the study, the former emphasises the direct and indirect mentions a member receives as a domain of influence considering the average distance in the domain of influence. The second is the share of direct or indirect mentions a specific user receives. From the combination of the indicators, we identified 132 members who are considered to have relative prestige. Of these, 15 are associated users of the TSAC cluster (i.e., @Geoventur) recognised as those members who are easily reachable by all others and who are likely to have control of the network resources (Fig. 8).

6 Discussion and Conclusions In this study, the social capital of a tourism cluster is assessed from the perspective of social networks when the associated companies interact on the Twitter platform with mentions actions. Specifically, the assessment is carried out from its resource approach, from the framework of social networks analysis and using the information available on the virtual platform. The results, at the network level, showed that members associated with the TSAC cluster fostered the formation of a community on the Twitter platform in which social capital was generated. During the period of study, the companies formed a single-component network of mentions that is not complete, Perhaps because each firm is unlikely to connect with every other member of the network, as suggested by Crowe (2007). As a small cluster, it is to be expected that the phenomenon of network closure, as argued by Allcott et al. (2007), will occur. However, the TSAC cluster mentions

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network is a structure that is not densely connected and has a low volume of relationships. This is because the minimum number of interactions is being used to connect all members and ties between neighbours are weaker than between pairs of nodes, confirming that it is a network with low connectivity. This leads to a lower likelihood of encouraging reciprocity, forming trust and developing cooperative behaviours (Peng & Wang, 2013). Although the associated members of the TSAC cluster join together to expand their contacts network, foster cooperation and promote cohesion among cluster members, when they interact with mentions, it is observed that the companies remain their individualistic nature (Casson & Giusta, 2007). As a result, they form a network structure with loosely cohesive sub-clusters, with slight intra- and inter-cluster interactions and where neighbours of each pair of sub-cluster nodes do not usually mention each other. At least one associate member is present in each sub-cluster suggesting that each company maintains its own network of individual ties to initiate or promote the resources transfer represented by the exchange of mentions. This implies that individual networks facilitate the individual competitiveness of each company, that the resource flow is likely to be limited or interrupted, and that there is a lack of cohesion in the cluster as a unit (KC et al., 2018). Referent to the analysis at the node level, the results showed that there is contact between some users associated with the TSAC cluster through mentions, probably because they share a common interest. The roles identified in the TSAC cluster members are as follow: (1) Members associated with the characteristic of being central by connecting with others highly informative until they have a relationship of mutual effort and achieve a high structural social capital (i.e. @Tsacaragon, @Cajaruralaragon). Also, those with the characteristic of nodes that receive valuable information (i.e., @Gobaragon, @camarazaragoza). (2) Members who are closer to the rest of the nodes (i.e., @Gobaragon, @FCQorg, @Arinobalneario), as well as those who are further away (i.e., @Tsacaragon, @Cajaruralaragon, @Camarazaragoza). This is that by mentioning or being mentioned more times by others, they are possibly achieving greater proximity, accessibility and opportunity for resource sharing and in turn cognitive social capital. (3) Members associated who are highly involved in capturing the attention of a large number of users, without being the ones who facilitate the transfer of resources in an intermediary position (i.e., @Camarazaragoza, @Cajaruralaragon). On the contrary, some non-associated members are those who participate in the network by disseminating relevant resources to the TSAC cluster members as mediators (i.e., @aragontv, @aragonalimentos, @HorecaZaragoza, @Ceeiaragon). (4) Members associated who are relatively popular because they are probably considered to be able to mediate on common initiatives due to their good reputation or because they can get information to many more people more quickly. Among those who have received the most mentions and are close to all the others are @DPTeruel, @Cajaruralaragon, @Tuhuesca, @Gobaragon. (5) Members associated that have an influence on others causing possible changes in their opinion or behaviour (i.e., @Camarazaragoza, @Zaragozaturismo, @DPTeruel, @Cajaruralaragon, @Ilunionhotels). A particular case is that of the

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user @Tsacaragon, which within the network should be in a good position to promote common initiatives in the tourism sector, however, it does not have the necessary quantity or quality of links. (6) Users who have a relative prestige for having received direct and indirect mentions as users who convey trust, opportunities and/or support and who are likely to have control of the network’s resources. Among the five that are considered to have the most prestige are @Gobaragon, @FCQorg, @Tsacaragon, @Camarazaragoza, @Esnepi. To sum up, in the network of mentions of the TSAC cluster, it was possible to identify the actors that stand out because they directly link users with whom they have social proximity, allowing them to obtain and strengthen their resources of connectivity, trust, support, cooperation, etc. As well as those who link with distant and unknown users, acting as a bridge to mobilise new information, while at the same time providing effective social cohesion, avoiding disconnection between different groups (Anderson & Jack, 2002; Granovetter, 1973). Optimal social capital results are achieved in an association when there is a balance in the roles of its members (Agnitsch et al., 2006; Etxabe, 2018). Therefore, ensuring that the TSAC cluster network has its members highly connected, that they help to establish relationships between other members who are not well known, and that they encourage the exchange of material and/or symbolic resources will not only allow individual competitiveness but will also facilitate and encourage the development of common initiatives in the tourism sector. This study contributes to the literature on social capital by investigating it in the context of social networks, because it presents a way to measure social capital from its resource approach, under the perspective of social networks, in the tourism sector and using the information available on the Twitter platform. This work has followed an orderly process where tools for understanding, identifying and measuring network structure at the global and individual levels were applied. The results obtained have implications for the business management of cluster members who need to identify associate and non-associate members with whom social capital can be increased through the exchange of resources. Among the limitations that can be covered by future research, without being a direct disadvantage, in this study the data extraction was done in a temporal period. It would be interesting to conduct the study under a longitudinal design that provides real-time information on the evolution of social capital as a function of the interactions between network members. On the other hand, this study has been carried out by analysing the network of mentions of an association of companies in the tourism sector. Future work could replicate the present study in different associations and analyse a network based on different types of interactions present on the Twitter platform. Acknowledgements The corresponding author gratefully acknowledges the Universitat Politècnica de Catalunya for the financial support of her pre-doctoral grant FPU-UPC, with the collaboration of Banco de Santander.

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The Economic Value of Recreational Assets During the COVID-19 Pandemic on the Example of Bóbr Valley Railway “Bobertalbahn” Paweł Piepiora

, Justyna Bagińska

, and Zbigniew Piepiora

Abstract The purpose of this paper is to answer a research question: has the economic value of recreational assets of Bóbr Valley Railway during the Covid-19 pandemic increased? A motivation to conduct the analysis was the information concerning the possibility of the restoration of the examined line. We carried out a survey and estimated the economic value of recreational assets of the railway using the contingent valuation method. Due to the Covid-19 pandemic, we collected the questionnaires fully online. We conducted the research in Poland in the second half of 2020. The economic value of recreational assets of Bóbr Valley Railway amounted almost PLN 5.2 million (EUR 1.1 million) in 2019 and PLN 222.8 million (EUR 47.6 million) in 2020. The research question was positively confirmed. The reason was a 3-fold increase in the intensity of tourist traffic from the year 2019 to 2020—in spite of ongoing Covid-19 pandemic and a 15-fold reduction in the reference rate by the National Bank of Poland at the beginning of 2020. Keywords Value · Recreational assets · CVM · WTP · Bóbr Valley Railway “Bobertalbahn”

P. Piepiora Department of Sports Didactics, Wroclaw University of Health and Sport Sciences, Wrocław, Poland e-mail: [email protected] J. Bagińska Wroclaw Business University of Applied Sciences, Wrocław, Poland e-mail: [email protected] Z. Piepiora (✉) Institute of Spatial Management, Wrocław University of Environmental and Life Sciences, Wrocław, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. H. Bilgin et al. (eds.), Eurasian Business and Economics Perspectives, Eurasian Studies in Business and Economics 25, https://doi.org/10.1007/978-3-031-36286-6_23

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1 Introduction Motivation for the research was the information about the possible reactivation of the Bóbr Valley Railway “Bobertalbahn”. Defining the economic value of recreational assets of a given property or area allows us to determine user’s perception of this property or area. Valuation helps in the correct allocation of funds in order to maximize the benefits of a given good, property or area. Furthermore, determining the value is also important when carrying out, for example, investment projects to restore disused railway lines such as the Bóbr Valley Railway “Bobertalbahn”. The paper was written also to consider the impact of the Covid-19 pandemic on the economic value of recreational assets. At this point, it should be recalled that SARS-CoV-2 is a virus belonging to the coronavirus group, that causes an acute pulmonary disease COVID-19. On 17 November 2019 the Covid-19 epidemic started in China, which was considered a pandemic on 11 March 2020 (Zhu et al. 2020). Thus, the purpose of the article is to evaluate the recreational assets of Bóbr Valley Railway, which may be useful to decision makers while concerning the restoration of this line. The aim of this paper is to answer a research question: has the economic value of recreational assets of Bóbr Valley Railway during the Covid19 pandemic increased? Authors’ contribution is the first ever study during the Covid-19 pandemic of an unused railway concerning the economic value of its recreational assets. After literature study (Bowker et al., 2007; Lee et al., 2017; Lim, 2020; Zhang and Liu, 2016) the contingent valuation method (CVM) was applied, specifically one of its varieties: willingness-to-pay (WTP) survey was conducted and after calculating the results, the conclusions were drawn. The essay consists of: introduction, literature study, object of the research, data and methodology, results and discussion, and conclusion and references.

2 Literature Study The core issue of literature study was to verify the usage of contingent valuation method (Arrow et al., 1993; Davis, 1963; Folmer & Gaber, 2000; Horowitz & McConnell, 2002; Isoni, 2011; Knetsch & Sinden, 1984; Rogall, 2004; Throsby, 2003) in the recent years with regard to railway transport. The existing research proves the usefulness of the CVM method in a variety of transport-related research topics, such as the usage of the WTP for the train fare research (Nonthapot et al., 2016). Very few papers (Woo et al., 2021; Arashima et al., 2021; Severino et al., 2021) contain an analysis related to train transport conducted during the Covid-19 pandemic. Woo et al. (2021) investigated declining Mass Transit Railway’s (MTR’s) ridership in the face of growing social unrest and COVID-19 pandemics. Arashima et al. (2021) estimated the value of conventional easy access system to train

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platforms for wheelchair users due to the spreading of COVID-19. Severino et al. (2021) developed multi-stage models of tourist activities for enhancing the development of operational businesses. The research enabled to take into consideration risks when planning tourist routes by railway, determine the order of construction or start of routes, and assess their profitability. Additionally, only four other papers (Bowker et al., 2007; Zhang & Liu, 2016; Lee et al., 2017; Somogyi & Csapó, 2018; Lim, 2020) concerned valuation of tourist railways before Covid-19 pandemic. Bowker et al. (2007) estimated the net economic value to trail users and the local economic impacts of the Virginia Creeper Rail Trail in the USA. They indicated that the trail was a highly valuable asset to the people who enjoy using it and to local businesses who benefit from trail-related tourist expenditures. The integrated valuation methodology and results could facilitate quantification of recreational trail economic benefits in other locations. Zhang and Liu (2016) investigated the value of industrial heritage railway lines in China in Ang’ang Xi district, Yimianpo town and Hengdaohezi town by utilizing the CVM. Lee et al. (2017) assessed the economic feasibility of tourist trains. The surveyed subjects were willing to pay fares that were higher than prices currently published at the time of the study, thereby demonstrating that the economic value they invest on tourist trains may be higher than published prices at the time. The study is significant in that it succeeded in quantifying the satisfaction level of tourist train passengers using quantitative data (additional funds people are willing to pay). Somogyi and Csapó (2018) provided evaluation method for landscape preferences of Hungarian railway lines passengers, showing how the landscape around the railways might become a travel attraction. They stated that surveyed types of landscape appearance would be needed in order to generate travel decisions for tourists and also how the travel experience itself could become a tourism product. Lim (2020) assessed the economic value of a variety of types of sightseeing trains service according to WTP and with the use of open-ended question surveys. The WTPs vary by different types of train services such as recreational activities, slow-moving operation, seating type, tourist commentary and locally connected tour service. To the best knowledge of the authors, our study is the first one concerning the economic value of recreational assets of an unused railway during the Covid-19 pandemic.

3 Object of the Research The Bóbr Valley Railway is located in the south-west of Poland, in the Sudety mountains. It is a section of line 283 between Jelenia Góra and Lwówek Śląski, which runs through the picturesque Bóbr Valley Landscape Park. The line used to connect Jelenia Góra with Żagań (Potocki et al., 2014; Piepiora & Piepiora, 2020). At the end of the nineteenth century, the construction of a railway line between Jelenia Góra and Lwówek Śląski was deemed economically unprofitable due to the need to build 3 tunnels and 2 bridges. However, the floods in Silesia changed the

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situation, especially the great flood in July 1897. After the flood, the idea of flood protection was developed by professor engineer Otto Intze while his Jelenia Góra collaborator, Curt Bachmann, was responsible for the final preparation and the technical side of the projects. On 3 July 1900, the Silesian Flood Protection Act entered into force, which made it possible to carry out regulatory works and, inter alia, construction of a dam in Pilchowice on the Bóbr river. To reduce the costs of transporting construction materials, machines and people, and, as a result, the costs of building the dam, it was decided to combine its creation with the construction of a railway line from Jelenia Góra to Lwówek Śląski. This line was constructed in the years 1904–1909 under the supervision of the construction inspector Mr. Kurowski, and then Mr. Winter, PhD, while the dam in Pilchowice was built in the 1904–1912 period. It is worth adding that apart from the economic (transport) function, the railway line from Jelenia Góra to Lwówek Śląski also played a tourist function. The line consisted of a 33 km section of tracks built in the difficult terrain, including three tunnels (187 m, 154 m and 320 m), a 130 m truss bridge hanging over the Pilchowice Lake, one of the highest bridges in Poland, and a stone, four-span viaduct in Pilchowice. In addition, it boasted a number of smaller bridges, viaducts and several picturesque stations. Today, this unused route is considered by many to be one of the most beautiful sections of railways in Poland. On 11 December 2016, however, the last train of Koleje Dolnośląskie rode along it, because it was quite damaged, and, according to the timetable, the journey time of the 33 km route was 1 hour 50 minutes (Piepiora et al., 2014; Dominas & Przerwa, 2017; Piepiora, 2020). In 2020, there was information in the media that Tom Cruise was to blow up a railway bridge on the Bóbr Valley Railway on Lake Pilchowice in his new film “Mission Impossible 7”. As a result of that, tourist traffic in Pilchowice increased threefold (Gierak, 2020; Piepiora, 2020). In September 2020, increased tourist traffic in the Wleń commune, caused by the information about the alleged blowing up of the bridge over Lake Pilchowice, allowed railway enthusiasts to create a tourist attraction. The Bóbr Valley Railway was reactivated in the form of trolley rides, but only on weekends, and from the railway station in Wleń through the railway tunnel and back. Perhaps the entire railway line from Jelenia Góra to Lwówek Śląski will be reactivated, following the recent trend to reactivate other unused railway lines in the region, such as railway line No. 340 Mysłakowice–Karpacz (German: “Riesengebirgsbahn”), closed in April 2000, taken over by the Dolnośląskie province in June 2021. At present, in accordance with the EU’s European Year of Rail 2021 initiative, there are plans to reactivate the Mysłakowice–Karpacz line by the end of 2023 (Piepiora, 2020).

4 Data and Methodology The authors used the contingent valuation method, also called the method of declared preferences. The basis for the valuation is determining, through appropriate surveys, what amounts (of money) the individuals are willing to pay for access to the

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values of the environment, for some ecological goods or in order to receive as compensation for the loss of the possibility of using the environment (Becla et al., 2012; Bowker et al., 2007; Lara & da Silva, 2019; Larumbe 2021; Mulley et al., 2016; Woo et al., 2021; Winpenny, 1991). The formula for the annual stream of willingness-to-pay benefits has been described with the following Eq. 1. YWTP = M WTP
L

ð1Þ

YWTP—annual willingness-to-pay benefit WTP MWTP—median of WTP value L—number of visitors Source: Own study based on: (Piepiora & Mądro, 2018). Formula for perpetual annuity has been described with the following Eq. 2. PV =

YWTP i

ð2Þ

PV—perpetual annuity of WTP YWTP—annual willingness-to-pay WTP i—interest rate Source: (Piepiora & Kujawa, 2018). The skewness of the distributions was examined by the authors as the disparity by the mean value and the mode. The authors adopted the following study assumptions: minimum size of the statistical sample was 261 respondents, maximum statistical error was set at 6% while confidence interval was 95% and fraction size was set at 0.5. Interest rate was determined at the level of the reference interest rate of the National Bank of Poland and it was 1.5% in 2019 and 0.1% in 2020. 1 Euro is 4.68 PLN (Podstawowe, 2021; BDL, 2021; Ostasiewicz et al., 2000; Exchange, 2021). The procedure was as follows: the survey was conducted online from 14 August to 12 December 2020. In order to answer the research question the authors constructed a questionnaire. They chose the contingent valuation method (willingness-to-pay option). To assess the number of visitors, the authors asked: How many visitors did you meet in Pilchowice in the Wleń commune (excluding those who came with you) during your last stay? In order to obtain WTP1, the authors asked: If the Pilchowice-Zapora station in the Wleń commune could be reached directly by train again, how much PLN would you be willing to pay for a ticket from Jelenia Góra? (a picturesque route along the Bóbr Valley through Jeżów Sudecki and Siedlęcin, 11.1 km long). In order to obtain WTP2, the authors asked: If Pilchowice in the Wleń commune could be reached directly by train again, how much PLN would you be willing to pay for a ticket from Lwówek Śląski? (a picturesque route along the Bóbr Valley through Dębowy Gaj, Marczów, Wleń, Pilchowice-Nielestno with a length of 21.5 km). Then, the respondents were asked to declare the province in which they

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live, their gender, age group, education and the average monthly net income (“on hand”).

5 Results and Discussion The questionnaire was available through a website (Piepiora, 2022) concerning the tourism, culture and history of the Sudety Mountains, “Na szlaku”—a tourist and sightseeing journal available online (Piepiora, 2020), and through social media accounts and mailing groups of Sudety Mountains enthusiasts in the period of August to December 2020. The question regarding the province of residence yielded 239 answers. As we can see in Table 1, respondents from Dolnośląskie province prevailed (75.7%). They were followed by the residents of: Śląskie, Opolskie, Łódzkie and Wielkopolskie provinces. Since the Sudety Mountains (including the research area) are situated in the Dolnośląskie province, most of the respondents naturally were from this area. The respondents from outside Dolnośląskie province have either been to the area in question or have their families or friends there and generally know the history of the investigated railway line. Question 2 regarded the respondents’ gender and yielded 273 answers. Males were slightly more numerous in the research—a little over 51%. Age group was the third question with 274 answers. As we can see in Table 2, the respondents were

Table 1 Province of residence

Province Dolnośląskie Śląskie Opolskie Łódzkie Wielkopolskie Lubelskie Lubuskie Świętokrzyskie Kujawsko-Pomorskie Małopolskie Mazowieckie Podkarpackie Podlaskie Pomorskie Warmińsko-mazurskie Zachodniopomorskie Total Source: Authors’ own study

No. of respondents 181 14 12 8 8 4 3 2 1 1 1 1 1 1 1 0 239

Share (%) 75.73 5.86 5.02 3.35 3.35 1.67 1.26 0.84 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.00 100.00

The Economic Value of Recreational Assets During the COVID-19 Pandemic. . . Table 2 Age group

Age (years) 18–24 25–34 35–44 45–54 55–64 65 and more Total

No. of respondents 162 25 31 29 18 9 274

401 Share (%) 59.12 9.12 11.31 10.58 6.57 3.28 100.00

Source: Authors’ own study Table 3 Education Education Primary school and lower secondary school Vocational education Secondary school and post-secondary college Higher education Total

No. of respondents 6 3 64 204 277

Share (%) 2.17 1.08 23.10 73.65 100.00

Source: Authors’ own study

mainly young people of 18–24 years of age—almost 60%. They were followed by respondents aged 35–44 and 25–34. Question 4 regarded education (258 answers). As we can see in Table 3, the respondents with higher education prevailed (75%), while 23.5% of the respondents had secondary and post-secondary college education. The respondents were also asked about the monthly income in their household. Aggregated data on the monthly income in the respondent’s household was as follows: median was PLN 3000, mean value was PLN 3097.64, mode was PLN 4512.41 and As = 3097.64–4512.41. Skewness of the distribution was PLN— 1414.77 thus the distribution of monthly income per person in the respondent’s household shows left-hand skewness. The authors formulated also the following question: “How many visitors did you meet in Pilchowice in the Wleń commune (excluding people who came with you) during your last stay?”. It allowed the authors to assess the number of visitors: – in 2019: 3893 people, – in 2020: 11,139 people. Another question: “If it was possible to reach the Pilchowice-Zapora station in the Wleń commune again directly by train, how many PLN would you be willing to pay for a ticket from Jelenia Góra?”, allowed for calculation of the mode, median and mean value for WTP1. As a result, the mode for WTP1 was PLN 10, the median for WTP1 was PLN 10 and the mean value for WTP1 was PLN 11.81.

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Then, the authors calculated the skewness (As) of the distribution. As = PLN 11.81-PLN 10 = PLN 1.81. The WTP distribution turned out to be right-skewed, so the authors used the median WTP1 for further calculations. Another question was asked: “If it was possible to get to Pilchowice in the Wleń commune again directly by train, how many PLN would you be willing to pay for a ticket from Lwówek Śląski?”. It allowed the authors to calculate the mode, median and mean value for WTP2. The mode feature for WTP2 was PLN 0. The median for WTP2 was PLN 10. The mean value for WTP2 was PLN 14.04. The authors calculated the skewness (As) of the distribution. As = PLN 14.04-PLN 0 = PLN 14.04. As a result, the WTP2 distribution turned out to be right-skewed, so the authors used the median of WTP2 for further calculations. Then the authors calculated the Pearson’s linear WTP1 correlation coefficients. They were as follows for the following relations between the items: • WTP1 and respondent’s age: -0.183616387, • WTP1 and the respondent’s level of education: 0.007676122, • WTP1 and respondent’s income level: -0.034646659. Thus, we can only talk about a very weak, negative linear correlation between WTP1 and the age of the respondent. Next, authors calculated the Pearson’s linear WTP2 correlation coefficients. They were as follows for the following relations between: • WTP2 and respondent’s age: -0.358217667, • WTP2 and the respondent’s level of education: 0.022869349, • WTP2 and respondent’s income level: -0.068820409. Thus, we can only talk about a clear, negative, but low linear correlation between WTP2 and the age of the respondent. In the next stage of research procedure the authors summed up the medians of WTP1 (PLN 10) and WTP2, obtaining the median WTP = PLN 20. Following the formula YWTP = MWTP
L, the authors calculated the annual willingness-to-pay benefit stream for 2019 and 2020: • YWTP2019 = PLN 20×3893 = PLN 77860. • YWTP2020 = PLN 20×11139 = PLN 222780. According to the PV = annuity. • • • •

YWTP i

formula, the authors calculated the perpetual

PV2019 = PLN 77,860/1.5% = PLN 5,190,666.67. That is PLN 5.2 million (EUR 1,109,116.81). PV2020 = PLN 222,780/0.1% = PLN 222,780,000. That is PLN 222.8 million (EUR 47,602,564.1). * (1 Euro is 4.68 PLN (Exchange, 2021))

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Through the analysis a three-fold increase in the intensity of tourist traffic from the year 2019 to 2020—in spite of ongoing Covid-19 pandemic was noted, which was a different outcome than in the study of Woo et al. (2021) who observed a decline in tourist traffic during the pandemics. Therefore, we agree with Bowker et al. (2007) who stated that the financial valuation results imply that “the trail is a highly valuable asset to the people who enjoy using it”. Wleń commune is, just like many other communities, interested in developing and preserving recreational trails to aid their users and as tourist attractions, to encourage economic growth. Moreover, alongside with the restoration of many closed railway lines, Polish rail authorities should consider creating new tourism products by developing rail tour routes in an effort to combine local tourism resources with rail service and to create value in regional areas as it was the case of South Korea (Lee et al., 2017).

6 Conclusion The authors positively answered the research question: the economic value of recreational assets of Bóbr Valley Railway during the Covid-19 pandemic has increased. The reason for this was a 3-fold increase in the intensity of tourist traffic in Pilchowice in the Wleń commune despite the Covid-19 pandemic and a 15-fold reduction in the reference rate by the National Bank of Poland at the beginning of 2020. The Pearson’s linear correlation coefficient between WTP1 and the respondent’s age was -0.183616387, so there was a very weak, negative correlation between WTP1 and the age of the respondent. The Pearson’s linear correlation coefficient between WTP2 and the respondent’s age was -0.358217667, so there was a clear, negative, but low correlation between WTP2 and the age of the respondent. It is worth noting that the respondents were predominantly young people with higher education and residents of the Lower Silesia Province, probably due to the proximity to the Bóbr Valley Railway. The limitation of the research is that the respondents were mostly from the Dolnośląskie province and the majority were 18–24 years old. In the future, authors shall focus on evaluation of the economic value of railway bridge over Lake Pilchowice and estimation of the share of this value in the economic value of recreational assets of Bóbr Valley Railway.

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