Economics and Finance Readings: Selected Papers from Asia-Pacific Conference on Economics & Finance, 2022 9819919789, 9789819919789

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Evan Lau Rayenda Khresna Brahmana Lee Ming Tan   Editors

Economics and Finance Readings Selected Papers from Asia-Pacific Conference on Economics & Finance, 2022

Economics and Finance Readings

Evan Lau · Rayenda Khresna Brahmana · Lee Ming Tan Editors

Economics and Finance Readings Selected Papers from Asia-Pacific Conference on Economics & Finance, 2022

Editors Evan Lau Department of Economics Faculty of Economics and Business Universiti Malaysia Sarawak UNIMAS Kota Samarahan, Malaysia

Rayenda Khresna Brahmana School of Economics, Finance and Accounting Coventry University Coventry, UK

Lee Ming Tan East Asia Research Singapore, Singapore

ISBN 978-981-99-1978-9 ISBN 978-981-99-1979-6 (eBook) https://doi.org/10.1007/978-981-99-1979-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

APEF Advisory Board

Conference Chair Dr. Evan Lau Poh Hock Associate Professor, UNIMAS

Committee Members Dr. Kai-Hong Tee Loughborough University, UK Dr. Chor Foon TANG Centre for Policy Research and International Studies (CenPRIS), Universiti Sains Malaysia Assoc. Prof. Dr Erginbay U˘gurlu Istanbul Aydın University FEAS, Department of Economics and Finance Dr. Rayenda Khresna Brahmana School of Economics, Finance and Accounting, Coventry University Dr. Mohd Norfian Alifiah Department of Accounting and Finance, Faculty of Management, Universiti Teknologi Malaysia Prof. Dr. Mansor H Ibrahim Deputy President Academic INCEIF (International Centre for Education in Islamic Finance) Dr. Biagio Simonetti University of Sannio, Benevento, Italy; WSB University in Gdansk, Poland; National Institute of Geophysics and Volcanology (INGV), Naples, Italy Dr. Benjamin García-Paez Economics Department of the National University of Mexico Dr. Pedro A. Mart in-Cervantes Department of Finance and Accounting, University of Valladolid

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APEF Advisory Board

Conference Organiser

East Asia Research

Supporting Journals Journal of Risk and Financial Management—MDPI Research on Enterprise in Modern Economy Theory and Practice Journal of Indonesian Economy and Business Research on World Agricultural Economy Revue Economie, Gestion et Société Management and Economics Research Journal Li Falah: Jurnal Studi Ekonomi dan Bisnis Islam

Preface

The 2022 Asia-Pacific Conference of Economics and Finance (APEF 2022) was held on 15–16 December 2022 at the Holiday Inn Singapore Atrium in Singapore. For this edition, thirty-nine research papers from Australia, Austria, China, Czech Republic, Egypt, Greece, India, Indonesia, Japan, Macau, China, Norway, Philippines, Poland, South Africa, United Kingdom, United States, and Singapore were successfully presented. The conference programme consisted of an opening speech by Dr. Evan Lau, an Associate Professor from UNIMAS, keynote presentations were given by Denise Cheok, Asst. Director, APAC Economist, Moody’s Analytics, Singapore, who presented on ‘Asia-Pacific Outlook: A Step Down in Growth’, along with Dr. Kai-Hong Tee, Loughborough University, UK, who presented on ‘On Hedge funds as market mispricing chasers’. This publication contains eleven of the best papers written by researchers who attended the 2022 Asia-Pacific Conference on Economics & Finance (APEF 2022). These papers would serve as significant contributions to the understanding of various economics and finance issues. APEF 2023 will be an in-person conference, to be held on the 14–15 Dec 2023 at the Holiday Inn Singapore Atrium. More details about the conference are available at APEF.ear.com.sg. I welcome you to this conference and look forward to your participation. Dr. Evan Lau APEF 2022 Conference Chair Kota Samarahan, Malaysia

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East Asia Research (EAR)

Established in Singapore in 2015, East Asia Research (EAR) envisions to be the gateway to improving lives and enhancing productivity in Asia through promoting cross-geographical exchange of ideas and knowledge in various faculties. This will be achieved through the dissemination of knowledge from the Asia-focused research conferences and publications by EAR. EAR academic conferences provide a meaningful platform for researchers, postgraduates, academicians, and industry practitioners to share unique insights and drive innovation. This is a great opportunity for expanding contact networks beyond a singular field and kick-starting a strategic collaboration. Such partnership can bridge the resources and expertise of multiple disciplines to spearhead pioneer movements, giving rise to breakthroughs in long-standing issues.

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Contents

Digital Renminbi: Impacts on Economic Integration of the Greater Bay Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weng Chi Lei and Xinru Wang

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Proposition of Development and Growth Through Economic Development Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ashot Davoyan

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A European Study on Financial Risk Attitude and Cognitive Decline in Aging Societies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michail Chouzouris, Antigone Lyberaki, and Platon Tinios

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Research on the Impact of Consumer Innovation on Purchase Intention of New Energy Vehicles—Regulated Intermediary Effect . . . . . Liu Yang, Guangyi Xu, and Xu Jianzhong

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Impact of Tobacco Consumption on Food and Non-food Households Consumption Patterns: The Case of Indonesia . . . . . . . . . . . . Salim Fauzanul Ihsani and Heni Wahyuni

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Tax and Fee Cuts, Investment in Innovation, and High-Quality Development of Equipment Manufacturing Enterprises . . . . . . . . . . . . . . . Xu Jianzhong and Dan Liu

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Need for Economic Remedies with the Increase in Product Liability Cases in the Twenty-First Century . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Sonia Mukherjee Measuring Financial Inclusion in Indonesia: Asserting the Role of Digital Financial Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Sugeng Triwibowo and Nony Nurbasith

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Contents

The Use of Mobile Technologies for Shopping During the Pre-Covid-19 Pandemic Versus Covid-19 Pandemic Time; A Gender-Based Quantitative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Kelaniyage Shihan Dilruk Fernando The South Korean Export Benchmark: Validity of the Export-Led Growth Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Mayar Bakeer and Hebatallah Ghoneim Tax and Non-tax Policies Towards the Finance of Sustainable Economy: The Mediating Role of Eco-Innovation . . . . . . . . . . . . . . . . . . . . . 181 Emmanuel Ebo Arthur, Raymond Kwame Adane Darfo-Oduro, Solomon Gyamfi, Yee Yee Sein, Jan Stejskal, and Viktor Prokop

About the Editors

Evan Lau serves as the Associate Professor and Head of Strategic in the Faculty of Economics and Business, Universiti Malaysia Sarawak (UNIMAS). He was the Deputy Dean for Research and Postgraduate at the Faculty of Economics and Business from 2016–2017 and the Director of the Centre for Business, Economics and Finance Forecasting (BEFfore), UNIMAS from 2013–2016. He holds editorial positions in numerous international journals. He was at the Faculty of Economics at Cambridge from October 2013–April 2014 as a visiting scholar. He was appointed as the Visiting Professor in Universitas Sebelas Maret from June-July 2019 and with Universitas Hayam Wuruk from February 2021–August 2021. He was selected for the World Class Professor (WCP) program by the Ministry of Education and Culture, Indonesia, twice in 2020 and 2021. He was appointed the Visiting Research Fellowship (VRF) for the Central Bank of Malaysia in May 2021 for a year. Evan speaks at numerous international conferences in countries like Indonesia, UAE, Sri Lanka, Italy, India, Phillippines, and Malaysia. Besides, he is also an active academic workshop instructor. He provides lectures, consultations, and supervision to students and receives positive evaluations from both undergraduates and postgraduates. He often shares his life story and research findings in class. Today, his journal articles publications stand at 106, and he has 90 research papers published as chapters in books, conference proceedings, working papers, newsletters, and newspaper articles. His excellence in research has brought him several award-winning awards, including the Young Researcher Award in UNIMAS, the Excellent Service Award, research medals from Research Expos, best papers, and Highest-Impact Journal Paper Award. To date, he has 83 postgraduate students under his supervision. He has examined 63 postgraduate theses and 142 undergraduate research projects. As an active researcher, he has been awarded a total of 28 research grants. He was listed among the Top 9% of economists in Malaysia since 2008 and the Top 10% in Asia since 2012 by the Research Papers in Economics (RePEc) database. He is among the highly cited authors in UNIMAS. Apart from the academic journey, he joined several running events and enjoyed travelling around the world.

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About the Editors

Rayenda Khresna Brahmana Ph.D., is currently at the School of Economics, Finance and Accounting, Coventry University. He also serves BigaAlpha.id as a senior advisor. Brahmana has an MSc in Investment from the University of Birmingham and a Ph.D. in Behavioral Finance from Universiti Sains Malaysia. He has published in reputable journals such as Business Strategy and the Environment, International Journal of Finance and Economics, Research in International Business and Finance, Global Finance Journal, Journal of Strategy and Management, Management Research Review, and Economics Bulletin. His research area is behavioral corporate finance, behavioral finance, and corporate strategy. Lee Ming Tan is the founder of East Asia Research and he obtained his Master of Applied Finance from the University of Adelaide. He is deeply interested in how humans function and react with each other. An insight into how people’s minds think and how they work together is invaluable in just about every field. Outside of work, Anthony Tan enjoys outdoor activities and occasional computer games.

Digital Renminbi: Impacts on Economic Integration of the Greater Bay Area Weng Chi Lei and Xinru Wang

Abstract The degree of economic integration in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), as reflected in the mobility of trade and capital flows, has been strengthened by free trade agreements, but obstacles including border effects, capital controls, differences of exchange rate systems and inadequate cross-regional coordination remain. Digital renminbi (e-CNY) has been tested in Shenzhen, a core GBA city since April 2020. If e-CNY is adopted in the GBA, the area will effectively become a single currency zone. Whether the GBA constitutes an “optimum currency area” (OCA) depends on its degree of economic integration. This paper computes real interest rate differential (RID), uncovered interest rate differential (UID) and deviation from purchasing power parity (PPD) of each regional pair based on data of interest rates, exchange rates and price indexes from 2016M2 to 2022M7. All UID, PPD and RID series have means within about 1 percent point from 0, indicating high degrees of financial integration, real integration and economic integration. With the exception of Guangdong-Macau RID, all series are stationary, implying meanreverting behavior. Hence, the parities are expected to hold both in the short run and in the long run, which is a condition for an OCA in the GBA. Furthermore, the regression analysis finds that the test launch of e-CNY in Shenzhen (adjusted for the COVID-19 outbreak) has significant impacts on all RIDs, Guangdong-Macau PPD and Hong Kong-Macau PPD. With merely two and a half years of test launch, the introduction of e-CNY already had impacts on overall economic integration in the GBA. Keywords Economic integration · Economic development policy · Exchange rate regime · Financial integration · Greater Bay Area · Optimum currency area W. C. Lei (B) Faculty of Business and Law, University of Saint Joseph, Macao, China e-mail: [email protected] URL: http://www.usj.edu.mo X. Wang Guangzhou Panyu Polytechnic, College of Finance and Economics, Guangzhou, China e-mail: [email protected] URL: http://www.gzpyp.edu.cn © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 E. Lau et al. (eds.), Economics and Finance Readings, https://doi.org/10.1007/978-981-99-1979-6_1

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W. C. Lei and X. Wang

1 Introduction The Greater Bay Area (GBA) was formalized in the “Framework Agreement on Deepening Guangdong-Hong Kong-Macau Cooperation in the Development of the Greater Bay Area” in 2017. Located in the Pearl River Delta, the GBA consists of nine cities in Guangdong province (Guangzhou, Shenzhen, Zhuhai, Foshan, Dongguan, Zhongshan, Jiangmen, Huizhou, and Zhaoqing) and the two special administrative regions (SARs), Hong Kong and Macau. Under the cooperation framework, the “9 + 2” cities maintain their own specializations in technological innovation, manufacturing, finance and tourism, and at the same time, work together toward further liberalization of trade and finance across the regions, therefore strengthening economic integration in the area. The GBA was re-emphasized as an important national development strategy in China’s Five-Year Plan for 2021 to 2025 (Huaxia, 2021; Lu et al., 2021). This calls for an evaluation of the progress the GBA has made in economic integration in the past five years. In terms of trade in goods and services, bilateral free trade agreements, called Closer Economic Partnership Arrangement (CEPA), have continued to mitigate trade barriers between Mainland China and Hong Kong and between Mainland China and Macau. However, existing capital controls and a lack of cross-regional policy coordination have hindered financial flows across the markets. One obvious disparity lies in the fact that the three regions have different exchange rate systems. While the renminbi follows a semi-floating exchange rate regime, the Hong Kong dollar (HKD) is pegged to the US dollar (USD), and in turn, the Macau pataca is fixed with the HKD. Figure 1 shows that the renminbi to USD exchange rate has gone through ups and downs since the conceptualization of the GBA in 2016. This ponders the question of whether the adoption of a single currency would remove obstacles to the mobility of goods and capital in the GBA, leading to a higher degree of economic integration. 7.2 7.0 6.8 6.6 6.4 6.2

2016

2017

2018

2019

2020

2021

2022

Fig. 1 Monthly average exchange rate of renminbi per USD, 2016M1–2022M7. Source International Financial Statistics of the International Monetary Fund (IMF, 2022)

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The single currency hypothesis is solidified by the launch of the digital renminbi (e-CNY), which has been tested since April 2020. Even though the introduction of e-CNY in Hong Kong and Macau is still under discussion, one of the pilot programs of e-CNY is in the GBA in Shenzhen (Blaschke et al., 2021; Meneses, 2021). The e-CNY is designed to improve the accessibility of financial services, and it should therefore ease cross-border transactions and boost trade and financial flows (Gang, 2022). In fact, one of the main goals of the e-CNY is the internationalization of the currency (Gang, 2022; People’s Bank of China [PBOC], 2021). Both Hong Kong and Macau have already played important roles in the process of renminbi internationalization. Hong Kong is the largest offshore renminbi hub in the world and has direct investment links with Mainland stock exchanges through Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect (Forbes, 2022). Recently, renminbi-denominated government bonds have also been issued in Macau (Moura, 2021; Tu, 2022). As an accelerator of the internationalization of renminbi, the e-CNY can have profound impacts on the economic integration of the regions. Motivated by major reforms of the renminbi and the unique circumstances of the GBA, this paper aims to assess the impacts of the e-CNY on the economic integration in the area. To achieve this research objective, this paper uses a price-based approach to evaluate the degrees of real integration and financial integration among the regions of Guangdong, Hong Kong and Macau before and after the introduction of the eCNY. As will be further discussed in the next section, such analytical framework has been widely adopted in related literature. Besides, the price-based approach makes use of interest rates, price indexes and exchange rate data that are constantly updated by international organizations and made available to the public. As the initiation of the GBA and the development of the e-CNY were merely recent, this paper seeks to contribute new empirical findings and offer insights into the feasibility of a single currency zone in the GBA.

2 Literature Review 2.1 Optimum Currency Area and Economic Integration Mumdell (1961) pioneered in the research of optimum currency area (OCA). An OCA is defined as a geographic area in which it is suitable to adopt a single currency or several currencies with the goal of unification. Optimality of such currency zone can be evaluated based on both internal and external conditions of the member states in the area. Due to factor mobility, the economies should have similar unemployment rates and inflation rates. At the same, due to the free movement of goods across the regions, the degree of trade openness should be high. Under these circumstances, a fixed exchange rate is desirable in the area, as it lowers exchange rate risks, brings price stability and thus creates a more favorable environment for trade and investment.

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Subsequent studies proposed various measures for evaluating whether a group of countries or regions characterized as an OCA. Mckinnon (1963) focused on the degree of economic openness of member states. On the other hand, Kenen (1970) believed that product diversification within the area, and therefore relatively low reliance on trade with the rest of the world were key to the sustainability of an OCA. Haberler (1971) and Fleming (1971) emphasized price equalization across regions and the resulting price stability. Ingram (1969, 1973) found a fixed exchange rate system to be advantageous when free capital flows could restore the balance of payments in the case of interest rate disparity. These cited criteria can be summarized as such that regions should have high degrees of real integration and financial integration to constitute an OCA. Real integration concerns openness in the goods and services markets, and financial integration requires openness in the capital markets. Together, the two imply overall economic integration among the regions. In the current paper, we assess the degree of economic integration in the GBA. By doing so, we also evaluate the feasibility of the GBA to operate as a single currency zone.

2.2 Symmetry of Shocks The works of Mumdell (1961) and Fleming (1971) were collectively known as the Mundell-Fleming model, which suggested a trilemma that an economy could not simultaneously maintain a fixed exchange rate, capital mobility and independent monetary policy. In a currency union, money supply is under the discretion of one central bank. Hence, the same set of monetary policies is prescribed to all member states. If a currency union faces the same external shocks, as in the case of an OCA, then unified monetary policies will be sensible. However, if member states face asymmetric shocks, then the monetary union translates into a loss of monetary autonomy for each member state. Hence, in the OCA literature, Tower and Willett (1976) stressed that symmetry of the shocks faced by member states was key to the success of a currency union. If the member states have different levels of tolerance against inflation and unemployment, policy coordination under a supranational central bank will be difficult (Tower & Willett, 1976). Empirically, many studies performed vector autoregression (VAR) estimations to assess the correlation of economic shocks in order to determine whether a group of regions made up an OCA. Blanchard and Quah (1989) decomposed economic fluctuations into aggregate demand shocks and aggregate supply shocks, and found the former had short-term impacts while the latter had long-term impacts on output and price levels. Bayoumi and Eichengreen (1993, 1994) found that the European Union and the East Asian countries characterized OCAs. The authors also pointed out that the nature and the scale of the shocks should be considered. Subsequently, Clarida and Gali (1994) introduced monetary shocks in addition to supply and demand shocks. Huang and Guo (2006) distinguished global shocks and domestic shocks and did not find a monetary union of East Asian countries to be beneficial. Shafighin and Gharleghi (2016) applied a five-variable VAR model, and estimated that Japan, South

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Korea, Malaysia, Indonesia and the Philippines had the economic foundation for an OCA. As will be discussed in the next sub-section, this kind of VAR estimations can be used to find out if the GBA regions are subject to symmetric shocks. While their results serve as good references for this paper, our research question— how economically integrated the GBA regions are, cannot be fully answered with these empirical approaches. Factors that determine economic integration within the GBA, such as bilateral free trade agreements and adoption of e-CNY, are largely internal factors rather than external shocks.

2.3 Research on Currency Integration of Renminbi, Hong Kong Dollar and Macau Pataca As the process of renminbi internationalization continued, various studies looked into the possibility of currency integration of renminbi, HKD and pataca. Yu (2002) and He (2006) saw the benefits of regional monetary cooperation in Asia, as it could better cope with economic shocks like the 1997 Asian financial crisis. Zhu and Chen (2004) highlighted the reduction of transaction costs, lower foreign exchange reserve requirements and mitigation of internal exchange rate risks as the advantages of a renminbi zone. Zhu and Chen (2008) further analyzed renminbi behavior using an evolutionary game theory model and suggested a gradual promotion from regional monetary cooperation to international monetary cooperation. Focusing on regional currency integration, Zeng and Liu (2004) proposed to replace the renminbi, the HKD and the Macau pataca with a new “yuan” to improve its international competitiveness. In contrast, the empirical analyses of Chen (2005, 2006) and Chen and Zhu (2010) found low symmetry of economic shocks faced by Mainland China and Hong Kong and concluded there was a weak economic foundation for the integration of renminbi and HKD. Other studies like Li et al. (2003) were more positive that the degree of economic integration between the Mainland and Hong Kong would gradually increase, creating more favorable circumstances for a currency zone. More recently, Zhang and Li (2013) and Zhang (2014) used a structural vector autoregression (SVAR) method to assess the feasibility of a renminbi zone across the Greater China regions, and found evidence of impact symmetry and endogenous mechanism of an OCA. Yang (2021) analyzed trade data of Mainland China, Hong Kong and Macau from 2002 to 2018 to test the OCA hypothesis of the GBA. According to Yang (2021), trade of Hong Kong and Macau were highly dependent on the Mainland, so their responses to global shocks were synchronous. These were indicators that monetary policy coordination in the area would be desirable (Yang, 2021). While not all these papers provided empirical findings, most offered insights on the symmetry of shocks faced by the Mainland, Hong Kong and China. The financial crises in the late 1990s and the late 2000s motivated quite a number of these studies.

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However, the GBA was only initiated in 2017, which faced different challenges in the global economic environment. Hence, we aim to provide new insights based on the current circumstances of the GBA. Besides, we emphasize economic integration between each of the three GBA regions. It is more sensible for us to analyze data of Guangdong, rather than country-level data used in previous studies.

2.4 Assessment of Economic Integration Using a Price-Based Approach Cheung et al. (2003) were one of the first to propose a price-based approach to assess the degrees of real and financial integration among regions in China. The analytical framework quantifies economic integration as the extent to which real interest rate parity holds. The real interest rate parity can be decomposed into purchasing power parity and uncovered interest rate parity. The former reflects the extent of real integration, and the latter reflects that of financial integration. This is because if the movements of goods and assets are rather frictionless across regions, the arbitrage forces will lead to equalization of prices and interest rates. Cheung et al. (2003) examined data of interest rates, exchange rates and price indexes of Mainland China, Hong Kong and Taiwan from 1996M2 to 2002M6, and found evidence of increasing price and interest rate parities. Since Cheung et al. (2003), various studies have applied the price-based approach to look into economic integration in different regions and over different periods of time. Zhou and Liu (2013), for instance, provide a relevant reference for this paper. Zhou and Liu (2013) found supportive evidence of increasing degree of economic integration among Guangdong, Hong Kong and Macau regions from 2007M1 to 2011M12. However, during Zhou and Liu’s (2013) period of study, provincial data of consumer price index (CPI) were not available, and the authors analyzed national data instead (National Bureau of Statistics of China, 2022). Furthermore, economic events that could be impactful on economic integration in the area, including the GBA initiative, the development of e-CNY, and the COVID-19, happened well after their period of study. Hence, this paper seeks to fill in the research gap by providing an up-to-date investigation.

3 Theoretical Basis As proposed in Cheung et al. (2003), this paper finds its theoretical basis in the following identity:     rt,k e − rt,k ∗e ≡ i t,k − i t,k ∗ −Δst,k e − πt,k e − πt,k e ∗ −Δst,k e ,

(1)

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where r t,k e is the expected k-period real interest rate, it,k is the k-period nominal interest rate, π t,k e is the expected inflation rate in k periods, and Δst,k e is the expected depreciation in the first economy. * denotes variables of the second economy. The identity says that real interest rate differential (RID) equals uncovered interest rate differential (UID) minus the deviation from purchasing power parity (PPD). In other words, RID = UID—PPD. RID, UID and PPD can be computed for each of the Guangdong-Hong Kong (GH), Guangdong-Macau (GM), and Hong Kong-Macau (HM) regional pairs. For example, when computing GH RID, Guangdong is the first economy and Hong Kong is the second economy in Eq. (1). This identity links real interest rate parity, uncovered interest rate parity and purchasing power parity. Assuming perfect mobility of goods and assets and rational expectations, arbitrage leads to equalization of interest rates and prices in the first and the second economies. Both uncovered interest rate parity and purchasing power parity hold. In the identity, both UID and PPD equal zero. When the right-hand side of (1) equals zero, so does the left-hand side: RID = 0. Real interest rate parity holds when there are both financial integration and real integration between the economies. Hence, the RID can be interpreted as an indicator of overall economic integration. Conversely, non-zero differentials indicate deviations from the parity conditions, and therefore lower degrees of integration. Empirically, each of the differentials can be computed from the data of interest rates, price indexes and exchange rates, so the differentials are expected to change from period to period.

4 Empirical Analysis 4.1 Methodology For this empirical analysis, we collected time series data of interest rates, exchange rates and consumer price indexes of Mainland China, Guangdong, Hong Kong and Macau. In order to update each time series with the latest data available, we obtained data from three databases—the National Bureau of Statistics of China (2022), the Statistics and Census Service of Macau (2022), and IFS (2022) of the IMF. When doing so, some computation and conversion were necessary because the three data sources applied different calculations and had different data frequencies for some time series. The final dataset covers 79 months from January 2016 to July 2022. The RID, UID and PPD series were computed according to Eq. (1) and expressed as annualized percentage points. The remaining of this section first presents their descriptive statistics, followed by unit root tests and regression results.

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Table 1 Descriptive statistics Max

Min

Std. dev

UID_GH

Mean 0.870406

22.69193

−12.91986

6.161294

1.25563522

PPD_GH

0.100614

21.02998

−25.70809

8.259399

0.10827383

RID_GH

0.769792

21.12562

−11.98464

4.958201

1.37994828

UID_GM

1.126607

22.90311

−12.75727

6.167609

1.62356304

PPD_GM

0.024317

22.27757

−15.12854

6.884

0.0313966

RID_GM

1.10229*

7.047893

−5.060943

2.257945

UID_HM

0.256202*

0.407496

0.099722

0.054417

41.8467241 −1.553E-07

PPD_HM RID_HM

−0.07630 0.332498

14.50342

−14.46191

4.365751

14.65925

−14.17647

4.369365

t stat

4.33906398

0.67636988

Note * Indicates p < 0.0001

4.2 Results and Findings Table 1 shows the descriptive statistics of the UID, PPD and RID series. According to Eq. (1), the closer to zero the differentials are, the stronger are the parity conditions. The means of UID, PPD and RID are close to zero, ranging from about 0.02 to 1.12 percentage point. With the exceptions of GM RID and HM UID, all the means are statistically insignificant. Though statistically significant, GM RID and HM UID are not economically significant with means of only about 1.1 and 0.26% points respectively. These give supportive evidence of financial integration, real integration and therefore economic integration in the GBA. Even though the differences are only within 1% point, among the regional pairs, HM has a mean UID that is closest to zero, GM has a mean PPD nearest to zero, and in terms RID, HM’s closest to zero. Several important insights can be highlighted here. First of all, HM UID is small because of the currency peg between HKD and Macau pataca. The financial institutions in the two regions have maintained interest rates at par. For the same reason, the HM pair also has the smallest mean RID. However, recall that if UID and PPD are of the same signs, they offset each other on the right-hand side of Eq. (1). The offsetting happens among the GH and GM regional pairs, but not in the case of HM. Hence, due to differences in purchasing power between Hong Kong and Macau, their interest rates are driven apart in real terms. The degree of financial integration between Hong Kong and Macau is high, so even though the degree of real integration is not the strongest among the three regional pairs, their overall economic integration is still the highest among the three. Second, the smallest GM PPD indicates that Guangdong and Macau have the highest degree of real integration. This is due to the large goods flow between the regions. This coincides with the fact that the Mainland is the largest trading partner of the two SARs, and that bilateral free trade agreements (i.e., CEPA) have been signed between the Mainland and each SAR, but not between the SARs themselves. These observations imply that the adoption of a single currency in the GBA can lead to a

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stronger hold of the parity conditions, especially the uncovered interest rate parity between the Mainland and each of the SARs, leading to even greater degrees of economic integration in the GBA. With the exception of HM UID, the large standard deviations, ranging from 0.05 to 8.26% points, mean that all the differentials display considerable fluctuations. These ups and downs can be easily visualized in Fig. 2. When the flows of goods and assets across the GBA regions have undergone changes since 2016, so have the degree of economic integration. Panel (C) shows how HM UID has been stable over the years. Again, this is due to the fixed exchange rate between the currencies of Hong Kong and Macau. Table 2 shows the results of the augmented Dickey and Fuller (1981) (ADF) unit root test with no intercept and trend, with intercept only, and then with both intercept and trend. If a time series is stationary, it will revert to its mean in the long run after external shocks. All the RID, UID and PPD series, except for GM RID and HM UID are stationary in the unit root tests with no intercept. Since their means are not statistically significantly different from zero, there is evidence that the parity conditions hold both in the short run and in the long run. Hence, the overall high degree of economic integration in the GBA can be anticipated in the future. The results are consistent with the findings of earlier studies reviewed in Sect. 2. On the other hand, HM UID is stationary in the unit root test with intercept. As shown in Table 1, HM UID has a statistically significant positive mean. The positive mean, though it is only about 0.26% point, is persistent. Finally, there is evidence that GM RID has a unit root. It is neither stationary nor trend-stationary. Hence, before regressions can be performed, we have stationarized the series by first differencing. To test whether the introduction of e-CNY had any impact on the economic integration in the GBA, the following regression model is estimated: Di,t = c + β1 EC N Yt + β2 C O V I Dt + ∈t ,

(2)

where Di = U I D, P P D, R I D of regional pair i = G H, G M, H M. Each differential is regressed on constant c, the dummy variables of ECNY and COVID and ∈t is the model’s residuals. ECNY accounts for the periods from 2020M4 to 2022M7, during which e-CNY was test-launched in Shenzhen and other pilot cities in Mainland. This period is shaded in all the panels in Fig. 2. In the period of study, the outbreak of the COVID-19 could cause a structural break and have significant impacts on the differential series. While the test launch of e-CNY continued, the anti-epidemic measures have not been removed till the end of the sample period. Hence, to distinguish its impacts from ECNY, COVID is included in the model. In particular, COVID covers 2020M2 to 2022M7 because the three regional governments have taken various measures against the pandemic, such as restrictions of visitor entry since February 2020 (Centre for Disease Control and Prevention, 2022). Without including COVID in the regression model, ECNY would capture both the effects of the e-CNY introduction and the pandemic. Table 3 presents the regression results. The launch of e-CNY since April 2020 has significant impacts on GH PPD, HM PPD and all the RID series, including the

10 Fig. 2 Series of UID, PPD and RID

W. C. Lei and X. Wang Panel (a) Guangdong-Hong Kong UID, PPD, and RID 30 20 10 0 -10 -20 -30 2016

2017

2018 uid_gh

2019 rid_gh

2020

2021

2022

ppd_gh

Panel (b) Guangdong-Macau UID, PPD, and RID 30

20

10

0

-10

-20 2016

2017

2018 ppd_gm

2019 rid_gm

2020

2021

2022

2021

2022

uid_gm

Panel (c) Hong Kong-Macau UID, PPD, and RID 15 10 5 0 -5 -10 -15 2016

2017

2018 ppd_hm

2019 rip_hm

2020 uid_hm

stationarized GM RID. The coefficients of the UID and PPD series have signs that are opposite to those of their means shown in Table 1. Even though not all of them are statistically significant, they work in the direction of improving the uncovered interest rate parity and purchasing power parity conditions. However, since UID and PPD can offset each other, the overall impacts on the RID series are less clear.

Digital Renminbi: Impacts on Economic Integration of the Greater Bay …

11

Table 2 Unit root test for stationarity at level UID_GHt

No intercept and no trend

Intercept only

Intercept and trend

Stationary

Stationary

Stationary

PPD_GHt

Stationary

Stationary

Stationary

RID_GHt

Stationary

Stationary

Stationary

UID_GMt

Stationary

Stationary

Stationary

PPD_GMt

Stationary

Stationary

Stationary

RID_GMt

Non-stationary

Non-stationary

Non-stationary

RID_GMt – RID_GMt-1

Stationary

Stationary

Stationary

UID_HMt

Non-stationary

Stationary

Stationary

PPD_HMt

Stationary

Stationary

Stationary

RID_HMt

Stationary

Stationary

Stationary

Table 3 Regression results #1 (without intercept c)

#2 (with intercept c)

e-CNY

e-CNY

COVID

COVID

UID_GH

−4.897925

4.580000

−4.897925

3.181960

PPD_GH

−13.35344*

12.43426*

−13.35344*

12.25432*

RID_GH

8.455515*

−7.854258*

8.455515*

UID_GM

−4.885976

4.833609

−4.885976

PPD_GM

−6.892818

5.597975

−6.892818

−6.005046**

6.235400**

−9.072358* 3.184608 5.047349 −6.088825**

diff(RID_GM)

6.235400**

UID_HM

0.011949

0.253609

0.011949

0.002649

PPD_HM

6.460622*

−6.836283*

6.460622*

−7.206969*

RID_HM

−6.448673*

7.089892*

−6.448673*

7.209618*

Note * Indicates p < 0.05, , and ** indicates p < 0.01

With merely two and half years of test launch in Shenzhen, it is remarkable that e-CNY already has statistically significant impacts on economic integration among all three regional pairs of the GBA. It is therefore expectable that wider adoption of e-CNY will have significant impacts on the economic integration in the GBA.

5 Concluding Remarks Long before the conceptualization of the GBA in 2016, Guangdong, Hong Kong and Macau have experienced increasing economic integration. The Mainland has been the largest trading partner of each of the SARs. The signing of bilateral free

12

W. C. Lei and X. Wang

trade agreements, CEPA, fueled the mobility of goods and services across the borders. Furthermore, the rapid development of Guangdong in manufacturing and technology among other industries has deepened the cooperation between the province and the two SARs. In terms of financial integration, the fixed HKD-pataca exchange rate has led to stable interest rate parity between Hong Kong and Macau. While the renminbi has continued to adopt a managed floating system, it has also undergone the process of internationalization. Both Hong Kong and Macau have taken up important roles in the process as they served as platforms for offshore renminbi. The recent test launch of the e-CNY in Shenzhen and other pilot cities was seen as one big step in the internationalization of the currency. Even though the adoption of e-CNY in Hong Kong and Macau is not yet realized, it has certainly been officially discussed and has drawn great attention in the area. Therefore, this paper raises the research question of how well the GBA fits the criteria of an OCA. Specifically, it attempts to evaluate the degrees of real integration and financial integration in the GBA in the short run and in the long run. Besides, the e-CNY has already been launched for two and half years. We should examine whether there have been significant impacts on the economic integration in the GBA. We apply the price-based approach due to Cheung et al. (2003) to evaluate the parity of interest rates and the parity of purchasing power across the three regions. We obtain monthly data of interest rates, exchange rates and price indexes of Guangdong, Hong Kong and Macau from 2016M2 to 2022M7 from local and international official databases. The UID, PPD, and RID of each regional pair have means within 1.13% point. Besides, 7 out of the 9 differential series have statistically insignificant means. These provide evidence of high degrees of real integration, financial integration, and thus overall economic integration in the GBA. Furthermore, 8 out of 9 differential series display mean-reverting behavior. Hence, in the long run, the differentials are expected to approach their close to zero means after any external shocks. The empirical results found in this paper reinforce the findings in the literature that the regions meet the trade and financial openness criteria of an OCA. In fact, the regression results show that the test-launch of e-CNY in the GBA, after controlling for the impacts of the COVID-19 pandemic, already has significant impacts on the overall economic integration in the GBA, particularly in PPDs of the GM and HM regional pairs. Even though the adoption of a single currency in the GBA is still under discussion, many view the test launch of e-CNY as a forerunner of its establishment. Since both the GBA initiation and the launch of the e-CNY took place fairly recently, related studies are limited. This paper contributes an empirical assessment of the current standing and provides an outlook of the GBA while the area follows the blueprint of the national development plan. As the internationalization of renminbi continues and the adoption of e-CNY widens, future studies that continue the research path can foresee more concrete results. Acknowledgements This study is supported by Higher Education Fund of the Macao SAR Government (Project number: HSS-USJ-2021-02).

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Proposition of Development and Growth Through Economic Development Index Ashot Davoyan

To prohibit a great people, however, from making all that they can of every part of their own produce, or from employing their stock and industry in the way that they judge most advantageous to themselves, is a manifest violation of the most sacred rights of mankind. Adam Smith, The Wealth of Nations, 1776

Abstract In modern economics, in addition to indicators of economic growth, the term “economic development” is regarded as relatively important. The term is considered a complex, multifactorial concept. In this article, I studied the importance of economic development and carried out a quantitative assessment through a number of indicators characterizing economic development in different countries. Consequently, the Economic Development Index was calculated, which included 11 sub-indexes, covering data of 102 countries from 2012 to 2019. Notwithstanding the quantitative assessment of economic development, it must be noted that it is, in essence, a qualitative phenomenon with complex and dynamic characteristics. Keywords Economy · Development and growth · Economic development index · Evaluation · Weight coefficient · Analysis

1 Introduction The processes of nature and society are always in a state of “renewal and development”: some phenomena disappear, others emerge, which is the dialectic process of change. In this regard, an interesting example is the following thought in the book “Theory of Economic Development” by the famous American-Austrian economist Joseph Schumpeter: “But add as many mail-coaches as you please, you will never get a railroad by so doing” (Schumpeter, 2010). As a result of development, a new A. Davoyan (B) London School of Economics and Political Science, Executive Global Masters in Management Program, London, UK e-mail: [email protected] URL: http://www.lse.ac.uk © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 E. Lau et al. (eds.), Economics and Finance Readings, https://doi.org/10.1007/978-981-99-1979-6_2

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A. Davoyan

qualitative degree of access to quality is created. While it is true that development is a change, not every change can be considered to be development. However, every change, regardless of its nature, has a quantitative assessment. During the change, the nature of the phenomenon or process may remain unaffected for a certain period, but it can not continue indefinitely; there comes a time when there is a qualitative change in that particular process or phenomenon. For example, when the temperature of water decreases below 0 °C and becomes negative, it turns to ice, and when it rises above 100 °C, it starts to evaporate. Thus, the change is qualitative which is possible to quantify. Such changes follow the pattern of Hegel’s law of materialist dialectic “transition from quantitative change to qualitative change”. According to Hegel’s law of materialist dialectic, each new quality of the phenomenon is the result of accumulated quantitative changes. The quantity and quality of change are separate components and the study of change will become more comprehensive if the necessary quantitative and qualitative research is conducted. From the point of scientific justification and practicality, economic development needs quantitative description, which makes it necessary to have adequate modern methods for quantitative evaluation, such as stochastic methods, networking, non-linear programming, differential equations, etc. However, since their application is not always possible and they provide only approximate results, indexes developed and published by international organizations and non-governmental organizations such as the World Bank, International Monetary Fund, United Nations, World Economic Forum, Heritage Foundation, etc., are used. As a result of this study, I calculated a new index and named it the Economic Development Index. According to Economic Development Index, top performing countries are the following: Luxembourg, Finland, Ireland, Denmark, and Germany. Worst performing countries include Costa Rica, Cote d’Ivoire, Botswana, Tajikistan, and Bolivia. Economic Development Index is a more effective measurement for development than other indexes considering its combination of a variety of indexes in contrast to one index which measures specific areas of development. For example, the most prominent measure of development and growth, Gross Domestic Product is limited to measuring the production capability of an economy within a specific time period and not its development. We can note that producing more petrol-run cars or cigarettes adds to the gross development and growth, but it does not take into account their consequences on the environment or health.

2 Economic Development Index Economic development can mean different things to different people, and there is no standard definition of this term in the literature. Many economists, including Adam Smith, the “father of modern economics”, Gary Becker, William Lewis, Theodore Schultz, Simon Kuznets, and others have studied economic development. According to Schultz, a Nobel Prize winner, the most important factor in economic development is human capital, and the ultimate goal of development is the continuous improvement of the quality of life and well-being of the population (Theodore, 1979). Kuznets,

Proposition of Development and Growth Through Economic …

17

also a Nobel Prize winner, was one of the first economists who tried to delineate the categories of economic growth and development. According to Kuznets’s definition, economic development (or, as the author calls it, modern economic growth) is the “long-term rise in capacity to supply increasingly diverse economic goods to its population, this growing capacity based on advancing technology and the institutional and ideological adjustments that it demands” (Kuznets, 1971). Todaro developed Kuznetsi approaches, considering economic development as dynamic progress in improving the living standards of the people, due to which the capacities of the entire population of the country increase, and favorable conditions are created for the full realization of human potential (Todaro, 1996). Summarizing definitions of economic development in the literature, I propose the following definition: In time and space, economic development is a qualitative, positive, and inclusive change aimed at the reproduction of an economy. In the olden days, economies were largely based on the plantation of crops and rearing of animals, and a common man would earn $1.5 to $2 per day. However, after the industrial revolution, technological advancements led to dramatic growth in incomes. Inventions such as electricity, TV, telephone, and later, air jets, cars, and electrical consumer appliances contributed significantly to economic growth. Joseph Schumpeter called these inventions “creative destructions”. He opined that products characterized as creative destructions form new markets. According to his theory, this process of industrial mutation changes the economic structure from within, destroying the old economic structure and creating a new one. Nowadays, start-ups are the frontrunners of creative destructions, although the pay-offs of success are too high not to attract established companies. In his book “The Theory of Economic Development”, Schumpeter discussed the difference between economic growth and economic development. Later, the theory of economic development was contributed by Schultz, Kuznets, Drucker, Baumol, and many others. The growth and development of an economy is still generally measured by the Gross Domestic Product (GDP). However, it must be noted that the GDP, as its definition suggests, is limited to measuring the production capability of an economy within a specific time period and not its development. For example, producing more petrol-run cars or cigarettes adds to the GDP, but it does not take into account their consequences on the environment or health. Thus, in my opinion, it is necessary to make a quantitative assessment of economic development. In this study, I have calculated a new index and named it the Economic Development Index which has 11 sub-indexes and covers 102 countries. The statistical database includes data for the period 2012–2019. The sub-indexes included in the Economic Development Index are as mentioned below. • Global Competitiveness Index The index has been published by the World Economic Forum since 2005. It assesses the indicators that influence the long-term growth and development of the world’s economies, as well as provides an opportunity to identify the pros and cons of those economies in order to develop a long-term development strategy. The results of

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A. Davoyan

Table 1 Sub-indexes of the Economic Development Index 1

GCI

Global Competitiveness Index

2

GII

Global Innovation Index

3

EF

Economic Freedom Index

4

Legatum

Legatum Prosperity Index

5

SPI

Social Progress Index

6

DB

Doing Business Index

7

Democracy

Index of Democracy

8

HDI

Human Development Index

9

GDP PPP

Country GDP ratio (in terms of purchasing power parity)

10

Hcap

Human capital ratio

11

QLI

Quality of Life Index

the World Economic Forum evaluations and databases of international organizations (World Bank, UN bodies, World Health Organization, etc.) are used as sources of information. The index is calculated every year and includes 120 indicators (for example, higher education and training, product-market efficiency, innovations, technological readiness, etc.), which are combined into 4 sub-indexes consisting of 12 pillars. • Global Innovation Index The Global Innovation Index was developed as a result of a collaboration between INSEAD and World Business, a British magazine. The Global Innovation Index consists of two sub-indexes: investment in the field of start-ups (input sub-index) and the result of innovative activities (output sub-index). Each sub-index is based on several pillars, which consist of 79 indicators (human capital and research, infrastructure, ICT access, etc.). • Index of Economic Freedom The Index of Economic Freedom was created by the Heritage Foundation and the Wall Street Journal in 1995 (Foundation, n.d.). The index evaluates economic freedom in 186 countries in the following four main sections: • • • •

Rule of law (property rights, judicial effectiveness, and government integrity); Government size (tax burden, government spending, and fiscal health); Regulatory efficiency (business freedom, labor freedom, and monetary freedom); Market openness (trade freedom, investment freedom, and financial freedom).

The index is calculated every year on the basis of 10 pillars characterizing economic freedom. Scores range from 0 to 100, as a result of which the overall score of the economic freedom index is calculated. The higher the score of an indicator, the greater the degree of economic freedom in a given country.

Proposition of Development and Growth Through Economic …

19

• Legatum Prosperity Index The index has been published by the Swiss Legatum Institute since 2006. It evaluates the prosperity and well-being of the citizens of 149 countries of the world through 104 indicators included in 9 sub-indexes (for example, security, business activity, etc.). The Legatum Prosperity Index is published annually (Institute, n.d.). • Social Progress Index The index was developed by the initiative of Oxford University. The methodology of the index was developed by the Harvard Business School and by Michael Porter, in collaboration with the Rockefeller Foundation, the Massachusetts Institute of Technology, and a number of other reputable organizations (Porter, n.d.). The Social Progress Index is based on 12 key components and contains 52 indicators. • Doing Business Index The index is published annually by the World Bank (The World Bank, n.d.): it assesses the existence of conditions for business in 189 countries on the basis of 11 pillars, and is dedicated to the regulation of small and medium enterprises, the clarification of regulations, and the assessment of their applicability in practice. The index includes 41 indicators (for example, property registration, access to electricity, liquidation of the enterprise, etc.). • Democracy Index The Democracy Index is developed by The Economist Intelligence Unit providing an overview of democracy in 167 countries (The Economist Intelligence Unit, n.d.). The Democracy Index is based on 5 main pillars: 1. 2. 3. 4. 5.

Electoral process and pluralism Civil liberty Government activities Political participation Political culture

The Democracy Index is rated on a scale of 0–10, which is calculated by the scores of 60 indicators grouped into the above 5 categories. • Human Development Index The index was developed by the UN. It considers the human potential as a driving force for a country’s economic development, calculated for 188 countries (UNDP, n.d.). The Human Development Index is a composite assessment of the following three dimensions of human development.

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• living a long and healthy life: measured by life expectancy • being educated: measured by literacy • having a decent standard of living: measured at the level of gross national income per capita, according to purchasing power parity. The Human Development Index is rated on a scale of 0–1. • Quality of Life Index The index is calculated for 177 countries through 30 indicators, which are presented in 7 pillars, each of which is included in the index with its weight coefficient: 1. 2. 3. 4. 5. 6. 7.

Stability—14% Civil rights—16% Health services—16% Security—16% Climate—14% Values—16% Popularity—8%.

The range of change of scores is (0–100) (2110). The database of Quality of Life indicators is collected from databases of the World Bank, the OECD, the UN, and other international organizations. Quality of life characterizes the efficiency in all directions of human life, material care, the level of satisfaction in the demand for social goods, the level of mental, cultural, and physical development, and security of life. According to the UN, the quality of life is presented to assess the socio-economic situation of the country’s population. The index is developed by the Economist Intelligence Unit, and its main components are: 1. 2. 3. 4. 5. 6. 7. 8. 9.

People’s health (measured by life expectancy) Public and family life Material well-being Political stability The safety of society and individuals Geographical location and climatic conditions Work guarantee Political and personal freedom The level of democracy.

• GDP (Expressed in Terms of Purchasing Power Parity) The indicators of gross domestic product (GDP), expressed in terms of purchasing power parity, are applied for conducting a comparative analysis of living standards and quality of life in different countries, as it takes into account the relative cost of living and inflation rates. These indicators are calculated and published by various international organizations.

Proposition of Development and Growth Through Economic …

21

• Human Capital Index The index, as well as the report underlying it, is published by the World Bank. The index assesses the mobilization potential of economic and professional skills of citizens of different countries (Bank, n.d.). The Human Capital Index measures how much capital each country loses due to a lack of education and health. The index was first published in 2008 for 157 countries. The overall score of the human capital index falls in the range (0–1), where 1 is the maximum score of the index. According to Fischer, human capital is a measure of a person’s ability to earn a living. Human capital has the capability to accumulate and reproduce its mental, physical, and emotional potential. According to G. Becker, the ratio of the cost of human capital and the income averages between 12 and 14%. The “Multiple Imputation” method (using the SPSS software package) was used to fill in the missing data for some sub-indexes, years, or countries. Given that the overall index of economic development includes indicators of different proportions (the scores of some sub-indexes vary in the range of 1–7, others range from 0 to 100), in order to combine them into one index, it is necessary to bring them under a common dimension. For that purpose, I have normalized the indicators in the range of (0–1). I have calculated the weight coefficients for each of the 11 sub-indexes of economic development for each year by factor analysis using the SPSS software package. The weight coefficients reflecting the relative strength of each sub-index are presented below. The Economic Development Index was calculated using the following formula. K =

11 ∑

αi Ni

i=1

Table 2 Weight Coefficients of the sub-indexes of the Economic Development Index GCI

2012

2013

2014

2015

2016

2017

2018

2019

0.083

0.087

0.084

0.087

0.089

0.086

0.093

0.092

GII

0.104

0.106

0.109

0.099

0.108

0.109

0.102

0.101

EF

0.061

0.059

0.061

0.054

0.049

0.052

0.061

0.059

Legatum

0.051

0.041

0.039

0.045

0.043

0.037

0.035

0.040

SPI

0.093

0.092

0.082

0.099

0.101

0.095

0.087

0.099

DB

0.032

0.029

0.030

0.028

0.024

0.029

0.030

0.026

Democracy

0.082

0.092

0.088

0.092

0.089

0.093

0.094

0.087

HDI

0.110

0.113

0.113

0.108

0.117

0.115

0.109

0.113

GDP PPP

0.121

0.119

0.124

0.118

0.116

0.120

0.119

0.114

Hcap

0.139

0.133

0.139

0.143

0.141

0.137

0.139

0.141

QLI

0.124

0.129

0.131

0.127

0.123

0.127

0.131

0.128

22

A. Davoyan

where i = 1,2,… 11 are the observed 11 sub-indexes, α_i—are the weight coefficients of sub-indexes, N_i—are the scores of sub-indexes. Based on the results of the Economic Development Index, the 102 countries observed were divided into 3 groups: • Countries with high levels of economic development • Countries with medium levels of economic development • Countries with low levels of economic development. Figures 1, 2 and 3 below represent the top ten countries in the above three groups.

Fig. 1 Top 10 countries with high levels of economic development, ranks, 2012–2019

Fig. 2 Top 10 countries with medium levels of economic development, ranks, 2012–2019

Proposition of Development and Growth Through Economic …

23

Fig. 3 Top 10 countries with low levels of economic development, ranks, 2012–2019

The indicators of the Economic Development Index for 2012–2019 have been averaged to assess the level of economic development of individual countries over the observed period, as well as to conduct a comparative analysis across countries (Fig. 4). As a result of the study of dynamics of the indicators of the Economic Development Index for 2012–2019, I have identified countries that showed the maximum dynamics in the level of economic development during the observed eight years (both in terms of growth and recession), as shown in Fig. 5. The most outstanding characteristic feature of this research is that its results are applicable for studies of development policies aimed at maintaining the level of economic development and for continuous improvement, selecting target areas for development policies.

3 Conclusion Economic development is a complex and multifactorial concept that can mean different things to different people in different countries; there is no common definition of the term. Thus, we define economic development as a qualitative, positive, and inclusive change in an economy aimed at the reproduction of the economy. Since it is not effective to assess the level of economic development of countries through the GDP or other macroeconomic indicators, the Economic Development Index can be

24

A. Davoyan

Fig. 4 Country rankings observed in Economic Development Index for 2012–2019

Fig. 5 Countries that recorded the biggest dynamics based on the results of the Economic Development Index in 2012–2019

Proposition of Development and Growth Through Economic …

25

used to measure economic development based on different indicators expressing development in the respective economies. The Economic Development Index is intended to help policymakers develop and assess relevant government programs, as well as adjust them in specific areas where there is a need for policy improvement to meet the necessary development levels.

References Bank, T. W. (n.d.). Retrieved 10 15, 2021, from https://www.worldbank.org/en/publication/humancapital Foundation, H. (n.d.). Retrieved October 12, 2021, from http://www.heritage.org/index/. Institute, L. (n.d.). Retrieved October 12, 2021, from https://www.prosperity.com/rankings Kuznets, S. (1971, December 11). The Nobel Prize. Retrieved December 5, 2021, from https://www. nobelprize.org/prizes/economic-sciences/1971/kuznets/lecture/ Porter, M. (n.d.). Social Progress Imperative. Retrieved October 12, 2021, from http://www.social progressimperative.org/about/origins Retrieved October 15, 2021, from https://www.worlddata.info/quality-of-life.php. Schumpeter, J. (2010). Capitalism. Routledge. The Economist Intelligence Unit . (n.d.). Retrieved October 15, 2021, from http://www.eiu.com The World Bank. (n.d.). Retrieved October 12, 2021, from http://www.doingbusiness.org/rankings Theodore, S. (1979, December 8). The Nobel Prize. Retrieved December 15, 2021, from https:// www.nobelprize.org/prizes/economic-sciences/1979/schultz/lecture/ Todaro, M. (1996). Economic development (6th ed.). Addison-Wesley Publishing Company. UNDP. (n.d.). Retrieved October 15, 2021, from http://hdr.undp.org

A European Study on Financial Risk Attitude and Cognitive Decline in Aging Societies Michail Chouzouris, Antigone Lyberaki, and Platon Tinios

Abstract In this study we investigate the relationship between willingness to take financial risks and cognitive decline in the European region. We use panel data from the Survey of Health, Ageing and Retirement in Europe (SHARE) including a measure for financial risk preference and an index for cognitive abilities based on the evaluation of episodic memory, verbal fluency and numeracy skills. Additionally, the dataset allow us to control for demographic factors and individual characteristics that may be related to cognitive skills and risk attitude. We performed generalized regression models to examine the effect of cognitive skills on risk attitude controlling for the individual characteristics of the sample. Our findings demonstrate a variation in all the components of cognitive functioning across the European regions while gender differences are also significant. Finally, analysis revealed the existing correlation between risk attitude and cognitive aging. Keywords Risk attitude · Cognitive decline · Financial · SHARE

1 Introduction The continuous increase in life expectancy and the population aging will force the older population to reorganize their life plan in order to ensure their well-being in the M. Chouzouris (B) · P. Tinios Department of Statistics and Insurance Science, University of Piraeus, Pireas, Greece e-mail: [email protected] URL: http://www.unipi.gr/ P. Tinios e-mail: [email protected] URL: http://www.unipi.gr/ M. Chouzouris Department of Accounting and Finance, University of Western Macedonia, Kozani, Greece A. Lyberaki Department of Economic and Regional Development, Panteion University, Athens, Greece e-mail: [email protected] URL: http://www.panteion.gr/ © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 E. Lau et al. (eds.), Economics and Finance Readings, https://doi.org/10.1007/978-981-99-1979-6_3

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M. Chouzouris et al.

later part of life. Both the absolute number of elderly people will increase, but also the share of elderly, the healthy years and the relatively fewer numbers of working-age people (Bloom et al., 2011). According to the World Health Organization (2022), by 2050 the world’s population aged 60 years and older is expected to rise up to 2 billion, up from 900 million in 2015. This shift in distribution of population highlights the importance of long-term planning, especially in terms of asset allocation during the life and prior to retirement. The classic model of life cycle planning describes the spending and saving habits of people over the course of a lifetime who attempt to smooth consumption throughout their lifetime by borrowing when their income is low and saving when their income is high (Ando & Modigliani, 1963). Modeling approaches such as the Life Cycle Hypothesis (LCH) have been widely used in the past to describe individuals’ plan of spending and saving choices during their entire life. Nevertheless, these approaches adopt simplifying assumptions, e.g., consumption expenditure is determined by the lifetime resources of the consumer. These assumptions have proven inadequate to describe the current transition to longer life expectancy (Graham & Isaac, 2002). Thaler and Benartzi (2004) find that people systematically save less than their optimal life cycle savings rate would predict. For example, in deciding their savings, apart from their current income, also consider the expected circumstances in the future but are influenced by their past experiences, as well as by cognitive issues (Chouzouris et al., 2022a, 2022b). Modern research shows that financial—planning and financial choices are directly related to the levels of cognitive skills (Agarwal & Mazumder, 2013). Cognitive functioning refers to multiple mental abilities, including learning, thinking, reasoning, remembering, problem-solving, decision-making and attention (Fisher et al., 2019). These abilities define how information acquired by an individual is utilized throughout the decision-making process. Several studies have emphasized the impact of cognitive abilities on decision-making, suggesting that lower cognitive ability individuals have lower risk performance leading to suboptimal behavior (Andersson et al., 2016; Kirchler et al., 2017). For example, Frederick (2005) using a sample of college students has shown that students with higher cognitive performance were more likely to be patient in assessing tradeoffs and less prompt to errors. It has been proven that age-related cognitive—skill decline affects financial decision-making (Dohmen et al., 2017) and several studies show that there exists a negative correlation between one’s age and their investment skills (Martin et al., 2019). Nevertheless, such trends appear to fluctuate among geographical regions and evidence exists that higher levels of education, allow more profound cognitive skills in later life, leading to higher stability in the investment choices. At the same time, as shown by Breuer et al. (2014), Cox et al. (2015), financial planning choices are significantly affected by the risk attitude profile, a parameter which is external to cognitive skills and probably dependent on the age (Borghans et al., 2008; Dohmen et al., 2011; Donkers et al., 2001), gender (Booth & Katic, 2013) and education (Fernandes et al., 2014). These differences may be explained by the disparity in earning and education between men and women (Bucher-Koenen et al., 2016). Moreover, risk tolerance is affected by the pension system (Van Rooij

A European Study on Financial Risk Attitude and Cognitive Decline …

29

et al., 2007), the variability of which among different countries appears to reflect the extent of discretion in taking risks allowed by the system. In this research we study how, for EU residents aged 50+, cognitive skills are related to risk attitude. We analyze data from SHARE waves 6 and 8, collected between 2015–2020 from 29,354 individuals. The focus on individuals aged over 50 is a consequence of data availability; SHARE, similarly to the HRS in the US or JSTAR in Japan is a panel dataset designed to track the aging process (Chouzouris et al., 2022a, 2022b). This being said, we can explore whether the aging societies will be confronted with an increase in aggregate risk aversion. We provide a metric of cognitive skills based on the evaluation of episodic memory, verbal fluency, numeracy and relate it to the risk attitude, evaluated as the financial-wealth investment allocation controlling for individual characteristics such as age, sex and education.

2 Method 2.1 Data We conduct a panel analysis using data from the Survey of Health Ageing and Retirement in Europe (SHARE) wave 8 were used that took place at 2020 in twenty-eight European countries; twenty-six Continental EU Members in addition to Israel and Switzerland. Also, data from wave 6 were used—cognitive module, which records the cognitive performance—to track the cognitive decline of the participants. The combination of two waves led to a total sample of 29,435 individuals from 17 countries. SHARE is a multidisciplinary database that was established to better understand the trajectories of economic, health and social conditions of individuals aged 50 or older. Respondents give detailed information on current demographics, household consumption, health status, cognitive functions, income and healthcare. All countries are on the same fieldwork schedule, use the same survey specifications given by a model contract, and administer the same questionnaire allowing for a direct comparison among the participants (Bergmann et al., 2017).

2.2 Eligibility Criterion The analysis restricted to individuals who met four criteria; first, are at least 50 years old, second respondents have not been diagnosed with Parkinson’s disease, Alzheimer’s disease, senility or dementia, third, the household manages at least one bank account, fourth, have full records between wave 6 and 8. The final sample of 29,453 individuals resides in four geographical European regions used to classify them: North countries (Sweden, Denmark, Finland), Western countries (Austria, Germany, Netherlands, Switzerland, Belgium, Luxembourg), Southern countries

30

M. Chouzouris et al.

(Spain, Italy, Greece, Portugal) and Eastern countries (Poland, Estonia, Slovenia, Czech Republic).

2.3 Cognition The SHARE database includes information regarding the cognitive ability of the respondents using subjective and objective measures of different aspects of cognitive functioning: memory, numeracy and verbal fluency. Memory test extract from a 10 word delay recall test; interviewers read out a list of 10 words and participants are asked to recall these worlds immediately (immediate recall) and after the end of the session (delayed recall). The score on each task is the sum of the recalled worlds, ranging from 0 to 10. Verbal fluency measures respondent’s ability to name as many different animals as they could think within one minute, summing up the number of listed animals. Numeracy is assessed by asking five questions that involve simple calculations based on daily life scenarios. Participants who manage to answer correctly the first question are asked a more difficult follow-up while those who failed are asked an easier one. For each of the above measures we create z scores, computing how many standard deviations an observation is far from mean, and then a standardized average score was constructed for the cognitive performance.

2.4 Risk Attitude The SHARE database includes detailed information related to the financial profile of the participants such as the total amount respondents hold in bank accounts, stocks, bonds, retirement accounts, etc. The measure of risk attitude is based on the willingness of respondents to take financial risk, ranging from substantial risk—choosing bonds or contractual savings—to high-risk investments like stocks. Based on their preferences we categorize individuals into three groups. The first group includes those who choose bonds, contractual savings or retirement accounts (substantial risk takers), the second, those who divide their portfolio equally between stocks and bonds or use mutual funds (average risk takers), and the last group, those who choose mostly stocks mutual funds (high-risk takers).

A European Study on Financial Risk Attitude and Cognitive Decline …

31

2.5 The Econometric Model The objective of the analysis is to estimate the probability of each level of risk attitude as a function of cognitive performance, demographic and socioeconomic characteristics. The main idea of the ordered probit model is the existence of a latent continuous variable underlying the observed ordinal responses. The latent continuous variable, y ∗ is a linear combination of some predictors, x, plus a disturbance term such as yi∗ = xi β + ei , ei ∼ N (0, 1), ∀i = 1, . . . , N where, yi the observed variable takes on values 0 through m given by the following expression yi = j ⇐⇒ μ j−1 < yi∗ ≤ μ j j = 0, . . . , m and μ j−1 = −∞ and μ j = +∞ The regression analysis starts with a baseline model, controlling for covariates. In our sample these are gender, age and cognitive decline which is collinear with stage in the course. The general form of the ordered probit model is     P(yi = j ) =  μ j − xi β −  μ j−1 − xi β

(1)

For j = m the general equation is P(yi = m) = (μm − xi β) − (μm−1 − xi β) = 1 − (μm−1 − xi β)

(2)

The estimation of the regression coefficients β and the values of μ, are achieved using the maximum likelihood estimation. This process allows the estimation of probabilities associated to each score, as follows   P(yi = 0) = P μ−1 < yi∗ ≤ μ0     = P −∞ < yi∗ ≤ μ0 = P yi∗ ≤ μ0 = P(xi β + εi ≤ μ0 ) = P(εi ≤ μ0 − xi β) = (μ0 − xi β)   P(yi = 1) = P μ0 < yi∗ ≤ μ1 = P(μ0 < xi β + εi ≤ μ1 ) = P(μ0 − xi β < εi ≤ μ1 − xi β) = (μ1 − xi β) − (μ0 − xi β)   P(yi = 2) = P μ1 < yi∗ ≤ μ2 = P(μ1 < xi β + εi ≤ μ2 ) = P(μ1 − xi β < εi ≤ μ2 − xi β)

32

M. Chouzouris et al.

= (μ2 − xi β) − (μ1 − xi β)   P(yi = 3) = P μ2 < yi∗ ≤ μ3 = P(μ2 < xi β + εi ≤ μ3 ) = P(μ2 − xi β < εi ≤ μ3 − xi β) = (μ3 − xi β) − (μ2 − xi β) where  represents the accumulated probability of the standardized normal distribution and yi is the risk attitude categories, varying from no willingness to take financial risk (yi = 0) to those who classified as high-risk takers (yi = 3).

3 Results The sample consists of 29,435 individuals (42.9% male) with a median age of 71 years. Participants from North countries had the highest overall cognitive score (verbal mean 23.6; immediate recall mean 5.6; delayed recall mean 4.5; numeracy mean 4.4), while respondents from South countries had the lowest overall score (verbal mean 16.6; immediate recall mean 4.7; delayed recall mean 3.3; numeracy mean 3.7). Males have a better performance to verbal fluency and numeracy while woman record higher scores on memory tasks (see Appendix, Table 5). Table 1 reports the score on each cognitive task for the total sample and for each group of countries mentioned above. Likewise, Fig. 1, shows that the average cognitive score drawing a decline trend; a finding in accordance with bibliography which highlights the negative correlation between age and cognition (see Appendix, Fig. 1). The SHARE interview collects detailed information about the sociodemographic characteristics of the participants including data about the gender, the age, the job situation and the years of education. In our analysis, the average age of the respondents was 72 years, while there was an age difference between the other countries and the rest of the group of countries (p-value < 0.05). The highest level of formal Table 1 Cognitive score by a group of countries (mean, SD) Western

North

Southern

Eastern

Other

Total

Verbal

22.0 (7.1)

23.6 (7.3)

16.6 (6.8)

21.9 (7.6)

18.8 (7.9)

20.8 (7.6)

Immediate

5.7 (1.7)

5.6 (1.7)

4.7 (1.7)

5.4 (1.7)

5.1 (1.8)

5.4 (1.7)

Delayed

4.4 (2.2)

4.5 (2.0)

3.3 (1.9)

3.9 (2.2)

3.7 (2.0)

4.0 (2.1)

Numeracy

4.3 (1.2)

4.4 (1.1)

3.7 (1.7)

4.1 (1.4)

3.5 (1.9)

4.1 (1.5)

Cognitive score

0.2 (0.7)

0.3 (0.7)

−0.3(0.7)

0.1 (0.7)

−0.1 (0.8)

0.1(0.8)

N

9924

3858

7266

7760

627

29,435

A European Study on Financial Risk Attitude and Cognitive Decline …

33

Table 2 Demographic characteristics by a group of countries Western

North

Southern

Eastern

Other

Total

Age (mean, SD)

71.3 ± 8.9

71.5 ± 9.0

71.3 ± 9.5

71.4 ± 8.9

68.1 ± 9.2

70.9 ± 9.2

Gender (% male)

44.2

46.7

43.9

38.5

40.7

42.9

Education (mean)

Post secondary

Post secondary

Lower secondary

Upper secondary

Post secondary

Upper secondary

Household size (mean, SD)

1.8 ± 0.8

1.8 ± 0.6

2.2 ± 0.9

2.0 ± 1.0

2.1 ± 1.0

1.9 ± 0.9

Employed (%)

16.6

22.9

15.4

15.0

26.1

16.9

Unemployed (%)

1.2

0.8

2.5

1.3

0.8

1.5

Homemaker (%)

5.6

0.3

19.9

1.3

12.9

7.5

Retired (%)

73.8

73.1

57.6

79.6

49.7

70.0

Permanently sick or disabled (%)

2.1

1.8

2.1

2.0

5.3

2.1

education that respondents have completed is the upper secondary while significant differences are reported in Southern countries compared to the rest sample (Table 2). For the purpose of the analysis, we have recorded participants willingness to invest in risky assets varying from low-risk takers, such as those who invest in bonds or contractual savings to high-risk lovers who prefer stocks with high returns. The 71.5% of the individuals report no willingness to take any risk while 9.4% are categorized as high-risk lovers. These findings vary across countries with Southern countries characterized by risk aversion, almost 90% of respondents are not willing to take any risk. On the other hand, North countries score the higher percent of risk lovers, nearly 29%. Moreover, significant variation of risk attitude is reported examining the behavior of men compared to women. While men have a propensity for risky choices, woman prefer to take no—risk or substantial risk. Also, this trend persists for different age groups (see Appendix, Fig. 3). In order to examine participants choice consistency regarding their willingness to take financial risks, we compare their assets allocation between the two waves. Interestingly, the 89% of individuals who preferred not to take any financial risk at wave 7 has a consistence choice in wave 8 choosing not to deviate from its previous choice. In contrast, significant variations recorded in those who chose to take average or high risk, with almost half of them choosing not to take any risks four years later (Table 3). Following the examination of demographic factors, the risk attitude and the cognitive profile of the responders we now explore whether one of the above factors had any

34

M. Chouzouris et al.

Table 3 Risk attitude change between waves (as row %) Wave 8 No willingness to take Low risk Average risk High risk financial risk Wave 6 No willingness to take 89.0 financial risk

6.3

1.3

3.5

Low risk

45.0

41.8

4.5

8.7

Average risk

33.2

16.7

24.7

25.5

high risk

29.7

10.7

7.6

52.0

impact on risk attitude. The following regression analysis controls for demographic (age, gender, education level) and economic characteristics (household size, employment status) while including country-fixed effects to capture differences among countries in institutions or culture. The models in Table 4 confirm the negative effect of age, gender and household size on risk attitude. The baseline model captures the negative effect of age in risk attitude while models (II)–(IV) have greater explanatory power by adding more variables. Compared with the baseline model, the effect of age is smaller, which may be explained by the nature of the explanatory variables and the correlation with responders’ age. Table 4 Model for risk attitude (marginal effects) Model I

Model II

Model III

Model IV

Age

−0.34***

−0.28***

−0.27***

−0.30***

Cognitive decline

−0.03***

−0.02***

−0.02***

−0.01*

Numeracy

0.04***

0.03***

−0.04***

0.02***

Financial resp

0.56***

0.4***

0.54***

0.50***

Gender (1 = female)

−0.05***

−0.05***

−0.04***

−0.03***

Household size

−0.01***

−0.01***

0.01***

Below high school

−0.06***

−0.06***

−0.03***

Above high school

0.06***

0.05***

0.03***

Pensioner

−0.02***

−0.02**

Unemployed

−0.09***

−0.07***

Housekeeping

−0.08***

−0.04**

Western countries

0.12***

North countries

0.24***

Southern countries

−0.05*** −0.01***

Eastern countries AIC

25,275

24,577

24,204

21,358

Pseudo R2

0.215

0.237

0.244

0.333

Note * p < 0.05 ** p < 0.01 *** p < 0.001

A European Study on Financial Risk Attitude and Cognitive Decline …

35

Controlling for sociodemographic characteristics (models II-III) we notice the negative effect on risk attitude. Individuals with less year on education have significant lower probabilities of taking risky decisions, while unemployed and housekeeping have also smaller probabilities of taking risk choices compared to employed. Also, financial respondents tend to take great risks, confirming the hypothesis that financial literacy—as expressed through the households’ economic management— is a decisive factor for household to take financial risks and participate in stock market. The last column report the results taking into account the effect of countries. Northern countries have the greatest probabilities of following risky choices with the Mediterranean countries have a risk free profile.

4 Discussion The current study revealed that cognitive score plays an important negative role in predicting risk attitude. In particular, using a nationally representative sample of 29,435 individuals across Europe we showed that risk preferences are closely connected with the decline of cognitive performance. Our findings demonstrate a variation in all the components of cognitive functioning across the European regions. The average cognitive score was 0.1, Northern countries record 0.3 while Eastern 0.1 and Southern −0.3. Focusing on each component of the cognitive measure, Scandinavians have the highest performance in almost each task followed by Western countries. On the other hand, Southern counties record the lower score, notably below the average score in each task. These findings are in line with previous studies that the level of cognitive performance differs across countries. Nonetheless, the observed differences may not fully explained only by the studied factors but also from life course experiences, nutrition and perceived stress (Formanek et al., 2019). Our findings suggest that gender differences in cognitive score are statistically significant in three out of four tasks (verbal task, immediate and delayed memory recall). Weber et al. (2014) suggest that improvements on health and the economic prosperity, the living conditions and the cognitive stimulation can explain the magnitude of gender differences. Ardila et al. (2011) report higher arithmetical and spatial abilities in favor of men while women achieve higher score in verbal abilities. Analysis revealed the existence of a correlation between risk attitude and cognitive aging. Controlling for the cognitive performance of the respondent, the age effect decreases, an impact which can be attributed to cognition. This trend holds across counties, confirming the negative relationship between risk attitude and age. A study by Bonsang and Dohmen (2015) examines the hypothesis that cognitive ability decline is correlated with the decline in willingness to take risks. Their findings highlight a pronounced decline in cognitive skills when they control for age. More precisely, their findings suggest that about 70% of the change in risk attitude can be attributed to cognitive aging, providing useful evidence in favor of the hypothesis that the swift in risk attitude at older age is in part driven by cognitive decline. Similarly, Falk et al. (2015) working on a sample of 76 countries across the world

36

M. Chouzouris et al.

confirm the existence of an age pattern between the willingness to take risks and the level of cognitive skills. Using a subjective assessment of cognitive skills based on mathematical competency concludes that risk preferences vary substantially across countries. This study faces limitations. First, to compensate for the absence of an exact risk attitude index, a proxy for risk attitudes had to be produced by classifying respondents according to their financial portfolio choices. Though this necessarily restricts the sample to those who face meaningful such choices, this proxy is theoretically warranted. Moreover, this study was based on a panel data analysis, on a five years period. A future study using longitudinal information for a bigger time span could examine the evolution of risk profile and the joint effect of aging and the evolution of risk attitude.

5 Conclusion In summary, this study tracked the changes on risk preferences examining the cognitive profile of more than 29,000 individuals over 50 years old using different cognitive tasks. Using a four-item task, we found significant differences in cognitive performance across countries and gender. Also, negative effect of age, gender and household size on risk attitude was reported. Nevertheless, an in-depth understanding of human behavior requires interdisciplinary approach controlling for physiological and sociological aspects.

Appendix See Table 5 and Figs. 1, 2 and 3.

Table 5 Cognitive performance by gender (mean, SD)

Man

Women

p-value

Verbal

20.9 (7.6)

20.7 (7.7)

n.s

Immediate

5.2 (1.7)

5.5 (1.8)

p < 0.05

Delayed

3.8 (2.0)

4.2 (2.2)

p < 0.05

Numeracy

4.2 (1.4)

4.0 (1.5)

p < 0.05

Cognitive score

0.04 (0.7)

0.08 (0.8)

p < 0.05

A European Study on Financial Risk Attitude and Cognitive Decline … Fig. 1 Cognitive score and age

Fig. 2 Risk attitude by a group of countries

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Fig. 3 Risk attitude by gender and age group

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Chowdhary, N., Barbui, C., Anstey, K. J., Kivipelto, M., Barbera, M., Peters, R., & Dua, T. (2022). Reducing the risk of cognitive decline and dementia. WHO Recommendations Frontiers in Neurology, 12. https://doi.org/10.3389/fneur.2021.765584. Cox, R., Brounen, D., & Neuteboom, P. (2015). Financial literacy, risk aversion and choice of mortgage type by households. The Journal of Real Estate Finance and Economics, 50(1), 74– 112. Dohmen, T., Falk, A., Huffman, D., Sunde, U., Schupp, J., & Wagner, G. G. (2011). Individual risk attitudes: Measurement, determinants, and behavioral consequences. Journal of the European Economic Association, 9(3), 522–550. Dohmen, T., et al. (2017). Risk attitudes across the life course. F95–F116. Donkers, B., Melenberg, B., & Van Soest, A. (2001). Estimating risk attitudes using lotteries: A large sample approach. Journal of Risk and Uncertainty, 22(2), 165–195. Falk, A., et al. (2015). The nature and predictive power of preferences: Global evidence. Fernandes, D., Lynch, J. G., Jr., & Netemeyer, R. G. (2014). Financial literacy, financial education, and downstream financial behaviors. Management Science, 60(8), 1861–1883. Fisher, G. G., Chacon, M., & Chaffee, D. S. (2019). Theories of cognitive aging and work. In Work across the lifespan (pp. 17–45). Academic press. Formanek, T., et al. (2019). Differences in cognitive performance and cognitive decline across European regions: a population-based prospective cohort study. European Psychiatry, 58, 80–86. Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives, 19(4), 25–42. Graham, F., & Isaac, A. G. (2002). The behavioral life-cycle theory of consumer behavior: Survey evidence. Journal of Economic Behavior and Organization, 48(4), 391–401. Kirchler, M., et al. (2017). The effect of fast and slow decisions on risk taking. Journal of Risk and Uncertainty, 54.1, 37–59. Martin, R. C., Gerstenecker, A., Triebel, K. L., Falola, M., McPherson, T., Cutter, G., & Marson, D. C. (2019). Declining financial capacity in mild cognitive impairment: A six-year longitudinal study. Archives of Clinical Neuropsychology, 34(2), 152–161. Thaler, R. H., & Benartzi, S. (2004). Save more tomorrow™: Using behavioral economics to increase employee saving. Journal of Political Economy, 112(S1), S164–S187. Van Rooij, M. C., Kool, C. J., & Prast, H. M. (2007). Risk-return preferences in the pension domain: Are people able to choose? Journal of Public Economics, 91(3–4), 701–722. Weber, D., et al. (2014). The changing face of cognitive gender differences in Europe. Proceedings of the National Academy of Sciences, 111.32, 11673–11678

Research on the Impact of Consumer Innovation on Purchase Intention of New Energy Vehicles—Regulated Intermediary Effect Liu Yang, Guangyi Xu, and Xu Jianzhong

Abstract In order to explore the influence mechanism of consumers’ purchase intention of new energy vehicles, based on planned behavior theory, technology acceptance model and innovation diffusion theory, a research framework including consumers’ innovation, perceived product innovation, social impact and individual cognitive response is constructed from the perspective of consumers, and data are collected through questionnaires, The empirical research is carried out by using Mplus and SPSS software. The results show that consumer innovation has a direct positive impact on consumer purchase intention, and perceived product innovation plays an intermediary role between consumer innovation and purchase intention; Consumer innovation is positively regulated by social influence in the process of influencing perceived product innovation; And individual cognitive response plays a positive regulatory role between perceived product innovation and purchase intention. Finally, it provides suggestions for new energy vehicle enterprises to promote consumers’ purchase intention. Keywords Consumer innovation · Perceived product innovation · Social impact · Individual cognitive response · Innovation diffusion theory

This paper is funded by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation. This paper is supported by the Natural Science Foundation of Heilongjiang Province (LH2019G014). L. Yang (B) · G. Xu · X. Jianzhong Department of Economics and Management, Harbin Engineering University, Harbin, China e-mail: [email protected] URL: http://www.hrbeu.edu.cn G. Xu e-mail: [email protected] URL: http://www.hrbeu.edu.cn X. Jianzhong e-mail: [email protected] URL: http://www.hrbeu.edu.cn © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 E. Lau et al. (eds.), Economics and Finance Readings, https://doi.org/10.1007/978-981-99-1979-6_4

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1 Introduction In recent years, the shortage of energy has become more and more obvious, and the technology of new energy vehicles has attracted more and more attention. In 2020, the executive meeting of the State Council adopted the development plan of new energy vehicle industry, put forward four representative promotion measures, and stressed that from 2021, the proportion of new energy vehicles in public areas such as public transport, leasing and logistics distribution will not be less than 80% in the national ecological civilization pilot area and key areas of air pollution prevention and control. At the same time, domestic new energy vehicle technology has also entered a dividend period of rapid development. According to the statistics of China Automobile Association, the cumulative sales volume of new energy vehicles (passenger vehicles) in the first half of 2021 reached 1.07 million, a year-on-year increase of 220.9%, which has equaled the total sales volume of new energy vehicles in 2020 (1.109 million). However, the actual proportion of this figure is still small compared with the huge domestic passenger car market. The domestic market is still in its infancy, with huge demand space and exploitable potential. Many scholars have found that consumers who are innovative when facing new products will affect their perception of product innovation (Anwarr et al., 2020), We can further explore the influencing factors of perceived product innovation from multiple aspects such as consumer individual factors and situational factors (Zolfagharian & Paswan, 2009). Ju and Lee (2021) found that the perceived product innovation will be affected by the innovative characteristics of consumers themselves. Raju and Lonial (2001) pointed out that product innovation based on consumer perspective rather than enterprise perspective can improve enterprise service level and enhance customer satisfaction. As a representative of emerging innovative products, new energy vehicles will greatly stimulate consumers’ innovation, and consumers’ purchase intention will be affected by consumers’ innovation. Consumers’ perception of product innovation is consumers’ subjective perception of product or service innovation (Ben and Frank, 2015). At the same time, consumers’ purchase intention will also be affected by multiple factors in real life. From the perspective of internal and external attribution, the influence sources are divided into social influence and individual cognitive response. Some studies have concluded that the achievement of purchase intention will be affected by both. At present, the research on consumer innovation is gradually maturing. Although some scholars have studied its concept and measurement (Page Moreau et al., 2001), there are relatively few studies on the combination of consumer innovation and internal and external psychological attribution. In addition, the research on consumerperceived innovation rarely involves developing countries, mainly developed countries (Chen et al., 2014). To sum up, taking the new energy vehicle industry as an example, this paper studies consumer innovation from the perspective of consumers, affects purchase intention through the role of perceived product innovation, takes the planned behavior theory, technology acceptance model and innovation diffusion theory as the theoretical support, taking developing countries as research objects,

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and incorporates the internal and external factors affecting consumers, in order to provide a new way for the development of new energy vehicle enterprises.

2 Literature Review and Assumptions 2.1 Consumer Innovation and Purchase Intention Innovation exists objectively, and consumers’ innovation will affect consumers’ green consumption behavior (Xie & An, 2020). As a new green product with advanced technology, new energy vehicles have the basic characteristics of saving resources and protecting the environment. To some extent, they are equipped with novel appearance characteristics, which can better meet the needs of consumers seeking breakthrough and change. The consumption of green products is usually driven by individual exploration and innovation of consumers. The study of consumers’ purchase intention of green products is inseparable from the analysis of individual exploration and innovation. Bartels and Reinders (2010) put the research object on green organic products and further discussed the relationship between consumers’ social identity and consumers’ innovation. Lao (2013) proposed in his research that consumer innovation has a significant impact on green consumption behavior, and its mechanism is to affect consumers’ green consumption intention through influencing consumers’ green consumption attitude, subjective norms and perceptual control, so as to affect green consumption behavior. In the face of new consumer products, consumers will stimulate their preference for innovative characteristics in the pursuit of product perception, which is no longer limited to the technology itself, but more the result of innovation. From the perspective of consumer innovation characteristics, Chen et al. (2010) and others observed that consumer innovation actually affects the adoption behavior of new products. Liu and Hao (2012) discussed the adjustment mechanism affecting the relationship between consumer innovation and new product adoption behavior from the structural perspective of consumer innovation, and finally confirmed that there is a positive relationship between consumers’ overall innovation and innovative behavior. Based on the above analysis, we propose the following assumptions: H1: consumer innovation positively affects purchase intention.

2.2 The Intermediary Role of Perceived Product Innovation Rogers first proposed the concept of perceived product innovation and defined it as consumers’ subjective judgment on the degree to which a product is different from other similar products in terms of novelty and practicability (Rogers, 1983). The technology of new energy vehicle itself is the key factor affecting consumers’ acceptance.

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Compared with the traditional power vehicle technology, new energy technology itself is novel and develops rapidly, which will become the most priority factor for consumers to choose to a great extent. From the perspective of technology, new energy vehicles involve the overall performance under the influence of power and control system, and also include the technical aesthetic concept of vehicle appearance. Existing studies believe that perceived product innovation is not only closely related to the benefit attributes of products, but also affected by information attributes, consumers’ cognitive style and individual characteristics (Chen et al., 2015), so we can further explore the influencing factors of perceived product innovation through consumers’ individual factors and situational factors (Zolfagharian & Paswan, 2009), Innovative consumers will affect their perception of product innovation. Many scholars have found that consumers’ perception of product innovation can have a significant impact on their attitudes and behaviors. Raju and Lonial (2001) pointed out that product innovation based on consumer perspective rather than enterprise perspective can improve enterprise service level and enhance customer satisfaction. Research by Jamal and Sharifuddin (2015) shows that perceived product innovation performance is positively promoting product purchase intention. Dahl and Moreau (2002) found that consumers’ perceived product innovation will significantly and positively affect consumers’ brand attitude, thus further affecting purchase intention. O’cass and Carison (2012) investigated 370 consumers with online shopping experience and found that perceived website product innovation will directly affect the number of customers visiting the website, improve customers’ purchase intention and reduce their switching behavior. To sum up, this paper proposes: H2: perceived product innovation plays an intermediary role between consumer innovation and purchase intention H2a: consumer innovation positively affects perceived product innovation H2b: perceived product innovation positively affects purchase intention.

2.3 Regulation of Social Impact Social impact refers to the subjective norms and impressions generated by consumers’ feeling of external pressure (Kelaman, 1958). Considering consumers’ external attribution, the planned behavior theory believes that consumers will be affected by the surrounding environment, and the amount of information that can be received around them will affect the subjective norms to a certain extent. Subjective norms refer to the influence of individual behavior from the social environment (ROGERS, 1995), When an individual does not feel the pressure to participate in the promotion of new energy around him or her, that is, when he or she does not feel the pressure to actively participate in the promotion of new energy around him or her, he or she will also feel the pressure to participate in the promotion of new energy around him or her. In some cases, the environmental concept respected by consumers will also

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affect the attitude and concept of novelty. As a prominent representative of environmental protection technology products, new energy vehicles will be promoted by environmental protection concept while driving innovative behavior. In recent years, scholars have gradually paid attention to the impact of consumers’ subjective norms on the sales of new energy vehicles in China. Some scholars have found that consumers’ subjective norms will affect the final achievement of acceptance intention. Based on the research of planned behavior theory, Ajzen (1991) concluded that consumers’ subjective cognition such as personal attitude and perceptual norms will affect their behavior intention, thus indirectly affecting their behavior. Zhang et al. proposed that due to consumers’ low environmental preference, consumers’ willingness to buy new energy vehicles is still small. Gan and Yuan (2011) studied the influencing factors of consumers’ adoption of online banking in the shopping process through the innovation diffusion theory, and believed that the relative advantages, complexity and convenience conditions of the environment will affect the willingness to innovate, among which the perceived risk and convenience conditions will have an indirect impact on the willingness to accept. From the perspective of innovation diffusion, Chen and Li (2018) believe that the accessibility of the external environment affects individual innovation, and then affects the willingness to accept shared bicycles. Based on the theory of innovation diffusion, the model built by Rogers (2003) regards innovation as a process of gradual elimination of uncertainty. During this period, it mainly depends on the relevant technical information collected from the external environment. Since then, it has been widely used in the factors affecting consumers’ willingness to adopt new technologies. Namely: H3: social impact plays a positive regulatory role between consumer innovation and perceived product innovation.

2.4 Regulation of Individual Cognitive Response Individual cognitive response refers to individual perceived usefulness and perceived ease of use (Huang and Korfiatis 2015), which can be attributed to the internal attribution of consumers. The technology acceptance model theory (Davis, 1989) believes that when consumers choose to buy a commodity, they will perceive the usefulness and ease of use of the commodity according to their own interests and actual conditions, and make a choice in combination with their personal preferences. Then affect their own use attitude and behavior. Fenech and O’Cass (2001) found that online consumers’ pursuit of innovative products and technologies will be reflected through perceived usefulness and perceived ease of use, which can significantly and positively affect their online shopping attitude. Wang and Wang (2013) and others are also based on TAM model, which shows that new energy vehicle technology further affects acceptance intention by affecting consumers’ subjective norms and intuitive control. On the other hand, personal perception will involve more detailed product adoption points. Zhou et al. (2012) based on TAM and IDT, by constructing a theoretical model of the

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Fig. 1 Theoretical model of this paper

influencing factors of adoption intention, concluded that consumers’ perceived cost, perceived ease of use and comparative advantage will affect adoption intention. Lu and Xu (2005) through theoretical analysis, it is concluded that under the planned behavior theory, consumers will be affected by self-perception, which will further affect the achievement of acceptance intention. Ju et al. (2017) Analyzed the choice behavior of travelers sharing cars, and discussed that consumers’ personal perception will affect their choice behavior. In the adoption of new products, consumers often have exploratory and innovative consumption behavior. Through research and analysis, Xiao and Zhang (2021) concluded that the control of individual perceived behavior is the influencing factor of new products. From this analysis, the higher consumers pay attention to their own needs, the more dating can stimulate their perception of product innovation. When the social impact also plays a regulatory role, individual cognitive response can drive the perception of product innovation and further affect the purchase intention. Therefore, it is proposed that: H4: individual cognitive response positively regulates perceived product innovation and purchase intention Based on the above theoretical analysis and relevant assumptions, the theoretical model of this study is shown in Fig. 1.

3 Research Design This model includes five variables: consumer innovation, individual cognitive response, social impact, perceived product innovation and purchase intention. The scale adopts Likert level 5 scale. See Table 1 for each question item and reference source. Limited by the current COVID-19 influence, this survey is mainly based on online questionnaires. First, the questionnaire is designed on the website of the questionnaire. Then, the network links generated by the students, friends and family members are posted in the circle of friends and their group chat. The coverage of consumers in most parts of the country is investigated, making the survey more universal. A

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Table 1 Variables and questionnaire items Variable

Item

Reference source

Consumer innovation (CI)

I prefer to accept new things

Roehrich (2004)

I think the new lifestyle is also a kind of progress New things appear around me. I’m willing to try I will try new things before the people around me Individual cognitive response (ICR)

My actual car demand will Radford and Bloch (2011) determine my purchase of new energy vehicles When I buy new energy vehicles, I will consider my current income The daily maintenance cost of vehicles, such as maintenance cost and maintenance cost, is the key consideration when I buy new energy vehicles I will care about the current use cost of new energy vehicles, such as charging fee, parking fee, etc

Social impact (SI)

I will accept the Murray and Schlacter (1990) recommendation of my classmates, friends, family and other people around me to buy new energy vehicles Before purchasing new energy vehicles, the test and evaluation of professional online media car reviewers influenced my decision The use of new energy vehicles instead of traditional fuel consuming vehicles can reduce the pollution to the ecological environment The policy support of government departments for the new energy vehicle industry will affect my decision to buy new energy vehicles (continued)

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Table 1 (continued) Variable

Item

Reference source

Perceived product innovation (PPI)

I know the technology of new energy vehicles very well

Dahl and Hoeffler et al. (2004)

When buying new energy vehicles, I pay more attention to the overall performance of the vehicle The novelty and appearance of new energy vehicles will be an important reason why I choose to buy them I will pay more attention to the innovation ability of new energy vehicle service manufacturers Purchase intention (PI)

I have been paying attention to Dodds et al. (1991) new energy vehicles recently I will consider buying new energy vehicles I will recommend others to buy new energy vehicles

total of 589 questionnaires were distributed and 518 were recovered, with a questionnaire recovery rate of 87.95%; Excluding 30 questionnaires with too long and too short response time and too high and too low comprehensive questionnaire scores, 488 valid questionnaires remained, and the effective rate of the questionnaire was 82.85%. See Table 2 for sample distribution. The sample distribution data shows that the proportion of men and women among the respondents is relatively balanced and basically the same. The age is mainly 18–40 years old (accounting for 70.9%), the education level is high (the number of people with college degree or above accounts for 90.1%), the occupation is mainly company employees, and the annual family income is between 100,000 yuan and 300,000 yuan.

4 Hypothesis Analysis 4.1 Reliability and Validity Test Cronbach’s was used in this study α Coefficient is used to test the reliability of the scale. Spss26 is used for reliability analysis. The results are shown in Table 3.

75 105 37

31–40

41–50

Over 51

1.84 9.84 29.92

9 48

Other

Education level Junior high school and below

Technical secondary 146 school/high school/college

39.75

13.11

17.42

21.11

7.58

21.52

15.37

29.51

26.02

6.76

Individual/freelancer

194

64

Professionals (such as doctors, teachers, lawyers, etc.)

Enterprise staff

85

Civil servant

103

144

26–30

Student

127

18–25

49.59

33

Occupation

Age

242

Woman

5 persons and above

4 persons

3 persons

1–2 persons

Master degree or above

Undergraduate

Classification index

99

155

234

0

54

240

Family car ownership

2 and above

1

0

500,000 yuan and above

400,000–500,000 yuan

300,000–400,000 yuan

200,000–300,000 yuan

100,000–200,000 yuan

106

334

48

45

42

60

99

136

21.72

68.44

9.84

9.22

8.61

12.30

20.29

27.87

21.72

20.29

31.76

47.95

0.00

11.07

49.18

Number/person Ratio/%

Annual household income 100,000 yuan and below 106

Number of families

Education level

246

Man

Gender

50.41

Number/person Ratio/% Variable

Classification index

Variable

Table 2 Sample distribution

Research on the Impact of Consumer Innovation on Purchase Intention … 49

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L. Yang et al.

Table 3 Reliability test

Item

Reliability

Overall reliability

CI

4

0.892

0.907

ICR

4

0.910

SI

4

0.910

PPI

4

0.907

PI

3

0.896

According to the data in the table, the reliability value of each dimension of the questionnaire is higher than 0.8, the result is stable and has a certain reliability. This article uses mplus8 3 exploratory factor analysis was used to test the structural validity of the scale. The aggregate validity was tested according to the factor load value and average variance extraction (AVE) of each test item. In the calculation results of each item load value, the standardized load value of different items under each dimension was higher than 0.5, the combined reliability CR value of each dimension was higher than 0.8, and the average variance extraction (AVE) was higher than 0.6. Based on the above parameters, as shown in Table 4, the topic relevance of each dimension in the model is strong, and all dimensions have good aggregate validity. Different competitive models were established to test the advantages and disadvantages of the fitting parameters of each model and the five factors model, as shown in Table 5. Except for the five factors model, the fitting parameters of other models did not meet the analysis standard, and the structural validity was poor. Therefore, the five factors model is the best model, and the scale has good discriminant validity.

4.2 Hypothesis Test (1) Main effect test Hypothesis H1 proposes that consumer innovation has a significant positive impact on purchase intention. In order to test this hypothesis, the purchase intention is set as the dependent variable, and the control variable and independent variable are added successively for regression. The results are shown in Table 6. It is easy to get that consumer innovation has a significant positive impact on consumers’ purchase intention (model 2, β = 0.497, P < 0.001), so H1 is assumed to be true. (2) Intermediary effect test The test results are shown in Table 6. (1) According to the above analysis, it can be concluded that the main effect is significant; (2) Using consumer innovation to regress perceived product innovation, the results show that the regression coefficient is positively significant (model 3, β = 0.244, p < 0.001,); (3) At the same time, consumer innovation and perceived product innovation are regressed to purchase

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Table 4 Aggregation validity analysis Title number CI

ICR

SI

PPI

PI

Load value

Standardized load

SE

t

p

T8

1.000

0.866

T9

0.899

0.786

0.043

20.686