Tilting at the Windmills of Transition: An Empirical Analysis of Spatial Systems of Entrepreneurship and Institutions in Russia [1st ed.] 9783030549084, 9783030549091

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Societies and Political Orders in Transition

Michael Schlattau

Tilting at the Windmills of Transition An Empirical Analysis of Spatial Systems of Entrepreneurship and Institutions in Russia

Societies and Political Orders in Transition Series Editors Alexander Chepurenko Higher School of Economics, National Research University, Moscow, Russia Stein Ugelvik Larsen University of Bergen, Bergen, Norway William Reisinger University of Iowa, Iowa City, IA, USA Managing Editors Ekim Arbatli Higher School of Economics, National Research University, Moscow, Russia Dina Rosenberg Higher School of Economics, National Research University, Moscow, Russia Aigul Mavletova Higher School of Economics, National Research University, Moscow, Russia

This book series presents scientific and scholarly studies focusing on societies and political orders in transition, for example in Central and Eastern Europe but also elsewhere in the world. By comparing established societies, characterized by wellestablished market economies and well-functioning democracies, with post-socialist societies, often characterized by emerging markets and fragile political systems, the series identifies and analyzes factors influencing change and continuity in societies and political orders. These factors include state capacity to establish formal and informal rules, democratic institutions, forms of social structuration, political regimes, levels of corruption, specificity of political cultures, as well as types and orientation of political and economic elites. Societies and Political Orders in Transition welcomes monographs and edited volumes from a variety of disciplines and approaches, such as political and social sciences and economics, which are accessible to both academics and interested general readers. Topics may include, but are not limited to, democratization, regime change, changing social norms, migration, etc. More information about this series at http://www.springer.com/series/15626 International Advisory Board: Bluhm, Katharina; Freie Universitðt Berlin, Germany Buckley, Cynthia; University of Illinois at Urbana-Champaign, Sociological Research, USA Cox, Terry; Central and East European Studies, University of Glasgow, UK Fish, Steve; Berkeley University, USA Ilyin, Michail; National Research University Higher School of Economics, Russia Melville, Andrei; National Research University Higher School of Economics, Russia Radaev, Vadim; National Research University Higher School of Economics, Russia

Michael Schlattau

Tilting at the Windmills of Transition An Empirical Analysis of Spatial Systems of Entrepreneurship and Institutions in Russia

Michael Schlattau Chair for International Management Bundeswehr University Munich Neubiberg, Germany

ISSN 2511-2201 ISSN 2511-221X (electronic) Societies and Political Orders in Transition ISBN 978-3-030-54908-4 ISBN 978-3-030-54909-1 (eBook) https://doi.org/10.1007/978-3-030-54909-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Acknowledgments

Success is not final, failure not fatal. It is the courage to continue that counts. Winston Churchill

This study has been my dissertation project conducted at the Bundeswehr University Munich at the chair of International Management. Although a dissertation project is usually a solitary and independent undertaking, a large number of people contributed to the project in their own way and I would like to attribute thanks accordingly. First and foremost, I would like to thank my doctoral supervisor Prof. Dr. Hans. A. Wüthrich who contributed to this process with new ideas, valuable assistance, and guidance without which this project would never have been possible. Hans, you have not only been the referee for this dissertation but also my mentor throughout the past years, and constantly offered invaluable help, comments, perspectives, and suggestions. Thanks to your support and flexibility, this dissertation has been written somewhere between Munich, Berlin, Moscow, New York, Madrid, Varna, and Halle (Saale) in a rather eventful period of my life. Secondly, big thanks also go to my second supervisor Prof. Dr. Andreas Brieden to whom I have to attribute a tremendous amount of gratitude for offering his valuable insights and perspectives on the methodological, statistical, and empirical aspects of my dissertation. Similarly, I want to thank Saskia Schiele for her steady and untiring methodological support, without which, occasionally, I would not have seen the wood for the trees. Additionally, I need to thank Prof. Aleksandr Chepurenko and Stepan Zemtsov for kindly welcoming me as a visiting scholar at Higher School of Economics (HSE) in Moscow, providing data, and particularly valuable input, ideas, and discussion. Likewise, I would like to thank the Harriman Institute at Columbia University in New York for hosting me in the summer of 2017 and providing enriching discussions and excellent grounds for my research. I also express my gratitude to Galina Belokurova, Michael Rochlitz, and Nikolai Petrov for kindly providing data to my research. v

vi

Acknowledgments

Many thanks go to my colleagues at the chair of International Management— Nicole, Olga, Steffi, and Philipp—and at the WOW institute—especially David, Erna, Jacqui, Martin, Sean, and Verena—for all the discussions, support, good hours, and lots of coffee in between. Last but not least, my deepest appreciation is reserved, naturally, for the patience, love, and support of my family and for my wife Viktoria, who in addition to love and encouragement kept my ideas and theories rooted in the reality of life in Russia. Munich, June 2020

Michael Schlattau

About the Book

Systems of entrepreneurship theory recognizes that decisions on entrepreneurial market entry are always embedded in a country’s or region’s institutional framework. In this regard, the context of the Russian Federation provides an ideal setting for investigation, especially in view of regionally differentiated processes of transition to a market economy. This dissertation investigates which institutional factors show significant influence on spatial entrepreneurship activity in Russia’s transition context and which factors act as institutional bottleneck constraints for the country’s system of entrepreneurship. Building on a comprehensive Orbis dataset, the analysis exploits spatial institutional variation by explicitly considering the substantial degree of heterogeneity across Russia’s regions. The research design follows a twofold methodological approach. Whereas a Tobit regression modeling approach comes along with considerable drawbacks such as spurious correlation, developing a predictive model based on the innovative geometric clustering approach by Brieden and Gritzmann resulted in higher statistical explanation power, higher accuracy of the impact of the predictor variables on entrepreneurial activity, and an appropriate consideration of the interrelatedness of institutions with regard to their impact on entrepreneurship. The results demonstrate that spatial entry can be explained by institutional factors at the regional level and emphasize the relevance of understanding entrepreneurial ecosystems as a system of interrelated elements whose overall function may be impeded by individual components. Whereas a lack of finance and sizable bureaucratic barriers significantly hamper entrepreneurial entry, higher levels of industry concentration unexpectedly do not constitute an actual barrier to entry (although concentration is detrimental for firm survival). Most importantly, there is substantial evidence that higher levels of regional democratization and the liberties that come with it are an essential prerequisite for higher rates of entrepreneurial entry and innovation in Russia.

vii

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 5

2

Entrepreneurship and Entrepreneurial Ecosystem . . . . . . . . . . . . . 2.1 Entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Understanding the Entrepreneur . . . . . . . . . . . . . . . . . . . 2.1.2 Entrepreneurship and Growth . . . . . . . . . . . . . . . . . . . . . 2.1.3 Types of Entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . 2.2 Systems of Entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Entrepreneurship and the Role of the Individual . . . . . . . . 2.2.2 Entrepreneurship and the Relevance of Context . . . . . . . . 2.2.3 Bottleneck Factors as Limiting Constraints . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . .

7 7 7 8 11 13 13 16 20 23

3

The Distinctive Layout of Russia . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Regional Layout and Spatial Heterogeneity . . . . . . . . . . . . . . . . 3.2 Political Administration and Heterogeneous Institutions . . . . . . . 3.3 Economy and Business Climate: Between Transition and Rent Dependency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Is Russia an Entrepreneurial Society? . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . .

31 31 36

. . .

38 44 47

The Institutional Framework for Entrepreneurship in Transition . . . 4.1 Structural Economic Factors as Fundamental Prerequisites . . . . . . 4.2 Institutional Drivers and Determinants of Entrepreneurial Activity . . . 4.2.1 Ensuring Property Rights . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Criminality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Corruption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.4 The Burden of Bureaucracy . . . . . . . . . . . . . . . . . . . . . . . 4.2.5 Financial Capital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.6 Human Capital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.7 Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51 51 66 66 74 77 80 86 89 93

4

ix

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Contents

4.2.8

Market Environment: The Effects of Oligarchy and the Structural Dominance of Incumbents . . . . . . . . . . . . . . . . . 100 4.2.9 Democratization and Entrepreneurship . . . . . . . . . . . . . . . 110 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 5

6

Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Motivation and Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Data and Sample Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Dependent Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Natural Entry Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Institutional Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.4 Structural Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.5 Detailed Description of Variables . . . . . . . . . . . . . . . . . . . 5.2.6 Data Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.7 Identification of Regional Clusters . . . . . . . . . . . . . . . . . . 5.2.8 A Preliminary Descriptive Analysis . . . . . . . . . . . . . . . . . . 5.3 Perspective 1: A Descriptive Regression Model Approach . . . . . . . 5.3.1 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Perspective 2: A Geometric Clustering Approach . . . . . . . . . . . . . 5.4.1 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

135 135 136 136 142 143 149 149 156 158 168 174 174 177 199 204 204 211 220 226

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Key Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Implications for Policy and Practice . . . . . . . . . . . . . . . . . . . . . . 6.3 Limitations and Future Research . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

233 233 237 241 242 243

. . . . . .

Annex A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Annex B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Annex C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287

Abbreviations

AO ASI BRICS CEE CEFIR CIS CT EBRD EFI EGP EU FAS FDI GDP GEM GRP GST HHI HSE ICSID IMF IT KSTE LGBT MAPE MAR MCAR NACE NMAR

Autonomous Oblast Agency for Strategic Initiatives Brazil, Russia, India, China, and South Africa Central and East European Countries Center for Economic and Financial Research Commonwealth of Independent States Configuration theory European Bank for Reconstruction and Development Economic Freedom Index Economic-Geographical Potential European Union Federal Antimonopoly Service Foreign direct investment Gross domestic product Global Entrepreneurship Monitor Gross regional product General systems theory Herfindahl–Hirschman index National Research University Higher School of Economics International Center for the Study of Institutions and Development International Monetary Fund Information technology Knowledge spillover theory of entrepreneurship Lesbian, gay, bisexual, and transgender Mean absolute percentage error Missing at random Missing completely at random Nomenclature statistique des activités économiques dans Communauté européenne (fr.) Not missing at random

la

xi

xii

NSE NSI OECD OLS OPORA PPP R&D RF SEZ SME SOE TASS TEA ToC ToW US USD USSR VAT WJP

Abbreviations

National Systems of Entrepreneurship National Systems of Innovation Organisation for Economic Co-operation and Development Ordinary least squares Russian non-governmental association for SMEs Purchasing power parity Research and development Russian Federation Special economic zone Small and medium-sized enterprise State-owned enterprises Informatsionnoye agentstvo Rossii TASS (ru.), major news agency in Russia Total early-stage entrepreneurial activity Theory of constraints Theory of weakest link United States of America US Dollar Union of Soviet Socialist Republics Value Added Tax World Justice Project

List of Figures

Fig. 3.1 Fig. 3.2 Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 4.4 Fig. 4.5

Fig. 4.6

Fig. 4.7

Map of the federal subjects of the Russian Federation (Source: Author’s illustration based on Geocurrents 2018) . . . . . . . . . . . . . . . . . Correlation of economic development and crude oil price . . . . . . . . Income per capita and total early stage entrepreneurial activity (TEA), 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Income per capita and total early stage entrepreneurial activity (TEA), 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GDP per capita and opportunity motivation, 2010 . . . . . . . . . . . . . . . . . GDP per capita and opportunity motivation, 2016 . . . . . . . . . . . . . . . . . Top income share in post-soc. countries (distribution of pretax national income (Russia) or fiscal income (other countries)) (Source: Author’s illustration according to Novokmet et al. (2018), Novokmet (2017), and Mavridis and Mosberger (2017)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wealth share distribution in Russia (distribution of pretax national income of adults (prior to taxes and transfers, except for pensions and unemployment insurance). The estimates used combine survey, fiscal, wealth, and national accounts data) (Source: Author’s illustration according to Novokmet et al. (2018)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Billionaire wealth in % of nat. income (total billionaire wealth (according to Forbes’s global list of dollar billionaires) divided by national income (measured at market exchange rates). Only citizen billionaires are reported (numbers for resident billionaires are practically identical)) (Source: Author’s illustration according to Novokmet et al. (2018)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

33 40 52 53 53 54

63

63

64

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xiv

Fig. 4.8

Fig. 4.9

Fig. 4.10

Fig. 4.11 Fig. 4.12

Fig. 4.13

Fig. 4.14

Fig. 4.15

Fig. 4.16

Fig. 4.17 Fig. 4.18

Fig. 4.19

Fig. 5.1 Fig. 5.2

Fig. 5.3

List of Figures

Income share distribution in Russia (distribution of pretax national income of adults (prior to taxes and transfers, except for pensions and unemployment insurance). The estimates used combine survey, fiscal, wealth, and national accounts data) (Source: Author’s illustration according to Novokmet et al. (2018)) . . . . . . . Gini coefficient estimates in Russia, 1988–2016 (Source: Author’s illustration according to Fidrmuc and Gundacker (2017), World Bank (2018a), and Rosstat (2018)) . . . . Raidership intensity across Russian regions, 2007–2010 (Source: Author’s illustration based on Geocurrents (2018) and data by Rochlitz (2014)) . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . Raidership attacks per year and average number of employees per target firm (illustration based on Rochlitz (2014)) . . . . . . . . . . . . . . . . . Average number of people per year involved in cases of business violence across Russian regions, 2006–2011 (Source: Author’s illustration based on Geocurrents (2018) and data by Belokurova (2012)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Corruption and entrepreneurial activity. Illustrations based on data from GEM (2018), World Bank (2018a), and ORBIS (2016), using 2008 and 2014 averages; countries close to the 100 percentile rank show relatively high levels of corruption control compared to the 0 rank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Average share of employees with higher education per region, 2006–2013 (Source: Author’s illustration based on Geocurrents (2018) and data by ICSID (2017)) .. . .. .. . .. . .. .. . .. . .. . .. .. . .. . .. .. . Special economic zones in Russia (Source: Author’s illustration based on Geocurrents (2018) according to the Ministry of Economic Development (2017)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Perceived level of competition from Russian microenterprise survey, 2013–2016 (illustration according to Szakonyi (2017, p. 2)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial concentration in the Russian manufacturing industry, 2010 and 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regional democracy ratings in Russia, 1991–2001 (Source: Author’s illustration based on Geocurrents (2018) and data by Petrov and Titkov (2013)) . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . Regional democracy ratings in Russia, 2001–2011 (Source: Author’s illustration based on Geocurrents (2018) and data by Petrov and Titkov (2013)) . . .. . . . . .. . . . . .. . . . . .. . . . . .. . .

65

66

72 73

76

79

92

97

101 103

113

114

Dynamic development of firm entry, 2007–2014 . . . . . . . . . . . . . . . . . . 141 Map of sample regions following the urbanization-based clustering scheme (Source: Author’s illustration based on Geocurrents 2018) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 EGP clustering scheme: cluster definition . . . . . . . . . . . . . . . . . . . . . . . . . . 166

List of Figures

Fig. 5.4 Fig. 5.5 Fig. 5.6 Fig. 5.7 Fig. 5.8 Fig. 5.9

Map of sample regions following the EGP clustering scheme (Source: Author’s illustration based on Geocurrents 2018) . . . . . . . Entry rate distributions across industries (selected industries) . . . . Developments in Internet use, wages, and employees with higher education, 2000–2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lookup tree, variable characteristics, and prediction collectives in data subset C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lookup tree, variable characteristics, and prediction collectives in data subset IKM . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . A comparison of GDP growth and entrepreneurial entry across prediction subsets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xv

167 173 197 212 215 219

List of Tables

Table 1.1

Overview of dissertation structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

Table 3.1 Table 3.2

Overview of the federal subjects of the Russian Federation . . . . . Russia’s business climate rankings in global comparison, 2008–2017 . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . .

34

Table 4.1 Table 4.2 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 5.6 Table 5.7 Table 5.8 Table 5.9 Table 5.10 Table 5.11 Table 5.12 Table 5.13 Table 5.14 Table 5.15

41

Overview of most and least concentrated industries, including SOE share .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . . .. 106 State share across market sectors, 2012 and 2016 . . .. . . .. . .. . . .. . 107 Overview of hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of average gross entry rates per year . . .. . . .. . . .. . .. . Entry rates per industry according to NACE classification, 2008–2011 . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . Overview of institutional variables and corresponding data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of variables and periods that require imputation of missing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of regional clusters: urbanization-based clustering scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of regional clusters: EGP clustering scheme . . . . . . . . . . Summary statistics of variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Correlation table of institutional and control variables . . . . . . . . . . Regression results, overall regional perspective . . . . . . . . . . . . . . . . . . Regression results, urbanization-based cluster perspective . . . . . . Regression results, EGP cluster perspective . . . . . . . . . . . . . . . . . . . . . . Overview of tentatively accepted and rejected hypotheses according to perspective 1 . . .. . . .. . . . .. . . . .. . . .. . . . .. . . .. . . . .. . . . .. . The number of training and test data observations across two sector-based subsets . . . .. . .. . .. . . .. . .. . .. . . .. . .. . .. . . .. . .. . .. . . .. . .. . Prediction model collectives, variable characteristics in data subset C . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

137 140 144 147 156 164 168 169 171 178 180 182 201 208 212 xvii

xviii

Table 5.16 Table 5.17 Table 5.18 Table 5.19

List of Tables

Prediction model collectives, variable characteristics in data subset IKM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prediction accuracy and statistical evaluation, data subset C . . . . Prediction accuracy and statistical evaluation, data subset IKM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of accepted and rejected hypotheses according to perspective 2 . .. . . .. . .. . .. . .. . . .. . .. . .. . . .. . .. . .. . .. . . .. . .. . .. . .. . . .. .

215 218 218 221

Chapter 1

Introduction

Taking a new step, uttering a new word, is what people fear most. Fyodor Dostoyevsky, Crime and Punishment.

There has been little discussion in the scientific community about the positive effects of entrepreneurship. High entry rates create growth, jobs, and a more adequate distribution of income; they are also substantial for economic development and countries’ innovation capabilities (McMillan and Woodruff 2002; Baumol 1990). However, the literature on entrepreneurship assumes that most of the findings are general and valid across countries. Moreover, entrepreneurship researchers have focused to a great extent on individuals’ characteristics and tended to neglect the regulating effect of context on entrepreneurial actions, although a great deal of the trade-offs and opportunity costs entrepreneurs face are regulated by national context, for example, through a broad range of different formal and informal institutional settings, different cultures, norms, and values and attitudes towards entrepreneurship, all of which affect entrepreneurial activity (Autio et al. 2014). To address this issue, Acs et al. (2014) have introduced a novel theoretical framework concept, i.e., National Systems of Entrepreneurship (NSE). NSE are resource allocation systems driven by individual-level opportunity pursuit through the creation of new ventures. Within those systems, efforts towards venture creation are regulated by country-specific institutional characteristics. Thus, NSE consider the contexts of entrepreneurs and not just their personal characteristics and aspirations, thereby recognizing that entrepreneurship processes are always embedded in a given country’s institutional framework (Acs et al. 2014, 2016). Moreover, the systemic approach of NSE allows for interactions between their components. Although business barriers can be examined in isolation, exploring their interrelatedness can provide a deeper level of understanding with regard to their interactions (Acs et al. 2014; Aidis 2005), particularly since potential bottlenecks may impede the performance of the entire system. Various studies have analyzed cross-country heterogeneity in entry rates and hypothesized a link with the domestic institutional context. These studies, © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Schlattau, Tilting at the Windmills of Transition, Societies and Political Orders in Transition, https://doi.org/10.1007/978-3-030-54909-1_1

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nevertheless, could only partially control for macroeconomic differences, legislation, sociocultural, and other country-specific factors, a major disadvantage of crosscountry studies (Bruno et al. 2013; Klapper et al. 2006; Djankov et al. 2002). Thus, as countries are usually not homogeneous, regional systems of entrepreneurship provide a useful focus for empirical research. Numerous studies have emphasized the local incentive structure for entrepreneurship and pointed out that regional market potential is an important feature that influences a firm’s decision to enter a market (Fritsch and Wyrwich 2014a, b; Baumol 1990). However, there is a significant spatial variation in the entrepreneurship context across regions that impacts entrepreneurial activity and that calls for a profound analysis. The context of the Russian Federation provides an ideal setting to address the question of how institutions influence entrepreneurial activity, especially in view of regionally differentiated processes of transition to a market economy. Russia continues to have one of the lowest shares of founders and innovative companies (Chepurenko 2011). Estrin (2017) has highlighted Russia as one of few countries with fewer than four entrepreneurs per thousand people in the working population. Moreover, Western best practices in an environment of apparently hampered institutions could neither prevent nepotism nor meet the special context of a transition economy (Chepurenko 2011). This outcome is in line with the entrepreneurship literature, which has argued that, in malfunctioning institutional frameworks, entrepreneurs do not take on new ventures or restrict their activities to unproductive types of entrepreneurship (Glaeser et al. 2003; Johnson et al. 1997; Baumol 1990). In Russia, the variety of economic and social conditions and the sizeable leeway for regional governments to shape regional institutions have resulted in considerable discrepancy among regional development paths. This cross-regional variation in both informal and formal institutions may have a substantial impact on spatial firm entry rates across the country’s regions (Bruno et al. 2013; Popov 2001). Estrin (2017, p. vi) has added that differences in the entrepreneurial evolution of transition economies are largely shaped by deep-rooted historical, geographical, and cultural disparities and that a comprehensive analysis of these developments is long overdue. Accordingly, the following research questions are addressed: 1. Which spatial institutional factors are most important in shaping regional entrepreneurial activity in Russia? 2. Is the performance of the Russian system of entrepreneurship subject to institutional bottleneck constraints? The empirical work to address this question is driven by a combination of different data sources and a twofold methodological approach. For the database, the exogenous or outcome variable is derived from a comprehensive longitudinal enterprise data set sourced from Bureau van Dijk’s Orbis database. From this data set, I calculate entry rates per year, Russian region and industry. On the endogenous or predictor side, I consider a broad set of sources for institutions at the regional level. Additionally, as Russian regions are assumed to be subject to a substantial degree of heterogeneity, the study considers different types of regional clusters in the analysis, i.e., groups of Russian regions with similar combinations of challenges. In

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order to do so, I follow Natalia Zubarevich’s (2013) concept of “Four Russias” and Zemtsov and Baburin’s (2016) concept of economic-geographical location potential (EGP). Regarding the cross-regional design of this study, the results can be expected to be more comparable within the same country than in cross-country settings. Notwithstanding high degrees of spatial heterogeneity and specific regional development paths, Russia’s regions are still subject to the same federal law, a common market, the same history, and similar social/cultural characteristics that influence the appearance of entrepreneurship. Considering these factors, I expect my statistical models to be more reliable regarding the ceteris paribus assumption and omitted variable bias, in contrast to cross-country studies. Regarding the research strategy, in the first perspective of my twofold methodological approach, I follow Klapper et al.’s (2006) and Bruno et al.’s (2013) regression modeling approach, which relies on natural entry rates as an intuitive benchmark to assess the impact of institutions on entrepreneurial activity. However, particularly when dealing with institutional factors, multicollinearity must be considered, and depending on the way in which it is considered in the modeling approach, other challenges, such as spurious correlations, may arise. Hence, in order to generate more reliable results, and particularly findings that are applicable in a practical context, in a second perspective, I aim to develop a predictive model for entrepreneurial entry in Russia’s regions. This model was realized in close cooperation with Andreas Brieden, holder of the Chair of Statistics at Bundeswehr University Munich, and my dear colleague Saskia Schiele, based on the innovative geometric clustering approach by Brieden and Gritzmann (2012). This way, it is possible to achieve higher statistical explanation power and a higher accuracy of the impact of the predictor variables on entrepreneurial activity, in addition to adequately accounting for the interrelatedness of institutions with regard to their impact on entrepreneurship. In terms of incremental contributions, I expect this study to add value to several discourses in the scientific literature. As the systemic NSE approach accounts for interactions between system components, this study particularly has the potential to provide a deeper understanding of institutional impacts, given the interactions between different types of institutions. Additionally, this work aims to offer further insights in considering the specific context of an economy in transition, especially from a regional perspective. Based on the studies to be conducted, I expect to identify true, reliable institutional effects on entrepreneurial activity. Apart from the theoretical discourse, the findings can be expected to offer a valuable foundation to policy-makers and reformists who aim to make Russia’s economy more diverse, modern, and innovative. As the NSE literature believes the performance of systems of entrepreneurship is constrained by so-called bottlenecks, discovering institutional root causes of inhibited entrepreneurial activity is a prerequisite for intelligent policy design. The remainder of this thesis is structured as follows (as also illustrated by Table 1.1). First, Chap. 2 provides a basic understanding of the thesis’s understanding of entrepreneurship and emphasizes why spatial context in particular matters in systems of entrepreneurship. The third chapter provides an overview of the

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Table 1.1 Overview of dissertation structure

Chapter 1 Introduction to main topic and structure of the dissertation Introduction to research questions

Chapter 2

Defining relevant aspects of entrepreneurship

Chapter 3

Demonstrating the relevance of a systems perspective and the spatial institutional context for entrepreneurial activity

Examination of the distinctive regional layout of Russia

Consequences for economy and entrepreneurial activity

Chapter 4 Theoretical foundation on institutional drivers for entrepreneurship

Establishing hypotheses

Chapter 5 Descriptive analysis of data

Chapter 6

Research perspective 1

Research perspective 2

A descriptive regression model approach

A geometric clustering approach

Conclusion

Revisiting the research questions Key contributions Policy implications Limitations and future research Concluding remarks

distinctive layout of Russia. In addition to providing some fundamental information on the country’s unique regional layout, it covers important aspects such as the extent of Russia’s spatial heterogeneity, its structural economic challenges, and its entrepreneurial climate. Next, Chap. 4 outlines the theoretical foundations on different regional institutions and aims to combine findings from the scientific literature with the specific context of Russia’s regions. Based on these illustrations, a set of hypotheses on the relationship between spatial institutional context factors and entrepreneurial activity in Russia is derived. Chapter 5 discusses the methodology and lays the foundation for the empirical analyses of this thesis. After presenting a detailed view of the data used in this

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dissertation (in Sect. 5.2), I take two methodological perspectives on my research question. In Sect. 5.3, I apply a descriptive regression model approach in order to determine and compare the impact of different factors on spatial entrepreneurial activity in Russia. In Sect. 5.4, I design a prediction model for entrepreneurial entry in Russia’s regions based on a geometric clustering approach that uses the most important institutional context factors. Finally, Chap. 6 revisits the research questions and demonstrates the overall implications of the study’s findings for theory and policy-making. It also points out the limitations of the dissertation, offers points of reference for future research, and closes with some concluding remarks.

References Acs, Z. J., Autio, E., & Szerb, L. (2014). National systems of entrepreneurship: Measurement issues and policy implications. Research Policy, 43(3), 476–494. Acs, Z. J., Audretsch, D. B., Lehmann, E. E., & Licht, G. (2016). National system of innovation. Journal of Technology Transfer, 42(5), 997–1008. Aidis, R. (2005). Entrepreneurship in transition countries: A review (Working Paper no. 61). Centre for the Study of Economic & Social Change in Europe, UCL School of Slavonic and East European Studies. Autio, E., Kenney, M., Mustar, P., Siegel, D., & Wright, M. (2014). Entrepreneurial innovation: The importance of context. Research Policy, 43(7), 1097–1108. Baumol, W. J. (1990). Entrepreneurship: Productive, unproductive, and destructive. Journal of Political Economy, 98(5), 893–921. Brieden, A., & Gritzmann, P. (2012). On optimal weighted balanced clusterings: Gravity bodies and power diagrams. SIAM Journal of Discrete Mathematics, 26(2), 415–434. Bruno, R. L., Bytchkova, M., & Estrin, S. (2013). Institutional determinants of new firm entry in Russia: A cross-regional analysis. Review of Economics and Statistics, 95(5), 1740–1749. Chepurenko, A. (2011). Entrepreneurship and SME policies in fragile environments: The example of Russia. In F. Welter & D. Smallbone (Eds.), Handbook of research on entrepreneurship policies in Central and Eastern Europe (pp. 190–209). Cheltenham: Edward Elgar. Djankov, S., La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (2002). The regulation of entry. Quarterly Journal of Economics, 117(1), 1–37. Estrin, S. (2017). Foreword. In A. Chepurenko & A. Sauka (Eds.), Entrepreneurship in transition economies – Diversity, trends, and perspectives (pp. iv–vii). Berlin: Springer. Fritsch, M., & Wyrwich, M. (2014a). The long persistence of regional levels of entrepreneurship: Germany 1925 to 2005. Regional Studies, 48(6), 955–973. Fritsch, M., & Wyrwich, M. (2014b). The effect of regional entrepreneurship culture on economic development – Evidence for Germany. Jena Economic Research Papers 2014 – 014, Friedrich Schiller University and Max Planck Institute of Economics Jena. Glaeser, E., Scheinkman, J., & Shleifer, A. (2003). Injustice of inequality. Journal of Monetary Economics, 50(1), 199–222. Johnson, S., Kaufmann, D., & Shleifer, A. (1997). Politics and entrepreneurship in transition economies (Working Paper No 57). William Davidson Institute/Michigan. Klapper, L., Laeven, L., & Rajan, R. (2006). Entry regulation as a barrier to entrepreneurship. Journal of Financial Economics, 82(3), 591–629. McMillan, J., & Woodruff, C. (2002). The Central role of entrepreneurs in transition economies. Journal of Economic Perspectives, 16(3), 153–170.

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Popov, V. (2001). Reform strategies and economic performance of Russia’s regions. World Development, 29(5), 865–886. Zemtsov, S. P., & Baburin, V. L. (2016). How to assess advantages of economic-geographical position for Russian regions? R-Economy, 2(3), 385–401. Zubarevich, N. (2013). Four Russians: Human potential and social differentiation of Russian regions and cities. In M. Lipman & N. Petrov (Eds.), Russia 2025 – Scenarios for the Russian future (pp. 67–85). Basingstoke: Palgrave Macmillan.

Chapter 2

Entrepreneurship and Entrepreneurial Ecosystem

Choose a job that you like, and you will never have to work a day in your life. Confucius, Philosopher

2.1

Entrepreneurship

Before reflecting on entrepreneurial activity in Russia and making assumptions about its institutional preconditions, it is critical to start this analysis by addressing two general questions: “What is entrepreneurship?” and “Why does entrepreneurship matter?” Thus, the following chapters aim to provide a better understanding of the concept of entrepreneurial action, clarity on the link between entrepreneurship and economic growth, and the awareness that this connection may vary between different types of entrepreneurship.

2.1.1

Understanding the Entrepreneur

Although entrepreneurship is the subject of an ample body of scientific literature, scholars have widely acknowledged the difficulty of achieving a consensus definition (e.g., Baumol 1993; Bull and Willard 1993). Nevertheless, it is possible to identify various key elements that characterize entrepreneurial activity. Starting with scientific perception, it is natural to follow the Schumpeterian understanding that entrepreneurs introduce novel products or processes based on discontinuity and creative destruction (Schumpeter 1934). According to various authors, the indispensable precondition for the former is the identification and exploitation of opportunities (Kirzner 1979, 1997). Likewise, Acs and Armington’s (2006, p. 7) understanding of entrepreneurial behavior revolves around the concept of “seizing an economic opportunity,” and in the same way, Shane and Venkataraman (2000, p. 218) have stated that the defining elements of entrepreneurship are the examination of “opportunities to create future goods and services” and the ways they are © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Schlattau, Tilting at the Windmills of Transition, Societies and Political Orders in Transition, https://doi.org/10.1007/978-3-030-54909-1_2

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“discovered, evaluated and exploited.” Consequently, we may conclude that the pursuit of opportunities is at the core of entrepreneurial activity.1 From another angle, Casson (2003, p. 225) has described an entrepreneur as “someone who specializes in taking judgmental decisions about the coordination of scarce resources.” Similarly, Spulber (2014) has stated that entrepreneurship primarily consists of individual actors’ decisions. Both authors have thus emphasized that an entrepreneur is, above all, an individual. Lazear (2002, p. 1) has even described individuals engaging in entrepreneurial activities as “the single most important player(s) in a modern economy.” Finally, apart from opportunity perception and individuals’ decisions, scholars have stressed the importance of “owning and running a firm on one’s own account and risk” (Acs and Armington 2006, p. 7) as another key element of entrepreneurship, i.e., bringing the entrepreneurial venture into play as a vehicle for entrepreneurial activity (Gartner 1989; Reynolds et al. 2005). Lumpkin and Dess (1996, p. 136) have even described entrepreneurial or new firm entry as “the essential act of entrepreneurship,” hence pointing towards the most salient notion of entrepreneurial activity. By assessing the essence of this multifaceted set of perspectives on entrepreneurship and by establishing the understanding for this thesis, I consider entrepreneurs as individuals who pursue the discovery, assessment, and exploitation of commercial opportunities through the creation of new ventures, i.e., in the form of a legally registered firm. The particular focus on the latter is further outlined in the following chapters.

2.1.2

Entrepreneurship and Growth

Entrepreneurship promotes economic growth; this perception has been documented in previous studies (e.g., Thurik 2009; Acs 2006; Acs and Armington 2006; van Stel et al. 2005) but has not always been as evident as we might assume today. In the post-World War II era until today, two comprehensive ideas of organizing market economies have evolved: the managed economy and the entrepreneurial economy (Audretsch and Thurik 2001, 2004). Illustrating those two distinct concepts of market economic structure, and particularly the shift from the former to the latter perspective, is vital to understand the relationship between entrepreneurship and economic growth. The idea of the managed economy is the older of the two and was largely inspired by the work of Robert Solow (1956), who hypothesized that increasing labor and capital supply is the best path towards primarily manufactured goods-driven growth. However, the fact that a substantial share of variation in growth still could not be

1 Those opportunities may either address commercial or social needs when it comes to social entrepreneurs (Zahra et al. 2009).

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explained by these factors led to the introduction of the “Solow residual,” which was thought to account for technical change and productivity growth. Later, scholars such as Romer (1986) and Lucas (1988, 1993) sought a better understanding of the mechanisms behind Solow’s residual and provided evidence that knowledge spillovers substantially contribute to economic growth; however, in those models, entrepreneurs still played no role. The managed economy concept was also well supported by economic reality, as large enterprises represented the predominant form of organization until the 1970s and 1980s, and labor and capital were extensively used in large-scale production (Teece 1993; Brock and Evans 1989). In this setting, entrepreneurship and small firms were considered rather a luxury.2 Instead, homogeneity, continuity, and stability were the mainstays of the managed economy (Audretsch and Thurik 2001). The leap into the era of the entrepreneurial economy occurred in the mid-1970s, when the importance of entrepreneurship began increasing, a trend that has continued until today. As some of the first scholars, Acs and Audretsch (1993), have shown, the employment share of small firms in the manufacturing sector of the United Kingdom rose from 30% in 1979 to 40% in 1986, in Western Germany from 55% in 1970 to 58% by 1987, or in the north of Italy from 44% in 1981 to 55% in 1987. Additionally, this development was accompanied by a shift from the manufacturing to the service sector, a decrease in both average firm and plant size, and a rising level of technology (Acs and Armington 2006). Both concepts of economic organization have different consequences for its acting agents. Unlike the managed economy, the entrepreneurial economy is characterized by fundamentally different features, such as flexibility, diversity, creativity, and turbulence. As Wennekers and Thurik (1999) have noted, firm failure in a managed economy is viewed negatively, as posing a drain on society’s resources; hence none or few resources are invested in high-risk ventures. In contrast, the entrepreneurial economy views firm failure through a different lens. In this context, it is considered more as an experiment to head in a new direction within an innately risky environment. It assumes that the process of searching for new ideas is facilitated by learning through failure. However, at this point, the contribution of entrepreneurship to economic growth in an entrepreneurial economy remains to be clarified. Hence, the specific links that the literature has identified are outlined in the following paragraphs. The first link between entrepreneurial activity and economic growth assumes that small firms have advantages in serving market needs and that these advantages stem from an increasing fragmentation of customer markets and technology development. Both developments create opportunities that are, first and foremost, attractive for young, small firms and rather unsuitable for large-scale operations. With regard to market fragmentation, in recent decades, globalization and cross-cultural stimuli,

2 Numerous studies from this period argue that small firms operate with lower efficiency compared to large firms (Weiss 1976; Pratten 1971), found a positive relationship between firm size and employee remuneration (Brown et al. 1990), or revealed that only a small share of overall innovative activity could be attributed to small firms (Audretsch 1995; Acs and Audretsch 1990).

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along with growing levels of income and wealth, have caused an increased demand for variety (Jackson 1984). Additionally, we have observed the steadily growing importance of the services sector (Wölfl 2005). Taking into account the comparatively small average size of most services, apart from airlines, shipping, and various business and financial services, this development offers a vast, growing array of opportunities for entrepreneurs. In many cases, small firms are the most suitable suppliers of new and specialized products or services, whereas large-scale corporations have not been capable of exploiting such market niches (Jovanovic 1993). Regarding technology development, by the end of the twentieth century until today, new technologies have led to a major decline in the level of transaction costs and reduced the importance of economies of scale in many industries (Thurik 2009). In contrast to the managed economy era of mass production, when economies of scale seemed to be the critical factor in achieving efficiency, small firms in the entrepreneurial economy benefit from being more flexible. They are encouraged to work in smaller units of organization that cooperate through technology (Lee et al. 2012), which also brings new found competitive advantages. The second and probably more important link is the entrepreneur’s function as a conduit for innovation in the economy. Building on Solow’s basic idea and by refining the main features of Romer’s and Lucas’ enhancements, Acs et al. (2004, 2009) have introduced a knowledge spillover theory of entrepreneurship (KSTE). In this light, entrepreneurship can be considered the “missing link” in making knowledge economically relevant. The mechanism behind KSTE works as follows: knowledge created by incumbent firms, universities, or similar institutions eventually results in knowledge spillovers to entrepreneurs who identify and exploit the opportunities that knowledge created for profit. Since high uncertainty also indicates a higher risk of failure, incumbent firms are involved in routine processes of largescale innovation. They also have a tendency to apply new knowledge to their existing product portfolio instead of innovating (Baumol 2002; Carlsson 1989). Entrepreneurs help overcome this innovative inertia through individual initiative, creativity, and flexibility. They serve as a conduit for commercializing ideas and knowledge that would otherwise have stalled in large-scale organizations due to institutional rigidities (Spulber 2014; Audretsch et al. 2006; Acs and Armington 2006; Acs et al. 2004). Moreover, as the knowledge context of an economy increases, entrepreneurship becomes even more essential because the quantity of knowledge-based entrepreneurial opportunities that would not have been commercialized otherwise concurrently increases (Keilbach 2009). The literature provides broad evidence for this mechanism by showing that entrepreneurs represent a crucial element in the innovation process of an economy and that small firms tend to be more innovative than their larger counterparts. Baumol (2004) has shown that new firms are more likely to introduce particularly radical and groundbreaking innovations. Other studies have also demonstrated that small firms have a relative innovative advantage over larger enterprises in highly innovative industries (Acs and Audretsch 1987) or a comparative advantage in inventing radical new products (Prusa and Schmitz 1991; Rothwell 1983, 1984). For example, 71% of firms listed

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in the Inc. 500 list of the fastest-growing firms in the United States developed their business ideas while being employed by a larger firm (Pathak et al. 2013). It must be noted that the relationship between entrepreneurship and economic growth is not necessarily unidirectional, particularly since higher rates of growth can be expected to create more opportunities, which in turn promote entrepreneurial activity. However, the issue of causality and mutual sensitivities of entrepreneurial activity and growth is addressed at a later point of this thesis, i.e., when addressing structural determinants of entrepreneurship in Sect. 4.1. To summarize, although some of the aforementioned effects may only be temporary in nature (e.g., in emerging industries, many startups usually fail to survive), the impact of globalization, new technologies, and most of all entrepreneurship’s role as a conduit for knowledge spillovers and innovation are undoubtedly long term in nature and support the entrepreneur’s vital role with regard to economic growth.

2.1.3

Types of Entrepreneurship

Although the previous chapter outlined how higher rates of entrepreneurship positively relate to economic growth and innovation, it is important to mention that more entrepreneurship is not necessarily always better. Entrepreneurship can emerge in manifold forms, and not everything considered “entrepreneurial” is favorable from a societal and economic point of view (Shane 2009; Wennekers et al. 2005; Baumol 1990). This raises the question of which types of entrepreneurial activities have the most desirable effects on economic development. To answer this question, the following chapter aims to differentiate between different types of entrepreneurial activities. The most common classification distinguishes between different motivations to engage in entrepreneurship and indicates whether an entrepreneur created his venture in order to seize a potential business opportunity (opportunity entrepreneurship) or because there were no better choices for work and earning a living (necessity entrepreneurship) (Reynolds et al. 2002, p. 12). Opportunity-driven entrepreneurship represents an intentional and voluntary occupational choice (i.e., the entrepreneur is pulled by opportunity), whereas necessity-driven entrepreneurship is a decision based on the lack of other suitable or satisfactory options. Necessity entrepreneurship can also be viewed as so-called reluctant entrepreneurship, i.e., people are pushed into self-employment because they face the threat of unemployment or have actually lost employment. Depending on their set of alternatives, entrepreneurship might essentially be considered a survival strategy (Welter 2010). Both concepts, opportunity and necessity entrepreneurship, can be associated with a different impact on economic growth; hence, the quality of entrepreneurship

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matters. Studies utilizing Global Entrepreneurship Monitor (GEM) data3 generally view necessity entrepreneurship as a negative factor with regard to economic growth and development because necessity entrepreneurs are hardly able to produce positive externalities such as innovation or job creation (e.g., Acs and Varga 2005; Wennekers et al. 2005; Wong et al. 2005).4 In contrast, the impact of opportunitybased entrepreneurial activity on economic growth and social welfare is significantly greater (Chepurenko et al. 2011; Hessels and van Stel 2011; Valliere and Peterson 2009; Acs et al. 2008; Hessels et al. 2008; Koellinger 2008; Acs 2006; van Stel et al. 2005). Consequently, with a more stable entrepreneurial environment and increasing levels of economic development, the proportion of necessity entrepreneurs tends to decrease, whereas the proportion of opportunity entrepreneurs increases (Bosma and Levie 2010). Unlike the motivation-driven classification of entrepreneurship, Baumol (1990) has focused on different outcomes of entrepreneurial activity. According to Baumol’s understanding, the economic contribution of entrepreneurship depends on the allocation of an entrepreneur’s efforts between productive and unproductive (or even destructive) entrepreneurial activities. Productive entrepreneurship refers to any activity that promotes economic output or social welfare, which particularly includes the introduction of new products or new production processes, as well as job generation (Davidsson and Henrekson 2002; Foss and Foss 2002; Dallago 2000; Baumol 1990, 1993). Unproductive entrepreneurship, on the other hand, typically refers to rent-seeking or can be related to informal entrepreneurship,5 whereas destructive entrepreneurship mainly includes, but is not limited to, illegal activities that aim at obtaining transfers, different forms of corruption, or even violence. Again, we can assume that the economic impact of entrepreneurial activities depends more on the productive contribution of the entrepreneurs than on their absolute numbers, i.e., on their allocation between productive and unproductive activities (Baumol 1990, p. 3). There is, however, one conceptual challenge in the classifications of different types of entrepreneurship: namely, there is no entirely coherent link between necessity and unproductive entrepreneurship or opportunity and productive entrepreneurship. This seems reasonable since even opportunity-driven entrepreneurship can 3

GEM (https://www.gemconsortium.org/) is one of the most comprehensive and ongoing research projects related to entrepreneurship and is based on more than 200,000 interviews each year, with 18 years of available data by the time of this thesis. 4 One should be careful to jump to quick and universal conclusions; particularly in earlier stages of economic development, microfinance institutions successfully aim at promoting economic development and welfare by empowering necessity-motivated entrepreneurs such as small farmers and micro-entrepreneurs. 5 Informal entrepreneurship exclusively encompasses entrepreneurial activities outside of formal institutional boundaries, i.e., illegal, even though legitimate under certain circumstances (Webb et al. 2009). This definition covers activities within the shadow economy and may include the use of undocumented workers, counterfeiting, unregistered and/or tax-avoiding businesses, skirting of regulations, or street vending. All of these have their limited contribution to economic growth in common.

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eventually lead to undesirable outcomes. Given that unproductive entrepreneurship particularly flourishes when framework conditions are weak,6 purely rational choices can be even more opportunity-driven than, for example, a high-growth Silicon Valley venture (Chepurenko et al. 2011). Nevertheless, based on the vast body of literature arguing that substantial, positive implications for economic development and economic growth are attributable to opportunity entrepreneurship in particular, this thesis focuses on opportunitydriven entrepreneurial activity.

2.2 2.2.1

Systems of Entrepreneurship Entrepreneurship and the Role of the Individual

The previous chapters clarified the fundamental role of innovative and opportunitydriven entrepreneurs due to their major economic contribution. However, almost no one engages in an entrepreneurial venture in order to accomplish innovation or national-level economic and job growth; instead, people desire personal profits, autonomy, creativity, and other benefits when making the decision to seize entrepreneurial opportunities (Locke and Baum 2007). Nonetheless, an individual’s choice to become an entrepreneur or not does not merely depend on the existence of a potential business opportunity but varies between different characters and is particularly dependent on the way people deal with the potential entrepreneurial decisions they face. Entrepreneurship scholars have spent considerable effort on explaining which traits and capabilities stimulate individuals to become entrepreneurs. In this light, several characteristics seem to be particularly essential for the role of an entrepreneur and can be classified into four categories: attitudes, abilities, aspirations, and the opportunity costs of becoming an entrepreneur. These categories are outlined in the following paragraphs. Attitudes With regard to the first category, individuals are subject to different attitudes that make them either more or less likely to engage in entrepreneurial activity. First, individuals are heterogeneous in their desire for independence, in their personal development, in their tendency to follow entrepreneurial role models, and to some degree in their pursuit of welfare. Fauchart and Gruber (2011, p. 935) have stressed that creating a new firm is an act “infused with meaning” as it is “an expression of an individual’s identity, or self-concept.” According to Birley and Westhead (1994), personal wealth creation might play a role, but in most cases, it is not the principal stimulus for choosing to pursue entrepreneurial activity. Motives such as vision,

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challenge, or independence are far more important for potential entrepreneurs (Amit et al. 2001). Second, individuals are heterogeneous in their self-perception of skills, which also impacts their decision to create entrepreneurial ventures. A number of studies support this view by linking entrepreneurial activity to levels of self-confidence or to the perceived level of required capabilities (Gatewood et al. 1995; Townsend et al. 2010). Third, individuals are heterogeneous in their assessment and perception of and propensity towards risk, and these differences affect the choice to create an entrepreneurial venture or not and entrepreneurial decision-making in general. In this light, a higher tolerance for uncertainty and risk positively influence the likelihood of becoming an entrepreneur (Wadeson 2006; Douglas and Shepherd 2000; Knight 1921), as does the self-perceived ability to manage risk (Podoynitsyna et al. 2012) or a higher acceptance level with regard to affordable loss (Dew et al. 2009). Fourth, individuals are heterogeneous in their cultural heritage and value systems, i.e., their observed cultural support and acceptance of entrepreneurial activity. For example, Holt (1997) has shown that individuals from different cultural backgrounds diverge in terms of values such as individualism, self-enhancement, openness to change, conformity, and security, which affect their attitude towards entrepreneurship. Abilities The second category of personal determinants states that an individual’s choice to pursue the creation of an entrepreneurial venture depends on his set of abilities. According to the literature, this set is twofold and encompasses both human capital in general and entrepreneurial abilities. Individuals are heterogeneous with regard to both aspects, and these differences affect their decisions related to the perception of entrepreneurial opportunity. Human capital in general comprises formal education, training, professional experience, and a person’s set of capabilities and skills. All of these aspects represent crucial assets for entrepreneurial activity, and all of them affect entrepreneurial decisions and outcomes (Davidsson and Honig 2003; Florin et al. 2003). Particularly when it comes to opportunity evaluation, a positive assessment and hence entrepreneurial entry are more likely if the opportunity is related to the entrepreneur’s personal expertise (Mitchell and Shepherd 2010; Haynie et al. 2009). As for entrepreneurial abilities, Choi and Shepherd (2004) have related positive opportunity assessments to the capability of the management team and its perceived knowledge of customer demand. Furthermore, business skills, the ability to create business plans, and an individual’s ability to seize external resources such as capital, suppliers, information, or knowledge are considered to be positively related to opportunity assessment outcomes (Patel and Fiet 2009; Casson and Wadeson 2007; Liao and Welsch 2003). Aspirations The third category accounts for the fact that individuals are heterogeneous in their aspirations. Aspirations are most commonly related to higher rates of firm growth in

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the future, but they also might impact the entrepreneurial entry decision itself. In this sense, first, higher entrepreneurial aspirations of individuals predict greater levels of resources and are thus likely to increase the possibility of successfully creating new ventures (Brush et al. 2008). Second, entrepreneurs vary in the extent and character of their experiences; these dissimilarities affect both their entrepreneurial aspirations and entrepreneurial decision-making. For example, former start-up experience might boost opportunity assessment and entry decision speed (Forbes 2005), whereas international management experience might raise the potential future size of the entrepreneurial ventures. Regarding the former, entrepreneurial experience builds entrepreneurial self-efficacy and facilitates the utilization of effectual logic to evaluate and make decisions (Dew et al. 2009). Individuals with such experience tend to be more aggressive in their entrepreneurial decision process, invest more personal assets, and are more likely to engage in creating a new venture (Cassar and Friedman 2009). As for the latter, international management experience or experience as a supervisor is essential for making decisions about the intended future size of a venture (Cassar 2006) and for managing continuously growing organizational complexities in high-aspiration entrepreneurship (Autio 2011). Opportunity Costs The last category refers to potential founders’ perceived opportunity costs. Differences in individuals’ opportunity costs help explain the choice to engage in an entrepreneurial venture. Opportunity costs can be understood as the loss in value of rejected alternatives when another alternative (e.g., creating a venture) is chosen. In the case of a potential entrepreneur, the loss in value typically refers to the entrepreneur’s time and money and, in most cases, the relatively safe harbor of employment. The concept of opportunity cost helps explain why individuals with entrepreneurial attitude, high abilities, and considerable aspirations might be drawn towards creating an entrepreneurial venture, even if they choose to remain in or decide to go into regular employment. Hence, in this light, a crucial aspect in terms of opportunity costs is the salary an employed individual receives. If salaries are reasonably high and relatively secure, the short-term benefit might outweigh the expected payoff from attempting an entrepreneurial venture (Campbell et al. 2012; Carnahan et al. 2012; Amit et al. 1995). Apart from salary considerations, the evaluation of opportunity costs is also determined by the likelihood of potential outcomes and, accordingly, the risk/reward trade-off of pursuing an entrepreneurial opportunity. This trade-off implies that higher-potential rewards are associated with higher levels of risk, and in this light, opportunity costs represent a crucial part of this risk. Intangible aspects are also important, for instance, potential embarrassment or social pressure in case of failure due to having invested capital from relatives or failed expectations. This also includes so-called social legitimacy costs that potential entrepreneurs might face given their affiliation to a social, cultural or ethnic group as well as organizational culture (Autio et al. 2014). Against this backdrop, individuals might view entrepreneurial activity as too risky and costly given the danger of struggles related to developing a business idea into a successful firm.

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2.2.2

Entrepreneurship and the Relevance of Context

2.2.2.1

Why Context Matters

For a long time, entrepreneurship researchers have largely focused their activities on individuals and individual decision-making. In addition, the literature on entrepreneurship has frequently assumed that its findings are valid across countries. However, there should be serious doubts about this; various studies have analyzed cross-country heterogeneity in entrepreneurial activity and hypothesized a link with the domestic institutional context (Cullen et al. 2014; Meyer et al. 2009; Klapper et al. 2007). Only recently have the narrow focus on individual characteristics and the tendency to ignore the regulating effects of context on individual action been recognized as a pivotal gap in the entrepreneurship literature (Autio and Acs 2010; Davidsson 2006; Phan 2004). Addressing this gap is particularly important because a great deal of the opportunity costs and trade-offs entrepreneurs face, as well as the formation of their attitudes, abilities, and aspirations towards entrepreneurship, are regulated by the context, namely, through a broad range of different formal and informal institutional settings, different cultures, norms, and values (Autio et al. 2014). The context7 consequently determines what potential entrepreneurs experience, which decisions they make, and what outcomes can be expected to look like. One of the first concepts to address the substantial role context plays in microeconomic processes was the national systems of innovation (NSI) framework, introduced by Nelson (1993) and Lundvall (1992). The central principles of this framework state that knowledge is an important economic resource that is created and built up via interacting and cumulative innovation processes. These processes are embedded in national institutional contexts; thus, context matters for innovation outcomes. Filippetti and Archibugi (2011, p. 180) have identified three key assumptions behind this concept, i.e., nations show systematic differences in economic performance, economic performance is not just conditional to technological and innovation capabilities but also to the quality of institutions, and, finally, policies on technology and innovation constitute effective instruments to promote economic performance. The understanding of “system” in the NSI framework is seen as “a set of institutional actors that, together, play the major role in influencing innovative performance” (Rosenberg and Nelson 1993, p. 4f). Systems hence consist of numerous elements that work together to deliver overall system performance. However, those systems cannot be designed from scratch; rather, they are inherited on a country level, and to adjust and improve overall system performance, it is particularly important to understand system structure and the interplay between elements (Acs et al. 2016; Samara et al. 2012; Carlsson et al. 2002). Whereas the NSI concept adequately accounts for institutional complexity and the context-embedded character of innovation processes, it is also subject to some conceptual weaknesses. One major source of criticism is its narrow focus on large-scale technological innovation 7

Quite frequently also referred to as the “entrepreneurial ecosystem.”

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(Gustafsson and Autio 2011). Little attention has been paid to other types of innovation or individual action at all, although, as illustrated above in Sect. 2.1.2, on the whole, entrepreneurs tend to be more innovative than large incumbents and are more likely to introduce technological breakthroughs and radical product innovations. Based on this apparent lack of connection between individual agency, a nation’s system-level institutions, and overall economic outcome, Acs et al. (2014) have provided a more comprehensive solution: a novel theoretical framework that was introduced as national systems of entrepreneurship (NSE). By building on the NSI and KSTE frameworks, the NSE concept considers both why entrepreneurship is a crucial factor to increase economic performance and why some individuals decide to engage in entrepreneurial activity while others do not. With regard to the former, authors have described NSE as resource allocation systems driven by entrepreneurial churn (Acs et al. 2014; Reynolds et al. 2005). Entrepreneurial churn is a trial and error process, meaning that resources are allocated to their most productive use in the long run. According to this logic, resources allocated towards productive uses stay there, while resources in unproductive uses (i.e., failed ventures) are released for other purposes. This gradually increasing allocation of resources to productive uses ultimately boosts economic performance (Bartelsman et al. 2004). With respect to individuals’ entry decision, NSE as resource allocation systems account for the fact that the institutional settings in which individual entrepreneurial choice is embedded reflect the costs and benefits of actions on the individual level. Context also regulates who engages in entrepreneurial activity by equipping individuals with the necessary abilities, facilitating entrepreneurial attitudes and aspirations, and through shaping opportunity and social legitimacy costs that must be faced upon entrepreneurial entry (Acs et al. 2016; Sorensen 2007; Cassar 2006). This understanding was influenced by Baumol (1990, 1993, 2005), who found that institutions design the incentive structure for choosing entrepreneurship as favorable to other alternatives. To summarize, Acs et al. (2014, p. 479) have provided a comprehensive definition of national systems of entrepreneurship that I complement by additionally including the essential aspect of opportunity costs, pointed out in Sect. 2.2.1: An NSE is the dynamic, institutionally embedded interaction between entrepreneurial attitudes, ability and aspirations by individuals given their opportunity costs, and it drives the allocation of resources through the creation of new ventures.

2.2.2.2

Institutions and Context

The previous chapter built on the assumption that institutions are the determining factor of context. However, in the common manner of speaking, the term

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“institution” is used in a variety of ways, and a more detailed understanding of the relevant terms has yet to be provided.8 Building on North (1990, p. 3), institutions can be defined as a set of humanly devised behavioral rules that govern and shape human beings’ interactions. Hodgson (2001, p. 295) has provided a somewhat broader interpretation by describing institutions as “durable systems of established and embedded social rules and conventions that structure social interactions.” Both definitions share their conception of institutions as so-called rules of the game; for example, they consider institutions as helping people in forming expectations of what other individuals, e.g., state or economic agents, will do. Institutions can comprise both formal and informal elements. Formal institutions are the visible rules of the game, such as the rules and legislation enforced by governments and authorities. In comparison, informal institutions can be seen as the invisible rules of the game that take effect via tacit agreements, social norms and codes of conduct, customs, and ideology (Lin and Nugent 1995; Knight 1992). With respect to the rules of the game, the notion of institutions may even comprise all potential framework factors and patterns that are systematic in nature and emanate externalities that affect individuals’ choices (i.e., as potential entrepreneurs) acting within their context (i.e., the system of entrepreneurship). This, for example, also covers aspects such as the prevailing levels of corruption, criminality, or market conditions (Puffer et al. 2010). Notably, the costs and benefits of entrepreneurial action determined by the institutional context do not have to be static but may vary over time due to both endogenous developments and exogenous shocks.

2.2.2.3

The Importance of the Regional Dimension

Although the NSE concept in essence concentrates on the national context of entrepreneurial action, Acs et al. (2014) have also stated that countries are not homogeneous, and regional systems of entrepreneurship thus provide a useful focus for empirical research. Additionally, the previous chapters mentioned a comprehensive body of literature that analyzed institutional contexts across countries; however, these studies could only partially control for macroeconomic differences, legislation, sociocultural, and other country-specific factors, which is a major disadvantage of cross-country studies (Bruno et al. 2013; Klapper et al. 2006; Djankov et al. 2002). Moreover, Acs and Armington (2006) have pointed out that substantial spatial differences are easily lost in aggregating them at the national level. Thus, it is obvious to conclude that states on a national level are often not adequate or useful as a unit of analysis.

In recent decades, institutional theory has experienced fast and multidimensional growth as a field of study; hence, a comprehensive synthesis is beyond the scope of this chapter. The purpose of this chapter is rather to provide an expedient understanding of the term “institution” for the scope of this thesis.

8

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19

With particular regard to entrepreneurial entry, various studies have supported the assumption that the regional context particularly matters. Reynolds et al. (1994) have analyzed a set of countries that showed considerable regional variation in entrepreneurial entry rates, i.e., the most entrepreneurial regions showed entry rates of two to four times the size of those of the least entrepreneurial regions. Other scholars have argued that the regional geographical setting has a substantial impact on economic agents’ behavior (Nelson 1993; Lundvall 1992). Referring to the innovation-driven character of entrepreneurship, Asheim and Gertler (2004, p. 292) have emphasized a regional perspective by stating, “geography is fundamental, not incidental, to the innovation process itself: that one simply cannot understand innovation properly if one does not appreciate the central role of spatial proximity and concentration in this process.” Various studies have likewise argued and suggested that entrepreneurial activity and framework conditions may vary substantially across different regions of a country, even if there are nation-wide common, formal rules (Westlund et al. 2014; Andersson 2012; Aoyama 2009). The following paragraphs attempt to provide a deeper understanding of how exactly spatial contextual differences affect entrepreneurial activity. First, local variation in agglomeration effects and occupational structure may affect the level of spatial entrepreneurial activity. The former contributes to entrepreneurial entry through demand effects, such as population growth (Acs and Armington 2006). On the other hand, with regard to occupational structure, labor force and input factors are both available in greater variety and at lower cost in agglomerations (Krugman 1991a, b). Thus, the formation rate for each industry sector should increase with a higher local density of establishments in that sector. Second, local absorptive capacity needs to be emphasized. Absorptive capacity can be understood as the ability to “recognize the value of new information, assimilate it and apply it to commercial ends” (Cohen and Levinthal 1990, p. 128) and is thus a precondition for knowledge spillovers to occur as a source of entrepreneurial opportunities. Absorptive capacity requires a sufficient degree of existing knowledge in a region to enable and moderate spillovers between economic agents, i.e., potential entrepreneurs (Cohen and Levinthal 1990). Hence, spatial concentration and proximity are a crucial prerequisite for knowledge-intensive entrepreneurial activities (Ghio et al. 2015; Acs et al. 2012, 2013). This also includes an adequate quality of infrastructure, which enables skilled workers to move and work together across different locations, thus fostering geographical knowledge diffusion (Breschi et al. 2010; Bathelt et al. 2004; Owen-Smith and Powell 2004; Coe and Bunnell 2003).9 Third, another relevant aspect of regional framework conditions that affect entrepreneurial activity is spatial industry concentration vs. variation. The basic idea originates with Jacobs (1969) and argues that a diversified industry structure

9

Additionally, an extensive body of literature provides evidence in favor of a link between highskilled workers’ mobility and knowledge diffusion (Singh and Agrawal 2011; Boschma et al. 2009; Rosenkopf and Almeida 2003; Almeida and Kogut 1999; Stephan 1996; Arrow 1962).

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enables the interaction and recombination of dissimilar types of knowledge, thereby promoting the creation of breakthrough ideas and innovations (Boschma et al. 2011). In this view, spillovers between related industries primarily lead to incremental innovations, whereas spillovers between different industries are highly conducive to radical innovations (Castaldi et al. 2015; Boschma and Capone 2014). Further evidence has been provided by Glaeser et al. (1992), who found that industries grow faster in a diversified and highly competitive industrial environment. Further, Feldman and Audretsch (1999) have linked regional industry structure to higher levels of innovative outcome. Implications for entrepreneurial activity might also be triggered by the intensity of local competition between nascent entrepreneurs and incumbent firms, which also differs on a regional level (Fritsch and Noseleit 2013). In this regard, regional dominance of large industry sector companies in particular is expected to lead to lower rates of new firm formation (Acs and Armington 2006; Mason 1994). Finally, and probably most importantly with regard to the NSE framework, local entrepreneurial activity is largely shaped by different preconditions in the regional institutional environment. There is broad evidence that differences in local market potential have a significant impact on influencing firms’ decision to enter (Fritsch and Wyrwich 2014a, b). Additionally, variations in regional labor force qualification, the availability of supportive infrastructure, i.e., consulting and financing, and the availability and quality of inputs need to be emphasized as critical determinants that vary on a regional level (Fritsch 2014; Audretsch and Keilbach 2004; Westlund and Bolton 2003). Some studies have also attributed differences in entrepreneurial activity to the presence of a regional culture of entrepreneurship10 (Glaeser et al. 2012; Fritsch and Wyrwich 2014b; Obschonka et al. 2013; Andersson and Koster 2011) or “a positive collective programming of the mind” (Beugelsdijk 2007, p. 190) towards entrepreneurship. According to Etzioni (1987), entrepreneurial culture can be understood as regional variation in the social acceptance and legitimacy of entrepreneurship, which may vary across regions. Consequently, higher levels of acceptance lead to higher demand for entrepreneurial activity and for higher amounts of resources dedicated to it. Overall, the demonstrated relationships underline the importance of taking a spatial perspective on entrepreneurial entry, which is why the analyses of this thesis place a strong, consistent focus on regional systems of entrepreneurship.

2.2.3

Bottleneck Factors as Limiting Constraints

Section 2.2.2 described how systems of entrepreneurship consist of a set of interrelated elements that jointly contribute to overall system performance, i.e., the extent of entrepreneurial activity. Pertaining to this understanding, a range of system

10

The concept is also known as “entrepreneurship capital” (Audretsch and Keilbach 2004).

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theories can provide important conclusions on the structure and function of entrepreneurial systems.11 This set of theories refers to general system theory and more specifically to a set of theories that are focused on organization and production contexts, i.e., configuration theory (CT), theory of constraints (ToC), and theory of weakest link (ToW). Systems theory, originally described by Bertalanffy’s (1950) general system theory (GST), is probably the oldest stream of literature in this regard. It has an interdisciplinary approach to describing and understanding any possible set of elements that work together to produce some result. In this sense, a system is a configuration of elements, connected and joined together by a web of relationships. Broadly interpreted, systems can be a single organism, an organization or entire societies. Building on GST, theoretical approaches were later applied to other scholarly fields, for example, by Talcott Parsons (since the 1950s) and Niklas Luhmann (since the 1970s) in the area of sociology. The approach also proved particularly successful in the area of organization theory and process engineering (Miller 1996; Veliyath and Srinivasan 1995). Configuration theory focuses on the organizational structure of a firm and defines configuration as one constellation of all a firm’s potential strategic and organizational characteristics (e.g., Meyer et al. 1993; Miller and Mintzberg 1988). It argues that for each given set of strategic characteristics, there exists an ideal set of interdependent organizational characteristics that together enable firms to achieve superior performance (e.g., Miller 1999; Ketchen et al. 1993; Van de Ven and Drazin 1985). This is also supported by Dess et al. (1993, p. 775f), who have defined configurations as “represent[ing] a number of specific and separate attributes which are meaningful collectively rather than individually.” The theory of constraints’ basic setup is quite similar to that of configuration theory. The latter regards an organization as a chain composed of many links, all of which contribute to the organization’s goal and overall performance, and each single link strongly depends on the others. However, ToC emphasizes that overall performance is constrained by its weakest link (i.e., the least-performing single attribute). In order to improve overall system performance, naturally, the first step is to identify the weakest link, i.e., the bottleneck (Dettmer 1997; Klein and DeBruine 1995; Goldratt 1990). Another similar approach is described by the theory of weakest link, which states that individual system elements are not entirely substitutable with one another (Harrison and Hirshleifer 1989). This was originally pointed out in an entirely different sphere, by Liebig’s law of the minimum with regard to biological

11

This perspective is also a fundamental element of the GEDI and REDI indices developed by Acs et al. (2014) and Szerb et al. (2013). The former refers to the cross-country Global Entrepreneurship Development Index (GEDI, https://thegedi.org), whereas the Regional Entrepreneurship Development Index (REDI) focuses on EU regions. Both were designed to analyze and understand how entrepreneurship ecosystems work and contribute to economic growth.

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populations and ecosystems.12 Liebig argued that growth is determined not by the total amount of available resources but rather by the scarcest resource as the limiting factor. As a consequence, growth can only occur at the rate permitted by the most constraining factor. To provide a practical example, more watering and fertilizer cannot achieve increased crop yield if the length of the day is the constraining factor; adding more fertilizer cannot substitute daylight, as both elements are complements rather than substitutes. Taking the described characteristics into consideration in view of a regional system of entrepreneurship, we can conclude the following. First, according to GST and CT, we know that systems consist of a broad set of elements, i.e., institutions, that work together to produce a result, i.e., entrepreneurial activity. Moreover, those elements are interrelated; hence, we can conclude that the outcome they produce is more than the sum of its individual parts. Second, ToC suggests that overall entrepreneurial activity can be constrained by limiting factors in the system, i.e., bottlenecks. In order to increase overall entrepreneurial activity, it is important to identify and address those potential bottlenecks. Finally, TWL teaches us that, if system performance is subject to a specific bottleneck, performance cannot be enhanced simply by improving other system elements that are easy to adjust. For example, if we observe a lack of entrepreneurial skills in the system population, this cannot easily be overcome by increasing the availability of investment capital. We are now aware that systems of entrepreneurship can be constrained by institutional bottlenecks. Generally, bottleneck factors in this thesis are frequently referred to by scientific literature as weak institutions, institutional voids, or institutional barriers to entry; however, actual bottlenecks refer to the least-performing factors from among a set of low-performing ones. Consequently, the existence of bottlenecks impedes the overall performance of systems of entrepreneurship, and a poorly performing system of entrepreneurship in turn explains why individuals decide not to engage in entrepreneurial activity, even when confronted with potentially promising business opportunities. A vast body of literature supports the conclusions drawn from the system theories by illustrating the harmful consequences of institutional bottlenecks for the entrepreneurship system and thus to the economy as a whole. Where supporting institutions are weak, i.e., where bottlenecks exist, there are not only lower levels of productive entrepreneurship but also preconditions in which unproductive or even destructive entrepreneurship can flourish (Baumol 1990, 1993). The mechanics behind this are rather simple: if, in a given system of entrepreneurship, the rewards for rent-seeking outweigh its costs, then unproductive entrepreneurship will thrive. North (1990) has argued a similar point by stating that, if the environment supports piratical behavior, piratical ventures will evolve. It is important to highlight that it is not necessarily the same individuals who will engage in productive, unproductive, or destructive entrepreneurship; rather, different players will engage in entrepreneurial activity depending on different institutional incentives

12

Originally, the minimum law was developed by Carl Sprengel (1828) as a principle in agricultural science and was later popularized by Justus von Liebig.

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(Aidis et al. 2008). What is even more important for the analysis at hand is that literature along the lines of entrepreneurship theory argues that absent, weak, or bad institutions obstruct venture creation (Bruton et al. 2010; Lerner and Schoar 2005). High-quality institutional environments are preconditions to sound entrepreneurial activity, whereas absent, low-quality, or bad institutions impede entrepreneurial activity (Puffer et al. 2010; Boisot and Child 1996). In summary, the understanding of institutional bottlenecks as illustrated above corresponds well with the regional systems of entrepreneurship framework: institutions affect entrepreneurial entry because it is a social activity that is embedded in a particular environment and not determined by isolated, individual decisions. Hence, the overall theoretical framework of this thesis accounts for institutional impacts on entrepreneurship that function as constraints or stimuli on the individual choice to create a venture.

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Chapter 3

The Distinctive Layout of Russia

Imagine that you had to share a room with an aggressive madman all your life. Moreover, you also had to play chess with him. On the one hand, you had to play so that you would not win and anger him with your victory; on the other, you had to play so subtly that he would not suspect that you allowed him to beat you. When the madman disappears, this precious skill and life-long experience of survival with a madman turns out to be redundant. Fazil Iskander (Cited from Ledeneva (2013, p. 6)).

As we are now aware of the significance of entrepreneurs and regional systems of entrepreneurship and their contribution to economic growth, we can now address Russia as the central playing field of our analysis. This chapter provides an introduction to the unique layout of the Russian Federation and aims to explain why Russia is a quasi-natural experimental setting to analyze spatially diverging rates of entrepreneurial activity. Parts one and two of this chapter focus on Russia’s regional and administrative structure and identify some of the factors that contribute to the country’s spatial heterogeneity. In doing so, they particularly stress their meaning for entrepreneurial activity. The chapter also considers the country’s economic structure and business climate before closing with a brief analysis of the status quo of entrepreneurship in Russia.

3.1

Regional Layout and Spatial Heterogeneity

In brief, Russia is impressive: it is geographically the largest country in the world and covers almost 13% of the world’s land mass. Overall, the territory of the Russian Federation encompasses an area of 17,075,400 km2, spanning 11 time zones and more than 9500 kilometers in east-west distance from St. Petersburg to Vladivostok. From north to south, Russia embraces about 40
of latitude (42
–82
) or a distance of over 4000 km. Along its enormous border, we encounter 14 neighboring states: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Schlattau, Tilting at the Windmills of Transition, Societies and Political Orders in Transition, https://doi.org/10.1007/978-3-030-54909-1_3

31

32

3 The Distinctive Layout of Russia

Azerbaijan, Belarus, China, Estonia, Finland, Georgia, Kazakhstan, North Korea, Latvia, Lithuania, Mongolia, Norway, Poland (Kaliningrad Oblast), and Ukraine; the total is 16 if one considers maritime boarders with Japan and the United States. As a whole, the territorial dimension alone results in considerable geographical and climatic heterogeneity. Additionally, Russia comprises a vast variety of different ethnicities, adding another dimension to the country’s level of heterogeneity. Russia’s regions are home to more than 170 different ethnic groups. According to the 2010 census, ethnic Russians make up roughly 80% of the population. Ethnicities of a different origin account for the other 20%, encompassing Tatars (3.9%), Ukrainians (1.4%), Bashkir (1.1%), Chuvash (1%), Chechens (1%), and Armenians (0.9%), among others (Rosstat 2018). Obviously, one might expect that Russia’s ethnic composition and the specific cultural traits of different groups influence individuals’ characteristics in terms of entrepreneurial attitudes, abilities, and aspirations and hence facilitate different views on entrepreneurial activity. However, in their study of characteristics of Russian entrepreneurs, Djankov et al. (2005) have found that there is neither a statistically significant difference in the ethnic composition between Russian entrepreneurs and non-entrepreneurs nor a difference in religious beliefs between the two groups. Hence, we should not assume ethnic affiliation in itself is a decisive factor to drive regional differences in entrepreneurship. From an administrative perspective, until 2014, the Russian Federation encompassed 83 administrative territorial divisions1: 21 republics, 9 krais (territories), 46 oblasts (provinces), 1 autonomous oblast, 4 autonomous okrugs, and 2 cities with federal status that are considered separate regions, namely, the cities of Moscow and St. Petersburg (Fig. 3.1 and Table 3.1 provide an overview.). In 2014, the Republic of Crimea and the City of Sevastopol joined the federation as the 84th and 85th federal subjects of the nation.2 Republics are considered nominally autonomous units, and each has its own constitution and legislature, which are represented by the federal government in international affairs and are meant to be the home for a specific ethnic minority. Oblasts, on the other hand, are the most common type of federal subject and are administered by a governor and locally elected legislature. They are commonly named after their administrative centers, i.e., the region’s major city. In addition, there is one autonomous oblast and several okrugs that were established to house substantial or predominant ethnic minorities, which usually also provided the name for the region. With regard to executive and legislative aspects, the territorial entities largely work the same way regular oblasts do. As the last type of federal subject, krais (English: territory) also share the same 1 Originally, the Federation encompassed 89 territorial divisions in 1993; however, the number was reduced due to several mergers. 2 Following the 2014 Crimean crisis, Russia incorporated the Republic of Crimea and the City of Sevastopol as constituent members into the Russian Federation, namely, as federal subjects with the status of a republic (i.e., Republic of Crimea) and a federal city (i.e., City of Sevastopol). Internationally, this is highly disputed, and neither federal subject is recognized as a part of Russia by most countries.

18

25

62

5

50

14 57 51 7

69

6

28

Moscow

16

52

80

30

71

63

33

74

37

4

78

41 73

68

45

St. Petersburg

56

67

42

36

12

82

77

29

76

66

10

24

79

53

48

60

43

8 27

70

23

75

35

39

21

38

81

Disputed Regions (Republic of Crimea and Federal City of Sevastopol)

Federal Cities

Okrugs and Autonomous Oblast

Krais

Republics

Oblasts

40

59

55

22

3

1

72

47

19 8

26

60

11

49

58

83

2

Fig. 3.1 Map of the federal subjects of the Russian Federation (Source: Author’s illustration based on Geocurrents 2018)

46

15

13

31

9

20

44

65

17

3.1 Regional Layout and Spatial Heterogeneity 33

34

3 The Distinctive Layout of Russia

Table 3.1 Overview of the federal subjects of the Russian Federation Nr.

Region

Oblasts

Nr.

Region

Republicsa

2

Amur mur Region

7

Chechen Republic

3

rkhangelsk Region Arkhangelsk

10

Chuvashi Republic

4

strakhan Region Astrakhan

14

Kabardino-Balkaria Kabardino-Balkarian Republic

5

elgorod Region Belgorod

18

Karachaevo-Cherke Karachaevo-Cherkessian Republic

6

ryansk Region Bryansk

23

Komi Republic

8

helyabinsk Region Chelyabinsk

46

Adygey Republic of Adygeya

11

kutsk Region Irkutsk

47

Republic of Altai

12

anovo Region Ivanovo

48

Bashko Republic of Bashkortostan

15

aliningrad Region Kaliningrad

49

Republic of Buryatia

16

aluga Region Kaluga

50

Daghes Republic of Daghestan

19

emerovo Region Kemerovo

51

Republic of Ingushe Ingushetia

22

rov Region Kirov

52

Republic of Kalmyk Kalmykia

24

ostroma Region Kostroma

53

Republic of Karelia

27

urgan Region Kurgan

54

Republic of Khakas Khakassia

28

ursk Region Kursk

55

Republic of Mariy-E Mariy-El

29

eningrad Region Leningrad

56

Republic of Mordovia Mordov

30

petsk Region Lipetsk

57

Republic of North O Ossetia-Alania

31

agadan Region Magadan

58

Republic of Sakha (Yakutia) (

33

oscow Region Moscow

59

Tatarsta Republic of Tatarstan

34

urmansk Region Murmansk

60

Republic of Tuva

36

izhni Novgorod Region Nizhni

61

Udmurt Republic of Udmurtia

37

ovgorod Region Novgorod

38

ovosibirsk Region Novosibirsk

39

msk Region Omsk

1

Altai Territory

40

renburg Region Orenburg

17

Kamchatka Territory Territor

41

ryol Oblast Oryol

20

Khabarovsk Territory Territo

42

enza Region Penza

25

Krasnodar Territory

45

skov Region Pskov

26

Territo Krasnoyarsk Territory

62

ostov Region Rostov

43

Perm Territory

63

yazan Region Ryazan

44

Primorsk Territory

65

akhalin Region Sakhalin

69

Stavropol Territory

66

amara Region Samara

83

Zabaykalsky Territo Territory

67

aratov Region Saratov

68

molensk Region Smolensk

70

verdlovsk Region Sverdlovsk

9

Chukotka Autonom Autonomous District

71

ambov Region Tambov

21

Khanty-Mansijsk Autonomous Au Region

72

omsk Region Tomsk

35

Nenets Autonomou Autonomous district

73

ula Region Tula

81

Yamalo-Nenets Aut Autonomous Region

74

ver Region Tver

75

yumen Region Tyumen

76

lyanovsk Region Ulyanovsk

77

adimir Region Vladimir

Krais

Okrugs

Autonomous Oblast 13

Jewish Autonomous District

Federal Citiesa

78

olgograd Region Volgograd

79

Vologda Region

Moscow

80

Vozronezh Region

Saint Petersburg

82

Yaroslavl Region

a The Republic of Crimea and the Federal City of Sevastopol joined the Russian Federation in 2014. Their status as part of the Russian state is internationally disputed.

3.1 Regional Layout and Spatial Heterogeneity

35

characteristics as oblasts, except that the title “territory” is historic in nature since those areas were once considered frontier regions. In spite of its vast territorial dimensions, Russia is one of the most sparsely populated countries on earth, with an average population density of only 8.37 inhabitants per square kilometer. Population density is highest in the European part of the country, particularly due to the milder climate. Population figures peak in Moscow and St. Petersburg (with populations of around 12 and 5 million inhabitants, respectively), both of which are particularly distinct from other Russian cities, Saint Petersburg due to its role as a historic capital and cultural center, and Moscow due to the special and central role it has played since the Soviet Union era. In addition, urbanization and agglomerations play a central role in Russia’s regional layout. Overall, 74% of Russia’s population is urban, a particularly high level. Apart from Russia’s two metropoles, there are 12 other cities with populations of more than or almost one million inhabitants, which altogether host more than 20% of Russia’s population. Those large urban centers are home to Russia’s middle class and provide higher living and educational standards, a well-developed university system and high degrees of Internet use. There is also a substantial number of industrial towns that accommodate between roughly 50,000 and 500,000 inhabitants whose appearance was largely shaped by large-scale industry in the Soviet era. Among these, there are also more than 150 one-company towns that accommodate around 10% of the country’s urban population. Against the background of Russia’s vast dimensions, it is also not surprising that the average distance between cities with more than one million inhabitants amounts to a remarkable 1171 km (Rosstat 2018). This makes it particularly difficult for knowledge spillovers to occur, or at least, they occur at a very limited regional scope, as the huge geographical distances impede the mobility of knowledge. Moreover, agglomerations in a Russian context are not always accompanied by more favorable conditions for business and economy. In the Russian Federation, economic output is based in large part on large-scale industry and natural resources. In both cases, urban centers with large populations do not necessarily predict higher economic outputs, as one would assume based on Chap. 2. One of the most measurable symptoms of spatial heterogeneity is gross regional product (GRP). In terms of per capita GRP, spatial disparities are most obvious in the federal cities and the main oil- and gas-extracting regions, i.e., the Tyumen (including the Yamal-Nenets and Khanty-Manti Autonomous Districts) and Sakhalin region, at one end, and a set of comparatively underdeveloped republics at the other extreme. With regard to the latter, a concentration of regions with comparably low levels of development can clearly be found in the North Caucasus3 and, to a somewhat lesser extent, in the Tuva and Altai regions in southern Siberia. Admittedly, regional economic disproportions have decreased since the early 2000s. The ratio in terms of per capita GRP between the richest and poorest regions, i.e., the

3

Specifically, the Republics of North Ossetia, Kabardino-Balkaria, Karachaevo-Cherkessia, Dagestan, Kalmykia, Ingushetia, and Chechnya.

36

3 The Distinctive Layout of Russia

Tyumen Region and the Republic of Ingushetia, fell from about 30 times in 2007/ 2008 to 13 times in 2016 (Rosstat 2018). However, this reduction of disparities was largely achieved by a continuous, rapid growth of oil and gas revenues that were centralized in the federal budget and redistributed to less-developed regions. This becomes particularly evident when we look at federal transfers, which account for more than 50% of budget revenues in 12 regions.4 With regard to Chechnya and Ingushetia, federal transfers are close to 85–90% (Chepurenko 2011). It follows that positive trends that are based on the redistribution of oil and gas rents are not sustainable; if the federal redistribution of funds cannot be maintained, regional disparities can be expected to grow again.

3.2

Political Administration and Heterogeneous Institutions

In order to further understand cross-regional heterogeneity, particularly with regard to institutional framework conditions, it is necessary to address Russia’s system of regional political administration. The Russian constitution provides its federal government the capacity to deal with matters of foreign policy, trade policy and customs, currency and exchange rate, as well as macroeconomic policy, which includes the legal framework for the common market and financial regulations. Apart from that, in principle, substantial legislative powers are decentralized to the regional level. The governor, as head of the executive branch,5 usually has considerable control over state budgeting, local commercial courts, supervisory bodies and various regulatory authorities, as well as a considerable role in legislation, including a large degree of leeway to interpret and apply the law (OECD 2003; Popov 2001). After the collapse of the Soviet Union, the far-reaching regional competencies led to regionally differentiated processes of transition to a market economy. In some regions, reforms and modernization initiatives were implemented relatively quickly, whereas other regions approached transition rather sluggishly or hardly at all. Moreover, some regional administrations granted preferential treatment to their economic elites, e.g., in the form of preferential tax discounts and leases or special permissions, while impeding others by withholding licenses or imposing administrative hurdles or excessive controls by tax authorities and supervisory agencies. The extent of utilizing such instruments varies between different regions, as indicated by the Center for Economic and Financial Research (Shchetinin et al. 2007). For example, in 2001, entrepreneurs in the Kurgan oblast faced an average of 10 unannounced inspections compared to 2.5 in the Samara oblast. In the end, this led to a considerable degree of heterogeneity

4

That is, the North Caucasus Republics, Kalmykia, Altai, Tuva and the Far East regions Kamchatka Territory, Amur Region, and the Jewish Autonomous Region (Chepurenko 2011). 5 The heads of the executive body of a regional subject of the Russian Federation are commonly referred to as governors, but titles may change between different regions.

3.2 Political Administration and Heterogeneous Institutions

37

in regional institutional factors (Bruno et al. 2013; Remington 2011; Berkowitz and DeJong 2005), with some regions having a more favorable climate for entrepreneurial activity compared to those with a more hostile one. This, however, is only one side of the coin. In recent years, Russia has become less and less federalist, but institutional heterogeneity across regions still plays a palpable role. In fact, developments until today have led to a situation in which the political system of the Russian Federation still lacks tangible incentives for regional policy-makers to create sustainable and sound institutional framework conditions to promote entrepreneurial activity. In this regard, first, the means of appointing governors has recently changed several times, and the federal government has seen a growing influence.6 Second, today, governors can be dismissed at any time depending on the will of the federal president and merely by having lost his confidence. This is well illustrated by a series of governor dismissals that occurred in the first 2 years of Dmitry Medvedev’s presidency (i.e., between 2008 and 2010), when more than 30 governors were replaced. Thus, even from an impartial perspective, the new governors seemed to be appointed based on their loyalty to the Kremlin rather than for their ability to govern, as numerous appointments both had no knowledge of the region they were appointed to govern and were also fairly inexplicable choices based on their respective track records. It appears that this has not changed; recent governor dismissals in 2017,7 on the one hand, led to young technocrats advancing who certainly have experience in governance and administration at the federal level but who are still largely unfamiliar with the regions to govern, e.g., regarding the governors of regions such as Novgorod, Nizhny Novgorod, Kaliningrad, Udmurtia, Buryatia, and Nenets (Zubarevich 2017). On the other hand, the appointment of Siloviki8 to governor positions also still seems to be a popular choice for the federal government. For example, in the early 2000s, ex-military members were appointed to gubernatorial office in Ulyanovsk, Voronezh, Krasnoyarsk, and Khakassia, but admittedly, their quality of governance was fairly poor. Instead of achieving economic and social results, the Siloviki entered into conflicts with local elites, appointed corrupt people to important positions, and embezzled state resources. However, their weak performance did not prevent them 6 Between 1995 and 2005, governors were directly elected by the residents of the federal subjects. From 2005 to 2012, governors were appointed by the regions’ legislative bodies based on a list of recommended candidates by the President of the Russian Federation. The most recent change occurred in 2012, when the federal legislature allowed regions to choose whether to elect their governor either directly by popular vote or if the governor should be appointed by parliament (Remington 2015). 7 The neglect of economic governance performance can be illustrated on the basis of the following example. Budget revenues in the Nenets Autonomous Okrug are heavily dependent on the price of oil, which dropped by more than 20% in 2016. Thanks to rigorous budget restrictions, the local governor could prevent an economic breakdown, and by the first half of 2017, budget revenues had risen again by almost 80%. However, regional population and elites were rather discontent; thus, the federal government ignored the economic achievements, and the governor was dismissed (Zubarevich 2017). 8 Representatives of the security services and especially veterans of the Federal Protective Service.

38

3 The Distinctive Layout of Russia

from being appointed again as governors of the regions of Tula, Yaroslavl, and Tver in 2017 (Kashin 2016).9 Thus, it seems that the rules of engagement for regional governors are rather unclear, except perhaps for the federal government’s priority of maintaining social stability. Nonetheless, they are less and less related to the regional economic performance or the creation of a favorable business climate (Remington 2015). In fact, governors of the worst-performing regions in terms of economic performance have stayed in office, while better-performing regional heads have been replaced (Zubarevich 2017). Additionally, Russia’s fiscal system is structured top-down, and the limited state tax income from entrepreneurial activity10 fills federal and not regional or local budgets. Thus, generating a higher density of entrepreneurial ventures does not provide financial or other compensation for regional or municipal authorities (Chepurenko 2011). Hence, it can be concluded that there are relatively weak political incentives to create sound entrepreneurial framework conditions, given that governor success is tied to political allegiance rather than measurable economic results, and higher rates of entrepreneurial activity do not result in immediate compensation. Overall, this condition further contributes to spatial heterogeneity in the institutional framework conditions for entrepreneurship.

3.3

Economy and Business Climate: Between Transition and Rent Dependency

The economic transformation of the Russian Federation into a market economy has brought mixed results. Whereas economic growth in the first half of the 2000s led to rising levels of income and wealth for the country, the 2007/2008 global economic crisis and the sharp drop in oil prices in 2015 showed that Russia’s economy was built on shallow foundations. Even though the Russian economy achieved hesitant growth again in 2017, supported by increases in oil prices and macroeconomic stabilization, economists often emphasize Russia’s structural economic problems and its unfavorable business climate. A Glance at Russia’s Economy Today, the Russian economy is still highly concentrated and lacks broad diversification. Although Russia spent an average of 1.1% of its GDP on R&D in 2016 and has a considerable reputation in some sectors (OECD 2018), for example, in nuclear energy and aerospace, innovation is largely concentrated in large-scale corporations. Consequently, the variety of exportable products with comparative advantages is narrow, and a low capacity for higher-value-added exports is apparent (EBRD 9

For example, Tula’s 2017 appointed governor seemed to have appeared essentially out of nowhere. His biography, however, revealed that his main qualification was 15 years of service as Mr. Putin’s personal bodyguard. 10 Income tax for solo owners and profit tax for firms.

3.3 Economy and Business Climate: Between Transition and Rent Dependency

39

2012). The quasi-ongoing state of economic crisis since 2008 has further contributed to market concentration in numerous sectors by exposing and eventually erasing firms with worse access to capital, weaker political connections and lower margins (Szakonyi 2017). These structural economic problems are in no small part imputable to a prolonged illness, namely, an overemphasis on exports of natural resources. Many papers and debates on Russia’s economy in recent years have noted the necessity of altering the country’s composition of output and trade. Hence, the topic is not entirely new, and Russian politicians have repeatedly emphasized the need for a radical shift away from a natural resource-based economy. At first glance, one might assume that concentration is not as bad as it seems, given that the share of oil and gas production in GDP accounts for an average of roughly 25% (Simola and Solanko 2017), meaning that three-quarters of the GDP is still driven by other sectors. Movchan (2015), however, attempts a deeper analysis by referring to a measure of so-called oil-based GDP. This measure additionally accounts for the fact that Russia imports about 60% of its total consumption and finances those imports mainly with its oiland gas-dominated exports. Moreover, state budget expenditures encompass roughly 20–22% of GDP, funded at least 60% by mineral extraction and excise taxes, export duties or VAT, and other taxes, thus adding another 13% to the oil-based GDP measure. Considering the influx of oil and gas revenues that are reinvested, spent in other sectors or result in consumption, one can estimate an additional impact ranging between 10% and 13% of GDP. Overall, the share of GDP that depends on oil and gas revenues consists of 67–70% (Movchan 2015). As a result, one can conclude that the state of Russia’s economy is only partially contingent on domestic policies and technological innovations, as well as sanctions or the choice of seeking rapprochement with the West. As of today, the decisive factor that impacts Russia’s economy remains the price of oil and gas (cf. Fig. 3.2). Grasping the Russian Business Climate Concerning the general business climate, there is a dichotomous opinion in Russia today that features both positive and negative aspects. According to the Doing Business Index 2018, since 2008, Russia has moved from the 106th to the 35th rank among a total of 190 countries (however, not before hitting its absolute low of 123rd place in 2011). The latest report emphasizes that a number of visible improvements have been made in recent years, especially with regard to the process of starting a business (World Bank 2017). This can primarily be attributed to reforms that simplified registration and post-registration formalities and eased capital requirements to start a business.11 Moreover, reforms improved the flow of information between state agencies and reduced the registration time required for opening corporate bank accounts. In fact, new ventures can now be registered with four procedures and within 10 days, in contrast to nine procedures and a time period of 11 For example, some Western nations, such as Austria or Spain, impose significantly higher entry barriers in terms of registration time and fees compared to Russia (Doing Business 2018).

40

3 The Distinctive Layout of Russia 18000

120

16000

GDP per capita (Current US$)

12000

80

10000

60 8000 6000

40

4000

Average Closing price per Barrel Crude Oil (in US$ )

100 14000

20

2000 0

0

GDP per capita (Current US$)

Average Closing price in US$ per Barrel Crude Oil

Fig. 3.2 Correlation of economic development and crude oil price

43 days 15 years ago, in addition to reducing the costs to start a business from 12% to 1% of income per capita. Progress has also been made in the area of enforcing contracts, which is attributable to the establishment of electronic courts that allow several steps in legal proceedings to be completed electronically. In this way, Russia could lower the costs of resolving commercial cases to levels 5% below the OECD average, in addition to reducing the average processing time to 337 days or 241 days less than the OECD average of 578 days. Furthermore, a new law that improves the collateral registry system has simplified access to credit, and registering property was eased by a reduction of the application time for state registration of title transfer (World Bank 2017). Table 3.2 illustrates Russia’s recent progress according to a range of economic indices compared to peer groups of Central and East European (CEE) countries, Commonwealth of Independent States (CIS) countries, and other BRICS nations. However, the Doing Business Index exclusively refers to Russia’s economic centers Moscow and St. Petersburg, which are not necessarily representative of other regions. Furthermore, although changes in framework conditions have boosted outlooks towards a rise in ventures, overall entrepreneurial activity in Russia still lags behind expectations (Yakovlev 2014; Golikova et al. 2007; Yasin et al. 2006). This perspective is taken by the Economic Freedom Index, which draws a fairly different picture of the Russian economy. The index captures whether individuals can “work, produce, consume and invest in any way they please” and particularly values minimal constraints in terms of the free movement of labor, capital, and goods (Heritage Foundation 2017). According to the index, from 2008 to 2017, Russia has only moved from the 134th to the 114th rank on a global scale. Besides some

Russia Avg. CEE countries Avg. CIS countries China India Brazil South Africa

35 36.6

58.6

78 100 125 82

87.8

83 120 122 35

Rank 2017

106 47.8

Doing business index Rank Country 2008

" " # #

5 20 3 47

" 29.1

" 71 " 11.2

Δ

Russia Avg. CEE countries Avg. CIS countries China India Brazil South Africa 126 115 101 57

81.1

134 54.3

111 143 140 81

78.8

114 45.7

Economic freedom index Rank Rank Country 2008 2017

" # # # 15 28 39 24

" 2.3

" 20 " 8.6

Δ Russia Avg. CEE countries Avg. CIS countries China India Brazil South Africa 30 50 64 45

90.9

51 56.1

27 40 80 61

72.9

38 53.3

Global competitiveness index Rank Rank Country 2008 2017

Table 3.2 Russia’s business climate rankings in global comparison, 2008–2017

" " # #

3 10 16 16

" 18

" 13 " 2.8

Δ

Russia Avg. CEE countries Avg. CIS countries China India Brazil South Africa

37 41 50 43

99.1

68 52.6

22 60 69 57

72.5

45 39.0

Global innovation index Rank Rank Country 2008 2017

" # # #

15 19 19 14

" 26.6

" 23 " 13.6

Δ

3.3 Economy and Business Climate: Between Transition and Rent Dependency 41

42

3 The Distinctive Layout of Russia

progress in the areas of tax policy and trade freedom, the report stresses weaknesses in three salient areas: rule of law, regulatory efficiency, and openness of markets. Especially with regard to the first of these areas, the index emphasizes an uneven enforcement of rule of law across the country and finds the legal system inconsistent in applying the law and also vulnerable to political pressure. Aidis and Adachi (2007) have also supported this view, stating that enforcement of laws in Russia occurs in a selective or arbitrary manner. Moreover, corruption in government, public administration, and business is ubiquitous and backed by a lack of accountability and impunity. This facilitates the predatory nature of some regulatory authorities and inspection agencies and makes differences in institutional quality particularly striking across regional levels. This is largely due to Soviet history, as citizens have become accustomed to a malfunctioning and corrupt legal system, and the prevailing mindset was to get around the law rather than understand it as a mechanism to protect one’s fundamental rights (Aidis and Adachi 2007). Second, onerous regulations still impede the development of the private sector. For example, the rigid labor code is in strong need of reform and hampers employment and productivity growth. Furthermore, the regulation system as a whole continues to suffer from limited transparency when establishing regulations. Firms in Russia still face a lack of stability, consistency, and reliability in the regulatory environment because regulations are often applied as the regional authorities see fit (World Economic Forum 2017; Aidis and Adachi 2007). Third, structural and institutional constraints, as well as government interference in the marketplace, keep the private sector below its potential. Instead, large-scale, state-owned incumbents and an inefficient public sector dominate many industries, and investment in several sectors of the economy is capped. In particular, the financial sector is fairly concentrated to the detriment of private domestic and foreign banks (World Economic Forum 2017). The Misery of Rent Addiction To draw a first intermediate conclusion, it seems that Russia’s economy is still characterized by an extensive focus on the resource sector, and, aside from the progress made in some aspects of its business climate and entrepreneurial framework conditions in order to achieve economic diversification, many impediments remain. Both facets are only symptomatic of Russia’s structural economic deficiencies and the failure of economic modernization initiatives. There is, however, one possible root cause for those deficiencies, which was inherited from the Soviet period: a special form of rent dependence or rather rent addiction (Gaddy and Ickes 2013). Every economy that is subject to resource abundance needs a system in place to direct the flow of rents drawn from it. In the Soviet Union, free and direct dissipation of rents was limited, as they could neither be directed to private accounts nor be transformed into consumption. Of course, some financial streams ended up benefiting party leaders and plant directors, but most resources were directed into the production of goods, which enhanced the authority and legitimacy of Soviet leadership, hence shaping a phenomenon called addiction through production (Gaddy and Ickes 2013). Essentially, rents were invested in large-scale heavy industry enterprises following the logic that higher

3.3 Economy and Business Climate: Between Transition and Rent Dependency

43

consumption of more metal, energy, workers, and infrastructure would lead to higher degrees of power and prestige for their directors. Together with the collapse of the USSR, this rent distribution system fell apart; however, its remnants still perpetuate the current state of rent addiction. Today’s system combines vigorous state influence with private ownership of enterprises. Companies involved in rent creation are supposed to maintain supply chains and the production of the rent addicts, i.e., the ineffective, large-scale enterprises inherited from the Soviet economy. This is done by providing input and services (e.g., materials, fuel, components, or transportation), whereby prices are largely non-transparent and used as rent-transfer channels to the addicts. On the other hand, those suppliers are obliged to buy machinery and equipment produced with their implicit subsidies (Gaddy and Ickes 2013). In this light, the state’s main interest is the maintenance of the system in securing private business owners’ support for the rent distribution system. In an analogy to Adam Smith’s invisible hand, Frye and Shleifer (1997) have characterized this state-driven behavior as the “grabbing hand” of government. In this light, regional governments are more interested in maintaining the rent distribution system and expropriating profits within their region than in facilitating innovation and favorable economic framework conditions.12 Chepurenko (2010) has supported this view by arguing that Russian incumbents still redistribute rather than generate new added value, and the use of strategies to privatize profits and externalize costs is quite common. Regarding grabbing hand behavior, Russian entrepreneurs have found creative ways to avoid the rent-seeking interference of corrupt officials or third-party interests. A common approach is either to restrict company performance and growth to a critical threshold or to divide growing businesses into smaller firms at different addresses in order to escape official notice from inspectors, tax authorities, etc. (Tavernise 2002). Although this does not interfere with entrepreneurial entry itself, it forestalls the positive effects of entrepreneurship by constraining entrepreneurial performance and growth, and it thus limits the positive effects of entrepreneurship on an overall economic level. Other studies, for example, from Aidis et al. (2008) and Molz et al. (2009), have argued that the Russian government is the major factor in creating institutional barriers for business and entrepreneurship. To summarize, incumbents in Russia have a high degree of market power at their disposal, and today, neither incumbents nor regional governments have shown sufficient interest in facilitating an increase in competition with system outsiders, i.e., innovative entrepreneurs, functioning market mechanisms, and the transparent

12

An illustrative example is provided by Fox and Heller (2000). In the early 1990s, the Swedish company Assidomän (today Sveaskog AB) acquired a 57% majority stake in the Karelia-based Segezhabumpron Paper Mill, one of Russia’s largest pulp and paper mills. Soon after, the company advanced a US$100 million plan to modernize the plant. However, concerns were raised that jobs could be cut. Backed by the local government, the mill’s employees challenged the legality of Assidomän’s initial purchase of shares and the plant’s managers faced physical threats and violence. After the regional government, the co-owner of the mill refused to contribute its share of working capital investment to keep the factory open, the restructuring plan was abandoned, and Assidomän wrote off its ownership of the paper mill (Fox and Heller 2000).

44

3 The Distinctive Layout of Russia

formation of prices. Even though there are notable improvements, for example, with regard to business incubators, dedicated industrial parks, and other types of initiatives (EY 2013), greater emphasis could be put on establishing sound framework conditions on an overall level in order to overcome institutional inertia and promote higher formation rates of entrepreneurial ventures.

3.4

Is Russia an Entrepreneurial Society?

The previous chapter illustrated some of Russia’s structural economic deficiencies shed light on some of the contextual hardships entrepreneurs face. In this chapter, I conclude my initial overview at Russia by gauging the status quo of entrepreneurial activity there and by drawing a clearer picture of Russia’s entrepreneurs. The Status Quo of Entrepreneurial Activity in Russia By now, we have considered the business climate in Russia; however, we still lack insight into the actual status quo of entrepreneurial activity there, which we now more closely investigate in the following paragraphs. Starting with a short retrospective into the early 2000s, initially, the development of small businesses in Russia had a relatively positive development. Russian SMEs started to utilize local resources and covered growing demand from Russia’s emerging middle class. This reflected the rapid growth in real income until the mid-2000s, and along with it, a rise in domestic consumption that led to increased turnover in the trade, retail, and services branches. Several Russian gazelles in the areas of food processing, textiles, clothing and footwear, IT, and consulting sectors have emerged and account for an estimated share of 12–15% of active firms, compared to 4–8% in developed countries (Chepurenko 2011; Yudanov 2008). Between 1999 and 2008, the total number of incorporated SMEs rose from 900,000 to 1.34 million, and the number of their employees from 6.2 to 11.4 million (Nabiullina 2009). However, for a long time, entrepreneurial activity was not considered an important contributor to economic development, and active political support of small-scale entrepreneurial activity has long been neglected (Aidis 2015). According to Aidis and Adachi’s (2007) Amadeus data set, cross-industry entry rates in Russia between 1998 and 2002 were relatively low compared to Western Europe and North America, which typically showed entry rates in the 5–15% range in the same period. Other transition economies also showed higher entry rates than Russia, given fairly similar initial conditions (Aidis and Mickiewicz 2006; Estrin et al. 2006; McMillan and Woodruff 2002). Bruno et al. (2013), on the other hand, have found that Russian entry rates are not exceptionally low by international standards, as they estimated them in an interval between 11.9% and 2.4% throughout 1996–2008, compared with 7.09% in Europe and 6.65% in the United States. However, they also found that Russia has lower survival rates or higher exit rates of firms. The downside of Russia’s weak performance in terms of entrepreneurial activity has not gone unnoticed by politicians. During his 2008–2011 term, president

3.4 Is Russia an Entrepreneurial Society?

45

Medvedev launched various initiatives to address the small and unsatisfactory role SMEs played in the Russian economy, for example, by establishing venture capital funds to address the lack of funding, by creating the Agency for Strategic Initiatives (ASI) to improve entrepreneurial framework conditions, and by fostering the creation of industrial parks. Moreover, in 2012, the government created the role of an ombudsman to protect the rights of entrepreneurs. In the same year, Moscow was identified as one of the top 20 global cities for start-ups, especially as Moscow-based ventures appeared to quickly adopt and integrate new technologies, process innovations, and business models, in addition to being run by particularly well-educated founder teams (i.e., 69% have a Master’s degree). However, weaknesses could be identified with regard to the availability of funding and the firms’ level of economic output (Aidis 2015). Additionally, regional differences must again be considered, and a strong focus on improving framework conditions in Moscow while neglecting other regions is insufficient to substantially improve the situation on a national level. Today, small- and medium-sized firms account for approximately 20% of GDP, 23% of employment, and 7% of exports in Russia (compared, e.g., with an 80% employment share and more than 50% export share in China) and still do not play a major, vital role in economic growth (TASS 2017). The distribution across sectors shows that small firms’ activities still largely concentrate on trade, retail, and services branches. On the other hand, various other branches still suffer a lack of entrepreneurial activity. For example, despite Russia’s vast geographical dimensions and wealth in natural resources, few entrepreneurs engage in the forestry, agriculture, and fisheries sector (Bleck 2011). Another aspect to consider is the uneven distribution of entrepreneurial activity across the country (Chepurenko 2011; Migin 2006). GEM data provide a slightly more positive outlook. Total early stage entrepreneurial activity (TEA)13 almost doubled in the last decade, from a relatively low value of 3.5% in 2008 to 6.3% in 2016, indicating that at least 6.3% of the relevant population is more or less involved in creating an entrepreneurial venture (which is even higher than a share of 4.6% in Germany). However, the relation of improvement-driven opportunity entrepreneurship relative to necessity-motivated entrepreneurs in 2016 only amounts to 1.3 compared to, e.g., Germany (2.7) or the United States (6.4), indicating that there is still a lack of quality in entrepreneurial activity. Higher shares of opportunity-motivated entrepreneurship would be crucial with regard to economic development and innovation.

13 TEA indicates the level of entrepreneurial activity at early stages, determined by the percentage of the 18–64 year-old population who are either nascent entrepreneurs or owner-managers of a new business. This is not a simple sum of the first two measures. If an entrepreneur is involved in both types of activity, he is counted only once. Nascent entrepreneurs are involved in setting up a business (up to 3 months old), whereas owner-manager own a new business that is up to 3.5 years old.

46

3 The Distinctive Layout of Russia

In Search of a Profile of the Russian Entrepreneur Since entrepreneurship is driven by both context and individual characteristics, I briefly consider the major characteristics of entrepreneurs in Russia. Several studies have investigated these, often in comparison to US entrepreneurs or between entrepreneurs and non-entrepreneurs. First, in general, studies suggest that the individual characteristics of Russian entrepreneurs are quite similar to those of Western entrepreneurs (Ojala and Isomäki 2011), except that Stewart et al. (2003) and Ageev et al. (1995) have found lower degrees of aspiration as a motive for engaging in entrepreneurial activity. Estrin and Mickiewicz (2010) also do not see any evidence that entrepreneurs in Russia show systematically different characteristics than their equivalents in other countries (e.g., Russian entrepreneurs show low confidence in their own management skills in Russia, but so do Japanese entrepreneurs). There are, however, notable differences between Russian entrepreneurs and non-entrepreneurs, as there are in other countries. Russia’s entrepreneurs appear to have more different previous professional activities than the country’s non-entrepreneurs. They are also less locally bound than other individuals and show a higher propensity towards risk (Djankov et al. 2005). With regard to gender, Wishniewsky (2008) has identified a somewhat higher tendency for men to pursue an entrepreneurial career than for women. Estrin et al. (2007) and Reynolds et al. (2002) have supported this point and suggested that men are even two times more likely to be entrepreneurs than women. Most notably, Russian entrepreneurs differ substantially from non-entrepreneurs in their family background and social network. Family members of entrepreneurs generally have higher education, better jobs, and more wealth, which also relates to the finding that new Russian ventures tend to use rather low amounts of external capital (Ojala and Isomäki 2011). Additionally, the share of childhood and school friends that are also entrepreneurs is twice as high for entrepreneurs than for non-entrepreneurs. Moreover, fathers of entrepreneurs are more likely to have been company directors (19% vs. 11%) and were also more likely members of the Communist Party (50% for entrepreneurs compared to 35% for non-entrepreneurs) (Djankov et al. 2005). Most interestingly, according to Wishniewsky (2008), individuals’ arguments for not choosing to become an entrepreneur include a desire for a stable income as well as negative personal experience with regard to bureaucracy, corruption, or crime. Consequently, apart from some national features, it appears that, by and large, similar attitudes, abilities, and aspirations shape the perceptions of opportunities on an individual basis and contribute to decision-making in favor of an entrepreneurial career, which makes institutional framework conditions in Russia a far more interesting subject of research. In brief, Chap. 3 revealed that Russia’s entrepreneurs largely share the same characteristics as entrepreneurs in other parts of the world. However, they are exposed to rather difficult framework conditions, namely, a stiff political and economic structure, which have facilitated the emergence of concentrated industries, an oligarchical structure and rent addiction to the detriment of innovative entrepreneurial activity. In addition, although Russia’s regions form one common market, regulated by the same federal law and characterized by other similar characteristics

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with regard to policy, society, and culture, there are still notable differences. Those differences are present in both the country’s regional institutions (e.g., due to ample competence in policy design and varying incentives for local authorities) and the spatial levels of entrepreneurial activity. Together, those aspects call for a more detailed analysis of how regional institutions in Russia affect entrepreneurial activity.

References Ageev, A., Gratchev, M., & Hisrich, R. (1995). Entrepreneurship in the Soviet Union and PostSocialist Russia. Small Business Economics, 7(5), 365–376. Aidis, R. (2015). Is Russia an entrepreneurial society? A comparative perspective. In S. Oxenstierna (Ed.), The challenges for Russia’s politicized economic system (pp. 77–95). New York: Routledge. Aidis, R., & Adachi, Y. (2007). Russia: Firm entry and survival barriers. Economic Systems, 31(4), 391–411. Aidis, R., & Mickiewicz, T. (2006). Entrepreneurs, expectations and business expansion: Lessons from Lithuania. Europe Asia Studies, 58(6), 855–880. Aidis, R., Estrin, S., & Mickiewicz, T. (2008). Institutions and entrepreneurship development in Russia: A comparative perspective. Journal of Business Venturing, 23(6), 656–672. Berkowitz, D., & DeJong, D. N. (2005). Entrepreneurship and post-socialist growth. Oxford Bulletin of Economics and Statistics, 67(1), 25–46. Bleck, S. (2011). Klein- und mittelständische Unternehmen als volkswirtschaftlicher Stabilisierungsfaktor in der Russischen Föderation – aktuelle Entwicklungen und Zukunftsaussichten (in German). Deutsches Institut für Bankwirtschaft – Schriftenreihe, 6, 02/2011. Bruno, R. L., Bytchkova, M., & Estrin, S. (2013). Institutional determinants of new firm entry in Russia: A cross-regional analysis. Review of Economics and Statistics, 95(5), 1740–1749. Chepurenko, A. (2010). Small entrepreneurship and entrepreneurial activity of population in Russia in the context of the economic transformation. Historical Social Research, 35(2), 301–319. Chepurenko, A. (2011). Entrepreneurship and SME policies in fragile environments: The example of Russia. In F. Welter & D. Smallbone (Eds.), Handbook of research on entrepreneurship policies in Central and Eastern Europe (pp. 190–209). Cheltenham: Edward Elgar. Djankov, S., Miguel, E., Qian, Y., Roland, G., & Zhuravskaya, E. (2005). Who are Russia’s entrepreneurs? Journal of European Economics, 3(2–3), 587–597. Doing Business. (2018). Doing business report 2018 – Reforming to create jobs. Washington, DC: The World Bank. EBRD. (2012). Diversifying Russia: Harnessing regional diversity. London: European Bank for Reconstruction and Development. Estrin, S., & Mickiewicz, T. (2010). Entrepreneurship in transition economies: The role of institutions and generational change (IZA Discussion Paper no. 4805). Bonn: IZA Institute of Labor Economics. Estrin, S., Meyer, K., & Bytchova, M. (2006). Entrepreneurship in transition economies. In M. Casson, A. Basu, B. Yeung, & N. Wadesdon (Eds.), The Oxford handbook of entrepreneurship. Oxford: Oxford University Press. Estrin, S., Aidis, R., & Mickiewicz, T. (2007). Institutions and entrepreneurship development in Russia: A comparative perspective (Working Paper No. 867). William Davidson Institute, University of Michigan.

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EY. (2013). Russia attractiveness survey 2013 – Shaping Russia’s future. London: Ernst & Young Global. Fox, M. B., & Heller, M. A. (2000). Corporate governance lessons from Russian enterprise Fiascos. New York University Law Review, 75, 1720–1780. Frye, T., & Shleifer, A. (1997). The invisible hand and the grabbing hand. American Economic Review, 87(2), 354–358. Gaddy, C. G., & Ickes, B. W. (2013). Russia’s dependence of resources. In M. Alexeev & S. Weber (Eds.), The Oxford handbook of the Russian economy (pp. 309–340). New York: Oxford University Press. Geocurrents. (2018). Customizable maps of the Russian federation for PowerPoint. Accessed January 2018, from http://www.geocurrents.info/customizable-base-maps Golikova, V., Gonchar, K., Kuznetsov, B., & Yakovlev, A. (2007). Russian industry at the crossroads: What prevents our companies from getting competitive (in Russian). HSE Policy Paper – Voprosy Economiki, 3, 4–34. Heritage Foundation. (2017). Index of economic freedom. The Heritage Foundation. Accessed December 2017, from https://www.heritage.org/index/ Kashin, O. (2016). How do you get to be a governor in Vladimir Putin’s Russia? The New York Times, September 8, 2016. https://www.nytimes.com/2016/09/09/opinion/how-do-you-get-tobe-a-governor-in-vladimir-putins-russia.html Ledeneva, A. V. (2013). Can Russia modernise? Sistema, power networks and informal governance. Cambridge: Cambridge University Press. McMillan, J., & Woodruff, C. (2002). The Central role of entrepreneurs in transition economies. Journal of Economic Perspectives, 16(3), 153–170. Migin, S. (2006). Classification of Russian federation members in terms of small business development (in Russian). Accessed January 2018, from http://www.nisse.ru/analitics.html?id¼ks_ RF Molz, R., Tabbaa, I., & Totskaya, N. (2009). Institutional realities and constraints on change: The case of SME in Russia. Journal of East-West Business, 15(2), 141–156. Movchan, A. (2015). Just an oil company? The true extent of Russia’s dependency on oil and gas. Carnegie Moscow Center. Accessed October 2018, from http://carnegie.ru/commentary/61272 Nabiullina, E. (2009). On the project “development of small and medium sized business” of the list of projects on the main tasks of the government of the Russian Federation until 2012. Theses of the Contribution by the Minister for Economic Development of the Russian Federation to the Government of Russian Federation (in Russian), Accessed August 2018, from http://www. economy.gov.ru OECD. (2003). Trade politics in Russia: The role of local and regional governments. Paris: Organisation for Economic Co-Operation and Development. OECD. (2018). OECD database. https://data.oecd.org/ Ojala, A., & Isomäki, H. (2011). Entrepreneurship and small businesses in Russia: A review of empirical research. Journal of Small Business and Enterprise Development, 18(1), 97–119. Popov, V. (2001). Reform strategies and economic performance of Russia’s regions. World Development, 29(5), 865–886. Remington, T. F. (2011). The politics of inequality in Russia. Cambridge: Cambridge University Press. Remington. (2015). Institutional variation among Russian regional regimes: Implications for social policy and the development of non-governmental organizations. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 6(26), 2215–2237. Reynolds, P., Bygrave, W. D., Autio, E., Cox, L.W., & Hay, M. (2002). Global entrepreneurship monitor: 2002 Executive report. London. Rosstat. (2018). Accessed from www.gks.ru Shchetinin, O., Zamulin, O., Zhuravskaya, E., & Yakovlev, E. (2007). Monitoring the administrative barriers to small business development in Russia: 5-Th Round (Policy Paper no. 22). Center for Economic and Financial Research at New Economic School (CEFIR).

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Simola, H., & Solanko, L. (2017). Overview of Russia’s oil and gas sector (BOFIT Policy Brief 2017 no. 5). Institute for Economies in Transition, Bank of Finland – BOFIT. Stewart, W., Carland, J. C., Carland, J. W., Watson, W., & Sweo, R. (2003). Entrepreneurial dispositions and goal orientations: A comparative exploration or United States and Russian entrepreneurs. Journal of Small Business Management, 41(1), 27–46. Szakonyi, D. (2017). Monopolies rising: Consolidation in the Russian economy (PONARS Eurasia Policy Memo no. 491). Washington, DC: Institute for European, Russian and Eurasian Studies (IERES), George Washington University. TASS. (2017). The influence of SME ecosystems on the global economy. Accessed May 2018, from http://tass.com/sp/947894 Tavernise, S. (2002, September 27). Red tape frustrates Russia’s entrepreneurs. The New York Times. Accessed June 2018, from https://www.nytimes.com/2002/09/27/business/red-tape-frus trates-russia-s-entrepreneurs.html Wishniewsky, G. (2008). Opinions of Russian managers on whether to become an entrepreneur. Journal of International Business and Economics, 8(2), 170–180. World Bank. (2017). Doing business 2018 – Reforming to create jobs. Washington DC: World Bank. World Economic Forum. (2017, November 20). Russia’s “Monotowns” are running out of steam. Can This Plan Revive Them? Accessed July 2018, from https://www.weforum.org/agenda/ 2017/11/russia-monotowns-out-of-steam-revival/ Yakovlev, A. (2014). Russian modernization: Between the need for new players and the fear of losing control of rent sources. Journal of Eurasian Studies, 5(1), 10–20. Yasin, E., Grigoriev, L., Kuznetsov, O., Danilov, Y., & Kosygina, A. (2006). Investment climate in Russia (in Russian). Obchshestvo i Economika, 5, 3–56. Yudanov, A. (2008). Competition in Russia: The causes of success and failure (in Russian). Moscow: KnoRus. Zubarevich, N. (2017, October 12). The fall of Russia’s regional governors. Carnegie Moscow Center. Accessed May 2018, from https://carnegie.ru/commentary/73369

Chapter 4

The Institutional Framework for Entrepreneurship in Transition

I sit on a man’s back, choking him, and making him carry me, and yet assure myself and others that I am very sorry for him and wish to ease his lot by any means possible, except getting off his back. Leo Tolstoy, Writings on Civil Disobedience & Nonviolence

The previous chapter has shown that spatially diverging rates of entrepreneurial activity in Russia may be related to substantial variation in regional institutions. Recall that the first part of this thesis’s research question concentrates on identifying those spatial institutional factors that influence Russia’s entrepreneurship activity. Reflecting on this question, in this chapter, I examine a broad set of regional institutional factors in Russia with regard to their assumed impact on entrepreneurial entry. My aim is to develop a range of hypotheses to be tested in the remainder of this dissertation.

4.1

Structural Economic Factors as Fundamental Prerequisites

Before addressing the issue of specific institutional impacts on entrepreneurship, I want to investigate the most “basic” type of institution, i.e., how structural economic characteristics relate to entrepreneurial activity. We already know from Sect. 2.1.2 that entrepreneurial activity is expected to facilitate economic growth in the long run. The following paragraphs aim to respond to the question of whether structural economic factors, which significantly shape the context of entrepreneurial action, also serve as necessary and relatively short-term preconditions for entrepreneurial entry itself. By doing so, I also address the issue of causality. Overall Economic Development and Economic Growth The overall economic situation is highly likely to affect the attractiveness of becoming an entrepreneur because the macroeconomic situation defines a large share of the © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Schlattau, Tilting at the Windmills of Transition, Societies and Political Orders in Transition, https://doi.org/10.1007/978-3-030-54909-1_4

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context of an entrepreneur’s activities, for example, resource availability, opportunities, etc. (Sarkar et al. 2018; Fiess et al. 2010; Parker 2004). In this regard, the most essential indicators of economic well-being are the level of economic development (measured in income per capita) and economic growth. With regard to the level of economic development, various studies have provided evidence for the existence of a relationship between economic development and entrepreneurial activity. However, there is evidence for both a negative and positive relationship. The former has mainly been provided by studies that refer to time periods within the first three-quarters of the twentieth century and build on large cross-sections of countries, for example, by Iyigun and Owen (1998), Schultz (1990), and Kuznets (1971). In contrast, studies referring to more recent time periods have tended to identify a U-shaped pattern. This pattern is described by high rates of entrepreneurial activity in low-income countries, reduced rates in medium-income countries, and higher rates of entrepreneurship in high-income countries. Acs et al. (1994) have proved a U-shaped development in the share of self-employment between 1966 and 1990 for a sample of OECD countries and for several individual countries. Carree et al. (2002), meanwhile, have found empirical support for a U-shaped relationship between per capita income and the rate of self-employment using a multiple-equation regression analysis based on data for 23 OECD countries between 1976 and 1996. Finally, Wennekers et al. (2005) have utilized 2002 GEM data for nascent entrepreneurship in 36 countries and also found support for a U-shaped relationship. This relation can be illustrated using income and GEM data on total early stage entrepreneurial activity per year in a set of approximately 60 countries (cf. Figs. 4.1

Early -s tage Entrepreneurial Ac t ivit y

60

50

40

30

20

10

0 -

10.000

20.000

30.000

40.000

50.000

GDP per capita, Purchasing Power Parity (PPP)

Fig. 4.1 Income per capita and total early stage entrepreneurial activity (TEA), 2010

60.000

4.1 Structural Economic Factors as Fundamental Prerequisites

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Early -s tage Entrepreneurial Ac t ivit y

40 35 30 25 20 15 10 5

0 -

20.000

40.000

60.000

80.000

100.000

120.000

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GDP per capita, Purchasing Power Parity (PPP)

Fig. 4.2 Income per capita and total early stage entrepreneurial activity (TEA), 2016

10

Motivation Index

8

6

4

2

0 -

10.000

20.000

30.000

40.000

50.000

60.000

GDP per capita, Purchasing Power Parity (PPP)

Fig. 4.3 GDP per capita and opportunity motivation, 2010

and 4.2). However, this measure does not differentiate between different entrepreneurial motivations and encompasses both opportunity- and necessity-driven entrepreneurship. If we restrict entrepreneurial activity to opportunity-driven entrepreneurs only, we find a linear relationship as illustrated by Figs. 4.3 and 4.4.

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4 The Institutional Framework for Entrepreneurship in Transition 12

Motivation Index

10

8

6

4

2

0 -

20.000

40.000

60.000

80.000

100.000

120.000

140.000

GDP per capita, Purchasing Power Parity (PPP)

Fig. 4.4 GDP per capita and opportunity motivation, 2016

Notably, in all illustrations, data dispersion is relatively high, and correlations are fairly weak. Nevertheless, the general relationships fit well with existing empirical findings, as many new opportunities for entrepreneurship emerge at the higher end of economic development, whereas necessity entrepreneurship is either negatively correlated with per capita income (Wennekers et al. 2005) or shows no relation at all (van Stel et al. 2007). Some obvious reasons can explain this relationship. Beyond a certain level of economic development, and with rising levels of GDP per capita, employment shares in agriculture and manufacturing decline, whereas the service sector’s share increases, offering more opportunities for entrepreneurs (Klapper et al. 2007; Wennekers et al. 2005). According to Desai et al. (2003), greater GDP per capita levels are also likely to provide greater infrastructure and larger market potential for start-ups. The latter can be attributed to the fact that higher levels of income and wealth augment consumer demand for variety, hence forming new market niches that small businesses can occupy. Additionally, economic development also affects individual needs. Referring to Maslow’s (1970) hierarchy of human motivations, higher levels of prosperity give meaning to immaterial needs such as a growing desire for self-realization, independence, and autonomy, after material needs are already satisfied, thus leading to higher rates of entrepreneurial entry (Wennekers et al. 2005). Apart from the absolute level of development, the overall (cyclical) economic performance or economic growth is also assumed to provide incentives for entrepreneurial entry. Against this background, however, there are two potential effects that may lead to contrary impacts. On the one hand, entrepreneurial activity may be

4.1 Structural Economic Factors as Fundamental Prerequisites

55

induced by a recession-push effect, as employees might be pushed into selfemployment due to high unemployment and few job offerings, and hence lower opportunity costs of entrepreneurial entry. On the other hand, a potential prosperitypull effect might boost business opportunities and expected gains from entrepreneurial activity (Aidis et al. 2009; Verheul et al. 2006; Parker 2004). A considerable amount of literature confirms this effect (Lee et al. 2011; van Stel et al. 2007; Djankov et al. 2006; Claessens and Klapper 2005; Reynolds et al. 2002; Kawai and Urata 2002; Shane 1996). Again, similar to the level of economic development, the relationship between economic growth and opportunity-driven entrepreneurship is different compared to the relationship between economic growth and necessitydriven entrepreneurship. GDP growth rates have a significantly positive effect on the entry rates of opportunity-driven firms, as they mirror higher demand for goods and services, thus creating more opportunities to start new businesses; they have no effect on necessity rates, though, because necessity entrepreneurs are not affected by changes in consumer demand (van Stel et al. 2007). While Russia is included in the data of some of the aforementioned cross-country studies, unfortunately, there is a lack of specific studies on the relationship of economic development and growth on entrepreneurial activity in Russia and its regions. Finally, an important aspect that remains to be mentioned is disentangling the direction of causality, i.e., whether the level of economic development or the rate of economic growth is a determinant for firms’ entry or whether higher rates of entrepreneurial entry lead to more economic growth and per capita GDP. Although there is ample evidence for both arguments, and one must acknowledge that this is still an important area of future research (Klapper et al. 2007), this work follows the understanding that higher levels of entrepreneurial entry benefit economic growth and innovation in the long term and, at the same time, opportunity-driven entrepreneurial entry depends on economic well-being in the short term. Nonetheless, I do not make any explicit hypotheses at this point because I intend to use variables on economic development and growth as control variables in the empiric analysis. Economic Uncertainty and Risk Following the arguments of the preceding paragraphs, it is necessary to ask how economic crisis conditions (e.g., recessions or depressions) or general situations of increased economic uncertainty and risk affect the decision to start a firm. Again, the literature often provides different perspectives that suggest both the existence of a positive and negative relationship between economic risk and entrepreneurial activity. Regarding the supporters of a positive relationship, there is at least some evidence that contexts of higher economic risk encourage the entry of small firms. On the one hand, situations of economic risk may work as a powerful push factor, urging people into self-employment due to the lack of viable alternatives (Dawson and Henley 2012). On the other hand, such situations might also create entrepreneurial opportunities in the form of more options and a greater need for new entrepreneurial ventures as agents of change (Wennekers et al. 2005; Thurow 2003; Audretsch and Thurik 2001). Nevertheless, regarding the literature in favor of a negative relationship, it is also well established that the number of new (opportunity-driven) firms varies pro-cyclically; i.e., entry rates are lower in periods of higher economic risk and

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higher in periods of reduced risk (Beiler 2017; Lee and Mukoyama 2015; Pugsley and Sahin 2014; Yu et al. 2014). There are several causes for this effect. First, high levels of risk might work as an impediment to action. Elevated levels of economic risk correlate with higher uncertainty of outcomes and thus nourish the fear of failure, which is de-motivating and negatively affects the potential entrepreneur’s aspirations. Additionally, it contributes to avoiding investments, and hence it is negatively associated with firm entry (Morgan and Sisak 2016). McKelvie et al. (2011) have also found that potential entrepreneurs hesitate to act when the outcomes of their actions cannot be sufficiently assessed or predicted. Consequently, the difficulty in predicting the consequences of actions represents a considerable impediment to action. Second, positive demand shocks in economic boom periods help existing firms expand and increase the likelihood of entry for opportunity-motivated and incorporated ventures. Contrary to the “higher risk creates entrepreneurial opportunities” argument from before, Devece et al.’s (2016) empirical work, based on the 2007–2008 global financial crisis and the prior economic boom, shows that a poor economic context is more likely to limit business opportunities due to reduced demand and purchasing power and also more likely to hamper the entrepreneur’s ability to exploit these opportunities due to limited access to resources. Studies on the relation between economic crisis conditions or increased economic uncertainty and entrepreneurial entry with a particular focus on Russia are fairly scarce. To my knowledge, only Iwasaki et al. (2016) have investigated how situations of economic risk and uncertainty relate to entrepreneurial activity in Russian regions. In their work, the authors attempt to quantify the extent to which the 2008 global financial crisis and the subsequent period of high economic risk affected firm creation and destruction in Russia over a period of 6 years. Their findings suggest two major effects. First, it was observed that firms’ market entry was discouraged by the global financial crisis after 2008 and until 2014. In addition to reduced firm entry, firm exits exhibited a substantial surge, amplified by the economic downturn in European and Asian trading partners. Second, this effect could be observed across all federal subjects, indicating that economic risk negatively affected Russian entrepreneurs regardless of their geographical location. However, both the level and volatility of firms’ entry and exit rates varied substantially across regions and over time. Most affected regions were characterized by their vulnerability to outside crises and, often, their reliance on the oil sector. In those regions, detrimental effects from the export sector spread quickly to the overall business environment, leading to reduced entry rates. On the contrary, if a given region was less exposed to economic risk due to relative isolation from its source, the negative impact on entry was lower (Iwasaki et al. 2016). To conclude, positive medium-term perspectives on the economic framework conditions and the absence of economic risk or uncertainty encourage new business entry; I thus derive the following hypothesis: H1: The regional level of economic risk has a negative impact on market entry of new firms.

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Unemployment and Entrepreneurship The relationship between entrepreneurial activity and unemployment provides ground for a rich, vivid debate in economics. One material strand of this debate revolves around the concept of the so-called refugee effect. This concept elaborates on assumptions of the simple theory of income choice, according to which individuals choose between employment, self-employment, and unemployment. In doing so, they are generally assumed to apply rational behavior and utility maximization by taking into account the relative costs of these choices (Bergmann and Sternberg 2007; Uusitalo 2001; Oxenfeldt 1943; Knight 1921). The refugee effect argues that unemployed individuals who face low prospects of wage employment choose selfemployment as the best available alternative, as the opportunity cost of starting a firm is less than being unemployed. Increased unemployment thus leads to an increase in entrepreneurial activity (Halicioglu and Yolac 2015; Evans and Jovanovic 1989). Several studies have examined this relationship and found that unemployment has a positive impact on entrepreneurial entry and new business formation (e.g., Fritsch et al. 2014; Reynolds et al. 1994; Evans and Leighton 1989, 1990; Highfield and Smiley 1987). However, some authors have questioned this evidence, indicating either no relationship (Naude et al. 2008) or a negative relationship between unemployment and rates of entrepreneurial activity (e.g., Blanchflower 2000; Audretsch and Fritsch 1994; Garofoli 1994). Others have argued that unemployment may only act as a push factor for necessity-motivated entrepreneurship and as a negative or inverse indicator for entrepreneurial opportunity, which is expected to be scarce during economic downturns, when unemployment is usually on the rise (Wennekers et al. 2005; Verheul et al. 2002; Audretsch and Thurik 2004; Evans and Leighton 1990). There may be some explanations for these inconclusive observations. First, the relationship between unemployment and entrepreneurial activity might vary with the business cycle. Some empirical work has shown that unemployed individuals are more inclined to engage in entrepreneurial activity and create ventures when unemployment levels are particularly high compared to periods of relatively low unemployment (Carmona et al. 2015; Congregado et al. 2012). Second, we must consider the spatial dimension of the relationship. Cueto et al. (2015) have found that if unemployment increases in a region, self-employment decreases. Yet, the authors argue, if unemployment grows in neighboring regions, the incentives for entering self-employment in the given region increase, suggesting that a refugee effect indeed exists. Fritsch and Schroeter (2011) have also emphasized the relevance of spatial differences with regard to the unemployment-entrepreneurship relation, as they have found the relation to be stronger in high-density areas than in low-density ones. The other strand of the debate on the relation between entrepreneurial activity and unemployment notes that the effect might also work the opposite way, i.e., higher rates of entrepreneurial entry are expected to reduce the level of unemployment rather than being subject to changes in the latter. This relationship, known as the Schumpeter effect, is closely related to the mechanisms described in Sect. 2.1. The Schumpeter effect assumes that higher entrepreneurial activity leads to higher economic growth, which consequently results in lower unemployment rates

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(Halicioglu and Yolac 2015; Carmona et al. 2012; Parker et al. 2012; Faria et al. 2008; Audretsch et al. 2001). Referring to both strands of literature, we may assume a bidirectional causality between unemployment and entrepreneurial activity. Nevertheless, one must also acknowledge that those effects do not necessarily take place simultaneously, similar to the entrepreneurship-economic growth/development relationship discussed above. Hence, whereas higher levels of unemployment may stimulate entrepreneurial activity in one period, higher rates of newly created businesses are likely to reduce unemployment in subsequent periods. This appears logical from the perspective that it requires less time for unemployed individuals to identify business opportunities and to open a new firm than it takes for those firms to grow sufficiently large enough to be able to hire new workers and contribute to economic growth (Audretsch et al. 2001). I close my analysis of the unemployment-entrepreneurship relationship with a brief look at the Russian labor market, as this might lead to some useful insights before deriving a final hypothesis. Against this background, the Russian labor market is both highly interesting and specific. This can best be illustrated on the basis of the labor market developments in the 1990s and 2000s. Russia’s great recession during the 1990s was characterized by an overall 40% decline in GDP between 1991 and 1998. Most interestingly, the attrition in economic output was not accompanied by a similar decline in employment. Instead, employment reduction was less than 15% within this period, which was three times less than the average elasticity observed in other Central and East European countries, where every percentage point of GDP reduction resulted in roughly one percentage point of employment decline (Gimpelson and Kapeliushnikov 2016). In the early 2000s, employment levels rose, and the unemployment rate decreased from its peak level of 14.6% in 1999 to a moderate 6.2% in 2008. However, as Russia faced another 8% decline in GDP in the wake of the 2008–2009 global financial crisis, unemployment again increased less and lasted for less time than expected. Similarly, during the postcrisis period, which was characterized by diminished growth, the employment ratio stayed at high levels, while unemployment remained low (Gimpelson and Kapeliushnikov 2016). A few potential factors could be at work here. On the one hand, the effects observable in the Russian labor market are partly associated with ongoing demographic change and the fact that the country was able to raise its share of tertiary education within the population, which lowered the natural rate of unemployment. However, it still seems contradictory that unemployment rates were less than half of those in the Euro area. Consequently, on the other hand, the low rates of unemployment are more likely to be attributable to government policy that favored jobs and social stability over efficient labor markets. Russia’s labor regulation is among Europe’s most restrictive and pressures companies not to reduce staff headcount. Hence, instead of firing people, low-wage floors have contributed to keeping the labor force in employment (Gurvich and Vakulenko 2017; Gimpelson and Kapeliushnikov 2016). For employees, this rule obviously increases the relative security of employment and makes taking the risk of entrepreneurial activity comparatively unattractive.

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To summarize, we may conclude that the Russian labor market tends to behave in a relatively inelastic and inert way, and thus a potential linear relationship between unemployment and entrepreneurship may not be of a considerable size. However, if we follow the argumentation of the previous paragraphs, that does not necessarily mean that a refugee effect does not exist. Accordingly, I formulate the following hypothesis: H2: The regional level of unemployment has a positive impact on market entry of new firms. Spatial Wage Differentials and the Distribution of Income and Wealth From a structural economic perspective, another contextual facet is likely to impact entrepreneurial activity, namely, personal income and wealth. In this regard, two perspectives are interesting and call for a more profound analysis of the empiric literature. On the one hand, entrepreneurial intentions might be related to average income (or rather wage) levels within a particular country or region. On the other hand, the distribution of income and wealth across the population may also shape an individual’s tendencies to engage in entrepreneurial activity. First, I take a deeper look into to the former aspect. Similar to the preceding paragraph on the entrepreneurship-unemployment relation, the literature on the entrepreneurship-wage relationship assumes that individuals make occupational choices largely based on rational behavior and utility maximization. In terms of wage levels, this means that individuals choose entrepreneurial entry over paid employment if the expected utility derived from self-employment is higher than that of paid employment (Douglas and Shepherd 2000). Although this does not mean that utility cannot be derived from other or nonmonetary factors, as described above, the opportunity costs of lost wages can be assumed to play a substantial role in an individual’s occupational choice. This relationship is also linked to economic development because increasing real wages raise the opportunity cost of engaging in entrepreneurial activity relative to its return, enticing potential entrepreneurs to seek paid employment. Consequently, individuals in richer countries or regions face higher wage levels and thus higher opportunity costs of lost income when launching a new venture. Moreover, next to the opportunity costs of occupational choice, with rising economic development, fewer individuals are willing to take the risk associated with becoming an entrepreneur compared to the relatively safe income provided by paid employment (Valdez and Richardson 2013; Aidis et al. 2009; McMullen et al. 2008; Wennekers et al. 2005; Iyigun and Owen 1998; Lucas 1978). Russia constitutes an ideal natural experimental field for the illustrated relationship because it is subject to several anomalies with regard to average regional wage levels. Generally, scholars acknowledge that, in sound labor markets, wages usually correlate with local amenities. Consequently, they compensate for high housing costs in the location, as well as for poor living conditions or an extreme climate (Moretti 2011). An ample amount of empirical research has demonstrated that Russia’s remote northern and far eastern periphery regions in particular can be characterized as amenity-poor regions (Giltman 2016; Nazarova 2016; Pilyasov

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2016; Saxinger 2016; Saxinger et al. 2016; Nalimov and Rudenko 2015; Kryukov and Moe 2013; World Bank 2010). Accordingly, average wage levels can be expected to be higher for employees with the same qualification and productivity than in other parts of the country. This has been documented in several empiric studies (Giltman 2017; Oshchepkov 2015; Berger et al. 2003; Bignebat 2003; Greenwood et al. 1991) and by official data (Rosstat 2018).1 However, this equilibrium is affected considerably by Russian minimum loan regulations. Until 2007, the minimum wage was determined by the federal government and imposed equally across all regions, except for those in the High North and Far East, where the so-called northern wage multipliers applied. In September 2007, the central government gave the regions the power to set their own minimum wage levels above the federal base threshold. Consequently, most regions immediately used this possibility and enacted sharp increases in the minimum wage levels, in many cases significantly more than the 109% mandated by the federal law (Muravyev and Oshchepkov 2013). However, in regions that usually provided better working and living conditions than the remote northern and the far eastern regions, the surge in minimum wages may have subsequently diminished entrepreneurs’ ability to adjust wages to local amenities (Bartolucci et al. 2017). Additionally, stiff employment protection regulation in Russia increases entrepreneurs’ costs when hiring employees, thus contributing to labor market rigidity and weakening the attractiveness of growing a venture or engaging in entrepreneurial activity overall (Giltman 2016). However, some remote regions are characterized by the prevalence of monotowns (i.e., settlements with one dominating employer)2; as a result, we find monopsonic structures in the labor market. The average wage level in those settlements is biased because the monopsonic company sets wages and workers are forced to either accept it or try to find work elsewhere, which is obviously scarce in supply (Pilyasov 2016; Commander et al. 2011; World Bank 2010). Further, although employment and wage levels in a monopsony are usually lower than in a competitive labor market, which should imply lower opportunity costs for entrepreneurial entry, the conditions for entrepreneurship in a monotown are far from beneficial. To name only a few, scarce business opportunities, intense competition, and increased transport costs due to the remote location may deter potential entrepreneurs from pursuing an entrepreneurial career. Overall, we lack a profound body of literature on the wage-entrepreneurship relation across Russian regions. One exception is a notable study by Chepurenko et al. (2015). Based on a regionally representative survey from 2011 that was conducted in 79 regions of the country with a sample of 56,900 respondents, the authors observed an immediate rise in the share of opportunity-driven entrepreneurial activity as a response to regional wage level increases. Better financing conditions

1 Based on Rosstat (2018), wages in the High North during the 2000s were 1.6 to 2 times higher than the average national wage and, quite recently, in 2018, even 2.4 times higher. 2 There are roughly 319 monotowns in Russia, encompassing a considerable share of the country’s urban population and approximately 40% of GDP. The largest monotown is Tolyatti, home to AvtoVAZ, Russia’s largest carmaker (World Economic Forum 2017).

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for their ventures were assumed to be the main drivers of this effect. However, when considering a 1-year lagged effect after the wage increases, the relationship was negative (Chepurenko et al. 2015). It appears that wage increases in the previous year induced individuals to consider the opportunity costs of engaging in entrepreneurial activity, which is highly supportive of the aforementioned assumption. Thus, I derive the following hypothesis: H3: The regional level of average wages has a negative impact on market entry of new firms. Finally, the second aspect, the distribution of wealth across the population, remains to be addressed. Given the previous paragraphs, one can reasonably assume an impact of wage levels on entrepreneurship. However, as wealth (which is normally the result of accumulating wages or income) usually is not distributed equally among members or groups in a society, I am particularly interested in the heterogeneity or asymmetry of its distribution. Against this background, two thoughts need to be mentioned beforehand. First, the most common approach of investigating wealth inequality is using data on income distributions as a proxy. Although in most theories it is inequalities in the distribution of wealth instead of income that determine particular outcomes, scholars are usually challenged by the absence of data that makes inequality of income data a reliable substitute for analysis (Ravallion 2012; Aghion et al. 1999; Bénabou 1996). In my analysis, I face similar constraints; hence, I base my assumptions on income distributions, as well. Second, the lion’s share of the relevant literature clearly focuses on the relationship between wealth inequality and economic growth in general rather than investigating the specific impacts on entrepreneurial activity. Nevertheless, from this literature, we may also draw interesting and highly relevant conclusions for entrepreneurship in particular. To start my investigation, a minor strand of literature suggests that greater initial inequality fosters economic activity and mainly focuses on arguments concerning a higher savings propensity among wealthy individuals (Bénabou 2000; Deininger and Olinto 2000; Forbes 2000; Bourguignon 1981). This perspective argues that higher wealth leads to greater marginal propensities to save, which consequently leads to higher savings and more investments from the wealthy. This again turns into more financing for entrepreneurs than in cases where wealth is divided across a broader share of the population. Consequently, this implies a positive effect of inequality on entrepreneurial entry. On the contrary, the broader and notably more comprehensive strand of literature argues that when a wealthy but small share of the population controls a large fraction of a country’s or region’s assets, it is expected to be harmful for entrepreneurial activity.3 Four relevant channels for this harmful effect can be identified. (1) A lack of resource availability: it is quite obvious that entrepreneurial entry usually requires 3

Empirical evidence on entrepreneurship in particular is provided by studies from Valdez and Richardson (2013) and Wennekers et al. (2005) and on economic activity in general from Bagchi and Svejnar (2015), Ostry et al. (2014), Knowles (2005), Rajan and Zingales (2004), Barro (2000), Forbes (2000), Deininger and Squire (1998), Perotti (1996), Alesina and Rodrick (1994), as well as from Persson and Tabellini (1994).

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overcoming a certain threshold in accumulating a combination of resources and attributes necessary to create a venture. Heterogeneity in the access to resources, i.e., due to unequal distributions of wealth or income, can be assumed to limit the ability of individuals or entire groups and communities to acquire enough resources to seize a business opportunity in the form of a new venture (Sarkar et al. 2018; XavierOliveira et al. 2015; Webb et al. 2014; Ardagna and Lusardi 2010; De Mel et al. 2010; Parker 2004). (2) Imperfections in the credit market: since low levels of collateral by the poor limit their access to credit and financing, consequently, less capital usually reduces an individual’s availability to undertake entrepreneurial investments and also (3) human capital investments (Lyubimov 2017; Thewissen 2013; Barro 2000; Perotti 1992). The last channel to mention is (4) sociopolitical instability, which is nourished by social tension or poor social indicators. The latter are common characteristics of unequal countries (Wilkinson and Pickett 2009; Gupta 1990) and create an environment that is not conducive to entrepreneurial activity. In sum, the channels mentioned are not mutually exclusive and usually interact in their impact on entrepreneurial entry. Moreover, it can be seen that inequality does not necessarily affect entrepreneurial activity directly but may also reinforce the effects of other institutional impediments, some of which are explained in more detail in the following chapters. Additionally, some effects, such as the availability of resources and the savings and investments rate, materialize in a relatively short period of time, whereas others may require a longer time horizon to come into effect (Halter et al. 2014). Once more, the example of Russia is quite interesting when it comes to analyzing the effects of unequal distributions of income or wealth. On the one hand, between 2000 and 2007, the average wealth per adult rose eightfold from US$2940 per person in 2000. On the other hand, since 2007, growth has been slow and uneven, and while household wealth per adult is currently an estimated US$16,770, the latter is hardly above its 2006 level (Credit Suisse 2017). Unfortunately, even the progress made in raising per capita income does not equally reached all parts of society. Since the beginning of the transition in the 1990s, disparities in income and wealth have sharply increased in Russia. Among the former Soviet republics, Russia is one of the countries with the highest rise in inequality during its early years of transition to a market economy (see Fig. 4.5). Moreover, wealth concentration has increased substantially over the 1995–2015 period. One field where this disparity was particularly obvious was Russia’s stock market around in the early 2000s, when the top ten families or investor groups in Russia owned 60.2% of the Russian stock market (Guriev and Rachinsky 2005). Although the stock market situation has slightly eased since then, Fig. 4.6 illustrates that the share of the top ten percentile of Russian households in the country’s wealth has steadily approached the 70% threshold. According to the latest Global Wealth Report in 2017, the top decile of wealth holders owned 77% of all household wealth in Russia (Credit Suisse 2017). If we look at the assets owned by Russian billionaires as the share of national income (cf. Fig. 4.7), the value peaked at 43% (which is considerably higher compared to Western countries or China) before declining to roughly 29% in 2016, first due to the effects of the 2008/2009 economic

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28% Top 1% (Russia)

24%

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Fig. 4.5 Top income share in post-soc. countries (distribution of pretax national income (Russia) or fiscal income (other countries)) (Source: Author’s illustration according to Novokmet et al. (2018), Novokmet (2017), and Mavridis and Mosberger (2017)) 80%

70%

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30%

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Fig. 4.6 Wealth share distribution in Russia (distribution of pretax national income of adults (prior to taxes and transfers, except for pensions and unemployment insurance). The estimates used combine survey, fiscal, wealth, and national accounts data) (Source: Author’s illustration according to Novokmet et al. (2018))

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45% Russia (citizen billionaires)

40%

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Fig. 4.7 Billionaire wealth in % of nat. income (total billionaire wealth (according to Forbes’s global list of dollar billionaires) divided by national income (measured at market exchange rates). Only citizen billionaires are reported (numbers for resident billionaires are practically identical)) (Source: Author’s illustration according to Novokmet et al. (2018))

crisis and later due to economic sanctions and the general downturn after 2013–2014. Furthermore, one should also pay attention to the exceptionally low level of wealth of the vast majority of the Russian population: 82 out of 100 Russian citizens have an accumulated wealth of less than US$10,000 at their disposal. Additionally, this wealth mostly encompasses assets that primarily serve individuals’ basic needs, such as living in their own apartments, rather than consisting of liquid or more investable forms of assets, for example, bank accounts (Credit Suisse 2017). The uneven distribution of wealth can most sharply be illustrated by comparing the proportion of income received by the top 10% of recipients to the bottom 50%. According to this, the bottom 50% of the Russian population owns less than 5% of the nation’s wealth and thus hardly appears as a sponsor to particularly high rates of innovation-driven opportunity entrepreneurship. With regard to income disparity (see Fig. 4.8), the situation appears less severe than compared to wealth disparity, since we even observe a slightly converging trend of top and bottom shares of the population in recent years. However, the diverging gap in terms of wealth ownership and the accumulation of capital at the disposal of a limited group of people, combined with limited opportunities to receive a loan or external financing, are compelling reasons to expect negative implications for entrepreneurial entry. Spatial differences in wealth dispersion also tend to be large. On the upper end of the scale, Moscow showed Gini coefficients of around 62–59% in the 1990s and is still ahead of the field, although the coefficient dropped to 43% through 2015. The lower end of the ranking of roughly 23% in the 1990s (Ingushetia and Leningrad Region)

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60%

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Fig. 4.8 Income share distribution in Russia (distribution of pretax national income of adults (prior to taxes and transfers, except for pensions and unemployment insurance). The estimates used combine survey, fiscal, wealth, and national accounts data) (Source: Author’s illustration according to Novokmet et al. (2018))

constantly rose until 2015. Today, the Tver and Pskov regions can be found at the lower end with index values of 34% (Rosstat 2018). Figure 4.9 illustrates the overall development of wealth distribution inequality in Russia measured by the Gini coefficient. Unfortunately, to my knowledge, hardly any studies have investigated the effects of inequality on entrepreneurship in Russia in particular. In the only directly related work, Kihlgren (2003) has noted the lack of a middle class with sufficient resources to engage in entrepreneurial activity, except in major cities such as Moscow, St. Petersburg, and others. According to the author, and in support of the argumentation above, income inequality in Russia also reduces investment in human capital. The bottom 50% of society may be unable to afford specific education and training, and consequently, it is not capable of identifying and pursuing potential opportunities. Finally, income and wealth inequality has been particularly damaging to entrepreneurship because, rather than investing on a broad scale, the rich top 10% of society has excessively indulged in palpable consumption and spending on imported luxury goods, particularly benefiting the construction sector in major cities. Moreover, many have preferred to send their wealth abroad, and large amounts of money have left the country to the detriment of the domestic economy and potential entrepreneurs who are in desperate need of financing (Kihlgren 2003). In sum, this leads to the following hypothesis: H4: The regional level of inequality in income and wealth distribution has a negative impact on market entry of new firms.

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50

45

40

35

30 Rosstat 25 World Bank 20

Fig. 4.9 Gini coefficient estimates in Russia, 1988–2016 (Source: Author’s illustration according to Fidrmuc and Gundacker (2017), World Bank (2018a), and Rosstat (2018))

4.2 4.2.1

Institutional Drivers and Determinants of Entrepreneurial Activity Ensuring Property Rights

Property rights are regularly mentioned as one of the most crucial preconditions of entrepreneurship. From an institutional perspective, the issue of property rights is of particular interest when it comes to Russia: the country was subject to a considerable change of property rights in recent decades, beginning with a tremendous transfer of property in the 1990s via a mix of privatization and, to some extent, theft. Since the early 2000s, one could observe a reverse development, as the state reclaimed substantial shares of several economic sectors, in part through legal subterfuge and compulsion. Entrepreneurial Activity and Property Rights in Russia According to Vanberg (1998), property rights serve two elementary purposes: they define ownership rights (i.e., “what is ownership?”) and assign them (i.e., “who owns what?”). Hence, they set the basic rules of the game for economic actors. Laws dealing with property rights do not directly assign rights and claims to individual actors but rather provide rules that enable them to derive ownership rights in

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particular circumstances and under various contracts (Hayek 1973, p. 108, 1978, p. 37). Furthermore, two distinct views on property rights exist: the perspective of de jure (legal) rights and that of de facto (economic) rights. The former describes the constitutionally defined rights the legal framework grants to individuals, which legal authorities are expected to enforce if they are violated. On the other side, de facto property rights describe the actual power of an individual to decide about the use of an economic good, to benefit from its usage or consumption, and to transfer it to other individuals (Mackaay 1999, p. 247; Barzel 1997, p. 3). Both perspectives can deviate to a certain degree, which is particularly the case when it comes to Russia. In general, property rights on paper are on par between Russia and OECD economies, and Russia has a sound legal framework in place that is in line with OECD principles (Estrin and Prevezer 2011). However, guaranteeing their de facto application and protection from violations constitutes the other side of the coin and provides vital ground for objection and criticism, as is discussed below. A sound, strong system of property rights is regarded as a core determinant of economic activity in general (Acemoglu et al. 2005; Rodrik et al. 2004; Aron 2000; Keefer and Knack 1997; North and Weingast 1989) and for entrepreneurial activity in particular. Entrepreneurs who consider creating a venture find it particularly important that property rights guarantee the “find and keep” element of entrepreneurship. In other words, entrepreneurs will not invest if their investment is not sufficiently protected or if they fear being unable to harvest the fruits of their venture. Several studies have supported the assertion that a sound property rights system ensures that entrepreneurs’ innovations are protected from other actors and thereby fosters entrepreneurial entry (Huarng et al. 2012; Acs and Sanders 2008; Harper 2003; Acemoglu et al. 2001). An effective enforcement of property rights can also enhance a potential entrepreneur’s access to capital from banks or other risk-averse investors. Acemoglu and Johnson (2005), for example, have provided evidence that property rights have positive effects on investment, financial development, and longrun economic growth. On the contrary, a lack of property rights and weak contract enforcement increase the risk of entrepreneurial activity and lower the likelihood of benefits. This in turn reduces the incentives for potential entrepreneurs to invest in a venture. With particular regard to Russia, Johnson et al. (2002) have found that weak property rights discourage entrepreneurs from reinvesting earned profits into their venture. Finally, while strong property rights are expected to encourage entrepreneurial entry for both necessity and opportunity-motivated entrepreneurship, they are particularly important for the latter, as opportunity entrepreneurs have more to lose. Hence, whereas poor protection of property rights may not discourage all types of entrepreneurial activity equally, it might make more complex or sophisticated forms of entrepreneurship particularly unattractive (Estrin et al. 2009). Aside from evidence for the general relationship between property rights and entrepreneurial activity, several studies have revealed that property rights play a fundamental role in determining entrepreneurial activity in Russia. A weak enforcement of property rights is a key barrier for entrepreneurial activity in the country, as violations are quite common and entrepreneurs tend to rely on informal solutions rather than opting for formal channels to resolve disputes (Chepurenko et al. 2011;

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Aidis et al. 2009; Aidis and Adachi 2007). For example, in the 1990s and early 2000s, 80% of Russian entrepreneurs had to endure broken contracts, which are only one manifestation of the weak property rights situation in Russia (Radaev 2002). The World Justice Project’s (WJP) Rule of Law Index and the property rights subindex of the Economic Freedom Index (EFI) paint a similar picture. With regard to the former, data for Russia was included in the index for the first time in 2011, with Russia ranking 55th out of 66 countries. In the latest report in 2016, Russia ranked 92nd out of 113 countries. Moreover, there is also reason to believe that substantial in-country inconsistency exists in property rights enforcement (Levina et al. 2016). Against this backdrop, apparently little has changed with regard to the overall situation of property rights protection. Russia has constantly ranked among countries in the lower quartile and still shows deficiencies in the enforcement of regulations and the efficiency of its civil courts. Shapers of Property Rights in Russia At this point, although the hypothesized direction of the relationship between property rights and entrepreneurial activity in Russia is already fairly evident, let us continue to dig deeper into the issue to obtain a more thorough understanding. There are two potential key shapers of property rights in Russia that are also related to potential entrepreneurs’ choices. Those shapers encompass, first, the bargaining power of state authorities compared to property rights holders and, second, the effectiveness of the rule of law and contract enforcement via the country’s court system. Both aspects are examined in the following. With regard to the first key shaper, Frye (2017) considers property rights as a bargain between state power and property right holders. On one side, representatives of state power seek to remain in office while maximizing tax revenue from economic turnover. Property rights holders, on the other hand, pursue revenue maximization of their property. The ruling side might be interested in exchanging guarantees for property rights in turn for revenue and political support, whereas the terms of this trade are determined by the resources that each side has at its disposal. If state power does not depend on property rights holders’ contributions, the latters’ terms will be lower. If this logic is applied to the case of Russia, state power obviously has easy access to oil revenues; thus, there is a rather low incentive to provide property rights to actors outside the resource sector, as state power depends far less on their contributions to remain in office (Haber et al. 2003). Apart from this, politicians in office are not the only party in the bargaining for property rights, as large-scale incumbent firms can mobilize workers as potential voters in elections. This is one explanation why large, politically connected firms tend to receive better treatment than smaller and less connected firms (Markus 2015; Wang 2015; Frye 2004). This situation can result in legal dualism (Frye 2017). In such a system, secure property rights may be viewed as a club good that is only guaranteed to some market participants but not to others. Although the roots of legal dualism can be traced to Russia’s Soviet history, it is not entirely unique to modern-day Russia and can also

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be found in other nations.4 This system enables authorities to utilize the country’s legal system to reward key constituencies on whose political support they rely while simultaneously facilitating economic activity and competition in areas that do not directly endanger valuable constituencies of the ruling authorities. The latter is particularly relevant because market participants would otherwise hide or consume their assets rather than investing them due to the fear of losing them to a politically more powerful competitor. However, as the generation of tax revenue is also important for the ruler, this situation would not be in his interest. In this light, dual legalism is designed to safeguard key constituencies and likewise to minimize income losses in nonpolitical cases. Consequently, property rights in run-of-themill disputes are much better protected than in politically sensitive disputes (Frye 2017). This view is also supported by Hendley (2011), who has found that, despite a societal lack of trust in the capacity of law, a high proportion of individuals is still willing to resolve disputes via formal legal channels. Moreover, apart from obvious evidence for politicized cases, there is a vast majority of commonplace cases that courts resolve according to the written law. Concerning the second key shaper of property rights, rule of law and the ability of property right holders to enforce contracts remain to be addressed. If rule of law is sufficiently strong, courts and the legislative system protect property rights, and potential entrepreneurs are not affected by corruption or third-party influence. Belief in courts and the legal system affects entrepreneurial behavior, as these institutions promise predictability and stability and thus facilitate entrepreneurial activity. For example, to grow beyond a certain size, firms need to engage in arms-length anonymous trades with customers or suppliers in geographically remote areas. Those dealings require a greater degree of formal contractual assurance. Additionally, when goods become more complex, they usually need to be ordered and bought in advance of production. In this situation, courts protect suppliers that otherwise might be reluctant to produce complex products due to the higher risk (McMillan and Woodruff 2002). Finally, entrepreneurs who believe in the legal system are more likely to offer trade credit and take on new trading partners (Johnson et al. 2002; Hendley et al. 1999; Frye and Shleifer 1997). On the contrary, an unreliable and unpredictable setting in which entrepreneurs cannot rely on legal means if their rights are violated is highly detrimental to entrepreneurial activity (McMullen et al. 2008). As a result, even weak courts can be useful in making it easier for new firms to enter. In Russia, state courts of arbitration generally deal with commercial disputes, whereas courts of general jurisdiction deal with the majority of civil and criminal cases as well as cases relating to administrative offences. State courts of arbitration are generally perceived to work more efficiently than those of general jurisdiction.

4 Authoritarian leaders like Mubarak in Egypt or Pinochet in Chile have also implemented dual legal systems, e.g., separate courts for foreign investors to attract capital in Egypt or a Supreme Court that was subservient to the regime in Chile, whereas the Constitutional Tribunal was not (Frye 2017; Hilbink 2008; Moustafa 2007).

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Nevertheless, the view of Russian courts as being biased and subject to corruption and political interests is prevalent in public opinion and media, as well as in academic publications (Lambert-Mogiliansky et al. 2007; Ledeneva 2006; Burger and Sorokina 2003; Satarov 2003). However, despite recognizing flaws and dysfunctionalities in the Russian legal system and despite low levels of trust in it, there is broad evidence that citizens use the courts of arbitration quite frequently for everyday disputes and that they provide some degree of security in terms of property rights (Frye 2017; Shvets 2013; Titaev 2012; Trochev 2012).5 With regard to the assumption made before, this strongly supports that even imperfect courts help promote entrepreneurial entry. In summary, after considering the complex facets of property rights and entrepreneurial entry in Russia, I expect the following relation: H5: The regional level of security of property rights has a positive impact on market entry of new firms. The Phenomenon of Raidership: Innovative Piracy in the Twenty-First Century A quasi-unique and Russia-specific type of property rights violation is the phenomenon of Reiderstvo, or commonly raidership, which Zhuravskaya (2008, p. 2) once referred to as “the problem most acute, urgent and illustrative of the present state of affairs” in Russia. The raidership phenomenon can be briefly illustrated by the following case. Put yourself in the position of an entrepreneur who runs a young, growing business that eventually becomes one of the leading players in its market niche. One morning, guards of a security agency refuse entry to your own business premises, presenting an official document that states that you are no longer the owner of the business. Even though the document appears to be forged, it carries the official seal and signature of local authorities, so even police cannot help. By the time you initiated legal procedures, went through a lengthy process, and eventually won in front of a court, the company would have been stripped down, its assets would have been sold, and those responsible would have long since disappeared. It seems obvious that Russian raidership should not be mistaken for a hostile corporate takeover as occurs in Western economies, which usually rely on legal methods within the framework of the law. Raidership relates to the illegal expropriation of entrepreneurs, and attackers usually employ methods such as bribe payments, forged documents, intimidation, exercise of physical violence, and the support of corrupt authorities, for example, tax authorities, law enforcement bodies, or judicial 5

For example, Titaev (2012) analyzed 10,000 judicial cases and concluded that state courts of arbitration are efficient in protecting property rights through the assurance of competitiveness and equality of the involved parties in court regardless of their government connections. On the other hand, he found that state courts of general jurisdiction are still subject to arbitrariness and submissiveness with regard to state authority. Interestingly, respondents of a study conducted by Frye (2017, p. 105) acknowledge that political relations matter in court hearings but are simultaneously convinced that the facts of the case are equally decisive.

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authorities. Moreover, charges against companies or owners are either entirely fabricated, or they selectively accuse companies of offenses for which others could have been blamed but normally are not (Hanson 2014). One of the most typical corporate raiding strategies is the initiation of bankruptcy proceedings. Historically, this can be traced to a bankruptcy law amendment in 1998 that allowed creditors holding a debt of 500 times the minimum wage per month that was overdue for 3 months to file a demand for bankruptcy, including the privilege to appoint a temporary firm manager.6 Those managers often abused their mandate to expropriate the previous owners and take over the entire firm (Volkov 2004). In 2001, 30% of all bankruptcy cases under the new law could be related to hostile takeovers or illegal expropriation (Rochlitz et al. 2016). Even though the numbers of bankruptcy cases peaked in 2002 after the adoption of an updated bankruptcy law, the use of bankruptcy filings is still a common strategy for raidership attacks. Another common mechanism is the involvement of state agencies. Whereas illegal state involvement between 1999 and 2002 was mentioned in 37% of all observed cases, this number rose to 61% between 2003 and 2010. In 21% of the observed cases, the judiciary was involved, followed by security services (19%), tax agencies (17%), regional administrations (15%), and regulatory agencies (8%) (Rochlitz 2014). This finding has also been supported by Gans-Morse (2012), who has found that threats to property rights from predatory state agencies increased significantly after 2003, accompanied by an increase of bribe payments to corrupt state officials in order to solve corporate conflicts. In terms of raidership, there is a considerable degree of heterogeneity observable across Russian regions. Moreover, Fig. 4.10 reveals interesting regional patterns based on Rochlitz’s (2014) intensity index of raiding cases measured by the average number of afflicted firms in a given region. In addition to an accumulation of raidership events in Moscow, St. Petersburg and Tver, as well as in Tuva in South Siberia and in Primorsk in the Far East, there are two regional centers where raiding cases seem to concentrate. The first is located in the Ural Mountains and encompasses the highly industrialized regions of Perm, Sverdlovsk, and Chelyabinsk. Many of the cases observed in this area relate to the early 2000s, when large industrial conglomerates attempted to consolidate their corporate empires. The second center is located in the Southern regions of Samara, Penza, Saratov, Ulyanovsk, Voronezh and Volgograd, and the Republic of Chuvashia (Rochlitz 2014) and reflects cases that occurred primarily between 2005 and 2010 and that do not focus exclusively on large-scale industry. The threat of being a potential raidership target also affects existing firms and the choices of potential or nascent entrepreneurs. With regard to existing firms, illegal attacks tie up resources for firms under attack, and these cannot be invested otherwise or put to more sustainable, long-term purposes. Even if firms are not subject to

Subsequent to the bankruptcy law amendment, the number of bankruptcy court filings reached over 12,000 by June 1999, compared to roughly 6000 filings issued between 1993 and 1997 (Lain 2017). 6

Fig. 4.10 Raidership intensity across Russian regions, 2007–2010 (Source: Author’s illustration based on Geocurrents (2018) and data by Rochlitz (2014))

17 – 20 cases

13 – 16 cases

9 – 12 cases

5 – 8 cases

1 – 4 cases

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Fig. 4.11 Raidership attacks per year and average number of employees per target firm (illustration based on Rochlitz (2014))

an attack, they are likely to invest less if corporate raids frequently occur in the region in which they operate because they face uncertainty about if and how property rights protection will be enforced or not. This is also in line with a survey conducted by Levina et al. (2016). Based on the investment decisions of 1950 firms in 60 Russian regions between 2011 and 2013, regions with relatively high numbers of raidership attacks in relation to GRP experienced significantly lower rates of investment. Since 2005, the focus of attackers switched from large-scale industry corporations to smaller businesses in the services, retail, transport and construction sectors, as well as to relatively young, innovative, and dynamic firms (Rochlitz 2014). Figure 4.11 illustrates how the average size of target companies decreased from 3000 employees per firm in the first half of the 2000s to an average of roughly 750 employees per firm from 2005 on. The consequences for entrepreneurial activity are severe: if potential entrepreneurs have to worry about their firm being taken away from them as soon as it becomes profitable, despite their ambitions and innovative ideas, they may think twice before engaging in a new venture, and they may have a lower tendency to start the business and invest in the first place. Additionally, this situation provokes evasion mechanisms. In Rochlitz et al.’s (2016) survey, Russian entrepreneurs repeatedly stated that they aim to limit connections to authorities to an absolute minimum, i.e., to fly under the radar. In cases where pressure through predatory behavior is perceived to be particularly high, companies might be tempted to move into the informal economy, as registry data might end up in the wrong hands and could be used for attacks. As registry data is used to determine entrepreneurial entry rates, this evasion behavior reduces entry rates and simultaneously lessens

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entrepreneurial activity’s positive effects for economic growth due to its limited impact in the informal sector. Expanding further on the thought of target attractiveness, Frye (2017) has described the situation as less severe for nascent entrepreneurs and suggested a U-shaped relationship between company size and the likelihood of a raidership attack. Even though large firms are more visible and represent a more attractive target, they also may resist a corporate takeover with greater resilience and power, for example, through mobilizing workers in public demonstrations or through their employees’ weight in public elections, which raises the price for the attacker (Frye 2017; Frye et al. 2014). On the other extreme, as outlined before, small and new firms might be relatively safe from attacks, as attackers are either not aware of them or because the reward for the attackers is still too small. Consequently, moderately sized firms are the most vulnerable with regard to their property rights. Nonetheless, this is still deterrent for potential entrepreneurs who aspire to grow their business beyond a certain threshold and can thus be expected to negatively affect overall entry. Consequently, I derive the following hypothesis: H6: The regional level of perceived risk from raidership has a negative impact on market entry of new firms.

4.2.2

Criminality

Many politicians and researchers addressing the subject of criminality in Russia believe that today, violent criminality no longer constitutes an issue of great relevance for Russian entrepreneurs, given that the state reclaimed control of the rampant situation in the period after the collapse of the Soviet Union (Firestone 2010; Volkov 2002). In contrast, Belokurova (2012), for example, has raised serious doubts about this assessment and is supported by a long period of high-level violence against Russian entrepreneurs in the 2000s, as well as persistently high levels of violence compared to other countries. I take this as an incentive to further investigate the relationship between regional levels of criminality and entrepreneurship in Russia. In general, concerning crime and its implications for entrepreneurs, one must distinguish between organized crime on the one hand and general or unrelated crimes against businessmen on the other. With regard to the former, organized crime largely tends to operate in regions that are characterized by institutional voids. Organized crime aims to fill those voids and challenges the state’s authority in regulating economic and noneconomic relationships among individuals. The government either does not perceive such activity as threatening or is not capable of preventing it. Common instruments to achieve criminal objectives include the trade in criminal goods, money laundering, extortion, corruption, and, most notably, the threat or use of violence (Astarita et al. 2017). Unfortunately, existing studies

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have largely tended to concentrate on organized crime and usually do not consider unrelated or general patterns of crime and business-related violence as a crucial element of entrepreneurial framework conditions (Belokurova 2012). Based on data from Belokurova (2012), Fig. 4.12 illustrates that those patterns indeed exist. In general, the literature assumes that crime, be it organized or unsystematic, has a negative impact on economic and entrepreneurial activity. According to Goulas and Zervoyianni (2015), this impact takes effect via three channels. First, higher levels of crime decrease capital productivity. A criminal environment undermines confidence in the rule of law and the security of property rights, thus facilitating a poor business climate, which discourages investment in innovation and entrepreneurship. Additionally, high crime rates contribute to a public perception of instability and uncertainty, which in turn increases the risk of entrepreneurial choices and reduces the likelihood of benefits drawn from entrepreneurial activity. Moreover, high levels of crime reduce incentives for domestic capital accumulation and instead make investments in foreign countries or regions more attractive. Second, higher levels of crime also boost the opportunity costs of public crime control as public resources are reallocated from growth-enhancing policies, e.g., promoting education, infrastructure, and sound framework conditions or granting the supply of capital to productive and innovative uses, towards law enforcement policies that ensure protection from and the fight against criminality. Last, a greater extent of criminality can also reduce the labor supply to legal activities, given that potential entrepreneurs can also achieve income through illegal activities. In a sufficiently criminal environment, and for individuals who do not categorically reject criminal activities, this might be an attractive alternative to playing by the rules. However, the empirical evidence on the crime-entrepreneurship relation is thin and somewhat equivocal. Studies have instead mainly focused on the relation between crime and overall economic activity and found it difficult to identify robust effects (Chatterjee and Ray 2009; Peri 2004). The relationship between crime levels and firm creation is slightly clearer. Greenbaum and Tita (2004) have used data for five US cities between 1987 and 1994 and analyzed the impact of rising crime levels on entrepreneurial entry, exit, and performance in terms of growth. Their results confirm that a rise in violence most negatively affects service sector firms in low-crime regions. Other studies are dedicated to the situation in emerging economies. Benyishay and Pearlman (2014) have found that microenterprises in Mexico suffer an average estimated loss of 1.7 months of profit per year due to criminal offenses and that entrepreneurs facing such risks limit their plans for investment or expansion. This relationship also holds after controlling for other types of crime, e.g., homicides and assaults, which usually have little direct impact on companies, or mugging, which does not lead to outright expropriation for firm owners but might cause severe shocks in income. In their cross-sectional study of companies in Central Asia and Eastern Europe, Krkoska and Robeck (2009) have found evidence that enterprises incur considerable losses from crime. Again, those entrepreneurs that face the highest losses are the least likely to choose to make new investments. A set of studies from South Africa has also named crime the single most important perceived constraint on engaging in an entrepreneurial venture, as an even larger

Fig. 4.12 Average number of people per year involved in cases of business violence across Russian regions, 2006–2011 (Source: Author’s illustration based on Geocurrents (2018) and data by Belokurova (2012))

> 25 cases

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obstacle than lacking financial means or the risk of business failure (Bhorat and Naidoo 2017; Cichello et al. 2011; McDonald 2008). On the contrary, Grabrucker and Grimm (2018) have found little evidence for the link between crime and entrepreneurship, i.e., only a negligibly small relationship between robbery and burglary crime rates and self-employment rates in South Africa. With regard to Russia, the body of literature on the relationship between crime rates and entrepreneurial activity becomes even thinner. Based on a survey of businesses operating in various Russian regions, Kuzmina et al. (2014) have found that lower levels of investment are attributable to a poor criminal situation in that region, which perfectly aligns with the results of the studies discussed above. According to Wishniewsky (2008), in his survey of Russian managers on whether to become an entrepreneur, crime is among the top five environmental factors mentioned that potential entrepreneurs perceive as barriers for entrepreneurial entry. In sum, I expect the following relation: H7: The regional level of perceived public safety has a positive impact on market entry of new firms.

4.2.3

Corruption

Post-Soviet countries show levels of corruption that are among the highest in the world, and the presence of corruption in Russia is difficult to deny. In the following paragraphs, I first present an overview of the situation of corruption in Russia and its regions before investigating the relationship between corruption and entrepreneurial activity. Broadly speaking, corruption serves as a comprehensive term for a range of illegitimate actions of economic agents related to the intentional subordination of public interests to individual interests (Ledeneva and Shekshina 2011). More specifically, it can be characterized as a monopoly of power over people who depend on the holders of power in a specific situation. The abuse of his power provides the holder of the monopoly with the capacity to extract additional benefits for himself, which is most commonly achieved via nepotism, extortion, deception, various forms of embezzlement, and through extralegal services at the public’s expense (Shlapentokh 2013; Rodriguez et al. 2006; Mauro 1995). Secrecy, complicity, and camouflage of corrupt acts are also characteristics that are commonly observed in connection with the corrupt behavior of economic agents (Ledeneva and Shekshina 2011). There is considerable evidence that corruption in Russia is a particularly grave and common phenomenon. A regular Levada Center survey on corruption, which is conducted at least once every 2 years, provides valuable insights into everyday corruption across Russia’s regions. Based on interviews of 1600 citizens over the age of 18 in 48 of the country’s regions, the share of respondents that had the occasion to give a bribe or render a service during interaction in order to receive important documents from local authorities ranged between 9% and 1% in the years

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between 2013 and 2017 (Levada Center 2017). If we narrow our focus to entrepreneurs, the picture is even more dramatic. Various studies have analyzed the extent of business-related corruption in Russia, and estimates of entrepreneurs required to pay bribes to state bureaucrats range from between 45% and 56% (Ospian 2012; Gyllenstedt et al. 2008) and up to 80% (Chamber of Commerce and Industry Russia 2017) or even 90% (Johnson et al. 2002). In light of these numbers, the majority of the Russian population believes that corruption embraces basically all spheres of social life; however, the alleged main perpetrators and beneficiaries are assumed to be those holding offices in government and administration (Fond Obshchestvennost Mneniye 2011). Although it has publicly addressed the urgency of the problem on several occasions and has initiated certain anti-corruption initiatives, the Russian government has yet to deliver serious results on a large scale. Nonetheless, the consequences of corruption in Russia are severe: corruption poses serious long-term concerns to society because it undermines labor ethics and particularly affects younger generations by making them believe that bribes and personal relations are the best and only way to become successful. Similarly, corruption makes public officials indifferent to public interests, since they are aware that their professional success is determined less by their honesty and job performance and more by informal relationships (Shlapentokh 2013). Additionally, a considerable share of businesses in Russia is both victim and perpetrator of corruption. Common examples of those activities encompass the creation and application of informal leverage, for example, by acting on sensitive information or via the use of compromising documents, to informally influence competitors, employees, or regional decision-makers. Those actions are regularly justified by a presumed necessity for business, due to competitive pressure or due to the corrupt environment (Ledeneva and Shekshina 2011). As corruption in Russia thus constitutes a serious concern, the specific relation between corruption and entrepreneurial activity must be determined. In contrast to the intuitive expectation that there is a negative impact on entrepreneurship, corruption can have a complex and inconclusive impact, as the following paragraphs outline. With regard to one stream of literature, studies either could not identify any direct effects between corruption and entrepreneurial activity (Aidis et al. 2012) or even found positive effects (Belitski et al. 2016). One possible explanation for those observations is that, under certain circumstances, corruption can grease the wheels of entrepreneurship. Particularly in countries with inefficient governments and institutional frameworks, corruption can accelerate the process of acquiring starting capital or help overcome bureaucratic red tape and regulatory rigidity, thus facilitating entrepreneurial entry and firm performance (Dreher and Gassebner 2013; Mauro 1995). With the growing efficiency of the relevant authorities, this effect decreases. When regulations become less burdensome and exorbitant but more transparent, instead, the need for entrepreneurs to bribe corrupt officials in order to receive permits, documents, or other requirements decreases. In contrast, the second stream of literature argues for the negative consequences that corruption exerts on entrepreneurship. Corruption affects the amount of rewards that can be created through entrepreneurial activity. On one hand, corruption can be

4.2 Institutional Drivers and Determinants of Entrepreneurial Activity Corruption & TEA, Avg. 2008 - 2014

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Fig. 4.13 Corruption and entrepreneurial activity. Illustrations based on data from GEM (2018), World Bank (2018a), and ORBIS (2016), using 2008 and 2014 averages; countries close to the 100 percentile rank show relatively high levels of corruption control compared to the 0 rank

considered a special and nontransparent tax imposed on entrepreneurial activity. This tax increases the costs for bureaucracy, services, and infrastructure and thus lowers the amount of proceeds that potential entrepreneurs can capture from engaging in a venture (Paunov 2016; Barone and Narciso 2015; Fiorentini and Peltzman 1995; Wintrobe 1995). For a set of Central and Eastern European countries, including Russia, Fig. 4.13 visualizes how better controls for corruption in a given country can relate to higher rates of entrepreneurial activity and firm entry (although firm entry in Russia is comparatively high, given its corruption levels). Some studies have strongly supported these assumptions, for example, those from Dutta and Sobel (2016) and Anokhin and Schulze (2009), which both found evidence for a negative impact of corruption on entrepreneurship, or Paunov (2016), who has found evidence that corruption lowers machinery investments for innovation. On the other hand, the presence of corruption exposes potential entrepreneurs to an increased risk that parties involved in their value chain might be opportunistic and try to appropriate some of their profits. Particularly in early entrepreneurial stages, entrepreneurs are highly dependent on legal contracts and indicators of the integrity of contract partners. However, as the impersonal enforcement of law is not warranted, the potential entrepreneurs are subject to the goodwill of their partners, placing their expected returns at a higher risk. The increased uncertainty in assessing the likelihood of potential proceeds for the entrepreneur again inhibits starting and growing new ventures (Puffer et al. 2010; Anokhin and Schulze 2009; Bowen and DeClercq 2008). In this regard, Rothstein and Uslaner (2005) as well as Rose-Ackerman (2001, 2004) have discussed how government efforts to suppress corruption establish trust in framework conditions, which in turn facilitates higher rates of entrepreneurial entry. Guseva (2007) and Aidis and Adachi (2007) have provided similar results. In addition, the latter authors have shown that, for all economic actors in transition economies, it is difficult to run a business in an honest manner without also engaging in illegal practices, such as corruption or bribing. Hence, the highly corrupt nature of business operations is highly detrimental to business development. Again, little literature has investigated this relationship by explicitly addressing the case of Russia. Using firm-level data for 48 developing, emerging, and transition

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countries, including Russia, Paunov (2016) has found that corruption particularly affects small firms in terms of receiving certificates, as they usually suffer a disadvantage in negotiations with bribe-seeking public officials due to the smaller amount of resources at their disposal. With regard to larger, export-driven, foreign-, or publicly owned firms, the study could not identify any impact. A less recent study from Safavian et al. (2001), based on a data set of 304 microenterprises in the Samara area, investigates how corruption affects microenterprise growth and has provided similar results. The authors found that corruption affects firms differently, i.e., larger and more successful firms show a higher tolerance for corruption, and small firms are more sensitive to the effects that impede firm growth through corruption. To summarize, although the literature provides different perspectives on the relationship between corruption and entrepreneurship, the level of corruption in Russian regions can be expected to affect the risk-reward profile of entrepreneurship. Thus, it may help explain the likelihood for individuals to choose pursuing an entrepreneurial opportunity through entrepreneurial entry. Based on the above paragraphs, I follow the argumentation that lower degrees of corruption should relate to increasing rates of entrepreneurial activity and thus derive the following hypothesis: H8: The regional level of corruption has a negative impact on market entry of new firms.

4.2.4

The Burden of Bureaucracy

There is a common saying: “Russian entrepreneurs fear bureaucrats more than criminals” (Smolchenko 2005, p. 1). This quote implies another important context factor for entrepreneurial activity in Russian regions that is addressed in the following paragraphs. Generally, the literature mentions different ways bureaucracy may affect entrepreneurial entry. I take this into account by examining two different facets pertaining to the bureaucracy-entrepreneurship relation, namely, administrative barriers in general, which largely consist of an excessive amount of bureaucratic red tape, and bureaucratic moral hazards that result in sizeable levels of agency pressure. Administrative Barriers and Bureaucratic Red Tape In non-OECD countries, administrative barriers, and particularly bureaucratic red tape,7 rank as one of the major obstacles to entrepreneurial activity (Brunetti et al. 1997; De Soto 1989), making it an interesting and highly relevant institutional framework factor to analyze. With regard to red tape, both monetary and

7

Red tape refers to excessive regulation or stiff conformity to formal administrative rules, which is considered (at least partly) redundant and impedes or even prevents action or decision-making of individuals or firms.

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nonmonetary costs are imposed on economic agents dealing with bureaucracy, and both are likely to shape the entry decision of potential entrepreneurs. The monetary costs are relatively obvious and relate to administrative and processing fees for applications and requests associated with the processes of starting and registering a company, acquiring the necessary permits or fulfilling other official demands related to business activities. As for the nonmonetary burden of bureaucracy, entrepreneurs need to spend substantial time and resources on bureaucratic processes, for example, to obtain mandatory information and documents to start a business or to receive the necessary authorizations and permits. In this regard, the number of steps to be completed may involve appointments in various administration offices, waiting in lines, making different payments, etc. As long as the incurred costs of this process, i.e., both monetary and nonmonetary costs, are lower than the expected entrepreneurial reward, potential entrepreneurs will likely decide to engage in an entrepreneurial venture and work their way through bureaucratic red tape, ceteris paribus. Consequently, the lower the perceived bureaucratic costs, the better off the potential entrepreneur will be, and the higher is the likelihood of entrepreneurial entry (Frâncu 2014; Fredriksson 2014). When looking at the situation in the field, Russia has a fairly infamous reputation for challenging its entrepreneurs with excessive governmental red tape and regulations. However, first, it is important to mention that Russia has achieved substantial improvements in this area in recent years. Since 2008, the annual Doing Business Reports cite various examples of administrative relief, such as easing the process of starting a business by reducing the number of days required to open a corporate bank account and eliminating requirements to deposit the charter capital before company registration or requirements to notify tax authorities of opening a bank account. Additionally, several notarization requirements have been suspended in recent years. In addition, Russia has implemented reforms that have made paying taxes less costly, improved access to credit, eased dealing with construction permits, streamlined property transferring procedures, and improved conditions for cross-border trading (Doing Business 2018).8 Overall, this has resulted in a considerable reduction of bureaucratic red tape. In 2009, starting a business in Russia’s major cities required fulfilling between 9 and 14 procedures, which took the entrepreneur between 22 and 37 days to accomplish and resulted in percentage of income per capita costs between 1.3% and 2.7%. In 2017 and 2018, those figures decreased to an average of four necessary procedures that required eight to 11 days and cost approximately 1.1% of income per capita (Doing Business 2014, 2018). However, regardless of the progress achieved, the administrative burden for potential entrepreneurs in Russia is still comparably high because the bureaucratic environment presents impediments with regard to both the number of regulatory requirements and their inconsistency. Concerning the number of requirements, Paneyakh (2008) has noted the significant amount of time spent addressing

8 Except for import restrictions on fresh foods in response to Western sanction after the annexation of Crimea in 2014.

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regulatory reporting requirements and, by calculating the number of required reports to be filed by Russian companies in 2004, has identified the poorly thought-out reporting system as root cause. Although local and sector-specific reporting requirements were explicitly excluded, medium-sized firms on average faced reporting requirements of 23 reports within 22 workdays. Taking a closer look, out of 30 general reports, only 14 were required by federal laws, whereas 16 were defined by bylaws from internal departmental regulations or decrees issued by regional governments. With regard to specific branch requirements, 14 reports out of 26 were required by federal laws, and 12 were required by internal departmental regulations (Paneyakh 2008). Although Paneyakh’s study refers to the year 2004, few incentives to reduce reporting requirements have been initiated since then. With regard to the second aspect, i.e., the inconsistency of regulations, rules pertaining to entrepreneurship and business activity in general show an apparent lack of a uniform, consistent, noncontroversial, and continuous set of official guidelines, which in turn increases the perceived opportunity costs for entrepreneurs. This is primarily due to the country’s administrative structure. In Russia, various entities such as local governments, cities, and municipal authorities are entitled to issue regulations and bylaws for businesses. Additionally, a considerable number of control departments, for example, fire inspections, health authorities, and tax administrations, also possess the right to issue regulations. Unfortunately, bylaws designed by those agencies are, to a great extent, subject to substantial shortcomings. First, in contrast with their actual purpose, they are usually not mere technical clarifications of existing laws but rather concurrent quasi-laws. Violations of those bylaws are usually sanctioned in the same way as breaking the original law. Second, as they do not have the status of a law in a legal sense, these bylaws are usually designed with less care and diligence; they are rather complicated, and there is tremendous potential for contradictions between laws and bylaws from other authorities (Paneyakh 2008). In a 1997 study, the Russian Federal Department of Justice analyzed 1544 regional laws in the Kurgan, Sverdlovsk, Tyumen and Chelyabinsk Oblasts, and the Khanty-Mansi and Yamalo-Nenets Autonomous Okrugs, with the result that almost 20% of the laws analyzed contradicted federal legislation and the country’s constitution (Paneyakh 2008; Minin 2000). Furthermore, the regulatory environment is also impaired by inconsistencies due to numerous Soviet regulatory documents that are still in force, which creates additional confusion for both regulators and their subjects (Aidis and Estrin 2006; OECD 2005). This leads to a highly unpleasant situation for entrepreneurs. Specific regulatory requirements can be followed individually, but, jointly, their costs may be unbearable. Additionally, regulators apply many laws either partially or not at all; thus, entrepreneurs do not know which requirements to implement. Against this background, outright regulatory compliance is basically impossible for entrepreneurs (Paneyakh 2008). Consequently, entrepreneurs are exposed to the danger of appearing to be violators regardless of their behavior. In turn, this fosters the undesirable outcome that entrepreneurs may be obliged to resolve this dilemma with the respective authorities in informal ways. The negative consequences for entrepreneurial entry provoked by these administrative impediments find support in a range of studies that provide evidence that

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regulatory quality in general, the absence of excessive and contradictory red tape, and the quality of market entry regulations in particular are assumed to positively affect entrepreneurial entry rates (Stenholm et al. 2013; Bjørnskov and Foss 2008; Nyström 2008; Klapper et al. 2006). In a recent study, Nistotskaya and Cingolan (2016) have also suggested that rates of business incorporation are directly and positively affected through perceptions of higher regulatory quality, or, vice versa, they support a negative relationship between the degree of bureaucracy and industry entry rates. It is, however, important to note that even in spite of the high costs red tape imposes on society and the economy, almost no one has proposed eliminating it altogether. If anything, the socially optimal level of regulation is perceived to be overall positive. Nonetheless, self-interested bureaucrats tend to create more barriers and more low-quality regulation compared to the social optimum at the expense of society and entrepreneurial activity (Guriev 2004), or, in Tanzi’s (1998, p. 582) words, “when rules can be used to extract bribes, more rules will be created.” Hence, by considering the theoretical and empirical evidence outlined, the following relationship is expected: H9: The regional level of administrative barriers has a negative impact on market entry of new firms. Bureaucratic Moral Hazard and Agency Pressure The existence of excessive bureaucracy and contradictory regulation also provides fertile ground for corruption (Bardhan 1997). Aside from the negative effects of corruption illustrated in Sect. 4.2.3, I now explicitly address the issue of public officials’ opportunistic behavior, the so-called bureaucratic moral hazard, and particularly its unfavorable consequences for entrepreneurship. Opportunistic behavior from bureaucrats, or bureaucratic moral hazard, may pose a substantial impediment to entrepreneurial activity. For young entrepreneurs, corrupt officials who solicit bribes or offer paid protection (Russian Крыша, English roof) constitute a serious threat to growing and developing their business, similar to the existence of corruption in general. However, an ample number of cases provides evidence that refusing such payments is likely to trigger visits and checks from inspection agencies, i.e., fire and health inspection, tax agencies, police, or even the Federal Security Service. This phenomenon is deeply rooted in Soviet history and was nurtured into a culture of inspection and penalization, amplified by the discretionary power of administration officials (Aidis and Adachi 2007; Estrin et al. 2007). Even in post-Soviet Russia, an extensive degree of discretionary power allows officials to inspect firms at basically any time, and there are no restrictions on the frequency or duration of inspections (Estrin and Prevezer 2011). Less recent but nonetheless illustrative evidence from an SME survey conducted by OPORA (2005) shows that, in the early 2000s, young and small firms were inspected seven times a year on average. Of the 80 regions covered in the survey, Moscow-based firms were hit hardest, particularly by tax and sanitary inspections. The investigations forced entrepreneurs to spend roughly 11.5% of their monthly earnings on kickback

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payments in comparison with the national average of 8.9% (Aidis and Adachi 2007). The hostile nature of checks mainly evolves in their potential for abuse, since even routine investigations might disturb operational processes and the activities of small firms for several days, particularly by restraining accounting departments in their capacity to perform daily duties. In-depth reviews and checks may easily accompany the confiscation of computers and IT infrastructure or stacks of accounting and other financial documents. Neither tax inspection nor other law enforcement or inspection agencies take responsibility for the damage and negative consequences caused by those procedures, even if no violations can be found. Hence, formal rules are used as a tool to exercise pressure, since both inspector and entrepreneur know that neither party is interested in a situation in which bargaining becomes confrontation. In-depth reviews constitute an unprofitable and time-consuming process for inspectors, while for the entrepreneur, they will likely cause severe financial losses (CEFIR 2007). This effect is further aggravated by the problematic and double-edged incentive scheme for public agencies and administration officials in Russia. The “stick system”9 evaluates and rewards lower ministry departments according to the count of initiated and investigated cases under their relevant provisions. Any decrease in the number of those indicators is considered a sign of poor performance. Consequently, this incentive scheme strongly supports an accusatory bias against entrepreneurs, regardless of their true culpability. As soon as an entrepreneur attracts the attention of potential prosecutors, they usually have few ways to defend themselves, except perhaps for personal relations to politicians or the responsible agencies (Nazrullaeva et al. 2013; Gerber and Mendelson 2008). Ultimately, it appears that the administration official only performs his actual duty. In cases where entrepreneurs are reluctant to submit to administrative pressure, Russian law provides a comprehensive repertoire to initiate minor or significant criminal cases. An ample body of literature has identified a growing tendency for the criminal persecution of entrepreneurs in Russia’s different regions by various law enforcement bodies (Nazrullaeva et al. 2013; Zhalinskiy and Radchenko 2011; Gans-Morse 2012). In an illustrative statement, the ombudsman for business rights, Boris Titov, also remarked that it is “hard to find another social group persecuted on such a large scale” (Titov 2012). Although steps were undertaken in 2009–2010 to make the criminal code more tolerant of entrepreneurs, which at least resulted in a decline of pretrial detentions (Firestone 2010), the discretionary power of administration officials and the various legal means at their disposal still pose a meaningful threat to entrepreneurs.10

For a more a detailed analysis of the mechanisms behind the “stick system,” refer to Paneyakh (2011) and Nazrullaeva et al. (2013). 10 This can be illustrated by the recent case of Dmitry Trubitsyn, a young innovative entrepreneur who venture Tion manufactures high-tech air-purification systems for homes and hospitals. The entrepreneur faced up to 8 years in jail after investigators accused him of leading a criminal conspiracy to, in essence, innovate too fast and too freely. The allegations began when competitors started a smear campaign against Tion. Luckily, the charges were eventually dropped as unsubstantiated in 2018 (Sibnovosti 2018; Higgins 2017). 9

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Given this situation, future entrepreneurial benefits can be considered contingent, thus reducing the likelihood and amount of entrepreneurial profit. However, to undertake investments and entrepreneurial efforts, individuals need to be certain that they will be able to reap the fruits of their efforts instead of being subject to expropriation or the deviating interests of agencies or other interest groups (Acemoglu and Robinson 2012; North and Weingast 1989). This perception is likely to reduce entrepreneurial activity because engaging in ventures may not seem to be an attractive investment. Nistotskaya and Cingolan (2016) have argued that if investors perceive public administrators as autonomous and unbiased in fulfilling their duties (including a sufficient degree of independence, especially from political actors11), potential entrepreneurs interpret this as a signal of the reduced contingency of their future entrepreneurial profits and lower risk of their investment returns. Hence, control agencies that are indeed perceived as service providers to public interests are appreciated by potential entrepreneurs and investors and positively impact their risk-return assessment of entrepreneurial opportunities and the likelihood of expected long-term entrepreneurial outcomes (Nistotskaya and Cingolan 2016; Beazer 2012; Blunt et al. 2012; Bearfield 2009). Naturally, in this regard, the following relation is expected: H10: The regional level of the perceived threat of agency pressure has a negative impact on market entry of new firms. Another commonly mentioned aspect to consider concerning the relation between entrepreneurship and administrative barriers is the issue of taxation. When asking entrepreneurs, perhaps not surprisingly, complaints about the burden of the imposed tax rate are virtually universal. However, when investigating the effects of taxation on entrepreneurial activity, there is evidence for a mutual relation. On the one hand, higher tax rates may lead to weaker incentives for opportunity-driven entrepreneurship as they reduce potential gains (Belitski et al. 2016; Aidis et al. 2012; Koellinger and Minniti 2009; Henrekson 2005). On the other hand, tax income is necessary to provide infrastructure and support for new and small firms in terms of information, advice, training, or finance (Wennekers et al. 2005; Desai et al. 2003). Consequently, as there is apparently no unidirectional relationship when it comes to taxation, the factor is not considered for further analysis.

11

Political actors may affect agency behavior, which may result in the denial of permits and authorizations for entrepreneurs, pressure from control agencies, or a lack of realistic prospects with regard to public tenders. In particular, the last case, where interested parties aim to bias administrators towards benefiting their preferred bidder, is not uncommon in Russia. There is evidence that a considerable share of contracts in state-funded healthcare projects or contracts related to the 2018 FIFA World Cup were assigned to businessmen with close relations to the Moscow-based or regional political elite (Grey et al. 2014; Nemtsov 2014; Suhotin 2014).

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Financial Capital

Ask an entrepreneur about the largest challenge when creating her or his venture, and the conversation will likely turn to the issue of financing. Although capital is the lifeblood of every firm, it is particularly vital during the funding process of an entrepreneurial venture. In the case of limited access to capital, entrepreneurs may be unable to invest, seize opportunities, and carry out their entrepreneurial projects. The Financing-Entrepreneurship Relation: Just a Matter of Credit Risk? In general, there is little debate that potential entrepreneurs and young firms face higher capital constraints relative to incumbent firms, since banks show a lower willingness to lend to new entrepreneurs compared to their incumbent counterparts. In principle, default risk is negatively related to firm age and firm size (Evans 1987), and there are several plausible reasons for this relation. The main factor that increases new entrepreneurs’ credit risk is asymmetric information, since the entrepreneur usually has a better understanding of the innovative capacity, future prospects, and potential returns of his venture than the lender. On the other hand, the lender may also be quite apprehensive of potential moral hazard by the entrepreneur, as the latter may have an incentive to provide false information about his venture in order to obtain the required financing (Benavente et al. 2007). Additionally, potential entrepreneurs, as long as they are not serial founders, usually lack a reliable credit history and a track record of managerial ability or accounting skills (Pissarides 1999). This further hampers an adequate assessment of credit risk and hence increases the potential risk of default for creditors. Due to the very nature of entrepreneurship, early stage entrepreneurial ventures normally lack a comfortable amount of collateral, and they may also face liquidity constraints resulting from an insufficient provision of equity capital or late payments from clients or business partners (De Soto 1989; Smallbone and Welter 2001). Moreover, the intangible nature of innovative entrepreneurial assets, particularly in IT, service-, or technology-intense branches, often makes them unsuitable as collateral (Benavente et al. 2007), which adds to the low willingness to finance ventures from de novo entrepreneurs. Consequently, it should be obvious why potential entrepreneurs may face capital constraints with regard to their ventures. Nonetheless, capital is usually a basic precondition to start a venture because the development and construction of products, bringing them to market and hiring employees, cost money. If capital is inaccessible or access is heavily impeded, this should be reflected by lower firm entry. The literature on entrepreneurship provides ample evidence for this assumption. Evans and Jovanovic (1989) have reported a positive relationship between the financial asset base of individuals and the probability of becoming self-employed. Similarly, by employing European data for the year 2000, Grilo and Irigoyen (2006) have shown how the perception of a lack of finance negatively affects the likelihood of being self-employed. With Evans and Leighton (1989) as well as Hurst and Lusardi (2004), two more studies provide evidence that people are more likely to seize entrepreneurial opportunities if they have greater financial capital at their disposal. Demirguc-Kunt et al. (2006) have combined firm-level data sourced from

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the World Business Environment Survey with country-level institutional variables and found that developed financial systems increase the likelihood for firms to be incorporated. Likewise, based on a large sample of 76 countries using the World Bank Entrepreneurship Survey, Klapper et al. (2007) have stated that the financial system matters for per capita firm entry rates, although the significance of their results is not robust. Additional studies have named the lack of financing as one of the most important obstacles to entrepreneurial entry (Beck and Demirguc-Kunt 2006; Klapper et al. 2006; Pissarides 1999), and some have even provided evidence with particular regard to transition economies and Russia (Pissarides et al. 2003; Aidis 2003; EBRD 2002; Puffer and McCarthy 2001). Access to Capital and Financing Behavior in Russia Against the background of the preceding paragraph, it is reasonable to address the finance-entrepreneurship relation with a particular focus on the Russian context in order to gain a more comprehensive understanding of the relevant country specifics. Taking into account the insights provided by the scholarly literature, there is broad evidence of capital constraints for small and young firms; however, they are even more severe in transition economies, since they may be augmented by a range of other market failures. The most notable effects in this regard are that, first, banks and financial services providers in such economic environments are subject to an even higher lack of reliable information about potential borrowers’ credit risk compared to more advanced economies (Beck et al. 2008; Love and Mylenko 2003; Miller 2003; Pagano and Jappelli 1993). Second, the institutional environment frequently includes an unfavorable legal framework for the asset collateralization of young and small companies (Galiani and Schargrodsky 2010; Deininger and Feder 2009; Carter and Olinto 2003). Finally, there is also limited competition in the financial sector, which further decreases the willingness of lenders to take risks for a specific expected return (Beck et al. 2010; De Haas and Naaborg 2005). These effects strongly contribute to underdeveloped capital markets and a poor availability of credit, which are both particularly true in Russia (Aidis and Adachi 2007, p. 396). Pissarides (1999) has assumed that the underdevelopment of Russia’s banking sector may be rooted in the circumstance that, in the early stages of transition, Russian banks were relatively inexperienced in private sector lending and the influence of historically determined working practices may still be felt today. Further evidence confirms that government-owned banks tend to privilege government-owned firms and, in part, large-scale private firms, as well, by providing credit with favorable conditions. At the same time, Russian banks rarely lend to de novo private firms (Tonoyan et al. 2010; Filatotchev and Mickiewicz 2006; Lizal and Svejnar 2002). Based on an analysis of 15 transition countries, including Russia, Klapper et al. (2002) have found that firms in 6 out of the 15 countries have total liability ratios lower than one, meaning that the average firm lends less than US$1 for every US$2 invested. This is fairly low compared to the median leverage ratio of US$1.73 for Western European firms in the same sample. Moreover, Russia’s private business sector is characterized by an apparent lack of the use of long-term debt, mainly driven by a shortage in long-term bank credit

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(Aidis 2005). Between 2010 and 2015, the official share of long-term debt financed investment in firms’ fixed assets was only 9.4% (Popova et al. 2017). Chernykh and Theodossiou (2011) used a large and highly representative cross-section sample of 881 Russian banks, including public, domestic, and foreign private banks across different sizes and regions, and analyzed long-term credit provision (i.e., with maturities longer than 3 years) to Russian private firms. Their results revealed remarkably low levels of long-term lending by Russian banks. On average, Russian banks allocated only about 2.5% of their assets to long-term loans to private businesses, with an even lower median ratio of merely 0.5%. About two-thirds of all long-term loans are granted by a small number of large, state-controlled banks,12 spearheaded by Sberbank, which accounted for 47% of all long-term private business credit. At the same time, small- and medium-sized private domestic banks focused exclusively on providing short-term financing by almost completely neglecting long-term credit lending (Chernykh and Theodossiou 2011). The authors assumed the root causes were primarily borrowers’ low quality and low transparency, consequent high credit risks, a generally weak protection of creditor rights, and a low efficiency of bank-level risk management systems. Since 2014, the limited access of Russian banks to foreign financial markets, following the financial sanctions of Western countries, has further contributed to this situation and left Russian banks with even fewer resources of refinancing at their disposal. This further constrains external funding for Russian entrepreneurs’ investment opportunities (Gurvich 2016). Notably, the impact of lacking external finance on entrepreneurial entry in Russia’s regions may be smaller than expected. On the one hand, some scholars have argued that access to finance is more important for the growth of small firms but less important for their initial creation (Grilo and Thurik 2005; ISEAED 2001). On the other hand, entrepreneurs, as a consequence of limited access to external sources of financing, often try to substitute formal external capital with informal sources of financial capital, for example, from their family, friends, or personal network (Korosteleva and Mickiewicz 2008). A recent survey on financial literacy among Russian entrepreneurs initiated by the Bank of Russia and conducted by the NAFI Analytical Center in cooperation with the RF Chamber of Trade and Industry, OPORA, and the Russian Microfinance Center also found that the planning horizons of most small firms are limited to 1 year, and only a 5% minority engages in multiyear, long-term planning (NAFI 2017). This might contribute to both the low degree of utilization of long-term capital but also to the apparently low relevance of longterm external capital for entrepreneurial activity in Russia. The study also states that only 26% of the responding entrepreneurs address funding gaps by taking up 12 Russia’s banking system is relatively concentrated at the top with the largest 20 banks accounting for three-quarters of system assets. Government-related banks Sberbank and VTB Group accounted for 60% of the banking sector assets at the end of 2015. Private banks hold roughly 16% in assets, foreign-owned banks 13%, and small or specialized banks 11%. In particular, the last often operate in one-industry cities and are systemically important for their respective regions in many cases (IMF 2016).

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external capital via bank credit. The majority prefers cutting costs (41%), taking private loans from friends or relatives (24%), or selling assets (9%) (NAFI 2017). However, the issue of causality in this case is not entirely clear, since there is no empirical evidence whether entrepreneurs are forced into informal funding due to the lack of formal finance or if they generally show higher demand for informal sources of financing in order to avoid restrictive credit terms. For the sake of thoroughness, it must be noted that access to external funding for Russian entrepreneurs appears to have improved in recent years. Various state support programs have been implemented to provide entrepreneurs with financial support across Russian regions, for example, via access to grants for starting their businesses, microcredit, credit guarantees, or credits on concessional terms. Consequently, loans to SMEs doubled between 2008 and 2013, both with respect to new loans approved and overall outstanding loans. Along with the achieved improvements in credit funding, venture capital activities also increased between 2008 and 2014, with investments doubling over this period. However, international capital market sanctions in response to the annexation of the Crimean peninsula brought a slump in 2014 and 2015, and even though lending conditions loosened in 2014, credit conditions for entrepreneurs reversed in 2015 and early 2016. Moreover, high inflation and particularly high interest rates charged to small firms in international comparison contributed to the difficult accessibility of financial means for entrepreneurs. Similar effects of decline could be observed in the venture capital market in 2015 (OECD 2017). Briefly, then, although access to external finance seems to have improved in recent years, there is enough evidence to assume a negative impact on entrepreneurial activity caused by a lack of short- and long-term financing. I thus hypothesize the following relationships: H11: The regional level of available short-term financial capital has a positive impact on market entry of new firms. H12: The regional level of available long-term financial capital has a positive impact on market entry of new firms.

4.2.6

Human Capital

Searching for context factors that drive entrepreneurial dynamics inevitably leads to one particular aspect that, to a large extent, shapes an entrepreneur’s individual set of characteristics: human capital. Education and the human capital created from it have been demonstrated to be one of the most conclusive drivers for entrepreneurial activity (Unger et al. 2011; Van der Sluis et al. 2008). This is motivation enough to investigate the particularities of the human capital-entrepreneurship relation in the Russian context.

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Human capital facilitates various aspects of entrepreneurial activity and seems to be one of the crucial elements in the early stages of entrepreneurship. As highlighted in Chap. 2, entrepreneurial entry consists of two interrelated processes, the identification of opportunities, and their subsequent exploitation in the form of a new venture. Individuals with higher degrees of education may perform better in both processes, since human capital contributes to an individual’s abilities, attitudes, and aspirations. Knowledge of industry sectors, technological capabilities, and specific market expertise provide valuable insights into product innovations or market niches that are not available to outsiders or unskilled workers. Moreover, higher levels of education can provide the necessary capabilities and skills so that the potential entrepreneur can more effectively discover and evaluate opportunities as they arise (Unger et al. 2011; Davidsson and Honig 2003). Other authors have defined those capabilities as entrepreneurial absorptive capacity, which, among other factors, refers to the combination of specific technical and business skills required by a potential entrepreneur to effectively pursue the exploitation of knowledge and opportunities (Audretsch and Belitski 2013; Qian and Acs 2013; Qian et al. 2013). Next to equipping potential entrepreneurs with industry-specific skills, higher levels of education are also likely to shape entrepreneurial attitudes and aspirations, since entrepreneurs might be attracted by the nonmaterial benefits of entrepreneurship, such as greater autonomy (Van Gelderen and Jansen 2006) or the achievement of personal goals (McClelland 1975). Higher levels of human capital are expected to increase one’s perception of self-confidence, which encourages individuals to pursue entrepreneurial activity. In addition, a potential entrepreneur’s perception of risk may be reduced, as individuals might consider their chances on the labor market as relatively favorable in case their business fails (Shane and Venkataraman 2000). In this regard, tertiary education is particularly important and plays a vital role for both the performance and creation of new ventures (Audretsch et al. 2012; Wennberg et al. 2011). Acosta et al. (2011) have examined different mechanisms of knowledge spillovers and shown that the number of university graduates is the most important factor in explaining the creation of new firms. Next to technical expertise, tertiary education also provides a comprehensive repertoire of management and leadership skills required to mobilize necessary resources to create a new firm, thus facilitating entry (Levie and Autio 2008). Similarly, Fogel et al. (2006) have shown that countries with higher shares of citizens with tertiary education demonstrate higher rates of firm entry. Furthermore, the empirical literature in this respect emphasizes that geographical proximity is particularly relevant. A theory that prominently examines the human capital-entrepreneurship relation from a more holistic perspective was introduced in Chap. 2, the knowledge spillover theory of entrepreneurship. Although most of the aforementioned studies do not directly employ KSTE as their theoretical framework, they are largely based on the same argumentation. The KSTE literature recognizes that regions differ in their endowment of human capital, and thus the local context affects new firm creation and growth. Researchers in this field have argued that entrepreneurial entry is a vehicle for potential entrepreneurs to commercialize their human capital or to turn knowledge that spills over from incumbent firms’ R&D

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efforts or universities into personal returns. In particular, the number of young hightech firms created around universities depends on regional factors and universities’ characteristics, for example, their student numbers in both natural and social sciences (Audretsch and Lehmann 2005; Audretsch et al. 2004). With regard to knowledge spillovers from incumbent firms, geographic proximity is of similar importance, and the positive spillover effect on firm creation declines rapidly with increasing distance (Lee et al. 2013). Returning to the specific context of Russia, several studies have provided evidence for a positive relationship between human capital and higher levels of entrepreneurship. For example, in their study of 400 entrepreneurs and 440 non-entrepreneurs, Djankov et al. (2005) found that next to perceptions of local institutional environments, human capital-shaped characteristics such as cognitive ability are of major importance for entrepreneurial activity in Russia. In a more general approach, Gabelko and Vinogradov (2010) have identified the amount of specific human capital as a decisive predictor of the share of persons to engage in entrepreneurial activity in Russia. Figure 4.14 provides further support for this assumption. The map confirms the considerable heterogeneity in the share of employees with higher education across Russian regions, which might eventually shape differences in regional firm entry. However, it is also important to acknowledge the complexity of this relation. For example, Aidis and Estrin (2006) have provided indications that the relation between human capital and entrepreneurial entry in Russia is not entirely clear because they could not find any evidence that higher education was strongly associated with rates of new start-ups or the likelihood of being self-employed in Russia. This is not entirely unreasonable given that Russia’s population is generally characterized by high levels of education, especially in the areas of engineering and technology (Frank et al. 2012; Estrin et al. 2007), but still experiences rather low levels of entrepreneurial activity. This perspective is also supported by the latest GEM reports, which, despite low levels of entrepreneurial activity, also indicate quite high levels of human capital in Russia (GEM 2016). One cause for these observations might be that, most obviously, individuals with higher levels of education have a greater probability of achieving success and earning a secure salary as an employee without facing the risk of self-employment (Gimeno et al. 1997; Campbell 1992). This in turn reduces the likelihood of becoming an entrepreneur. Other authors have argued that relatively high levels of human capital merely offset other discouraging forces within the entrepreneurial environment, as Estrin et al. (2007) have proposed. Another problem that might hamper the direct positive impact of human capital on entrepreneurship is the lack of specific entrepreneurial know-how. Based on its highly educated population, Russia should be able to create a variety of new inventions. However, it seems that the country lacks the ability to commercialize such ideas because Russians may have little experience in seizing entrepreneurial opportunities and bringing innovative ideas to the market in the form of a product (Frank et al. 2012). Nevertheless, despite the observed indistinctness in the case of Russia, there are well-founded reasons to assume an overall positive relationship between the level of

20 – 25 %

< 20 %

35 – 40 %

30 – 35 %

Fig. 4.14 Average share of employees with higher education per region, 2006–2013 (Source: Author’s illustration based on Geocurrents (2018) and data by ICSID (2017))

25 – 30 %

> 40 %

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human capital in a given region and the level of entrepreneurial entry. Furthermore, it is particularly interesting to shed light on the specific case of Russia. Consequently, I intend to test the following hypothesis: H13: The regional level of human capital has a positive impact on market entry of new firms.

4.2.7

Infrastructure

Among businesspeople, scholars, and policy-makers, it is widely acknowledged that infrastructure matters for economic activity and growth. There is ample evidence that higher levels and quality of infrastructure raise the productivity of both human and physical capital. Since infrastructure is a fairly comprehensive concept, first, we need to clarify to which set of entrepreneurial and economic preconditions the term refers. For example, Reynolds et al. (2002) have created a framework of entrepreneurial infrastructure, which, among other factors, refers to the availability of physical infrastructure. Samli (2011, p. 86) has distinguished between basic infrastructure (i.e., water and supply of electrical power), physical infrastructure (roads, railways, airports, ports, etc.), and high-tech infrastructure (which refers to telecommunication and broadband Internet access). Isenberg (2011) considers infrastructure as physical aspects that relate to transportation (i.e., highways, railways, airports, etc.) and communication (broadband Internet, telecommunications, etc.). Using the essence of these concepts, this thesis’s understanding of infrastructure relates to the physical preconditions for entrepreneurial activity (i.e., physical infrastructure) and its informational aspect (i.e., communication infrastructure). Based on this, the following paragraphs intend to provide a more thorough understanding of the individual aspects of infrastructure and their expected impact on entrepreneurial activity in Russia. Physical Infrastructure 1. Transport Transport and logistics in Russia are challenged by the fact that it is by far the largest country in the world and covers a broad range of hostile, inhospitable climate, and environmental zones that are rather difficult to access. Nonetheless, transport infrastructure, if well-developed and maintained, is considered one of the most important pillars of economic growth. Based on an analysis of Russia in the period from 1996 to 2004, there is evidence that, in addition to neighbor effects and the existence of resources, an adequate transport infrastructure is one of the most essential factors for regional growth (Lugovoi et al. 2007). With regard to entrepreneurial entry, in their analysis of entrepreneurship in BRIC countries, Estrin and Prevezer (2011) have identified transport infrastructure as one of the most significant institutional factors in terms of the impact on new firm creation. The overall argument authors share is that entrepreneurial opportunity cannot be pursued as long as individuals are not supported by a sufficient level of

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infrastructure. Additionally, Brown et al. (2008) have explored how entrepreneurial entry depends on a measure of the size of the markets that can be reached and the supporting transportation infrastructure to cover market access. The authors have found that new ventures overwhelmingly favor regions with favorable market access. Furthermore, the authors’ market potential measure strongly correlates with regional infrastructure characteristics such as distance to ports, transportation nodes, etc., which implies that entrepreneurial entry can be promoted through improvements in transportation infrastructure. By taking a closer look at Russia’s transport infrastructure, several issues attract attention. Although Russia commands the largest railway system in the world,13 tracks and railcars are in poor condition and lack investment. The same applies to ports, which require modernization and larger capacities. When it comes to inland waterways, the main ones flow from south to north, while goods instead need to be transported on the west-east route. Finally, air traffic in essence is fast but relatively expensive, and customs delays tend to further increase costs (Capgemini 2018; Boute et al. 2010). However, there have been some notable developments in recent years. With regard to its comparative index ranking in the World Bank Logistics Performance Index14 from 2007 until 2016, Russia was able to achieve improvements in the tracking and tracing of shipments, the timeliness of shipments, and the general competence and quality of logistics services from transport operators or customs brokers. Nevertheless, the quality of trade- and transport-related infrastructure (roads, railroads, ports, etc.) and the efficiency of customs clearance did not show any significant improvements compared to other countries in the ranking. Whereas Russia ranked 94th in terms of infrastructure in the global ranking of 160 countries, it still found itself in 141st place with regard to customs clearance procedures. In terms of the ease of arranging competitively priced international shipments, we can even observe decreasing ratings in the last decade (a rank of 115 in 2016, compared to 94 in 2007). Although some skepticism on the index construction may be warranted, since scores are determined based on subjective expert assessments rather than on quantitative factors, Yergaliyev and Raimbekov (2016) have found a strong relationship between the index rankings and countries’ innovativeness. However, although infrastructure and economic activity may often correlate, infrastructure investment is not always the root cause. In the past, infrastructure investments to promote economic activity were most effective when entrepreneurs’ demand for infrastructure greatly exceeded supply, as was the case in several African countries. In countries with a sufficient and adequate infrastructure, as is the case of Russia, the observable effect is expected to be considerably smaller. The

13 Due to the considerable distances, railway transportation is much more important in Russia compared to Western Europe (Capgemini 2018). 14 The World Bank Logistics Performance Index is a benchmark index to measure the performance of trade logistics across countries, and it is based on a worldwide survey of operators on the ground (World Bank 2018b).

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increased efficiency in infrastructure may only play a strong, positive role in Russia’s poorer regions, as is the case in the North Caucasus republics, Siberia, and the Far East area (Kolik et al. 2015). Apart from that, there are reasonable doubts that transport infrastructure is one of the key-limiting factors that impedes Russia’s entrepreneurial activity. And although costs for transportation, logistics, and communication are relatively high, they still pale in comparison with other constraints entrepreneurs face, such as a lack of the rule of law, limited access to finance, and corruption (Movchan 2016). 2. Electricity Other sources deem Russia’s infrastructure comparably good compared to other transition countries or BRIC states. This is particularly the case for the electrical power supply. The availability of electrical power supply can be measured by cost, time, and procedures to obtain connection to the power grid, the reliability of the supply, and the transparency of tariffs, and it is considered one of the main obstacles to business activity as reported by entrepreneurs in more than 131,000 companies in 139 economies covered by the World Bank Doing Business Reports. Next to higher risks, higher costs, and lower profits when running a business, the perception of inherent risks in the power supply and barriers to actually obtain access to electricity also act as constraints on entrepreneurial entry and might decrease the likelihood of pursuing an entrepreneurial career. With regard to the Doing Business Report, Russia performed outstandingly in the field of access to electricity: it was ranked 10th out of 190 nations in 2018. This performance is characterized by a high reliability of the electricity supply, a high transparency of electricity tariffs, and affordable costs to obtain connection to the grid (Doing Business 2018). Carlin et al.’s (2006) evaluation of constraints on entrepreneurship has also provided evidence that a lack of electricity is not a major issue in CIS states and other post-Soviet countries and that there is no negative impact on entry rates for new businesses from this factor. Based on an analysis of 5000 firms in 26 countries in the former Soviet Union and Central and Eastern Europe, Desai and Olofsgård (2011) have found that infrastructural constraints including electricity affect large-scale firms more than small and young enterprises, and even then, the results are significant only at the 10% level; hence, they do not indicate a strong negative relationship between access to electricity and entrepreneurial entry in Russia. 3. Land and Facilities The last aspect worth mentioning in light of infrastructure requirements for entrepreneurship is access to land and business or production facilities, although the literature on this matter is relatively limited. In one of few studies, Pissarides et al. (2003) have mentioned access to land and buildings as among the highestrated obstacles to entrepreneurial entry. Sarkar et al. (2018) could not find any relation between inequality in landholding and entrepreneurship in urban areas; however, in rural areas, a higher concentration of land ownership and thus more difficult access to it were detrimental for entrepreneurial activity. In contrast, Carlin et al.’s (2006) analysis does not provide any evidence that access to land is a problem in CIS states or other post-Soviet countries. Yukhanaev et al. (2015)

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have investigated the perceptions of Russian SME owners regarding the quality of the institutional environment via a set of semi-structured interviews. As in the case of Carlin et al., their study also led to ambiguous results. Whereas, on the one hand, it still seems difficult to receive allotments of land to construct buildings, warehouses, or other business premises, given that those procedures are usually accompanied by high costs for the entrepreneur, other participants see this process as gradually improving. For example, official institutions are considered to have become more accessible for potential entrepreneurs, and municipal authorities provide information on free plots of land, vacant municipal buildings for rent, or tenders and supporting programs for entrepreneurs (Yukhanaev et al. 2015). In sum, the aspect of access to land and facilities seems to be more urgent for later steps in the business life cycle than for potential entrepreneurs and nascent entrepreneurship. Special Economic Zones and Industrial Parks A special strategy for infrastructure provision to facilitate regional entrepreneurial activity that is frequently used in Russia is the establishment of special economic zones (SEZ) or industrial parks. This concept is largely based on the idea of providing the necessary physical infrastructure (i.e., roads, rail, electricity, water, gas, and facilities) in a specific area. Those areas are commonly located in suburbs or just outside a city’s residential areas and are usually in proximity to centers of higher education and research, for example, universities or technical colleges. The provision of a geographically concentrated set of prerequisites reduces a broad range of expenses for young businesses and provides them with a supply of specialized workers, suppliers of specific inputs and services, and technological knowledge spillovers among centers of R&D or other companies in order to facilitate innovation and growth (Ratinho and Henriques 2010; Geng and Hengxin 2009). An ample body of literature has argued that such industrial zones are specifically suitable to nurture nascent entrepreneurial activity and SME development (Sandler and Kuznetsov 2015; Farole 2011; Akinci and Crittle 2008; Meng 2003; Shaw and Yeoh 2000). Given the success of one of most renowned industrial zones in encouraging hightech startups and enhancing entrepreneurial activity, the US-based Silicon Valley, various countries have made government-backed attempts to reproduce effort although with varying degree of success.15 Russia is quite active among those countries and started various attempts to create industrial and innovation clusters by providing favorable preconditions for businesses in the form of regional industrial zones or parks, predominantly in regions of relatively weak infrastructure, low production capacity, and a considerable potential for economic growth. Currently, there are roughly 28 SEZs (illustrated in Fig. 4.15) in which regional governments have offered greenfield and brownfield projects to local and foreign investors, and

15 One distinct project worth mentioning is Skolkovo Innovation City in Russia. Further examples of clusters established elsewhere include Silicon Alley in New York City, Cambridge Science Park and Tech City in London, Silicon Wadi in Israel, Bangalore in India, Zhongguancun in China, and Hsinchu Science Park in Taiwan, China.

Порт

Port SEZ

Technology Innovative SEZ

Fig. 4.15 Special economic zones in Russia (Source: Author’s illustration based on Geocurrents (2018) according to the Ministry of Economic Development (2017))

Tourism and Recreational SEZs

Industrial Production SEZ

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they are supported by incentives such as an established infrastructure, tax allowances, customs concessions, and simplified administrative procedures (Maslikhina 2016; Yankov et al. 2016). The effectiveness of those attempts in facilitating entrepreneurial activity and innovation, however, has fallen short of expectations. Unfortunately, there are few related studies, and none of them provides an empirical evaluation of Russia’s attempts to facilitate entrepreneurial entry via the creation of industrial parks. On the one hand, there have certainly been positive results. For example, Sosnovskikh (2017) provides an overview of scientific literature on SEZs in Russia.16 In his synopsis, the author presents a range of positive effects that have already been accomplished, for example, attracting FDI to the parks and regional economies, increasing employment (one created job within a given park results in four or five jobs outside it), improving the region’s infrastructure, and promoting collaborations between firms inside and outside the industrial parks although only on a small scale (Sosnovskikh 2017). On the other hand, industrial park projects in Russia appear to suffer from several conceptual shortcomings. In the earlier stages of creating special industrial zones in Russia, the physical infrastructure was not yet created, and zone administrations tried to attract investors to virtually empty territories by promising the provision of required infrastructure, while the investor established his facilities. The high inherent risk in this offer was not very inspiring for potential investors, nor did it contribute to entrepreneurial activity in a broad sense, since only large-scale incumbents have the resources to develop the required business premises and facilities. Potential entrepreneurs tend to prefer brownfield projects and access to established real estate infrastructure because they usually lack the financial means to engage in construction activities (Sosnovskikh 2017). Another characteristic that particularly affects potential entrepreneurs who wish to be located in a specific SEZ is the absence of competition between firms within the parks: investors either do not want to come to parks where competitors operate or do regional administrations, or managing companies show greater interest in attracting a variety of companies from the same industry sector. As a result, competitors are often located in other regions, which, owing to Russia’s geographical vastness, can be perceived as quasi-separate markets (Tsukhlo 2007; Brown and Earle 2000). The avoidance of a competitive environment reflects a special trait of Russian business culture, whereby firms try to prevent competition rather than trying to pursue competitive advantages. Naturally, without the need to strive for competitive advantages, there are fewer incentives to innovate. Since competition is not perceived as a stimulus and mutual benefits from knowledge spillovers and sharing resources are seen as a threat rather than a virtue, this thwarts the intended function of special industrial zones in facilitating entrepreneurial

16

Studies include Maslikhina (2016), Yankov et al. (2016), Ablaev (2015), Ivanova et al. (2015), Sandler and Kuznetsov (2015), Burnasov et al. (2013), Dudkina (2013), Gareev (2013), and Romanova and Lavrikova (2008).

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activity and innovation (Orlov 2013; Shastitko et al. 2009; Michailova and Husted 2003). Overall, concerning the arguments outlined on physical infrastructure, there is no general and unequivocal relationship. On the one hand, the energy and property aspects of infrastructure do not appear to be a major obstacle to entrepreneurial entry in Russia’s regions in contrast to the more substantiated aspect of transport and logistics infrastructure. Nevertheless, there are still vital preconditions for many entrepreneurial activities. In conclusion, we may assume that physical infrastructure in total has a positive effect on entrepreneurship in Russian regions but presumably a relatively small one. According to this, the following relation is expected: H14: The regional provision of physical infrastructure has a positive impact on market entry of new firms. Communication Infrastructure: Internet Penetration In addition to physical infrastructure, access to high-quality communication and information infrastructure, i.e., telecommunications and particularly broadband Internet, is another potentially vital contextual factor for entrepreneurial activity. This is especially the case because information technologies and the Internet can be a significant leveler for entrepreneurship: they contribute to the creation and distribution of information, and, moreover, they are facilitators for the development of and collaboration via social networks. Hence, access to the Internet implies access to information, new technologies, new ideas, and valuable networks (Jiménez et al. 2014). As opportunity-based entrepreneurship largely depends on the search for exploration and the exploitation of entrepreneurial opportunities, access to information plays a crucial role in the process of identifying business opportunities and business models, as well as in the decision to create an entrepreneurial venture. In addition, information technology grants low-cost access to a broad range of new customers, which has made it relatively easy and attractive to pursue an entrepreneurial career. The scientific literature largely confirms the fruitful relation between entrepreneurship and Internet infrastructure. Based on a sample of 514 SMEs in a European middle-income region, Lucchetti and Sterlacchini (2004) have analyzed the use and effectiveness of Internet and communication technology and concluded that higher Internet penetration promotes higher human capital and a stronger foreign market presence of the companies involved. Henderson et al. (2009) have argued that the Internet has become a necessary precondition for successful business transactions. In their analysis of drivers of regional entrepreneurship in rural and metropolitan areas, they have found a positive impact of broadband access on the income and revenue of entrepreneurs in metropolitan regions. Regarding studies that investigate the concrete relationship between Internet access and entrepreneurial activity in Russia, literature is relatively scarce. To my knowledge, there is only one study that provides valuable insights, i.e., that of Chepurenko et al. (2015). Based on their research, among other factors, it seems

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that regions with relatively high Internet literacy and penetration show higher levels of opportunity-based entrepreneurship but with a 1- or 2-year lag. Moreover, the authors have suggested that providing individuals with computers and stable Internet access, as well as promoting IT literacy among the regional population, facilitates a greater increase in entrepreneurial entry than, for example, simply increasing smallscale funding (Chepurenko et al. 2015). A look at sector statistics confirms that entrepreneurs find promising markets based on IT technology. In particular, the Russian online retail channel has gained popularity, growing on average by 19% a year between 2013 and 2017, in comparison with only 7% across Europe (Bogod and Sukharevsky 2017). Considering this evidence, I hypothesize the following relationship: H15: The regional level of access to communication infrastructure has a positive impact on market entry of new firms.

4.2.8

Market Environment: The Effects of Oligarchy and the Structural Dominance of Incumbents

Open, fair, functioning, and competitive markets are considered to provide fertile grounds for entrepreneurial activity. The free flow of goods, services, and financial capital within and across regional and national borders positively affects entrepreneurial activity because it creates competitive pressure, which in turn stimulates opportunity-motivated entrepreneurship and innovation (Kuckertz et al. 2016; Miller et al. 2013). Additionally, the ability to identify and exploit opportunities across regional and national boarders, i.e., access to larger markets, also encourages the creation of new firms. With regard to Russia, three noteworthy aspects of national and regional market structure need to be considered when discussing the market environment-related context for entrepreneurial activity: (1) the dominance of stateand oligarch-owned incumbent firms, (2) a potential lack of suppliers for young and entrepreneurial firms resulting from the former aspect, and (3) a relatively high degree of regional market segmentation due to Russia’s regional structure. I address these issues in the following paragraphs. Market Dominance, Oligarchy, and State Ownership In general, there are many reasons to assume that the market mechanisms and the state of competition in Russian markets are not exactly in good condition. Above all, there is a noticeable degree of market dominance by incumbent firms, which affects entrepreneurial activity and manifests itself in several ways. First, Russian large-scale incumbents tend to show high degrees of horizontal market dominance and usually hold high market shares in their respective sector. Based on an annual survey by the Analytical Center for the Government of the Russian Federation that encompassed approximately 1000 firms, mainly microenterprises with fewer than 100 employees, across all Russian regions, an apparent trend with regard to competition can be noted (Analytical Center 2018; Szakonyi 2017). Whereas the share of

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50 40.3

Share of Firms (%)

40 32.8 30 27.4 20.3 20

20.3

10

15.1 13.9 10.6 4.7

0.7 0 2013

2014

2015

2016

Year Very strong competition

Strong competition

Weak competition

No competition

Moderate competition

Fig. 4.16 Perceived level of competition from Russian microenterprise survey, 2013–2016 (illustration according to Szakonyi (2017, p. 2))

respondents that face “very strong” and “strong” competition fell from 32.8% and 40.3% in 2013 to 27.4% and 20.3% in 2016, respectively, the “no competition” indicator signaling the absence of any competition increased from 0.7% to 13.9% (cf. Figure 4.16). In 2016, almost one quarter of the surveyed firms indicated that they either face weak or no competition at all. Since the study does not focus on exceptional innovators with unique niche market products, which would at least provide a comforting explanation for this development, those figures rather point out a concerning dysfunction of market mechanisms. In light of this development, Russia’s Federal Antimonopoly Service (FAS) highlighted a rise of politically connected cartels across various sectors, for example, in the areas of defense, construction, and pharmaceuticals. Those cartels cooperate to secure state contracts, split up markets, and set prices. Moreover, market consolidation continues because rivals tend to be swallowed by larger companies, and mergers and acquisitions continue largely unrestrained, especially in the agricultural, insurance, oil, and gas markets (KPMG 2017; Szakonyi 2017; FAS 2016). An amplifying factor for horizontal industry dominance of Russian large-scale incumbents is also their tendency to show high levels of local concentration. Since the collapse of the Soviet Union and the following breakdown of many industries, the concentration effects of industries intensified again. According to Golovanova (2008), industries with relatively small shares of the country’s industrial production were largely affected by spatial concentration trends from 1998 to 1999, whereas in the 2000 to 2004 period, the largest industrial sectors were primarily affected. This tendency was also observable in the first decade of the twenty-first century, when

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spatial concentration increased constantly during the period from 2000 to 2009 (Belov 2012). In another study based on the analysis of 83 Russian federal subjects during the more recent period of 2005–2015, Maslikhina (2017) has estimated the scale and tendencies of spatial concentration in Russia’s manufacturing industry and found an overall increase in the industry’s spatial concentration. In support of those findings, covering the time period from 1991 to 2013, Rastvortseva and Ternovskii (2016) have confirmed that from an overall perspective, spatial industry concentration has clearly increased. There are few exceptions to this development, for example, the food industry or the production of nonmetallic mineral products (Rastvortseva and Chentsova 2015). Using Maslikhina’s (2017) sectoral data, spatial concentrations within the Russian manufacturing sector can be illustrated. Figure 4.17 confirms that only non-mineral products’ production and metallurgical production showed moderate levels of local concentration, whereas all other manufacturing sub-branches showed Herfindahl-Hirschman (HHI) index values above 25%, indicating high concentration levels. The group of regions with relatively high levels of industry concentration primarily includes highly urbanized regions, for example, Moscow, St. Petersburg, Moscow Oblast, Tatarstan, and Sverdlovsk. Additionally, production tends to be shifted from the eastern parts of the country to the west, with deconcentration taking place mainly in the Far East and in the Volga Federal District regions (Maslikhina 2017). In terms of entrepreneurial activity, this implies that, on the one hand, quasi-monopolies and high market shares of incumbents in concentrated regions impede competition from young, small firms by means of market dominance. On the other hand, the local concentration of industries leaves few entrepreneurial opportunities for potential suppliers, subcontractors, or entrepreneurs in related industry sectors in regions with low industry density because the dominating firms tend to cover all aspects of their supply chains in house, as the following shows. Second, large-scale incumbents in Russia also show high degrees of vertical integration or at least have exclusive buyer-seller relationships. The high levels of vertical integration are attributable to the chronic shortages of the Soviet legacy and the early transition stage. Vertically integrated ownership of the supply chain permits firms to avoid the hold-up problems or lock-in effects with suppliers and buyers that characterized Russia in this period (Shapiro et al. 2009). Hence, extensive vertical integration reflects the firms’ attempts to circumvent risks and uncertainties in the market environment as much as possible by ensuring control over raw materials sources; controlling the markets for final products; and commanding the entire infrastructure to shift capital, production capacities, and flows of materials and products in between (in addition to the obvious benefits of the generation of economies of scale). In case of other institutional deficiencies, for example, the lack of access to finance or a secure and reliable rule of law, conglomerates of integrated firms benefit from their access to internal means of financing (Perotti and Volpin 2004; Rajan and Zingales 2003) and higher bargaining power in negotiations and disputes (Kumar et al. 2003; Sonin 2003; Yakovlev and Zhuravskaya 2003) compared to individual entrepreneurs and small firms. Both aspects, horizontal dominance and vertical integration, are common characteristics of conglomerates owned by either the Russian state or Russia’s oligarchs.

0%

10%

20%

30%

40%

50%

Fig. 4.17 Spatial concentration in the Russian manufacturing industry, 2010 and 2015

Herfindahl Hirs c hman Index

60%

2010

2015

4.2 Institutional Drivers and Determinants of Entrepreneurial Activity 103

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They are the economic players that exert the most pressure on small entrepreneurial firms. With regard to the first type of player, the share of Russia’s state-owned enterprises (SOE) of the country’s GDP and the overall economic contribution of the public sector are tremendously high. Prudent estimations, for example, from the European Bank for Reconstruction and Development (EBRD) or Di Bella et al. (2019), have appraised the share of Russia’s public sector in GDP at around 35% in 2010 and 33% in 2016. However, according to Abramov et al. (2017), these figures seem grossly understated. More extensive estimates for 2015 have determined that the total contribution of the public sector to the country’s GDP is close to 70% (Abramov et al. 2017; Szakonyi 2017; Mereminskaya 2016). These figures illustrate how the Russian state has vigorously reestablished itself as the largest holder of economic assets by nationalizing a broad set of important firms since the early 2000s. However, the ability of effortlessly gaining monopoly profits encourages complacency and favors maintenance of the status quo by creating entry barriers for potential competitors rather than striving for innovation. Using a sample of 114 of Russia’s largest firms over the time period from 2006 to 2014, Abramov et al. (2017) has confirmed this assumption and shown that, on average, state-owned firms perform worse than private companies and higher shares of government ownership lead to lower degrees of labor productivity and profitability. Unfortunately, the FAS has not proven to be a very effective instrument to prevent negative consequences for the functioning of the market economy, as the agency categorically ignores the activities of state-owned incumbents.17 The second type of player to consider in Russia’s market environment is large oligarch-owned corporations, which, compared to state-owned firms, share many similar characteristics in terms of industry dominance and vertical integration. The concept of oligarchs is widely used for Russia’s new business elite, who not only possesses considerable resources and wealth but are also capable of exerting substantial market power and political influence (Guriev and Rachinsky 2005). Most of today’s oligarchs emerged from Russia’s privatization efforts in the 1990s, when former politicians and industry directors strived to take ownership of state property in the highly chaotic environment of early transition. Many took ownership of their conglomerates via the “loans-for-shares” scheme, where they obtained underpriced state assets in return for providing loans to the indebted government (Guriev and Rachinsky 2005). In doing so, oligarchs mainly followed the strategy of seeking ownership of large, cash-generating but not necessarily productive firms.18

17 Almost two-thirds of the companies on FAS’s register with market shares over 35% are SMEs. There are more than 60,000 anti-monopoly cases filed every year (which is by far the highest number in the world, compared to the US or EU average of roughly 100 cases per year). In this light, the agency prosecutes small-scale entrepreneurs such as sole proprietors, taxi drivers, or rural dairy producers, whereas incumbents with substantial market power (e.g., Gazprom, Rostec, Sberbank, etc.) are widely ignored (Szakonyi 2017). 18 By this are meant “accounting profits” rather than actual economic value added. Those companies are largely subject to rent-seeking and rent-addiction behavior and make extensive use of transfer pricing. Rent addiction refers to the process described in Sect. 3.3. Transfer pricing encompasses

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Moreover, they tend to create vertically integrated production chains, with corporate structures in which most enterprises are consumers and suppliers to each other, as described above (Fidrmuc and Gundacker 2017; Guriev and Rachinsky 2005). Oligarch empires that have been built this way comprise large parts of Russia’s primary and secondary sectors. For example, basically all major corporations in the Russian oil business are vertically integrated; many metallurgic firms possess their own sources of ore and coal; and many large-scale firms own transport infrastructure such as ports, railway tracks, and carriages. Table 4.1 provides an overview of the economic sectors that are most affected by high rates of industry concentration. According to Di Bella et al.’s (2019) estimates, based on official data from Rosstat and the International Monetary fund, even the country’s least-concentrated industries still show Gini coefficients of critical size. In this view, we may conclude that if industries are not dominated by SOEs, usually oligarch-conglomerates stepped in. Although there are some sectors in which we can observe some privatization tendencies, or at least a relative growth in sales from private firms, Table 4.2 shows that, aside from the mining and extraction sector, the finance industry in particular was subject to continuing renationalization. Consequences for Entrepreneurship Based on the above paragraphs, we can assume that the existence of vertically integrated production chains and market domination across economic sectors, either by state-owned or oligarch firms, has several consequences for potential entrepreneurs and the creation of new ventures; the impact oligarchs have on entrepreneurial activity is still vividly discussed in the scientific literature. On the one hand, Fidrmuc and Gundacker (2017) have argued that oligarchs may support entrepreneurial activity by posing a counterweight to autocratic politicians who might otherwise pursue policies that damage business activity. Particularly in weak institutional environments, the combination of economic and political power may be necessary to overcome entry barriers and guarantee protection for investments that might otherwise be extracted by corrupt officials or organized crime. Hence, oligarchs are likely to support business-friendly but unpopular legislation that politicians might not otherwise introduce. It is questionable whether this is suited to promote entrepreneurial activity on an economic level; it may be more likely that oligarchs use their economic and political leverage to lower competition and increase entry barriers for potential competitors, avoid taxation, suppress wage increases to maintain profits, and support legislation that mainly benefits their personal business interests. This facilitates economic inefficiencies that not only affect potential entrants in the same industry but may also spill over to other sectors of the economy (Fidrmuc and Gundacker 2017). Since the typical strategies of incumbent firms (owned by either the state or oligarchs) encompass picking up numerous firms around as quickly as possible, before someone else steps in, potential entrepreneurs (semi)legal ways to shift or tunnel resources away from one firm towards another. Usually this occurs via overstated costs and understated sales at the expense of the former firm’s shareholders, employees, and municipality.

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Table 4.1 Overview of most and least concentrated industries, including SOE share Top 15—Most concentrated industries

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14

15

Top 15—Least-concentrated industries State Economic Gini share # sector coefficient (%) 1 Waste disposal 0.70 26 2 Security and 0.71 16 investigation 3 Hotel 0.78 5

Gini coefficient 0.95 0.95

State share (%) 22 40

0.95

53

0.95

73

4

Sewage

0.78

34

0.94

57

5

Restaurants

0.79

6

0.94

3

6

0.81

0

0.94

69

7

0.82

2

0.94

14

8

0.82

6

0.94 0.93 0.93 0.93

5 2 15 47

9 10 11 12

0.82 0.83 0.84 0.84

3 3 8 2

Crude oil and gas extraction Research and development

0.93

67

13

0.84

0

0.92

51

14

0.85

3

Other transport equipment Average

0.92

55

15

0.85

1

0.94

38.2

Employment and recruiting Production of TV, films Building maintenance Coal mining Forestry Real estate Specialized construction Furniture production Polygraphiccopying information Veterinary activity Average

0.81

7.7

Economic sector Telecommunications Management consulting Insurance-pension funds Postal-courier services Electricity, gas, steam Metallurgical production Land-pipeline transport Chemicals production Beverage production Motor vehicles Oil and coke refining Air transport

Table based on an IMF working paper from Di Bella et al. (2019)

and small firms again face few incentives to pursue opportunities, grow their venture above a certain threshold, or even aspire to reach a substantial market share.19 Acemoglu (2008, 2012) has provided evidence for both positive and negative effects. In his model, an oligarchic economy prevents high tax rates, which may be 19

Hoffman (2003, p. 205, cited from Gorodnichenko and Grygorenko 2008) provides an illustrative example of oligarchs’ firm acquisition strategies: “Like many others [oligarchs], Khodorkovsky was shooting in the dark. He could not figure out which factories were potentially lucrative, so he bought many. . . Khodorkovsky purchased large blocks of shares in timber, titanium, pipe, copper smelting, and other industries, more than one hundred companies in all. . . He hired Andersen Consulting to survey the crazy quilt industry he had assembled and the management consultants told him he had gathered up the equivalent of a South Korean conglomerate.”

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Table 4.2 State share across market sectors, 2012 and 2016 Economic sector Agriculture, hunting, forestry, logging Fishing, hatcheries and related services Mining and extraction Manufacturing Electricity, gas, steam and hot water Construction Wholesale and retail trade, repairs Hotels and restaurants Transport and communications Financea Real estate, renting, and related services

Sales share (2012) 4 3 34 20 57 6 11 16 52 50 11

Sales share (2016) 2 2 44 21 52 4 9 5 48 59 9

Δ
2
1 10 1 25
2
2
11 24 9
2

Table based on an IMF working paper from Di Bella et al. (2019) The market share of state-owned financial services firms is estimated based on overall asset volume instead of sales share

a

beneficial for entrepreneurial activity. Oligarchs may also provide higher degrees of property rights protection, but only for the economic elite and their associated suppliers. Nonetheless, Acemoglu deems it more likely that oligarchs tend to artificially increase entry barriers for the industries in which they operate in an attempt to avoid wage demand from new entrants and, as mentioned before, keep wages low and profits high. Based on an earlier analysis of World Bank data, Broadman’s (2001) argumentation is largely in line with this perspective. He has argued that exorbitant levels of vertical integration in connection with firms’ horizontal dominance in industry sectors are likely to forestall new firm entry. Evidence for the years 1994–2006 in 71 Russian regions confirms that regions with new-elite governors (e.g., siloviki or people with no official positions in the former Soviet administration) showed significantly fewer small- and medium-sized firms than otherwise similar regions governed by old elites. The author interprets this result as Vladimir Putin’s attempt to secure power in distant, resource-abundant regions by promoting inexperienced but loyal new elites to oversee those regions. As both oligarch and state-owned incumbents were allowed to monopolize those regions’ resources, entrepreneurial activity remained at low and impeded levels (Shurchkov 2012). In Search of Suppliers Another detrimental effect for entrepreneurship, and a more or less natural consequence of the vertical integration situation described above, is a relative scarcity of suppliers for intermediary or preprocessed goods required by entrepreneurs. On the one hand, for potential entrepreneurs, it might be difficult in general to locate potential suppliers because suitable firms are scarce, market information might be inadequate, or transaction costs could be high (McMillan and Woodruff 2002), which prevents them from realizing their business idea. On the other hand, even if

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suppliers are theoretically available, they might to some extent be locked in with their incumbent trading partners and unable to engage in transactions with new entrepreneurs to prevent competition for their existing partners. Whereas large-scale firms or international companies can compensate for this barrier through imports, potential entrepreneurs might be deterred due to the higher impediments of international sourcing, barriers imposed by customs authorities, or potentially higher amounts of required investments. Moreover, supply for manufacturing activities often requires a certain minimum production volume that might be difficult to achieve for a start-up firm. Auerswald (2015) has stressed the importance of buyer-supplier relationships with subcontractors by referring to them as a key aspect to the functioning of large firms in advanced industrialized countries. According to Auerswald, this relationship can be compared to a rainforest ecosystem, in which microenterprises, innovative SMEs, and large corporations mutually reinforce one another in terms of innovation and productivity. There is some empirical evidence for this perspective. For example, based on empirical fieldwork on emerging and survival entrepreneurs, Fairer-Wessels (2013) has identified a lack of partnerships and a lack of suppliers as crucial determinants for entrepreneurial activity, and, according to a study on 150 US-based founders of high-growth firms, access to both customers and suppliers is the second-most valuable business-related resource (Morris 2013). With regard to transition economies in particular, Pissarides et al. (2003) have found that “suppliers are unable to deliver” is among the top three barriers to business. Unfortunately, to my knowledge, there are no current empirical studies on supplier availability in Russia and its effect on entrepreneurial activity. Nonetheless, there are some indications. For example, the absence of a developed supplier community is a well-known problem in the Russian automotive industry. Russian automotive firms have tended to produce up to 70% of their car parts in-house, which in turn has led to limited innovation and little incentive to adopt new technology (Pope 2008; Moran 2003). At least, according to more current reports, such as the Deloitte report on the manufacturing supplier situation in Russia, this situation has improved (Deloitte 2016). Regional Market Segmentation The last market-related aspect that is likely to exert significant impact on entrepreneurial entry in Russia is the particularly high degree of regional market segmentation. Segmentation indicates the existence of substantial barriers to the trade of goods between different regions, i.e., the absence of integrated markets in which entrepreneurs can pursue business activities regardless of regional boarders. In principle, if there are no barriers to trade, one should observe equal prices for the same goods across all regions, as price increases of a specific good in one region will give rise to arbitrage (i.e., buying the good in regions at a cheaper price and selling it in another region until an equilibrium is reached). There is, however, one natural impediment between regions: if it is sufficiently large, the distance between regions raises the costs of intra- and interregional transactions in the form of transportation costs. In sound and established markets, this would result in a pattern of fairly

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109

“concentric circles” around a given region, with market segmentation increasing with rising distance. Since Russia’s regional authorities still mandate substantial degrees of political economic power, aside from transportation impediments, other types of transaction costs might also exist, for example, measures of regional protectionism such as price regulations and subsidization provisions (Gluschenko 2008), which are manifestations of the desire to control important economic activities inside a given region’s boundaries (Broadman 2001). There is some empirical evidence for this observation, although scientific literature on this topic is not very extensive. Particularly in the early years of transition, several authors, such as Berkowitz et al. (1998) and Goodwin et al. (1999), investigated market separation in Russia and provided evidence for a rather poor integration of Russian regional markets. Regarding the period from 1992 through 2000, spatial disconnectedness accounts for roughly 70% of the price differential between Russian regions, whereas artificial market barriers, such as shipping conditions, regional protectionism, and organized crime, are responsible for only 30% (Gluschenko 2008), implying a considerable degree of market segmentation. In the late 1990s and early 2000s, market integration appeared to increase to levels comparable to the United States, as indicated by Berkowitz and DeJong (2003) and Gluschenko and Kulighina (2006), although only if one neglects particularly remote and difficult-to-access regions. Akhmedjonov and Lau (2012) have observed market integration in the markets for diesel, gasoline, electricity, and coal covering a period from 2003 to 2010. In analyzing the differences between regional and national prices, the authors found that a given regional market is integrated with the national one to degrees between roughly 35% and 57%. Investigating the textile industry based on a 44-region sample for a period from 2002 to 2009, the same authors found that 72% of regional markets were integrated (Lau and Akhmedjonov 2012). The results of Perevysin and Skrobotov’s (2017) analysis of 69 goods between 2003 and 2015 support these results. In their analysis, the law of one price does not hold for 32% of the analyzed products. However, in the last decade, an opposite effect can be observed. In a more recent analysis based on the case of Novosibirsk Oblast, Gluschenko (2018) has provided evidence that the region is neither integrated nor moving towards integration with the markets of more than half of the other regions, and price divergence occurs between one-third of them. Most interestingly, Novosibirk Oblast and particularly its immediate neighboring regions show substantial levels of market separation. The observed degree of market separation, combined with high levels of market dominance and vertical integration of enterprises, still tends to preserve aspects of the Soviet system’s structural autarky, when local administrations were in charge of consumer goods production for their respective local markets. Simultaneously, this situation consolidates administrative-geographic market boundaries rather than economic ones. Overall, this is detrimental for establishing a unified national economic market and proves that entrepreneurs may, at least to some degree, face barriers in accessing trans-regional supplier or customer markets and more generally in pursuing business activities across regional borders. Consequently, this impedes efficient and competitive markets and simultaneously hampers entrepreneurial entry.

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In summary, given the extensive arguments of the above paragraphs, there is ample evidence that the market dominance of state-owned or oligarch-led incumbent firms and the resulting lack of suppliers for entrepreneurs negatively affect the likelihood of entrepreneurial entry. The high degrees of regional market segmentation in Russia appear to further aggravate this effect. Accordingly, I hypothesize the following relationships: H16: The regional level of incumbent corporate power has a negative impact on market entry of new firms. H17: The level of industry concentration has a negative impact on market entry of new firms. H18: The regional availability of suppliers has a positive impact on market entry of new firms.

4.2.9

Democratization and Entrepreneurship

The final paragraphs of this chapter are dedicated to the relationship between democracy and entrepreneurship. The two concepts are more closely related than they might first appear; they represent two facets of personal freedom. By using their economic freedom, entrepreneurs serve markets as they see fit and constantly challenge the economic, social, and political status quo. Yet it becomes clear that this ability also depends on the guarantee to use this freedom. Hence, democracy as a guarantor of this freedom might be a crucial determinant for entrepreneurial activity. Unfortunately, the body of literature on the role of democracy in this regard, and particularly in a country as heterogeneous as Russia, is rather limited. Nevertheless, this role is an interesting field of research, especially as within-country differences in the political systems of a country’s federal entities can be almost as large as the variation of political systems between countries.20 For example, even democratic states can include persistent areas of autocracy, whereas nondemocratic states might have to acknowledge more democratic regional governments, aspects that particularly support a cross-regional perspective. However, assessing the role of democracy in the context of entrepreneurial activity is a fairly difficult task. In particular, similar political institutions in two different regions may affect their respective economic environment in a different way. At the same time, politically different institutions in two disparate regions may lead to the same economic activity and performance. Thus, the following paragraphs first shed light on spatial variation in Russian democracy and, second, analyze its potential impact on entrepreneurial activity.

20 Besides Russia, similar examples can be found in Mexico, Brazil, Argentina, and India (Libman 2013; Gervasoni 2010; McMann 2006; Gibson 2005).

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Democracy in Russian Regions Frankly, Russia’s status as a democracy is contested. With regard to the Freedom House democracy indicators, since the early 2000s, Russia has dropped in a steady negative trend from its mediocre “partly free” rating almost to the very end of the scale (to an inglorious 178th rank out of 210), and, as of today, it is mostly designated as an authoritarian regime. Against the background of this development and the introductory notes of this chapter, the case of Russia is particularly interesting for two reasons. First, development processes in the center of the country tend to be rather quick, whereas similar processes in the regions lag significantly behind; thus, with regard to democratic developments at the national level, some regions may be more conservative and inert. With regard to early transition, sub-national variation in Russian democracy was, to a considerable extent, attributable to the weak 1990s administration of Boris Yelzin, which had only limited ability to directly engage in regional political transformation. Consequently, region-specific features, such as ethnic and economic characteristics (particularly economic pluralism and competition), geographical characteristics such as the presence of large urban centers, regional leadership and elites, and external influences shaped the attributes of regional democracy,21 resulting in a wide variety of bicameral parliaments, electoral systems, different forms of legislative assemblies, etc. (Ross 2000; Petrov 1998). Unfortunately, as this argument also holds for antidemocratic developments, this phenomenon was not only observable during the chaotic democratization process of the 1990s but also during the de-democratization tendencies of the 2000s (Petrov and Titkov 2013). When the 1990s’ tenuous administration was replaced by the strong, central government of Vladimir Putin in 2000, massive efforts were put into the recentralization of the Russian state. Although, in the first years of the 2000s, the regions’ loss of formal and informal rights and authorities began relatively slowly and lacked systematic efforts, it gradually increased until gubernatorial elections were abolished in 2004. Hence, second, a side effect of more authoritarianism at the federal level may have resulted in higher levels of democracy at the regional one. In particular, the weakened role of regional governors with regard to their influence on federal and regional levels led to, at least in some cases, an increase of political pluralism and competition and a somewhat more pronounced separation of powers (Petrov and Titkov 2013). However, as numerous institutions of regional democracy were recentralized in the 2000s, a considerable degree of regional variation in democracy was achieved (Libman 2013). Thus, today’s differences between political regimes in Russia’s regions rarely manifest themselves as differences in formal institutions but rather as differences in informal practices. Those practices, among others, may cover facets such as the regional political structure (i.e., the real balance of the authorities, including their appointment and independence), democratic elections on various

21 Petrov and Titkov (2013) have provided an extensive review of regional democracy in Russia, including region-specific root causes.

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levels (national, regional, local, including their competitiveness, potential intervention of authorities, courts, etc., restrictions in the exercise of active and passive electoral rights and voting irregularities), the existence of political pluralism (i.e., the existence of stable parties and factions in legislative assemblies, coalitions in and after elections, political competition, and polycentricity), the existence and involvement of civil society (i.e., the presence of nongovernmental organizations, referendums, bottom-up social activities including rallies, demonstrations, strikes, etc.), and the openness and transparency of political life in general (Libman 2013; Petrov and Titkov 2013). Moreover, one must recognize that the view on regional democracy in Russia may be somewhat distorted as it almost exclusively encompasses regions with comparatively low levels of democracy and hybrid regimes but hardly any with evolved democratic systems (Libman 2013). Consequently, we can conclude that (1) similar institutions work quite differently in different regions and (2) institutions become solid and efficient in one region but not in others. Based on a meaningful assessment of regional democratization in Russia by Petrov and Titkov (2013), we may contrast some insightful developments across the periods from 1991 to 2001 and 2001 to 2011, which are illustrated in Figs. 4.18 and 4.19, using a score system from 10 (low levels of democratization) to 50 (high levels of democratization). With regard to the more recent of the two periods, regions with relatively high levels of regional democracy are the Sverdlovsk and Perm regions, followed by St. Petersburg, Irkutsk, and Samara. Moreover, the clusters around the Karelia and Arkhangelsk regions, Yaroslavl and Nizhni Novgorod, as well as Krasnoyarsk and Novosibirsk are also within the top ten regions in terms of regional democracy. This composition illustrates that high-level regions consist of relatively large regions that host large cities and that are dominated by industry rather than agriculture. Additionally, another common attribute of the high democratization regions is the absence of a consolidated power elite and the occurrence of several political conflicts through the present, which mostly manifest as struggles for power between governors and mayors of capital cities.22 In contrast, at the lower end of the scale, we find republics such as Chechnya, Ingushetia, Kabardino-Balkaria, Mordovia, North-Ossetia-Alania, Kalmykia and Tuva, the Chukotka and Jewish Autonomous District, as well as the Kursk region. For the most part, the low performers consist of North Caucasian and other national republics with ethnic- and clan-specific characteristics in both their political systems and cultures. Obviously, it is considerably more difficult for democracies to take root in light of ethnic conflicts and demands for separatism, as in the case of Chechnya, which, as a particularly extreme case of sovereignty claims, has even demanded independence. Apart from those regions’ attempts to take on attributes of independent countries during the 1990s, such as usurping powers from the federal government (Ross 2000), one can still observe limitations and frequent violations of citizens’ rights and freedoms. On the other hand, the assumption that lower levels

22 With regard to this group, there used to be rivalries or even conflicts between the city and regional heads in the Perm, Novosibirsk, and Nizhny Novgorod regions.

20 - 24 < 20

35 - 40

30 - 34

Fig. 4.18 Regional democracy ratings in Russia, 1991–2001 (Source: Author’s illustration based on Geocurrents (2018) and data by Petrov and Titkov (2013))

25 - 29

> 40

4.2 Institutional Drivers and Determinants of Entrepreneurial Activity 113

20 - 24 < 20

35 - 40

30 - 34

Fig. 4.19 Regional democracy ratings in Russia, 2001–2011 (Source: Author’s illustration based on Geocurrents (2018) and data by Petrov and Titkov (2013))

25 - 29

> 40

114 4 The Institutional Framework for Entrepreneurship in Transition

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115

of democratization are merely rooted in the ethnic composition of a region does not apply universally because we also find other regions within low democracy section, for example, the Kursk and Jewish AO regions, closely followed by the Kemerovo and Kurgan regions. Moreover, a large share of those federal regions is rather small and not home to particularly large cities, except Ufa in the Republic of Bashkortostan, which is ranked just outside the final ten group. Overall, the polarity between higher- and lower-level democratization appears to be oriented from north to south rather than from west to east. Generally, by comparing developments from the 2001 to 2011 period in contrast to the 1990s, most regions in the top and final groups tend to be subject to substantial continuity in their evaluation. For example, among the top ten group, only Krasnoyarsk rose eight places in the ranking compared to the 1990s period. However, overall, there are 14 cases where we can observe changes in position of more than 20 places, led by the Republic of Khakassia (
36) and Kemerovo region (
35) or Altai Territory and Voronezh region (both +33) in the other direction. The sharp changes in the ranking particularly emphasize the transitional and volatile nature of Russian electoral democracy. Regional Democracy and Entrepreneurship As there is ample evidence for spatial variation in the (perceived) levels of democratization, the following paragraphs aim to clarify whether democracy is also a relevant precondition for economic activity in general and entrepreneurial entry in particular. Generally, the effects of democracies on economic well-being and activity tend to diverge and have been widely discussed in the scientific community. Starting with the perspective that assumes a positive link, in their comprehensive study, Acemoglu et al. (2019) have estimated the impact of democracy on economic growth using a panel of countries from 1960 to 2010. The authors have provided empirical evidence that democracy has a significantly positive effect on growth and suggest that countries in transition from an undemocratic system to a democracy attain about 20% higher GDP per capita in the subsequent 25 years than countries that stay nondemocratic. However, the interesting aspect of this relation is the way the positive impact of democratization on economic or particularly entrepreneurial activity occurs. With regard to this, the literature distinguishes two ways democracy can affect entry. First, democracies are considered to perform better in providing public goods. Since democratic leaders are accountable to the public rather than to elites, they invest more in human capital, infrastructure, safety, etc. In turn, this is conducive to entrepreneurial entry, as those investments increase the attractiveness of the region for individuals to engage in entrepreneurial activity. In contrast, the impact of influential interest groups and the personal interests of politicians or autocrats might lead to a suboptimal provision of public goods. In this sense, Rodrik (2007) has demonstrated that democratic key elements, such as unbiased regulatory institutions or institutions for social insurance and macroeconomic stabilization, etc., are the most significant source of economic growth across countries.

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Second, democracies are assumed to perform better in providing a stable environment for businesses and to better protect property rights from the predatory interests of various interest groups, for example, within the government or the economy (although democracies are also accompanied by a hint of instability, attributable to insecure electoral outcomes and their impact on public policy). The relevance of stable, predictable institutions stems primarily from the importance of creating reliable expectations for investments and business decisions. The view that democracies create and maintain stable business environments through laws and the protection of individual property rights is supported by scientific evidence from Acemoglu and Johnson (2005), who have confirmed a strong link between the security of property rights and democracy. Even though democracies may be more likely to engage in the redistribution of resources than is the case in some autocracies, the negative effect of income redistribution for entrepreneurial activity can be assumed to be offset by lower barriers to entry, the encouragement of fair and open competition, and fostering innovation. In support of this perspective, Freedom House indicators suggest that oppressive regimes are more likely to limit business opportunities (Freedom House 2018). In a combined analysis of Doing Business indicators and Freedom House data, governments in countries identified as “not free” were found to impose more red tape, build more barriers to trade, and apparently fail to enforce contracts more often (Freedom House 2018). In addition to self-imposed barriers of trade, other countries may also be less likely to engage in international trade with authoritarian regimes, which in turn impedes the development of entrepreneurial opportunities and the possibility of seizing them. Moreover, democratic systems and their inherent characteristics, such as political openness, free elections, pluralism, transparency, etc., are expected to show disciplinary effects. They can serve to deter corruption and undesirable connections to politicians and administrations, particularly because political opponents have an incentive to uncover and publish abuses of office. Political connections may be seen as less valuable if the respective officials can be voted out of office (Faccio 2006). For example, Persson et al. (1997) have shown that different democratic government bodies discipline each other to their citizens’ benefit, whereas a lack of transparency facilitates corruption and opportunism, which are assumed to have a negative impact on entrepreneurial activity. For the sake of completeness, there are also examples that contradict the entrepreneurship-fostering effects of democracies. For example, the world’s largest democracy, India, experienced slow economic growth for decades, whereas other countries achieved rapid economic growth under nondemocratic systems. For example, Asian states such as China, Singapore, as well as South Korea and Taiwan before democratization all created strong market economies in the absence of democratic practices. Focusing on the case of China, Friedman (2009) has stated, “one-party non-democracy certainly has its drawbacks. But when it is led by a reasonably enlightened group of people, as China is today, it can also have great advantages. That one party can just impose the politically difficult but critically important policies needed to move a society forward in the 21st century.” However, from a personal perspective, I view this quote with the utmost skepticism, but at least

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some scholars have made similar arguments. Barro (1997), for example, has also denied that more political rights have stimulating effects on economic growth. Additionally, in an extensive literature review until the mid-2000s, Gerring et al. (2005) have concluded that the net impact of democracy on economic performance over the last 50 years is null or even negative. However, those arguments should be considered carefully, as some of them tend to be based on countries with very low levels of development (e.g., Brooks 2013; Posner 2010; Aghion et al. 2008). Offering a compromise, Andersen and Aslaksen (2008) as well as Boschini et al. (2009) have proposed that more tangible and definite constitutional details might have a stronger influence on economic activity than simply the overall presence of a democracy. Unfortunately, there are few specific studies on the relationship between regional levels of democracy and entrepreneurial activity in Russia. As one of few studies, that of Bruno et al. (2013) has analyzed political fluidity in Russian regions, which can be, at least in principle, related to higher levels of democracy and found it to increase the rates of entry for small firms, whereas it reduced the likelihood of entry for larger ones. Consequently, the entry of larger-scale firms appears to be encouraged by continuity in the regional political field, characterized by the reelection of incumbent governors or new governors from the same elite. Conversely, small firms’ entry rates rise following a more competitive (i.e., democratic) political environment (Bruno et al. 2013). Although there are some indications of a negative relationship between democratization and entrepreneurship, the majority of the empirical evidence provided supports a positive link between the two concepts. Hence, I close this chapter by deriving the following and final hypothesis: H19: The regional level of democratization has a positive impact on market entry of new firms.

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Chapter 5

Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

All we can know is that we know nothing. And that’s the height of human wisdom. Leo Tolstoy, War and Peace

5.1

Motivation and Objective

The overall objective of this dissertation is to examine the question of which spatial institutional factors are most important in shaping regional entrepreneurial activity in Russia and whether the performance of entrepreneurial activity in Russia is restrained by any particular institutional bottlenecks. This chapter aims to provide answers to these questions. The foundation of the empirical analyses in this chapter is a cross-regional perspective that is promising with regard to Russia’s specific regional outline. On the one hand, Russia’s regions are subject to the same federal law; they share a nation-wide common market and have similar characteristics with regard to policy, society, and culture, which all affect the appearance of entrepreneurial activity (Bruno et al. 2013; Klapper et al. 2006; Djankov et al. 2002). On the other hand, there are considerable differences across various regions, along with variations in competence in policy design for local governments and administrations, which cause a sufficient degree of spatial institutional variation. As a result, the Russian context provides an almost natural experimental setting to analyze how institutional factors affect entrepreneurial activity (Bruno et al. 2013; OECD 2002). In addition, I expect the empirical results to be more comparable within the same country’s borders, in contrast to cross-country studies, and the statistical models to be more reliable regarding the ceteris paribus assumption and omitted variable bias. This allows the exploitation of sub-national institutional variation without being concerned about differences in institutional structures at the national level. Additionally, as Aidis and Adachi (2007) have suggested, cross-country comparative data may not offer many useful indications because, purely in terms of formal legislation, Russia does not perform that poorly. Moreover, instead of employing cross-sectional data, the following analyses are based on a panel data set. This is mainly to avoid a bias in the institution-entrepreneurship relation by one-time events such as political © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Schlattau, Tilting at the Windmills of Transition, Societies and Political Orders in Transition, https://doi.org/10.1007/978-3-030-54909-1_5

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reforms or regulation (e.g., as implied by initiatives of then-President Medvedev, who wanted to end “nightmares to business,” or the 2011 police reform) or the impact of the global financial crisis that hit Russia in 2008 after the “fat years.” Moreover, spatial levels of entrepreneurial entry tend to be path-dependent and persistent over time, as emphasized in Sect. 2.2.4. This makes a longitudinal element in the analyses particularly interesting. Now, to achieve a reliable answer to the research questions of this dissertation, I require a truly capable research strategy. In this light, I want to test the hypothesized relationships between spatial institutional determinants and the corresponding rates of entrepreneurial activity (as summarized in Table 5.1) based on two different methodological perspectives. Beforehand, a preliminary descriptive analysis of the data used (Sect. 5.2) serves as the foundation for both methodological approaches and aims at identifying special characteristics in the data set that need to be considered from both methodological perspectives. Next, the first methodological perspective (Sect. 5.3) follows studies from Klapper et al. (2006) and Bruno et al. (2013) and is based on a descriptive Tobit regression model. The purpose of this approach is to empirically validate the assumed relationships between spatial institutions and entrepreneurship by testing the hypotheses listed in Table 5.1. Regarding the aspect of potential bottlenecks, standardized coefficients allow for the direct comparison of coefficient effect sizes and the identification of particularly negative impacts on entrepreneurial activity. Given the constraint of institutional data availability, the observation timeframe for this study ranges from 2007 to 2011. However, one considerable disadvantage of descriptive regression analysis is that it may be subject to both a sizeable risk of overfitting and spurious correlation. Consequently, resulting from close cooperation with Andreas Brieden and Saskia Schiele, I utilize an innovative algorithm approach that can identify hidden multidimensional structures by creating clusters of objects with similar sets of characteristics, that is, the geometric clustering approach from Brieden and Gritzmann (2012). In this second approach (Sect. 5.4), I develop a prediction model based on institutional data from 2007 to 2010 (i.e., the training data) to predict entry rates for 2012 by using 2011 institutional data (i.e., the test data). By doing so, I not only circumvent the risk of overfitting and spurious correlation, but I also account for the interrelatedness of predictor variables. This way, I can identify true, reliable relationships between institutional predictors and firm entry rates, as well as potential bottlenecks that are also valid for an actual use case.

5.2 5.2.1

Data and Sample Selection Dependent Variable

Entrepreneurial activity can be a rather difficult concept to grasp. It may be defined as the status of being self-employed (Reynolds et al. 2005), as behavioral attitude in

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Table 5.1 Overview of hypotheses Structural economic factors H1: The regional level of economic risk has a negative impact on market entry of new firms H2: The regional level of unemployment has a positive impact on market entry of new firms H3: The regional level of average wages has a negative impact on market entry of new firms H4: The regional level of inequality in income and wealth distribution has a negative impact on market entry of new firms Property rights H5: The regional level of security of property rights has a positive impact on market entry of new firms H6: The regional level of perceived risk from raidership has a negative impact on market entry of new firms Criminality H7: The regional level of perceived public safety has a positive impact on market entry of new firms Corruption H8: The regional level of corruption has a negative impact on market entry of new firms Bureaucracy H9: The regional level of administrative barriers has a negative impact on market entry of new firms H10: The regional level of the perceived threat of agency pressure has a negative impact on market entry of new firms Financial capital H11: The regional level of available short-term financial capital has a positive impact on market entry of new firms H12: The regional level of available long-term financial capital has a positive impact on market entry of new firms Human capital H13: The regional level of human capital has a positive impact on market entry of new firms Infrastructure H14: The regional provision of physical infrastructure has a positive impact on market entry of new firms H15: The regional level of access to communication infrastructure has a positive impact on market entry of new firms Market environment H16: The regional level of incumbent corporate power has a negative impact on market entry of new firms H17: The level of industry concentration has a negative impact on market entry of new firms H18: The regional availability of suppliers has a positive impact on market entry of new firms Democratization H19: The regional level of democratization has a positive impact on market entry of new firms

the sense of an entrepreneurial orientation (Lumpkin and Dess 1996) or as a cognitive attribute of individuals similar to opportunity perception (Shane and Venkataraman 2000). This work, however, bases its understanding of entrepreneurship on Lumpkin and Dess’ conception referred to in Sect. 2.1, since “the essential act of entrepreneurship [is] new entry” (1996, p. 136). Moreover, as also illustrated

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in Sect. 2.1, firm entry is a relatively easy and intuitive measurement of the outcome of entrepreneurial activity. Hence, for this study, entrepreneurial activity is measured by utilizing gross entry rates, which are calculated as the share of new entrants on incumbents per year, region, and industry (according to the latter’s two-digit NACE classification1). The data to measure firm entry is derived from a comprehensive longitudinal enterprise data set based on Bureau van Dijk’s Orbis database.2 With regard to the firm entry measure, two important conceptual aspects need to be taken into account before addressing the more practical facets of the variable construction: the difference between gross and net entry and the difference between overall and de novo entry. Regarding the former, as a matter of principle, high gross entry does not necessarily imply high net entry if firms’ survival rates are low and many firms are forced to exit (Brown and Earle 2006; Rutkowski and Scarpetta 2005).3 This perspective is important because institutional determinants may not only affect entry decisions of potential entrepreneurs but also business decisions of already established ones and ultimately their decision to abandon entrepreneurial activity. Hence, net entry reflects not just entry barriers but also obstacles to grow firms from seed to growth or expansion stages. In this light, institutional shortfalls might be strongly accountable for hampering company development and not just for discouraging market entry. Consequently, the most preferable measure would be net entry instead of gross entry. Unfortunately, as regards this thesis, net entry cannot be calculated for analysis because suspension or bankruptcy dates in Orbis are available for few companies. Furthermore, inactive companies are removed from the database after approximately 3 years, whereas it may take more than 2 years for new companies to be included, given the time it takes to prepare annual accounts and to include information in the database. Together, this does not allow for the calculation of net entry rates over a larger period. Nevertheless, I assume that there is a considerable impact of institutional factors on net and gross entry, since both entry barriers and the expectation of survival barriers may influence the entry decision in the first place. Moreover, the literature suggests a strong correlation of exit rates and entry rates (Klapper et al. 2006). Although analyses of firm entry and exit in Russia are relatively scarce, studies from Rinaldi (2008), Iwasaki (2014), and Sprenger (2014), who have investigated the firm-level determinants of survival in Russia, have confirmed that context factors affect both market entry and exit in a similar fashion. Thus, I deem gross entry a reliable measure of entrepreneurial activity with regard to this analysis. Regarding the second conceptual aspect, we may also distinguish between entry of pre-existing firms (de alio entry) and actual, de novo entry. A positive facet is the circumstance that, for the observation period of this study, it can generally be

1

NACE refers to the Statistical Classification of Economic Activities in the European Community. The data set is based on an August 2016 excerpt from Orbis. The database contains extensive information covering private and listed companies in a broad set of countries. 3 Net entry would be measured by the gross number of firm entry minus all suspended or bankrupt businesses per year. 2

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assumed that the bulk of businesses in Russia were legally registered, perhaps excepting small petty traders, farmers, or a certain share of business activities that are generally conducted informally.4 However, particularly in Russia, entrepreneurs might either register several new businesses to remain under a certain size threshold in order to avoid the attention of corrupt state bureaucrats, or large enterprises may register small shell businesses to disperse profits and benefit from the lower tax rates for smaller firms (Aidis and Adachi 2007). The phenomena of one firm being registered as multiple small entities or shell businesses or serial founders establishing new firms are essentially a type of diversifying entry rather than de novo entry. On the one hand, this is important as de alio and de novo firms follow different growth and innovation patterns. Some empirical studies have found that entrepreneurial firms behave contrary to Gibrat’s law of proportionate growth, since growth rates are relatively high in the first years following firm entry but quickly decrease with advancing firm age (Haltiwanger et al. 2013; Mata and Portugal 2004; Lotti et al. 2003). Hence, analyzing de novo entry exclusively would be of primary interest. On the other hand, Orbis unfortunately does not allow for differentiation between de alio and de novo entry. Nevertheless, regarding interpretation, we may at least bear in mind the implication that the observed number of firms in the data set might be slightly overestimated, and the real rates of entry are likely to be marginally lower than the ones calculated. Having addressed these issues, I now concentrate on the practical facets of the dependent variable construction. To guarantee comparability of the computed entry rates across regional and international boarders, and by following the approach of Klapper et al. (2006) and Bruno et al. (2013), several filter criteria were applied to the initial Orbis data extract. 1. All legal forms except public and private limited liability companies, or their national equivalents, were removed for three reasons. First, the main benefit of legally registered incorporation is limited liability, which enables entrepreneurs to take risks. In contrast, the advantages of choosing other forms of legal firm structure (e.g., proprietorships or partnerships) may differ substantially across countries, which in turn impede international comparability. Second, incorporated firm entry usually requires the entrepreneur to cope with a certain minimum investment threshold. According to Scase (1997, 2003), incorporation refers to entrepreneurs who reinvest profits and aim for business development. On the other hand, sole proprietorship may reflect notions such as starting a business out of the necessity to sustain a family or to raise income, due to the lack of better alternatives (Scase 1997, 2003). As the following analyses concentrate on opportunity-motivated entrepreneurship as described in Sect. 2.1, exclusively considering incorporated firms allows other forms of potentially necessity-driven entrepreneurial activity to be left aside. 4

Notably, according to Rosstat, in 2016 employment in the informal sector of Russia’s economy peaked at a record level for the last 10 years with 21.2% (Rosstat 2018). Regarding the observation period of this thesis, those figures are more moderate.

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5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

Last, the coverage of proprietorships and other unincorporated legal forms in Orbis is rather poor, for example, due to lower reporting requirements. 2. Only companies with a minimum of financial information (assets, sales) were considered. This is mainly to avoid biases caused by “phantom” companies that merely exist for tax reasons and which can thus be excluded. 3. Companies that only report consolidated annual reports were removed as well to avoid double-counting companies and subsidiaries. 4. Moreover, I removed several industries based on their NACE code. First, certain branches of primary industries were removed due to their country- or regionspecific character (agriculture, forestry, fishing, and mining, i.e., NACE codes 1-10). Furthermore, branches from the government and public sector and closely related industries were removed as well, for example, the social and health sector, activities of organizations, private households, and extra-territorial organizations and firms that cannot be classified (NACE codes 84-88, 90-94, 97-99). The overall data extract from Orbis includes 1,951,364 firms across 83 Russian regions and 66 industries that were either active or funded between 2006 and 2014. For the final calculation of entry rates, I excluded region-industry observations based on fewer than three firms in a given industry. Whereas the literature suggests that gross entry rates in Russia until the early 2000s were extremely low by international standards (Aidis and Adachi 2007), studies with more recent observation periods have indicated that entry in Russia does not appear to be particularly low in an international comparison. For example, Bruno et al. (2013) have registered entry rates between 11.9 and 2.4% throughout 1996–2008, compared with 7.09% in Europe and 6.65% in the United States. With regard to Table 5.2 and Fig. 5.1, the present data tell the same story. We can identify higher-than-average entry rates for Russia compared to EU and Eastern European post-socialist countries.

Table 5.2 Comparison of average gross entry rates per year Year 2007 2008 2009 2010 2011 2012 2013 2014

EU entry rates (%) 9.05 8.54 8.42 9.18 8.50

Post-socialist entry rates (%) 9.50 12.13 9.26 8.76 9.12 7.60

Russian entry rates (%) 15.03 13.64 11.28 12.30 12.28 12.55 12.42 8.58

Russian entry rates (sample) (%) 15.54 14.14 11.33 12.47 12.59 12.77 12.48 8.84

The “Russian Entry Rates” column refers to the overall Orbis extract. Due to limited data availability for independent variables with regard to regions and time period, the final analysis sample is restricted to 33 out of 83 regions and a time period spanning from 2007 to 2011 (more details follow in Sects. 5.2.3 and 5.2.6). The composition of the EU- and post-socialist columns is outlined in Sect. 5.2.2

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141

20,00%

Entry Rate (in %)

16,00%

12,00%

8,00%

4,00%

0,00%

2007

2008

2009

2010

2011

2012

2013

Entry Rate EU

Entry Rate Post-socialist Sample

Entry Rate Russia (total)

Entry Rates Russia (sample)

2014

Fig. 5.1 Dynamic development of firm entry, 2007–2014

This is generally in agreement with literature that suggests that typical entry rates in Western Europe and North America are within a range from 5 to 15%, whereas developing and transition economies show slightly higher rates of entry (Aidis and Adachi 2007). With regard to the sample regions for this study, the average entry rates are marginally higher but largely representative of the figures for Russia as a whole. Interestingly, there are obvious differences in the presented entry rates in contrast to the very low 2.4% entry rate for Russia in 2008 observed by Bruno et al. (2013), despite using the same database and similar filter criteria. I assume this might be primarily due to technical issues in the Orbis database. Data coverage in Orbis for approximately 2 years before the current date is rather fragmented and unreliable. Again, this is attributable, among other factors, to the time it takes companies to prepare and publicize annual reports, for government entities and information offices to encompass this information and eventually forward it to Bureau van Dijk’s Orbis database. This would explain the relatively low 2.4% average entry in 2008 calculated by Bruno et al. (2013) in comparison to 13.64% in my case. Regarding the validity of my calculations, I am not apprehensive of any negative consequences for the following analyses, as entry rates in the final samples only range from 2007 to 2011 (research perspective 1) and 2008 to 2012 (research perspective 2); hence, any relevant information for this period can be expected to be included in the data base by the time of the data excerpt.

142

5.2.2

5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

Natural Entry Rates

To capture the impact of institutional context factors on entry, it is highly expedient to know how entry rates would look if there were relatively few institutional barriers. Following Klapper et al. (2006), I expect industries that naturally have relatively low barriers to entry to be most affected by artificial institutional barriers. Klapper et al. (2006) expect the rate of entry in a given industry in the United States to be a suitable proxy for the natural capacity for entry in that industry because, in the United States, the costs of entry appear to be relatively low and only amount to 0.5% of per capita GDP (Djankov et al. 2002). This implies relatively low barriers of entry. Hence, these natural entry rates reflect the quasi-natural propensity for entry in consideration of technological barriers in that industry, for example, organizational efficiencies and advantages that incumbents gain from experience as well as economies of scale but are not subject to any artificial institutional barriers. In the following analysis, I develop this thought with some modifications. As Klapper et al. (2006) have also noted, one should beware of a certain degree of glorification in supposing that entry rates in the United States do not suffer from barriers. The apparent lack of comparability between the formal and informal institutional environments in Russia and the United States is also notable. Additionally, the average costs of entry as determined by Djankov et al. (2002) may not adequately reflect additional costs imposed by informal or intangible institutional factors. With that in mind, I interpret natural entry rates as the natural propensity for entry in a relatively sound institutional environment and utilize average entry rates from the European Union5 as a natural entry benchmark. The decision to use EU entry rates as primary benchmark stems from an analysis of the persistence of industry entry rates.6 In their study, Klapper et al. (2006) refer to US entry rates as relatively persistent over time. The authors experimented with average natural entry rates for different time periods in the 1990s and found them to remain highly significant and of similar magnitude, regardless of the specific period. Their results thus support that industry structures tend to be relatively stable over time and across countries, which should be reflected by natural entry rates and which make US entry rates a particularly useful benchmark for the natural propensity for firm entry (Klapper et al. 2006; Cable and Schwalbach 1991; Dunne and Roberts 1991). According to these arguments, I base my analysis primarily on EU natural entry rates. Even though EU rates are comparatively low, they tend to be more stable

5

The sample refers to the EU-27, excluding Greece, Malta, and Cyprus due to technical reasons. The average correlation of industry entry between consecutive years is 95.25% in the EU sample, with an average deviation of 0.64%. Industry entry in the post-socialist sample correlates with 86.84% and an average standard deviation of 1.75%. The highest fluctuation can be observed in Russia, with an average correlation of 75.28% and an average standard deviation of 1.68% (cf. Annex A.1). 6

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143

across time and thus constitute an adequate benchmark to analyze spatial entry in Russia. I also utilized average entry rates from a set of post-socialist countries7 as a robustness test for the validity of my results, as explained in detail in Sect. 5.3.1. Although subject to slightly higher fluctuation, they seem to be an appropriate benchmark, especially with regard to the more comparable socio-economic background. The data for the European Union countries is sourced from Eurostat, whereas post-socialist natural entry rates are calculated based on Orbis. Natural entry rates are subject to the same filter criteria8 and refer to the same NACE two-digit industries as outlined in Sect. 5.2.1. Table 5.3 provides an overview of entry rates per industry for the samples used. The highest rates of firm entry in Russia can be observed in the postal and courier activities sector, followed by branches such as services to buildings and landscape activities, sewerage, retail-related branches such as wholesale trade and warehousing, and support activities for transportation. Other high-entry branches are administrative branches such as office services and support, as well as legal and accounting activities, tourism, and information service activities. In contrast, in terms of entry rates, the EU leaders are branches that are likely to be driven more strongly by innovation, such as activities of head offices and management consultancy activities, electricity, gas, steam and air conditioning supply, food and beverage service activities, scientific research and development, and other professional, scientific, and technical activities, as well as employment activities that may reflect the demands of a more flexible labor market.

5.2.3

Institutional Factors

With regard to institutional factors at the regional level, the analyses of this dissertation consider a broad range of data sources. Table 5.4 provides a summary of all explanatory institutional variables including availability and data sources. A detailed description of the individual indicators is provided in Sect. 5.2.5. Concerning the regression analysis to be conducted according to research perspective 1 (Sect. 5.3), direct comparisons of coefficient effect sizes are an essential part of the estimation strategy. To allow for such comparisons, all institutional factors were standardized prior to the analysis. Regarding standardization, I applied z-transformation using the following formula: 7 The sample covers the Eastern European countries of Bosnia and Herzegovina, Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Macedonia, Poland, Romania, Serbia, Slovakia, Slovenia, and Ukraine. Unfortunately, data availability for other post-socialist countries (i.e., Commonwealth of Independent States, CIS) was scarce and highly fragmented. 8 Data based on Orbis used the same filter criteria as outlined in Sect. 5.2.1; data based on Eurostat only used criteria 1 and 4 as the other were already accounted for in the figures provided by Eurostat.

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5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

Table 5.3 Entry rates per industry according to NACE classification, 2008–2011

NACE 10 11 12 13 14 15 16

17 18 19 20 21

22 23 24 25 26 27 28 29 30 31 32 33

Industry Manufacture of food products Manufacture of beverages Manufacture of tobacco products Manufacture of textiles Manufacture of wearing apparel Manufacture of leather and related products Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials Manufacture of paper and paper products Printing and reproduction of recorded media Manufacture of coke and refined petroleum products Manufacture of chemicals and chemical products Manufacture of basic pharmaceutical products and pharmaceutical preparations Manufacture of rubber and plastic products Manufacture of other non-metallic mineral products Manufacture of basic metals Manufacture of fabricated metal products, except machinery and equipment Manufacture of computer, electronic, and optical products Manufacture of electrical equipment Manufacture of machinery and equipment n.e.c. Manufacture of motor vehicles, trailers, and semi-trailers Manufacture of other transport equipment Manufacture of furniture Other manufacturing Repair and installation of machinery and equipment

EU entry rates (%) 6.58 6.58 6.58 6.05 6.05 4.74

Post-soc. entry rates (%) 8.10 7.16 5.73 7.30 7.90 6.62

Russian entry rates (%) 9.52 9.29 6.26 11.23 8.58 9.45

Russian entry rates (sample) (%) 9.93 9.10 4.70 11.87 9.68 9.26

6.32

9.03

11.48

11.97

5.51

6.84

11.19

11.17

5.51

7.66

10.03

10.94

10.21

7.90

11.54

11.26

6.06

7.52

11.50

10.31

6.06

4.99

7.76

8.81

4.70

6.50

12.36

12.00

5.55

6.77

12.17

12.04

5.95 5.95

5.26 7.65

11.62 12.63

12.23 12.94

5.52

6.06

10.05

9.36

5.52 4.32

6.17 5.29

10.53 10.29

11.67 10.81

6.05

5.71

9.43

9.63

6.05

8.14

10.14

11.18

6.27 6.27 9.01

8.79 9.30 11.38

12.63 10.18 10.30

12.99 10.73 11.64 (continued)

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145

Table 5.3 (continued)

NACE 35 36 37 38 39 41 42 43 45 46 47 49 50 51 52 53 55 56 58 59

60 61 62 63 64 65

Industry Electricity, gas, steam, and air conditioning supply Water collection, treatment, and supply Sewerage Waste collection, treatment, and disposal activities; materials recovery Remediation activities and other waste management services Construction of buildings Civil engineering Specialized construction activities Wholesale and retail trade and repair of motor vehicles and motorcycles Wholesale trade, except of motor vehicles and motorcycles Retail trade, except of motor vehicles and motorcycles Land transport and transport via pipelines Water transport Air transport Warehousing and support activities for transportation Postal and courier activities Accommodation Food and beverage service activities Publishing activities Motion picture, video and television program production, sound recording, and music publishing activities Programming and broadcasting activities Telecommunications Computer programming, consultancy, and related activities Information service activities Financial service activities, except insurance and pension funding Insurance, reinsurance, and pension funding, except compulsory social security

EU entry rates (%) 15.38

Post-soc. entry rates (%) 16.59

Russian entry rates (%) 13.06

Russian entry rates (sample) (%) 13.37

8.73 8.73 8.73

5.91 10.82 10.69

17.65 18.18 15.63

15.68 17.37 15.86

8.73

13.26

15.98

14.59

9.67 9.67 9.67 8.23

11.19 8.50 10.85 10.19

12.08 10.27 12.88 13.69

12.79 10.77 13.21 14.67

8.82

9.15

15.93

16.85

9.65

10.48

9.15

9.52

8.57

10.97

14.29

14.47

8.98 9.65 8.48

8.68 8.55 9.67

10.43 10.65 14.83

10.91 13.48 16.21

13.97 7.64 11.88 8.29 10.91

14.84 8.29 13.55 8.45 11.80

17.34 10.34 11.16 11.06 13.98

18.93 10.73 11.37 10.58 13.62

6.21

7.16

6.36

6.03

10.56 11.73

9.55 13.49

11.21 14.57

11.33 15.38

16.31 10.98

17.56 10.71

17.35 15.49

18.65 14.83

8.16

3.79

11.56

10.81

(continued)

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5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

Table 5.3 (continued)

NACE 66 69 70 71 72 73 74 75 77 78 79

80 81 82 95 96 Total

Industry Activities auxiliary to financial services and insurance activities Legal and accounting activities Activities of head offices; management consultancy activities Architectural and engineering activities; technical testing and analysis Scientific research and development Advertising and market research Other professional, scientific, and technical activities Veterinary activities Rental and leasing activities Employment activities Travel agency, tour operator, and other reservation service and related activities Security and investigation activities Services to buildings and landscape activities Office administrative, office support, and other business support activities Repair of computers and personal and household goods Other personal service activities

EU entry rates (%) 10.76

Post-soc. entry rates (%) 12.26

Russian entry rates (%) 15.14

Russian entry rates (sample) (%) 16.61

8.59 13.59

11.02 13.90

17.50 13.39

17.23 12.36

8.34

9.55

10.82

11.46

12.17 10.67 13.54

12.20 12.95 13.61

14.29 14.54 10.42

12.80 15.09 12.17

10.29 10.77 12.04 8.82

13.08 11.61 13.61 10.24

11.43 13.62 13.42 16.00

12.43 14.15 14.90 16.26

10.62 11.93

10.46 14.34

9.62 19.14

9.68 18.78

14.92

15.07

16.60

16.80

6.94

10.03

10.41

11.88

10.87 8.80

15.18 9.81

10.34 12.30

10.96 12.60

The industry sector with NACE code 68 (real estate activities) was excluded because Eurostat did not provide the data. Additionally, Eurostat provides combined figures for some sectors (e.g., NACE 10-12), which is why the EU entry rates column provides similar entry rates for some closely related industries

zvi ¼

xvi  xv σv

ð5:1Þ

with zvi denominating the standardized observation value i of a given variable v, xvi the original variable value, xv the mean value for variable v, and σ v the standard deviation of the given variable. Standardization was performed for all observations i of all institutional variables v. Additionally, with regard to parametrization, prior to the standardization procedure, some variables were inverted. In terms of the regression approach in Sect. 5.3, if a given institutional factor had a significant effect on entry, it should particularly hamper entry in those industries with a naturally high propensity for entry. Since the model specification aims at interpreting higher values of the given institutional variable as higher levels of institutional barriers (in other

op_ad_nocntrl op_ad_noprsc Financial capital H11 op_fin_short

H10

Corruption H8 op_art_corr Bureaucracy H9 reg_administ

reg_safety

H3 avwage H4 reg_mincgini Property rights H5 patent_coeff H6 raiding_cases Criminality H7 buscrm_bus

Hyp. Variable Structural factors H1 rsk_econ H2 unempl 2006, 2009–2014 2006–2013 2006–2013 2006–2013 2007–2011 1990–2010 1990–2010

Business trends, asset depreciation, etc. Unemployment level (according to ILO methodology), in percent Average wage, rubles per month Gini coefficient (wealth distribution)

Number of patent applications per 10,000 people Number of raidership cases (covered by media)

Victims of business violence covered by media (number of victims explicitly identified as businessmen) Citizen satisfaction with executive authorities’ performance in public safety

2006–2007, 2010, 2012 2007–2011 2006–2007, 2010, 2012 2006–2007, 2010, 2012 2006–2007, 2010, 2012

Corruption indicator (rnk)

Citizen satisfaction with executive authorities’ performance in general Freedom from pressure of control agencies (rnk) Freedom from pressure of law enforcement agencies (rnk)

Availability of financial means—short term (1 year, rnk)

2008–2011

Availability

Description

Table 5.4 Overview of institutional variables and corresponding data sources

Opora Rossii

Opora Rossii Opora Rossii

ICSID, UniSIS

Opora Rossii

ICSID, UniSIS

Belokurova (2014)

Rosspatent Rochlitz (2014)

Rosstat ICSID, UniSIS

RA expert ICSID, Rosstat

Source

(continued)

5.2 Data and Sample Selection 147

2006, 2007, 2010, 2012 2006–2010, 2014 2000–2013 2000–2013

Level of regional democracy

Gross regional product growth Gross regional product, per capita

2007–2012 2007–2012

2006, 2007, 2010, 2012 2006, 2007, 2010, 2012 2006, 2007, 2010, 2012 2005, 2007–2010, 2013

Measurement of corporate power (regional M&A intensity) Herfindahl-Hirschman index, measure of industry concentration (per NACE 2digit industry) Regional availability of suppliers (rnk)

Quality of transport and logistics infrastructure (rnk) Availability/quality of power supply (rnk) Availability of business/production premises (rnk) Normalized index of household and firm access to and the use the of Internet and communication technologies

2000–2013

Percentage share of employees with higher education in a region Availability of engineers and technicians (rnk) General level of local government efficiency in education (rnk) 2006, 2007, 2010, 2012 2007–2010

Availability 2006–2007, 2010, 2012

Description Availability of financial means—long term (>3 years, rnk)

Rosstat Rosstat

Petrov and Titkov (2013)

Opora Rossii

Zephyr (BvD) ORBIS (BvD)

Opora Rossii Opora Rossii Opora Rossii Inst. of the Information Society

Opora Rossii ICSID, UniSIS

ICSID, UniSIS

Source Opora Rossii

UniSIS and Rosstat are governmental statistical databases (http://www.fedstat.ru/user/about.do, www.rosstat.ru), RA Expert is a Russian rating agency (http:// www.raexpert.ru/ratings/regions/), and Opora Rossii is the Russian business association of small- and medium-sized enterprises (http://new.opora.ru). The ICSID database (https://iims.hse.ru/en/csid/databases) was created by the HSE International Center for the Study of Institutions and Development and gathers comprehensive data on Russian regional economics, politics, and social development. Internet use data was obtained from the Institute of the Information Society (http://www.iis.ru). In addition to the factors considered in this thesis, Baranov et al. (2015) have provided an excellent, comprehensive overview on how (not) to measure Russian regional institutions

op_art_spl H18 Democratization H19 crn_democracy Control variables CV grp_growth CV grp_pc

Market environment H16 ma_intensity H17 HHI_ind

op_hr_ingtech educ Infrastructure H14 op_infa op_art_en op_art_prp H15 ICT_idx_std

Hyp. Variable op_fin_long H12 Human capital H13 heduc

Table 5.4 (continued)

148 5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

5.2 Data and Sample Selection

149

words, higher costs of entry to be borne by the potential entrepreneur), this should be reflected in the variable parametrization. Hence, after inverting and standardization, higher variable values generally imply a lower quality of institutional framework conditions (i.e., higher barriers), whereas lower values imply a higher quality (i.e., lower barriers). All ordinal ranked variables, for example, those sourced from Opora Rossii and RA Expert, as well as the data on raidership and business violence, are already available in the correct scale, and the inversion procedure encompasses the following variables: avwage, patent_coef, reg_safety, reg_administ, heduc, ICT_idx_std, and crn_democracy. Regarding the second methodological approach in Sect. 5.4, a series of more complex data transformation steps is required. Thus, I do not discuss those steps here but rather in the respective chapter.

5.2.4

Structural Controls

In addition to the endogenous variables discussed above, this dissertation aims to control for an influence of structural factors generally believed to be important for entrepreneurial activity. Taking into account the theoretical explanations from Chap. 4, I expect that the economic performance of a given region influences the local rate of new firm entry. Regions with higher levels of macroeconomic development or regions experiencing positive growth may have higher rates of opportunity-driven new firm entry. Thus, on the one hand, I control for the regions’ general level of development and macroeconomic performance. Regarding this, I include GRP per capita as a variable. On the other hand, I control for economic performance by including the growth rate of real GRP. Both variables are standardized and sourced from Rosstat, with per capita GRP measured as the sum of total value added by all economic activities in a given region and GRP growth as its annual delta measured as a share of the previous year.

5.2.5

Detailed Description of Variables

Structural Economic Factors I obtained one part of the structural economic factors data, i.e., average wages and unemployment figures, from the Russian Federal State Statistics Service (Rosstat). The average wage variable was measured in rubles per month. The unemployment measure considers persons above the age of 15 without revenue that are able to work and are actively searching for job. Both variables are available up until 2014. The economic risk variable was drawn from RA Expert rating data. The Russian RA Expert rating agency issues a well-known investment attractiveness rating for Russian regions that measures the quality of regional institutions. The ratings encompass two major components, investment risk and investment potential, and

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5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

consist of several sub-rankings that are based on information from state statistical services and private consultancies. The sub-rankings cover aspects such as the quality of public administration, political and legal risks, and other factors. Overall, each sub-indicator consists of an ordinal ranking of roughly 80 regions each year from low (rank 1) to high risk (rank 80). Unfortunately, one major drawback of the agency data is that the rating agency does not disclose the particularities of its rating methodology. The economic risk variable in particular captures the investment risk within a given region, according to factors such as the level of social tension, number of unemployed people, share of people with income below subsistence level and the various ethnic characteristics of a specific region. Data for this variable is available from 2006 to 2014, except for the years 2007 and 2008. Finally, to measure inequalities in the distribution of income in Russian regions, I used a Gini coefficient estimate provided by the International Center for the Study of Institutions and Development (ICSID) database from the Higher School of Economics, Moscow. The database provides economic and political indicators for Russian regions from 1998 to 2014 and was created by the ICSID as part of the research project, “Institutions and Economic Development: The Role of Bureaucracy and Experiments as a Method of Analysis and Evaluation of Reforms” (supported by the Basic Research Program of the Higher School of Economics, 2011–2013). The Gini coefficient was calculated based on household budget surveys (for a sample size of roughly 50,000 voluntarily participating households) conducted by Rosstat. The Rosstat estimates were adjusted to reflect the distribution of per capita income, as determined by macroeconomic calculations. Since the survey covers primarily lower-income groups, it may even underestimate the level of inequality (ICSID Codebook 2017). Property Rights With regard to property rights protection, there are few possibilities to directly measure the level of security of a potential or established entrepreneur’s property rights. Hence, I used a proxy measure to approximate the regional level of perceived security from property rights violations; that is, it was a performance indicator based on the number of filed patents. Patents warrant the protection of intellectual property and are thus an instrument for entrepreneurs and business owners to protect their innovation and ideas from unfair competition. They are designed to guarantee that an entrepreneur can seize the economic benefit of his commercialized innovation. Although not every entrepreneur and every venture are linked to a patented innovation, a higher use of patents as an instrument should indicate either higher innovative activity or a higher trust in protection through this tool. Thus, I assume that in regions with higher numbers of filed patents, the overall perception of property rights security is considered to be higher. I obtained the patent_coeff indicator by using Rosspatent data on the coefficient of innovative activity in Russia’s regions relating to the number of applications for patents, utility models, or registered designs per 10,000 citizens. As an alternative to assess a region’s property rights situation, I employed an indicator prepared by Rochlitz (2014) representing the number of raider attacks per

5.2 Data and Sample Selection

151

region that were covered in the media. Although the latter may be potentially biased due to erratic media development, coverage, and freedom across regions, different types of media nevertheless provide the advantage of reflecting the immediate perception of property rights security potential entrepreneurs have in a given region. The data is available over a period from 1999 to 2010, and each case of raidership required at least two different sources to report on the same incident to be considered. Overall, Rochlitz (2014) has identified more than 300 cases of raider attacks, which, considering the survey design and explanations of the author, is even likely to understate the extent of corporate raiding in Russia. Criminality With regard to factors that determine perceived levels of regional criminality, I employed two different types of measures. First, I used data based on regional institutional measures that were gathered regularly by central government agencies.9 The original data is stored in the Unified Interdepartmental Statistical Information System (UniSIS); however, I drew the data from the comprehensive stock of regional institutional data in the ICSID database. The measure I used is reg_safety, which refers to the citizens’ satisfaction with the executive authorities’ performance in public safety, available from 2008 to 2011. Satisfaction is measured as the percentage of total positive responses in the survey. Thus, the variable covers values in a [0, 1] interval, implying that values close to zero represent high levels of criminality or a poor public safety situation, whereas values close to one represent low levels of criminality or a good public safety situation. Nevertheless, since official statistics may, for various reasons, be distorted, I deemed it useful to supplement official data on crime levels with information from alternative sources. One such source refers to attacks on business owners or executives. Belokurova (2012, 2014) has gathered comprehensive data on the number of business-related attacks publicized in media, police and press releases, and court decisions. From several different indicator variations prepared by the author, I chose one that appears to be most relevant for the present analysis. buscrm_bus relates to the number of victims of business violence covered by media that were explicitly identified as businessmen. I assume that this estimate is promising because higher numbers of cases that affect entrepreneurs are likely to receive broad media attention and are consequently likely to impact the public perception of overall criminality levels or business-related criminality in particular. Corruption I derived data on the degrees of corruption per region from OPORA Rossii reports, Russia’s business association of small- and medium-sized enterprises, which 9 Since gubernatorial elections were abandoned in 2004, those evaluations were set up by a presidential decree in order to assess the public opinion about the functioning and transparency of regional administrations. They were intended to be used to allocate fiscal transfers to the best performers among Russian regions (the indicators have been prepared until 2010/11 pursuant to the President’s Decree No. 825 of 28 June 2007, “On Evaluating the Performance of Regional Government Authorities in the Russian Federation”).

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composes widely known indices of the entrepreneurial climate. OPORA analyzes different facets of the regional business climate in Russia and provides regional rankings for each set of indicators. The indicator rankings are available for the years 2006, 2007, 2010, and 2012; however, the number of regions covered varies between 35 and 40 in each report. Apart from regional sampling, ranking methodology has also not been entirely consistent over the observation period. In order to account for these shortcomings in the data, first, I focused on the set of 33 common regions that are covered in all four reports. Because the number of observed regions varies each year, I normalized the rankings to a [0, 1] interval in each year. Second, this was also useful regarding the issue of methodology. By using the relative ranking instead of absolute index values, I considered positive or negative changes in the underlying factor, such as corruption. This way, changing nuances in the way of measuring the absolute corruption index are not of severe consequence because the ranking still indicates whether there has been a positive change towards lower levels of corruption or a negative change towards higher levels of corruption.10 Lastly, since, at times, the reports provide different measures of the same institutional factor (i.e., in this case, corruption), I consolidated relevant indicators into a single measure, reflecting the general prevalence level of corruption in a given region and year.11 With regard to the final data, values close to zero represent very favorable ranking positions, i.e., low levels of corruption, whereas values close to one represent poor positions, i.e., high levels of corruption. Bureaucracy The measure for administration barriers (i.e., reg_administ) is based on the citizens’ satisfaction with the executive authorities’ performance in general (similar to the reg_safety variable described above). I assumed that higher rates of public satisfaction with the work of authorities and public agencies are a mirror for lower artificial bureaucratic barriers, which are expected to correspond to higher rates of entrepreneurial entry. The indicator, which is available for the years 2007–2011, is sourced from the ICSID database. Again, satisfaction is measured as the percentage of total positive responses in the survey. Hence, the variable carries values in a [0, 1] interval, implying that values close to zero represent low administrative performance

10

This procedure is the same for all variables drawn from OPORA reports, relating to administrative barriers, financial capital, human capital, and all other categories. 11 Considering all Opora reports, they cover a set of eight indicators relating to corruption. Specifically, the indicators considered were bribes to public officials (вэятки чиновникам), sum of illegal payments to officials as share of company revenues (Доля незаконных выплат чиновникам в выручке компаний), freedom from corruption and raiding (Свобода от коррупции и рейдерства), total extent of corruption (Общий уровень коррупции), freedom from corruption in typical situations (Свобода от коррупции в типичных ситуациях), and the frequency of illegal actions against entrepreneurs by officials/by representatives of the Ministry of Internal Affairs/by employees of control and supervisory bodies (как часто предприниматели региона сталкиваются с противоправными действиями со стороны чиновников/ представителей мвд/сотрудников контрольно-надзорных органов).

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and low levels of satisfaction, whereas values close to one represent high administrative performance and high levels of satisfaction. Variables with regard to agency pressure were drawn from OPORA, similar to the corruption variable. Overall, I based my analyses on two related indicators, i.e., freedom from pressure by control agencies (op_ad_nocntrl/Свобода от давления со стороны проверяющих инстанций) and freedom from pressure by law enforcement agencies (op_ad_noprsc/Свобода от давления со стороны правоохранительных органов), both available in 2006, 2010, and 2012. The parametrization of all indicators was also similar to the corruption indicator, i.e., a [0, 1] interval with values close to zero signaling few administrative barriers, or little pressure from agencies. Financial Capital I based two variables on the availability of financial capital from the OPORA reports, i.e., op_fin_short, which describes the availability of financial resources for the short term (up to 1 year) and op_fin_long, denominating the long term (more than 3 years) (Доступность финансовых ресурсов на краткосрочный период (до 1 года/более 3 лет). Both variables are available in 2007, 2010, and 2012. The variables carry values in a [0, 1] interval, with values close to zero signaling good access to capital and values close to one signaling difficult access to capital. Human Capital Regarding the availability of human capital, I based my analysis on three different proxy measures. First, I derived a measure of specialist availability from the OPORA reports (op_hr_ingtech), i.e., the availability of engineers and technicians (Доступность квалифицированных инженеров и технических специалистов), available for the years 2007, 2010, and 2012, and with values close to zero signaling good availability of engineers and technicians and values close to one signaling poor availability. Second, I used a variable measuring the share of employees with higher education as a percentage of all employees in a given region (heduc). The data with availability between 1998 and 2013 was originally provided by Rosstat; however, I drew it from the ICSID database. Finally, I also accounted for an indicator that was prepared by the former Ministry of Regional Development and also drawn from the ICSID database. The educ variable captures the general level of local government efficiency in the area of education. Efficiency is measured by an ordinal ranking of 83 regions each year from best performance (rank 1) to worst performance (rank 83). Infrastructure With regard to physical infrastructure, I tried to cover several aspects with the underlying data. I created op_infra as the annual average of two OPORA variables, i.e., the quality of transport infrastructure (Качество транспортной инфраструктуры) and the quality of logistics infrastructure (Качество логистической инфраструктуры). The indicators are available in 2007, 2010, and 2012.

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op_art_en consolidates four OPORA indicators, i.e., availability of new energy generation capacities (Доступность новых энергетических мощностей), the quality of electricity supply (Качество электроснабжения), the availability of electricity tariffs (Доступность тарифов на электроэнергию), and the risk of tariff increases (риск повышения тарифов). The indicators used were drawn from the reports in 2006, 2007, 2010, and 2012. Regarding the availability of facilities (op_art_prp), again, the OPORA reports cover a range of relevant indicators. Since the same indicators are not provided across all reports, three indicators were consolidated into a single index that reflected the general availability of office and production property in a given region and year. Specifically, the indicators considered were availability of production facilities (Доступность производственных помещений), availability of office and commercial premises (Доступность офисных и торговых помещений), and availability of production and office space in the area (наличие производственных и офисных площадей). The data is available for 2006, 2007, 2010, and 2012. Finally, the communication infrastructure variable (ICT_idx_std) was drawn from reports of the Institute of the Information Society. The variable was calculated as the average of two index indicators which measured (1) household access to the Internet and communication technology (ICT) and their use by the population in a given region and (2) ICT accessibility for companies in the region. Data is available for the periods 2005, 2007–2010, and 2013. Both measures reflect index values, which I normalized in a [0, 1] interval for each year, with values close to zero implying low ICT infrastructure potential, whereas values close to 1 represented high potential. Market Environment Regarding market environment factors, I relied on one measure for each related hypothesis. First, in order to account for market dominance of incumbent firms, I created a measure of corporate power. To do so, I employ the Zephyr database from Bureau van Dijk. Zephyr is a comprehensive database of deal information and contains information on M&A deals, IPOs, as well as private equity and venture capital deals or rumors relating to any of the former. The data excerpt12 covers the time period from 2007 to 2012, considers all deals with Russian target companies, and takes into account all deals that were either completed-confirmed, completedassumed, or announced in a given region and year. Consequently, for each year and region combination, I calculated the aggregated sum of deal values in rubles as a share of the respective GRP. I expected that the higher this share was, the greater incumbent corporate power would be in a given region, and it would be reflected by the ability of large-scale incumbents to avoid market pressure by acquiring potential competitors. Second, I also considered market competitiveness to be a similarly important determinant to entrepreneurial entry in a given region and industry (Analytical Center 2018; Szakonyi 2017). Thus, I considered a measure of the normalized

12

The excerpt was downloaded on the 5th of July, 2017.

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HHI to account for market concentration. I based my calculations on the Orbis database and concentrated on active companies without considering those with no recent financial data in the years from 2007 to 2012. I used the following formula: PN HHI i,t ¼ P N

2 j¼1 a j

2

ð5:2Þ

j¼1 a j

with aj ( j 2 [1; N]) representing the turnover of firm j, N as the number of firms in the given industry, and i denominating the industry according to NACE two-digit code and t year. One notable drawback of this calculation is that the annual turnover of a given firm is only considered for a single industry, i.e., the industry pertaining to the firm’s primary line of business. Naturally, firms may operate across various regions and industry branches. Unfortunately, I also do not have any information at my disposal to split a firm’s turnover proportionately across its different areas of business activities (or across different regional markets). Last, to assess the impact of a lack of suppliers in a given region on entrepreneurial entry, I once again utilized information gathered by OPORA Rossii. Considering all OPORA reports, a set of four indicators related to suppliers’ availability. Again, in some instances, the different indicators were only provided in one or two reports. Hence, the four indicators were consolidated into a single index13 that reflected the general availability of suppliers, machinery, and equipment or other service companies in a given region and year. The indicators are available in the years 2006, 2007, 2010, and 2012. The variable encompasses a [0, 1] interval, with values close to zero signaling good availability of suppliers and values close to one signaling poor availability. Democratization The institutional impact of regional levels of democratization was assessed by utilizing the regional democracy ratings prepared by Carnegie Center Moscow (Petrov and Titkov 2013). The rating reflects expert opinions on the openness and transparency of the political life, the plurality of political actors, the fairness of elections, etc. in the respective regions. Overall, the expert opinions cover ten individual items. The scale for each item reaches from one to five. All items were then aggregated to an integral indicator of local democratization. In this sense, the theoretically highest obtainable rating was 50, whereas the lowest score would be 10. Moreover, to account for the durations of legislative terms, the political penetration

13 Specifically, the indicators considered are availability of machinery and equipment suppliers (Доступность поставщиков машин и оборудования), the availability of suppliers of business services (Доступность поставщиков бизнес-услуг), availability of component suppliers (Доступность поставщиков комплектующих) and the existence of SME subcontracting/supply for large firms in a given region (насколько в регионе развита такая практика когда малые предприятия выполняют какие-либо работы или поставки по заказу крупных?).

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of society and to smooth out sharp fluctuations, the authors applied a moving average to the yearly data. Thus, the assessment not only reflects the democratization in a given year but is rather an assessment of the previous 5 years (e.g., the 2010 assessment considers a period starting in 2006).

5.2.6

Data Imputation

Whereas entry rates calculated for Russian regions are theoretically available through 2014, other sources are somewhat limited in terms of data availability (illustrated by Table 5.5). Overall, data on regional institutional determinants covering the same regions are available for 33 out of 83 regions and a broadest common range of 5 years from 2007 to 2011. However, as is apparent in Table 5.4, the availability of institutional data for Russian regions is quite fragmented over time. Resulting from the use of a broad range of data sources, the panel data set over the 2007–2011 timeframe for institutional factors is subject to the frequent occurrence of missing data and is hence an unbalanced panel. The preferred solution of addressing missing data, discarding incomplete observations, is not applicable in this case. Since data on particular institutions is missing for entire years, sorting out missing values would result in a data set in which only 1 year (i.e., 2010) of institutional data remained for analysis. This confronts us with the problem that we lack a longer joint time period in which we can compare the impacts of all relevant institutional factors on entrepreneurial Table 5.5 Overview of variables and periods that require imputation of missing data Variable buscrm_bus crn_democracy educ ICT_idx_std op_ad_nocntrl op_ad_noprsc op_art_corr op_art_en op_art_prp op_art_spl op_fin_long op_fin_short op_hr_ingtech op_infra raiding_cases reg_safety rsk_econ

Period with available data 1990–2010 2006–2010, 2014 2007–2010 2005, 2007–2010, 2013 2006, 2010, 2012 2006, 2010, 2012 2006–2007, 2010, 2012 2006–2007, 2010, 2012 2006–2007, 2010, 2012 2006–2007, 2010, 2012 2007, 2010, 2012 2007, 2010, 2012 2007, 2010, 2012 2007, 2010, 2012 1999–2010 2008–2011 2006, 2009–2014

Period that requires imputation 2011 2011 2011 2011 2007–2009, 2011 2007–2009, 2011 2008–2009, 2011 2008–2009, 2011 2008–2009, 2011 2008–2009, 2011 2008–2009, 2011 2008–2009, 2011 2008–2009, 2011 2008–2009, 2011 2011 2007 2007–2008

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entry. Comparing standardized coefficients of institutional determinants across different time periods is not an option because we have different underlying samples on the one hand, and potential relationships may be influenced by different time-related particularities on the other. Both issues are likely to result in biased conclusions. Additionally, since I am interested in analyzing not only cross-regional variation in institutions but also its dynamics over time as outlined in Sect. 5.1, I am highly interested in following a panel data approach. Thus, rather than removing observations or variables with missing data, I chose to impute (i.e., artificially fill in) missing values. There is no immediate reason to believe that data is not missing at random (NMAR). Most of the data sources used rely on regular reports provided by private companies or governance agencies, which unfortunately lack publications in some years of our observation period. Since the reasons for disruptions in the publication series are likely to be matters of organization, funding, or other administrative issues, I do not expect them to be related to the surveyed data. Consequently, I assume that data is missing completely at random (MCAR). A variety of approaches can be used for imputation. I applied two different approaches, the first of which was a simple mean imputation. I computed missing values in a given year as the mean value between the prior and following year that have data available. In case of two subsequent years of missing values, the mean value was constructed in two steps (i.e., the one-third and two-thirds threshold of the linearly perpetuated value). In case values were missing at either the beginning or the end of the observation period and there was no data available to construct mean values, I simply computed a linear trend based on the values of previous or following years. Although the empirical literature is rather critical with regard to simple mean imputation, this method has some useful advantages in this case. In particular, as institutional determinants tend to be relatively stable and usually do not change substantially from 1 year to another, assuming a linear trend between existing data points seems reasonable. Additionally, this way, it is possible to maintain the full sample size, which can also be advantageous for bias and precision (Kleinke et al. 2011). Using an alternative approach for imputation was intended as an additional robustness check for the linearly imputed data. Regarding this, I based my calculations on a more sophisticated imputation procedure suggested by Bingham et al. (1998). This approach is particularly suitable for panel data because it combines cross-sectional and longitudinal information. According to this method, I imputed data as follows. Let M be a data matrix for a given variable of l lines and c columns, with l representing different regions, c measurement years, and xl, c a single element in the matrix. First, I started by computing column means xc. As many of the missing data were drawn from sources that work with ordinal ranking data, the column means tended to have values very close to each other (which is the case if the number of regions covered by the given ranking is the same in each column; minor deviations depended on the fact that one or some regions may be not included in a particular year). Consequently, I calculated the overall mean of all column means for all available years.

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5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

C¼

n 1X nx n c¼1 c

ð5:3Þ

Second, I computed individual deviations from the overall mean. dl,c ¼ xl,c  C

ð5:4Þ

Third, given that D is the matrix containing all individual deviations, I calculated the row means of D, i.e., dl . Finally, for the years of missing data, xl, c was imputed using xl,c ¼ C þ d l. I used all available regions and years for this procedure, not just the final 33-region sample, to consider as much information as possible. The procedure has been empirically shown to produce more reliable results than simple mean imputation (Kleinke et al. 2011; Bingham et al. 1998) and works both under MCAR and also not-too-severe MAR conditions. Since the method has been successfully applied in a number of studies (Larsson et al. 2008; Stemmler and Petersen 2005; Ackerman et al. 2004; Seiffge-Krenke and Stemmler 2003), I consider it a suitable mechanism to check the robustness of the results based on linear imputation. Unfortunately, one notable drawback of the applied variant of this method is that it produces the same values per region for each year, which are subject to missing data. In case more than 1 year of data is missing for a given variable, this consequently leads to all but one of the imputed periods being dropped in the following regressions due to multicollinearity. Nonetheless, since I do not employ this approach as the main foundation of my analysis but rather as an assurance to check the reliability of the linearly imputed data, I consider this shortcoming acceptable. The issue of imputation is fairly controversial because it bears the danger of producing biased results and underestimated variance. Imputation strategies can also severely distort the distribution of variables, particularly since mean imputation may distort relationships between variables by shifting correlation estimates towards zero (Kleinke et al. 2011). However, from the perspective of this dissertation, I prefer estimating data so that I can provide at least a vague picture of the relevance of institutional determinants for entrepreneurship across Russian regions rather than none at all. Undoubtedly, there are inconsistencies, but I believe that, as long as the process of missing data imputation is fully, transparently outlined, the benefit of the resulting analyses is worth the imperfection of imputation.

5.2.7

Identification of Regional Clusters

5.2.7.1

The Heterogeneity of Russian Regions

As Putnam (1993, S. 83) has noted it in his seminal book Making Democracy Work, “it is best to begin a journey of exploration with a map.” One of the most striking

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features of Russia’s spatial layout is the vast diversity of its regions. Thus, in order to adequately account for the heterogeneity of Russian regions in this analysis and to avoid that potential institutional effects might be lost by capturing only the mean of contrary impacts in different regions, I seek to design clusters of similar types of regions and to compare institutional impacts across those regional clusters. This idea is primarily motivated by findings from Zubarevich (2015), who has argued that several groups of Russian regions can be identified with specific combinations of similar development issues. In the following, I apply two alternative methods and maps to identify clusters of Russian regions according to a set of similar characteristics.

5.2.7.2

Method 1: An Urbanization-Based Approach

The first conceptual approach to address heterogeneity in Russian regions accounts for the fact that entrepreneurial entry as a process is characterized by its social environment. However, in Russia, differences in the social environment are only to some extent explained by regional differences. Zubarevich (2013) has proposed that many aspects of regional heterogeneity can be better understood by a change of angle and by utilizing a center-periphery criterion, a concept commonly known as the “Four Russias.” With regard to Russia, she draws a hierarchical system of populated localities, divided into the largest, less large, small, and rural periphery, with population size and urbanization serving as the major criteria for division. Based on this idea, three different “Russias” can be identified within one country. The first consists of major cities with populations of over half a million people; the second includes medium-scale towns of around 50,000–500,000 inhabitants; and the third is made up of small towns, semi-rural areas, and the rural countryside. The three urbanization-based elements are complemented by the North Caucasus region as the fourth area. Obviously, category boundaries overlap somewhat (Zubarevich 2013). With respect to entrepreneurial activity, this distinction sounds promising because a number of studies have indicated that new business formation is subject to different effects between agglomerations and rural areas (Fritsch 2013; Fritsch and Noseleit 2013; Fritsch and Schroeter 2011).14 It is important to emphasize that Zubarevich’s concept of the four Russias does not fit into the regional structure of the Russian Federation at its heart, except perhaps for Moscow, St. Petersburg, and the Northern Caucasus regions. It is instead a perspective that looks beyond regional borders and restructures Russia’s regional outlet into different types of settlements and agglomerations according to their population and urbanization characteristics. Based on the structure of the data at hand, it is obvious that this concept cannot be reflected entirely in this study’s

14

For example, Fritsch and Schroeter (2011) have analyzed German regions and drawn the conclusion that the levels of agglomeration and population density shape the relation between new firm entry and growth, competition, and a number of indirect effects.

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5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

research design. However, I attempt to use the core idea of Zubarevich’s concept and categorize regions according to the characteristics that are largely representative of the majority of a given region. In the following paragraphs, the different types of Russian regions according to Zubarevich’s basic concept are outlined as a basis for the formation of regional clusters. The clustering procedure utilizes 2010 census data on regional and urban population per region, as well as population data of Russian cities with more than 50,000 inhabitants. First Russia (Large Urban Centers) First Russia is comprised of large urban centers whose inhabitants share higher living and educational standards, a well-developed university system, and high degrees of Internet use. More than 20% of Russia’s population lives in 14 cities with populations of around a million or more inhabitants.15 In this Russia, one can find a particularly high concentration of the Russian middle class. Since the collapse of the Soviet Union, the share of white-collar workers has risen notably, and there is a considerable number of medium-sized businesses and a substantial demand for employees with high qualifications. Furthermore, these cities are usually the first to adopt new trends in consumer lifestyle, although salaries are substantially lower than those in the federal capitals. According to Zubarevich (2013), cities above 500,000 inhabitants also tend to be more prone to dynamics of change and innovation. Based on this idea, I assigned sample regions to cluster one if they host at least one major city with a population of at least 500,000 inhabitants, and this population constitutes at least one-third of the region’s population. Moreover, the population share of both small- to medium-sized towns with 50,000–500,000 inhabitants and the population share of the rural population do not exceed one third of the region’s total population.16 First Russia (Moscow and St. Petersburg) A unique role belongs to the two federal cities Moscow and St. Petersburg, home to more than 11% of all Russians. Both cities are characterized by relatively high levels of economic development, reflected by a high average per capita income of US $47,000 in Moscow and US$22,000 in St. Petersburg. Moreover, both cities incorporate a great deal of the country’s financial and human resources. Regarding the latter, 50% of Moscow’s and 44% of St. Petersburg’s residents above 15 years have higher education (Zubarevich 2013). Additionally, with up to 40%, both cities have the highest shares of the Russian middle class, accompanied by the country’s most diverse labor market and the highest-paid jobs. Although both cities share a great deal of the characteristics of First Russia, the inhabitants of both federal cities still 15 Particularly cities like Kazan, Novosibirsk, Rostov-on-Don, and Yekaterinburg experienced a rapid evolution towards major service industry centers. Other cities, for example, Chelyabinsk, Omsk, Perm, Ufa, and Volgograd, have shown similar developments. Even though their economic dependence on former Soviet large-scale industry such as oil refineries or metallurgical plants is still high, the latters’ importance as the previously largest employers has waned, and the cities have gradually moved towards modernization (Zubarevich 2013). 16 With regard to the thresholds, in all clusters, an evaluation tolerance of up to 5% was allowed.

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161

have a higher standard of living, a higher per capita income, and a more pronounced international influence than all other Russian cities or regions. Based on these arguments, I assigned the federal cities Moscow and St. Petersburg to their own cluster (i.e., cluster five). Second Russia Second Russia entails industrial towns that accommodate between roughly 50,000 and 500,000 inhabitants. Most of those cities’ profiles were shaped in the Soviet era, and the cultural imprint from large-scale industry can still be felt today. Those towns are home to more than 25% of Russia’s population and are characterized by diverse conditions in terms of economy. On the one hand, oil- and gas-producing towns provide adequate wages, followed by those with large steel and coal industry enterprises, which are mostly oriented towards export markets. Far behind in terms of wages, we find towns that mainly host industries that depend on export substitutions, for example, the food and machine-building industries. Additionally, there is a substantial number of one-company cities. One common characteristic of those settlements is a particularly high number of blue-collar workers and lower-level public sector employees. Moreover, the number of small businesses is relatively low due to low consumer spending power and apparently high levels of cronyism. The region suffers from a persistent drain of young people, since, aside from the towns that rely on oil and gas extraction, there are few alternative options for work in most towns. Hence, population in most of the medium-sized industrial towns has rapidly declined. In addition, the local population suffers from a lack of mobility and low qualifications. Consequently, there is little fertile ground for liberal ideas, initiatives for modernization, and innovation in a broader sense because people still value a strong paternalist state, large-scale social policy, and more than anything else stability, employment, and wages (Zubarevich 2015). Regarding the clustering procedure, I assigned regions to cluster two if the share of the population living in towns with 50,000–500,000 inhabitants comprised at least one-third of the total population. Additionally, the population share of towns with more than 500,000 inhabitants and the population share of rural population both did not exceed one-third of the region’s total population. Third Russia Russia’s vast peripheral territory of small villages, semi-urban settlements, and countryside towns represents third Russia and is home to one-third of the country’s population. Depopulation and a high share of elderly people are prominent characteristics in third Russia. Although such types of settlements can be found all over the country, there is a strong concentration in the regions of Central Russia, the northwest, Ural, and Siberia. Compared to the rest of the country, levels of education and mobility are very low. The highest share of jobs can be found in the public and agriculture sectors; however, a large share of workers is employed in the informal sector. Moreover, there is a substantial share of people who earn their living by gathering mushrooms, berries, or pinecones, as well as by fishing (Zubarevich 2015). Overall, high rates of pensioners (e.g., 40% of the female population) and

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5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

high involvement in the shadow economy leave little room for entrepreneurial ambitions. With regard to clustering, cluster three contains regions whose share of people living in towns of fewer than 50,000 inhabitants comprises at least two-thirds of the region’s entire population. Moreover, there are no cities with more than 500,000 inhabitants in these regions, and the share of towns with 50,000–500,000 inhabitants does not exceed one-third of the total population.17 Fourth Russia Whereas the first through third Russias focused on the hierarchy of settlement forms, the definition of fourth Russia deviates from this approach. Fourth Russia refers to the barely developed regions of the North Caucasus and, although to a lesser extent, to some regions of southern Siberia, such as the Tuva and the Altai regions. These areas accommodate roughly 6% of the Russian population and differ to a great extent from the rest of Russia. Although there is one large agglomeration (Mahkachala, with around 580,000 inhabitants), it is home to only a negligible share of an educated middle class and those who can prefer to leave. Compared to other parts of Russia, the North Caucasus area is characterized by a late onset of urbanization, and the demographic transition to a modern society is far from complete with high levels of birth rates and a patriarchal, clan-based societal structure. Moreover, the region is characterized by large-scale ethnic differences and a history of perennial instability, with violence occurring on a regular basis (Zubarevich 2015). This has also led to several conflicts in the post-Soviet period, mostly in Chechnya where Russia fought two wars in the 1990s to suppress uprising attempts for the region’s independence. Furthermore, Islamist militancy and clan feuds are also not easily addressed and contribute to a harsh institutional environment. However, with regard to the study sample, there is no region that belongs to the North Caucasus area; hence, cluster four is not considered in the analysis. In sum, based on the 33-region sample, Fig. 5.2 and Table 5.6 provide an overview of the final clusters and corresponding regions.

17 It needs to be emphasized that some allocation decisions between clusters two or three were ambiguous from a technical perspective. Hence, expert assessments and manual cluster allocation were applied for individual cases. For example, there is the notable case of Tyumen Oblast, which is the major producer of oil and gas in Russia and which made the region by far the richest federal subject of the country in terms of GRP per capita. Although Zubarevich (2013) has allocated the city of Tyumen, which hosts roughly 500,000 inhabitants, to second Russia, it only makes up roughly 17% of the oblast’s overall population. Moreover, the oblast is characterized by considerable inequalities, as profits from resource exploitations are not equally distributed among different shares of the population, leading to a poverty rate in the region’s rural parts that is significantly higher than in Tyumen city (Buccellato and Mickiewicz 2009). Hence, I adhere to the basic idea of the clustering strategy and allocate Tyumen oblast, according to the characteristics of the majority of its population, to cluster three.

Industrial regions with medium sized & small cities

Rural regions

Moscow & St. Petersburg

Cluster 2:

Cluster 3:

Cluster 5:

Fig. 5.2 Map of sample regions following the urbanization-based clustering scheme (Source: Author’s illustration based on Geocurrents 2018)

Industrial regions with cities > 500.000

Cluster 1:

5.2 Data and Sample Selection 163

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5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

Table 5.6 Overview of regional clusters: urbanization-based clustering scheme Region Cluster 1 Novosibirsk region Republic of Tatarstan Perm territory

Avg. entry (%) 13.57 16.21 15.17 14.42

Nizhny Novgorod region Samara region

13.80

Tomsk region

13.67

Volgograd region

13.31

Lipetsk region

13.27

Ryazan region

12.94

Tula region

12.16

Voronezh region Yaroslavl region

11.97 11.93

5.2.7.3

14.00

Region Cluster 2 Sverdlovsk region Irkutsk region Chelyabinsk region Primorsk territory Chuvashia Republic Arkhangelsk region Vladimir region Moscow region Murmansk region Novgorod region Rostov region

Avg. entry (%) 13.05 14.94 14.15 13.94

Region Cluster 3 Republic of Buryatia Tyumen region

Avg. entry (%) 12.69 14.94 14.39 13.16

13.68

Republic of Bashkortostan Belgorod region

13.22

Orenburg region

12.23

13.14

Stavropol territory

11.60

13.12

Krasnodar territory Leningrad region

11.60

11.62

Cluster 5

14.03

11.02

Saint Petersburg Moscow

14.33 13.73

12.85

12.29

11.38

11.83

Method 2: Economic Geographical Location

The second approach to address heterogeneity in Russian regions and to adequately incorporate it into this study’s research design is the concept of economicgeographical location potential (EGP), which is based on the work of Zemtsov and Baburin (2016). A region’s EGP is primarily associated with its proximity to markets, traffic flows, industrial centers, and other facilities. In this light, a central position of an object within a system of related elements (i.e., countries or regions) provides economic and social benefits, such as lower transport costs or a concentration of trade and migration flows. On the other hand, economic agents such as potential entrepreneurs are likely to carry additional costs in peripheral regions, for example, to gain access to markets. The authors have suggested that even though the costs to be borne by economic agents in different regions have rapidly declined with an increasing development of transport and information technology, heterogeneity across regions persists. There is still a high discrepancy in living conditions, and

5.2 Data and Sample Selection

165

remote and underdeveloped areas are still less attractive to qualified workers, investors, and thus entrepreneurs (Zemtsov and Baburin 2016).18 Zemtsov and Baburin (2016) have consequently formalized and assessed the potential benefits of the economic-geographical position of each Russian region as a probabilistic category based on the use of gravity models and by differentiating between interregional EGP (i.e., economic potential from exchange with other Russian regions) and international EGP (i.e., economic potential from exchange with other countries). Both measures relate to the maximum potential volume of trade and market access, given adequate institutions and infrastructure. In this regard, the interregional EGP for each particular region i is calculated by the following formula: EGPReg: i ¼

J X GRP j 2 j¼1 Ri,j

ð5:5Þ

with i as the subscript for each particular region, GRP as the gross regional product, j as the subscript for all other Russian regions (J ¼ 83), and Ri, j as the respective distance (km) between two regions by rail. For regions without railways, data on automobile roads and river routes was used. The international EGP for each particular region was calculated using the following formula: EGP

Int:

i

¼

Q X q¼1

GDP  2 q  min Ri,p þ Rp,q 1:5

! þ

N X n¼1

! GDPn  2  Ri,e þ Re,n 2

ð5:6Þ

with i as the subscript for each particular Russian region, GDP as the gross domestic product, q as the subscript for a given foreign country (Q ¼ 170), Ri, p as the distance (km) from a particular region to a Russian port or major airport region p, and Rp, q as the distance from a port region p to the distant country q (km). Hence, min (Ri, p2 + Rp, q1.5) describes the adjusted square distance to a given country q via the nearest port or airport. This concept relates to the idea that foreign trade activity is concentrated in nine regions with nonfreezing major ports and thus all-season infrastructure connection to other countries.19 Land-bound infrastructure to neighboring countries is considered as well. In this light, n describes one of N ¼ 15 border countries, whereby economic interrelations with n are carried out mainly by land

18 This can easily be illustrated by the example of automobile factories in Russia that are concentrated in the Kaluga and Leningrad regions, i.e., those close to large and growing consumer markets such as Moscow and St. Petersburg. By contrast, distant Russian regions, such as the Republics of Altai and Tuva, are subject to unfavorable landlocked positions far from the main traffic flows and major economic centers, which might contribute considerably to their poor economic development. 19 That is, ports in the Arkhangelsk, Kaliningrad, Leningrad, Murmansk, Rostov, St. Petersburg, Krasnodar, and Primorsky regions, plus Moscow’s international airports.

166

5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

high

Regional EGP Potential

Cluster 2

Cluster 3

low

Cluster 1

low

high

Internat. EGP Potential

Fig. 5.3 EGP clustering scheme: cluster definition

through a set of adjacent regions (e).20 The authors assume that international landbound trade is mostly conducted via those regions. However, due to higher transport costs, it is assumed to be less intense than via sea; thus, at 1.5, the applied coefficient for sea-bound international relations is lower than for land-bound international relations. The resulting measures of regional and international EGP are used as a basis to define homogenous groups based on their EGP potential. I used average EGP potential values per region over the 2007–2011 period. Due to the high range of values, the regional and international EGP measures were transformed to their natural logarithm before clustering using k-means clustering. Based on the Calinski/Harabasz pseudo-F for regional and international EGP potential, I defined three homogenous clusters, illustrated in Fig. 5.3.21 Cluster one is defined by the combination of the lowest regional and lowest international EGP. This cluster entails the most remote regions located in Ural, Siberia, and the Far East, which are characterized by the least potential for interregional and international cooperation, trade, and exchange. Cluster two, in contrast, is comprised of regions with both high regional and international EGP levels. These can be found close to the Moscow and St. Petersburg agglomerations. Finally, cluster three is also characterized by high degrees of international EGP; however, it has a comparatively low level of regional EGP, which particularly refers to border regions or regions with ports. Based on the 33-region sample, Fig. 5.4 and Table 5.7 provide an overview of the final clusters and assigned regions. 20

For an overview on border countries n and regions e, refer to Annex A.2 (Table A.4). Although the Calinski/Harabasz pseudo-F was maximized for a number of six clusters (39.02), I want to avoid defining clusters with only one or two regions and confronting the danger of lacking observations for the subsequent analyses. As the three-cluster pseudo-F was only slightly lower (34.07), I decided to use three clusters. A plot of the identified clusters, i.e., the basis of the scheme presented in Fig. 5.3, is provided in Annex A.2 (Fig. A.1). 21

high regional potential, high international potential

low regional potential, high international potential

Cluster 2:

Cluster 3:

Fig. 5.4 Map of sample regions following the EGP clustering scheme (Source: Author’s illustration based on Geocurrents 2018)

low regional potential, low international potential

Cluster 1:

5.2 Data and Sample Selection 167

168

5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

Table 5.7 Overview of regional clusters: EGP clustering scheme Region Cluster 1 Novosibirsk region

Avg. entry (%) 14.18 16.21

Republic of Tatarstan Republic of Buryatia Sverdlovsk region

15.17

Perm territory

14.42

Tyumen region Irkutsk region

14.39 14.15

Chelyabinsk region

13.94

Samara region

13.80

Tomsk region

13.67

Chuvashia Republic Republic of Bashkortostan Orenburg region

13.22

5.2.8

14.94 14.94

Region Cluster 2 Saint Petersburg Moscow Vladimir region Ryazan region Moscow region Tula region Yaroslavl region Novgorod region Leningrad region

Avg. entry (%) 12.68 14.33

Avg. entry (%) 12.52 14.00

13.73

Region Cluster 3 Nizhny Novgorod region Primorsk territory

13.12

Volgograd region

13.31

12.94

Lipetsk region

13.27

12.85

13.14

12.16 11.93

Arkhangelsk region Belgorod region Voronezh region

11.62

Murmansk region

11.83

11.38

Stavropol territory

11.60

Krasnodar territory Rostov region

11.60

13.68

12.29 11.97

11.02

13.16 12.23

A Preliminary Descriptive Analysis

Tables 5.8 and 5.9 provide summary statistics before applying standardization22 and correlations for the endogenous variables. Since I use a variety of institutional indicators that might be interrelated to a substantial degree, I pay particular attention to multicollinearity in order to account for the reliability of later results. The fact that more developed regions tend to show higher rates of GRP per capita, higher average wages, and, correspondingly, higher rates of Internet usage, which also correlates with higher shares of employees with tertiary education, explains a range of relatively high correlation coefficients between 60 and 76%. Interestingly, regions with high numbers of filed patents or high shares of employees with tertiary education also appear to be particularly affected by high degrees of business

22

Notably, all variables sourced from Opora Rossii, as well as the data on Internet use, were normalized during the process of variable construction. This is primarily due to the changing number of surveyed regions in each Opora report.

5.2 Data and Sample Selection

169

Table 5.8 Summary statistics of variables Variable Obs Dependent variable and natural entry rates Entry rate Rus. 10,725 Entry rate EU 8580 Entry rate post-soc. 10,725 Entry rate EU (avg. 08–12) 10,725 Entry rate post-soc. (avg. 08–12) 10,725 Structural economic factors rsk_econ 10,725 unempl 10,725 avwage 10,725 reg_mincgini 10,725 Property rights patent_coef 10,725 raiding_cases 10,725 Criminality buscrm_bus 10,725 reg_safety 10,725 Corruption op_art_corr 10,725 Bureaucracy reg_administ 10,725 op_ad_nocntrl 10,725 op_ad_noprsc 10,725 Financial capital op_fin_short 10,725 op_fin_long 10,725 Human capital heduc 10,725 op_hr_ingtech 10,725 educ 10,725 Infrastructure op_infra 10,725 op_art_en 10,725 op_art_prp 10,725 ICT_idx_std 10,725 Market environment MA_intensity 10,661 HHI_ind 10,725 op_art_spl 10,725 Democratization crn_democracy 10,725 Control variables grp_growth 10,725

Mean

Std. dev.

Min

Max

0.133 0.088 0.098 0.088 0.094

0.096 0.029 0.035 0.027 0.028

0 0.040 0.020 0.044 0.036

1 0.176 0.220 0.159 0.162

22.99 6.47 22,355 0.40

19.54 2.28 6727 0.03

1 0.80 12,198 0.34

71 13.70 48,400 0.55

2.33 0.54

1.97 1.53

0.46 0

12.47 12

3.58 28.53

7.10 9.02

0 12.90

50 52.60

0.52

0.23

0.03

0.98

41.45 0.50 0.50

12.85 0.23 0.25

14.50 0.04 0.03

72.00 1 1

0.50 0.50

0.25 0.25

0.03 0.03

1 1

27.26 0.49 35.08

5.83 0.26 23.50

18.50 0.03 1

51.80 1 87

0.49 0.50 0.51 0.54

0.27 0.22 0.23 0.14

0.03 0.04 0.03 0.25

1 0.94 1 1

0.26 0.08 0.51

1.39 0.15 0.23

0 0 0.03

16.40 0.85 1

33.23

5.24

20

45

3.78

6.42

19.60

14.00 (continued)

170

5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

Table 5.8 (continued) Variable grp_pc

Obs 10,725

Mean 235,883

Std. dev. 169,427

Min 80,715

Max 1,198,186

Summary statistics for linearly imputed data from 2007 to 2011; summary statistics for regional clusters and the reduced sample (innov. branches), as described in Sects. 5.2.7 and 5.3.1, are provided in Annex A.3

violence (76% and 74%, respectively). Finally, conceptually related indicators also show plausibly high degrees of correlation, for example, the availability of shortand long-term finance (63%) and the threat of raidership, business criminality, and regional inequality (62–76%). Only patent_coeff (the share of patent applications) and heduc (the share of employees with tertiary education) show a disproportionately high degree of correlation (81%), which can be explained by the fact that firms with particularly high shares of educated employees tend to be more innovative, resulting in higher numbers of filed patents. Against this background, with the exception of patent_coeff and heduc, Table 5.9 does not provide any clues as to which factors I would need to exclude from the following analysis due to correlations higher than 80%. Nevertheless, since there are in fact some variables with considerable levels of correlation, multicollinearity is indeed an issue to address. Research perspective 1 explicitly addresses this issue by avoiding the inclusion of different institutional factors simultaneously in the empirical model, as is further explained in Sect. 5.3.1. Nevertheless, by handling potentially interrelated variables independently, a significant risk of spurious correlation remains (Blalock 1963). This is why research perspective 2 later explicitly accounts for the aspect of interrelatedness of institutional predictors. Another important aspect to consider is the distribution of entry rates, which varies considerably across Russian industries. Most significantly, some industries have an exceptionally high density of entry rates equal to zero, as illustrated by an excerpt of industries in Fig. 5.5 and in more detail in Annex A.4. In some industries, entry is a scarce event in general, as in the tobacco industry in Russia (NACE industry 12). Apart from that, there are industries that, to some extent, show an approximately normal distribution of entry, except that they are also subject to a high number of zero entries (e.g., NACE industries 13, 20, 30, etc.). Lastly, some industries are indeed approximately normally distributed (e.g., NACE industries 38, 52, 82, etc.). There is, however, no clear predictor of entry rates equal to zero among the available set of variables.23 Particularly high rates of entry, i.e., entry

23

Coming from the observed distributions, I further investigated the issue of zero entry. Annex A.5 shows the impact of the given set of variables on the likelihood of observing an entry rate equal to zero (i.e., 1 on the Y-axis) vs. a non-zero entry rate (i.e., 0 on the Y-axis). The blue mean curve represents the likelihood of observing entries of 0 given the value on the X-axis. Except from minor effects for the HHI and post-soc. entry variables, there are no obvious relationships. The former may be explained by the fact that some industries are generally not very prone to entry in Russia (or other post-socialist countries), for example, tobacco or banking.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27)

rsk_econ unempl avwage reg_mincgini patent_coef raiding_cases buscrm_bus reg_safety op_art_corr reg_administ op_ad_nocntrl op_ad_noprsc op_fin_short op_fin_long heduc op_hr_ingtech educ op_infra op_art_en op_art_prp ICT_idx_std MA_intensity HHI_ind op_art_spl crn_democracy grp_growth grp_pc

(1) 1 0.30 0.02 0.37 0.24 0.16 0.27 0.24 0.06 0.19 0.17 0.01 0.07 0.06 0.24 0.31 0.15 0.02 0.19 0.04 0.17 0.04 0.00 0.02 0.44 0.09 0.21

1 0.01 0.31 0.46 0.30 0.47 0.01 0.10 0.12 0.17 0.08 0.08 0.22 0.43 0.30 0.10 0.19 0.22 0.09 0.27 0.02 0.02 0.05 0.11 0.34 0.29

(2)

1 0.45 0.26 0.15 0.35 0.19 0.00 0.09 0.03 0.14 0.19 0.05 0.47 0.09 0.21 0.06 0.11 0.05 0.70 0.08 0.04 0.04 0.08 0.22 0.76

(3)

1 0.62 0.52 0.63 0.06 0.14 0.17 0.11 0.10 0.27 0.04 0.60 0.23 0.31 0.10 0.22 0.00 0.59 0.08 0.00 0.01 0.05 0.03 0.63

(4)

Table 5.9 Correlation table of institutional and control variables

1 0.50 0.76 0.19 0.34 0.10 0.01 0.07 0.10 0.03 0.81 0.15 0.15 0.16 0.06 0.01 0.64 0.16 0.00 0.08 0.06 0.00 0.36

(5)

1 0.62 0.19 0.11 0.03 0.05 0.04 0.09 0.11 0.48 0.14 0.13 0.06 0.01 0.12 0.31 0.00 0.01 0.06 0.07 0.00 0.31

(6)

1 0.27 0.19 0.07 0.10 0.08 0.06 0.04 0.74 0.16 0.21 0.05 0.07 0.05 0.57 0.03 0.00 0.09 0.11 0.04 0.44

(7)

1 0.25 0.60 0.29 0.29 0.13 0.26 0.21 0.14 0.09 0.32 0.09 0.09 0.21 0.00 0.00 0.08 0.26 0.09 0.08

(8)

1 0.04 0.58 0.50 0.21 0.18 0.27 0.02 0.00 0.24 0.11 0.03 0.15 0.05 0.00 0.34 0.10 0.04 0.02

(9)

1 0.14 0.15 0.07 0.11 0.02 0.15 0.01 0.21 0.15 0.12 0.01 0.03 0.01 0.03 0.16 0.08 0.14

(10)

1 0.61 0.32 0.31 0.03 0.27 0.10 0.30 0.12 0.24 0.05 0.03 0.00 0.47 0.25 0.01 0.13

(11)

1 0.27 0.18 0.10 0.01 0.13 0.02 0.23 0.17 0.05 0.09 0.00 0.32 0.08 0.05 0.18

(12)

1 0.00 0.33 0.06 0.35 0.33 0.13 0.01 0.02 0.00 0.28 0.19 0.01 0.02

(14)

(continued)

1 0.63 0.11 0.25 0.01 0.34 0.25 0.30 0.07 0.04 0.00 0.46 0.13 0.01 0.19

(13)

5.2 Data and Sample Selection 171

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27)

rsk_econ unempl avwage reg_mincgini patent_coef raiding_cases buscrm_bus reg_safety op_art_corr reg_administ op_ad_nocntrl op_ad_noprsc op_fin_short op_fin_long heduc op_hr_ingtech educ op_infra op_art_en op_art_prp ICT_idx_std MA_intensity HHI_ind op_art_spl crn_democracy grp_growth grp_pc

Table 5.9 (continued)

1 0.17 0.35 0.04 0.02 0.03 0.67 0.15 0.01 0.03 0.01 0.01 0.48

(15)

1 0.18 0.37 0.42 0.33 0.17 0.12 0.00 0.05 0.23 0.01 0.22

(16)

1 0.27 0.05 0.26 0.27 0.09 0.00 0.00 0.09 0.03 0.27

(17)

1 0.45 0.36 0.06 0.12 0.00 0.35 0.06 0.04 0.16

(18)

1 0.26 0.08 0.15 0.00 0.23 0.17 0.00 0.22

(19)

1 0.05 0.08 0.00 0.37 0.00 0.09 0.02

(20)

1 0.03 0.04 0.08 0.14 0.16 0.57

(21)

1 0.02 0.05 0.07 0.06 0.02

(22)

1 0.00 0.00 0.02 0.01

(23)

1 0.01 0.03 0.00

(24)

1 0.08 0.04

(25)

1 0.04

(26)

1

(27)

172 5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

Fig. 5.5 Entry rate distributions across industries (selected industries)

5.2 Data and Sample Selection 173

174

5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

higher than 0.5 for a given region, industry, and year, are an issue to consider, as well. However, their occurrence can be more easily explained since exceptionally high rates of entry are primarily related to low numbers of incumbents in a particular region and industry per year (i.e., approximately fewer than 10). Finally, I performed an initial assessment of the marginal relationship between the entry rate and the potential predictor variables. The scatterplots and the blue trend curve provided by Annex A.6, however, do not show any clear trends that might indicate high or low entry rates implied by the particular values of a given predictor. I conducted a similar analysis based on the structural dimensions of the data set in order to investigate if the linear relationship between entry rate and predictor variable may differ across regions and industries (based on a simple OLS model, shown in Annex A.7). In total, all coefficients only showed marginal deviations from zero regarding traditional methods of analysis.24

5.3 5.3.1

Perspective 1: A Descriptive Regression Model Approach Research Design

In the first study, to capture institutional impacts on entrepreneurial entry, my overall research design follows the difference-in-difference approaches of Klapper et al. (2006) and Bruno et al. (2013), based on an observation timeframe from 2007 to 2011 and by making some modifications. To analyze the relationship between entry and spatial institutional environment, I employed a Tobit estimation model.25 I used regional entry rates in Russia as a dependent censored [0, 1] variable, calculated as the share of new entrants per year on incumbents. The impacts of spatial institutional factors are captured by interaction terms of natural entry rates and the respective institutional factor. The model specification aims to test how institutions differently affect entry rates given the natural entry rate. For each regression, the Inst.r, t parameter was separately loaded with one of the institutional variables presented in Sect 5.2.3. To consider the effect that the current

24 There are marginal effects observable for the MA_intensity, op_ad_noprsc, op_art_corr, op_art_spl, op_fin_long, and op_infra variables in some regions. Regarding the industry perspective, only HHI_ind shows some effects because it is one of the few variables that vary across industries instead of regions. Hence, these variables are, to some extent, promising to show significant results in the analyses of the following chapters. 25 The Tobit model is suitable for corner solutions, where non-negativity constraints force (theoretically negative) values to be zero. Under these circumstances, OLS estimates would be distorted downward and inconsistent, whereas Tobit estimates are asymptotically normal and consistent (Stewart 2009; Amemiya 1973). Since the distribution of the entry rate variable implies a strong concentration of censored entry rates with value 0 (as illustrated in Annex A.6), the model is expected to result in more reliable results than OLS models.

5.3 Perspective 1: A Descriptive Regression Model Approach

175

institutional environment may influence entry either today or in the future, the institutional factor variables were parametrized for the same year as the dependent entry variable (tInst ¼ tEntry) and with a 1-year lag (tInst  1 ¼ tEntry). The basic model specifications are as follows: R i X   X EntryRater,i,t ¼ α þ β  Nat:EntryEU þ x Inst: β D þ βi Di r,t r r 20082012 r¼1

þ

t X

r¼1

βt Dt þ er,i,t

ðIÞ

r¼1 R   X β r Dr EntryRater,i,t ¼ α þ β  Nat:EntryEU 20082012 x Inst:r,t1 þ r¼1

þ

i X r¼1

βi Di þ

t X

βt Dt þ er,i,t

ðIIÞ

r¼1

with r denominating the Russian region, i the industry according to NACE two-digit code, and t year. The basic specifications also include region, sector, and time dummies (Dr, Di, Dt) to control for fixed regional, industrial, and time effects.26 Finally, er, i, t is an error term following the common distributional assumptions. The estimation results for both models are reported with a particular interest in the sign of the β coefficient stemming from the interaction term of the given institutional factor and the natural entry rate. To facilitate the interpretation of the Tobit regression model coefficients, the following transformations were made: first, all institutional variables were standardized so that coefficient effect sizes of the same samples could be compared (as described in Sect. 5.2.3). In addition, the model specification aims at interpreting higher values of the given institutional variable as higher level of institutional barriers, which is why I expect a negative β coefficient in case of a significantly negative impact on entry. Last, Tobit marginal effects are reported on a truncated expected value at 0 (lower bound) and 1 (upper bound). This enables interpreting the magnitude of the β coefficients as the percentage decrease of the entry rate for a region moving from the best to the poorest institutional context. Robustness of Results In addition to the baseline model specification, I consider some variations in order to check the robustness of the empirical results. Complementary to the basic specifications, I aim to control for the influence of other factors generally believed to be important for entrepreneurial activity, as described in Sect. 5.2.4. As recommended 26

To account for unobserved regional effects, I created dummy variables for regions. Regional differences apart from the considered institutional factors may result in different levels of entrepreneurial activity across regions (Busenitz et al. 2000; Baumol 1996; North 1990). Similarly, I controlled for time effects across all regions (Caprio and Klingebiel 2002) and for specific industry effects.

176

5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

by Schjoedt and Bird (2014) and Carlson and Wu (2012), in order to determine if the effects of controls are meaningful in the analysis, I initially ran the analyses without and then with control variables. Hence, I introduced a set of two control variables, i.e., GRP per capita and the growth rate of real GRP, in addition to the dummy variables already included, to the basic specifications I–II, resulting in two additional specifications, i.e., III–IV. If the results do not differ, I expect that the control variables do not account for the findings. Additionally, as a central implication from Chap. 3 and Sect. 5.2.7 of this thesis, I considered it imperative to account for the high degrees of spatial heterogeneity across Russian regions. In this regard, I utilized two different approaches of regional clustering in order to analyze if different types of regions were subject to different effects of institutional factors on entrepreneurial entry. Furthermore, this strategy aims to avoid the result that different effects among different types of regions fade out in an overall perspective on all regions.27 Consequently, specifications I–IV were performed separately for each cluster based on the two alternate cluster approaches, i.e., the urbanization-based and the EGP perspective. Aside from the controlled and cluster-based specifications, I conducted a set of additional robustness tests to further substantiate the reliability of the identified relationships and the tested hypotheses. The results of these checks, however, are not explicitly presented in the discussion part of this thesis but rather provided in the annex. Against this background, the following procedures were performed. First, I ran the analysis with a reduced sample that only encompassed highly innovative branches. Compared to, for instance, the retail, gastronomy, or construction sectors, start-ups in innovative and knowledge-intensive industries are considered to have a higher impact on regional growth and employment, as well as other indirect beneficial effects for economic development.28 However, variation in the entry of high-innovation entrepreneurship might be encouraged or restrained by different institutional peculiarities compared to less innovative industry branches. Against this background, I ran model specifications I and II on a reduced sample focusing on high-innovation industries only.29 The results are provided in Annex B.1. The following chapter refers to these results in cases of noteworthy findings or differences compared to specifications I–IV. 27

As HSE professor Alexander Chepurenko strikingly puts it, this would be similar to making conclusions by “measuring the average temperature of all patients in a hospital.” 28 For example, in a study focusing on Germany, Fritsch and Schroeter (2011) have confirmed a stronger overall economic impact of new firms in knowledge-intensive service branches. This perspective is also supported by prior results from Falck (2007) and Fritsch and Noseleit (2009), which confirm that particularly high-innovation start-ups that were able to survive a critical minimum period have the highest economic impact. 29 The reduced sample selection is based on the 2009–2015 long-term view of the Reuters 2016 State of Innovation report (Reuters 2016), which highlights the following as the most constantly innovative industries: automotive, biotechnology, cosmetics and well-being, food, beverage and tobacco, home appliances, information technology, medical devices, pharmaceuticals, semiconductors, and telecommunications. In accordance with Sect. 5.2.1, I exclude the state sector-dominated aerospace and defense and oil and gas industries.

5.3 Perspective 1: A Descriptive Regression Model Approach

177

Second, I wanted to account for the fact that the results could be driven by the peculiarities of the industry structure in the EU. Hence, I considered natural entry rates from a range of European post-socialist countries in an alternative set of model specifications. I ran specifications I–IV by substituting EU natural entry rates with those from the set of Eastern European post-socialist countries. The results of the alternate entry rate specification are largely in line with the basic approach.30 Last, by comparing the results to those using the method of Bingham et al. (1998) as an alternative approach for data imputation (as introduced in Sect. 5.2.6), I checked for any potential biases caused by the linear imputation procedure.31 Based on the results of the test, the estimation results can be assessed as reliable and robust.

5.3.2

Results and Discussion

The following paragraphs provide an overview and a detailed discussion of the results of Sect. 5.3s regression analyses. Given that a second research approach follows in Sect. 5.4, all results should be interpreted as interim results. With regard to the presentation of results, a few important comments need to be mentioned beforehand. I tested each hypothesis on institutional framework factors based on one to three different measures of the institutional factor (cf. Table 5.4). In the following, and according to the methodological approach, the regression results are provided in three tables: Table 5.10 illustrates the regressions based on the overall regional sample, Table 5.11 provides results using the urbanization-based cluster perspective, and Table 5.12 shows the results using the EGP cluster perspective. Additionally, all tables entail different model specifications (i.e., utilizing lagged and control variables), as discussed in Sect. 5.3.1. For reasons of conciseness, the results tables only provide coefficients and standard errors for significant results. The reported effect sizes can be compared in Table 5.10 and within each individual cluster of the other tables, since the basic precondition for comparison are identical samples. The illustrated results are based on imputed data. However, to further enhance the robustness of results, I also performed individual regressions for every institutional variable for all years between 2006 and 2014 where data for the given factor was

30

The composition of the post-socialist natural entry data is outlined in Sect. 5.2.2. The model specifications utilizing post-socialist natural entry rates lead to different significant results in only 10.4% of all regressions compared to using EU industry natural entry rates (Annex B.2). Given the fact that post-socialist entry rates show higher volatility than EU rates, I deem this discrepancy to be in a tolerable range. 31 In total, the Bingham et al. (1998) approach produces different significant results in only 4.5% of all regressions compared to the linear imputation method (Annex B.3). We may thus conclude that the likelihood of a serious estimation bias due to employing simple linear imputation is comparatively low.

Baseline regression (I) (II) Variables Structural economic factors rsk_econ 0.068** – (0.022) unempl – – avwage 0.112** – (0.036) reg_mincgini 0.103*** 0.095** (0.025) (0.029) Property rights patent_coef 0.088** – (0.033) raiding_cases – – Criminality buscrm_bus – 0.056*** (0.016) reg_safety 0.043* 0.062** (0.021) (0.022) Corruption op_art_corr – – Bureaucracy reg_administ – – op_ad_nocntrl – – – – –

0.041* (0.021) – – –

–

–

0.049** (0.017) 0.056* (0.022)

–

0.089** (0.033) –

– –

– 0.118** (0.037) 0.107*** (0.025) 0.104*** (0.029)

–

–

Regression incl. controls (III) (IV)

Table 5.10 Regression results, overall regional perspective

– – –

– – –

–

–

– –

– – – 0.071* (0.028)

– – –

– – –

Baseline regression (I) (II)

– – – 0.076** (0.026) Market environment ma_intensity – HHI_ind 0.056*** (0.012) op_ad_noprsc – Democratization crn_democracy –

Variables Financial capital op_fin_long op_fin_short op_fin_short Human capital heduc op_hr_ingtech educ Infrastructure op_infra op_art_en op_art_prp ICT_idx_std

–

– 0.056*** (0.012) –

– – – 0.082** (0.027)

– – –

– – –

–

–

– –

– – – 0.077** (0.028)

– – –

– – –

Regression incl. controls (III) (IV)

178 5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

– 10,725

– 8580

– 10,725

– 8580 Obs.

10,725

8580

10,725

8580

This table shows Tobit regression marginal effects with censoring at zero and one. +, *, **, and *** denote significance at 10%, 5%, 1%, and 0.01% levels. White (1980) standard errors are reported in parentheses. The dependent variable is entry of new firms per year, region, and industry as a proportion of incumbent firms. Each independent variable has been regressed independently (i.e., each specification-variable combination is based on a separate regression). The null hypothesis of joint zero coefficients for all regressions is always rejected at the 1% level. Results for insignificant variables and region, sector, and time dummies are not reported. The “Regression incl. controls” section includes GRP per capita and GRP growth to account for the regional economic context

op_art_spl Obs.

5.3 Perspective 1: A Descriptive Regression Model Approach 179

–

–

raiding_cases

0.046*

–

op_ad_noprsc

–

–

–

–

op_fin_long

op_fin_short

Financial capital

–

–

(0.023)

–

–

–

op_ad_nocntrl

–

reg_administ

Bureaucracy

op_art_corr

Corruption

0.821*

–

–

–

–

–

–

–

–

–

–

–

–

(0.382)

–

–

–

–

0.076** –

reg_safety

(0.029)

–

–

–

–

–

buscrm_bus

Criminality

–

(0.089)

–

(0.055)

–

–

0.156** 0.221*

patent_coef

Property rights

reg_mincgini

0.238**

(0.074)

–

–

0.141*

avwage

–

–

(0.071)

–

–

–

unempl

–

–

–

rsk_econ

5

–

–

–

–

–

–

–

3

–

–

(0.031)

0.055+

–

–

–

–

–

–

5

–

–

(0.07)

0.168*

(0.055)

–

–

–

2

–

–

–

(0.055)

–

–

–

(0.073)

0.157** 0.178*

(0.071)

0.140*

–

–

1

–

–

(0.033)

–

–

–

(0.087)

–

–

–

–

(0.027)

0.060*

(0.023)

0.048*

(0.033)

–

–

–

–

0.103** –

–

–

–

–

–

–

–

–

–

–

–

–

3

–

–

(0.029)

0.057+ 0.075** –

–

–

(0.398)

0.669+

–

(0.092)

0.293**

–

–

0.143** 0.144+

–

(0.048)

–

0.097* –

(0.034)

–

–

–

–

–

–

–

–

0.079*

(0.031)

0.105*** –

(0.086)

0.183*

–

–

(0.053)

0.111*

–

–

–

2

1

3

1

2

(I)

Structural economic factors

Variables

Regression incl. controls (III)

(II)

Baseline regression

Table 5.11 Regression results, urbanization-based cluster perspective (IV)

–

–

–

(0.052)

0.116*

–

–

–

1

–

–

–

–

–

–

(0.038)

–

–

–

–

–

(0.035)

0.072*

(0.031)

–

–

–

3

–

–

–

(0.09)

–

–

–

–

–

–

–

–

(0.07)

0.141*

(0.055)

0.128*

–

–

–

–

–

–

0.216* –

–

–

–

2

0.151*** 0.102*** –

(0.016)

0.030+

–

(0.392)

1.110**

–

(0.073)

0.250***

–

–

5

–

–

–

–

–

–

–

–

–

(0.411)

0.840*

–

(0.094)

0.295**

–

–

5

180 5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

–

–

–

–

op_hr_ingtech

educ

–

–

–

–

op_art_prp

ICT_idx_std

–

–

HHI_ind

op_art_spl

3920

Obs.

(0.041)

(0.049)

3550

–

–

2595

–

–

660

(0.042)

0.069+

–

3136

–

–

2840

–

–

–

2076

–

–

– –

–

528

(0.047) 3920

0.086+ –

–

(0.013)

0.029*

0.186*

–

3550

–

–

(0.022)

2595

–

–

0.063** –

–

–

(0.082)

0.211*

–

–

–

–

(0.049)

0.111*

–

(0.015)

–

–

–

–

–

–

–

–

0.038*

–

–

–

–

–

–

–

–

0.063** –

–

(0.237)

0.553*

–

–

(0.058)

0.142*

–

–

–

(0.022)

–

–

–

–

–

–

–

–

(0.084)

–

–

–

–

–

–

–

–

(0.084)

–

(0.037)

(0.078)

–

–

0.097**

–

–

–

–

0.226** –

(0.076)

0.203** –

–

–

–

–

0.066**

0.069+ (0.024)

–

–

0.113*

–

660

(0.044)

0.101*

–

(0.016)

0.038*

–

–

–

(0.044)

0.098*

–

–

–

–

3136

–

–

–

–

(0.038)

0.094*

–

–

–

–

(0.024)

0.063**

–

2840

–

–

–

–

–

–

–

–

–

–

–

2076

–

–

–

–

–

–

–

–

–

–

–

528

(0.045)

0.093*

–

(0.012)

0.029*

–

(0.263)

0.631*

–

–

(0.062)

0.151*

(0.096)

0.366***

–

–

This table shows Tobit regression marginal effects with censoring at zero and one. +, *, **, and *** denote significance at 10%, 5%, 1%, and 0.01% levels. White (1980) standard errors are reported in parentheses. The dependent variable is entry of new firms per year, region, and industry as a proportion of incumbent firms. Each independent variable has been regressed independently (i.e., each specificationvariable-cluster combination is based on a separate regression). The null hypothesis of joint zero coefficients for all regressions is always rejected at the 1% level. Results for insignificant variables and region, sector, and time dummies are not reported. Results that are significant at the 10% level are only reported for the urbanization-based cluster five due to the relatively small sample size. The “Regression incl. controls” section includes GRP per capita and GRP growth to account for the regional economic context

–

crn_democracy

Democratization

–

ma_intensity

0.173*

–

–

op_art_en

Market environment

–

–

op_infra

Infrastructure

–

–

heduc

Human capital

5.3 Perspective 1: A Descriptive Regression Model Approach 181

Bureaucracy reg_administ

Corruption op_art_corr

reg_safety

raiding_cases Criminality buscrm_bus

– – – –

3

–

– –

–

–

–

–

–

0.087** – (0.033)

–

–

–

–

0.084* (0.037)

–

–

0.077* (0.039)

–

0.141*** (0.034)

–

0.123* – (0.056) – –

(II) 1

–

Baseline regression (I) 1 2 Variables Structural economic factors rsk_econ – 0.109** (0.038) unempl – – avwage – 0.194** (0.06) reg_mincgini 0.114* 0.092*** (0.057) (0.026) Property rights patent_coef – –

Table 5.12 Regression results, EGP cluster perspective

– –

–

3

–

–

0.065* (0.027)

–

–

–

0.047** – (0.018) – –

–

–

0.084** – (0.031)

– –

–

2

–

0.086* (0.037)

0.093** (0.033)

–

0.138* (0.054) –

0.124* (0.057)

–

–

–

–

–

–

–

–

0.109** (0.039) – 0.203** (0.062) 0.095*** (0.026)

–

–

–

–

–

–

–

– –

–

Regression incl. controls (III) 1 2 3

0.115** (0.043)

–

0.149*** (0.035)

–

0.140* (0.064) –

–

– –

–

(IV) 1

0.067* (0.031)

–

–

–

–

–

0.081** (0.031)

– –

–

2

–

0.069* (0.033)

–

–

–

–

–

– –

–

3

182 5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

– – –

– – –

–

–

–

– – –

op_art_en op_art_prp ICT_idx_std

–

– 0.078* (0.039)

–

– – 0.116** (0.036)

–

Infrastructure op_infra

– –

–

–

–

– –

op_hr_ingtech educ

–

–

–

0.083* (0.039) – –

–

–

–

– –

–

–

op_fin_short Human capital heduc

–

–

–

– –

–

–

–

–

– 0.079** (0.024) –

–

–

op_ad_noprsc Financial capital op_fin_long

Market environment ma_intensity – HHI_ind 0.041* (0.017) op_art_spl – Democratization crn_democracy –

–

–

op_ad_nocntrl

–

–

– –

– – 0.092** (0.035)

–

– –

–

–

–

–

–

–

–

– –

0.077* (0.036) – – –

– –

–

–

–

–

–

–

– 0.041* (0.017) –

– – –

–

– –

–

–

–

–

–

–

– 0.079** (0.024) –

– – 0.134*** (0.039)

–

0.082* (0.039) – –

–

–

–

–

–

–

– –

– – –

–

– –

–

0.088 * (0.035) –

–

–

–

–

– –

– – –

–

– 0.099* (0.04)

–

–

–

–

–

–

–

– –

– – 0.093* (0.038)

–

– –

–

–

–

–

–

(continued)

–

–

– –

0.069* (0.035) – – –

– –

–

–

–

0.072* (0.03) –

5.3 Perspective 1: A Descriptive Regression Model Approach 183

3 3575

(II) 1 3380 2 2340

3 2860

Regression incl. controls (III) 1 2 3 4225 2925 3575 (IV) 1 3380 2 2340

3 2860

This table shows Tobit regression marginal effects with censoring at zero and one. +, *, **, and *** denote significance at 10%, 5%, 1%, and 0.01% levels. White (1980) standard errors are reported in parentheses. The dependent variable is entry of new firms per year, region, and industry as a proportion of incumbent firms. Each independent variable has been regressed independently (i.e., each specification-variable-cluster combination is based on a separate regression). The null hypothesis of joint zero coefficients for all regressions is always rejected at the 1% level. Results for insignificant variables and region, sector, and time dummies are not reported. The “Regression incl. controls” section includes GRP per capita and GRP growth to account for the regional economic context

Variables Obs.

Baseline regression (I) 1 2 4225 2925

Table 5.12 (continued)

184 5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

5.3 Perspective 1: A Descriptive Regression Model Approach

185

available without imputation. I only present significant results for the imputed data if the regressions on the non-imputed data also provided significant results of the same coefficient sign. Although a direct comparison of the coefficient size is not feasible due to the different sample period and size, we may at least check if the given institutional factor exerts a significant impact on entrepreneurial entry without possibly being biased by artificial data imputation. The only exception to this procedure is the lagged regressions (specifications II and IV) of variables obtained from Opora Rossii, since, unfortunately, coherent observation periods of two or more consecutive years with non-imputed data are not available. Since the following chapters present many results, and to ensure auditability in the following discussion, the results are cited as follows. When discussing the results of one of the results tables, each coefficient reference includes a subscript that is divided into three parts (e.g., braiding _ cases, II, 3, or breg _ administ, IV, 1). The first part refers to the variable under examination, while the second refers to the model specification. The third part of the subscript refers to the respective cluster (i.e., 1, 2, 3 in the urbanization-based and EGP perspectives and additionally 5 in the urbanization-based perspective; there is no cluster indicator when referring to the non-clustered regional perspective in Table 5.10). This way, the cited coefficients are intended to help easily navigate the results tables.

5.3.2.1

Structural Economic Factors

Economic Uncertainty and Risk Starting with economic uncertainty and risk, from an overall regional perspective (Table 5.10), the results indicate that the perception of higher economic risk implies higher entry rates (brsk _ econ, I ¼ 0.068). Additionally, the economic risk variable has a rather immediate impact, i.e., the higher risk-higher entry effect is only significant within the same year. Notably, the effect disappears once we consider controls for economic development and growth. The urbanization cluster perspective also does not lead to further insights because we do not observe any significant effects across all clusters. The EGP cluster (Table 5.12), however, points out significant effects in the high international and regional EGP cluster two, i.e., the regions in and around Russia’s federal capitals (i.e., brsk _ econ, I, 2 ¼ 0.109 and brsk _ econ, III, 2 ¼ 0.109). Again, we observe an immediate effect in the same year, which this time also holds once economic controls are considered. We may conclude that those regions in particular are home to well-qualified workers and provide a modern, innovative, and internationally connected environment. Those preconditions may provide attractive market niches when economic conditions impair incumbents’ market performance and appear to make entrepreneurship an appealing alternative in times of economic hardship. Notably, this relationship may only work in regions with a sufficiently strong middle class and enough purchasing power, even in times of economic stagnation. Nonetheless, it has to be admitted that the fact of only observing sameperiod effects might also point in the direction of endogeneity. The presence of

186

5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

endogeneity would imply that a growing share of new companies increases economic risk. In such a situation, the economic risk variable might cover higher and more intense competition that eventually leads to asset depreciation, higher portions of loss-making companies and more bankruptcies, particularly if we remember that Klapper et al. (2006) have suggested a strong correlation of entry and exit rates. Hypothesis H1 predicted that entrepreneurial entry would be negatively related to higher rates of economic risk. Indeed, there are indications that this is the case; however, the observed results also leave considerable doubt as to a clear causal relationship. Hence, hypothesis H1 was not supported. Unemployment as a Push Factor for Entrepreneurship? Regarding the unemployment variable, Tables 5.10, 5.11, and 5.12 do not imply any significant effects from variation in unemployment on entrepreneurial entry. Hence, even if there is indeed a refugee effect causing unemployed individuals to choose self-employment as the best available alternative for earning a living, it is rather likely that they end up as sole proprietors or in the informal economy. Another root cause might be attributable to Russia’s low elasticity of employment; as noted in Sect. 4.1, the restrictive labor market policy in Russia largely favors job maintenance and social stability over efficient labor markets, and it pressures companies not to reduce staff headcount. Hence, overall variation in unemployment might be too low to substantially affect entrepreneurial entry. Consequently, hypothesis H2 was not supported. Spatial Wage Differentials and the Distribution of Income and Wealth In terms of the spatial wage differential regressions in Table 5.10, the results suggest a significantly negative impact of increasing wages on entrepreneurial entry (bavwage, I ¼ 0.112, and bavwage, III ¼ 0.118). The relationship also holds when additional economic controls are considered, when the effect increases slightly. The given evidence supports the hypothesis that higher levels of wages provide an alternative source of income, which raises the opportunity costs of engaging in entrepreneurship and makes the latter occupational choice less attractive. At the same time, those salaries can be earned without bearing the risk of entrepreneurial activity. The urbanization-based cluster perspective in Table 5.11 provides even more interesting insights. It supports the same-period effect for cluster one (bavwage, I, 1 ¼ 0.141, and bavwage, III, 1 ¼ 0.140) and reveals a same-period and a lagged cluster five effect (bavwage, I, 5 ¼ 0.238, bavwage, II, 5 ¼ 0.293, bavwage, III, 5 ¼ 0.250, and bavwage, IV, 5 ¼ 0.295). All effects also hold once economic control variables are incorporated into the regressions. Hence, it seems that entrepreneurial activity in the federal capitals and in regions that host large urban centers is particularly affected by wage increases, possibly because well-qualified middle-class and white-collar workers simply have more to lose. On the other hand, salaries in cluster two and three regions are substantially lower than those in the federal capitals and cluster one region; thus the marginal effect of a rise in wages on entrepreneurial activity might be either too small or ineffective, or entrepreneurial opportunities are lacking anyway, and people are pushed into necessity entrepreneurship. In the latter case, the wage-opportunity entrepreneurship relation would be virtually nonexistent.

5.3 Perspective 1: A Descriptive Regression Model Approach

187

The EGP cluster perspective (Table 5.12) is quite supportive of these arguments. We observe a significant negative effect of increasing wages for the same-year regressions of cluster two (bavwage, I, 2 ¼ 0.194 and bavwage, III, 2 ¼ 0.203). Again, the federal capitals and the closely located, highly urbanized regions are affected most, even though the long-term effect previously observed in the capitals is mitigated by including the surrounding regions. In total, the empirical results confirm that the average wage measure shows a significantly negative relation towards higher levels of entrepreneurial entry, although only for the highly urbanized clusters. Thus, the results support hypothesis H3, but with a restriction to the country’s urbanized centers. The last structural aspect to discuss is the inequality of income and wealth distributions, measured by the Gini coefficient. From an overall regional perspective, Table 5.10 suggests a significantly negative impact across all periods and specifications, which means the more unequally income and wealth are distributed, the less entry can be expected in the same and the following year (breg _ mincgini, I ¼ 0.103, breg _ mincgini, II ¼ 0.095, breg _ mincgini, III ¼ 0.107, and breg _ mincgini, IV ¼ 0.104). Across all cross-regional specifications, the Gini coefficient effect sizes are the second highest, after the wage impact. The concentration of wealth in the arms of a few thus leaves entrepreneurs without financial means to realize their ideas in the form of newly founded companies, regardless of the cluster or type of region. According to expert and Rosstat employee assessments, because the estimation method for the Gini coefficient variable relies on surveys that primarily cover lower-income groups, the index might even underestimate the level of inequality (ICSID Codebook 2017). We may thus expect the real impact of an unequal distribution of wealth and income to be even higher. From the urbanized cluster perspective (Table 5.11), we observe similar results. Observations of significant effects concentrate on the highly and medium-urbanized clusters one and two, which also hold for the controlled specifications (e.g., breg _ mincgini, III, 1 ¼ 0.157, breg _ mincgini, IV, 1 ¼ 0.116, breg _ mincgini, III, 2 ¼ 0.178, and breg _ mincgini, IV, 2 ¼ 216). A possible conclusion of these effects could be that the federal capitals provide alternative means for financing and other support for entrepreneurial activity. Hence, pursuing entrepreneurial opportunities might be dependent to a lesser degree on personal wealth or income, whereas the absence of the latter has more severe consequences in less modern and developed regions. This effect could be particularly severe in cluster two, which is characterized by high numbers of blue-collar workers living in mid-sized industrial towns and partially one-company cities. The fact that there is no effect at all for the Moscow/St. Petersburg cluster, although it is subject to high levels of inequality, also supports this conclusion. Taking into consideration the EGP cluster perspective, there are similar significant effects in clusters one and two. Whereas the cluster one effect for Russia’s remote regions disappears, presumably by virtue of the higher predictive power of the economic control variables (breg _ mincgini, I, 1 ¼ 0.114, breg _ mincgini, III, 1 ¼ 0.124), the cluster two results are stable across all specifications (breg _ mincgini,

188

5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

¼ 0.092, breg _ mincgini, II, 2 ¼ 0.084, breg _ mincgini, III, 3 ¼ 0.095, and breg _ mincgini, IV, 2 ¼ 0.081). Consequently, the empirical results confirm that the measure for an unequal distribution of income or wealth shows a significantly negative relation towards higher levels of entrepreneurial entry. Thus, hypothesis H4 was supported.

I, 2

5.3.2.2

Ensuring Property Rights

With regard to the factor that most studies have described as the indispensable precondition for entrepreneurial activity, I now focus on the results for the two variables related to property rights protection. Entrepreneurial Activity and Property Rights in Russia Starting with the first property rights measure (patent_coeff), the non-clustered cross-regional perspective (Table 5.10) suggests that a rising number of filed patents per capita indicate lower rates of entrepreneurial entry in the same period. The effect stays the same if we account for additional control variables (bpatent _ coef, I ¼ 0.088, and bpatent _ coef, III ¼ 0.089). This relationship appears somewhat peculiar; it implies that entrepreneurial entry benefits from lower levels of property rights. An in-depth look into the cluster perspectives also provides little help in explaining the observed effects. In the urbanization-based cluster perspective (Table 5.11), we identify the same effect that is exclusively driven by cluster five across all specifications (bpatent _ coef, I, 5 ¼ 0.821, bpatent _ coef, II, 5 ¼ 0.669, bpatent _ coef, III, 5 ¼ 1.110, and bpatent _ coef, IV, 5 ¼ 0.840). The EGP cluster perspective (Table 5.12) also does not provide further insights, since the cluster three effect (bpatent _ coef, I, 3 ¼ 0.123) quickly disappears after accounting for economic controls, whereas cluster one only shows significant effects in the controlled specifications (bpatent _ coef, III, 1 ¼ 0.138 and bpatent _ coef, IV, 1 ¼ 0.140). Overall, the effects observed across all regions and in the regional clusters lead to the thought that the property rights proxy might be questionable from a conceptual angle. Instead of tracing lower rates of entry to higher numbers of filed patents, the relationship might stem from higher numbers of large and innovative firms that are located in Moscow and St. Petersburg, i.e., in urbanization cluster five. Those are the regions where the highest numbers of filed patents are registered. Since the bulk of new patents relates to large-scale incumbent R&D, the proxy variable does not just indicate a higher quality of safe property rights but most likely higher competition from incumbents. This could at least point towards an explanation for the EGP cluster one effects, since the mechanisms of incumbent competition can be expected to be more intense in the limited markets of Russia’s periphery. In either case, higher numbers of patents are thus not likely to indicate a warranty for intellectual property protection from entrepreneurs. Hypothesis H5 predicted that entrepreneurial entry would be positively related to the perceived security of property rights. As shown by the empirical results, the employed proxy measure for property rights was both not positively correlated with

5.3 Perspective 1: A Descriptive Regression Model Approach

189

higher levels of entrepreneurial entry and rather likely to primarily measure competition from incumbents. Thus, hypothesis H5 was not supported. The Raidership Phenomenon The second property rights measure captures the number of raiding cases reported in the media (raiding_cases). Based on all three results tables, there is no indication of a significant relationship between the amount of raidership in a region and rates of entrepreneurial entry. Hypothesis H6 predicted that entrepreneurial entry would be negatively related to the perceived threat from raidership. As the empirical results show, the perception of a raidership threat does not lead to the assumption of any correlation with lower or higher levels of entrepreneurial entry. Thus, hypothesis H6 was not supported.

5.3.2.3

Criminality

The overall perspective on the regional sample (Table 5.10) indicates that both criminality-related variables prove a negative impact of perceived criminality levels on entrepreneurial entry in the same year and following years. The highest impacts (breg _ safety, II ¼ 0.062 and breg _ safety, IV ¼ 0.056) can be attributed to the lagged specifications using reg_safety. Hence, if more people perceive their environment as relatively safe, apparently more people decide to engage in entrepreneurial ventures in the subsequent period. The coefficient sizes are quite similar to the effects suggest by the buscrm_bus variable (bbuscrm _ bus, II ¼ 0.056 and bbuscrm _ bus, IV ¼ 0.049). A change of perspective towards the urbanization-based clusters (Table 5.11) confirms the identified relationships and reveals further details. In contrast to the effects of cases of business violence, which tend to be somewhat sporadic (bbuscrm _ bus, II, 1 ¼ 0.183 and bbuscrm _ bus, III, 5 ¼ 0.030), the results on perceiving the environment as relatively safe are more stable. It seems that cluster one in particular is subject to the identified relationship because the effects hold across all specifications (bbuscrm _ bus, I, 1 ¼ 0.076, bbuscrm _ bus, II, 1 ¼ 0.105, bbuscrm _ bus, III, 1 ¼ 0.075, and bbuscrm _ bus, IV, 1 ¼ 0.102). The EGP cluster perspective (Table 5.12) largely confirms these results. From this perspective, entrepreneurial activity in the country’s remotely located cluster one regions appears to be particularly sensitive to safety concerns, as suggested by the reg_safety coefficients across all specifications (breg _ safety, I, 1 ¼ 0.087, breg _ safety, II, 1 ¼ 0.141, breg _ safety, III, 1 ¼ 0.093, and breg _ safety, IV, 1 ¼ 0.149). The buscrm_bus variable only shows significant results in one particular case, i.e., bbuscrm _ bus, II, 2 ¼ 0.047. Concomitant, greater levels of safety are clearly beneficial for entrepreneurial activity in both Russia’s urbanized regions apart from the federal capitals, as well as in the country’s remote regions. It appears that security is indeed an issue of concern for potential entrepreneurs. Hypothesis H7 predicted that entrepreneurial entry would be negatively related to perceived levels of criminality. As the empirical results show, criminality levels

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were significantly correlated with lower levels of entrepreneurial entry, which is why hypothesis H7 was supported.

5.3.2.4

Corruption

Hypothesis H8 predicted that entrepreneurial entry would be negatively related to perceived levels of corruption. According to Table 5.10, there is no indication that, from an overall regional perspective, corruption affects current or future rates of entrepreneurial entry. However, the regional cluster perspectives demonstrate to some interesting relationships. The urbanization-based clusters (Table 5.11) suggest that highly urbanized and industrial regions in particular are affected by a negative impact of corruption levels on entrepreneurial entry in the following year, i.e., cluster one (bop _ art _ corr, II, 1 ¼ 0.079 and bop _ art _ corr, IV, 1 ¼ 0.072) followed by cluster two (e.g. bop _ art _ corr, II, 2 ¼ 0.097). The cluster one effect also holds in the controlled specifications. In contrast to the lagged effects from the urbanization clusters, the EGP perspective (Table 5.12) provides evidence that there is an immediate, same-year effect in EGP cluster one (bop _ art _ corr, I, 1 ¼ 0.084 and bop _ art _ corr, III, 1 ¼ 0.086) and a lagged negative impact in EGP cluster three (bop _ art _ corr, IV, 3 ¼ 0.069). Given the fact that corruption in Russia is still a serious and common phenomenon, the negative effects on entrepreneurial entry seem plausible. However, there is no concise pattern regarding which regions are affected most by the negative effects of corruption. Although there are some indications that remote regions may be affected more than the country’s federal centers, in contrast, there is also evidence that highly urbanized and industrialized regions are specifically affected more than less urbanized regions. As shown by the empirical results, higher levels of corruption were significantly correlated with lower levels of entrepreneurial entry. Thus, hypothesis H8 was supported.

5.3.2.5

The Burden of Bureaucracy

Administrative Barriers and Bureaucratic Red Tape The first bureaucracy-related hypothesis predicted significantly negative impacts from the level of administrative barriers (measured by reg_administ) on entrepreneurial entry. Neither the overall regional perspective nor the urbanization-based cluster perspective leads to any significant relationships between regional levels of administrative barriers and rates of entrepreneurial entry. There are, however, interesting results in the EGP perspective (Table 5.12), where we observe lagged and significantly negative effects of higher administrative barriers on entry in clusters one and two, which also hold for the controlled specifications (breg _ administ, II, 1 ¼ 0.077 and breg _ administ, IV, 1 ¼ 0.115, breg _ administ, II, 2 ¼ 0.065, and breg _ administ, IV, 2 ¼ 0.067). Although the effect direction is quite

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plausible, it seems curious that the EGP perspectives demonstrate a significant negative impact of administrative barriers on entry, whereas neither the overall regional perspective nor a single cluster in the urbanization-based perspective can identify any reliable relationships. Additionally, not observing any significant effects for EGP cluster three, which is conceptually located between clusters one and two, further adds to the peculiarity of the observed results. Nevertheless, it appears that, except for the country’s border regions, the dependency on interacting with authorities accompanied by high levels of artificial bureaucratic hurdles to be overcome by entrepreneurs is a crucial determinant for entrepreneurial entry. Since hypothesis H9 predicted that administrative barriers have a significantly negative impact on the market entry of new firms, based on the given evidence, hypothesis H9 was supported, with some geographical restrictions for border regions. Bureaucratic Moral Hazard and Agency Pressure However, that would tell only half of the story; thus, we now turn to the second bureaucracy-related hypothesis on the perceived threat of agency pressure (measured by op_ad_nocntrl and op_ad_noprsc) and its impact on entry. Whereas the non-clustered regional perspective does not show any significant results, the results provided by the urbanization-based cluster perspective appear to be inexplicable at first glance. Based on this perspective, we observe different coefficient signs for different types of regional clusters. Regarding Table 5.11, in a rather odd pattern, entrepreneurial entry appears to be most negatively affected by high levels of agency pressure in clusters one and two (bop _ art _ nocntrl, III, 2 ¼ 0.103, bop _ art _ noprsc, I, 2 ¼ 0.046, bop _ art _ noprsc, II, 1 ¼ 0.055, or bop _ art _ noprsc, III, 2 ¼ 0.048). Although we cannot make a direct comparison for cluster one, it seems that at least in cluster two, the negative implications by control agencies are considerably higher than those from law enforcement agencies. The most surprising effect is that, in contrast to clusters one and two, in clusters five and three, higher degrees of agency controls and prosecution seem to facilitate higher rates of firm entry (e.g., bop _ art _ nocntrl, II, 3 ¼ 0.143, bop _ art _ nocntrl, IV, 3 ¼ 0.128, bop _ art _ nocntrl, II, 5 ¼ 0.144, bop _ art _ noprsc, II, 3 ¼ 0.168, and bop _ art _ noprsc, IV, 3 ¼ 0.141) rather than deterring it. This time, the prosecution agency effect on entrepreneurship is higher. Most interestingly, the rural cluster three regions are subject to the same relationship as the federal capitals cluster. Unfortunately, the EGP perspective (Table 5.12) does not provide enough reliable results to clarify. Only in cluster three, i.e., in the border and port regions with poor regional interlinkage, we observe a negative agency pressure effect on entry (bop _ art _ nocntrl, IV, 3 ¼ 0.072). Naturally, regarding the contradictory results, the indications for coincident positive and negative stimuli by agency pressure on entrepreneurial entry across different types of regions call for a more profound analysis of the underlying relationship. Referring to the theoretical assumptions in Chap. 4, controls of regulatory requirements and the prosecution of illegitimate action within the framework provided by the law are not negative in themselves. Quite the contrary, by definition,

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they are designed to guarantee fair market conditions and an equal playing field for all market participants. Hence, it is mainly opportunistic behavior or bureaucratic moral hazard that produces a negative outcome of bureaucratic actions with regard to entrepreneurial activity. Since this phenomenon is closely related to the presence of corruption, I re-ran the regressions by including corruption as a moderator in order to analyze whether the relation between entrepreneurial entry and agency pressure was different for different levels of corruption. The results are intriguing. Once corruption is considered as a moderator, the effects of the urbanization clusters reveal interesting insights for both variables op_ad_nocntrl and op_ad_noprsc, which are illustrated in Annex B.4. Whereas in specification I of cluster five, the positive control agency pressure effect disappears once corruption serves as moderator, for specification II, a significantly positive moderator coefficient and significantly negative coefficients for the control and corruption variables suggest that higher levels of corruption exacerbate the negative effect of agency pressure on entrepreneurial entry. On the other hand, this suggests that low levels of corruption may lead to a higher quality of bureaucratic actions, which in turn facilitate higher levels of firm entry. For cluster one, the moderation analysis reveals that, for higher levels of corruption, any positive effects of agency controls on entry become weaker. In case of cluster three, the moderation analysis does not suggest any significant moderator effects apart from the still significantly positive control and prosecution agency effects on entry. Consequently, we may conclude that, in a considerable share of Russia’s rural regions, control and prosecution tend to perform better than their reputation suggests and that particularly in those regions a stable and functioning administrative environment is a vital precondition to entrepreneurial activity. These conclusions can also be noted graphically. Annex B.4 illustrates the relation between agency pressure and firm entry for different levels of corruption in a selection of several industries. Although dispersion is rather high and the correlations are relatively low, the analysis suggests that, given low levels of corruption, stricter controls facilitate higher levels of entry on average, whereas in regions with higher levels of corruption, more pressure from control agencies leads to lower levels of entrepreneurial entry. For the most part, regions in the medium corruption corridor do not show any correlations between agency pressure and entry. Hypothesis H10 predicted that the perceived threat of agency pressure has a negative impact on market entry of new firms. However, by considering the insights from before, the actual relationship is more complex than expected. Hence, hypothesis H10 in its initial intention was not supported.

5.3.2.6

Financial Capital

Let us now address the results of variables relating to the availability of long- and short-term financial means. Starting with the short-term measure, hypothesis H11 predicted that the availability of short-term capital would be positively related to entrepreneurial entry. However, neither the overall regional perspective nor the

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urbanized or EGP clusters suggest any significant relationships between the shortterm availability of capital and entrepreneurial entry. At this point, we cannot say if there have already been some adaptation trends as a reaction to poor credit availability, dried up capital markets, and scarce financing opportunities for start-ups other than owners’ savings or the famous FFF: family, friends, and fools. Since the empirical results do not suggest any significant correlations between the availability of short-term capital and new firm entry, hypothesis H11 was not supported. With regard to the second factor under analysis, i.e., the availability of long-term capital, the overall regional perspective also could not identify any significant effects. On the other hand, the cluster perspectives suggest surprising and somewhat odd results. First, urbanization-based cluster two suggest that poorer availability of long-term financial means is beneficial for entrepreneurial entry when considering economic controls (Table 5.11, bop _ fin _ long, III, 2 ¼ 0.060). Second, we observe a similar effect for EGP cluster three (Table 5.12, bop _ fin _ long, III, 3 ¼ 0.088), also pointing towards the relation that a more difficult availability of long-term capital with credit periods of more than 3 years implies higher rates of new firm entry. This relationship seems irrational at first glance. Once again, endogeneity might be an issue because we only observe same-period effects. From this perspective, a high number of newly created firms with high capital needs as opposed to a limited amount of financial resources and low willingness to lend may even further aggravate the availability of long-term financial means. Hypothesis H12 predicted that higher regional levels of long-term capital availability have a positive impact on market entry of new firms. Since the results do not support the original assumption, hypothesis H12 was not supported.

5.3.2.7

Human Capital

In assessing the human capital and entrepreneurial entry relation, I employed three different proxy measures for the human capital concept: the percentage share of employees with higher education in a given region (heduc), an availability ranking of engineers and specialized technicians (op_hr_ingtech), and an ordinal ranking of regions with regard to the regional performance in terms of education (educ). Since Table 5.10 does not show any significant relationships from an overall regional perspective, let us start by looking at the urbanization-based cluster perspective (Table 5.11). Based on the lack of significant results, the first measure heduc does not seem to represent a relevant issue in this case. In contrast, op_hr_ingtech leads to significant results, particularly for cluster one in the immediate and cluster three in the lagged specifications (bop _ hr _ ingtech, I, 3 ¼ 0.113, bop _ hr _ ingtech, III, 3 ¼ 0.111, bop _ hr _ ingtech, II, 1 ¼ 0.066, and bop _ hr _ ingtech, IV, 1 ¼ 0.063). What is striking is that this human capital indicator suggests a negative impact on entrepreneurial entry, i.e., a poor availability of engineers or specialists in a region is positively related to higher rates of entrepreneurial entry in the current or following year. On the other hand, there is one exception, i.e., a negative cluster five coefficient (bop _ hr _ ingtech, I, 5 ¼ 0.069), which points in the opposite direction. The educ

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variable also supports the initial assumption that a better performing education system is supportive of entrepreneurial entry (beduc, I, 5 ¼ 0.226 and beduc, IV, 5 ¼ 0.366). Looking at the EGP cluster perspective (Table 5.12) provides a quite similar picture. Again, educ suggests a positive effect on entry by better performing education systems, particularly in cluster one (beduc, II, 1 ¼ 0.078 and beduc, IV, 1 ¼ 0.099). In contrast, the cluster two heduc results indicate a negative impact of higher shares of overall employees with tertiary education on entrepreneurial entry (beduc, I, 2 ¼ 0.083 and beduc, III, 2 ¼ 0.082). This raises the question of how this fits together. In trying to solve the obvious ambiguity, we may argue as follows. The present results indicate that individuals with higher education likely prefer to work as employees of incumbent firms instead of bearing the risk of creating their own ventures. This notion corresponds with the results for the average wage indicator from Sect. 5.3.2.1 that the incentive for highly qualified employees (whose share of total employees is captured by heduc) to leave the security of a decent and unconditional salary is comparatively small in Russia. The same relationship is also reflected by the op_hr_ingtech variable. In regions with a theoretically high availability of experts such as engineers or technicians, which are unfortunately tied up in incumbent firms, we can expect a negative impact on rates of new firm creation, especially in branches that require high degrees of specific expertise. On the other hand, the educ results suggest that entrepreneurial entry nonetheless appears to benefit from a better performance of the country’s education system, although many of its most educated and qualified graduates will likely end up in paid employment rather than creating their own ventures. The fact that there is a different op_hr_ingtech effect in urbanization cluster five is at least an indicator of somewhat better start-up conditions in the federal capitals than in the rest of the country. Hypothesis H13 predicted that higher regional levels of human capital have a significant positive impact on market entry of new firms. In sum, there are clear indications that this is indeed the case. However, the proxy measures of human capital also suggest some regional variation in this relationship. Hence, I cannot come to a universally valid conclusion, which is why hypothesis H13 in its original intention was not supported.

5.3.2.8

Infrastructure

Since I made two hypotheses on the entrepreneurship-infrastructure relation, the measures of physical infrastructure are addressed first, followed by a consideration of the communication infrastructure indicators. Physical Infrastructure From a physical infrastructure perspective, the overall regional analyses shown in Table 5.10 imply no significant correlations with entrepreneurial entry. In contrast to the overall regional perspective, entry in urbanization-based cluster five (i.e., the federal capitals) seems to be particularly affected by the quality of transport and

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logistics infrastructure (Table 5.11, bop _ infra, II, 5 ¼ 0.142 and bop _ infra, IV, 5 ¼ 0.151). Consequently, a better quality of transport and logistics infrastructure implies higher rates of entrepreneurial entry in the following year. The strong coefficients in cluster five are likely subject to the comparatively high quality of infrastructure and higher rates of entrepreneurial activity in the federal capitals. Nonetheless, by looking at the EGP perspective, similar effects are observable for EGP cluster three (Table 5.12, bop _ infra, II, 3 ¼ 0.077 and bop _ infra, IV, 3 ¼ 0.069). Notably, this effect is one of the few significant results in the high international-low regional potential regions of the EGP perspective and comparable in size to the corruption and agency pressure indicators. Thus, it appears that the infrastructure effect on entrepreneurship is particularly relevant in border regions that depend on trade infrastructure such as roads, tracks, or ports and trade flows from neighboring countries. In contrast, entrepreneurial entry in other regions does not show any reliable sensitivity towards fluctuation in the regional quality of transport or logistics infrastructure. It seems that the lack of sophisticated infrastructure has the greatest impact in areas where it is taken for granted (the federal capitals) or is an essential precondition for a majority of existing business models (the border regions). There is, however, one peculiar effect relating to power supply in urbanization cluster five (bop _ art _ en, III, 5 ¼ 0.098). The observation suggests that a lower availability and reliability of power supply in Moscow and St. Petersburg indicate higher rates of entry in the same period. Interestingly, the effect appears only once economic control variables are considered. Two possibilities could be behind this observation. First, endogeneity may be an issue; the effect under observation only occurs in model specification III. We also cannot rule out the possibility that a high number of newly created firms that simultaneously require access to the federal capitals’ power grids cause bottlenecks to a greater extent in the responsible administration and shortages. Second, potential effects from unknown variables may play a role as well in this case. Unfortunately, since we only observe one single effect in place of a more systematic pattern, it is not possible to investigate the potential root causes of the issue further. Hence, without being able to provide any satisfactory explanation, I leave the energy effect for future scholarly analysis. In summary, a better provision of transport and logistics infrastructure in particular meets the demands of potential entrepreneurs in the federal capitals and stimulates the creation of new firms in trade-dependent border regions. Consequently, hypothesis H14 was supported, although with a regional limitation. Communication Infrastructure and Internet Penetration When it comes to the communication infrastructure variable, we can observe surprising and rather counterintuitive effects. In Table 5.10, the results from the overall regional regressions indicate a significantly negative impact of better index values in terms of household and firm access to (and the use of) the Internet and communication technologies on entrepreneurial entry. This effect holds for both the baseline and controlled model specifications (bICT _ idx _ std, I ¼ 0.076, bICT _ idx _ std, II ¼ 0.071, bICT _ idx _ std, III ¼ 0.082, and bICT _ idx _ std, IV ¼ 0.077). The urbanization-

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based cluster perspective (Table 5.11) shows that this effect is particularly relevant for the high-urbanization cluster one (bICT _ idx _ std, II, 1 ¼ 0.097 and bICT _ idx _ std, IV, 1 ¼ 0.094) and the federal capitals in cluster five (bICT _ idx _ std, II, 5 ¼ 0.553 and bICT _ idx _ std, IV, 5 ¼ 0.631). Particularly as regards the latter, the effect size is one of the highest in cluster five. Additionally, we also observe a relatively strong and one of few effects for cluster three; however, this is only the case for the non-lagged specifications (bICT _ idx _ std, I, 3 ¼ 0.203 and bICT _ idx _ std, III, 3 ¼ 0.211). The results from the EGP perspective also highlight that the observed effects are strongest around the highly urbanized federal capitals (Table 5.12, bICT _ idx _ std, I, 2 ¼ 0.116, bICT _ idx _ stdI, I, 2 ¼ 0.092, bICT _ idx _ std, III, 2 ¼ 0.134, and bICT _ idx _ std, IV, 2 ¼ 0.093). Based on these observations, it could be assumed that an improved access and use of the Internet and communication technologies negatively affects entry rates. The results suggest that this relationship holds for Russia’s two megacities and their surrounding area, which are equipped with a well-developed communication infrastructure, as well as for the country’s rural regions, which perform slightly worse in terms of communication infrastructure. In contrast with the major part of scientific literature and foremost rational thinking, the conclusion that the better availability of the Internet is related to lower rates of firm entry is reasonably hard to make. In order to investigate the problem and determine what is happening here, let us recall the correlation table from Sect. 5.2.8. By doing so, we remember comparatively high rates of correlation between the Internet access and use variable (ICT_idx_std) and avwage (0.70), patent_coeff (0.64), and heduc (0.67). It seems obvious that the use and availability of high-quality communication infrastructure are particularly good in economically well-developed regions (such as the federal capitals Moscow and St. Petersburg). In those regions, we observe comparatively high wages (avwage) and higher numbers of incumbent firms with significant market shares, which generate patents (patent_coeff) and employ relatively high shares of well-educated workers (heduc). Most notably, for all these variables, we already identified significantly negative effects on entrepreneurial entry in previous chapters. Keeping in mind the high rate of correlation of these variables with the Internet access measure, this might be the reason for the negative Internet effect on entrepreneurial entry, rather than assuming a direct negative impact. Figure 5.6 provides an indication of the interrelated development of the respective institutional factors. Notably, this relationship is particularly strong in and around the highly urbanized federal capital regions. In this light, it appears that the variables employed are likely subject to multicollinearity. Due to the interrelatedness of the variables described, and based on the model employed in this chapter’s research approach, I am not able to clearly separate the individual effects of the investigated institutional factors at this point. Instead, the observed coefficients may be subject to spurious correlation. One fact that may add to this conclusion is the reduced effect sizes in the EGP regressions once the strongly correlated variable on economic development (0.57) is considered in the controlled specifications. Using controls might have helped to compensate in part for the effects of spurious correlation in these cases. Unfortunately, the

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urbanization-based clustering perspective does not support this assumption since it does not provide similar effects for clusters one and five. Ultimately, in any case, the change in perspective from Sect. 5.4s alternative research approach can provide clarification and a more comprehensive picture of this relationship. To summarize, based on the available evidence, I am not able to make any reliable conclusions. Hence, hypothesis H15, which predicted that the level of regional access to communication infrastructure would have a positive impact on entrepreneurial entry, was not supported.

5.3.2.9

Market Environment

Three hypotheses were defined against the background of the relation between potential market environmental factors and entrepreneurial activity. The market factors relate to the impact of regional market dominance by incumbents, the industry concentration in a given industry, and a potential lack of available suppliers. Let us consider the results. Regional Market Domination of Incumbents Starting with the first indicator, the overall regional perspective does present any evidence for a significant relationship between the regional degree of corporate power, measured by the aggregated sum of M&A deal values as share of GRP (ma_intensity), and entrepreneurial entry. However, it becomes interesting once we change our point of view towards the urbanization cluster perspective. Contrary to the hypothesized relation, we observe a significantly positive and lagged effect in cluster two (Table 5.11, bma _ intensity, I, 2 ¼ 0.173 and bma _ intensity, III, 2 ¼ 0.186). Both observations suggest that higher corporate power results in higher rates of entrepreneurial entry. There are no ma_intensity effects observable from the EGP cluster perspective.

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Again, since we do not observe any lagged effects, the most plausible explanation for the observed relation may be the presence of endogeneity. It is not unlikely that higher rates of newly created firms also trigger higher volumes of M&A deals targeted at those firms. In this light, higher degrees of incumbent corporate power might not entirely deter entrepreneurial entry but rather impede competition by acquiring potential competitors in early development stages. Hypothesis H16 predicted that the regional level of incumbent corporate power has a significant negative impact on market entry of new firms. Although there are indications that there is a high degree of competitive pressure by incumbents that does affect entrepreneurial activity, the empirical results do not support the original intent of the formulated hypothesis. Thus, hypothesis H16 was not supported. Industry Concentration In contrast to the regional corporate power indicator, industry concentration reflects on competition in Russia from another angle. The Herfindahl Hirschman index (HHI_ind) provides a measure of industry concentration and thus the potential intensity of competition in a given industry across the entire country. Based on the HHI variable, we observe an immediate and significantly negative effect from higher industry concentration on firm entry across all regions (Table 5.10, bHHI _ ind, I ¼ 0.056 and bHHI _ ind, III ¼ 0.056). There is strong support for these observations from the urbanization clusters, especially clusters five and two. The former cluster supports a significantly negative impact of higher industry concentrations on entry across all specifications (Table 5.11, bHHI _ ind, I, 5 ¼ 0.038, bHHI _ ind, II, 5 ¼ 0.029, bHHI _ ind, III, 5 ¼ 0.038, and bHHI _ ind, IV, 5 ¼ 0.029), the latter at least in specifications I and III (Table 5.11, bHHI _ ind, I, 2 ¼ 0.063, bHHI _ ind, III, 2 ¼ 0.063). The EGP clusters also suggest that the industry concentration-entry relation is not limited to particular types of regions because there is evidence for significant impacts in the high international and regional EGP potential regions in cluster two, as well as in the low EGP potential cluster one regions (Table 5.12, bHHI _ ind, I, 1 ¼ 0.041, bHHI _ ind, III, 1 ¼ 0.041, bHHI _ ind, I, 2 ¼ 0.079, and bHHI _ ind, III, 2 ¼ 0.079). In total, the HHI_ind measure provides compelling evidence for the negative implications of high market concentrations across numerous industry sectors in Russia. As illustrated in Sect. 4. 2.8, the ability to effortlessly gain monopoly profits fosters complacency and creates entry barriers rather than promoting competition and innovation. In this regard and if also confirmed by the analysis in Sect. 5.4, the results on industry concentration are broadly supportive with regard to studies from Fidrmuc and Gundacker (2017), Szakonyi (2017) and Shurchkov (2012). Additionally, Annex B.1 also confirms that industry concentration particularly impedes entry in high-innovation branches. Hypothesis H17 predicted that overall industry concentration has a significant negative impact on market entry of new firms. As shown by the empirical results, there is substantial evidence for this assumption. Thus, Hypothesis H17 was supported.

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Lack of Suppliers With regard to the third hypothesis related to market environment factors, across all regressions, there are no indications of a significant relationship between supplier availability and entrepreneurial entry. The reduced sample for innovative branches in Annex B.1 also does not lead to any conclusions on significant relations between a lack of suppliers and rates of entrepreneurial entry. Thus, as demonstrated by the lack of supportive empirical results, a lower availability of suppliers does not appear to be directly correlated with lower levels of entrepreneurial entry. Consequently, hypothesis H18 was not supported.

5.3.2.10

Democratization

Finally, concerning the last category of institutional factors, I tested for a positive relationship between the regional degree of democratization and entrepreneurial entry. Neither Table 5.10 (the overall regional perspective) nor Table 5.12 (the EGP cluster perspective) suggests any significant effects on entrepreneurial entry. In contrast, the urbanization cluster perspective suggests that there is an effect for cluster five that holds across all specifications. The results indicate that higher levels of regional democracy are indeed beneficial for entrepreneurial entry in the same and the subsequent year (Table 5.11, bcrn _ democracy, I, 5 ¼ 0.069, bcrn _ democracy, II, 5 ¼ 0.086, bcrn _ democracy, III, 5 ¼ 0.101, and bcrn _ democracy, IV, 5 ¼ 0.093). This relationship might be based on the fact that we observe both relatively high rates of entrepreneurial entry in the federal capitals, and Russia’s federal capitals are usually first when in terms of liberal ideas and initiatives for modernization, particularly compared to other regions of the country. As a recent example, in 2017, Russia’s liberal opposition has filled various positions and achieved various majorities in the capital’s core local councils (Ragozin 2017). Moreover, notions of protests usually trace their origins to the federal capitals, as with the 2011–2013 and the 2017–2018 protests. Consequently, Moscow’s and St. Petersburg’s moderate political climates contrast with the more conservative political attitude in other regions. Since the results demonstrated that democratization positively affects entry, hypothesis H19 was supported, although the effect is only visible in Russia’s comparatively democratic federal capitals.

5.3.3

Conclusion

What have we learned from this chapter? First and foremost, the results presented in Sect. 5.3 clearly demonstrate that Russian regions are indeed subject to considerable heterogeneity and that this heterogeneity influences the way in which institutional factors affect entrepreneurial activity. This was particularly obvious because several of the analyzed institutional factors did not show any significant impact on

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entrepreneurial activity from an overall regional perspective but rather showed different effects once we looked at different types of regional clusters. In fact, both urbanization and economic-geographic location matter for entrepreneurship in Russia. The strongest evidence in this regard is provided by the measure for the quality of transport and logistics infrastructure. Based on the evidence provided, it appears that a lack of quality in infrastructure reduces entry particularly where high-quality infrastructure is considered guaranteed, which is the case in Moscow and St. Petersburg. This relationship is also relevant for regions in which a sound and reliable infrastructure in form of roads, rail tracks, or ports is the foundation for a great deal of business models, as in most of the country’s border regions, where entrepreneurial activity depends on well-functioning incoming and outgoing trade flows. In contrast, entrepreneurial entry in other regions does not show greater sensitivity towards variation in the quality of transport or logistics infrastructure. Apart from spatial heterogeneity, let us reconsider the results of the hypotheses tested in this chapter. Table 5.13 provides an overview of the hypotheses, including the preliminary results based on the descriptive analysis in Sect. 5.3. We can thus draw some conclusions regarding which institutional factors play the most significant role in shaping entrepreneurial entry across Russia’s regions. 1. There is surprisingly little or clear evidence for the traditionally important factors from the entrepreneurship literature, namely, the protection of property rights, availability of finance, and human capital. Regarding the first, higher numbers of patent applications as a proxy for property rights only reflect guarantees of property rights protection to a limited extent; in contrast, they are more likely to reflect competitive pressure from large-scale incumbents, which are the principal generators of patent applications. There is also no clear evidence for a lack of finance to negatively affect entry, except for some indications which point in the other direction; given Russia’s difficult situation in terms of start-up capital, increasing demand from higher numbers of newly created firms may even worsen the already poor availability of financing. Interestingly, the hypothesis on human capital was not supported either. This is, however, mainly due to the fact that well-educated employees and experts are usually tied up in incumbent firms and face few incentives to do otherwise. The comfort of receiving an adequate salary accompanied by workplace safety and employment protection guaranteed by Russia’s strict labor regulation boosts the opportunity costs compared to bearing the risk of pursuing an entrepreneurial career. This argument is also supported by the average wage indicator, which illustrates the declining propensity for entry with rising rates of average wages. Nonetheless, corresponding with the majority of the existing literature, there is also evidence that entrepreneurial activity benefits from an increase in the quality and performance of the country’s education system, although most of Russia’s well-educated graduates will more likely favor paid employment over creating an entrepreneurial venture. 2. From the perspective of structural economic factors, the most notable outcome stems from the impact of unequal distributions of income and wealth on entry.

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Table 5.13 Overview of tentatively accepted and rejected hypotheses according to perspective 1 Structural economic factors H1: The regional level of economic risk has a negative impact on market entry of new firms H2: The regional level of unemployment has a positive impact on market entry of new firms H3: The regional level of average wages has a negative impact on market entry of new firms H4: The regional level of inequality in income and wealth distribution has a negative impact on market entry of new firms Property rights H5: The regional level of security of property rights has a positive impact on market entry of new firms H6: The regional level of perceived risk from raidership has a negative impact on market entry of new firms Criminality H7: The regional level of perceived public safety has a positive impact on market entry of new firms Corruption H8: The regional level of corruption has a negative impact on market entry of new firms Bureaucracy H9: The regional level of administrative barriers has a negative impact on market entry of new firms H10: The regional level of the perceived threat of agency pressure has a negative impact on market entry of new firms Financial capital H11: The regional level of available short-term financial capital has a positive impact on market entry of new firms H12: The regional level of available long-term financial capital has a positive impact on market entry of new firms Human capital H13: The regional level of human capital has a positive impact on market entry of new firms Infrastructure H14: The regional provision of physical infrastructure has a positive impact on market entry of new firms H15: The regional level of access to communication infrastructure has a positive impact on market entry of new firms Market environment H16: The regional level of incumbent corporate power has a negative impact on market entry of new firms H17: The level of industry concentration has a negative impact on market entry of new firms H18: The regional availability of suppliers has a positive impact on market entry of new firms

Tentatively unsupported Tentatively unsupported Tentatively accepteda Tentatively accepted

Tentatively unsupported Tentatively unsupported

Tentatively accepted

Tentatively accepted

Tentatively accepteda Tentatively unsupported

Tentatively unsupported Tentatively unsupported

Tentatively unsupported

Tentatively accepteda Tentatively unsupported

Tentatively unsupported Tentatively accepted Tentatively accepted (continued)

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Table 5.13 (continued) Democratization H19: The regional level of democratization has a positive impact on market entry of new firms

Tentatively accepteda

a

H3 was supported with geographical restrictions to the country’s major cities and highly urbanized regions; H9 was also supported with geographical restrictions, i.e., there were no effects for border regions; H14 was accepted with regional limitations, i.e., only for Russia’s federal capitals and border regions; H19 is only visible in Russia’s comparatively democratic federal capitals

What we learn from these results is that the concentration of wealth in the hands of only a few does not necessarily lead to higher investment or higher rates of newly created firms. More likely, the accumulated wealth is spent on consumption and imported luxury goods, in the construction and real estate sectors of the major cities, or sent abroad. As proposed by a recent paper of Novokmet et al. (2017), the offshore wealth owned by Russian citizens is estimated at roughly one trillion USD, which is three times as much as the country’s official foreign reserves. On the other hand, the average Russian’s wealth mostly encompasses assets that only serve basic needs. Consequently, the lack of financial means limits potential entrepreneurs’ ability to gather resources to seize a business opportunity. This is particularly important because the acquisition of external capital for entrepreneurial investments lacks attractiveness from the perspective of banks due to the high inherent risk, whereas Russia’s high interest rates often decrease the economic feasibility of entrepreneurs’ business models. 3. Next, the results of the first research approach showed that the perception of business violence and criminality, or more generally, a perceived lack of public safety, negatively affect the decision to pursue an entrepreneurial career by creating a venture. The perception of environmental insecurity challenges the predictability of a potential entrepreneur’s future returns and boosts the opportunity costs of the entry decision. This conclusion was also confirmed from the structural economic perspective, since regions with high and relatively risk-free average wages showed a significantly reduced propensity for entrepreneurial entry. 4. When it comes to entrepreneurship, the performance of Russia’s bureaucracy appears to be better than its reputation. Certainly, many regulations are still rigid and far from providing a modern and flexible ecosystem for firms. Moreover, there are tremendously high reporting requirements, particularly for payroll accounting. However, the results of my analysis indicate that, at least from a control and prosecution agency side, it is mainly corruption that shapes the efficiency of the agency’s mission and its effect on entrepreneurial activity. As the study demonstrated, in regions with low levels of corruption, agency controls appear to live up to their intended purpose and create a level playing field for market participants. This appears to be accounted for by potential entrepreneurs; as in those regions, we also observe higher rates of firm entry. On the other hand,

5.3 Perspective 1: A Descriptive Regression Model Approach

203

in the presence of corruption, the anticipation of opportunistic behavior and rent extraction by bureaucrats results in lower rates of entrepreneurial entry. 5. In terms of industry concentration, the preliminary results suggest negative implications for entrepreneurial entry caused by high market concentrations in a given industry. In this light, it can be assumed that incumbents try to create entry barriers in order to gain monopoly profits rather than playing on a level field, thus resulting in negative consequences for overall economic innovativeness. 6. Regarding the final institutional indicator, regional democratization, it appears that higher rates of regional democratization positively influence entrepreneurial activity. This is particularly the case for Russia’s federal capitals, which are usually the first when it comes to pursuing liberal ideas, starting initiatives for modernization, and challenging traditional ways of thinking. In sum, I was able to identify a range of institutional factors that shape entrepreneurial entry in Russia’s regions. Hence, the question remains of whether the performance of the Russian system of entrepreneurship is subject to any institutional bottleneck constraints. In terms of effects with comparatively strong negative impacts on entrepreneurial entry, the differences across the overall regional perspective and the clusters of the urbanization-based and EGP perspectives are remarkable. Notably, average wages and Gini coefficients show the highest effect sizes in the non-clustered perspective. However, when it comes to the regional clusters, we also observe high effect sizes for the ICT_idx_std (urbanization-based clusters 3 and 5, EGP cluster 2) and patent_coeff (EGP cluster 3) variables. Against the background of the limited explanatory value of the given variables, propositions on potential bottleneck factors cannot be made with sufficient certainty. Consequently, this leads us to the last consideration of this interim conclusion. Lastly and most importantly, we need to reflect on the statistical reliability of the relationships identified. The Tobit model approach of research perspective 1 aimed at avoiding potential issues of multicollinearity by individually regressing various institutional framework factors and to avoid endogeneity by considering the lagged institutional impact on entry. However, the approach is, as we have noted, subject to some methodological limitations. In this light, we observed a considerable risk of spurious correlation that became particularly clear when looking at the correlated institutional factors avwage, heduc, patent_coeff, and ICT_idx_std. Unfortunately, the choice of being subject to either multicollinearity or spurious correlation is like being caught between a rock and a hard place. Aside from this, the research design of perspective 1 is subject to some general limitations of regression approaches. Statistical inference relies on the understanding that we may generalize results derived from a sample to the entire population. In this light, measures on the significance of results usually provide an objective benchmark for the validity of the generalization. However, care should be taken because significant results can also be misleading. Particularly for large data sets, as in the case of the present analysis, Hair et al. (2006, p. 11) have stated that simply “by increasing sample size, smaller and smaller effects will be found to be statistically significant, until at very large samples sizes almost any effect is significant.”

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Likewise, Anscombe (1973) has illustrated the importance of in-depth data exploration instead of simply relying on correlations and p-values when deriving conclusions from data. Moreover, by separately considering predictor variables, research perspective 1 accounts for the interrelatedness of institutional framework factors only to a very limited degree, if at all. However, as we know from Chap. 2, to adjust and improve the overall performance of a system of entrepreneurship, it is essential to account for the interplay between system elements (Acs et al. 2014, 2016). To conclude, this is why the results presented need to be interpreted with caution. In order to adequately take into account the limitations described and to identify truly reliable relationships between spatial institutions and entrepreneurial entry as well as any potential bottlenecks, we require an alternative approach to address the research questions of this thesis. Such an alternative approach is described in the following chapter.

5.4 5.4.1

Perspective 2: A Geometric Clustering Approach Research Design

Although research perspective 1 identified a set of interesting relationships regarding spatial institutional framework factors and entrepreneurial entry, we also found a number of limitations to consider. Thus, the following chapter aims to develop a methodological approach that can capture true, reliable relationships between spatial institutional factors and entrepreneurial entry across Russia’s regions. In order to do so, there are three decisive issues to consider: 1. Following the systems of entrepreneurship literature, the approach needs to account for the interrelatedness of institutional factors. 2. It needs to avoid biased results that may be driven by endogeneity, multicollinearity, and spurious correlation. 3. The approach should not suffer from being subject to model overfitting, which is frequent in descriptive analytics (Harrell 2001, p. 60f). In order to account for those elements, I approached Andreas Brieden, holder of the chair of statistics at Bundeswehr University Munich, and my dear colleague Saskia Schiele. In close interdisciplinary collaboration and profound discussions, we drew from predictive modeling theory in order to develop a reliable alternative research approach. Compared to research perspective 1, developing a predictive model has the advantage that the relationships identified verifiably and measurably contribute to entrepreneurial entry, in contrast to identifying statistically significant effects but with actual effect sizes close to zero. Moreover, in contrast to a descriptive regression approach, a predictive model stands up to an actual use case; given a set of relevant spatial institutional factors, I aim to predict reliable rates of entrepreneurial entry that are as yet unknown. Thus, in case of entry rate prediction, the validity of the relationships identified is considerably higher.

5.4 Perspective 2: A Geometric Clustering Approach

205

Against this background, computation power, machine learning, and innovative algorithms allow for promising new approaches of data analysis and prediction, particularly when it comes to identifying causal and reliable predictor-outcome relations. Such an approach is the geometric clustering approach from Brieden and Gritzmann (2012), whose underlying algorithm identifies hidden multidimensional structures and creates clusters of objects with similar sets of characteristics. It has been successfully applied to cases of automatically reconsolidating farmland in the state of Bavaria, Germany (Borgwardt et al. 2011, 2014; Brieden and Gritzmann 2012), and on predicting patient outcomes dependent on a set of individual characteristics and medication (Hinnenthal 2017). In the following chapter, I utilize geometric clustering in order to define sets of relevant variable characteristics that can predict reliable estimates of entry rates. By doing so, I can identify reliable relationships between institutional predictor variables and outcomes in terms of entrepreneurial entry rates.

5.4.1.1

Methodological Approach

Geometric clustering is based on the idea of supervised learning algorithms. This means that an algorithm analyzes a defined set of training data and derives estimation criteria (i.e., a function or rather the prediction model), which can be used to predict new and unseen examples based on the available set of parameters (Alpaydin 2014, p. 9). Hence, in the first place, I divide the data set into training and testing data sets. To avoid the pitfall of underfitting, the number of observations in the training data set is significantly higher than in the test data. In order to evaluate the performance of the fitted model, I assess the accuracy of the prediction model based on the remaining test data, which can be read as unknown or new data. Traditional approaches allocate 80% of all observations to the training data set, whereas the remaining 20% are used as testing data. Although a random 80% vs. 20% allocation would be possible in the case of this thesis, I adhere to the more common practice for forecasting that reserves some data towards the end of the observation period for testing, whereas earlier data is used for training. This also seems to be more adequate and useful with regard to the potential use case, i.e., creating a predictive model for entrepreneurial entry that can predict future entry rates in a given region and industry based on the existing institutional framework. Regarding the goal of developing a reliable predictive model, it is crucial that, at the point of making predictions, we command all characteristics of the relevant institutional variables; in light of this requirement, I make use of the respective institutional variables and their impact on entrepreneurial entry in the subsequent year. This also helps circumvent the issue of potential endogeneity. Consequently, observations on institutional factors between 2007 and 2010, including the corresponding entry rates of the subsequent year, were allocated to the training data set, whereas the 2011 institutional data, including the 2012 entry rates, were used as test data. By using entirely new data, I aim to generate new and reliable findings, particularly compared to the first research perspective from Sect. 5.3.

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After defining the training and test data partitions, the geometric clustering algorithm aims to classify observations into collectives Cli as homogeneous as possible based on the training data set. At this point, it needs to be mentioned that, in light of the following analysis, the groups determined by the geometric clustering approach are referred to as collectives rather than clusters, particularly because, in this thesis, the term “cluster” is already used for similar types of regions, as defined in Sect. 5.2.7. Those collectives are formed by grouping similar value combinations of relevant variables (e.g., if we consider observations in the manufacturing sector, given specific levels of bureaucracy and human capital, those observations can be expected to experience an average entry rate of a specific percentage). Against the background of entry rate prediction, it is essential to derive collective boundaries that are as clear and precise as possible. Additionally, we need a sufficiently large number of observations in each collective to make reliable predictions. From a methodological point of view, the algorithm identifies collectives by expressing each observation as a point position in geometric space, whereby the distance between points can be measured. To determine homogenous collectives, the algorithm then aims to maximize the pairwise distances between the centers of each collective in the geometric space.32

5.4.1.2

Data Transformation

Since the geometric clustering approach is based on the concept of geometric space, we need quantitative values. However, some of the data are provided in a nominal scale. Against this background, data transformation allows the computation of collectives for non-metric data by using conditional probabilities for the outcome variable of interest, i.e., entrepreneurial entry. To transform the data, I used a transformation procedure described in Brieden and Gritzmann (2020). This approach is outlined in the following paragraphs. Initially, before performing any data transformation steps, I excluded all observations with fewer than ten firms per region, industry, and year. The rationale behind this was to avoid particularly high rates of entry (i.e., between 50 and 100%) that are due to the fact that even low numbers of newly created firms can trigger disproportionately high rates of entry in a region-industry-year cell with only few initially existing firms. These quasi-outliers do not follow a systematic pattern and are, on the one hand, likely to distort forecast results for more plausible rates of entry. On the other hand, they are extremely difficult to predict themselves. Next, the transformation was performed based on the training data set. The idea behind the data transformation approach is to replace any value of a given variable 32

For a more detailed explanation of the algorithm procedure, refer to Brieden and Gritzmann (2012). The main ideas on classifying heterogeneous data into homogeneous collectives according to the specific algorithm are based on the work of Brieden and Gritzmann (2003, 2004, 2010). A more detailed description of the methodology applied to the case of entrepreneurial entry is provided in a forthcoming paper of Schlattau et al. (2019).

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207

Vd ¼ vd for d ¼ 1, . . ., d, where d is the number of all potential predictor variables, by the conditional expected value E(Y | Vd ¼ vd) of the outcome variable, i.e., the entry rate, given the variable value Vd ¼ vd. Vd encompasses all institutional variables described in Table 5.4 and the data set’s structural nominal variables for region and industry (reg_id, NACE_2dig), the natural entry variables for EU and post-socialist entry rates, as well as the two regional cluster variables from Sect. 5.2.7. Finally, I complemented the structural set of variables with an industry sector variable (NACE_Sec) that refers to the NACE level one industry sector classification and is thus characterized by a lower level of granularity than the NACE two-digit classification. Annex C.1 provides an overview of these sectors. The main motive behind this less detailed sectoral view was that, for entry rate prediction, both the training and test data sets need a sufficiently high number of observations in each identified collective. This cannot be guaranteed on a two-digit NACE level, which is why the level one classification is more adequate for the purpose of prediction. The conditional expected value E(Y | Vd ¼ vd) of entry is now used as new value, replacing the original variable value. Following Kolmogorov (1973, p. 46) in a standard procedure in probability theory, I estimated the conditional expected value by calculating the conditional mean entry rate y across all Vd ¼ vd, i.e., the entry rate that can be expected on average given an arbitrarily large number of observations, as the best estimator in this case. The nominal scale variables of the data set can be transformed in a fairly straightforward way by computing mean entry for every nominal variable characteristic, for example, mean entry y per region, y per NACE level one sector, and so forth. With regard to metric and ordinal scale variables, however, the number of combinations between variable values and mean outcome values increases proportionally with the increase in the number of different variable values. To give a practical example, I would have to calculate the mean entry rate y for every individual expression of GRP present in the data set (it can reasonably be assumed that most of the exact region-year specific amounts of GRP measured in rubles can be observed only once or twice in the data set). This would lead to a very small ratio between actual and conditionally expected values. Moreover, there might not be major impacts of GRP increases of only a few thousand rubles or advancements of only one or two positions in corruption rankings on entry. Thus, it is expedient to classify variables with a high number of different values into classes of equal class width. Consequently, across the entire value range of each metric variable, I derived a set of quantiles and calculated y for each. The number of quantiles for a given variable is specific to this variable and determined based on the highest possible variance max Var yVq of mean entry for a given variable V and the potential number of quantiles q ¼ 2, . . ., 10. For the sake of usability, the maximum number of quantiles allowed is 10, and a sufficient number of observations in every quantile need to be available in the test data set, as well.33

33 This is another argument for the sectoral NACE view, since, in contrast to the two digit NACE view, this perspective warrants for a sufficient number of observations in every quantile.

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Observations in the test data set were not considered in determining variable quantile numbers and estimations of the conditional expected outcome. The predictor variable values of the test data set were only assigned to the corresponding quantile from the training data set and consequently replaced by the estimations for the conditional expected values of the training data set.

5.4.1.3

Definition of Data Subsets and Selection of Variables

Keeping in mind the goal of entry rate forecasting, and with regard to the fundamental differences of some industry sectors, I split both the training and test data sets in two separate subsets. The first subset comprises branches of the secondary economic sector, focusing on manufacturing and industrial production. This sector is usually relatively strong in transition economies such as Russia. The second part encompasses service branches of the tertiary economic sector. However, I do not include some sectors in the data subsets. NACE sectors that only encompass fewer than 35 observations (e.g., sectors D and L) were removed, since, in both sectors, we would be left with too few observations per collective once the test data was divided into several groups. Moreover, sectors that show particularly high degrees of discontinuity are not suitable for entry rate forecasts. In this light, sectors with a relatively large delta between the average training data entry rate and the test data entry rates (i.e., higher than 0.75%) were removed, as well. Annex C.2 provides an overview of the differences in entry and the number of observations per NACE sector for the training and test data sets. Finally, there were two data subsets available for the final analysis, one covering NACE sector C and another covering sectors I, K, and M. The data subsets and the number of observations across the respective training and test data sets are illustrated in Table 5.14. Before applying the geometric clustering algorithm based on the training data subsets, all relevant variables for the clustering algorithm had to be identified because running the algorithm by including the comprehensive set of all potential predictors would not make much sense from a computation intensity perspective. In fact, I chose the most promising variables based on the maximum variance in mean entry across all nominal characteristics or quantiles per variable. The reasoning behind this follows the analysis of variance (ANOVA) logic and assumes that if there are significant deviations in variance across nominal characteristics or quantiles, we can expect that there are different relationships at work. An overview of the variance in mean entry across the potential variables is provided in Annex C.3. Finally, the clustering algorithm was then applied to the training data of both data subsets, and for each subset, we achieved a set of collectives Cl ¼ (Cl1, . . ., Clk ) Table 5.14 The number of training and test data observations across two sector-based subsets Data subset (incl. NACE sector codes) Sectors C Sectors I, K, M

Training data observations 2504 1333

Test data observations 662 344

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209

where each collective Cl was characterized by combinations of similar institutional variable values. Then, for each collective, the expected outcome in terms of an average entrepreneurial entry rate was determined.

5.4.1.4

Statistical Evaluation

To evaluate the reliability of the prediction model and hence the reliability of the relationships identified between institutional factors and entrepreneurial entry, I applied simple left- and right-tailed hypothesis tests for each collective based on the predicted and actual cluster values. In order to do so, as a first step, I used the condition that all potential collectives per data subset were sorted and renumbered with regard to their predicted values. Thus, for the collective values of Cl1, . . ., Clk, I expected the following: f 1 ðCl1 Þ  f 2 ðCl2 Þ . . .  f k ðClk Þ:

ð5:7Þ

The main assumption for evaluating prediction accuracy then stated that the true prediction yi for the outcome in collective Cli satisfies the following inequation: f i ðCli Þ ∙ δil  yi  f i ðCli Þ ∙ δiu

ð5:8Þ

for i ¼ 2, . . ., k – 1, and for the left and right border, it holds yi  f 1 ðCl1 Þ ∙ δ1u and f k ðClk Þ ∙ δku  yi

ð5:9Þ

where δil is a lower and δiu an upper parameter to adjust fi(Cli). The adjustment is necessary to define a confidence interval within which we can assess the predicted values as reasonably acceptable. In setting the adjustment parameters, it is important that the defined confidence intervals do not overlap with those of the adjacent collectives. The defined confidence intervals could, however, also be defined more narrowly. However, I deemed being able to predict a reasonably precise corridor of entry more important than precisely forecasting fourth-decimal figures. Therefore, the following formulas were used to define the upper and lower adjustment parameters δil and δiu : 0:5 δiu ≔

δil ≔



 f i ðCli Þ þ f iþ1 ðCliþ1 Þ , for i ¼ 1, . . . , k  1 f i ðCli Þ

0:5½ f i ðCli Þ þ f i1 ðCli1 Þ , for i ¼ 2, . . . , k f i ðCli Þ

ð5:10Þ

ð5:11Þ

This way, the confidence interval defined by the adjustment factors was exactly the arithmetic mean of Cli and Cli + 1 for the upper limit, and Cli and Cli  1 for the

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lower one. In the next step of the hypothesis test procedure, I formulated the null hypothesis. According to the standard approach in statistics, I aimed to reject the null hypothesis and confirm the alternate hypothesis. The right-tailed hypothesis test was formulated as follows: H i0,l : yi  f i ðCli Þ ∙ δil ≕byil , for i ¼ 2, . . . , k, and

ð5:12Þ

H i1,l : yi > f i ðCli Þ ∙ δil ≕byiu , for i ¼ 2, . . . , k,

ð5:13Þ

which describe the assumption that yi is larger than f i ðCli Þ ∙ δil . With regard to the left-tailed hypothesis test, the following hypothesis pair H i0,u : yi  f i ðCli Þ ∙ δiu , for i ¼ 1, . . . , k  1, and

ð5:14Þ

H i1,u : yi < f i ðCli Þ ∙ δiu , for i ¼ 1, . . . , k  1,

ð5:15Þ

stands for the assumption that yi is smaller than f i ðCli Þ ∙ δiu . Consequently, for the collectives at the left and right borders, I obtained one hypothesis, whereas I tested two hypotheses for each of the intermediate collectives. Finally, to test the formulated hypotheses, I needed to formulate the corresponding test statistics. t iu ¼

byi,test  byiu , for i ¼ 1, . . . , k  1 i,test bσ ffiffiffiffiffiffi p

ð5:16Þ

byi,test  byil , for i ¼ 2, . . . , k i,test bσ ffiffiffiffiffiffi p

ð5:17Þ

ntest i

t il ¼

ntest i

where yi describes the actually observed rate of entry in the test data of a given collective and byiu and byil describe the rejection range according to the null hypothesis. i,test bσ ffiffiffiffiffiffi describes the estimated standard deviation of the given test data collective, p test ni

whereas b σ i,test is the estimated variance and ntest the size of collective i.34 Based on i the calculated test statistic, it is also possible to derive a corresponding p-value, i.e.,

34

Since the standard deviation of the test data collective is unknown, I needed to use an estimator. Following Chambers and Clark (2012, p. 86), I used an unbiased estimator for the unknown variance of outcome in a given collective Cli.

5.4 Perspective 2: A Geometric Clustering Approach

211

the probability of trusting the predicted outcome of an average entry rate in a given region and sector within its corresponding collective. Regarding the level of significance for the statistical evaluation, the general convention usually sets levels at or close to 0.05. However, significance levels should be chosen with diligent consideration of factors such as the sample size, test power, and joint probabilities for type I and II errors rather than mechanically adopting 5% significance levels for every application (Kim 2015). Consequently, I aimed to determine the level of significance as a decreasing function of sample size. Given a number between two and five potential collectives for each data subset, each collective may be expected to have an average number ntest of approximately 132–331 (C) and 69–172 i (IKM) observations. In the case of understated significance levels, even negligible deviations from the actual test value may impose substantial reductions on statistical significance. Given the present form of hypothesis tests, even if the predicted entry rates were very close to the test data value, we would not receive p-values close to zero. Instead, the primary goal should be to guarantee that any predicted values comply with the defined acceptance ranges, which are defined by formulas 5.5 and 5.6 and penalize estimates close to or outside of this range. Hence, considering the trade-off between type I and II errors, I followed the argumentation of Kim (2015) and adhered to Leamer’s (1978) line of enlightened judgment, i.e., the relation that minimizes the joint likelihood of both type I and II errors in order to guarantee sufficient statistical power of the hypothesis tests. Based on this, a significance level of 25% for relatively small collectives seems to be more adequate and would thus serve as benchmark in the applied hypothesis tests. Next, the null hypotheses could be rejected if the realization of the test statistic and the corresponding p-value were in the reject region.

5.4.2

Results and Discussion

I ran the geometric clustering algorithm on the 2007–2010 training data in the two data subsets and tested the results against previously unconsidered data. By doing so, I was able to identify a set of true institutional drivers of spatial entrepreneurial entry. The results for both data subsets are described in the following paragraphs. Data Subset C For subset C, the algorithm identified two entry collectives: collective 1, with an average entry rate of 10.12%, and collective 2, with an average entry of 12.38% (Table 5.15, Fig. 5.7). Regarding the variable characteristics that determine the two collectives, the results suggest the following. Regions with relatively low degrees of democratization (i.e., ratings of 30 at most) and simultaneously low degrees of income inequality belong to collective 1 and are subject to low degrees of entry. In contrast, if low democracy levels coincide with high degrees of income inequality (Gini coefficients higher than 0.4 given democracy levels of 30 or lower, as well as Gini coefficients higher than 0.42 given democracy levels of 31 and 32), a higher concentration in income has a strong and positive impact on entrepreneurial entry. Notably, only in the case of slightly above-average democracy and slightly above-

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Table 5.15 Prediction model collectives, variable characteristics in data subset C Variable Characteristics crn_democracy

reg_mincgini

[0;30]

Collectives

Urbanization-based cluster

1

2

[0.42]

[31;32]

0.1204

[0.42]

0.1149

[0.42]

0.1409

[0.42]

0.1165 0.1376 0.1264 0.1012

Average Entry in Collective

[0;30]

[31;32]

[0.40]

Collective 2

[0.42]

Collective 2

[0.42]

Collective 2

0.1238

reg_mincgini

reg_mincgini

crn_democracy

[33;38]

[39;45]

reg_mincgini

1

Collective 2

2

Collective 1

Collective 2

Fig. 5.7 Lookup tree, variable characteristics, and prediction collectives in data subset C

average income concentration, we need the urbanization cluster variable to decide between collectives 1 and 2. In this situation, the higher urbanized regions experience higher rates of entry, whereas regions with lower degrees of urbanization are allocated to low-entry collective one.

5.4 Perspective 2: A Geometric Clustering Approach

213

Altogether, these observations are in sharp contradiction to the results from Sect. 5.3; nonetheless, they are not implausible. Considering the relatively high amounts of initial investments are required in the manufacturing sector compared to the services sector (KPMG 2018), for example, with regard to product development and design, creation of prototypes, setting up production facilities and processes, etc., relatively wealthy individuals are more likely to provide the necessary investments. Given the reluctance of the financial sector to provide financial means for funding, serial or de alio entrepreneurs, oligarchs, or other individuals with substantial financial means have a relative advantage in gathering the required resources for funding compared to the average potential entrepreneur. Regions with higher rates of income or wealth concentration may thus be more prone to entry in the manufacturing sector. If we consider the IKM data subset, income inequality does not play a role at all, i.e., the concentration of financial resources appears to play a similarly important role, particularly when it comes to leveraging higher initial investments in the manufacturing sector. Another aspect that is also quite supportive of this relationship is the relative propensity of Russian oligarchs towards manufacturing industry enterprises, as outlined in former chapters. Moreover, the entry rates in the two manufacturing collectives are clearly lower on average than in the services sectors, which contributes to the assumption that there are higher entry barriers in the manufacturing sector in general, and that those barriers can be more easily overcome by high-net wealth individuals and serial entrepreneurs than the average de novo entrepreneur. Finally, if we remember the relatively low levels of wealth for the vast majority of the Russian population, with 82% of the country’s citizens owning less than US$10,000 in illiquid or barely investable forms of assets, the question arises as to whether higher rates of income equality on a very low level would be truly effective in promoting higher entrepreneurial activity in this industry sector. In sum, the results observed confirm and contribute to Bénabou’s (2000), Deininger and Olinto’s (2000), and Forbes’ (2000) understanding that a higher savings and investment propensity of wealthy individuals has a positive impact on firm creation, at least in the manufacturing and presumably other relatively investment-intensive sectors. In contrast, assumptions that heterogeneity in access to resources results in lower rates of entrepreneurial entry (as has been suggested by Sarkar et al. 2018; Xavier-Oliveira et al. 2015; Webb et al. 2014; Ardagna and Lusardi 2010) could not be confirmed. Notably, the observation horizon does not encompass the period of countersanctions on the import of Western meat, dairy products, fruits, and vegetables, which were introduced in 2014 as an answer to Western sanctions related to the Russia-Ukraine dispute. Since 2015, this has triggered a boom in regional food production that particularly benefited the country’s remote and rural regions.35 These developments and the increased demand for national and regional food products are

35

For example, the Altai region now accounts for 16% of the country’s cheese production, and production volumes grow at an average of 33% per year. In the course of this development, some dairy entrepreneurs in the remote region even became billionaires (Daschkowski 2015).

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also likely to have impacted the relationship between income inequality and entrepreneurial entry by increasing the attractiveness of creating a venture, even with lower initial investments, at least in affected branches such as the food production industries. Besides the Gini coefficient impact, rather astonishingly, another important impact factor for entrepreneurial entry is the regional level of democracy. According to the relationships identified, higher levels of regional democracy imply higher levels of entrepreneurial entry. Whereas in the lower three quantiles of spatial democratization, the respective entry collective is compiled by democratization, income concentration and occasionally urbanization, in the upper democratization quantile (i.e. the [39;45] quantile), the high entry collective 2 is determined exclusively by the level of regional democracy. Indeed, the results from data subset C contribute to the perspective that higher degrees of democratization promote entrepreneurial activity. It appears that the regionally limited democracy effect for the Moscow and St. Petersburg regions identified in research perspective 1 is actually more important on a broader scale than originally thought. Democracy truly seems to work as a contributor to an entrepreneurial mindset and as a guarantor of economic freedom. With regard to the former, the aspect of shaping an entrepreneurial mindset, these results support the notion that civil liberties are particularly important with regard to constantly evaluating and adapting traditional ways of doing things. Open and free debate is important for being open to new ideas, spreading innovations from abroad and triggering debates on the most efficient and suitable solutions to given problems. Overall, this describes the essence of an entrepreneurial attitude and simultaneously contributes to the formation of opportunities. In contrast, restricting civil liberties in order to alleviate threats to an established elite’s political or economic survival results in an impaired spread of productive ideas and technologies, although essentially everyone would desire entrepreneurial entry, technological change, and economic growth. Regarding the second aspect, i.e., ensuring economic freedom, particularly in weak institutional environments, the combination of economic power and a sufficient degree of freedom may be essential to overcome entry barriers and to ensure protection for investments that might otherwise be extracted by politicians, officials, competitors, or criminals. Democratic elections, independent courts, and free and impartial media establish checks and disincentives for predatory behavior and encourage private investments, conditions that in turn foster entrepreneurial entry. Even though the past has shown that this may not be the case for largescale political cases, it seems to work well for non-politicized, everyday business issues. Higher degrees of power dispersion also reduce the risk of expropriation or the threat that individual actors impose their intentions at the cost of an entrepreneur’s business. Additionally, democratic plurality in the form of manifold social forces may work as a prevention mechanism to avoid institutions being captured by narrow or extractive interests (Acemoglu and Robinson 2012; Putnam 1993).

5.4 Perspective 2: A Geometric Clustering Approach

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Table 5.16 Prediction model collectives, variable characteristics in data subset IKM Variable Characteristics NACE_Sec

Collectives

reg_administ

HHI_ind

1

[0.09]

0.1723

[0.09]

0.1369

[0.09]

0.1610 0.1105

Average Entry in Collective

I

Collective 1

K

reg_administ

[0.42]

Collective 4

NACE_Sec

[0.03]

Collective 4

Fig. 5.8 Lookup tree, variable characteristics, and prediction collectives in data subset IKM

Data Subset IKM The second data subset under analysis refers to the branches that cover accommodation and food service activities (I), financial and insurance activities (K), and professional, scientific, and technical activities (M). Based on this data set, the geometric clustering algorithm identified four entry collectives: collective 1 (entry

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rate of 11.05%), collective 2 (entry rate of 12.08%), collective 3 (entry rate of 13.68%), and collective 4 (entry rate of 15.75%), which are illustrated in Table 5.16 and Fig. 5.8. Now, let us consider the collective-determining factors. First, sector I observations can be allocated to the low-entry collective 1 regardless of any other variable characteristics. On the one hand, as illustrated in Annex C.1, the accommodation and food service branches generally observe lower rates of average entry compared to sectors K and M. Particularly in the case of the food services sector, low rates of entry may be attributable to the fact that relatively small firm sizes urge founders to choose other legal forms, such as sole proprietorships, which are not recorded by the present data. On the other hand, the business models of hotels, restaurants, or culinary establishments may depend to a lower degree on institutional context factors compared to more sophisticated types of business models. Notably, the relatively low rates of entry present in the observation period may have changed in the following years because the 2014 Olympic Winter Games in Sochi, the 2016 IIHF World Championship, and the 2018 FIFA World Cup in 2018 have pushed tourism in Russia back to growth after a slump in 2012/2013 (WTTC 2018). Simultaneously, due to the weak ruble between 2014 and 2017, Russians tended to prefer domestic destinations for their holiday plans, which eventually could have driven higher rates of entry in the respective sector. Next, entry in sector K is primarily influenced by the institutional barriers indicator (reg_administ), i.e., the citizens’ satisfaction with the executive authorities’ performance. Since the variable is divided in a lower share [(0.42), i.e., high-level performance], regions in the lower share are likely to experience lower rates of entry (13.68%) than in the upper one (15.75%). This seems plausible, since, particularly the financial sector, if it is not owned and controlled directly by the state, is usually heavily regulated and hence dependent on efficient administration. In sector M, differentiating between low and high levels of executive authorities’ performance is still an important factor; however, the degree of industry concentration (HHI_ind) now tips the scale towards higher or lower levels of entry. Consequently, given an above-average administrative environment, higher levels of industry concentrations, or, in other words, lower levels of competition, higher rates of entrepreneurial entry are likely (collective 4, 15.75%). In contrast, lower levels of industry concentration suggest lower entry (collective 3, 13.68%). The same applies for poorly performing administrative environments, where higher degrees of industry concentration still imply higher rates of entry (collective 3, 13.68%) than in the case of high competition (collective 2, 12.08%). Given that the performance of administrative environments is negatively affected by red tape, inefficient administrative processes, a high regulatory burden, and excessive reporting requirements, which all require a substantial amount of a potential entrepreneur’s time and money, the results observed clearly imply a negative impact on entrepreneurial entry. This is in line with study results from Brunetti et al. (1997) and De Soto (1989), who have argued that red tape ranks as one of the major obstacles to entrepreneurial entry, as well as with results from Frâncu (2014) and Fredriksson (2014), who have stated that higher-performing

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217

administrative environments increase the likelihood of entry. Moreover, the results support the notion of excessive regulatory reporting requirements, poorly thoughtout reporting systems and inconsistent regulations as noted by Paneyakh (2008) and Nistotskaya and Cingolan (2016). Although this seems to be a general problem across many Russian regions, it appears that regions with less-pronounced administrative deficiencies are subject to considerably higher rates of entrepreneurial activity. Now, if we consider the industry concentration indicator, the results are somewhat surprising. Again, the results are in sharp contradiction to those from Sect. 5.3, where higher rates of industry concentration were associated with incumbents’ ability to effortlessly gain monopoly profits, which in turn was expected to foster complacency and create entry barriers. Naturally, this negatively affects entrepreneurial entry. However, if we consider the Herfindahl Hirschman Index not as a single input measure, but as being interlinked with other institutional aspects, as is the case in the geometric clustering approach, it appears that particularly in concentrated industries, entrepreneurs may also identify market niches and opportunities to pursue in the form of newly created ventures. In contrast, industries with higher rates of competition appear to be subject to lower rates of entry. Apparently, then, higher levels of competition actually deter entrepreneurial activity. However, this relationship should be considered in the right context. Rather than turning our back on competition policy in general, these results merely emphasize that the presence of industry concentration by itself does not negatively affect rates of firm entry. As part of their DNA, entrepreneurs deliberately search and choose market niches and opportunities, which by all appearances tend to emerge in concentrated markets even more than in highly competitive markets. However, the prerequisite for this to be successful is that a well-functioning market environment allows entry to yield results. In contrast, what the results do not directly show is the fact that the newly created firms are quite likely to be driven out of those markets. It is not far-fetched that higher rates of entry in concentrated industries are also linked to higher subsequent exits, since pressure from large-scale incumbents in concentrated markets is common. Moreover, based on what we have learned about market actors in quasimonopoly industries in former chapters of this dissertation, we may reasonably assume that competition in those sectors is not necessarily a fair game, and potential entrants can expect to face considerable pressure by illicit means. Thus, merely taking a gross entry perspective in this case might not be very useful, since it is net entry rather than gross entry that is impeded by unfair market mechanisms and the lack of a level playing field for economic actors. Consequently, another aspect that should capture our attention is the particular distribution of HHI values in the given panel. In fact, merely the highest of the four quantiles shows HHI values that indicate moderate and high levels of concentration, whereas the others comprise rather low and tolerable levels. This supports the assumption that highly concentrated sectors offer entrepreneurial opportunities in theory but do not allow entrepreneurs to pursue them successfully in the long term. In addition to the existence of barriers of survival, this notion also supports the prior assumption that young firms may also hesitate to

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grow their venture above a certain threshold or avoid reaching a substantial market share (Fidrmuc and Gundacker 2017; Guriev and Rachinsky 2005), since this likely establishes those firms as potential targets for other economic players. Most importantly, although there is no guarantee of fair competition, these results show that potential entrepreneurs still do not forgo entrepreneurial entry at all, which is at least reassuring for the situation of entrepreneurship in Russia. Once again, when considering the differences in the results provided by research perspective 2 compared to the results from Sect. 5.3, accounting for the interrelatedness of institutional factors seems imperative to identify clear-cut institutional impacts on entrepreneurship. Additionally, different institutional effects may prevail in different industry sectors, which are demonstrated by the differing results for data subsets C and IKM. Both aspects are a valuable contribution of the algorithm approach used in this second research perspective. Statistical Evaluation of Results Tables 5.17 and 5.18 provide an overview of the statistical evaluation of the prediction models for the respective data subsets. For the evaluation of the reliability of the predicted rates of entry, I use the hypothesis test procedure introduced in Sect. 5.4.1.4. The tables provide the predicted rate of entry (the training data column) for a given collective compared to the actual rate of entry (the test data column). The test statistics and p-values for the upper and lower boundaries provide us with the measures of statistical reliability for each identified collective. Given a significance level of 0.25 and due to the fact that both collectives in data subset C show p-values within the acceptance range, I assess the entry rate Table 5.17 Prediction accuracy and statistical evaluation, data subset C Collective 1 2

Ø Entry Observations Ø Entry Observations

Training data (prediction) 0.1012 1357 0.1238 1147

Test data (actual) 0.1102 358 0.1266 304

t il –

t iu 0.80

pil –

piu 0.2117

4.16

–

0.0001

–

Table 5.18 Prediction accuracy and statistical evaluation, data subset IKM Collective 1 2 3 4

Ø Entry Observations Ø Entry Observations Ø Entry Observations Ø Entry Observations

Training data (prediction) 0.1105 264 0.1208 219 0.1368 540 0.1575 310

Test data (actual) 0.1162 66 0.1362 42 0.1401 157 0.1529 79

t il –

t iu 0.13

pil –

piu 0.5512

2.42

0.87

0.0078

0.8081

2.65

1.65

0.0040

0.0491

1.03

–

0.1518

–

5.4 Perspective 2: A Geometric Clustering Approach

219 0.3

0.25

Avg. Entry I, K, M

0.25

0.2

Avg. Entry All Sectors

0.2

0.15

Avg. Entry C

0.15

0.1

0.1

0.05

0.05

0

GDP growth (in %)

Entry Rate (in %)

0.3

0

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

-0.05

-0.05

-0.1

-0.1

Fig. 5.9 A comparison of GDP growth and entrepreneurial entry across prediction subsets

predictions as sufficiently adequate. Whereas the predicted rate of 12.38% in collective 2 is indeed close to the actual rate of 12.66%, the prediction for cluster 1 is somewhat lower than the actual rate of 11.02%. Regarding subset IKM, based on the provided test statistics and p-values, the hypotheses on accurate entry rate predictions can be accepted for collectives 3 and 4. In contrast, for collectives 1 and 2, we have to reject the hypothesis on the upper boundaries since both predictions are slightly higher than those defined by the acceptance range. Considering the mean absolute percentage error (MAPE)36 as an additional goodness-of-fit criterion, the MAPE values also support the overall accuracy of the prediction models for the respective data subsets, with error sizes in percentage terms of 5.40% for the subset IKM model and 2.59% for the subset C model. Notably, most of the actual entry rates are somewhat higher than the entry rate predictions. This might be partly related to the fact that entry still seems to be subject to changes in economic development. As illustrated by Fig. 5.9, the 2012 test data entry rates are somewhat higher than the average training entry rates in the period from 2008 to 2011, particularly given the slump in 2009. Whereas overall entrepreneurial entry in Russia and entry for the I, K, and M industry sectors decreased with a slight delay compared to economic growth, the manufacturing sector C reacted more immediately to economic up- and downswings. This corresponds well with the existing literature because manufacturing sector industries are usually assumed to be more cyclical than other types of goods and services (Konovalova and Maksimov 2017). Moreover, it emphasizes that higher growth rates reflect higher demand for

36

MAPE measures the size of the error in percentage terms by

1 C

C P Ai Pi Ai , with C denoting the

i¼1

number of collectives for a given model, Ai the actual entry rate for a collective i, and Pi the prediction for collective i.

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5 Institutions and Entrepreneurial Activity: A Quantitative Empirical Analysis

goods and services, which in turn creates more opportunities to start new ventures (van Stel et al. 2007). All things considered, there is sufficient statistical evidence to believe in the predictive power of the institutional factors identified. In summary, and referring to the initial research hypotheses of this thesis, we can make the following conclusions. Since hypothesis H9 predicted that the regional level of perceived administrative barriers has a significant negative impact on the market entry of new firms, and considering the evidence provided, hypothesis H9 was supported. Similarly, hypothesis H19 expected the regional level of democratization to have a significant positive impact on the market entry of new firms. In light of the results observed, hypothesis H19 was also supported. With regard to the regional level of inequality, we expected a significant negative impact on the market entry of new firms based on higher degrees of inequality. The same applies for higher degrees of incumbent corporate power in the form of industry concentration. This was not proven by the prediction model results; in contrast, considering the interrelatedness of institutional factors, we observed negative effects on entry by higher competition and higher degrees of income equality. Hence, hypotheses H4 and H17 were not supported. Again, regional heterogeneity seems to play a considerable role in shaping the relationship between the institutional framework and entrepreneurial entry, as shown by the role urbanization-based regional clusters play in data subset C. Lastly, regarding all other hypotheses, the prediction model approach could not identify substantial evidence for tangible relationships towards entrepreneurial entry. Consequently, all other hypotheses were not supported. This does not necessarily mean that other institutional factors do not affect entrepreneurial entry rates; however, in terms of effect size, they are simply not as powerful and relevant in comparison to the four relationships identified. Naturally, these four factors are also the most relevant ones when it comes to the question of potential bottlenecks.

5.4.3

Conclusion

The purpose of research perspective 2 was to extend our understanding of the relationships between spatial institutional factors and entrepreneurial entry across Russia’s regions. Although the granularity of the data used was smaller compared to perspective 1, the geometric clustering approach provided some decisive advantages. (1) By considering multiple institutional variables, the approach accounted for the interrelatedness of the institutional framework factors as claimed by the systems of entrepreneurship literature, (2) it avoided potential biases in the results that are driven by multicollinearity and spurious correlation, and (3) the approach does not suffer from being subject to model overfitting. Consequently, I was able to identify true institutional drivers of spatial entrepreneurial entry, which in fact differ substantially from the preliminary results from research perspective 1. Similar to research perspective 1, there are considerable indications that crossregional heterogeneity in institutions plays a role in shaping spatial entrepreneurial

5.4 Perspective 2: A Geometric Clustering Approach

221

Table 5.19 Overview of accepted and rejected hypotheses according to perspective 2 Structural economic factors H1: The regional level of economic risk has a negative impact on market entry of new firms. H2: The regional level of unemployment has a positive impact on market entry of new firms. H3: The regional level of average wages has a negative impact on market entry of new firms. H4: The regional level of inequality in income and wealth distribution has a negative impact on market entry of new firms Property rights H5: The regional level of security of property rights has a positive impact on market entry of new firms H6: The regional level of perceived risk from raidership has a negative impact on market entry of new firms Criminality H7: The regional level of perceived public safety has a positive impact on market entry of new firms Corruption H8: The regional level of corruption has a negative impact on market entry of new firms Bureaucracy H9: The regional level of administrative barriers has a negative impact on market entry of new firms H10: The regional level of the perceived threat of agency pressure has a negative impact on market entry of new firms Financial capital H11: The regional level of available short-term financial capital has a positive impact on market entry of new firms H12: The regional level of available long-term financial capital has a positive impact on market entry of new firms Human capital H13: The regional level of human capital has a positive impact on market entry of new firms Infrastructure H14: The regional provision of physical infrastructure has a positive impact on market entry of new firms H15: The regional level of access to communication infrastructure has a positive impact on market entry of new firms Market environment H16: The regional level of incumbent corporate power has a negative impact on market entry of new firms H17: The level of industry concentration has a negative impact on market entry of new firms H18: The regional availability of suppliers has a positive impact on market entry of new firms

Not supported Not supported Not supported Not supporteda

Not supported Not supported

Not supported

Not supported

Accepted Not supported

Not supported Not supported

Not supported

Not supported Not supported

Not supported Not supporteda Not supported (continued)

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Table 5.19 (continued) Democratization H19: The regional level of democratization has a positive impact on market entry of new firms

Accepted

a

Although hypotheses H4 and H17 were not supported in their original intention, I was nevertheless able to identify significant impacts on entrepreneurial entry. Furthermore, there are also ample indications for a relationship between the availability of finance and entrepreneurial activity (demonstrated by H4), although the indicators used in H11 and H12 did not show any significant results in research perspective 2

activity. Regarding data subset C, in one case, the degree of urbanization in particular tips the scale towards higher entry. Thus, again, urbanization matters for entrepreneurship in Russia. Table 5.19 provides a final overview of the accepted and rejected hypotheses derived from the results of the geometric clustering algorithm. Let us consider the institutional factors under analysis. 1. Inequality in the distribution of income and wealth: The subset C results suggest that higher degrees of inequality are beneficial for entry in the manufacturing sector. Although this appears to be in sharp contrast to the research perspective 1 results, where inequality was found to negatively affect entrepreneurial entry, there might be a common cause to both effects. In this sense, both results are likely to reflect a low availability of financial means for funding. Given that firms in the manufacturing sector in particular require considerable amounts of initial funding and that Russia is characterized by relatively difficult access to sources of funding, it is primarily wealthy individuals, serial entrepreneurs, and other economic actors with available funds who can make those investments. The number and wealth of such actors are reflected by higher levels of income concentration. In contrast, if we look at the IKM service sector data subset, income inequality does not play a similar role; hence, the concentration of financial resources appears to play a major role particularly when it comes to leveraging higher initial investments in the manufacturing sector.37 However, the issue arises that Russia has rates of income inequality comparable to the United States without observing similar negative effects on firm entry (Levada Center 2017). In fact, this may be another indicator of the lack of available financial means for business creation in Russia. Whereas the United States have considerable financial market depth, the most advanced venture capital market in the world and broad financing capabilities for potential founders, Russia faces rather low willingness to provide founding capital from financial market actors, high interest rates and collateral requirements, relatively low FDI, and a reduction in venture capital market volume of roughly 52.8% between 2007 and 2015 (OECD 37 One exception in this regard might be the financial services sector K, where at least in terms of banking services, considerable amounts of initial investment capital would be required.

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223

2017). This might also explain why higher-income inequality seems to have negative impacts on entry from an all-industries perspective (i.e., research perspective 1), whereas it appears to show a positive impact in industry sectors with relatively high-entry barriers in terms of initial funding requirements. 2. The burden of bureaucracy: The results on the measure of administrative barriers are rather straightforward and confirm the first impression from research perspective 1. Consequently, an administrative environment characterized by high amounts of red tape, inefficient administrative processes, a high regulatory burden, and excessive reporting requirements can be reasonably assumed to negatively affect entrepreneurial entry. The results thus contribute to one of the most frequently mentioned obstacles for business in Russia, and the performance of regional authorities thus seems to be a crucial factor for promoting entrepreneurial activity. On the one hand, the results reflect the high dependency of entrepreneurs on interactions with public agencies, and on the other, a better performance of regional authorities also suggests more freedom from artificial administrative barriers, which in turn facilitates new firm entry. 3. Industry concentration and competition: The present evidence suggests that higher levels of industry concentration (or lower levels of competition) indicate higher rates of entrepreneurial entry. This is certainly one of the most interesting results and stands in clear contradiction to the research perspective 1 results from Sect. 5.3. This relationship suggests that industry concentration in itself does not impede entry since entrepreneurs appear to identify enough market niches and opportunities even in concentrated markets. The evidence, however, does not account for the fact that firms are even more likely to be driven out again from those markets due to unfair practices of competition or high pressure from incumbents, emphasizing the importance of considering high industry concentrations as a significant barrier to survival rather than as a barrier to entry. 4. Democracy: Regarding regional democratization, there is strong evidence that higher degrees of democracy are beneficial for entrepreneurial activity in Russia. The present results suggest that the free dissemination of information and open debate facilitate receptiveness to new and creative ideas and foster both internal innovation and spreading innovations from abroad, i.e., democracy supports an entrepreneurial mindset and the entrepreneurial nature of creative destruction. Moreover, democratic elections, independent courts, free and impartial media, and a plural, active, and enthusiastic civil society create checks and disincentives for extractive and predatory interests and in turn stimulate investments such as entrepreneurial firm creation. Given the unexpected importance of these results, the following paragraphs discuss their extensive implications within the Russian context. At first, considering the positive impact of democracy on entrepreneurial activity, the results strongly identified support study the findings from Rodrik (2007), Faccio (2006), and Acemoglu and Johnson (2005), whereas they clearly contradict conclusions from Friedman (2009) or Barro (1997), who have argued against a reliable

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connection between democracy and higher levels of entrepreneurial or economic activity. In terms of comparing the present results with previous studies, looking at the results of Bruno et al. (2013) is particularly interesting. In their study, the authors argued that, in Russia, higher levels of democracy can be expected to diminish entry rates below their levels of natural entry because, in an institutionally weak environment where personal ties are particularly important, change in political elites and greater competition for political power may not necessarily lead to a higher quality in political institutions but rather result in the greater disruption of personal ties. Whereas large- and medium-sized firms are more dependent on their political ties, which make their entry more dependent on predictable power structures, only small firms are positively affected by higher levels of democracy. The latter is due to the fact that small firms usually do not have ample political connections at their disposal and instead rely on a fair and equal guarantee of economic rights (Bruno et al. 2013). This is plausible in principle; however, the results of this thesis point in a slightly different direction. The present results suggest that even founders of firms with higher growth aspirations (i.e., potential medium- and large-scale firms) benefit from the advantages provided by higher levels of regional democracy because, ultimately, they have less to fear if the mechanisms of regional democracy are more efficient. Consequently, they are more likely to make higher investments without fear of losing them or being forced to share the fruits of their investments with politicians, officials, or well-connected incumbents. Certainly, from an individual’s perspective, it may not matter if one’s investment is protected by democratic institutions or a political ally; from an overall economic perspective, however, protection via public checks, an active civil society, and disincentives for extractive behavior are far more effective. It remains to be seen why this is particularly the case in the manufacturing sector. Presumably, cash-generating manufacturing businesses are often the target of politicians’ and officials’ rent extraction desires. Against the background of being most likely subject to the grabbing hand of the government (Frye 2017) or an incumbent’s business interests, the benefits of higher levels of regional democracy may particularly benefit those sectors that suffer most from its absence, thus leading to higher rates of entrepreneurial entry. This may also explain why the particular effect cannot be observed, or is less pronounced, in other sectors (i.e., the IKM subset), where other institutional prerequisites may be more relevant. Although the consequences that result from these observations are rather straightforward, facilitating democratization in Russia still faces considerable challenges, which two particular aspects illustrate. When it comes to civil engagement, according to the Levada Center (2012), relational trust among Russians is significantly lower than in other countries, given that only 27% of respondents in Russia trust their fellow citizens compared to an average of 45% among 29 countries. Additionally, less than 15% of the Russian population was involved in informal voluntary engagement, whereas merely 1–3% committed themselves to official community or association work. In particular, NGO-related engagement has a difficult foothold in Russia. Infamous laws from 2012 and 2015 created different

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lists of NGOs and civic associations, either providing opportunities to “good” organizations or limiting the activities of “bad” ones. Whereas the former mainly refers to socially oriented organizations that provide social or health services, the latter encompasses “foreign agents” and “undesirable organizations,” i.e., associations that focus on issues such as human rights, environmental issues, or political topics, such as the promotion of democracy. As a result, the number of civic associations declined by roughly 33% (EU-Russia Civil Society Forum 2017; Zakharova 2016). Certainly, there are some civic initiatives and groups protesting against crisis measures in the economy, such as long-haul truck drivers, farmers, or pensioners; however, they were largely condemned to failure due to their small numbers and a lack of overall societal support. In terms of entrepreneurship, more appreciation of the freedom of expression, information, association, and assembly would foster the flow of ideas, creativity, critical thought, and innovation, in addition to enabling entrepreneurs to develop and share new ideas and innovations with the confidence of being supported by other members of society. Similar developments can be observed regarding Internet censorship, which, apart from the political dimension, from an entrepreneurial point of view, threatens the influx and free debate on new, creative, and innovative ideas. Whereas in 2016, roughly 76% of Russia’s population had access to the Internet, and in general the Internet still is the most free area of media coverage in the country, compared to directly or indirectly state-controlled television or newspapers, there is an infamous tendency towards more and more restrictions. Based on several legal initiatives, steps taken in recent years encompass Internet blacklists, mass surveillance by the FSB, data retention requirements, and even deactivating telecommunication services during opposition meetings.38 These developments are scarcely conducive to the country’s setting for Internet-related or e-commerce start-ups, since this scene in particular is accustomed to online freedom of movement, as well as to cultural and commercial exchanges beyond the limits of borders and geography.39 Installing black boxes will inevitably result in lower connection speeds, which will consequently put at risk any developments related to Internet of things appliances, autonomous driving, telemedicine, and other web-based innovations. Consequently, it should be seriously doubted that the described deterioration in terms of democracy is encouraging to pursue entrepreneurial opportunities. Regarding the question of potential bottlenecks that may constrain Russia’s entrepreneurial performance, the four factors described are of primary concern. Although hypotheses on other institutional factors could not be confirmed, this does not necessarily mean that other institutional factors do not affect entrepreneurial 38 For example, residents from Ingushetia stated that, for many years 3G and 4G, networks were disconnected every time a rally or public action was scheduled (Roskomsvoboda 2018). 39 For example, according to a recent survey from global investment bank Morgan Stanley, e-commerce ventures based in Moscow and St. Petersburg have received substantial funding over the past decade in a market that can be expected to reach US$52 billion by 2023 (Henni 2018). This is notable, as particularly in relatively remote regions, Internet-based entrepreneurial activity might help counterbalance other geographic disadvantages.

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entry rates at all. Rather, the command of required financial resources, the absence of administrative barriers, the perception of market niches and opportunities (against the background of industry concentration), and high democratization simply make the largest contributions towards rates of entrepreneurial entry. In this light, a lack or poor quality of these factors may systemically impede the overall functioning of entrepreneurial activity in Russia. To conclude broadly on research perspective 2, one last aspect should not remain unmentioned. Although research perspective 2 was useful in overcoming some drawbacks of the first research approach, there are nevertheless some limitations to note. In this regard, the predictive model approach is subject to lower data granularity; thus, it is not possible to predict entry for individual NACE two-digit industries. Additionally, concerning the use case of entry rate prediction, the training data observation period to develop the prediction model could be more current. This is particularly relevant in light of political and economic developments since 2013, such as the circle of sanctions and counter-sanctions, or Russia’s limited access to international capital markets, which might have affected the institutions-entry relation. Finally, we need to remember that missing data in some variables of the panel has been treated with missing data imputation. Nevertheless, regarding the significant relationships identified, only the regional democratization variable in 2011 was subject to imputation. Since the index considers a 5-year running average of the underlying sub-elements of democracy, we observe little variance in the democratization index on a year-to-year basis, i.e., the index value is relatively inert. Hence, we may conclude that the likelihood that this particular index is biased from the imputation procedure is comparatively small, which is why I do not expect any material distortions in the results.

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Chapter 6

Conclusions

The best time to plant a tree was 20 years ago. The secondbest time is now. Chinese proverb

At the end of this thesis, this chapter revisits the research question and summarizes the key contributions of the previous analyses. These key contributions are regarded from a theoretical and practical, i.e., policy-related, perspective. In order to provide answers to the questions, Which spatial institutional factors are most important in shaping regional entrepreneurial activity in Russia? and Is the performance of the Russian system of entrepreneurship subject to institutional bottleneck constraints? the present study followed a twofold approach. In the first step, I followed Klapper et al.’s (2006) and Bruno et al.’s (2013) Tobit regression approach in order to test the derived hypotheses on spatial institutional impacts on entrepreneurial entry. Due to several methodological limitations in the first approach, in a second step, I utilized Brieden and Gritzmann’s (2012) innovative geometric clustering approach in order develop a predictive model for spatial entrepreneurial entry rates based on institutional predictor variables. Based on this research strategy, I was able to identify significant, reliable institutional relationships towards entrepreneurial entry or rather those institutional factors that are most important in shaping regional entrepreneurial activity in Russia. Those factors also represent potential bottlenecks that may constrain the performance of the country’s or its regions’ systems of entrepreneurship.

6.1

Key Contributions

From a theoretical perspective, the relationships identified contribute to several discourses in the scientific literature. Apart from the specific institutional impacts on entry, the results of this dissertation emphasize the relevance of thinking of entrepreneurial ecosystems as dynamic, interdependent elements of an overall © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Schlattau, Tilting at the Windmills of Transition, Societies and Political Orders in Transition, https://doi.org/10.1007/978-3-030-54909-1_6

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system that drives the allocation of resources through the creation of new ventures, i.e., a system of entrepreneurship as proposed by Acs et al. (2014). Hence, my results emphasize that equipping individuals with the necessary abilities, attitudes, and aspirations and shaping the opportunity and social legitimacy costs of entrepreneurial activity are certainly matters of context (Acs et al. 2016; Sorensen 2007; Cassar 2006). In addition, regarding the issue of spatial heterogeneity in Russia, I could show that local entrepreneurial activity is considerably shaped by different preconditions in the regional institutional environment, as a number of authors such as Fritsch and Wyrwich (2014a, b), Audretsch et al. (2006), Berkowitz and DeJong (2005), and Baumol (1990) have proposed. Notably, economic-geographical location potential and particularly urbanization need to be considered as relevant aspects in this regard. Most importantly, the present results support the notion that regional systems of entrepreneurship can be constrained by institutional bottlenecks, which was proven by the fact that, given a set of relevant framework factors, lower-quality institutional factors or higher institutional barriers could be used to predict significantly lower levels of entrepreneurial entry. Hence, the institutional factors identified can be considered tangible constraints for the performance of the regional system of entrepreneurship in a given region (Bruton et al. 2010; Puffer et al. 2010; Lerner and Schoar 2005; Boisot and Child 1996). Besides these general contributions, I identified four significant institution-entry relations that can be noted as key contributions of this dissertation. First, the evidence provided on the positive effects of higher concentrations of income and wealth on entry in the manufacturing sector most probably reflects a low availability of financial means for funding, which is one of the most frequently mentioned obstacles to entrepreneurial activity in Russia. As a consequence of limited access to external sources of financing, wealthy individuals, serial entrepreneurs, and other economic actors with considerable funds at hand are most likely to provide substantial initial investments. On the other hand, given the underdeveloped capital markets, poor availability of credit, and the fact that Russian banks rarely lend to de novo private firms, substituting formal external capital with informal sources of financial capital, for example, from family, friends, or a personal network, is rarely sufficient compensation to promote higher levels of entrepreneurial activity on a broad scale. This widely corresponds with studies and arguments from scholars such as Popova et al. (2017), Tonoyan et al. (2010), Aidis and Adachi (2007), Beck and Demirguc-Kunt et al. (2006), Filatotchev and Mickiewicz (2006), and Klapper et al. (2006). Second, there is compelling evidence for the negative effects of higher levels of bureaucracy and administrative barriers on entrepreneurial entry. An administrative environment characterized by high amounts of red tape, inefficient administrative processes, a high regulatory burden, and excessive reporting requirements can thus be reasonably assumed to negatively affect a potential entrepreneur’s decision to create a venture. This is in line with the assumption that high numbers of rules and regulations pertaining to entrepreneurship, accompanied by a lack of consistency and continuity, result in significant opportunity costs for entrepreneurs. The present

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evidence thus confirms the notion that the quality of market regulations in particular positively affects entrepreneurial entry rates (Stenholm et al. 2013; Bjørnskov and Foss 2008; Nyström 2008; Klapper et al. 2007). Similarly, it supports the understanding from several Russia-focused studies, for example, by Nistotskaya and Cingolan (2016), Paneyakh (2008), or Aidis and Estrin et al. (2006) that have argued that higher regulatory quality and lower levels of bureaucracy support firm creation and incorporation. Third, the results of my analysis do not imply any actual barriers of entry caused by higher levels of industry concentration. Instead, higher degrees of sector competition are apparently more likely to deter entrepreneurial entry in Russia, since this might leave fewer market niches and opportunities for potential entrepreneurs. However, what the results do not directly show is the fact that firms are even more likely to be driven out again from these markets, for example, due to unfair practices of competition, exclusive supplier and customer relations, political relations, or other ways of exerting unfair competitive pressure. In this sense, concentrated markets are more likely to create survival barriers than mere entry barriers. This is in line with study results from Maslikhina (2017) and Rastvortseva and Ternovskii (2016), who have argued that incumbent market dominance in Russia most strongly affects young, small firms. Similarly, market-dominating players are more likely to drive out or acquire small firms that distinguish themselves, for example, by showing sufficient potential for success and growth. This conclusion adds to the understanding that small firms hesitate to grow their venture above a certain threshold or avoid reaching a substantial market share rather than waiving entry entirely (Fidrmuc and Gundacker 2017; Guriev and Rachinsky 2005). Hence, first and foremost, the lack of a level playing field and the existence of survival barriers should attract our attention in this case. Fourth, and probably most interesting, is the fact that there is substantial evidence that higher levels of regional democratization are beneficial for higher levels of entrepreneurial activity. It could be shown that democracy can work as a contributor to an entrepreneurial mindset, for example, by facilitating openness to new ideas, spreading productive ideas, technologies, and innovations from abroad and triggering debates on the most efficient and suitable solutions to problems. Additionally, as a guarantor of economic freedom, democracy creates disincentives for predatory behavior and establishes prevention mechanisms to avoid institutions being captured by narrow or extractive interests, thus encouraging private investment (Acemoglu and Robinson 2012; Putnam 1993). By emphasizing the positive effects of democracy on entrepreneurial activity, the evidence of this dissertation strongly supports the study results from Rodrik (2007), Faccio (2006), and Acemoglu and Johnson (2005), whereas it clearly contradicts conclusions from Friedman (2009) or Barro (1997), who have argued against a reliable connection between democracy and higher levels of entrepreneurial or economic activity. The results also disagree with assumptions from Boschini et al. (2009) and Andersen and Aslaksen (2008), who have proposed that tangible and definite constitutional details in particular have a stronger influence on economic activity than the mere overall presence of a democracy. Particularly with regard to the measure of democratization used, rather

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than formalized aspects, the real balance of power, fairness of elections on various levels, the existence of political pluralism, the existence and involvement of civil society, and the openness and transparency of political and civil life in general all positively affect entrepreneurial activity across Russia’s regions. Most importantly, and in contrast to the arguments provided by Bruno et al. (2013), it is not merely small, young firms that benefit from the advantages of democratization. All firm founders with higher growth aspirations and sufficiently large initial investments to provide have less to fear given the security provided by the mechanisms of public checks, an active civil society and disincentives for extractive behavior, as discussed in Sect. 5.4.3. Understanding democratization and the liberties that accompany it as an essential prerequisite for entrepreneurial entry and innovation is virtually groundbreaking, particularly when it comes to addressing the specific requirements of a transition economy context. Finally, in addition from these four key contributions, the analyses of this thesis also provide some interesting indications on the consequences of the opportunity costs of entrepreneurship and the impact of corruption in Russia. With regard to the former, sufficiently large and relatively secure wages appear to create relative advantages of regular employment over pursuing an entrepreneurial career, particularly for well-educated employees. Thus, although entrepreneurial activity in Russia generally benefits from the quality and performance of the country’s education system, most of its well-educated graduates likely favor paid employment over creating an entrepreneurial venture. Additionally, the perception of high levels of criminality or a lack of public safety also adversely affect the decision to pursue an entrepreneurial career by creating a venture because increasing insecurity challenges the predictability of an entrepreneur’s future returns and increases the opportunity costs of the entry decision. Regarding corruption, there are comprehensive indications that it plays a considerable role in shaping the efficiency of executive agencies in performing their duty. In this light, the data under analysis demonstrate that, in regions with low levels of corruption, agency controls serve their intended purpose and create a level playing field for market participants, which results in higher rates of entrepreneurial entry. In contrast, in the presence of higher levels of corruption, the threat of opportunistic behavior and rent extraction by bureaucrats leads to lower rates of entrepreneurial entry. However, the evidence on opportunity costs and corruption needs to be interpreted with caution because it mainly stems from the first research approach of this dissertation, which was subject to several limitations. Nevertheless, as a whole, even these findings provide interesting clues on the multifaceted institutional impacts on entrepreneurial activity across Russian regions and may serve as an interesting starting point for future analysis.

6.2 Implications for Policy and Practice

6.2

237

Implications for Policy and Practice

It is one thing to identify econometric links between the institutional context and entrepreneurial activity, but certainly another to act on them and recommend how entrepreneurial entry can be increased. However, the goal should be straightforward: to increase entrepreneurial activity. Hence, the following paragraphs aim to derive several political implications that can be drawn from the key results of this study without claiming completeness. First, starting with the scarcity of seed capital, the first initiatives made by the Russian government and the central bank need to be followed more consistently. Although various state support programs have been initiated to provide grants for starting businesses, microcredit, credit guarantees, or credits on concessional terms, lending volumes declined between 2014 and 2016 in the aftermath of the conflict between Russia and Ukraine. Additionally, high inflation and interest rates charged to small firms, as well as a small venture capital market accompanied by a low willingness to take risks, contribute to the lack of financing opportunities for entrepreneurs. Potential measures to combat the status quo may include the simplification of access to seed finance and increasing its volume, for example, by providing subsidized seed funding (without exclusively restricting intermediary access to large-scale government-controlled banks); lowering disincentives for banks and other financial institutions to engage in seed financing by reducing overly stringent regulatory requirements; lowering collateral requirements, for example, by a government-backed guarantee system; or facilitating the securitization of seed loans. Other measures might be improving access to leasing instruments or capital market instruments, as well as facilitating access to microfinance and crowdfunding, or providing founder grants, which has the advantage of reducing a great deal of an entrepreneur’s opportunity costs.1 Many of these ideas can be leveraged via international capital markets. However, Russian access to the former is currently restricted by international sanctions, which should not be expected to be removed until there is a mutually agreed-upon solution of the dispute with Ukraine that all parties comply with.2 Additionally, international willingness to provide investment capital recently took a blow from another side. The detention of US private equity investor Michael Calvey in February 2019 on the basis of accusations once again reflects the arbitrariness of justice in Russia and was a shock for FDI providers to Russia.3 In the first week after to the allegations, 1

However, caution needs to be advised in designing the requirements for additional sources of financing. For example, Chepurenko et al. (2011) has shown that, particularly in a transition context, increasing access to public funding may negatively affect the share of opportunity entrepreneurship in a given region if the funds are also open to other economic agents. 2 However, Russia observed a surge in corporate deposits subsequent to the first capital market sanctions, when Russian firms repatriated cash formerly held abroad due to fear of further escalation (Reuters 2016). 3 Since the 1990s, Calvey and his investment firm Baring Vostok have been major investors in Russia. In February 2019, Calvey was sent to pretrial detention due to allegations related to

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international investment funds withdrew roughly 130 million USD from the country (Brüggmann 2019). If international investors have to fear being imprisoned on unsubstantiated accusations and without a fair trial, there are few incentives for international actors to provide seed funding. It appears, unfortunately, that there is little political discernment on this matter. As Andrey Movchan (2019) from Carnegie Moscow Center recently commented: [O]ne senior official said: Investors are like pigeons on the barn floor: if you kick one of them, the others will take a flight, circle a couple of times, make some noise, and then land again. And what do we need them for anyway—so they can leave a mess everywhere and take our grain?Second, like the difficult access to finance, red tape and a high bureaucratic burden are not entirely new discoveries regarding obstacles to business in Russia. However, the Russian government is surprisingly reluctant to be more consistent in its efforts to fight bureaucracy. Even despite Russia’s love of disclosure procedures, documentation, stamps, seals and meticulous note-taking, there is no point to it; obviously, it does not help provide more proper documentation in case of alleged charges, it does not achieve more efficient markets by complying with regulatory requirements, and excessive reporting obligations for entrepreneurs do not enable authorities to do a better job or facilitate better economic assessments by Russia’s statistics agencies. Admittedly, some progress in reducing the bureaucratic burden to entrepreneurs has been made; regarding the Ease of Doing Business index mentioned earlier in this work, improvements in time and the procedures required to start a business have been made. Moreover, Russian state services have initiated some e-government initiatives, which is why most Russians can pay their fees and taxes online. This has decreased the time required by an average firm to file tax returns by half.

Based on the results of this dissertation and the impeding effect of administrative barriers on entrepreneurial activity, following the path of digitalization may unlock a considerable range of benefits in the fight against red tape, bureaucracy, and corruption. Digital technologies can significantly improve the public sector’s capacity, transparency, and accountability, for example, by automating tasks and reducing public officials’ discretion. This could raise the quality of public administration, improve the efficiency of entrepreneur-related services, shorten processing times, and reduce leakages in spending.4 For example, one area that regularly faces criticism and that would certainly benefit from higher levels of automation and transparency is the field of cross-border trade and transactions, particularly when it comes to time reductions to clear customs and border checks. Overall, one of the main advantages of providing administrative services digitally is that algorithms are not familiar with the concept of corruption and are hardly receptive of bribes; thus, digitalization initiatives of public administration can be expected to help reduce a large share of entrepreneurs’ opportunity costs.

embezzlement of funds from Vostochny bank, one of the firms in Baring Vostok’s portfolio. The supposed initiators are two politically connected minority shareholders of Vostochny Bank (Courtney 2019). 4 A regularly mentioned role model is Estonia, which claims that its digital administration initiatives have resulted in an annual reduction of more than 800 years of public and private sector work time, as well as in savings of about 2% of GDP (E-Estonia 2019).

6.2 Implications for Policy and Practice

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Simultaneously, on the one hand, it is crucial to extend assessments of regulatory impacts by explicitly considering entrepreneurship, ideally at a relatively early stage of legislative drafts, but certainly before implementation. On the other hand, more urgency needs to be placed on reforming burdensome regulation. This includes re-defining clear objectives and competences for regulatory agencies, as well as restricting competences regarding the issuance of bylaws by lower-level departments and shifting jurisdiction towards higher levels with more sufficient capacities, which could consider contradictions across different legal provisions. Nevertheless, there is a tremendous urgency in fighting Russia’s bureaucratic jungle because “the existing state regulatory system simply doesn’t let private business to be conducted without violating something” (Titov 2019). Concerning the third aspect, we saw that higher levels of sector concentration do not directly deter entrepreneurial entry; however, we may expect higher subsequent exits due to higher levels of incumbent pressure in those concentrated markets. Thus, what is particularly relevant from a policy perspective in this regard is to guarantee a level playing field for all actors, i.e., to secure low entry barriers to facilitate entry and to ensure fair rules in order to avoid high exit rates due to (unfair) incumbent pressure. Specifically, this could be achieved by fostering SOE privatization, at least in non-strategic areas. Reducing the overly strong presence of state-run enterprises, or at least showing the political will to enforce procurement policies that provide SME contractors with realistic chances in the vast sector of state procurement, which represented roughly 28.5% of GDP between 2015 and 2018 (Di Bella et al. 2019), may provide entrepreneurs with opportunities to succeed even in concentrated markets. Instead, even now, SOE procurement occurs either through non-competitive methods, or laws on SME quotas are used by subsidiaries of large-scale incumbents and conglomerates. SOE privatization also encompasses the finance sector, particularly since the allocation of funds currently benefits primarily large-scale players or state-run firms, thus adding to increasing market concentration. In turn, more competition in the banking sector itself plus incentives for financial intermediaries is needed to allocate higher shares of capital to private, small-scale ventures. Additionally, as described in Chap. 4 of this thesis, the FAS in its current state is barely a guarantor of fair and competitive markets, especially since the agency has a rather strong focus on SMEs with market shares over 35% and files a preposterously high number of roughly 60,000 anti-monopoly cases each year, covering sole-proprietors, taxi drivers, or rural dairy producers, whereas incumbents with substantial market power (e.g., Gazprom, Rostec, Sberbank, etc.) are widely ignored (Szakonyi 2017). Providing the FAS with a clearer mission and the capacity to comply with it is of utmost importance from an entrepreneurship policy perspective, since only true competition with fair rules leads to real entrepreneurial innovation. This is clearly a question of political priorities. A current and relevant example for the urgency of this issue is Russia’s digital technologies sector, where the system-immanent lack of competition causes considerable implications for pursuing opportunities. On the one hand, some Russian firms make better use of digital customer channels and other advantages of digital innovation than many European ones, for example, Russian banks compared to the

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German banking sector. On the other hand, numerous Russian industries exclusively compete in the national market with little demand for operational efficiency and innovation to succeed. This largely prevents the entire economy from being more innovative. In contrast, Russian firms from the metallurgy or oil and gas sectors that compete globally are rather efficient in exploiting the benefits from digital technologies in order to succeed against international competitors. Consequently, an evident solution to the status quo would be gradually transferring and applying the principle of fostering more entrepreneurial innovation through competition on the national level. An already existing environment of high potential start-ups in digital core technologies such as artificial intelligence, blockchain, or voice and image recognition might find an attractive domestic market and promising opportunities, for example, in the country’s sizeable industrial sector. This leads us, finally, to democratization as the fourth field of policy implications. Certainly, this is also the most delicate aspect concerning policy initiatives in Russia. Certainly, Russia should not mindlessly copy and imitate the patterns of democracy promoted by the West, something that did not work in the past and will hardly bring about the expected results in the future. Moreover, in light of the developments of the last decade, making recommendations of more intense efforts towards Western-style democratization to Russia’s political leadership may also appear quite naïve. Nevertheless, this dissertation noted the importance of democratic liberties as an essential prerequisite for entrepreneurial activity. Hence, instead, I want to raise a number of open questions for debate to political decision-makers. • How can we expect entrepreneurs to invest and bear entrepreneurial risk if there is no guarantee of legal certainty or independent courts, given that someone with convenient political relationships who acquires a liking for an entrepreneur’s firm may decide to politicize the given case, regardless of the law? • Why should we expect employees to leave the relatively safe haven of employment and pursue a risky entrepreneurial career, including the accompanying opportunity costs, if there is no active civil society, no free and impartial media, and no reliable public system of checks that creates disincentives for and protection from extractive and predatory interests they might face? • How can people be expected to think and discuss creatively and critically about innovative solutions as well as share ideas when individuals or groups (such as ethnic, religious, gender, LGBT, political, and other groups) are not free to express personal views on sensitive topics or to express their faith or belief in public without fear of surveillance or retribution? • Why should we expect entrepreneurs to create ventures to help fellow citizens with start-ups that not only generate profits but also contribute to society by concentrating on future-oriented challenges and solutions if there is a shrinking space for a plural and active civil society with a government that discredits foreign-funded groups, weakens independent civil-society actors, and uses selective prosecution and intimidation? Reflecting upon the current state of Russian democracy, there is currently no sizeable opposition questioning the status quo. However, a growing number of

6.3 Limitations and Future Research

241

spontaneous demonstrations across several regions reflect displeasure with rising social inequality, administrative mistakes, and corruption. Additionally, since the 1990s, opening up the country, promoting globalization and digitalization pluralized Russia’s society, and many young Russians value personal liberties and individual self-realization despite their identification with conservative values and Russian patriotism. Hence, it is to be hoped that in the long run these developments also work towards a positive vision for Russia’s future.

6.3

Limitations and Future Research

While the analyses of this dissertation were conducted as diligently as possible, this study is not without limitations. Where possible, appropriate measures have been taken to alleviate problems. First, earlier in this study, challenges with the underlying data sources were mentioned. In this regard, the outcome variable was constructed based on gross entry rather than net entry. Moreover, the underlying data also did not allow for differentiation between de alio and de novo entry, which is why the calculated entry rates are likely to be slightly overestimated. Second, sources for predictor data were somewhat limited in terms of availability and completeness, resulting in missing data for some observations. Since data elimination was not an option, the variable values in question were complemented by data imputation. There were, however, no indications that the data were not missing at random, and the use of two different imputation approaches showed only minimal deviation. Thus, although the issue of imputation is a controversial one, I believe that providing a slightly blurred picture is better than providing none at all. Moreover, the final relationships identified in research perspective 2 are not subject to data imputation, except for the democracy variable. This last, however, is subject to only a small risk of being biased from imputation because the variable is calculated by a 5-year moving average, and variance across years is rather inert. Third, by aiming to alleviate potential challenges of multicollinearity, the Tobit model approach of research perspective 1 appeared to be subject to a considerable risk of spurious correlation. Additionally, it did not sufficiently account for the interrelatedness of institutional framework factors. Research approach 2 successfully resolved these issues, although the granularity of the data used is smaller compared to perspective 1, and not all industry sectors were eligible for the predictive model approach. Nevertheless, the interrelation of system elements, avoidance of multicollinearity and spurious correlation, and avoiding overfitting were appropriately accounted for in the second approach. Finally, critics might object the fact that the underlying data is not up to date. Certainly, if the entry rate predictions should be considered for an up-to-date use case, the prediction model needs to be trained on more current data in order to account for recent political and economic developments, such as the sanctions and counter-sanctions related to the Russian economy.

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I readily acknowledge these limitations. However, in light of prior research, I submit that the results of this dissertation have the merits of providing new ways to think about the nature of spatial systems of entrepreneurship and the impact of institutions on entrepreneurial activity in Russia and other transition economies, particularly regarding the central relevance of democratic core principles, which constitutes an interesting, important finding concerning the special context of transition economies. The limitations of this work also offer interesting avenues for future research. On a general level, I firmly encourage other scholars to investigate the interaction of spatial institutions and entrepreneurial entry in transition economy contexts. Building on the system of entrepreneurship literature, more insight into the mechanisms of system element interactions is certainly required. Additionally, explicitly accounting for net entry might provide a better understanding of the differences between entry barriers and barriers to firm survival. Finally, I believe that there is great value in continuing to study the underlying mechanisms of the democracy-entrepreneurship interaction that I have proposed and found. In this regard, apart from traditional regression approaches, machine learning, AI, and innovative algorithms may pave the way for promising new approaches of data analysis.

6.4

Conclusion

The insights revealed through the theoretical and practical contributions of this dissertation constitute a valuable contribution to existing knowledge. The comprehensive analysis of this dissertation offers substantial evidence of the contextual factors that shape entrepreneurship in Russia, and it provides a valuable basis for understanding the subtleties of entrepreneurship in transition, as well as the policies and reforms required in specific regional contexts. Additionally, almost 30 years after the collapse of the communist system, a new generation is entering the markets, one that neither experienced the socialist system nor the first years of transition. Let us hope that this allows them to better cope with the difficulties of the context and embrace the opportunities it provides. The current situation, nevertheless, is not sustainable. When the collapse of the Soviet Union slowly began to gain momentum in the late 1980s, rebellious rock vocalist Viktor Tsoi sang the lyrics that most Russian’s knew and still know by heart: “Change—our hearts demand it. Change—our eyes demand it.” And although the type and extent of the change that is necessary today are arguable, as most Russians would probably agree, in view of facilitating higher levels of entrepreneurship, change is indispensable. Certainly, there are many paths that Russia’s economy may take in the future, but all of them will only come to bear when politicians act upon the present problems and work ambitiously to make the Russian Federation a better place for entrepreneurs. In this sense: “The best time to plant a tree was 20 years ago. The second-best time is now.”

References

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Annex A

A.1 The Persistency of Entry Rates

Table A.1 Average correlation of entry in consecutive years, 2008–2012, EU entry rates NACE code 10 11 12 13 14 17 18 20 21 24 25 26 27 29 30 31 32 36 37 38 39

2008 (%) 5.59 5.59 5.59 5.38 5.38 5.40 5.40 5.58 5.58 6.61 6.61 5.28 5.28 5.90 5.90 6.13 6.13 9.36 9.36 9.36 9.36

2009 (%) 6.08 6.08 6.08 5.63 5.63 5.30 5.30 5.87 5.87 5.51 5.51 5.01 5.01 5.64 5.64 6.04 6.04 8.11 8.11 8.11 8.11

2010 (%) 6.97 6.97 6.97 5.99 5.99 5.73 5.73 6.14 6.14 5.61 5.61 5.89 5.89 6.15 6.15 5.99 5.99 7.85 7.85 7.85 7.85

2011 (%) 7.66 7.66 7.66 7.22 7.22 5.62 5.62 6.66 6.66 6.05 6.05 5.92 5.92 6.51 6.51 6.93 6.93 9.58 9.58 9.58 9.58

2012 (%) 7.71 7.71 7.71 6.69 6.69 5.57 5.57 6.45 6.45 5.80 5.80 5.58 5.58 6.64 6.64 6.63 6.63 8.02 8.02 8.02 8.02

Std. dev. (%) 0.85 0.85 0.85 0.68 0.68 0.15 0.15 0.39 0.39 0.39 0.39 0.35 0.35 0.37 0.37 0.37 0.37 0.73 0.73 0.73 0.73 (continued)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Schlattau, Tilting at the Windmills of Transition, Societies and Political Orders in Transition, https://doi.org/10.1007/978-3-030-54909-1

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Table A.1 (continued) NACE code 41 42 43 15 16 19 22 23 28 33 35 45 46 47 49 50 51 52 53 55 56 58 59 60 61 62 63 64 65 66 69 70 71 72 73 74 75 77 78 79 80

2008 (%) 11.19 11.19 11.19 3.98 6.47 10.46 5.32 6.17 4.27 9.12 14.39 8.63 8.42 9.28 9.01 10.54 9.91 8.54 13.44 8.37 11.06 8.51 10.95 6.47 11.25 11.91 17.59 11.43 10.62 11.47 9.31 14.65 9.83 12.03 11.55 15.01 10.56 11.62 12.67 9.08 10.32

2009 (%) 9.13 9.13 9.13 4.31 6.19 10.41 4.45 5.17 4.22 8.79 15.90 7.81 8.88 9.71 8.04 7.66 9.15 8.58 13.45 8.03 12.12 7.88 11.10 5.27 10.12 11.08 17.16 11.54 7.76 11.03 8.47 13.47 7.90 12.17 10.42 12.96 11.35 10.35 10.47 8.63 11.34

2010 (%) 8.65 8.65 8.65 4.76 6.11 8.82 4.35 5.54 4.14 8.99 16.88 7.91 8.97 9.66 8.01 9.86 8.23 7.80 12.38 6.96 12.06 8.79 10.53 6.34 10.56 11.22 15.06 10.47 5.62 10.14 7.90 12.69 7.30 12.70 9.88 12.36 9.63 10.23 10.76 8.84 10.07

2011 (%) 9.71 9.71 9.71 5.91 6.52 11.16 4.67 5.32 4.63 9.13 14.36 8.59 9.01 9.93 9.21 7.87 11.30 9.00 16.59 7.20 12.29 7.97 11.07 6.75 10.32 12.69 15.44 10.49 8.63 10.41 8.69 13.55 8.32 11.80 10.85 13.81 9.62 10.86 14.25 8.72 10.75

2012 (%) 9.18 9.18 9.18 5.88 5.67 8.19 4.31 4.64 4.67 9.09 15.10 8.12 8.76 9.53 8.23 8.48 10.97 8.69 13.95 7.14 11.75 7.92 9.85 5.48 9.68 11.03 14.36 10.11 8.83 8.56 7.57 12.16 7.34 10.61 9.88 12.84 8.23 11.00 12.08 8.77 10.60

Std. dev. (%) 0.88 0.88 0.88 0.80 0.30 1.12 0.37 0.50 0.22 0.13 0.96 0.34 0.21 0.21 0.51 1.13 1.14 0.40 1.41 0.56 0.44 0.37 0.47 0.58 0.52 0.64 1.25 0.57 1.63 1.00 0.61 0.85 0.92 0.69 0.63 0.93 1.05 0.50 1.37 0.15 0.43 (continued)

Annex A

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Table A.1 (continued) NACE code 81 82 95 96

Correlation

2008 (%) 12.54 13.65 6.86 11.46 2008/2009 (%) 95.23

2009 (%) 11.92 14.65 6.90 10.86 2009/2010 (%) 96.67

2010 (%) 10.82 14.33 6.76 10.14 2010/2011 (%) 92.50

2011 (%) 12.45 17.06 7.25 11.02 2011/2012 (%) 96.60

2012 (%) 10.73 13.98 6.33 10.14 Total (%) 95.25

Std. dev. (%) 0.78 1.21 0.30 0.52 Ø Std. dev. (%) 0.64

Table A.2 Average correlation of entry in consecutive years, 2008–2012, post-socialist entry rates NACE code 10 11 12 13 14 17 18 20 21 24 25 26 27 29 30 31 32 36 37 38 39 41 42 43 15 16 19 22 23

2008 (%) 8.15 8.64 5.72 8.15 7.98 7.72 11.27 6.89 9.31 7.76 7.32 5.90 7.95 8.00 4.46 9.36 6.17 6.67 5.26 5.93 10.08 11.27 10.72 12.51 19.21 5.44 14.72 11.89 19.40

2009 (%) 7.83 6.04 5.16 7.07 7.15 6.13 8.26 7.22 7.40 6.70 7.48 4.23 5.99 6.95 4.55 7.11 6.07 5.37 5.51 4.91 7.72 8.57 9.02 12.01 18.96 4.08 14.01 9.07 7.89

2010 (%) 7.84 6.99 8.47 6.85 8.24 5.88 7.93 6.46 6.81 7.46 7.44 3.57 5.85 5.84 6.39 6.76 5.94 6.26 5.25 5.39 7.69 7.42 7.96 9.53 16.17 6.44 6.76 10.75 12.16

2011 (%) 8.56 6.97 3.57 7.11 8.23 6.74 8.65 6.81 7.11 9.66 7.83 6.25 6.19 6.28 5.66 7.36 6.08 6.39 5.13 6.59 7.09 7.88 9.50 11.48 12.02 7.69 7.77 11.06 13.58

2012 (%) 7.33 6.99 4.96 7.50 7.44 6.37 7.22 5.24 6.07 7.34 6.77 4.46 4.93 5.19 6.59 6.16 5.70 6.43 4.42 5.36 5.68 5.73 8.03 9.79 10.46 3.66 7.05 8.45 15.95

Std. dev. (%) 0.41 0.84 1.61 0.46 0.44 0.64 1.38 0.69 1.08 1.00 0.34 1.02 0.99 0.96 0.89 1.08 0.16 0.45 0.37 0.58 1.42 1.81 1.02 1.20 3.57 1.49 3.54 1.28 3.84 (continued)

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Table A.2 (continued) NACE code 28 33 35 45 46 47 49 50 51 52 53 55 56 58 59 60 61 62 63 64 65 66 69 70 71 72 73 74 75 77 78 79 80 81 82 95 96

Correlation

2008 (%) 15.87 10.70 15.58 13.17 10.16 12.74 13.19 11.68 10.46 12.16 19.03 11.64 14.76 10.28 14.76 8.59 10.98 16.57 21.99 11.96 6.57 16.74 15.83 18.30 14.62 14.52 18.14 17.53 18.89 15.57 16.97 13.82 12.30 17.65 15.91 13.06 19.61 2008/2009 (%) 86.89

2009 (%) 9.52 7.84 9.72 9.63 8.72 10.31 9.91 5.93 7.13 8.89 13.11 7.61 13.93 8.92 12.00 6.46 9.57 12.91 17.91 12.12 3.36 12.49 10.45 13.98 8.48 12.12 12.12 12.20 11.69 10.87 11.34 8.61 10.53 14.13 14.65 9.82 14.42 2009/2010 (%) 87.07

2010 (%) 9.16 6.84 8.55 8.97 8.68 9.40 9.95 8.11 9.31 8.69 12.28 6.72 12.49 7.69 9.59 6.82 8.38 11.63 14.69 9.97 3.22 10.70 9.18 11.73 7.19 10.39 10.44 12.07 11.45 9.86 13.08 9.52 8.84 12.44 13.12 8.32 13.00 2010/2011 (%) 88.55

2011 (%) 10.21 8.60 9.55 9.01 9.03 9.47 10.83 9.01 7.30 8.93 14.93 7.21 13.02 6.91 10.87 6.78 9.26 12.83 15.65 8.79 2.03 9.10 8.61 11.60 7.91 11.77 11.11 12.62 10.29 10.15 13.04 9.02 10.18 13.13 16.60 8.91 13.66 2011/2012 (%) 84.86

2012 (%) 8.11 6.77 8.13 7.65 7.54 8.36 8.51 6.42 10.26 7.33 12.28 5.80 10.46 6.22 7.52 5.04 6.32 9.75 10.67 6.73 2.96 7.66 7.35 9.22 6.08 7.14 9.17 11.25 6.70 8.33 13.59 7.60 8.14 10.45 10.94 7.27 11.77 Total (%) 86.84

Std. dev. (%) 2.73 1.44 2.70 1.86 0.84 1.48 1.55 2.05 1.42 1.59 2.54 2.02 1.46 1.45 2.42 1.13 1.54 2.23 3.73 2.02 1.54 3.15 2.95 3.06 2.99 2.42 3.12 2.24 3.97 2.45 1.85 2.15 1.44 2.37 2.03 1.97 2.70 Ø Std. dev. (%) 1.75

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251

Table A.3 Average correlation of entry in consecutive years, 2008–2012, Russian entry rates NACE code 10 11 12 13 14 17 18 20 21 24 25 26 27 29 30 31 32 36 37 38 39 41 42 43 15 16 19 22 23 28 33 35 45 46 47 49 50 51 52 53 55

2008 (%) 9.05 8.64 4.81 12.88 5.98 6.89 13.59 8.13 11.47 11.48 10.64 9.80 12.23 13.87 14.27 14.92 9.26 10.26 10.65 11.68 10.45 12.55 8.81 14.70 15.30 19.99 20.57 15.89 19.22 16.82 12.45 17.49 14.78 16.57 9.68 16.58 9.97 12.32 15.13 22.01 13.05

2009 (%) 8.87 9.84 5.77 8.03 8.46 8.78 9.79 11.94 9.48 8.26 11.55 6.57 10.35 9.67 9.05 10.03 9.27 9.29 9.66 8.03 9.00 11.58 9.26 6.43 11.81 14.91 18.31 13.80 17.83 8.10 8.06 9.84 13.26 14.80 9.04 12.08 9.51 10.41 13.94 19.92 10.60

2010 (%) 9.51 8.81 7.69 13.45 10.38 9.33 10.45 10.96 9.54 13.94 12.79 5.79 13.58 11.19 10.84 11.35 10.32 11.15 10.05 8.75 10.55 12.16 11.33 9.74 14.18 20.72 15.11 17.62 13.73 11.27 10.20 10.95 12.87 15.30 8.49 12.93 11.96 9.73 14.53 16.40 8.42

2011 (%) 10.66 9.84 6.75 10.58 9.50 12.79 12.10 13.71 9.63 12.48 11.03 8.87 13.29 13.96 12.32 14.23 11.37 11.40 10.80 9.27 10.58 14.25 11.32 10.35 10.94 15.00 18.75 15.20 13.16 12.13 10.37 13.25 13.83 17.07 9.39 15.56 10.27 10.12 15.70 11.04 9.29

2012 (%) 11.09 13.35 12.50 14.66 10.03 8.60 10.72 11.11 8.61 15.98 11.23 5.90 13.31 13.84 13.16 15.79 7.08 10.06 10.51 8.13 9.31 13.08 10.17 11.41 12.48 16.63 17.94 13.90 12.47 14.33 13.34 14.51 14.73 17.18 9.84 15.79 9.78 14.51 16.24 11.65 11.03

Std. dev. (%) 0.88 1.70 2.68 2.35 1.59 1.94 1.36 1.80 0.94 2.58 0.73 1.64 1.19 1.76 1.82 2.20 1.43 0.77 0.42 1.33 0.68 0.91 1.03 2.67 1.59 2.46 1.76 1.41 2.72 2.93 1.86 2.70 0.77 0.96 0.48 1.75 0.87 1.78 0.82 4.36 1.59 (continued)

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Table A.3 (continued) NACE code 56 58 59 60 61 62 63 64 65 66 69 70 71 72 73 74 75 77 78 79 80 81 82 95 96

Correlation

2008 (%) 11.79 12.80 15.22 7.85 14.32 16.34 16.94 16.92 16.89 18.87 18.06 14.31 15.23 10.86 19.01 8.56 9.73 16.14 16.67 17.12 13.10 21.25 18.37 10.92 11.03 2008/2009 (%) 72.40

2009 (%) 9.64 10.87 9.70 5.67 12.17 13.30 17.10 14.01 7.04 13.31 17.55 14.06 8.18 10.17 12.42 10.90 10.59 11.60 13.97 14.37 10.87 20.85 14.84 9.96 10.08 2009/2010 (%) 73.02

2010 (%) 11.52 10.92 17.49 6.61 9.67 13.70 18.93 15.28 11.09 15.75 19.32 13.34 10.19 17.86 13.79 9.95 12.29 12.53 11.81 17.02 6.74 18.21 16.66 9.66 10.75 2010/2011 (%) 78.30

2011 (%) 11.68 9.67 13.53 5.31 8.69 14.95 16.43 15.74 11.22 12.61 15.07 11.84 9.66 18.28 12.93 12.26 13.09 14.23 11.24 15.51 7.76 16.25 16.53 11.11 9.52 2011/2012 (%) 77.40

2012 (%) 12.47 9.14 12.07 4.86 9.18 13.61 15.01 17.48 12.40 15.18 17.48 11.66 10.39 15.37 13.55 12.18 16.45 15.45 14.30 13.27 7.26 16.99 17.28 9.68 11.15 Total (%) 75.28

Std. dev. (%) 0.95 1.26 2.66 1.06 2.13 1.13 1.26 1.23 3.15 2.19 1.38 1.11 2.38 3.41 2.38 1.40 2.34 1.71 1.94 1.50 2.44 2.01 1.15 0.62 0.62 Ø Std. dev. (%) 1.68

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253

A.2 Regional Clustering Based on Economic Geographical Location

Table A.4 Preferential land connections between Russian regions and neighboring countries

Country (n) Armenia Azerbaijan Belarus Estonia Finland Georgia Kazakhstan Kyrgyzstan Lithuania Latvia Mongolia Tajikistan Turkmenistan Ukraine Uzbekistan

Regions (e) Republic of North Ossetia—Alania Republic of Dagestan Smolensk and Pskov region Pskov and Leningrad oblast Republic of Karelia and Leningrad region Republic of North Ossetia—Alania Orenburg and Astrakhan region Orenburg and Astrakhan region Smolensk region Smolensk and Pskov region Republic of Buryatia Orenburg and Astrakhan region Astrakhan region Kursk, Bryansk, and Belgorod region Orenburg and Astrakhan region

Table created by Zemtsov and Baburin (2016) according to the Federal Statistical Service of Russia (Rosstat)

Fig. A.1 EGP clustering—results from k-means cluster analysis for three clusters

254

Annex A

A.3 Summary Statistics of Regional Clusters and the Reduced Innovative Branches Sample Table A.5 Summary statistics, urbanization-based cluster perspective, cluster 1 Variable Obs Mean Dependent variable and natural entry rates Entry rate Rus. 3920 0.136 Entry rate EU 3136 0.088 Entry rate post-soc. 3920 0.098 Entry rate EU (avg. 08–12) 3920 0.088 Entry rate post-soc. (avg. 08– 3920 0.094 12) Structural economic factors rsk_econ 3920 35.2 unempl 3920 6.49 avwage 3920 19,881 reg_mincgini 3920 0.40 Property rights patent_coef 3920 2.62 raiding_cases 3920 0.35 Criminality buscrm_bus 3920 1.60 reg_safety 3920 28.53 Corruption op_art_corr 3920 0.56 Bureaucracy reg_administ 3920 43.75 op_ad_nocntrl 3920 0.54 op_ad_noprsc 3920 0.52 Financial capital op_fin_short 3920 0.47 op_fin_long 3920 0.47 Human capital heduc 3920 26.19 op_hr_ingtech 3920 0.50 educ 3920 46.15 Infrastructure op_infra 3920 0.54 op_art_en 3920 0.47 op_art_prp 3920 0.50 ICT_idx_std 3920 0.49 Market environment MA_intensity 3856 0.48

Std. dev. 0.093 0.029 0.035 0.027 0.028

Min

Max 0 0.040 0.020 0.044 0.036

1 0.176 0.220 0.159 0.162

17.74 1.80 3541 0.03

4 2.60 13,518 0.34

71 10.30 30,164 0.55

1.13 0.68

0.80 0

6.01 3

1.83 7.21

0 12.90

8 40.85

0.17

0.10

0.97

12.82 0.20 0.27

18.60 0.20 0.08

72 0.92 0.98

0.21 0.22

0.06 0.11

0.94 0.98

3.61 0.26 22.61

18.50 0.03 2

36.60 0.97 82

0.22 0.23 0.21 0.11

0.09 0.04 0.11 0.25

0.9 0.94 0.93 0.76

2.26

0

16.40 (continued)

Annex A

255

Table A.5 (continued) Variable HHI_ind op_art_spl Democratization crn_democracy Control variables grp_growth grp_pc

Obs 3920 3920

Mean 0.08 0.55

Std. dev. 0.15 0.24

3920

34.75

4.50

3920 3920

3.22 190,556

6.68 52,471

Min

Max 0 0.12

0.85 1

25 19.60 94,850

45 12.90 344,093

Table A.6 Summary statistics, urbanization-based cluster perspective, cluster 2 Variable Obs Mean Dependent variable and natural entry rates Entry rate Rus. 3550 0.131 Entry rate EU 2840 0.088 Entry rate post-soc. 3550 0.098 Entry rate EU (avg. 08–12) 3550 0.088 Entry rate post-soc. (avg. 08– 3550 0.094 12) Structural economic factors rsk_econ 3550 34.24 unempl 3550 6.89 avwage 3550 23,370 reg_mincgini 3550 0.39 Property rights patent_coef 3550 1.69 raiding_cases 3550 0.40 Criminality buscrm_bus 3550 3.56 reg_safety 3550 27.05 Corruption op_art_corr 3550 0.56 Bureaucracy reg_administ 3550 36.61 op_ad_nocntrl 3550 0.56 op_ad_noprsc 3550 0.52 Financial capital op_fin_short 3550 0.50 op_fin_long 3550 0.55 Human capital heduc 3550 26.50

Std. dev. 0.098 0.029 0.035 0.027 0.028

Min

Max 0 0.040 0.020 0.044 0.036

1 0.176 0.220 0.159 0.162

19.8 2.03 6818 0.02

3 2.00 12,198 0.34

71 11.60 42,506 0.43

0.79 0.82

0.46 0

3.57 3

4.68 9.81

0 13.40

0.24

0.07

0.98

12.22 0.24 0.22

17.80 0.06 0.09

70.00 1 1

0.26 0.29

0.03 0.03

3.71

21.40

18 49

1 1 37.50 (continued)

256

Annex A

Table A.6 (continued) Variable op_hr_ingtech educ Infrastructure op_infra op_art_en op_art_prp ICT_idx_std Market environment MA_intensity HHI_ind op_art_spl Democratization crn_democracy Control variables grp_growth grp_pc

Obs 3550 3550

Mean 0.53 31.81

Std. dev. 0.27 20.76

Min

Max

3550 3550 3550 3550

0.47 0.55 0.46 0.55

0.26 0.21 0.24 0.09

0.03 0.06 0.03 0.33

0.89 0.91 0.94 0.70

3550 3550 3550

0.12 0.08 0.50

0.36 0.15 0.25

0 0 0.03

2.63 0.85 0.98

3550

34.03

4.93

3550 3550

3.62 199,161

6.94 62,143

0.03 1

1 87

25 17.70 97,529

43 14.00 360,166

Table A.7 Summary statistics, urbanization-based cluster perspective, cluster 3 Variable Obs Mean Dependent variable and natural entry rates Entry rate Rus. 2595 0.127 Entry rate EU 2076 0.088 Entry rate post-soc. 2595 0.098 Entry rate EU (avg. 08–12) 2595 0.088 Entry rate post-soc. (avg. 2595 0.094 08–12) Structural economic factors rsk_econ 2595 17.28 unempl 2595 6.99 avwage 2595 22,329 reg_mincgini 2595 0.41 Property rights patent_coef 2595 1.20 raiding_cases 2595 0.34 Criminality buscrm_bus 2595 1.65 reg_safety 2595 32.41 Corruption op_art_corr 2595 0.35

Std. dev. 0.108 0.029 0.035 0.027 0.028

Min

Max 0 0.040 0.020 0.044 0.036

1 0.176 0.220 0.159 0.162

15.03 2.27 7725 0.02

1 3.90 12,910 0.37

62 13.70 48,400 0.47

0.44 1.01

0.58 0

2.20 5

1.43 9.33

0 20.50

6 52.60

0.21

0.03

0.93 (continued)

Annex A

257

Table A.7 (continued) Variable Bureaucracy reg_administ op_ad_nocntrl op_ad_noprsc Financial capital op_fin_short op_fin_long Human capital heduc op_hr_ingtech educ Infrastructure op_infra op_art_en op_art_prp ICT_idx_std Market environment MA_intensity HHI_ind op_art_spl Democratization crn_democracy Control variables grp_growth grp_pc

Obs

Mean

Std. dev.

Min

Max

2595 2595 2595

43.77 0.36 0.39

12.72 0.25 0.24

14.50 0.04 0.03

69.40 0.92 0.90

2595 2595

0.51 0.46

0.29 0.24

0.05 0.04

1 0.89

2595 2595 2595

25.31 0.43 26.90

2.93 0.24 23.63

18.80 0.05 1

30.00 1 78

2595 2595 2595 2595

0.49 0.48 0.54 0.51

0.28 0.21 0.23 0.11

0.04 0.14 0.13 0.27

0.96 0.91 1 0.68

2595 2595 2595

0.10 0.08 0.48

0.27 0.15 0.20

0 0 0.12

1.64 0.85 0.89

2595

29.48

4.06

2595 2595

4.86 283,099

4.64 266,120

20

36

7.40 80,715

13.20 1,198,186

Table A.8 Summary statistics, urbanization-based cluster perspective, cluster 5 Variable Obs Mean Dependent variable and natural entry rates Entry rate Rus. 660 0.141 Entry rate EU 528 0.088 Entry rate post-soc. 660 0.097 Entry rate EU (avg. 08–12) 660 0.087 Entry rate post-soc. (avg. 660 0.094 08–12) Structural economic factors rsk_econ 660 9.80 unempl 660 2.05 avwage 660 31,694

Std. dev. 0.048 0.029 0.035 0.027 0.028

7.20 0.92 6872

Min

Max 0 0.040 0.020 0.044 0.036

2 0.80 21,065

0.314 0.176 0.220 0.159 0.162

24 4.10 44,586 (continued)

258

Annex A

Table A.8 (continued) Variable reg_mincgini Property rights patent_coef raiding_cases Criminality buscrm_bus reg_safety Corruption op_art_corr Bureaucracy reg_administ op_ad_nocntrl op_ad_noprsc Financial capital op_fin_short op_fin_long Human capital heduc op_hr_ingtech educ Infrastructure op_infra op_art_en op_art_prp ICT_idx_std Market environment MA_intensity HHI_ind op_art_spl Democratization crn_democracy Control variables grp_growth grp_pc

Obs 660

Mean 0.48

Std. dev. 0.04

660 660

8.44 3.30

660 660

Min

Max 0.43

0.55

2.54 4.41

5.61 0

12.47 12

23.10 21.27

15.95 5.42

3 14.40

50 29.40

660

0.68

0.15

0.34

0.90

660 660 660

44.74 0.47 0.68

9.00 0.13 0.17

30.20 0.30 0.31

61.10 0.68 0.84

660 660

0.69 0.63

0.06 0.18

0.57 0.31

0.80 0.80

660 660 660

45.36 0.42 19.00

3.84 0.12 10.89

38.70 0.17 4

51.80 0.57 35

660 660 660 660

0.53 0.43 0.64 0.92

0.19 0.09 0.12 0.07

0.20 0.33 0.44 0.79

0.75 0.61 0.86 1

660 660 660

0.31 0.08 0.49

0.14 0.15 0.11

0.13 0 0.39

0.57 0.85 0.73

660 660 660

34.7 3.79 516,965

5.28 7.41 209,284

27 12.80 235,410

40 13.10 859,355

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259

Table A.9 Summary statistics, EGP cluster perspective, cluster 1 Variable Obs Mean Dependent variable and natural entry rates Entry rate Rus. 4225 0.142 Entry rate EU 3380 0.088 Entry rate post-soc. 4225 0.098 Entry rate EU (avg. 08-12) 4225 0.088 Entry rate post-soc. (avg. 4225 0.094 08-12) Structural economic factors rsk_econ 4225 27.75 unempl 4225 7.59 avwage 4225 22,575 reg_mincgini 4225 0.41 Property rights patent_coef 4225 2.25 raiding_cases 4225 0.34 Criminality buscrm_bus 4225 1.91 reg_safety 4225 30.88 Corruption op_art_corr 4225 0.51 Bureaucracy reg_administ 4225 45.48 op_ad_nocntrl 4225 0.48 op_ad_noprsc 4225 0.47 Financial capital op_fin_short 4225 0.54 op_fin_long 4225 0.52 Human capital heduc 4225 26.13 op_hr_ingtech 4225 0.51 educ 4225 35.36 Infrastructure op_infra 4225 0.50 op_art_en 4225 0.49 op_art_prp 4225 0.44 ICT_idx_std 4225 0.55 Market environment MA_intensity 4225 0.50 HHI_ind 4225 0.08 op_art_spl 4225 0.48 Democratization crn_democracy 4225 34.58 Control variables grp_growth 4225 3.38 grp_pc 4225 258,752

Std. dev. 0.093 0.029 0.035 0.027 0.028

Min

Max 0 0.040 0.020 0.044 0.036

1 0.176 0.220 0.159 0.162

15.87 2.11 6666 0.02

1 2.50 12,754 0.36

62 13.70 48,400 0.47

1.29 0.77

0.58 0

6.01 4

2.02 9.53

0 14.60

8 52.20

0.22

0.10

0.98

10.32 0.20 0.22

23.60 0.18 0.08

70.00 0.98 0.94

0.27 0.26

0.06 0.11

3.74 0.25 23.07

18.80 0.09 1

36.60 1 87

0.25 0.22 0.21 0.10

0.04 0.10 0.11 0.27

0.96 0.91 1 0.76

2.19 0.15 0.23

0 0 0.04

16.40 0.85 1

6.25 6.84 211,387

20 19.60 97,529

1 1

45 13.40 1,198,186

260

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Table A.10 Summary statistics, EGP cluster perspective, cluster 2 Variable Obs Mean Dependent variable and natural entry rates Entry rate Rus. 2925 0.127 Entry rate EU 2340 0.088 Entry rate post-soc. 2925 0.098 Entry rate EU (avg. 08–12) 2925 0.088 Entry rate post-soc. (avg. 2925 0.094 08–12) Structural economic factors rsk_econ 2925 28.96 unempl 2925 4.64 avwage 2925 22,663 reg_mincgini 2925 0.40 Property rights patent_coef 2925 3.39 raiding_cases 2925 0.94 Criminality buscrm_bus 2925 7.66 reg_safety 2925 25.66 Corruption op_art_corr 2925 0.58 Bureaucracy reg_administ 2925 38.89 op_ad_nocntrl 2925 0.51 op_ad_noprsc 2925 0.55 Financial capital op_fin_short 2925 0.56 op_fin_long 2925 0.51 Human capital heduc 2925 30.47 op_hr_ingtech 2925 0.49 educ 2925 34.17 Infrastructure op_infra 2925 0.55 op_art_en 2925 0.52 op_art_prp 2925 0.54 ICT_idx_std 2925 0.59 Market environment MA_intensity 2861 0.14 HHI_ind 2925 0.08 op_art_spl 2925 0.53 Democratization crn_democracy 2925 33.33 Control variables grp_growth 2925 3.79 grp_pc 2925 259,130

Std. dev. 0.092 0.029 0.035 0.027 0.028

Min

Max 0 0.040 0.020 0.044 0.036

1 0.176 0.220 0.159 0.162

18.90 2.10 6951 0.05

2 0.80 12,198 0.34

66 9.00 44,586 0.55

3.06 2.51

0.46 0

12.47 12

12.11 6.74

0 12.90

50 41.30

0.20

0.18

0.97

11.52 0.23 0.26

18.10 0.06 0.09

61.10 0.92 0.98

0.22 0.23

0.03 0.03

1 0.91

9.08 0.26 26.42

19.10 0.03 1

51.80 0.97 82

0.23 0.19 0.25 0.21

0.09 0.14 0.07 0.25

0.86 0.91 0.93 1

0.15 0.15 0.22

0 0 0.03

0.57 0.85 0.96

3.87 5.99 178,256

27 12.80 99,683

40 14.00 859,355

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261

Table A.11 Summary statistics, EGP cluster perspective, cluster 3 Variable Obs Dependent variable and natural entry rates Entry rate Rus. 3575 Entry rate EU 2860 Entry rate post-soc. 3575 Entry rate EU (avg. 08–12) 3575 Entry rate post-soc. (avg. 08– 3575 12) Structural economic factors rsk_econ 3575 unempl 3575 avwage 3575 reg_mincgini 3575 Property rights patent_coef 3575 raiding_cases 3575 Criminality buscrm_bus 3575 reg_safety 3575 Corruption op_art_corr 3575 Bureaucracy reg_administ 3575 op_ad_nocntrl 3575 op_ad_noprsc 3575 Financial capital op_fin_short 3575 op_fin_long 3575 Human capital heduc 3575 op_hr_ingtech 3575 educ 3575 Infrastructure op_infra 3575 op_art_en 3575 op_art_prp 3575 ICT_idx_std 3575 Market environment MA_intensity 3575 HHI_ind 3575

Mean

Std. dev.

0.126 0.088 0.098 0.088 0.094

0.103 0.029 0.035 0.027 0.028

Min

Max

0 0.040 0.020 0.044 0.036

1 0.176 0.220 0.159 0.162

30.47 6.64 21,843 0.39

23.48 1.55 6582 0.01

2 2.70 12,910 0.36

71 10.30 42,506 0.42

1.55 0.45

0.72 0.99

0.51 0

4.07 5

2.23 28.11

2.75 9.28

0 13.40

12 52.60

0.48

0.25

0.03

0.90

38.8 0.52 0.49

15.15 0.27 0.27

14.50 0.04 0.03

72.00 1 1

0.42 0.48

0.22 0.26

0.03 0.04

0.95 1

25.97 0.47 35.49

2.54 0.26 21.35

18.50 0.03 1

30.00 0.98 78

0.46 0.49 0.56 0.49

0.25 0.23 0.21 0.10

0.03 0.04 0.03 0.28

0.90 0.94 0.94 0.70

0.08 0.08

0.12 0.15

0 0

0.73 0.85 (continued)

262

Annex A

Table A.11 (continued) Variable op_art_spl Democratization crn_democracy Control variables grp_growth grp_pc

Obs 3575

Mean 0.54

Std. dev. 0.23

3575

31.57

4.34

3575 3575

4.27 189,834

6.21 64,364

Min

Max 0.07

0.98

25 13.00 80,715

41 13.20 360,166

Table A.12 Summary statistics, reduced innovative branches sample Variable Obs Dependent variable and natural entry rates Entry rate Rus. 1720 Entry rate EU 1376 Entry rate post-soc. 1720 Entry rate EU (avg. 08–12) 1720 Entry rate post-soc. (avg. 08–12) 1720 Structural economic factors rsk_econ 1720 unempl 1720 avwage 1720 reg_mincgini 1720 Property rights patent_coef 1720 raiding_cases 1720 Criminality buscrm_bus 1720 reg_safety 1720 Corruption op_art_corr 1720 Bureaucracy reg_administ 1720 op_ad_nocntrl 1720 op_ad_noprsc 1720 Financial capital op_fin_short 1720 op_fin_long 1720

Mean

Std. dev.

Min

Max

0.116 0.086 0.089 0.086 0.086

0.090 0.036 0.040 0.033 0.034

0 0.050 0.036 0.055 0.049

1 0.176 0.220 0.159 0.162

28.66 6.43 22,284 0.40

19.51 2.27 6677 0.03

1 0.80 12,198 0.34

71 13.70 48,400 0.55

2.35 0.55

1.99 1.55

0.46 0

12.47 12

3.58 28.57

7.10 8.99

0 12.90

50 52.60

0.52

0.23

0.03

0.98

41.47 0.50 0.50

12.90 0.24 0.25

14.50 0.04 0.03

72.00 1 1

0.50 0.50

0.25 0.25

0.03 0.03

1 1 (continued)

Annex A

263

Table A.12 (continued) Variable Human capital heduc op_hr_ingtech educ Infrastructure op_infra op_art_en op_art_prp ICT_idx_std Market environment MA_intensity HHI_ind op_art_spl Democratization crn_democracy Control variables grp_growth grp_pc

Obs

Mean

Std. dev.

Min

Max

1720 1720 1720

27.29 0.49 35.07

5.90 0.26 23.54

18.50 0.03 1

51.80 1 87

1720 1720 1720 1720

0.50 0.50 0.51 0.54

0.25 0.22 0.23 0.14

0.03 0.04 0.03 0.25

0.96 0.94 1 1

1710 1720 1720

0.26 0.08 0.51

1.37 0.13 0.23

0 0 0.03

16.40 0.56 1

1720

33.22

5.26

20

45

1720 1720

3.78 235,704

6.42 168,338

19.60 80,715

14.00 1,198,186

The reduced innovative branches sample exclusively encompasses highly innovative industries according to the Reuters 2016 Innovation report (NACE codes 10, 11, 12, 21, 26, 27, 29, 61, 62, 63, 72; Aerospace and Defense as well as Oil and Gas have been excluded)

264

A.4 Distribution of Entry Rates per Industry

Annex A

Annex A

265

266

Annex A

Annex A

267

268

A.5 Predictability of Entry Rates Equal to Zero

Annex A

Annex A

269

A.6 Linear Relationship Between Entry Rates and Standardized Variables

270

Annex A

A.7 Linear Relationship Between Entry Rates and Regions/ Industries

Annex A

271

Annex B

B.1 Regression Results for the Reduced Innovative Branches Sample Table B.1 Regression results for the reduced innovative branches sample, overall regional perspective Baseline regression (I) (II) Variables Structural economic factors rsk_econ 0.149*** – (0.042) unempl – – avwage – – reg_mincgini Property rights patent_coef raiding_cases Criminality buscrm_bus reg_safety Corruption op_art_corr Bureaucracy reg_administ op_ad_nocntrl

Variables Financial capital op_fin_long

Baseline regression (I) (II) –

–

–

–

– – –

– – –

0.129** (0.048)

0.183*** (0.053)

op_fin_short Human capital heduc op_hr_ingtech educ

– –

– –

Infrastructure op_infra

–

–

0.066* (0.03) –

–

op_art_en

–

–

–

–

–

– –

– –

op_art_prp – ICT_idx_std – Market environment ma_intensity – HHI_ind 0.494*** (0.129)

– – – 0.371*** (0.097) (continued)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Schlattau, Tilting at the Windmills of Transition, Societies and Political Orders in Transition, https://doi.org/10.1007/978-3-030-54909-1

273

274

Annex B

Table B.1 (continued) Variables

Baseline regression (I) (II)

op_ad_noprsc

–

Variables op_art_spl Democratization crn_democracy

–

Baseline regression (I) (II) – – –

–

This table shows Tobit regression marginal effects with censoring at zero and one. +, *, **, and *** denote significance at 10%, 5%, 1%, and 0.01% levels. White (1980) standard errors are reported in parentheses. The dependent variable is entry of new firms per year, region and industry as a proportion of incumbent firms. Each independent variable has been regressed independently (i.e., each specification-variable-cluster combination is based on a separate regression). The null hypothesis of joint zero coefficients for all regressions is always rejected at the 1% level. Results for insignificant variables and region, sector, and time dummies are not reported. The reduced innovative branches sample exclusively encompasses highly innovative industries according to the Reuters 2016 Innovation report (NACE codes 10, 11, 12, 21, 26, 27, 29, 61, 62, 63, 72; Aerospace and Defense as well as Oil and Gas have been excluded)

Table B.2 Regression results for the reduced innovative branches sample, urbanization-based cluster perspective Baseline regression (I) 1&5 2 Variables Structural economic factors rsk_econ 0.184* – (0.075) unempl – – avwage – – reg_mincgini 0.111* – (0.047) Property rights patent_coef – – raiding_cases – – Criminality buscrm_bus 0.0853** – (0.028) reg_safety 0.109* – (0.046) Corruption op_art_corr – –

3

(II) 1&5

2

3

–

–

–

–

– – –

– –

– – –

– – –

– –

– –

– –

– –

–

–

–

–

–

0.116* (0.051)

–

–

–

0.079* (0.034)

–

–

– –

– – –

– – –

Bureaucracy reg_administ op_ad_nocntrl op_ad_noprsc

– – –

– – –

– – –

Obs.

745

560

415

0.144** (0.049)

0.115* (0.058) 596

448

332 (continued)

Annex B

275

Table B.2 (continued)

Variables Financial capital op_fin_long op_fin_short Human capital heduc op_hr_ingtech educ Infrastructure op_infra op_art_en op_art_prp ICT_idx_std

Baseline regression (I) 1 2

3

(II) 1

2

3

– –

– –

– –

– –

– –

– –

– –

– –

– –

– –

– –

–

–

–

– 0.113* (0.05) –

–

–

– – – –

– – – –

– – – 0.332* (0.165)

– – – 0.142* (0.07)

– – – –

– – – –

–

– –

–

– –

–

Market environment ma_intensity – HHI_ind 0.422* (0.21) op_art_spl – Democratization crn_democracy – Obs. 745

0.434* (0.18) – – 560

–

0.299* (0.123) –

– 415

– 596

–

0.643** (0.235) –

– 448

– 332

This table shows Tobit regression marginal effects with censoring at zero and one. +, *, **, and *** denote significance at 10%, 5%, 1%, and 0.01% levels. White (1980) standard errors are reported in parentheses. The dependent variable is entry of new firms per year, region and industry as a proportion of incumbent firms. Each independent variable has been regressed independently (i.e., each specification-variable-cluster combination is based on a separate regression). The null hypothesis of joint zero coefficients for all regressions is always rejected at the 1% level. Results for insignificant variables and region, sector, and time dummies are not reported. The reduced innovative branches sample exclusively encompasses highly innovative industries according to the Reuters 2016 Innovation report (NACE codes 10, 11, 12, 21, 26, 27, 29, 61, 62, 63, 72; Aerospace and Defense as well as Oil and Gas have been excluded)

276

Annex B

Table B.3 Regression results for the reduced innovative branches sample, urbanization-based cluster perspective Baseline regression (I) 1 2 Variables Structural economic factors rsk_econ – 0.109** (0.038) unempl – 0.232** (0.089) avwage – – reg_mincgini – 0.136** (0.052) Property rights patent_coef – – raiding_cases – – Criminality buscrm_bus – – reg_safety 0.119* – (0.055) Corruption op_art_corr – – Bureaucracy reg_administ – – op_ad_nocntrl – – op_ad_noprsc – – Obs. 660 475 Financial capital op_fin_long – – op_fin_short – – Human capital heduc – –

3

(II) 1

–

–

–

2

3 –

–

0.237*** (0.061) –

– –

–

–

0.374*** (0.107)

0.161** (0.052)

– –

– –

– –

– –

– –

– –

– –

– –

–

–

–

–

– – – 585

– – – 528

– – – 380

– – – 468

– –

– –

– –

– –

–

–

0.199*** (0.054) 0.165* (0.065) –

–

op_hr_ingtech

–

–

–

–

educ Infrastructure op_infra op_art_en op_art_prp ICT_idx_std

–

–

–

–

– – – –

– – – –

– – – –

– – – –

– – – 0.154** (0.058)

– – –

– – – – – –

(continued)

Annex B

277

Table B.3 (continued) Baseline regression (I) 1 2

3

Variables Market environment ma_intensity – HHI_ind –

– –

–

–

0.963*** (0.266) –

– 660

– 475

– 585

op_art_spl Democratization crn_democracy Obs.

–

(II) 1 – –

2

3

–

– –

–

0.495** (0.187) –

– 528

– 380

– – 468

This table shows Tobit regression marginal effects with censoring at zero and one. +, *, **, and *** denote significance at 10%, 5%, 1%, and 0.01% levels. White (1980) standard errors are reported in parentheses. The dependent variable is entry of new firms per year, region and industry as a proportion of incumbent firms. Each independent variable has been regressed independently (i.e., each specification-variable-cluster combination is based on a separate regression). The null hypothesis of joint zero coefficients for all regressions is always rejected at the 1% level. Results for insignificant variables and region, sector, and time dummies are not reported. The reduced innovative branches sample exclusively encompasses highly innovative industries according to the Reuters 2016 Innovation report (NACE codes 10, 11, 12, 21, 26, 27, 29, 61, 62, 63, 72; Aerospace and Defense as well as Oil and Gas have been excluded)

B.2 Deviations Between EU and Post-Socialist Natural Entry Regressions List of regressions Baseline regressions Overall/no clusters Cluster 1.1 Cluster 1.2 Cluster 1.3 Cluster 1.5 Cluster 2.1 Cluster 2.2 Cluster 2.3 Control regressions Overall/no clusters Cluster 1.1 Cluster 1.2 Cluster 1.3 Cluster 1.5 Cluster 2.1 Cluster 2.2

Concordance in % (26 variables) (I) (II) 0.880 1.000 0.880 0.880 0.880 0.880 0.880 0.920 0.760 0.760 0.800 0.800 0.760 0.880 0.880 0.920 (III) (IV) 0.885 0.885 0.923 0.962 0.962 0.731 0.808 0.962 0.846 0.808 0.731 0.885 0.962 0.923

Avg. concordance Ø 0.940 0.880 0.880 0.900 0.760 0.800 0.820 0.900 Ø 0.885 0.942 0.846 0.885 0.827 0.808 0.942 (continued)

278 List of regressions Cluster 2.3 Innov. branch regressions Overall/no clusters Cluster 1.1 and 1.5 Cluster 1.2 Cluster 1.3 Cluster 2.1 Cluster 2.2 Cluster 2.3 Overall concordance Average deviation

Annex B Concordance in % (26 variables) 0.885 0.846 (V) (VI) 0.920 0.880 1.000 0.960 0.920 0.960 1.000 0.920 0.960 0.960 1.000 1.000 0.960 1.000

Avg. concordance 0.865 Ø 0.900 0.980 0.940 0.960 0.960 1.000 0.980 0.896 0.104

The sample covers the Eastern European countries of Bosnia and Herzegovina, Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Macedonia, Poland, Romania, Serbia, Slovakia, Slovenia, and Ukraine. Data availability for other post-socialist countries (i.e. Commonwealth of Independent States, CIS) was scarce and highly fragmented

B.3 Deviations Between Results Based on Linear Imputation Versus Imputation According to Bingham et al. (1998) List of regressions Baseline regressions Overall/no clusters Cluster 1.1 Cluster 1.2 Cluster 1.3 Cluster 1.5 Cluster 2.1 Cluster 2.2 Cluster 2.3 Control regressions Overall/no clusters Cluster 1.1 Cluster 1.2 Cluster 1.3 Cluster 1.5 Cluster 2.1 Cluster 2.2 Cluster 2.3 Innov. branch regressions Overall/no clusters Cluster 1.1 and 1.5

Concordance in % (19 imputed variables) (I) (II) 1.000 1.000 0.955 0.955 1.000 0.955 1.000 0.955 0.818 0.955 0.955 1.000 1.000 1.000 1.000 0.955 (III) (IV) 0.955 1.000 0.955 0.909 0.909 0.955 1.000 0.955 0.955 0.955 0.955 1.000 0.955 1.000 1.000 0.864 (V) (VI) 0.909 1.000 0.955 0.864

Avg. concordance Ø 1.000 0.955 0.977 0.977 0.886 0.977 1.000 0.977 Ø 0.977 0.932 0.932 0.977 0.955 0.977 0.977 0.932 Ø 0.955 0.909 (continued)

Annex B

279

List of regressions Cluster 1.2 Cluster 1.3 Cluster 2.1 Cluster 2.2 Cluster 2.3 Overall concordance Average deviation

Concordance in % (19 imputed variables) 0.773 0.955 1.000 1.000 0.955 1.000 0.955 0.955 0.864 0.909

Avg. concordance 0.864 1.000 0.977 0.955 0.886 0.955 0.045

B.4 The Control Agency Pressure: Entry Relationship Moderated by the Level of Corruption Table B.4 Corruption-moderated baseline regressions for agency pressure in the urbanizationbased cluster perspective Baseline regression (I) (II) 1 2 3 5 1 Moderated regressions Control agency pressure—corruption moderated regression op_ad_nocntrl – – 0.932* – 0.823* (0.437) (0.382) op_art_corr – – – – – op_ad_nocntrl * op_art_corr

–

–

–

–

1.445* (0.669) Prosecution agency pressure—corruption moderated regression op_ad_noprsc – – – – –

2

3

5

– –

1.215* (13.3) –

–

–

22.70+ (13.3) 18.8+ (10.7) 33.09+ (19.0)

–

op_art_corr

–

–

–

–

–

–

0.887* (0.4) –

op_ad_noprsc * op_art_corr

–

–

–

–

–

–

–

Obs.

3920

3550

2595

660

3136

2840

2076

55.4* (22.7) 63.7* (26.0) 80.00* (33.0) 528

This table shows Tobit regression marginal effects with censoring at zero and one. +, *, **, and *** denote significance at 10%, 5%, 1%, and 0.01% levels. White (1980) standard errors are reported in parentheses. The dependent variable is entry of new firms per year, region and industry as a proportion of incumbent firms. Results for insignificant variables and region, sector, and time dummies are not reported. The null hypothesis of joint zero coefficients for all regressions is always rejected at the 1% level. Results that are significant at the 10% level are only reported for the urbanization-based cluster five due to the relatively small sample size

280

Annex B

NACE Code

Low Corruption

Medium Corruption

High Corruption

16

42

43

69

82

Fig. B.1 The impact of control agency pressure on entry in low, medium, and high corruption environments in selected industries

Annex B

NACE Code

281

Low Corruption

Medium Corruption

High Corruption

23

43

56

73

82

Fig. B.2 The impact of prosecution agency pressure on entry in low, medium, and high corruption environments in selected industries

Annex C

C.1 NACE Industry Sector Overview NACE sector C

D E

F G

H I J K L M

NACE sector description Manufacturing

Electricity, gas, steam, and air conditioning supply Water supply; sewerage, waste management and remediation activities Construction Wholesale and retail trade; repair of motor vehicles and motorcycles Transportation and storage Accommodation and food service activities Information and communication Financial and insurance activities Real estate activities Professional, scientific, and technical activities

Average entry (2008–2012, sample) (%) 11.27

NACE branches (2 digit codes) 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33 35

12.97

36, 37, 38, 39

15.64

41, 42, 43 45, 46, 47

12.72 13.87

49, 50, 51, 52, 53 55, 56

14.84 11.16

58, 59, 60, 61, 62, 63

12.07

64, 65, 66

15.35

68 69, 70, 71, 72, 73, 74, 75

9.70 13.61 (continued)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Schlattau, Tilting at the Windmills of Transition, Societies and Political Orders in Transition, https://doi.org/10.1007/978-3-030-54909-1

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284

NACE sector N S

Annex C

NACE sector description Administrative and support service activities Other service activities

NACE branches (2 digit codes) 77, 78, 79, 80, 81, 82

Average entry (2008–2012, sample) (%) 14.90

95, 96

11.16

Annex C.1 shows NACE sectors and the corresponding two digit industry branches, incl. average entry per sector from 2008 to 2012 for the 33-region analysis sample. Primary sector branches (agriculture, forestry, and fishing) were excluded. The secondary economic sector covers NACE sectors B to F (branches 10–43). The tertiary economic sector covers NACE sectors G to U (branches 45–95)

C.2 Average Training and Test Data Entry per NACE Industry Sector

NACE_Sec C D E F G H I J K L M N S

Training Data Ø Entry 0.1116 0.1337 0.1599 0.1226 0.1368 0.1452 0.1105 0.1222 0.1531 0.0966 0.1352 0.1516 0.1142

Test Data

Observations 2504 132 445 396 396 421 264 720 260 132 809 779 264

Ø Entry 0.1177 0.1136 0.1415 0.1458 0.1466 0.1608 0.1162 0.1136 0.1542 0.0987 0.1396 0.1382 0.1015

Observations

Training / Test

662 33 122 99 99 117 66 189 68 33 210 198 66

0.0061 0.0201 0.0184 0.0232 0.0098 0.0156 0.0057 0.0086 0.0012 0.0021 0.0044 0.0133 0.0127

With only 33 observations in the test dataset, NACE Sectors D and L were not considered for the final prediction model. Unfortunately, in both sectors too few observations per collective would be left once the test data is divided into several collectives.

With only 33 observations in the test dataset, NACE sectors D and L were not considered for the final prediction model. Unfortunately, in both sectors, too few observations per collective would be left once the test data is divided into several collectives

Annex C

285

C.3 Variance in Mean Entry per Predictor Variable

Subset C Variable crn_democracy reg_mincgini avwage ma_intensity grp_pc op_infa ICT_idx_std op_art_prp patent_coeff op_art_en heduc reg_administ unempl educ raiding_cases reg_safety op_hr_ingtech op_ad_noprsc op_fin_short op_art_spl op_ad_nocntrl buscrm_bus grp_growth op_art_corr HHI_ind rsk_econ op_fin_long

Subset IKM Varianz 0.000123099 9.13566E-05 7.65858E-05 5.56579E-05 5.12338E-05 4.49515E-05 4.36535E-05 4.31176E-05 3.66632E-05 3.45477E-05 2.39118E-05 2.19508E-05 1.68633E-05 1.65307E-05 1.55236E-05 1.26658E-05 1.17913E-05 1.02142E-05 9.02186E-06 5.72833E-06 4.10387E-06 3.67813E-06 3.26603E-06 2.23943E-06 2.02002E-06 1.24011E-06 3.03051E-07

Variable HHI_ind reg_administ educ reg_safety reg_mincgini ICT_idx_std op_infa rsk_econ avwage op_art_prp patent_coeff grp_growth op_fin_long op_art_en heduc crn_democracy op_ad_nocntrl op_art_corr op_hr_ingtech raiding_cases ma_intensity op_art_spl grp_pc unempl buscrm_bus op_ad_noprsc op_fin_short

Varianz 0.000204848 7.52201E-05 3.16024E-05 3.14202E-05 2.67473E-05 2.64282E-05 2.54027E-05 2.29326E-05 2.09232E-05 2.01788E-05 1.82845E-05 1.60663E-05 1.48789E-05 1.18463E-05 9.8905E-06 8.64299E-06 8.32437E-06 5.15417E-06 3.85941E-06 3.82117E-06 3.12211E-06 2.52803E-06 1.66796E-06 1.29137E-06 1.07876E-06 9.38118E-07 8.89008E-07

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© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Schlattau, Tilting at the Windmills of Transition, Societies and Political Orders in Transition, https://doi.org/10.1007/978-3-030-54909-1

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