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Contributions to Finance and Accounting
Shaofang Li
Financial Regulation and Bank Performance
Contributions to Finance and Accounting
The book series ‘Contributions to Finance and Accounting’ features the latest research from research areas like financial management, investment, capital markets, financial institutions, FinTech and financial innovation, accounting methods and standards, reporting, and corporate governance, among others. Books published in this series are primarily monographs and edited volumes that present new research results, both theoretical and empirical, on a clearly defined topic. All books are published in print and digital formats and disseminated globally.
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Shaofang Li
Financial Regulation and Bank Performance
Shaofang Li Southeast University Nanjing, China
ISSN 2730-6038 ISSN 2730-6046 (electronic) Contributions to Finance and Accounting ISBN 978-981-16-3508-3 ISBN 978-981-16-3509-0 (eBook) https://doi.org/10.1007/978-981-16-3509-0 Jointly published with Shanghai Jiao Tong University Press The print edition is not for sale in China (Mainland). Customers from China (Mainland) please order the print book from: Shanghai Jiao Tong University Press. © Shanghai Jiao Tong University Press 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 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 publishers, 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 publishers nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Preface
The book evaluates the impact of financial regulatory on bank performance in normal times and during the global financial crisis. Following the 2008–2009 global financial crisis, the wide-ranging international financial regulatory reforms and the reshape of financial regulation in most countries have sought to strength financial stability through both improved and new standards. In 2010, the Basel Committee on Banking Supervision (BCBS) incorporated the revised capital regulatory framework, known as the Basel III accord, to encourage enhanced regulation of bank capital. On December 7, 2017, the Basel Committee on Banking Supervision (BCBS) finalized the reforms outlined in Basel III, informally known as Basel IV, mandating significantly higher minimum capital requirements for banks. The Financial Stability Board’s Key Attributes of Effective Resolution Regimes for Financial Institutions set out “the core elements to allow authorities to resolve institutions in an orderly manner without [resorting to the] taxpayer” (FSB, 2014). The regulatory changes in the last decade have been investigated and well documented by the World Bank database Bank Regulation and Supervision (see Barth, Caprio, and Levine (2013)). However, less is known about the implementation of the regulatory rule. Even though countries share the same regulations, the implementation across countries may differ. Our focus will therefore be on the implementation of financial regulation and how well the supervisors pursue and implement financial regulation. Regulators might allow for regulatory forbearance in which banks would inflate the levels of their capital (see Huizinga and Laeven (2012)) or simply the regulators would postpone the decision to close insolvent banks (see Brown and Dinç (2011)). By using the most recent data in banking industry, Chap. 2 empirically investigates how bank capital and competitive conditions affect bank risk-taking. Using financial data of 7620 banks on 118 countries from 2001 to 2016, we show that banks with a higher Tier 1 ratio and a lower Tier 2 ratio are lower risk-takers. A bank with greater market power in a banking system tends to reduce its risk-taking activities. Our findings also highlight that the negative relationship between Tier 1 ratio and bank risk is more pronounced in more competitive conditions. During the financial crisis, Tier 1 capital acted as a stable funding source and reduced bank risk, but the v
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evidence on Tier 2 capital shows that a higher Tier 2 ratio results in a higher level of risk and increases bank instability. Chapter 3 aims to investigate the impact of both bank reform and competition on stability by analyzing the empirical relationships among banking sector reform, competition, and stability in transition economies. By using unbalanced data on 1121 banks from 22 transition countries between 1998 and 2016, our findings confirm the negative relationship between market power and stability, supporting the view that competition increases bank stability. The results also confirm that bank reform increases banking stability, supporting the view that better financial development leads to a more stable banking system. Both higher activity restrictions and more explicit guidelines for asset diversification increase bank stability, but this positive effect significantly weakens for banks with higher market power. More stringent capital requirements in combination with higher market power increase the risk of bank insolvency. Declaring insolvency power, private monitoring, financial statement transparency, and deposit insurance have only a direct impact on bank stability. Chapter 4 aims to examine the impact of bank regulation and supervision on competitive conditions in the banking sector in emerging economies and examines whether the unique characteristics in emerging markets shed light on the relationship between bank regulation and competition from different perspectives. Using a sample of 1,629 banks in 23 emerging economies between 1996 and 2016, this study employs the Panzar and Rosse (1987) methodology and constructs the competitive variable H-statistic as a measure of competition in the banking system. We also compute the Lerner index (see also Coccorese (2009); Koetter et al. (2012)) and the Boone indicator (2008) as alternative measures of competition. We investigate how competition evolved under different types of bank regulation and supervision. As our sample period covers several banking crises in some emerging economies as well as the recent global financial crisis, we also examine whether the relationship between bank regulation and competition changed during the banking crisis. Considering the different roles played by domestic banks and foreign banks, we also study whether the impact of bank regulation on foreign banks exhibits different patterns. Chapter 5 investigates the impact of bank competition and regulation on bank efficiency in the Asia-Pacific region during 2001–2016. The result reveals that market power is positively related to bank efficiency. We also find that stringent activity restrictions, strong official supervisory power, and low capital requirements are associated with high bank efficiency. Furthermore, market power has a stronger efficiency-increasing effect in a banking system characterized by the activity restrictions, supervisory power, and capital requirements described above. Foreign banks operating under increased activity restrictions in a host country with strong official supervisory power have relatively high efficiency. Nanjing, China
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Preface
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References Barth, J. R., Caprio, G. J., & Levine, R. (2013). Bank regulation and supervision in 180 countries from 1999 to 2011. Journal of Financial Economic Policy, 5(2), 111–219. Brown, C. O. & Dinç, I. S. (2011). Too many to fail? Evidence of regulatory forbearance when the banking sector is weak. The Review of Financial Studies, 24(4), 1378–1405. Coccorese, P. (2009). Market power in local banking monopolies. Journal of Banking & Finance, 33(7), 1196–1210. Huizinga, H. & Laeven L. (2012). Bank valuation and accounting discretion during a financial crisis. Journal of Financial Economics, 106(3), 614–634. Koetter, M., Kolari, J. W., & Spierdijk, L. (2012). Enjoying the quiet life under deregulation? Evidence from adjusted Lerner indices for US banks. Review of Economics and Statistics, 94(2), 462–480. Panzar, J. C. and Rosse, J. N. (1987). Testing for “monopoly” equilibrium. The Journal of Industrial Economics, 35(4), 443–456.
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Motivation of This Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Research Questions Addressed in This Study . . . . . . . . . . . . . . . . . . . 1.3 Structure and Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1 1 2 4 5
2 Quality of Bank Capital, Competition, and Risk-Taking . . . . . . . . . . . . 2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Data and Variable Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Variable Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 Descriptive Statistics and Correlation . . . . . . . . . . . . . . . . . . . 2.5 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.1 Basic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.2 Impact of Competition on the Relationship Between Capital Structure and Risk-Taking . . . . . . . . . . . . . . . . . . . . . . 2.6.3 Impact of the Financial Crisis on the Relationship Between Capital Structure and Risk-Taking . . . . . . . . . . . . . . 2.6.4 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9 9 9 11 13 13 14 16 19 25 25
3 Banking Sector Reform, Competition, and Bank Stability . . . . . . . . . . 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Banking Sector Reform in Transition Countries and Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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29 36 40 45 46
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3.3.1 Banking Sector Reforms in Transition Countries . . . . . . . . . . 3.3.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Data and Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Empirical Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.1 Basic Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.2 Impact of Bank Regulation on the Relationship Between Competition and Stability . . . . . . . . . . . . . . . . . . . . . 3.6.3 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
53 54 55 55 56 59 62 67 67
4 The Impact of Bank Regulation and Supervision on Competition . . . 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Previous Studies on Competition . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Bank Regulation and Effects on Bank Competition . . . . . . . 4.4 Estimation Methodology and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Estimation of Competition Variables . . . . . . . . . . . . . . . . . . . . 4.4.2 Data Sources, Sample Selection, and Variable Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Statistical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.4 Determinants of Bank Competition . . . . . . . . . . . . . . . . . . . . . 4.5 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Bank Regulation, Supervision, and the H-Statistic . . . . . . . . 4.5.2 Alternative Measures of Competition . . . . . . . . . . . . . . . . . . . 4.5.3 Impact of the Financial Crisis . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.4 Further Tests: Foreign Banks Versus Domestic Banks . . . . . 4.5.5 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5 Bank Competition, Regulation, and Efficiency . . . . . . . . . . . . . . . . . . . . 5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Related Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 The Impact of Competition on Bank Efficiency . . . . . . . . . . . 5.3.2 Literature Review on Bank Regulation and Bank Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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99 102 105 105 105 110 114 115 122 122 130 133 133 133 135 135 135
Contents
5.4 Variables Definition and Research Design . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Bank Efficiency Estimation and Main Variables Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Model Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Data Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.4 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Basic Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Impact of Bank Regulation on the Relationship Between Competition and Efficiency . . . . . . . . . . . . . . . . . . . . 5.5.3 Further Test: Foreign Banks Versus Domestic Banks . . . . . . 5.5.4 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 6.2 Summary of the Main Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
List of Tables
Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 2.6 Table 2.7 Table 2.8 Table 2.9 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 4.1 Table 4.2 Table 4.3 Table 4.4
Variable definition and data sources . . . . . . . . . . . . . . . . . . . . . . . Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pearson correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of capital structure and competition on bank risk-taking (non-performing loan ratio) . . . . . . . . . . . . . . . . . . . . Impact of capital structure and competition on bank risk-taking (log Zscore) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of competition on the relationship between capital structure and bank risk-taking . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of capital structure on bank risk-taking during the financial crisis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Robustness checks: alternative measures of competition and bank risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Robustness checks: subsamples . . . . . . . . . . . . . . . . . . . . . . . . . . . Variable definition and data sources . . . . . . . . . . . . . . . . . . . . . . . Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bank reform, competition and stability: basic analysis . . . . . . . . Bank reform, competition and stability: alternative measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Relationship between bank competition, reform, regulation and stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The impact of bank reform and regulation on stability: through the channel of competition . . . . . . . . . . . . . . . . . . . . . . . . Impact of bank competition and reform on bank stability: robustness checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variable definition and data sources . . . . . . . . . . . . . . . . . . . . . . . Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of bank regulation and supervision on H-statistic . . . . . . Impact of bank regulation and supervision on competition. Alternative measure of competition: Lerner index and Boone indicator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17 20 21 26 30 33 37 41 43 60 63 68 70 72 75 80 101 103 107
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Table 4.5 Table 4.6 Table 4.7 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
List of Tables
Bank regulation, supervision, bank crisis, and H-statistic . . . . . . Bank regulation, supervision, foreign ownership, and H-statistic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of bank regulation and supervision on competition: robustness check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variables definition and data sources . . . . . . . . . . . . . . . . . . . . . . Summary of profit inefficiency and cost inefficiency according to countries and years . . . . . . . . . . . . . . . . . . . . . . . . . . Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of bank competition and regulation and bank efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of bank regulation on the relationship between competition and efficiency . . . . . . . . . . . . . . . . . . . . . . . Impact of ownership on the relationship between bank competition, regulation and bank efficiency . . . . . . . . . . . . . . . . . Alternative measure of bank competition . . . . . . . . . . . . . . . . . . . Robustness checks based on alternative estimation methods . . . . Robustness checks based on different subsamples . . . . . . . . . . . .
116 119 123 142 146 148 149 153 157 164 167 171
Chapter 1
Introduction
1.1 Motivation of This Study Following the 2008–2009 global financial crisis, the wide-ranging international financial regulatory reforms and the reshape of financial regulation in most countries have sought to strength financial stability through both improved and new standards. In 2010, the Basel Committee on Banking Supervision (BCBS) incorporated the revised capital regulatory framework, known as the Basel III accord, to encourage enhanced regulation of bank capital (Basel Committee on Banking Supervision, 2010). On December 7, 2017, the Basel Committee on Banking Supervision (BCBS) finalized the reforms outlined in Basel III, informally known as Basel IV, mandating significantly higher minimum capital requirements for banks. The Financial Stability Board’s Key Attributes of Effective Resolution Regimes for Financial Institutions set out “the core elements to allow authorities to resolve institutions in an orderly manner without [resorting to the] taxpayer” (Financial Stability Board, 2012). The regulatory changes in the last decade have been investigated and well documented by the World Bank database Bank Regulation and Supervision (see Barth et al., 2013). However, less is known about the implementation of the regulatory rule. Even though countries share the same regulations, the implementation across countries may differ. Our focus will therefore be on the implementation of financial regulation and how well the supervisors pursue and implement financial regulation. Regulators might allow for regulatory forbearance in which banks would inflate the levels of their capital (see Huizinga & Laeven, 2012) or simply the regulators would postpone the decision to close insolvent banks (see Brown & Dinç, 2011). Given these concerns, this study investigates in the impact of the financial regulatory on bank behaviour. In particular, the primary objective of this book is to evaluate the impact of financial regulatory on bank performance in normal times and during the global financial crisis. To that end, this book proceeds from three perspectives: (1)
investigates how bank capital and competitive conditions affect bank risktaking. Particularly, based on the Basel Accords, this study classifies bank capital based on capital quality: Tier 1 capital with higher loss-absorbing
© Shanghai Jiao Tong University Press 2021 S. Li, Financial Regulation and Bank Performance, Contributions to Finance and Accounting, https://doi.org/10.1007/978-981-16-3509-0_1
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2
(2)
(3)
(4)
1 Introduction
capacity and Tier 2 capital with lower loss-absorbing capacity, and examine the effects of bank capital structure on bank risk-taking; explores the impact of both bank reform and competition on stability by analyzing the empirical relationships among banking sector reform, competition, and stability in transition economies; examines the impact of bank regulation and supervision on competitive conditions in the banking sector in emerging economies and examine whether the unique characteristics in emerging markets shed light on the relationship between bank regulation and competition from different perspectives; tests the impact of competition and regulation on bank efficiency based on international evidence in the Asia–Pacific region.
1.2 Research Questions Addressed in This Study This book is built on four main parts: Chap. 2 focuses on a direct link between quality of bank capital, competition, and banks’ risk-taking. Bank capital plays an important role in the financial system, because it acts as a financial cushion to absorb a firm’s losses and also reduces the incentive for banks to take excessive risks (Jokipii & Milne, 2011), but higher capital requirements would lead to a decrease in lending and would undermine banks’ profitability (Ben Naceur et al., 2018; Košak et al., 2015). Previous analyses also indicate that more competitive conditions among banks result in decreasing monopoly rents, which in turn leads to lower profits and capital adequacy. The latter renders the banks less able to absorb the losses and incentivizes excessive risk-taking (Keeley, 1990). However, les competition among banks may also lead to more risk-taking activities if large banks are deemed “too systemically important to fail” (Kelly et al., 2016). As the first research question, this part investigates how bank capital and competitive conditions affect bank risk-taking. Based on the Basel Accords, we classify bank capital based on capital quality: Tier 1 capital with higher loss-absorbing capacity and Tier 2 capital with lower loss-absorbing capacity. We then examine the effects of bank capital structure on bank risk-taking. We test whether bank risk-taking behaviour varies according to the quality of bank capital, and whether this relationship changes significantly in different competitive conditions and during the financial crisis. Chapter 3 focuses on the relation between bank sector reform, competition and bank stability. The recent global financial crisis between 2008 and 2010 influenced the global economy through banking systems across countries and led to unprecedented consequences. Some studies highlight that deregulation and excessive competition in financial sectors were important factors that drove the financial crisis (Brunnermeier, 2009; Carletti, 2008; Fernández et al., 2013; Llewellyn, 2008). The impact of bank regulation and competition on financial stability has thus attracted more attention from academics and policymakers since then (Acharya & Richardson, 2009; Beck et al., 2010, 2013; Schaeck & Cihák, 2014).
1.2 Research Questions Addressed in This Study
3
The banking systems in transition countries have undergone extensive reforms in the past two decades and this provides us with a unique setting in which to test the relationships among banking sector reform, competitive conditions, and financial stability. In this part, we investigate the impact of both bank reform and competition on stability by analyzing the empirical relationships among banking sector reform, competition, and stability in transition economies. Chapter 4 focuses on the impact of bank regulation and supervision on competition from emerging economies. Over the past two decades, because of the development of information technology, globalization, and deregulation, the banking system has undergone dramatic changes. Emerging economies have experienced considerable economic development and financial reforms, including financial liberalization, bank privatization, widening access to foreign banks, and restructuring national banking systems, which aimed to reshape competitive conditions in the banking sector. These changes decreased profitability in traditional bank activities and led to massive mergers and acquisitions (M&A) among financial institutions in emerging economies. While changes in the financial market are the main driving forces for bank consolidation in developed economies, financial supervisory authorities play an important role in the bank consolidation process in emerging economies (Gelos & Roldós, 2004). The overall differences in economic development, bank consolidation, and regulation between developed economies and emerging economies created distinctive features in competitive conditions in the banking industry in emerging economies. The goal of this study is to examine the impact of bank regulation and supervision on competitive conditions in the banking sector in emerging economies and examine whether the unique characteristics in emerging markets shed light on the relationship between bank regulation and competition from different perspectives. Chapter 5 focuses on the relationship between bank competition, regulation, and bank efficiency in the Asia–Pacific region. Since the 1980s, the global financial market has become increasingly integrated, and both the developed and developing countries have embarked on financial liberalization. In the process of financial liberalization, deregulation, and increased integration, banks have expanded their services and engaged in greater risk-taking activities to improve their productive efficiency (Luo et al., 2016). With increased competition following financial liberalization and deregulation, banks have to improve their efficiency (Andrie¸s & C˘apraru, 2014; Schaeck & Cihák, 2008). Since the 1990s, governments across the region have implemented financial reforms, including financial liberalization, bank privatization, widening access to foreign banks, and restructuring of the national banking systems, to enhance the competitive strength of the banking sector. Prudential norms on non-performing loans and capital requirements are also implemented to strengthen financial stability (Zhao et al., 2010). The banking sector in Asia–Pacific region underwent a restructuring process after the 1997–1998 Asian financial crisis and the recent global financial crisis (Noman et al., 2018; Williams & Nguyen, 2005). In this part, we examine how bank competition and regulation affect bank efficiency, using a sample of 1261 banks across 28 Asia Pacific countries for the period 2001–2016.
4
1 Introduction
1.3 Structure and Contents Apart from the Introduction and Conclusion, this book is organized in four chapters. Chap. 2 empirically investigates how bank capital and competitive conditions affect bank risk-taking. Particularly, based on the Basel Accords, we classify bank capital based on capital quality: Tier 1 capital with higher loss-absorbing capacity and Tier 2 capital with lower loss-absorbing capacity. We then examine the effects of bank capital structure on bank risk-taking. We test whether bank risk-taking behaviour varies according to the quality of bank capital, and whether this relationship changes significantly in different competitive conditions and during the financial crisis. Using financial data of 7620 banks on 118 countries from 2001 to 2016, the results show that banks with a higher Tier 1 ratio and a lower Tier 2 ratio are lower risk-takers. A bank with greater market power in a banking system tends to reduce its risk-taking activities. The main findings also highlight that the negative relationship between Tier 1 ratio and bank risk are more pronounced in more competitive conditions. During the financial crisis, Tier 1 capital acted as a stable funding source and reduced bank risk, but the evidence on Tier 2 capital shows that a higher Tier 2 ratio results in a higher level of risk and increases bank instability. Chapter 3 provides empirical analysis of the relation between banking sector reform, competition on bank stability in transition countries. The banking systems in transition countries have undergone extensive reforms in the past two decades and this provides us with a unique setting in which to test the relationships among banking sector reform, competitive conditions, and financial stability. This study investigates the impact of both bank reform and competition on stability by analyzing the empirical relationships among banking sector reform, competition, and stability in transition economies. By using unbalanced data on 1121 banks from 22 transition countries between 1998 and 2016, our findings confirm the negative relationship between market power and stability, supporting the view that competition increases bank stability. The results also confirm that bank reform increases banking stability, supporting the view that better financial development leads to a more stable banking system. Both higher activity restrictions and more explicit guidelines for asset diversification increase bank stability, but this positive effect significantly weakens for banks with higher market power. More stringent capital requirements in combination with higher market power increase the risk of bank insolvency. Declaring insolvency power, private monitoring, financial statement transparency, and deposit insurance have only a direct impact on bank stability. Chapter 4 focuses on the impact of bank regulation and supervision on competition in emerging economies. Using a sample of 1629 banks in 23 emerging economies between 1996 and 2016, this study employs the Panzar and Rosse (1987) methodology and constructs the competitive variable H-statistic as a measure of competition in the banking system. We also compute the Lerner index (see also Coccorese (2009), Koetter et al. (2012)) and the Boone indicator (2008) as alternative measures of competition. We investigate how competition evolved under different types of
1.3 Structure and Contents
5
bank regulation and supervision. As our sample period covers several banking crises in some emerging economies as well as the recent global financial crisis, we also examine whether the relationship between bank regulation and competition changed during the banking crisis. Considering the different roles played by domestic banks and foreign banks, we also study whether the impact of bank regulation on foreign banks exhibits different patterns. Our analysis shows that banking systems with higher concentration and fewer activity restrictions and entry barriers are more competitive. We also find that reducing foreign bank limitations and increasing capital strictness and official supervisory power also enhances competition in the banking sector. The results also provide evidence that competition in banking systems with fewer government-owned banks and fewer diversification guidelines tends to be more intensive. The results also confirm that higher private monitoring of banks and deposit insurance coverage significantly contribute to an increase in bank competition. Chapter 5 explores the relationship between bank competition, regulation, and efficiency in Asia–Pacific region. This part examines how bank competition and regulation affect bank efficiency, using a sample of 1261 banks across 28 Asia Pacific countries for the period 2001–2016. We employ a stochastic frontier analysis (SFA) model to estimate bank efficiency. Our findings confirm that market power is positively related to bank efficiency. Increased activity restrictions, strong official supervisory power, and low capital requirements are associated with high bank efficiency. Furthermore, market power has a stronger efficiency-increasing effect in a banking system characterized by the activity restrictions, supervisory power, and capital requirements described above. Foreign banks operating under strict activity restrictions in a host country with strong official supervisory power are highly efficient. The rest of this book proceeds as follows: Chap. 2 addresses the relationship between the quality of bank capital, competition and banks’ risk taking, Chap. 3 analyses the impact of bank sector reform and competition on bank stability in transition countries, Chap. 4 investigates the relationship between bank regulation and supervision on competition in emerging economies, Chap. 5 investigates the impact of bank competition and regulation on bank efficiency. Chapter 6 concludes the analysis.
References Acharya, V. V., & Richardson, M. (2009). Causes of the financial crisis. Critical Review, 21(2–3), 195–210. Andrie¸s, A. M., & C˘apraru, B. (2014). The nexus between competition and efficiency: The European banking industries experience. International Business Review, 23(3), 566–579. Barth, J. R., Caprio, G. J., & Levine, R. (2013). Bank regulation and supervision in 180 countries from 1999 to 2011. Journal of Financial Economic Policy, 5(2), 111–219. Basel Committee on Banking Supervision. (2010). Basel III: A global regulatory framework for more resilient banks and banking systems. http://www.bis.org/press/p100912.htm
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Beck, T., De Jonghe, O., & Schepens, G. (2013). Bank competition and stability: Cross-country heterogeneity. Journal of Financial Intermediation, 22(2), 218–244. Beck, T., Levine, R., & Levkov, A. (2010). Big bad banks? The winners and losers from bank deregulation in the United States. The Journal of Finance, 65(5), 1637–1667. Ben Naceur, S., Marton, K., & Roulet, C. (2018). Basel III and bank-lending: Evidence from the United States and Europe. Journal of Financial Stability, 39, 1–27. Boone, J. (2008). A new way to measure competition. The Economic Journal, 118(531), 1245–1261. Brown, C. O., & Dinç, I. S. (2011). Too many to fail? Evidence of regulatory forbearance when the banking sector is weak. The Review of Financial Studies, 24(4), 1378–1405. Brunnermeier, M. K. (2009). Deciphering the liquidity and credit crunch 2007–2008. Journal of Economic Perspectives, 23(1), 77–100. Carletti, E. (2008). Competition and regulation in banking. Handbook of Financial Intermediation and Banking, 126(5), 449–482. Coccorese, P. (2009). Market power in local banking monopolies. Journal of Banking & Finance, 33(7), 1196–1210. Fernández, A. I., González, F., & Suárez, N. (2013). The real effect of banking crises: Finance or asset allocation effects? Some international evidence. Journal of Banking & Finance, 37(7), 2419–2433. Financial Stability Board. (2012). Progress Report to the G20 on Strengthening the Oversight and Regulation of Shadow Banking. Gelos, R. G., & Roldós, J. (2004). Consolidation and market structure in emerging market banking systems. Emerging Markets Review, 5(1), 39–59. Huizinga, H., & Laeven, L. (2012). Bank valuation and accounting discretion during a financial crisis. Journal of Financial Economics, 106(3), 614–634. Jokipii, T., & Milne, A. (2011). Bank capital buffer and risk adjustment decisions. Journal of Financial Stability, 7(3), 165–178. Keeley, M. C. (1990). Deposit insurance, risk, and market power in banking. The American Economic Review, 80(5), 1183–1200. Kelly, B., Lustig, H., & Van Nieuwerburgh, S. (2016). Too-systemic-to-fail: What option markets imply about sector-wide government guarantees. American Economic Review, 106(6), 1278– 1319. Koetter, M., Kolari, J. W., & Spierdijk, L. (2012). Enjoying the quiet life under deregulation? Evidence from adjusted Lerner indices for US banks. Review of Economics and Statistics, 94(2), 462–480. Košak, M., Li, S., Lonˇcarski, I., & Marinˇc, M. (2015). Quality of bank capital and bank lending behavior during the global financial crisis. International Review of Financial Analysis, 37, 168– 183. Llewellyn, D. T. (2008). The Northern Rock crisis: A multi-dimensional problem waiting to happen. Journal of Financial Regulation and Compliance, 16(1), 35–58. Luo, Y., Tanna, S., & De Vita, G. (2016). Financial openness, risk and bank efficiency: Cross-country evidence. Journal of Financial Stability, 24, 132–148. Noman, A. H. M., Gee, C. S., & Isa, C. R. (2018). Does bank regulation matter on the relationship between competition and financial stability? Evidence from Southeast Asian countries. PacificBasin Finance Journal, 48, 144–161. Panzar, J. C., & Rosse, J. N. (1987). Testing for “monopoly” equilibrium. The Journal of Industrial Economics, 35(4), 443–456. Schaeck, K., & Cihák, M. (2008). How does competition affect efficiency and soundness in banking? New empirical evidence. European Central Bank Working Paper Series No. 932.
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Schaeck, K., & Cihák, M. (2014). Competition, efficiency, and stability in banking. Financial Management, 43(1), 215–241. Williams, J., & Nguyen, N. (2005). Financial liberalisation, crisis, and restructuring: A comparative study of bank performance and bank governance in South East Asia. Journal of Banking & Finance, 29(8), 2119–2154. Zhao, T., Casu, B., & Ferrari, A. (2010). The impact of regulatory reforms on cost structure, ownership and competition in Indian banking. Journal of Banking & Finance, 34(1), 246–254.
Chapter 2
Quality of Bank Capital, Competition, and Risk-Taking
2.1 Overview This chapter empirically investigates how bank capital and competitive conditions affect bank risk-taking. Using financial data of 7620 banks on 118 countries from 2001 to 2016, we show that banks with a higher Tier 1 ratio and a lower Tier 2 ratio are lower risk-takers. A bank with greater market power in a banking system tends to reduce its risk-taking activities. Our findings also highlight that the negative relationship between Tier 1 ratio and bank risk are more pronounced in more competitive conditions. During the financial crisis, Tier 1 capital acted as a stable funding source and reduced bank risk, but the evidence on Tier 2 capital shows that a higher Tier 2 ratio results in a higher level of risk and increases bank instability.
2.2 Introduction The recent global financial crisis (GFC) of 2007–2009 was transmitted through the banking system across different countries and led to unprecedented consequences for the world economy. A series of studies indicate that deregulation and excessive competition in the banking system are the main factors that led to the GFC (Brunnermeier, 2009; Carletti, 2008; Fernández et al., 2013; Llewellyn, 2008). In 2010, the Basel Committee on Banking Supervision (BCBS) incorporated the revised capital regulatory framework, known as the Basel III Accord, to urge for enhanced bank capital regulation. On December 7, 2017, BCBS published a document finalizing
This chapter was published in Emerging Markets Finance and Trade. © Shanghai Jiao Tong University Press 2021 S. Li, Financial Regulation and Bank Performance, Contributions to Finance and Accounting, https://doi.org/10.1007/978-981-16-3509-0_2
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the Basel III reforms, also known informally as Basel IV, and banks face significantly higher minimum capital requirements as a result of the new Basel Committee standards.1 Bank capital plays an important role in the financial system, because it acts as a financial cushion to absorb a firm’s losses and also reduces the incentive for banks to take excessive risks (Jokipii & Milne, 2011), but higher capital requirements would lead to a decrease in lending and would undermine banks’ profitability (Ben Naceur et al., 2018; Košak et al., 2015). Previous analyses also indicate that more competitive conditions among banks result in decreasing monopoly rents, which in turn leads to lower profits and capital adequacy. The latter renders the banks less able to absorb the losses and incentivizes excessive risk-taking (Keeley, 1990). However, les competition among banks may also lead to more risk-taking activities if large banks are deemed “too systemically important to fail” (Kelly et al., 2016). This study empirically investigates how bank capital and competitive conditions affect bank risk-taking. Particularly, based on the Basel Accords, we classify bank capital based on capital quality: Tier 1 capital with higher loss-absorbing capacity and Tier 2 capital with lower loss-absorbing capacity. We then examine the effects of bank capital structure on bank risk-taking. We test whether bank risk-taking behaviour varies according to the quality of bank capital, and whether this relationship changes significantly in different competitive conditions and during the financial crisis. Using the unbalanced financial information of 7620 banks from 118 countries between 2001 and 2016, we find that banks with a higher Tier 1 ratio and a lower Tier 2 ratio exhibit lower risk-taking. Further, a bank that has higher market power in a banking system tends to reduce its risk-taking activities. The results also confirm that banks with higher profitability, more funding, and higher quality of bank assets are exposed to lower risk. During a financial crisis, banks exhibit a higher level of risk-taking than in normal times. Our findings also highlight that the negative relationship between the Tier 1 ratio and bank risk are more pronounced in more competitive conditions. During the financial crisis, Tier 1 capital acted as a stable funding source and reduced bank risk, but the evidence on Tier 2 capital shows that a higher Tier 2 ratio results in a higher level of risk and increases bank instability. Our analysis extends the results of previous studies in three ways. First, although there are some studies focusing on the effect of capital requirements on risk-taking in the banking system, we focus on the capital structure and distinguish between Tier 1 and Tier 2 capital, thus considering the quality of bank capital, and examine the different effects of Tier 1 capital and Tier 2 capital on bank risk-taking. Second, we also consider the impact of competition on the relationship between bank capital and risk-taking while controlling for bank-specific characteristics and macroeconomic factors that may impact bank stability. Both the theoretical and empirical analyses show that competition affects both bank capital holding (Allen et al., 2011; Schaeck & Cihák, 2012; Chen, 2016) and bank risk-taking (Agoraki et al., 2011; Jiménez 1 KPMG, Impact of Basel 4 on EU banks, 2018. https://home.kpmg.com/xx/en/home/insights/2018/ 10/impact-of-basel-4-on-eu-banks-fs.html.
2.2 Introduction
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et al., 2013). This indicates that the effects of Tier 1 and Tier 2 capital on bank risk-taking may change under different competitive conditions. Finally, our analysis extends the results of previous studies as we also evaluate the relationship between Tier 1 capital and Tier 2 capital and bank risk-taking during the financial crisis, which could provide further empirical evidence for policy makers regarding bank capital regulation, especially during the financial crisis. The rest of the analysis is structured as follows: Sect. 2.3 summarizes the relevant literature. Section 2.3 mainly addresses the data collection, variable definition, and data statistics. Section 2.4 presents the research design. Section 2.5 presents the empirical analysis and robustness checks, and Sect. 2.6 concludes the paper.
2.3 Literature Review Previous literature that studies the relationship between bank capital and bank risk-taking shows different findings. Koehn and Santomero (1980) and Kim and Santomero (1988) show that higher capital requirement incentives banks to choose riskier assets (see also Altunbas et al., 2007; Shrieves & Dahl, 1992). However, Jokipii and Milne (2011) indicate that bank capital is used to absorb banks’ losses and insulate banks from potential insolvency, and holding additional capital is to protect banks against large losses during a cyclical downturn and reduce the risk of insolvency (see also Rajan, 1994; Ayuso et al., 2004; Jokipii & Milne, 2008). Bank capital also acts as an incentive device, which encourages banks to behave prudently and to reduce risk-taking (Ashraf et al., 2016; Bui et al., 2017), but because of the existence of deposit insurance schemes and implicit government bailout guarantees, bank managers and shareholders tend to expand their lending and increase the potential risk (Goodhart, 2013). In the previous studies, only few studies consider the difference between the quality of Tier 1 and Tier 2 capital. Demirgüç-Kunt et al. (2013) find that Tier 1 capital has a stronger and more positive effect on stock returns than Tier 2 capital. Barrell et al. (2011) suggest that an increase in the overall capital adequacy ratio reduces banks’ risk appetite in both ex-ante and ex-post terms and that increasing Tier 2 capital based on a given capital adequacy structure increases banks’ risk appetite (see also Ashcraft, 2008). Košak et al. (2015) analyze the determinants of bank lending behavior during the GFC and find that Tier 1 capital acted as a stable source of funding during that crisis but that, although Tier 2 capital spurs bank lending during normal times, it did not do so during the crisis (Koivu, 2012). Since the GFC, regulators have already acknowledged the need to readjust and recalibrate their regulatory measures. The intention of the Basel III Accord is to significantly increase the role of Tier 1 bank capital relative to Tier 2 bank capital (Basel Committee on Banking Supervision, 2010). Similarly, the EBA, the institution charged with setting harmonized supervisory standards for banks in EU Member States, announced that major European banking groups would have to increase their Core Tier 1 capital ratios to 9% of their risk-weighted assets by June 2012 (EBA,
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2 Quality of Bank Capital, Competition, and Risk-Taking
2011). Bank capital regulatory measures are very likely to affect the credit activity of banks (Gropp et al., 2018; Mésonnier & Monks, 2015). As regards the impact of competition on bank risking-taking, previous analysis indicates that competitive banking systems are more fragile, as competition facilitates banks’ undertaking of more risk-taking activities (Keeley, 1990). In a competitive banking system, banks benefit less from a lending relationship and this discourages their motivation to monitor borrowers’ risk properly, thus introducing higher risk (Allen & Gale, 2000, 2004). Boyd et al. (2004) also suggest that large banks in concentrated banking sectors would increase profits and decrease financial instability by providing more “capital buffers” that absorb external macroeconomic and liquidity shocks. Caminal and Matutes (2002) argue that less competition leads to reduced credit rationing and higher loans, increasing the probability of bank insolvency. However, Berger et al. (2009) indicate that by using alternative methods to allocate the risk exposure, bank risks may not increase even if market power encourages riskier asset portfolios. Tabak et al. (2012) conclude that competition affects risk-taking behavior in a non-linear way as both high and low competition levels enhance financial stability, whereas we find the opposite effect for average competition levels (see also Jiménez et al., 2013). In addition, bank size and capitalization are essential factors in explaining this relationship. Agoraki et al. (2011) suggest that banks with market power tend to undertake lower credit risk and have a lower probability of default, and capital requirements reduce risk in general, but, for banks with market power, this effect significantly weakens or can even be reversed. Laeven et al. (2016) analyze the role of bank capital, size, funding, and activities in explaining bank instability during the recent GFC and find that a higher capital ratio increases stock return, especially for large banks. Bank capital may have diverse effects on systemic stability, depending on specific policy environments: institutional frameworks enabling private market discipline and regulatory environments that foster information transparency and lower information asymmetries can substitute for capital in containing bank risk-taking. Some studies also investigate the impact of institutional environment on competition and also on risk-taking. The theoretical studies find conflicting conclusions on the relationship between bank regulation and bank performance, and a consensus on what regulatory practices are more effective for reducing bank risk has still not been reached (Barth et al., 2004, 2008). Acharya et al. (2006) claim that, although engaging in nonlending activities helps banks to achieve risk diversification, lack of experience in new services may also introduce higher volatility in incomes and higher instability. Stringent capital requirements may help improve banks’ corporate governance and reduce risk-taking behavior, but they are not necessarily related to lower loan losses (Kopecky & VanHoose, 2006; Pasiouras et al., 2006). Higher supervisory power also introduces more corruption in lending, which also increases bank risk-taking (Beck et al., 2006). Hellmann et al. (2000) claim that activities’ restrictions might incentivize bank managers to accept risky projects and Barth et al. (2004) and Laeven and Levine (2009) provide empirical findings supporting this statement. Anginer et al. (2018) show that a country’s institutional setting has a significant impact on the
2.3 Literature Review
13
relationship between bank capital and bank stability and that the impact of capital on bank risk is less pronounced for banks located in countries with better public and private monitoring of financial institutions and in countries with higher levels of information availability.
2.4 Data and Variable Definition 2.4.1 Data Sources We collect our data from different sources. The financial data for the sampled banks were collected from Bankscope. To avoid the potential bias introduced by doublecounting, this analysis employs the consolidated report; the unconsolidated report is used if consolidated information is unavailable. The country-level variables included to account for the impact of macroeconomic factors are collected from the World Bank Development Indicator Database. Additionally, we include a set of bank regulation and supervision indicators to control for the impact of institutional environment at the country-level, and the data are obtained from Barth et al. (2013).2 We keep or drop observations based on the following selection criteria: (1) for the estimation of Z-scores, we employ the mean of ROA and E/A (equity to total assets ratio) and the standard deviation of ROA (sd (ROA)) over 3 years, only keeping the banks with at least three consecutive observations; (2) we drop observations where the data on one of the variables used to estimate the Lerner index are missing; (3) for the estimation of the Lerner index, we set the minimum number of observations to 10 for each year in each country, thus deleting countries with fewer than 10 bank-year observations; and, (4) we also delete the observations where the data on one of the bank-specific and country-level control variables is missing. Using the above selection criteria, we finally have 7620 banks, accounting for 49,074 observations between 2001 and 2016. Our sample consists of commercial, savings, and co-operative banks from 118 countries. All data are adjusted based on the inflation rate and expressed in USD. To avoid the potential bias introduced by outliers, all bank-level variables employed in this study are winsorized at the 1st and 99th percentile levels.
2 In
the bank regulation and supervision information provided by Barth et al. (2013), however, the surveys were conducted in 1999, 2002, 2006, and 2011. Following the study of Anginer et al. (2014a), (2018), we employ the data from previous surveys until the newer survey data became available for matching the bank regulation and supervision variables with bank-specific variables and country control variables, that is, the data from the surveys conducted in 1999, 2002, 2006, and 2011 for the periods from 1998 to 2001, 2002 to 2005, 2006 to 2010, and 2011 to 2016, respectively. For a detailed description of the bank regulation and supervision information, see Barth et al. (2013).
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2.4.2 Variable Definition (1)
Bank risk-taking measures
This study uses two measures of bank risk-taking. First, we follow the study of Jiménez et al. (2013) and employ the ratio of non-performing loans to total loans (NPL) as a measure of bank risk-taking. Second, based on the more recent study by Ashraf (2017) (see also Agoraki et al., 2011; Gaganis et al., 2018; Tabak et al., 2012), we also use a market version of the Z-score as an alternative measure of bank risk-taking. Based on the recent study by Mare et al. (2017), we use the novel non-stationary estimator and define the Z-score as in Eq. (2.1): Zit =
R OAit + (E/A)it , sd R OA
(2.1)
it
where R OA is the moving average ROA, computed based on past and current information for each period; E/A is the ratio of equity to total assets for period t; and, sd ROA indicates the standard deviation of ROA computed based on past and current information for each period. A higher Z-score means a lower probability of insolvency and lower risk-taking. We also follow Anginer et al. (2014b) and employ the logarithm of the Z-score (log Zscore) to avoid the bias introduced by extreme values. (2)
Bank competition indicators
We first use the Lerner index as a measure of bank competition to evaluate banks’ competitive behavior. The higher the Lerner index, the higher the market power is. The Lerner index is defined in Eq. (2.2): Lerner Indexit = (Pit − MCit )/Pit
(2.2)
where Pit is the output price, measured by the ratio of total revenue to total assets for bank i in year t, and MCit is the marginal cost of bank i in year t.3 MCit is calculated based on Eq. (2.3): MCit =
TCit (α1 + α2 ln Qit + ϕ1 ln w1it + ϕ2 ln w 2it + ϕ3 ln w3it ) Qit
(2.3)
and the translog cost function is 3 Based
on earlier studies, we define the output price Pit as the ratio of total revenue to total assets; see also Angelini and Cetorelli (2003), Koetter et al. (2012), Fu et al. (2014), and Li (2019).
2.4 Data and Variable Definition
15
α2 βj ln wjit (ln Qit )2 + 2 j=1 3
ln TEit = α0 + α1 ln Qit +
2 1 γj ln wjit + ϕj ln wjit ∗ ln Qit 2 j=1 j=1 3
+ +
3 3
3
ρjk ln wjit ∗ ln wkit + αi + εit
(2.4)
j=1 k=1, k=j
where TEit is the total expenses and Qit denotes the output and is proxied by total assets of bank i in year t. Variables w1 , w2 , and w3 are input price variables: w1 is the interest expenses to total funding ratio and used as a measure of the average funding rate, w2 indicates the ratio of personnel expenses to total assets and used as the input price of personnel expenses, and w3 is the ratio of other non-interest expenses to fixed assets and employed as a proxy for the price of physical capital. Following Fu et al. (2014) and Koetter et al. (2012), we employ a stochastic cost frontier analysis to estimate Eq. (2.4) for each country in each year. As a robustness check, this analysis also constructs an alternative competitive measurement by using the Panzar-Ross model (Panzar & Rosse, 1987). Panzar and Rosse (1987) define the H-statistic to measure the market power, calculated as the sum of the elasticity of revenue with respect to three input prices. Similarly to the analysis of Anginer et al. (2014b), we use the reduced-form revenue equation and estimate the H-statistic for each country in each year4 : ln TRit = α +
3 j=1
ωj ln wjit +
πk Controlskit + αi + εit ,
(2.5)
k
where TRit is the total revenue of bank i in year t. Variables w1 , w2 , and w3 the same three input price variables used in the Lerner index estimation. A set of control variables are also included to control for bank-specific characteristics: Commercial loan ratio, the ratio of commercial loans to total assets, used to control for credit risk; Liquidity assets ratio, the ratio of liquidity assets to total assets, used to control for liquidity risk; Customer deposit ratio, the ratio of customer deposits to the sum of customer deposits and short-term funding, used to measure the bank’s funding structure; and, Equity ratio, the ratio of the equity to total assets, used to account for the leverage. Similarly to Coccorese (2009), all bank-specific factors that may have a potential effect on bank revenues but are not included in Eq. (2.5) are captured by including the bank fixed effects term (denoted by αi ). The H-statistic is defined as ω1 + ω2 + ω3 , according to Eq. (2.5). 4 Bikker
et al. (2012) indicate that a scaled revenue function leads to a significant upward bias and incorrectly measures the degree of competition; therefore, in our analysis, we employ the unscaled revenue equation to reduce the estimation bias.
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(3)
2 Quality of Bank Capital, Competition, and Risk-Taking
Capital structure measurement and control variables
To measure each bank’s capital structure, we follow Košak et al. (2015) and define Tier 1 ratio as the Tier 1 capital to total risk-weighted assets ratio, and Tier 2 ratio as the difference between the total capital ratio and Tier 1 ratio. We include several variables to control for bank-specific time-varying effects on bank risk-taking behavior. We define Customer deposit ratio as the ratio of total customer deposit to total assets as a proxy for bank funding, Commercial loan ratio as the ratio of corporate and commercial loans to gross loans to control for the bank asset structure, Liquidity asset ratio as a control of bank liquidity risk, Foreign dummy as an indicator of bank’s ownership, and also define Size, the logarithm of total assets, to control for bank size. We also include GDP growth, Inflation, and Interest rate at the country level to account for the impact of macroeconomic factors; the variable Market capitalization, which equals to the ratio of stock market capitalization to GDP, is also included to control for the stock market size in a given country. As our sample period covers the recent GFC and the European sovereign debt, we use the bank-crisis information for individual countries from a database compiled by Laeven and Valencia (2018) to define a dummy variable Crisis dummy, which equals one if the country experienced a financial crisis in a given year, and zero otherwise. (4)
Bank regulatory variables
According to Anginer et al. (2014b), bank supervision and regulation are important factors in determining banks’ risk-taking. We use the bank regulatory information provided in Barth et al. (2013) and include several bank regulatory variables to control for bank regulation changes. We first consider the effect of capital regulation and include the following variables: Overall capital stringency, measuring the amount of capital a bank must maintain, with higher values indicating greater stringency; Official supervisory power indicating whether the supervisory authorities have the authority to take specific actions to prevent and correct problems; Banking activities restriction, related to bank activity restrictions and measuring the degree to which banks are allowed to engage in securities, insurance, and real estate activities, with higher values indicating more restrictiveness; and, Explicit deposit insurance, a binary variable that equals one if the country has explicit deposit insurance in a given year, and zero otherwise. Table 2.1 summarizes the variable definitions and the data sources employed in the analysis.
2.4.3 Descriptive Statistics and Correlation In Table 2.1, we provide descriptive statistics for the variables included in our analysis. First, we present the results for the bank risk-taking variables. The average
2.4 Data and Variable Definition
17
Table 2.1 Variable definition and data sources Variables
Definitions
Data sources
Risk-taking variables NPL
Non-performing loans divided by Bankscope total loans
log Zscore
Calculated as the sum of ROA and equity-to-assets divided by the standard deviation of ROA of each bank over past 3 years
Own calculation
Standard deviation of ROA for each bank, computed over past 3 years
Bankscope
Standard deviation of ROE for each bank, computed over past 3 years
Bankscope
sd R OA OE sd R
Capital structure variables Tier 1 ratio
The ratio of Tier 1 capital to total Bankscope risk-weighted assets
Tier 2 ratio
Tier 2 capital ratio, which equals to total capital ratio minus Tier 1 capital ratio
Bankscope
Lerner index
Defined as the ratio of the difference between asset price and marginal cost to asset price
Own calculation
H-statistic
Defined and calculated based on the Panzar and Rosse (1987) model for each country at each year between 2001 and 2016
Own calculation
Customer deposit ratio
The ratio of total customer deposit to total assets
Bankscope
Commercial loan ratio
The ratio of commercial loans to gross loans
Bankscope
Liquidity assets ratio
The ratio of liquidity assets to total assets
Bankscope
Size
The logarithm of total asset in $ thousand
Bankscope
Foreign dummy
A dummy variable which equals Bankscope one if the bank is foreign owned, and zero otherwise
Competition measures
Bank-control variables
Country control variables (continued)
18
2 Quality of Bank Capital, Competition, and Risk-Taking
Table 2.1 (continued) Variables
Definitions
Data sources
GDP growth
Annual growth rate of GDP at market prices based on constant local currency
World Bank Development Indicator Database
Inflation
Inflation rate
World Bank Development Indicator Database
Interest rate
The real interest rate on loans
World Bank Development Indicator Database
Crisis dummy
A dummy variable which equals one if the country experienced a financial crisis on the given year, and zero otherwise
Laeven and Valencia (2018)
Market capitalization
Stock market capitalization to GDP
World Bank Development Indicator Database
Bank regulatory measures Overall capital stringency
A variable that captures both the Barth et al. (2013) overall capital stringency and the initial capital stringency based on answers to eight questions. It ranges from zero to eight, with a higher value indicating higher capital stringency
Official supervisory power
A variable indicates whether the supervisory authorities have the authority to take specific actions to prevent and correct problems. It ranges from zero to fourteen, with fourteen indicating the highest power of the supervisory authorities
Barth et al. (2013)
Banking activities restriction
A variable measures a bank’s ability to engage in securities, insurance, and real estate activities. The ranges from 0 to 12, and a higher score indicates more restrictions on banks to engage in such activities
Barth et al. (2013)
Explicit deposit insurance
A binary variable which equals one if the country have explicit deposit insurance on the given year, and zero otherwise
Barth et al. (2013)
This table reports the definitions and data sources of the variables included in our analysis
2.4 Data and Variable Definition
19
non-performing loan ratio (NPL) is 2.285, ranging from 0 to 21.02, with a standard deviation of 4.437; the average log Zscore is 3.882, ranging from −0.321 to 6.894, with a standard deviation of 1.264. As regards bank capital structure, the average values of Tier 1 capital per risk-weighted assets (Tier 1 ratio) and Tier 2 capital per risk-weighted assets (Tier 2 ratio) are 13.577% and 1.057%, respectively. Concerning the competition variables, the Lerner index has a mean of 0.177 and a standard deviation 0.421, H-statistic has a mean of 0.613 and ranges from 0.171 to 0.916. Then, we report some descriptive statistics of the bank-specific variables. The average the average Customer deposits ratio is 0.932, and the Commercial loan ratio has a mean of 0.199, and the liquidity assets take 8.2% of the total asset on average. The value of Size varies quite substantially, ranges from 4.615 to 21.843; this confirms the importance of the size effect in the banking system. In our sample, 2.9% of banks are foreign owned. We include five country-level variables, namely GDP growth, Inflation, Interest rate, Crisis dummy, and Market capitalization, to control for the changes in macroeconomic factors and stock market size. The average country in the sample has a market capitalization to GDP ratio of 25.49, a GDP growth of 1.85%, inflation of 2.55%, and an interest rate of 5.489%. The mean value of Crisis dummy is 0.375. Finally, we report descriptive statistics on the bank regulation variables. The average country in our sample has an overall capital stringency of 5.31, official supervisory power of 12.64, banking activities restriction of 8.33, and explicit deposit insurance of 0.591. The Pearson correlations among the variables are reported in Table 2.3. The correlation coefficients between NPL and the bank-level variables suggest that the bank would have a lower non-performing loan ratio if they have a higher Tier 1 ratio and operate in a less competitive market; a higher Tier 2 ratio incentivizes the bank to engage in higher risk-taking activities. The correlations between NPL and the country-level variables indicate that a higher GDP growth, larger stock market, and lower inflation and interest rate lead to lower amounts of non-performing loans. The alternative measure of bank risk-taking, log Zscore provides results similar to those of NPL (Table 2.2).
2.5 Research Design The empirical methodology is designed to examine the effects of capital structure and competition on bank risk-taking. The analysis is performed on the bank level and the model is Risk takingijt = α0 + α1 Tier1 ratioijt + α2 Tier2 ratioijt + α3 competitionijt + × bank controlsmijt + × country controlsnjt + × bank regulatorykjt + εit
(2.6)
20
2 Quality of Bank Capital, Competition, and Risk-Taking
Table 2.2 Summary statistics Variable
N
Min
Median
Max
NPL
48,636
Mean 2.285
STD 4.437
0.000
0.900
21.02
log Zscore sd R OA sd R OE
49,074
3.882
1.264
−0.321
3.994
6.894
49,128
0.483
3.942
0.000
0.196
30.684
49,002
5.533
39.903
0.006
2.060
45.119
Tier 1 ratio
49,287
13.577
5.757
5.180
12.260
147.5
Tier 2 ratio
49,287
1.057
2.132
−3.600
1.150
4.6
Lerner index
49,287
0.177
0.421
−0.623
0.127
35.083
H-statistic
47,807
0.613
0.528
0.171
0.669
0.916
Customer deposit ratio
49,287
0.932
0.114
0.000
0.967
1.000
Commercial loan ratio
49,287
0.199
0.247
0.000
0.142
33.675
Liquidity asset ratio
49,287
0.082
0.079
−0.051
0.058
0.977
Size
49,287
12.957
1.643
4.615
12.553
21.843
Foreign dummy
49,287
0.029
0.167
0
0
1
GDP growth
49,121
1.850
2.196
– 14.814
1.786
34.500
Inflation
49,121
2.548
2.943
– 27.633
1.994
103.823
Interest rate
46,619
5.489
3.930
0.500
4.675
55.383
Crisis dummy
49,287
0.375
0.484
Market capitalization
47,820
25.494
43.312
0
0
1
0.010
123.939
1254.465
Overall capital stringency
30,102
5.307
1.922
1
7
7
Official supervisory power
30,149
12.638
Banking activities restriction
29,842
8.329
1.249
5
13
16
1.134
3
9
Explicit deposit insurance
30,100
0.591
12
0.165
0
1
1
This table reports summary statistics of the variables. Our sample period is 2001–2016. All data are inflation-adjusted, and bank-specific variables are winsorized at the 1st and 99th percentile level to reduce the influence of outliers
where Risk takingijt indicates the risk-taking of bank i in country j and year t and is represented by the non-performing loan ratio (NPL) or the logarithm of the Z-score (log Zscore) as defined in Sect. 2.4.2. The competition in the banking system is measured by the Lerner index (Lerner index). The bank control variables controlsmijt include Customer deposit ratio, Commercial loan ratio, Liquidity asset ratio, Size, and Foreign dummy. The country control variables country controlsnjt are GDP growth, Inflation, Interest rate, Market capitalization, and Crisis dummy. Bank regulatorykjt represents the bank regulatory variables Overall capital stringency, Official supervisory power, Banking activities restriction, and Explicit deposit insurance. We are also concerned with whether competition changes the relationship between capital structure and bank risk-taking. We employ Eq. (2.7) below to examine how
(2)
−0.031*** −0.036*** 0.062*** −0.054*** −0.054*** −0.027***
0.198***
−0.009**
0.058***
−0.162*** 0.189***
−0.140*** 0.106***
Tier 1 ratio (5)
Tier 2 ratio (6)
Lerner index (7)
Customer deposit ratio (8)
0.092***
0.159***
Interest rate (15)
Crisis dummy (16)
0.044***
0.044***
0.028***
0.037***
0.009**
0.009**
−0.103***
0.132***
−0.011**
−0.087***
0.196*** 0.033***
−0.138***
−0.230*** 0.050***
0.114***
0.022*** −0.015***
0.058***
0.046***
−0.033*** −0.039*** −0.026***
0.046***
−0.021*** −0.045*** 0.023***
−0.097***
Market −0.195*** 0.083*** capitalization (17)
0.142***
−0.096*** 0.118***
Inflation (14)
GDP growth (13)
0.034***
−0.047***
0.128***
Foreign dummy (12)
0.034***
−0.048***
0.127***
Size (11)
0.032***
−0.115***
0.225***
Liquidity asset ratio (10)
0.037***
0.162***
Commercial loan ratio (9)
−0.073***
−0.0135*** 0.008*
−0.042*** 1
0.721***
−0.216***
0.135*** −0.006
1
(5)
−0.175*** 1
(4)
0.117***
(3)
log Zscore (2) sd R OA (3) OE (4) sd R
−0.362*** 1
NPL (1)
Table 2.3 Pearson correlations
1
(7)
0.022***
0.019***
0.113***
0.016***
0.083***
−0.058***
0.032***
−0.025*** 0.0480***
−0.060*** −0.106***
0.042***
0.092***
0.065***
0.084***
0.271***
0.079***
0.121***
−0.136*** 0.008*
0.101***
1
(6)
(9)
0.173***
0.079***
0.257***
0.256***
0.156***
0.222***
0.227***
1
(10)
(continued)
−0.142*** −0.128***
−0.073*** −0.065***
−0.429*** 0.354***
−0.307*** 0.318***
−0.103*** 0.158***
−0.277*** 0.232***
−0.402*** 0.258***
−0.285*** 0.381***
−0.346*** 1
1
(8)
2.5 Research Design 21
(4)
(5)
0.143***
0.126***
−0.068*** −0.065***
Inflation (14)
Interest rate (15)
Crisis dummy (16)
−0.347*** −0.203***
Official supervisory power (19)
Banking activities −0.244*** −0.282*** restriction (20)
0.005
Overall capital stringency (18)
−0.070***
Market −0.089*** −0.143*** capitalization (17)
0.235***
0.158***
0.067***
0.103***
GDP growth (13)
1
0.287***
0.462***
1
(14)
−0.004
1
(15)
0.0533***
(6)
(7)
1
(16)
(17)
−0.091*** −0.061***
−0.103*** −0.033***
−0.152*** −0.011**
−0.032*** −0.107***
−0.058*** −0.302*** −0.518***
0.319***
−0.143*** −0.421*** −0.0947*** 0.0980***
0.609***
(8)
(18)
0.126***
0.443***
0.373***
0.125***
0.0391***
−0.0204*** 0.464***
0.154***
−0.0280*** 1
−0.144*** −0.0521*** −0.178*** 1
−0.228*** −0.113*** −0.327***
0.143***
−0.493*** −0.161*** −0.156***
0.222***
0.383***
1
(13)
(11)
Foreign dummy (12)
(12)
−0.001
−0.026*** 0.039***
−0.041*** −0.051*** −0.042***
Banking activities −0.093*** 0.087*** restriction (20)
Explicit deposit insurance (21)
−0.042*** −0.037*** −0.022***
0.075***
−0.153*** 0.127***
0.0028
Official supervisory power (19)
(3) 0.0003
(2)
−0.078***
0.229***
Overall capital stringency (18)
NPL (1)
Table 2.3 (continued) (9)
(10)
0.112**
0.549***
1
(19)
(continued)
1
(20)
−0.244*** −0.249***
−0.275*** −0.263***
−0.31***
−0.058*** −0.018***
22 2 Quality of Bank Capital, Competition, and Risk-Taking
(2)
−0.289*** −0.075***
NPL (1)
(3)
(4)
(5)
(6)
−0.264*** −0.140*** −0.0824*** 0.0925***
(7) 0.138***
This table provides the information of correlation between variables. All variables are defined as in Table 2.1 ***, **, *indicate correlation between variables are significance 1%, 5%, and 10% respectively
Explicit deposit insurance (21)
Table 2.3 (continued) (8) 0.0621***
(9) 0.208***
(10) 0.041***
2.5 Research Design 23
24
2 Quality of Bank Capital, Competition, and Risk-Taking
bank competition affects bank risk-taking and whether different qualities of bank capital strengthen or mitigate risk-taking. Risk takingijt = α0 + α1 Tier 1 ratioijt + α2 Tier 2 ratioijt + α3 competitionijt + α4 Tier 1 ratioijt ∗ competitionijt + α5 Tier 2 ratioijt ∗ competitionijt + × controlsmijt + × controlsnjt + × bank regulatorykjt + εijt
(2.7)
The variables here are the same as defined in Eq. (2.6), except we include the interaction term between the bank capital variables and the Lerner index. Equations (2.6) and (2.7) are estimated based on the unbalanced panel regressions, as some banks exited the market through bankruptcy, liquidity, or through M & A. We also include different types of banks, according to the main activities. There is considerable heterogeneity of the sample banks in terms of size and activities. To control for the potential heterogeneity across banks, we employ two types of estimation. First, we follow the study of Williams (2016) and use the feasible generalized least square (FGLS) regression, to account for general heteroskedasticity and crosssectional correlation in the model errors. The FGLS estimator allow for heterogeneity across banks in terms of autoregressive characteristics due to the differences in bank activities, as well as difference in variance across the banks.5 The alternative is the dynamic generalized method of moments (GMM) estimator to account for potential endogeneity of our risk-taking variable. If lagged dependent variable is correlated with the panel-level effects, the estimator may become inconsistent. Our sample also has a short time dimension and large bank dimension. Following Naceur and Omran (2011) and Noman et al. (2018), we employ the twostep system GMM procedure of Arellano and Bond (1991).6 The Arellano–Bond estimator is particularly useful in obtaining unbiased and efficient estimates in short dynamic panels with lagged endogenous variables as an explanatory variable. We employ a cluster-robust estimator to account for potentially non-i.i.d. errors and to obtain consistent standard error estimates even in the presence of autocorrelation or heteroskedasticity within panels. The bank capital structure decision is endogenous and may depend on bank-specific variables and regulatory conditions in a country (see Byoun, 2008; Flannery & Rangan, 2008; Gropp & Heider, 2010; Memmel & Raupach, 2010). We use charter value and risk-weighted assets as instrument variables based on economic arguments of Berger et al. (2019) and Gropp et al. (2018).7 5 For
detail information of feasible generalized least square (FGLS) regression, see Wooldridge (2010) and Bikker et al. (2012). 6 See Baum et al. (2007) and Roodman (2009) for detail discussion of system GMM. 7 Gropp et al. (2018) confirm that banks increase their capital ratios by reducing their risk weighted assets, not by raising their levels of equity, and this finding is consistent with debt overhang. Berger et al. (2019) find that charter value is the most important factor in explaining target capital results, and also help explain the speed of capital adjustment results.
2.5 Research Design
25
We test for the relevance of these instruments or the endogeneity of bank capital ratios using the First Stage F-test, and we use the Hansen’s J test of overidentification to check their validity.
2.6 Empirical Analysis 2.6.1 Basic Analysis In this section, we investigate the impact of capital structure and competition on bank risk-taking, by using non-performing loan ratio (NPL) as a measure of bank risk-taking. The results for FGLS regressions (Columns (1)–(4)) and the GMM estimations (Columns (5)–(8)) are reported in Table 2.4. Columns (1) and (4) report the results including on bank-level variables, while controlling for both bank fixed effects and year fixed effects. Columns (2)–(4) and (6)–(8) include country-level control variables. In columns (2) and (6), we control for both country fixed effects and year fixed effects. To avoid the estimation bias introduced the potential correlation between country-level control variables and country fixed effects, we follow Anginer et al. (2018) and include the country-year fixed effects (Country × Year FE) to control for all time-varying country factors in Columns (3)–(4) and (7)–(8). For the GMM estimation, the results of First Stage F-test and Hansen’s J statistic show that the instrument variables are both relevant and valid. The results show that Tier 1 ratio is significant and negatively related to the non-performing loan ratio at the 1% level under two estimation methods, indicating that a higher Tier 1 ratio implies lower risk-taking and a higher stability of the banking system. However, Tier 2 ratio shows different results: it is positively and significantly related to the non-performing loan ratio at the 10% level or above, implying that a higher Tier 2 ratio increases bank risk-taking. These findings provide further empirical support for Ashcraft (2008), who asserts that increasing the overall capital adequacy ratio leads to a reduction in banks’ risk appetite, but increasing the proportion of Tier 2 capital in a given capital adequacy structure increases banks’ risk appetite. Concerning the effect of competition on bank risk-taking, we find that the Lerner index is significantly and negatively related to the non-performing loan ratio, indicating that banks with higher market power have fewer non-performing loans and lower risk. This finding is consistent with those of previous studies that focus on banks in different regions (e.gAgoraki et al., 2011; Noman et al., 2018). The results for the bank-specific variables show that banks with higher funding (Customer deposit ratio), and lower commercial loans (Commercial loan ratio) and liquidity assets (Liquidity assert ratio) have a lower non-performing loan ratio. We also account for the effect of bank size (Size) and ownership and find that larger banks and foreign banks tend to exhibit higher risk-taking.
Inflation
GDP growth
Foreign dummy
Size
Liquidity asset ratio
Commercial loan ratio
Customer deposit ratio
Lerner index
Tier 2 ratio
Tier 1 ratio
L.NPL
(−14.59)
(−29.03)
−0.0753*** (−20.35)
−0.0160 (−0.82) 0.0881***
(12.49)
(−0.25)
(9.76)
0.0611***
(25.83) 0.877***
(10.59) −0.0138
(1.83)
0.0899***
(69.35)
6.095***
(58.17)
1.367***
(−5.60)
−0.406***
(−101.50)
−1.225***
(39.93)
0.0621***
(−6.34)
−0.00467***
(3)
0.776***
0.0372***
(33.88)
(78.33)
0.00492*
3.035***
5.682***
(71.56)
−0.927***
−1.759***
(16.80)
(−61.26)
(−76.57)
2.743***
−0.759***
−0.956***
0.449***
(18.10)
(31.97)
(−46.21) 0.0357***
(−49.27)
0.0530***
−0.0421***
(2)
−0.0240***
(1)
0.250***
(−7.30)
−0.0366***
(8.35)
0.528***
(12.13)
0.0455***
(48.31)
4.850***
(57.25)
2.395***
(−2.88)
−0.134***
(−86.08)
−1.064***
(30.62)
0.0568***
(−46.80)
−0.0471***
(4)
(−3.09)
(1.11)
0.227
(2.00)
0.0431**
(3.65)
1.908***
(4.97)
0.966***
(−1.03)
−0.415
(−1.67)
−0.0945*
(3.55)
0.00546***
(8)
(−16.92) −0.0306
−0.124
−0.188***
(1.01)
0.162
(0.98)
0.0195
(4.97)
2.174***
(5.31)
1.342***
(0.15)
0.0376
(−3.46)
−0.172***
(−0.08)
(continued)
−0.00294
(−6.45)
−0.116***
(1.44)
0.363
(3.86)
0.138***
(6.10)
4.022***
(6.76)
2.885***
(−0.11)
−0.0517
(−2.15)
−0.0750**
(2.60)
(−4.95) 0.230***
(−3.62)
−0.245***
(32.02)
0.835***
−0.000756
−0.107***
(54.80)
0.954***
(7)
(0.02)
0.275
(0.06)
1822.5
(0.12)
7.430
(0.49)
14.09
(0.09)
24.60
(0.07)
24.06
(−0.12)
−5.828
(2.08)
0.0136**
(−0.06)
−0.476
(0.02)
−0.101***
(21.21)
(6) 0.137
0.898***
(5)
Table 2.4 Impact of capital structure and competition on bank risk-taking (non-performing loan ratio)
26 2 Quality of Bank Capital, Competition, and Risk-Taking
Yes
No
No
Year FE
Country FE
Country × Year FE
Yes
No
Yes
Yes
No
Yes
No
No
No
(33.03)
4.397***
(−33.29)
(−5.57)
(−0.28)
−0.00693***
−0.00381***
−0.0779
(80.99)
(26.84)
1.054***
(2.43)
(24.89)
0.212**
0.0750***
(2.84)
(11.11)
(4.25) 0.0553***
(3)
(2)
2.236***
(1)
Bank FE
Constant
Explicit deposit insurance
Banking activities restriction
Official supervisory power
Overall capital stringency
Market capitalization
Crisis dummy
Interest rate
Table 2.4 (continued) (4)
(6)
Yes
No
No
No
(16.72)
No
No
Yes
Yes
(3.15)
No
Yes
Yes
No
(0.05)
3.407***
Yes
No
No
No
(3.75)
Yes
No
No
No
(continued)
(5.21)
7.578***
(−2.10)
(−7.63) 3.710***
(−1.38) −0.528**
(−22.86)
(−2.50) −0.117
−0.631*** −0.631***
−0.135**
(−4.76)
(−2.77)
−0.00143***
(11.65)
0.848***
(5.90)
0.105***
−0.0957***
(−4.76)
−0.00110***
(16.87)
0.568***
(2.33)
0.0173**
(8) (−0.12)
(3.62)
3,442,862.3
(7) (−1.63)
0.182***
(0.11)
0.0141
(0.11)
4.549
(0.93)
0.511
(−0.05)
(85.37)
2.909***
(5)
0.721***
(−24.92)
−0.00522***
(33.42)
0.762***
(18.98)
0.0971***
(21.44)
2.6 Empirical Analysis 27
(6)
GMM
0.302
1.148
24.326***
41,403
(7)
GMM
0.163
2.114
15.881***
41,403
(8)
GMM
0.495
0.426
19.357***
23,010
The dependent variable is non-performing loan ratio (NPL). Our sample period is 2001–2016. Bank-level controls include Deposit ratio, Commercial loan ratio, Liquidity ratio, Size, and Foreign dummy. Country control variables include GDP growth, Inflation, Interest rate, Crisis dummy, and Market capitalization. Bank regulatory variables Overall capital stringency, Official supervisory power, Bank activities restriction, and Explicit Deposit insurance. All variables are defined in Table 2.1. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
GMM
Estimation method
FGLS
0.927 0.295 FGLS
354.27***
(5) 43,675
Hansen J statistic (p-value) FGLS
249.41***
(4) 27,006
Hansen J statistic
FGLS
338.12***
(3) 45,608 25.495***
236.74***
(2) 45,608
First Stage F-test
Wald Chi-square
(1)
48,636
Number of observations
Table 2.4 (continued)
28 2 Quality of Bank Capital, Competition, and Risk-Taking
2.6 Empirical Analysis
29
For the country-level variables, the results for GDP growth suggest that higher GDP growth corresponds to lower risk-taking and that the non-performing loan ratio is significantly higher during a financial crisis than during normal times. We also consider the impact of bank regulation on banks’ risk-taking behavior. When including the bank regulatory variables as control variables, we find that, if the regulators have lower stringency on capital requirements, higher official supervisory power and more restrictive on bank activities, and banking system has explicit deposit insurance, banks tend to have lower risk-taking activities. We also conduct the analysis using log Zscore as an alternative measure of bank risk-taking and the results are reported in Table 2.5. The findings in Table 2.5 are largely consistent with our main findings from using non-performing loan ratio as a measure of bank risk-taking: Tier 1 ratio is significantly and positively related to log Zscore and Tier 2 ratio is negatively and significantly related to log Zscore, confirming that banks with a higher Tier 1 ratio have lower probability of insolvency but higher Tier 2 ratio corresponds with higher risk-taking. The Lerner index is positively and significantly correlated to log Zscore, confirming that higher market power results in a lower probability of insolvency and risk, similarly to the findings of Leroy and Lucotte (2017) and Fu et al. (2014).
2.6.2 Impact of Competition on the Relationship Between Capital Structure and Risk-Taking A series of theoretical and empirical studies demonstrate that competition is an important factor affecting banks’ capital holding; based on the theoretical work of Allen et al. (2011), these studies find that competition motivates banks to hold higher levels of capital because this indicates their commitment to monitoring and attracts creditworthy borrowers. Schaeck and Cihák (2012) provide empirical support for the findings of Allen et al. (2011) and confirm that competition incentivizes banks to maintain higher capital ratios. On the other hand, Chen (2016) shows that credit market competition reduces banks’ incentive to hold capital. Although the findings on the relationship between competition and capital ratio are mixed, all these results confirm the impact of competition on bank capital holding. In this section, we are concerned with whether the relationship between Tier 1 ratio and Tier 2 ratio and bank risk-taking shows a different pattern under different competitive conditions. We include the interaction terms of Tier 1 ratio and Tier 2 ratio with the Lerner index in the analysis and the results are reported in Table 2.6.8 The results show that the interaction term between Tier 1 ratio and the Lerner index is statistically significant and positively related to the non-performing loan ratio (as shown in Columns (1)–(4)) and negatively correlated to log Zscore (as reported in Columns (5)–(8)). This indicates that, if a bank has a higher market 8 For
brevity, we only report the results of FGLS estimations in Tables 2.6 and 2.7. The results for the GMM estimations are consistent with those findings and are available upon request.
Inflation
GDP growth
Foreign dummy
Size
Liquidity asset ratio
Commercial loan ratio
Customer deposit ratio
Lerner index
Tier 2 ratio
Tier 1 ratio
L. log Zscore
−0.0174***
−0.0351***
0.000908
(25.60)
(18.57)
0.0415***
0.0355***
(−2.88)
−0.0654***
(−18.79)
−0.0408***
(−53.94)
−2.311***
(−23.47)
(1.50)
(−1.18)
−0.0289
(−54.43)
−1.797***
(−26.56)
−0.478***
(13.81)
0.537***
(70.10)
0.490***
(−1.87)
−0.00227*
(98.68)
0.0558***
(4)
0.0106
(0.35)
(−2.52)
(−4.60)
(−3.83)
−0.00840***
−0.0212*** 0.0101
(−70.78)
(−81.82)
(−14.07)
−1.922***
−1.795***
−0.0433**
−0.00780***
(−20.69)
(−2.65)
(4.25) −0.465***
(10.06) −0.478***
(38.32)
−0.0273***
0.140***
(50.62)
0.393***
(−2.41)
0.369***
(49.08)
(73.01)
0.846***
0.390***
0.417***
(−2.72)
(−1.71)
−0.000489**
(86.55)
(84.27) −0.000860***
(86.89)
0.0542***
(3)
0.0521***
(2)
−0.000706*
0.0434***
(1)
Table 2.5 Impact of capital structure and competition on bank risk-taking (log Zscore)
(3.70)
(−0.37)
−0.0137
(−2.66)
−0.0206***
(−6.54)
−0.876***
(−2.37)
−0.119**
(6.16)
0.447***
(3.15)
0.0555***
(−2.09)
−0.000275**
(7)
0.0708
−0.0177**
(6.02)
0.0225***
−0.247 (−0.99)
(0.38)
0.0200
(1.17)
0.00971
(−4.21)
−0.687***
(−5.19)
−0.485***
(0.24)
61.65
(−2.89)
−1.254***
(−4.25)
−10.94***
(−1.28)
−2.485
(−0.91)
−0.0742
−1.206 (−0.42)
(2.13)
0.0329**
(−0.07)
−0.000241
(5.29)
0.0590***
(70.94)
0.748***
(0.77)
0.0767
(−1.88)
−0.00451*
(0.44)
0.00852
(12.20)
0.0444***
(71.23)
(6) 0.565***
0.748***
(5)
(8)
(continued)
0.00348
(6.07)
0.0240***
(−1.10)
−0.0459
(−3.39)
−0.0291***
(−6.09)
−0.736***
(−3.68)
−0.241***
(4.33)
0.353***
(2.64)
0.0945***
(−2.47)
−0.0106**
(2.66)
0.0194***
(48.06)
0.786***
30 2 Quality of Bank Capital, Competition, and Risk-Taking
Yes
No
No
Year FE
Country FE
Country × Year FE No
Yes
Yes
No
(22.49)
Yes
3.160***
(74.21)
Yes
No
No
No
(67.82)
3.974***
(4.46)
(−7.69) (3.17)
(−87.45)
−0.261*** 0.000294***
−0.524***
(0.40)
0.00120***
(−42.88)
0.00226
(6)
(7)
Yes
No
No
No
(17.21)
1.567***
No
No
Yes
Yes
(−0.75)
No
Yes
Yes
No
(−0.18)
Yes
No
No
No
(2.52)
(continued)
Yes
No
No
No
(−1.54)
−0.377
(0.83)
(5.55)
(1.63) 0.0494
0.127***
(5.88)
(−0.34) 0.0262
(4.54) 0.0362***
−0.00624
0.0303***
0.809**
(0.36)
0.0000541
(−2.80)
−0.0502***
(−1.32)
−0.00438
(−0.51)
−14.80
(8) (0.68)
(−2.94)
(3.93)
0.000836***
−0.00526 (−0.73)
(−8.96)
−0.109***
(−14.66)
−0.0433***
(−2.54)
(−0.16)
−0.0957
(−0.91)
−0.0732
(0.82)
−0.00344
−0.216
(5)
−0.00721***
(4.99)
0.000300***
(−51.02)
−0.498***
(−16.88)
−0.0270***
−0.0516***
(0.20)
(4) (−6.40)
(3) (−14.32)
(2)
2.591***
(1)
Bank FE
Constant
Explicit deposit insurance
Banking activities restriction
Official supervisory power
Overall capital stringency
Market capitalization
Crisis dummy
Interest rate
Table 2.5 (continued)
2.6 Empirical Analysis 31
(6)
GMM
0.121
4.824
56.05***
41,686
(7)
GMM
0.158
3.974
44.82***
41,686
(8)
GMM
0.214
2.879
28.45***
23,193
The dependent variable is log Zscore. Our sample period is 2001–2016. Bank-level controls include Deposit ratio, Commercial loan ratio, Liquidity ratio, Size, and Foreign dummy. Country control variables include GDP growth, Inflation, Interest rate, Crisis dummy, and Market capitalization. Bank regulatory variables Overall capital stringency, Official supervisory power, Bank activities restriction, and Explicit Deposit insurance. All variables are defined in Table 2.1. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
GMM
Estimation method
FGLS
3.593 0.187 FGLS
318.93***
(5) 44,280
Hansen J statistic (p-value) FGLS
415.90***
(4) 27,142
Hansen J statistic
FGLS
368.11***
(3) 45,798 38.51***
436.12***
(2) 45,798
First Stage F-test
Wald Chi-square
(1)
49,074
Number of observations
Table 2.5 (continued)
32 2 Quality of Bank Capital, Competition, and Risk-Taking
Foreign dummy
Size
Liquidity asset ratio
Commercial loan ratio
Customer deposit ratio
Lerner index
Tier 2 ratio * Lerner index
Tier 2 ratio
Tier 1 ratio * Lerner index
Tier 1 ratio
(−15.07)
(−18.77)
(−0.71)
(10.77)
(12.32)
(19.47) 0.860***
(12.59) −0.0387
(3.18)
0.0657***
(150.41)
8.322***
(39.62)
1.137***
(−3.76)
−0.287***
(−57.02)
−1.892***
(1.52)
0.104
(22.57)
0.0445***
(34.45)
0.0687***
0.859***
0.0453***
(35.10)
(69.01)
0.00896***
3.130***
5.780***
(69.38)
−0.860***
−1.237***
(13.15)
(−53.01)
(−61.36)
2.671***
−1.653***
−1.431***
0.446***
(1.25)
(1.50)
(18.95) 0.0720
(28.32)
0.0789
0.0411***
(33.29)
(39.79)
0.0598***
0.0621***
0.0427***
(−14.75)
(6.67)
0.408***
(18.57)
0.0655***
(58.74)
5.524***
(59.32)
2.636***
(−2.46)
−0.121**
(−50.82)
−1.988***
(1.00)
0.00645
(19.38)
0.0442***
(26.60)
0.0682***
(−47.59)
(88.47)
0.0601***
(9.46)
0.357***
(50.22)
0.817***
(0.96)
(−1.08)
(−64.69)
−1.783***
(28.43)
0.475***
(5.56)
0.171***
(43.65)
0.755***
(0.83)
0.0151
(−64.48)
−2.358***
(26.52)
0.534***
(15.29)
0.575***
(53.72)
1.044***
(0.47)
0.00164
(3.08)
0.00376***
(−29.17)
−0.0399***
(91.95)
0.0680***
(8)
(−1.89)
−0.0338*
(−14.86)
(0.35)
0.0101
(−4.58)
(−1.74)
−0.0423*
(−2.87)
(continued)
(−3.43)
−0.0788***
(−28.29)
−0.0249*** −0.00981*** −0.00507*** −0.0411***
(−65.10)
−1.993***
−1.928*** (−84.56)
(21.68)
(−4.23)
−0.0489*** 0.486***
(38.84)
0.863***
(46.70)
0.794***
(−0.42)
(1.68) 0.00957
(−2.16)
−0.00119
−0.00250** 0.00189* −0.0190
(−23.05)
−0.0279***
(98.31)
0.0622***
(7)
(−27.31)
(−25.72)
−0.0278*** −0.0308***
(82.88)
0.0511***
(−59.16)
(−50.75)
−0.0628***
−0.0574***
−0.0266***
−0.0161***
(5)
(6)
Panel B: log Zscore (4)
(1)
(3)
(2)
Panel A: NPL
Table 2.6 Impact of competition on the relationship between capital structure and bank risk-taking
2.6 Empirical Analysis 33
Constant
Explicit deposit insurance
Banking activities restriction
Official supervisory power
Overall capital stringency
Market capitalization
Crisis dummy
Interest rate
Inflation
GDP growth
Table 2.6 (continued)
1.700***
0.543**
4.048***
2.432***
3.559***
(5.96) (continued)
1.343***
(−7.23) 4.312***
(7.02) 0.141***
0.0410***
−0.712*** −0.563***
(3.46)
(−2.87) (−27.30)
(−4.35) 0.0236***
−0.0103***
(4.84)
0.000309***
(−57.58)
(85.01)
3.050***
(−1.14)
−0.0000765
(−103.44)
(−16.92) −0.495***
−0.0554***
0.710***
(−31.52)
(3.28)
(−45.48)
0.00128***
(−6.73)
(−45.27) −0.542***
(0.33)
(−5.60) −0.0257***
−0.0515***
−0.0153***
−0.0387*** (−19.24)
(39.87)
0.0422***
(8)
(23.02)
0.0358***
(7)
−0.231***
0.00189
(0.40)
0.00188
(1.36)
0.00994
(−6.21)
(33.46)
0.845***
(16.46)
0.0815***
(22.13)
0.247***
(−9.40)
−0.0476***
−0.00431*** −0.00786*** −0.00633***
(83.80)
(0.65)
(27.25) 1.159***
(2.72) 0.0540
0.0795***
(16.52)
0.0520***
0.118***
(5.89)
(−22.33)
(−4.41) 0.0835***
−0.0761***
−0.0792***
(6)
(5)
(4)
Panel B: log Zscore (3)
(1)
(2)
Panel A: NPL
34 2 Quality of Bank Capital, Competition, and Risk-Taking
No
48,636
481.39***
FGLS
Country FE
Country × Year FE
Number of Observations
Wald Chi-square
Estimation method
FGLS
302.78***
45,608
No
Yes
Yes
No
FGLS
549.62***
45,608
Yes
No
FGLS
417.81***
27,006
Yes
No
No
No
(20.95)
FGLS
424.4***
49,074
No
No
Yes
Yes
(64.03)
FGLS
390.41***
45,798
No
Yes
Yes
No
(21.13)
FGLS
347.31***
45,798
Yes
No
No
No
(60.92)
(7)
FGLS
421.07***
27,142
Yes
No
No
No
(16.21)
(8)
The dependent variables are NPL and log Zscore in Panel A and Panel B, respectively. Our sample period is 2001–2016. Bank-level controls include Deposit ratio, Commercial loan ratio, Liquidity ratio, Size, and Foreign dummy. Country control variables include GDP growth, Inflation, Interest rate, Crisis dummy, and Market capitalization. Bank regulatory variables Overall capital stringency, Official supervisory power, Bank activities restriction, and Explicit Deposit insurance. All variables are defined in Table 2.1. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
Yes
No
Year FE
No
(29.31)
(1.98) No
(19.22)
Yes
(5)
(6)
Panel B: log Zscore (4)
(1)
(3)
(2)
Panel A: NPL
Bank FE
Table 2.6 (continued)
2.6 Empirical Analysis 35
36
2 Quality of Bank Capital, Competition, and Risk-Taking
power, the negative relationship between the Tier 1 ratio and risk-taking becomes less pronounced. In other words, in a more competitive market, the effect of the Tier 1 ratio in reducing bank risk will be stronger. The interaction term between Tier 2 ratio and Lerner index is insignificant under different specifications. Based on Allen et al. (2011) and Schaeck and Cihák (2012), one potential explanation is that the competition in the banking system drives compels to hold a higher level and higher quality of bank capital, which in turn reduces bank risk.
2.6.3 Impact of the Financial Crisis on the Relationship Between Capital Structure and Risk-Taking The results of our basic analysis confirm that Tier 1 ratio and Tier 2 ratio exhibit different effects on bank risk-taking. After the GFC, the results of studies on the causes of the GFC strengthen the importance of bank capital and urge for enhanced bank capital regulation (see Anginer et al., 2018; Bui et al., 2017). We are interested in whether Tier 1 ratio and Tier 2 ratio function differently during the financial crisis. In this section, we use financial crisis information on individual countries obtained from Laeven and Valencia (2018) and include the interaction terms of Tier 1 ratio and Tier 2 ratio with Crisis dummy as additional explanatory variables. Crisis dummy is a binary variable that equals one if, in a given year, a country is experiencing a systemic crisis, and zero otherwise. Table 2.7 presents the results of the analysis with these bank crisis interactions. The coefficient of the interaction variable with Tier 1 ratio is significantly and negatively related to the non-performing loan ratio (Columns (1)–(4)) and significantly and positively related to log Zscore (Columns (5)–(8)), suggesting that the effect of the Tier 1 ratio in reducing bank risk-taking is more pronounced during a financial crisis. We also find a positive (negative) relationship between the interaction term with Tier 2 ratio and the non-performing loan ratio (log Zscore), and the coefficient of the interaction term is significant in most specifications (it is only insignificant for the non-performing loan ratio in column (3)), confirming that the positive relationship of Tier 2 ratio in increasing bank risk-taking also become pronounced during financial crisis. Our findings confirm that the negative relationship for Tier 1 ratio and positive relationship for Tier 2 ratio become more pronounced during a financial crisis.
Foreign dummy
Size
Liquidity asset ratio
Commercial loan ratio
Customer deposit ratio
Lerner index
Tier 2 ratio * Crisis dummy
Tier 2 ratio
Tier 1 ratio * Crisis dummy
Tier 1 ratio
(−16.35)
(−26.63)
(−1.40)
(2.56)
(13.36)
(20.83) 0.943***
(22.73)
−0.0743
(1.86)
0.0840***
(73.03)
6.182***
(31.78)
1.067***
(−5.30)
−0.367***
(−81.78)
−0.933***
(1.37)
0.00735
(2.58)
0.00614***
(−55.41)
−0.100***
(−73.26)
0.213**
0.0469***
(36.95)
(80.82)
0.00492*
3.093***
6.306***
(75.13)
−0.808***
−1.555***
(35.99)
(−68.22)
(−92.49)
2.606***
−0.770***
−1.029***
0.856***
(14.87)
(15.83)
(10.90)
0.0412***
0.0571***
(8.91)
(−49.63)
(−49.43)
0.0197***
−0.0901***
−0.0779***
0.0144***
(−13.24)
(−8.66)
(10.78)
0.574***
(14.69)
0.0558***
(45.11)
4.496***
(85.07)
2.963***
(−0.72)
−0.0353
(−107.39)
−1.070***
(2.05)
0.0131**
(16.69)
0.0356***
(−40.83)
−0.116***
(−12.08)
(−3.77)
−0.0482***
(0.31)
0.00943
(−5.53)
−0.0117***
−0.0277*** (−14.75)
(−51.34)
−1.822***
−1.714*** (−59.54)
(−31.36)
(−1.53)
(10.75) −0.521***
(49.24)
0.382***
(47.90)
0.370***
(−4.63)
−0.0179
0.838***
(59.34)
0.402***
(−13.14)
(5.00) −0.00982***
(9.80)
0.00763***
(33.93)
0.0435***
(52.87)
0.0386***
(6)
−0.0293***
0.0127***
(27.29)
0.0181***
(70.01)
0.0368***
−0.0118***
−0.00816***
−0.0125***
(5)
−0.0513***
Panel B: log Zscore (4)
(1)
(3)
(2)
Panel A: NPL
Table 2.7 Impact of capital structure on bank risk-taking during the financial crisis
(−2.18)
−0.0485**
(0.90)
0.00137
(−48.95)
−1.621***
(−35.04)
−0.490***
(7.47)
0.230***
(51.54)
0.353***
(−2.53)
−0.00641**
(2.72)
0.00355***
(36.38)
0.0453***
(64.02)
0.0398***
(7)
(continued)
(−2.05)
−0.0467**
(−15.62)
−0.0344***
(−138.05)
−2.293***
(−28.78)
−0.516***
(15.09)
0.560***
(137.58)
0.469***
(−2.93)
−0.00668***
(2.83)
0.00292***
(30.69)
0.0464***
(51.63)
0.0384***
(8)
2.6 Empirical Analysis 37
Constant
Explicit deposit insurance
Banking activities restriction
Official supervisory power
Overall capital stringency
Market capitalization
Crisis dummy
Interest rate
Inflation
GDP growth
1.532***
(−21.81)
(−21.24)
(−5.08)
3.959***
−0.00428***
−0.00360***
−0.316
(73.43)
(18.05)
2.578***
(7.02) (continued)
1.880***
(−7.11) 3.797***
(8.61) 0.149***
0.0422***
−0.698*** −0.509***
(3.25)
(−3.79) (−25.14)
(0.73) 0.0224***
−0.0696***
3.819***
(4.06)
0.000252***
(−48.76)
(100.97)
3.118***
(−1.58)
−0.000105
(−65.48)
−1.138***
(−15.93)
−0.0241***
0.00130
(3.06)
0.00119***
(−22.06)
(−45.61) −1.169***
(0.51)
−0.0485***
(−9.49)
−0.0190***
−0.0399*** (−17.35)
(17.12)
0.0333***
(8)
(21.58)
0.0359***
(7)
−0.868***
0.00274
(0.04)
0.000193
(1.20)
0.00878
(6)
0.740***
(−27.05)
−0.00575***
(55.22)
2.445***
(24.28)
(−25.24)
(3.68) 2.272***
0.106***
−0.0756***
0.0662***
1.539***
(19.58)
(7.75)
(4.23)
0.213***
0.0627***
0.0598***
(−17.33)
−0.0839***
−0.0899***
(1.15)
(5)
0.0222
Panel B: log Zscore (4)
(3)
(1)
(2)
Panel A: NPL
Table 2.7 (continued)
38 2 Quality of Bank Capital, Competition, and Risk-Taking
No
48,636
591.09***
FGLS
Country FE
Country × Year FE
Number of observations
Wald Chi-square
Estimation method
Yes
FGLS
501.9***
45,608
No
Yes
FGLS
426.2***
45,608
Yes
No
No
FGLS
399.6***
27,006
Yes
No
No
No
(19.17)
FGLS
340.2***
49,074
No
No
Yes
Yes
(71.85)
FGLS
468.3***
45,798
No
Yes
Yes
No
(23.38)
(6)
FGLS
434.57***
45,798
Yes
No
No
No
(70.50)
(7)
FGLS
326.22***
27,142
Yes
No
No
No
(19.87)
(8)
The dependent variables are NPL and log Zscore in Panel A and Panel B, respectively. Our sample period is 2001–2016. Bank-level controls include Deposit ratio, Commercial loan ratio, Liquidity ratio, Size, and Foreign dummy. Country control variables include GDP growth, Inflation, Interest rate, Crisis dummy, and Market capitalization. Bank regulatory variables Overall capital stringency, Official supervisory power, Bank activities restriction, and Explicit Deposit insurance. All variables are defined in Table 2.1. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
Yes
No
Year FE
No
(29.69)
(−1.29)
No
(18.90)
Yes
(5)
Bank FE
Panel B: log Zscore (4)
(1)
(3)
(2)
Panel A: NPL
Table 2.7 (continued)
2.6 Empirical Analysis 39
40
2 Quality of Bank Capital, Competition, and Risk-Taking
2.6.4 Robustness Checks To check whether our findings are consistent, we also perform robustness checks based alternative measures of our main variables and also different subsamples.9 (1)
Alternative measures of competition and bank risk
First, we consider whether our results are robust across different measures of competition, and employ two other alternative measures of bank competition: (1) the Hstatistics based on the Panzar-Rosse model as defined in Sect. 2.4.2 and (2) the efficiency-adjusted Lerner index according to Koetter et al. (2012) (see also Fu et al., 2014). The results when using H-statistic (Columns (1)–(3) in Table 2.8) and efficiency-adjusted Lerner index (Columns (4)–(6) in Table 2.9) are consistent with our main findings.10 Second, the bank risk measure Z-score, which indicates the insolvency risk of the banks, is constructed based on ROA and equity to total assets ratio, which may highly correlated to Tier 1 ratio and Tier 2 ratio, and not really a good measure of bank risky in our analysis. To avoid the bias introduced by thepotential correlation between Z-score and capital ratio variables, we use sd ROA and sd ROE as alternative measures of bank risk. We find a negative (positive) relationship between Tier 1 ratio (Tier 2 ratio) and sd ROA (sd ROE ), and this relationship becomes more pronounced during a financial crisis and in a more competitive market, which are consistent with our main findings. (2)
Robustness check on subsamples
In this section, we perform our analysis based on different subsamples. First, we examine the relationship when considering only commercial banks. The results for commercial banks (Columns (1)–(3)) show that only Tier 1 ratio (Tier 2 ratio) has a negative (positive) impact on bank risk-taking and, during the financial crisis, a higher Tier 1 ratio is related with lower risk. Second, we perform our analysis based on subsamples of banks from different countries. As in our entire sample, 9661 out of 48,636 bank-year observations are from the U.S. banking system (accounting for 19.9% of the sample). We perform our analysis by excluding the observations from the United States and check whether our findings are consistent (Columns (4)–(6)). We also report the results including only countries with more than 200 observations (Columns (7)–(9)). Finally, as our sample period covers the Basel I, Basel II, and Basel III regulatory sub-periods, and the implementation of Basel regulatory varies by country. Thus, the definitions of the Tier 1 and Tier 2 capital ratios in our sample were changing 9 For brevity, we only report the results under FGLS, the results for the GMM estimations are largely
similar and are available upon request. bank-level control variables, country control variables, and bank regulatory variables are included in the regression but not report for brevity. 10 The
Tier 1 ratio * Lerner index
Lerner index
Tier 2 ratio * Efficiency−adjusted Lerner index
Tier 1 ratio * Efficiency−adjusted Lerner index
Efficiency-adjusted Lerner index
Tier 2 ratio * Crisis dummy
Tier 1 ratio * Crisis dummy
Tier 2 ratio * H−statistic
Tier 1 ratio * H−statistic
(1.61)
0.0550
(−1.85)
−0.00142*
(2.77)
(9.16)
(−5.02)
0.109***
(13.29)
H−statistic
0.242***
−0.00580***
−0.0250***
0.0269***
Tier 2 ratio
(−27.70)
(−34.77)
(−55.69)
NPL
−0.0337***
(−1.94)
(−1.42)
(1.01)
−0.979
(1.71)
0.0658*
−2.584* (2.61)
1.005***
(21.81)
(17.80) −0.509
0.0952***
0.0792***
(−34.58)
(−10.45)
−0.0213***
(−26.41)
−0.0158***
NPL
(6)
−0.0490***
(3.82)
0.00772***
(−71.25)
−0.0355***
NPL
(5)
(−34.05)
(18.19)
0.0347***
(−55.90)
−0.0299***
NPL
(4)
−0.0477***
(9.85)
0.260***
(−2.93)
−0.0175***
NPL
−0.0376***
NPL
(3)
Tier 1 ratio
(2)
Variables
(1)
Table 2.8 Robustness checks: alternative measures of competition and bank risk
(−55.33) 0.0141***
−0.361*** (−87.28)
(15.95)
0.00606***
(−64.03)
−0.0128***
(8) sd ROA
−0.171***
(11.29)
0.00460***
(−44.84)
−0.00736***
(7) sd ROA
(−76.41)
−0.155***
(1.42)
0.00113
(−39.91)
−0.0177***
(1.47)
0.000375
(−3.24)
−0.000651***
(9) sd ROA
(−65.47)
−1.594***
(23.32)
0.105***
(−96.15)
−0.271***
(10) sd ROE
0.265***
(−61.08)
−5.594***
(29.97)
0.127***
(−115.20)
−0.326***
(11) sd ROE
(continued)
(−86.55)
−1.823***
(43.21)
0.299***
(−45.87)
−0.241***
(−5.29)
−0.0257***
(−79.97)
−0.147***
(12) sd ROE
2.6 Empirical Analysis 41
27,006
383.5***
FGLS
Number of observations
Wald Chi-square
Estimation method
FGLS
440.8***
27,006
Yes
FGLS
264.6***
27,006
Yes
Yes
(7.31)
2.321***
(3)
FGLS
346.6***
27,006
Yes
Yes
(14.10)
3.749***
(4)
FGLS
416.27***
27,006
Yes
Yes
(−11.55)
−40.37***
(5)
FGLS
327.96***
27,006
Yes
Yes
(16.41)
4.348***
(6)
FGLS
147.6***
27,006
Yes
Yes
(35.32)
1.178***
(7)
FGLS
168.9***
27,006
Yes
Yes
(33.03)
FGLS
226.2***
27,006
Yes
Yes
(35.09)
1.237***
FGLS
353.33***
26,973
Yes
Yes
(41.02)
18.38***
FGLS
365.93***
26,973
Yes
Yes
(52.71)
18.45***
(−1.28)
1.125***
−0.138
(−1.21)
(11)
−0.00784
(10) (49.85)
(9)
(34.83)
(8)
FGLS
222.6***
26,973
Yes
Yes
(36.52)
17.30***
(12)
The dependent variable is Non-performing loan ratio. Our sample period is 2001–2016. Bank-level controls include Deposit ratio, Commercial loan ratio, Liquidity ratio, Size, and Foreign dummy. Country control variables include GDP growth, Inflation, Interest rate, Crisis dummy, and Market capitalization. Bank regulatory variables Overall capital stringency, Official supervisory power, Bank activities restriction, and Explicit Deposit insurance. The bank-level control variables, country control variables, and bank regulatory variables are included in the regression but not report for brevity. All variables are defined in Table 2.1. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
Yes
Country × Year FE
Yes
(−6.14)
(4.19)
Yes
−30.37***
(2)
1.361***
Bank and country variables
Constant
Tier 2 ratio * Lerner index
(1)
Table 2.8 (continued)
42 2 Quality of Bank Capital, Competition, and Risk-Taking
(8)
(9)
(10)
(11)
(12)
−1.301***
−1.093***
−1.377*** (−5.73)
Yes
24,616
Country × Year Yes FE
24,616
225.8***
Number of observations
Wald Chi-square
209.7***
Yes
319.8***
24,616
Yes
363.6***
21,642
Yes
332.41***
21,642
Yes
319.9***
21,642
Yes
462.15***
25,911
Yes
382.38***
25,911
Yes
Yes
(0.74) Yes
0.0733***
−2.095*** (−5.28)
0.00469
278.4***
25,911
Yes
Yes
(12.59)
(−42.98)
Yes
Yes
0.309***
(−5.22)
169.4***
5124
Yes
205.34***
5124
Yes
Yes
(−1.74)
−0.186*
(−1.31)
(9.36)
0.624***
(−8.14)
(continued)
361.3***
5124
Yes
Yes
(−6.03)
−4.924*** −1.406***
(10.46)
0.759***
(5.03)
(0.80)
Yes
(−6.03)
−1.406***
(9.36)
0.624***
(−8.14)
−0.0327 −0.123***
(−74.32)
−1.042***
(10.40)
0.0244***
(−18.02)
(30.63)
0.0815***
(−51.69)
−2.172***
(17.77)
0.0382***
(−59.21)
0.0550
Yes
(−68.39)
−0.956***
(14.37)
0.0338***
(−50.25)
(−41.37)
(1.58)
(1.17)
(−8.18)
−0.927***
(2.19)
0.0325**
(−3.21)
−0.113***
0.0927
0.0533
Yes
(−10.38)
(4.23)
(0.45)
(−92.36)
(2.12)
0.0421***
(−5.01)
(46.23)
−2.652***
(−88.02)
−1.207***
(−88.31)
(19.96)
0.00305**
(−4.13)
0.00369
(12.23)
(12.40)
0.0326***
(−18.12)
0.0950***
0.0308***
0.0316***
(−70.34)
(−11.82)
(7)
(−38.67)
(6)
Subperiod after Implementation of Basel III −0.307*** −0.297***
(5)
(4)
(3)
Countries with more than 200 observations
−0.0496*** −0.0710*** −0.0201*** −0.0336*** −0.0442*** −0.0261*** −0.0576*** −0.0778*** −0.0217*** −0.297***
(2)
(1)
Yes
Bank and country variables
Tier 2 ratio * Crisis dummy
Tier 1 ratio * Crisis dummy
Tier 2 ratio * Lerner index
Tier 1 ratio * Lerner index
Lerner index
Tier 2 ratio
Tier 1 ratio
Countries without US
Commercial Banks
Table 2.9 Robustness checks: subsamples
2.6 Empirical Analysis 43
FGLS
(2)
(3)
FGLS
FGLS
FGLS
(5)
(4)
FGLS
(1) FGLS
(6) FGLS
(7) FGLS
(8)
Countries with more than 200 observations
FGLS
(9) FGLS
(10) FGLS
(11)
FGLS
(12)
Subperiod after Implementation of Basel III
The dependent variable is Non-performing loan ratio (NPL). Our sample period is 2001–2016. Bank-level controls include Customer deposit ratio, Commercial loan ratio, Liquidity asset ratio, Size, and Foreign dummy. Country control variables include GDP growth, Inflation, Interest rate, Crisis dummy, and Market capitalization. Bank regulatory variables Overall capital stringency, Official supervisory power, Bank activities restriction, and Explicit Deposit insurance. The bank-level control variables, country control variables, and bank regulatory variables are included in the regression but not report for brevity. All variables are defined in Table 2.1. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
Estimation method
Countries without US
Commercial Banks
Table 2.9 (continued)
44 2 Quality of Bank Capital, Competition, and Risk-Taking
2.6 Empirical Analysis
45
substantially between 2001 and 2016. Based on the implementation timeline of Basel III for different countries, we focus our analysis on the most recent subperiod and perform our analysis on the countries that complete the implementation of Basel III regulatory.11,12 The results show that our main findings are consistent under these alternative sample selection criteria’s.
2.7 Conclusion In this analysis, we examine the relationship between capital structure, competition, and bank risk-taking using cross-country data from 118 countries in the period between 2001 and 2016. We consider the difference between Tier 1 and Tier 2 capital quality of bank capital. The results indicate that banks with a higher Tier 1 ratio and a lower Tier 2 ratio are lower risk-takers. A bank with higher market power in its banking system tends to reduce its risk-taking activities. The results also confirm that banks with more customer deposit and higher quality of bank assets are exposed to lower risk. During a financial crisis, banks exhibit a higher level of risk-taking than during normal times. Our findings also highlight a sharp contrast between the impact of different types of bank capital quality during the financial crisis and in different competitive conditions. During the financial crisis, Tier 1 capital acted as a stable funding source and reduced the bank risk, but the evidence on Tier 2 capital shows that a higher Tier 2 ratio results in a higher level of risk and increases bank instability. In a banking system, competition compels banks to hold more capital, which in turn reduces bank risk, but only in the case of Tier 1 capital. Our findings provide some policy implications for bank regulation as follows: The negative (positive) relationship between Tier 1 ratio (Tier 2 ratio) and bank risk-taking confirms the importance of the quality of bank capital in the banking system—more enhanced regulation of high-quality capital should be implemented to reduce bank risk-taking behavior and enhance effective risk management, and this is more important during a financial crisis. Although our results suggest that higher market power reduces bank risk-taking, increases in market power make the banking system less contestable, which makes the banking system less competitive. This finding emphasizes the importance of a regulatory framework that achieves a balance between reducing bank risk-taking and avoiding potentially excessive competition.
11 The
timeline for the adoption of Basel III regulatory framework by Basel Committee members can be find in the progress report progress report on adoption of the Basel regulatory framework provided by Basel Committee on Banking Supervision. See also https://www.bis.org/bcbs/implem entation/rcap_reports.htm. 12 The results of the interaction terms for Crisis dummy are dropped because of collinearity.
46
2 Quality of Bank Capital, Competition, and Risk-Taking
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Jokipii, T., & Milne, A. (2011). Bank capital buffer and risk adjustment decisions. Journal of Financial Stability, 7(3), 165–178. https://doi.org/10.1016/j.jfs.2010.02.002 Keeley, M. C. (1990). Deposit insurance, risk, and market power in banking. The American Economic Review, 80(5), 1183–1200. Kelly, B., Lustig, H., & Van Nieuwerburgh, S. (2016). Too-systemic-to-fail: What option markets imply about sector-wide government guarantees. American Economic Review, 106(6), 1278– 1319. Kim, D., & Santomero, A. M. (1988). Risk in banking and capital regulation. The Journal of Finance, 43(5), 1219–1233. Koehn, M., & Santomero, A. M. (1980). Regulation of bank capital and portfolio risk. The Journal of Finance, 35(5), 1235–1244. https://doi.org/10.1111/j.1540-6261.1980.tb02206.x Koetter, M., Kolari, J. W., & Spierdijk, L. (2012). Enjoying the quiet life under deregulation? Evidence from adjusted Lerner indices for US banks. Review of Economics and Statistics, 94(2), 462–480. Koivu, T. (2012). Monetary policy, asset prices and consumption in China. Economic Systems, 36(2), 307–325. Kopecky, K. J., & VanHoose, D. (2006). Capital regulation, heterogeneous monitoring costs, and aggregate loan quality. Journal of Banking & Finance, 30(8), 2235–2255. Košak, M., Li, S., Lonˇcarski, I., & Marinˇc, M. (2015). Quality of bank capital and bank lending behavior during the global financial crisis. International Review of Financial Analysis, 37, 168– 183. Laeven, L., & Levine, R. (2009). Bank governance, regulation and risk taking. Journal of Financial Economics, 93(2), 259–275. Laeven, L., Ratnovski, L., & Tong, H. (2016). Bank size, capital, and systemic risk: Some international evidence. Journal of Banking & Finance, 69(Supplement 1), S25–S34. https://doi.org/10. 1016/j.jbankfin.2015.06.022 Laeven, L., & Valencia, F. (2018). Systemic banking crises revisited. IMF Working Paper No. 206. International Monetary Fund. Leroy, A., & Lucotte, Y. (2017). Is there a competition-stability trade-off in European banking? Journal of International Financial Markets, Institutions and Money, 46, 199–215. https://doi.org/ 10.1016/j.intfin.2016.08.009 Li, S. (2019). The impact of bank regulation and supervision on competition: Evidence from emerging economies. Emerging Markets Finance and Trade, 55(10), 2334–2364. https://doi. org/10.1080/1540496X.2018.1547191 Llewellyn, D. T. (2008). The Northern Rock crisis: A multi-dimensional problem waiting to happen. Journal of Financial Regulation and Compliance, 16(1), 35–58. Mésonnier, J.-S., & Monks, A. (2015). Did the EBA capital exercise cause a credit crunch in the Euro Area? International Journal of Central Banking, 11, 75–117. Mare, D. S., Moreira, F., & Rossi, R. (2017). Nonstationary Z-Score measures. European Journal of Operational Research, 260(1), 348–358. https://doi.org/10.1016/j.ejor.2016.12.001 Memmel, C., & Raupach, P. (2010). How do banks adjust their capital ratios? Journal of Financial Intermediation, 19(4), 509–528. Naceur, S. B., & Omran, M. (2011). The effects of bank regulations, competition, and financial reforms on banks’ performance. Emerging Markets Review, 12(1), 1–20. Noman, A. H. M., Gee, C. S., & Isa, C. R. (2018). Does bank regulation matter on the relationship between competition and financial stability? Evidence from Southeast Asian countries. PacificBasin Finance Journal, 48, 144–161. Panzar, J. C., & Rosse, J. N. (1987). Testing for “monopoly” equilibrium. The Journal of Industrial Economics, 35(4), 443–456. Pasiouras, F., Gaganis, C., & Zopounidis, C. (2006). The impact of bank regulations, supervision, market structure, and bank characteristics on individual bank ratings: A cross-country analysis. Review of Quantitative Finance and Accounting, 27(4), 403–438.
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Rajan, R. G. (1994). Why bank credit policies fluctuate: A theory and some evidence. The Quarterly Journal of Economics, 109(2), 399–441. Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal, 9(1), 86–136. Schaeck, K., & Cihák, M. (2012). Banking competition and capital ratios. European Financial Management, 18(5), 836–866. https://doi.org/10.1111/j.1468-036X.2010.00551.x Shrieves, R. E., & Dahl, D. (1992). The relationship between risk and capital in commercial banks. Journal of Banking & Finance, 16(2), 439–457. Tabak, B. M., Fazio, D. M., & Cajueiro, D. O. (2012). The relationship between banking market competition and risk-taking: Do size and capitalization matter? Journal of Banking & Finance, 36(12), 3366–3381. https://doi.org/10.1016/j.jbankfin.2012.07.022 Williams, B. (2016). The impact of non-interest income on bank risk in Australia. Journal of Banking & Finance, 73, 16–37. https://doi.org/10.1016/j.jbankfin.2016.07.019 Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT Press.
Chapter 3
Banking Sector Reform, Competition, and Bank Stability
3.1 Overview This study tests the impact of banking sector reform and competition on bank stability based on unbalanced data from 22 transition countries from 1998 to 2016. The initial results not only highlight the positive relationship between market power and bank fragility, but also confirm the positive relationship between bank reform and stability. Our findings also show that both higher activity restrictions and more explicit guidelines for asset diversification increase bank stability, but this positive effect significantly weakens for banks with higher market power. More stringent capital requirements in combination with higher market power increase the risk of bank insolvency.
3.2 Introduction The recent global financial crisis between 2008 and 2010 influenced the global economy through banking systems across countries and led to unprecedented consequences. Some studies highlight that deregulation and excessive competition in financial sectors were important factors that drove the financial crisis (Brunnermeier, 2009; Carletti, 2008; Fernández et al., 2013; Llewellyn, 2008). The impact of bank regulation and competition on financial stability has thus attracted more attention from academics and policymakers since then (Acharya & Richardson, 2009; Beck et al., 2010, 2013; Schaeck & Cihák, 2014). With the development of information technology (Marinˇc, 2013) as well as the liberalization, deregulation, and integration of capital markets (Das & Ghosh, 2006; Koetter et al., 2012), the number of financial reforms has increased markedly. A number of studies demonstrate that financial reforms develop the financial market, This chapter was published in Emerging Markets Finance and Trade. © Shanghai Jiao Tong University Press 2021 S. Li, Financial Regulation and Bank Performance, Contributions to Finance and Accounting, https://doi.org/10.1007/978-981-16-3509-0_3
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3 Banking Sector Reform, Competition, and Bank Stability
increase the market share of private credit, reduce the financing cost, and raise risktaking (Cubillas & González, 2014). The banking systems in transition countries have undergone extensive reforms in the past two decades and this provides us with a unique setting in which to test the relationships among banking sector reform, competitive conditions, and financial stability. In this study, we investigate the impact of both bank reform and competition on stability by analyzing the empirical relationships among banking sector reform, competition, and stability in transition economies. By using unbalanced data on 1121 banks from 22 transition countries between 1998 and 2016, our findings confirm the negative relationship between market power and stability, supporting the view that competition increases bank stability. The results also confirm that bank reform increases banking stability, supporting the view that better financial development leads to a more stable banking system. Both higher activity restrictions and more explicit guidelines for asset diversification increase bank stability, but this positive effect significantly weakens for banks with higher market power. More stringent capital requirements in combination with higher market power increase the risk of bank insolvency. Declaring insolvency power, private monitoring, financial statement transparency, and deposit insurance have only a direct impact on bank stability. Our analysis extends previous studies in two ways. First, banking systems in transition countries are particularly interesting because banks play no economic role in planned economies; by contrast, the financial systems in most transition countries are now dominated by banks rather than the equity market. Since the 1990s, financial reforms have been implemented across transition countries, including financial liberalization, bank privatization, widening access to foreign banks, and restructuring national banking systems, which have changed the regulation system of the banking sector as well as reshaped the competitive conditions of banks. The recent financial crisis and global recession also enable us to test the resilience of new institutions and regulatory structures. Second, transition countries exhibit a number of unique characteristics, and government policies and regulations can differ from those in developed and developing economies. Therefore, we evaluate the impact of competition on bank stability while considering the impact of banking sector reforms and bank regulations as well as controlling for bank-specific characteristics and macroeconomic factors that may impact bank stability. We also perform robustness checks by using different estimation methods and subsamples. The rest of the paper is structured as follows. The third section summarizes the banking sector reform in transition countries and the related literature. The fourth section mainly addresses the sample selection, variable definition, and data statistics. The fifth section describes the empirical methodology employed in this study. The sixth section presents the empirical analysis and robustness checks and the last section concludes.
3.3 Banking Sector Reform in Transition Countries and Literature Review
53
3.3 Banking Sector Reform in Transition Countries and Literature Review 3.3.1 Banking Sector Reforms in Transition Countries Before the transition phase, banking sectors in transition countries were largely controlled by the state and lending activities were showing a marked preference toward state-owned firms, practically the only form of non-monetary financing of the budget deficit. Because of the implicit government guarantee, banks had no incentives to monitor their risk. As a result, they were saddled with a large number of bad loans concentrated in the state-owned banking sector (Perotti, 1993). Financial reforms have been implemented across transition countries since the 1990s, including resolving non-performing loans, privatizing state-owned banks, introducing foreign investors, and restructuring national banking systems. As discussed in the Introduction, this has changed the regulation system of the banking sector and reshaped the competitive conditions of the banking system. Transition countries have enacted strict bankruptcy laws to control the level of non-performing loans. For example, non-performing loans in Hungary were replaced by government securities and transferred to a government collection agency, and the stakes of large state-owned banks owned by the government were sold to foreign investors. Bad loans in the banking system in the Czech Republic were replaced by government bonds and the bad assets were transferred to a new bank (Bonin et al., 2014). The privatization programs of government-owned banks have experienced several stages (Bonin et al., 2005b). After the bank crises in the 1990s, transition countries started to introduce foreign investors into financial markets. To privatize banks in Poland, both domestic initial public offerings and foreign investment were used. The bank privatization programs in Romania, Bulgaria, Croatia, and the Czech Republic involved negotiations between governments and foreign banks (Bonin et al., 2014). With the introduction of foreign investors, the majority of government-owned banks were taken over by privately owned and well-capitalized financial institutions (Bonin et al., 2005a). Indeed, foreign investors now hold a large share of the banking sectors in transition countries (Fang et al., 2014). The initial bank regulation reforms focused on opening the financial market, facilitating banking system competition, and creating diversities of financial institutions. Financial reforms including liberalizing interest rates, reducing directed credit programs, and lowering bank activity restrictions were implemented (Bonin et al., 2014). New and prudential supervision guidelines concerning capital requirements, official supervisory power, loan classifications, information disclosure, and private sector monitoring were established following the rapid adoption of financial reforms. For example, bank lending in Croatia is now strictly constrained by central bank regulation, which mitigates the effects of booms. Moreover, formal Financial Stability Reports and macro-prudential policy responses have been introduced by Romania and several other transition countries to control systemic risk in the banking system (Bonin et al., 2014).
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3.3.2 Literature Review Previous analyses indicate that competitive banking systems are fragile. In a competitive banking system, banks benefit less from the lending relationship, and this discourages them from monitoring borrowers’ risk properly and thus raises risk (Allen & Gale, 2004). Boyd et al. (2004) also suggest that large banks in concentrated banking sectors can enhance profits and decrease financial instability by providing more “capital buffers” that absorb external macroeconomic and liquidity shocks. Further, concerning the competition–fragility nexus, although deposit insurance can mitigate bank instability by preventing banks from insolvency, it also leads to moral hazard by providing incentives for banks to undertake risk-taking activities. Thus, deposit insurance may increase bank instability in a more competitive market (Matutes & Vives, 1996) and thus constraints on deposit interest rates are needed to prevent excessive risk-taking in a competitive banking system (Hellmann et al., 2000). Some studies indicate that competitive banking systems are more stable. Less competition leads to reduced credit rationing and higher loans, increasing the probability of bank insolvency (Caminal & Matutes, 2002). However, Berger et al. (2009) indicate that by using alternative methods to allocate the risk exposure, bank risks may not increase, even if market power encourages riskier asset portfolios. On the impact of banking sector reform on risk-taking behavior, previous studies have mixed findings. From the traditional viewpoint, financial liberalization is generally associated with increased market competition, which in turn introduces greater risk-taking activities and intensified moral hazard issues (Grossman, 1992); Boyd et al., 1998). Dick (2006) also provides empirical evidence for this view based on bank information from the United States after the Riegle–Neal Act in the 1990s. However, other researchers have different views, stating that banks are more able to pursue economies of scale and scope and diversify income flows, and thus become less risky, if they are liberalized (Claessens & Klingebiel, 2001). Hellmann et al. (2000) claim that activity restrictions might incentivize bank managers to accept risky projects and Barth et al. (2004) and Laeven and Levine (2009) provide empirical evidence for this statement. Theoretical studies find conflicting conclusions on the relationship between bank regulation and bank performance, and no consensus on which regulatory practices are more effective in reducing bank risk has been reached (Barth et al., 2004, 2007). Acharya et al. (2006) claim that although engaging in non-lending activities helps banks diversify risk, the lack of experience in new services may also introduce higher volatility in incomes and higher instability. Stringent capital requirements may help improve banks’ corporate governance and reduce risk-taking behavior, but they are not necessarily related to lower loan losses (Kopecky & VanHoose, 2006). Greater supervisory power also introduces more corruption in lending and this raises banking instability (Beck et al., 2006).
3.3 Banking Sector Reform in Transition Countries and Literature Review
55
Only a few recent studies provide direct findings on the relationships among banking sector reform, competition, and bank performance by focusing on transition countries. Brissimis et al. (2008) confirms that banking sector reforms, by increasing competition and introducing more risk-taking activities, improve bank efficiency. Agoraki et al. (2011) confirm that banks with higher market power tend to have a lower probability of default and that higher activity restrictions and capital requirements increase bank stability. Fungáˇcová and Weill (2013) explore the role of bank competition on the occurrence of bank failures based on a sample in Russia and confirm that increasing bank competition could undermine bank stability. Fang et al. (2014) suggest that financial stability has improved substantially since the banking sector reform.
3.4 Data and Variables 3.4.1 Sample We collect banks’ financial data from Bankscope. To avoid the bias introduced by double counting in the sample, we use the consolidated financial report of each bank, and only the unconsolidated report if the consolidated report is not available. Information on banking sector reform is collected from the transition report provided by the European Bank for Reconstruction and Development (EBRD). To control for the impact of bank regulation, we also employ the bank regulation and supervision data provided by Barth et al. (2013).1 The country-level variables used to control for the change in macroeconomic factors and capital market size are collected from the World Development Indicators database. We drop observations based on the following selection criteria: (1) observations where the data on one of the variables employed to estimate the Lerner index are missing; (2) to estimate the Lerner index, we set the minimum number of observations to 10 for each year in each country; thus, we delete countries with fewer than 10 bankyear observations; (3) to compute the Z-score, we employ the moving average ROA (return on assets) and moving average standard deviation of ROA (sd(ROA)) over three years, and we only keep banks that have at least three consecutive observations; and (4) we delete observations where the data on one of the bank-specific and countrylevel control variables are missing.
1 For
the bank regulation and supervision information provided by Barth et al. (2013), however, the surveys were conducted in 1999, 2002, 2006, and 2011. Following the study of Anginer et al. (2014a), we employ the previous survey data until the new survey data become available for matching the bank regulation and supervision variables with the bank-specific variables and country control variables: the survey data found in 1999 for 1998–2001, the survey data found in 2002 for 2002– 2005, the survey data found in 2006 for 2006–2010, and the survey data found in 2011 for 2011– 2016.
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3 Banking Sector Reform, Competition, and Bank Stability
After applying the selection criteria, we finally have 1121 banks, which account for 4445 observations between 1998 and 2016. Our sample consists of commercial banks, saving banks, cooperative banks, and bank holding companies from 22 transition countries.2 All data are adjusted based on the inflation rate and expressed in USD.
3.4.2 Variables (1)
Bank stability measures
We follow Laeven and Levine (2009) and employ the Z-score to measure bank stability. The Z-score is an indicator of bank solvency extensively used in banking studies (e.g., Demirgüç-Kunt & Huizinga, 2010, Beck et al., 2013). It indicates the number of standard deviations by which returns would have to fall from the mean to wipe out all equity in the bank (Boyd & Runkle, 1993). Based on the recent study of Mare et al. (2017), we employ the novel non-stationary estimator and calculate the Z-score as follows: Z it =
R OAit + (E/A)it sd R OA
(3.1)
it
where R OA is defined as moving average ROA computed based on past and current information for each period; E/A is defined as the ratio of equity to total assets for the current period (t), and sd R OA indicates the standard deviation of ROA computed based on past and current information for each period.3 A higher Z-score indicates a lower probability of insolvency and higher bank stability. We also follow Laeven and Levine (2009) and employ the logarithm of the Z-score to avoid the bias introduced by extreme values. As a robustness check, we use ROA volatility (sd R OA ), ROE volatility (sd R OE ), and the non-performing loan ratio (NPL, defined as the ratio of non-performing loans to total loans) as alternative measures of bank stability. (2)
Bank competition indicators
This study first employs the Lerner index to measure bank competition and evaluates banks’ competitive behavior. The Lerner index is defined as follows:
2 Focusing only on European cooperative banks between 2006 and 2014, Clark et al. (2018) study the
relationship between competition and financial stability and show that a hump-shaped relationship exists between market power and stability. 3 Mare et al. (2017) also summarize the methods for computing the Z-score and outline some important limitations.
3.4 Data and Variables
57
Lerner Indexit = (Pit − MCit )/Pit
(3.2)
In our analysis, Pit indicates the output price, measured as the total revenue to total assets ratio for bank i in year t, and MCit indicates the marginal cost of bank i in year t.4 MCit is defined as 3 TCit MCit = ρk ln wkit + ϕ3 ln Tt α1 + α2 ln Qit + Qit k=1
(3.3)
and the translog total cost function is 3
1 2 ln TCit = βk ln wkit + γk ln wkit + α1 ln Qit 2 k=1 k=1 3
1 1 + α2 ln Q it2 + ρk ln Qit ln wkit + ϕ1 ln Tt + ϕ1 ln T2t 2 2 k=1 3
+ ϕ3 ln Tt ln Qit +
3
ϕk+3 ln Tt ln wkit + α0 + αi + εit
(3.4)
k=1
where T Cit is defined as total costs and Qit denotes output, proxied by the total assets of bank i in year t. We define w1 as the interest expenses to total funding ratio and employ this to measure the average funding rate; w2 indicates the ratio of personnel expenses to total assets and this is used as the input price of personnel expenses; and w3 is the ratio of other non-interest expenses to fixed assets and this is employed as a proxy of the price of physical capital. T is a time trend variable used to capture the impact of technological development. Equation (3.4) is estimated based on stochastic cost frontier analysis for each country in each year (see also Fu et al., 2014; Koetter et al., 2012). We also estimate the efficiency-adjusted Lerner index based on Koetter et al. (2012) as a robustness check. For another robustness check, we define an alternative competition indicator based on the Panzar–Rosse model (Panzar & Rosse, 1987). The Panzar–Rosse model constructs an H-statistic to measure market power, where the H-statistic is calculated as the sum of the elasticity of revenue with respect to three input prices. Based on Anginer et al. (2014b), we employ the reduced-form revenue equation and estimate the H-statistic for each country in each year5 :
4 We
define the output price Pit as the ratio of total revenue to total assets following Angelini and Cetorelli (2003), Koetter et al. (2012), and Fu et al. (2014). 5 Bikker et al. (2012) indicate that a scaled revenue function leads to a significant upward bias and incorrectly measures the degree of competition. Hence, in our analysis, we employ the unscaled revenue equation to reduce estimation bias.
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3 Banking Sector Reform, Competition, and Bank Stability
ln OPIit = α +
3
β j ln w jit +
j=1
πk Controlskit + αi + εit
(3.5)
k
where OPI it indicates the operating revenue of bank i in year t and w1 , w2 , and w3 are the bank input price variables as defined in the Lerner index estimation. We also add a set of control variables to control for the impact of bank-specific characteristics.6 All bank-specific factors that may affect operating revenue but are not included in Eq. (3.5) are captured by including bank fixed effects (FE; denoted by αi ). The Hstatistic, which is defined as the sum of the elasticities of revenues with respect to three input prices, is defined as β1 + β2 + β3 .7 Finally, we define the Herfindahl– Hirschman index (HHI), which is calculated as the sum of the square of the market share of each bank in the banking system to measure bank concentration. (3)
Banking sector reform measure
To measure banking sector reform in transition countries, we collect bank reform information from the transition reports provided by the EBRD. The EBRD follows the transition progress of former socialist countries in different sectors since the 1990s. The bank reform indicator for transition countries considers the degree of interest rate liberalization, bank credit allocation, presence of private banks, and whether bank supervision and regulation are prudent (see also Brissimis et al., 2008; Fang et al., 2014). This indicator varies between 1 and 4.33, with a higher value indicating greater financial development. (4)
Bank regulation variables
As we are also interested in the effect of bank regulation on the relationship between competition and bank stability, we employ the set of regulation variables provided by Barth et al. (2013) to examine the impact of bank regulation from different aspects. The first regulatory variable Activity restriction is related to banks’ activity restrictions and this measures the degree to which banks are allowed to engage in
6 The control variables include the following: Customer Loan, defined as the ratio of customer loans
to total assets, to control for credit risk; NEA, defined as the ratio of other non-earning assets to total assets, to control for the composition of the asset; Customer Deposit, defined as the ratio of customer deposits to the sum of customer deposits and short-term funding, to measure the funding structure of the bank; and Equity Ratio, defined as the equity to total assets ratio, to account for leverage. 7 To deal with the potential heteroskedasticity problem, we estimate the unscaled PR revenue model in Eq. (3.5) by using pooled feasible generalized least squares, and clustered standard errors are also used to account for general heteroskedasticity and cross-sectional correlation in the model errors (Arellano 1987). We also follow the analysis of Bikker et al. (2012) and employ an equilibrium ROE test to confirm whether banks operate in a long-run equilibrium. By using ROE as the dependent variable in Eq. (3.5), the H-statistic under the ROE test equals zero if the banking system operates in a long-run equilibrium.
3.4 Data and Variables
59
securities, insurance, and real estate activities (higher values indicate higher restrictions). Second, we consider the effect of capital regulation and include Capital stringency index, which measures the amount of capital a bank must maintain (higher values indicate greater stringency). We use Declaring insolvency power to measure official supervisory power (higher values indicate greater power). Diversification index captures whether there are explicit, verifiable, and quantifiable guidelines for banks’ asset diversifications and whether banks are allowed to make loans outside national borders (higher values indicate more diversification). Private monitoring index measures whether there are incentives for private monitoring by firms (higher values indicate more private monitoring). Finally, Financial statement transparency indicates the transparency of banks’ financial statement practices to examine the impact of external governance and Deposit insurance, which equals one if the country has deposit insurance and zero otherwise, is used to investigate the impact of deposit insurance on bank stability.8 (5)
Bank-specific and country-level control variables
We use several variables to control for bank-specific time-varying effects on bank stability. We define log EQ as the logarithm of the total equity to total assets ratio to control for leverage, log LLP as the logarithm of the ratio of loan loss provision to gross loans to control for loan quality, log DEP as the ratio of total customer deposits to total assets as a proxy of bank funding, FA as the ratio of fixed assets to total assets to measure a bank’s asset structure, and log (TA), defined as the logarithm of total assets, to control for bank size. All the bank-specific variables employed in this analysis are winsorized at the 1st and 99th percentiles to avoid the effects of outliers. We also use GDP growth and Inflation at the country level to account for the impact of macroeconomic factors. The binary variable Crisis equals one if the country was experiencing a systemic crisis in a given year and zero otherwise, following Laeven and Valencia (2018), to control for the economic cycle in a country. Table 3.1 summarizes the definition and data sources of the variables employed in this analysis.
3.4.3 Summary Statistics Panel A in Table 3.2 provides the statistics of the variables included in our analysis. First, we present the results for the bank stability measurements. The average log Zscore is 2.42, ranging from −5.85 to 12.13; sd(ROA) and sd(ROE) have means 8 We also include Foreign bank limitation, which measures whether foreign banks own domestic banks and may enter a country’s banking industry (lower values indicate greater stringency), and Entry barriers, which indicates whether various types of legal submissions are required to obtain a banking license (higher values indicate greater stringency). However, the results show no significant impact on bank stability.
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3 Banking Sector Reform, Competition, and Bank Stability
Table 3.1 Variable definition and data sources Variables
Definitions
Data sources
Calculated as the sum of ROA and equity-to-assets divided by the standard deviation of ROA of each bank over past 3 years
Bankscope
Standard deviation of ROA for each bank, computed over past 3 years
Bankscope
Standard deviation of ROE for each bank, computed over past 3 years
Bankscope
Nonperforming loans divided by total loans
Bankscope
Dependent variables log Zscore
OA sd R sd R OE NPL
Competitive condition indicators Lerner index
Lerner index is equal to the difference Own calculation between asset price and marginal cost, normalized by asset price
H-statistic
H-statistic is calculated as the sum of Own calculation the elasticity of revenue with respect to three input prices for each country at each year between 1998 and 2016 based on the Panzar and Rosse (1987) model
HHI
Hirschmann-Herfindahl index (HHI) Own calculation of concentration of total assets, which is the sum of the squares of the market shares (assets) of each bank in each country at each year
Bank-specific variables log TA
The logarithm of total asset in $ thousand
Bankscope
log EQ
The logarithm of total equity to total assets ratio
Bankscope
log LLP
The logarithm of ratio of loan loss provision to gross loans
Bankscope
NIM
The ratio of net interest income to total earning asset
Bankscope
log DPS
The logarithm of ratio of total customer deposit to total asset
Bankscope
FA
The ratio of fixed assets to total assets Bankscope (continued)
3.4 Data and Variables
61
Table 3.1 (continued) Variables
Definitions
Data sources
Country control variables GDP growth
Annual growth rate of GDP at market World Bank Development prices based on constant local Indicator Database currency
Inflation
Inflation rate
Crisis
A binary variable which equals one if Laeven and Valencia (2010) the country was experiencing a systemic crisis in a given year and zero othewise
World Bank Development Indicator Database
Bank reform index Bank reform index
An indicator which considers the European Bank for degree of liberalization of interest Reconstruction and rate, the allocation of bank credit, Development (EBRD) whether significant lending to private enterprise exits, whether private banks have a significant presence, and whether bank supervision and regulation are prudent. The indicator goes from 1 to 4.33, with higher numbers indicating higher stages of development
Bank regulation variables Activity restriction
A variable measures a bank’s ability to engage in securities, insurance, and real estate activities. The ranges from 0 to 12, and a higher score indicates more restrictions on banks to engage in such activities
Barth et al. (2013)
Capital regulatory index
A variable that captures both the overall capital stringency and the initial capital stringency based on answers to eight questions. It ranges from zero to eight, with a higher value indicating higher capital stringency
Barth et al. (2013)
Declaring insolvency power
A variable measure whether the supervisory authorities have the power to declare a deeply troubled bank insolvent. It ranges from 0 to 4 and higher values indicate greater power
Barth et al. (2013)
Diversification index
A variable that ranges from zero to two, with higher values indicating more diversification
Barth et al. (2013)
(continued)
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3 Banking Sector Reform, Competition, and Bank Stability
Table 3.1 (continued) Variables
Definitions
Private monitoring index
A variable that measures whether Barth et al. (2013) there incentives/ability for the private monitoring of firm, with higher values indicating more private monitoring
Data sources
Financial statement transparency
The transparency of bank financial Barth et al. (2013) statements practices, ranges from 0 to 6 and higher values indicate better transparency
Deposit insurance
A binary variable which equals one if Barth et al. (2013) the country has explicit deposit insurance scheme and zero otherwise
This table reports the definitions and data sources of the variables included in our analysis
of 1.82 and 15.4 with standard deviations of 2.78 and 24.01, respectively; and the average non-performing loans ratio (NPL) is 9.09, ranging from 0 to 114.8. We also summarize the competition variables. The Lerner index has a mean of 0.295 with a standard deviation 0.207; its H-statistic is 0.311, ranging from 0.153 to 0.848, and the average HHI is 0.158, ranging from 0.068 to 0.986. For the bank-specific variables, the average log TA is 14.39 and its value varies substantially, which confirms the importance of the size effect in a banking system. The average logarithm of customer deposits to total assets (log DPS) is −0.553, while log LLP has a mean of −4.244, and the average fixed asset ratio is 0.032. We include three country-level variables, namely GDP growth, Inflation, and Crisis, to control for the changes in macroeconomic factors and the economic cycle. An average transition country in the sample has GDP growth of 4.03% and inflation of 10.34%, and 18.8% of countries have experienced a crisis. Finally, we report the descriptive statistics of the bank reform indicator and bank regulation variables. An average country in our sample has a bank reform indicator of 2.87, activity restriction of 6.33, capital regulatory index of 7.07, declaring insolvency power of 1.61, diversification index of 1.35, private monitoring index of 6.96, and financial statement transparency of 4.85. In total, 91.6% of the observations have an explicit deposit insurance scheme. Panel B in Table 3.2 reports the sample distribution by calendar year and country. The number of bank-year observations in our sample ranges from 45 to 648, with the largest number in 2008 and the lowest number in 1998. Russia accounts for 38.5% of our sample observations.
3.5 Empirical Methodology The adopted empirical methodology is designed to examine the effects of banking sector reform and competition on bank stability. The analysis is performed at the bank level and the model is defined as follows:
4445 0.188
4445 2.866
3874 6.331
3886 7.072
3854 1.614
Bank reform index
Activity restriction
Capital regulatory index
Declaring insolvency power
4445 10.340
Inflation
Crisis
4445 0.032
4445 4.026
0.795
4445 −0.553
log DPS
GDP growth
1.118
4445 −4.244
log LLP
FA
0.520
4445 2.473
log EQ
0.091
0.658
1.572
1.517
0.616
0.390
9.098
4.835
0.033
3.496
4445 0.158
4445 14.389
0.530
log TA
4042 0.311
H−statistic
13.249 0.207
HHI
3492 9.092
4445 0.295
24.013
4438 15.400
Lerner index
2.784
4367 1.823
NPL
1.505
4445 2.419
STD
log Zscore sd R OA sd R OE
Mean
N
Variable
Panel A: Data statistics
Table 3.2 Summary statistics
0
3
3
1.000
4
10
11
4.000
1.000
75.201
−18.930 0.000
0.365 34.500
0.000
0.000
−14.800
−9.531
1.099
4.416
−1.139
0.986
0.848
2.446
13.663
0.135
0.481
0.253
4.330
7.596
0.932
2.581
2.769
15.562
0.162
0.725
0.430
11.145
16.118
1.998
3.343
Median P75
1
6
5
2.670
0.000
3.367
1.785
0.010
2
8
6
2.670
0.000
9.075
5.017
0.022
(continued)
2
8
7
3.330
0.000
15.170
7.296
0.043
−0.705 −0.250 −0.085
−4.883 −4.213 −3.520
2.146
12.079
0.124
0.003
0.124
114.810 1.315 0.678
27.451
−10.331
0.439
1.696
P25
164.821 3.872
5.152
0.068
0.153
0.000
0.000
0.338
19.010
12.130
−5.848 0.000
Max
Min
3.5 Empirical Methodology 63
2521 4.851
3252 0.916
Financial statement transparency
Deposit insurance
66
114
82
138
160
271
58
46
88
138
90
Armenia
Azerbaijan
Belarus
Bosnia and Herzegovina
Bulgaria
Croatia
Czech Republic
Estonia
Georgia
Hungary
Kazakhstan
1.48
2.02
3.10
1.98
1.03
1.30
6.10
3.60
3.10
1.84
2.56
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
260
648
564
438
184
170
123
108
94
75
58
45
Frequency
Year
48
Albania
1.08
Sample distribution by year
Frequency Percentage
Country
0.277
0.855
1.065
0.481
STD
Sample distribution by country
Panel B: Sample distribution by year and by country
3980 1.352
3712 6.957
Private monitoring index
Mean
Diversification index
N
Variable
Panel A: Data statistics
Table 3.2 (continued)
0
2
5
0
Min
5.85
14.58
12.69
9.85
4.14
3.82
2.77
2.43
2.11
1.69
1.30
1.01
Percentage
1
6
9
2
Max
1
5
6
1
P25
1
5
7
1
(continued)
1
5
8
2
Median P75
64 3 Banking Sector Reform, Competition, and Bank Stability
4445
Uzbekistan
Total
100
2.05
8.01
4.43
2.88
38.47
4.45
5.62
1.71
2.41
0.52
N
Mean
Total
2016
2015
2014
2013
2012
2011
2010
STD
4445
178
261
208
232
259
268
272
Min
100
4.00
5.87
4.68
5.22
5.83
6.03
6.12
Max
P25
Median P75
This table reports summary statistics of the variables for the full sample, and sample distribution according to year and country. Our sample period is 1998–2016. All data are inflation-adjusted and bank-specific variables are winsorized at the 1st and 99th percentile level to reduce the influence of outliers
356
91
Ukraine
197
Slovenia
198
Romania
1710
250
Poland
128
76
Macedonia (FYROM)
Slovakia
107
Latvia
Russia
23
Kyrgyz Republic
Variable
Panel A: Data statistics
Table 3.2 (continued)
3.5 Empirical Methodology 65
66
3 Banking Sector Reform, Competition, and Bank Stability
bank riski jt = α0 + α1 competition i jt + α2 bank re f orm indexi jt + × bank_contr olsmi jt + × countr y_contr olsn jt + αi + μt + εi jt
(3.6)
where bank riski jt indicates bank i’s stability in country j in year t and is measured as the logarithm of the Z-score (log Zscore) as defined in Eq. (3.1). The main explanatory variables of interest are competition, measured by the Lerner index (Lerner index), and banking sector reform, measured by the bank reform indicator (Bank reform index) provided in the EBRD’s transition reports. The bank-specific control variables include bank size (log TA), the equity ratio (log EQ), the loan loss provision ratio (log LLP), the net interest income ratio (NIM), the total customer deposits ratio (log DPS), and fixed assets (FA). The country-level control variables include GDP growth (GDP growth), the inflation rate (Inflation), and Crisis. We also include bank FE (αi ) to control for the unobserved time-invariant heterogeneity across banks and yearly FE (μt ) to control for the macroeconomic factors and monetary policy, which may vary over time. We are also concerned about whether bank regulation changes the relationships among bank competition, reform, and stability. We thus employ Eq. (3.7) to examine how bank regulation affects risk and whether the relationship between competition and stability is strengthened or mitigated: bank riski jt = α0 + α1 competition i jt + α2 bank re f orm indexi jt + α3 bank r egulaiton jt + α4 competition i jt ∗ bank re f orm index jt + α5 competition i jt + bank r egulaiton jt + × bank_contr olsmi jt + × countr y_contr olsn jt + αi + μt + εijt
(3.7)
All the variables are as defined in Eq. (3.6) except that we include the interaction term between the bank reform index and Lerner index, bank regulatory variables, and interaction terms between the bank regulatory variables and Lerner index. Equations (3.6) and (3.7) are estimated based on two types of estimation methods. First, we employ the bank FE model with robust standard errors and include the year dummy variables to control for the heterogeneity and changes in unobservable features. Second, we also consider potential endogeneity bias: the level of bank stability could affect bank competition and the degree of banking sector reform may affect not only bank stability, but also the competitiveness of banks. To address this potential endogeneity bias, we Anginer et al. (2014b) and use the two-stage least squares (2SLS) estimator. Based on previous studies (e.g., Leroy & Lucotte, 2017; Schaeck & Cihák, 2008), we use the first lag of the Lerner index, the first lag of the bank reform index, and personnel expenses (w2 ), which is a proxy of a bank’s efficiency, as instrumental variables (IVs). The first stage F-test and Hansen’s J test are respectively employed to test for the relevance and validity of the instruments of the Lerner index.
3.6 Empirical Analysis
67
3.6 Empirical Analysis 3.6.1 Basic Results In this section, we investigate the impact of bank competition and reform on bank stability in transition countries, after controlling for the effects of the bank-specific and country-level characteristics. Table 3.3 presents the results for both the FE regressions (columns (1)–(3)) and the IV estimations (columns (4)–(6)). Columns (1) and (4) of Table 3.3 present the results including only the Lerner index and country-level explanatory variables. The results confirm that the Lerner index is negatively and statistically significant at 5% for the different estimation methods. After including the bank-specific variables in columns (2) and (5), the results remain unchanged. This finding is consistent with those of previous studies that focus on banks from different regions and also suggest that competition enhances bank stability (e.g., Anginer et al., 2014a; Schaeck et al., 2009; Yeyati & Micco, 2007).9 Columns (3) and (6) of Table 3.3 report the results for the bank reform index. The results confirm the significantly positive correlation for the bank reform index, suggesting that higher banking sector development is positively related to bank stability in transition countries. The results for the bank-specific variables show that banks with higher equity capital exhibit higher stability, while a higher loan loss provision leads to bank fragility. We also account for the effect of bank funding (log DPS), size (log TA), and fixed assets (FA), but none of these factors has a significant effect on bank stability. For the country-level variables, the results for GDP growth and Inflation suggest that higher GDP growth and lower inflation correspond to higher bank stability. We also conduct ananalysis based onthe different measures of risk and compe tition. First, we use sd R OA , sd R OE , and NPL as alternative measures of bank risk. Panel A of Table 3.4presents the results. We find a positive relationship between the Lerner index and sd R OA as well as sd R OE , suggesting that higher market power increases the volatility of bank profit and leads to bank fragility. Higher market power also corresponds to higher non-performing loans. The results for the bank reform index confirm that financial development leads to a significant reduction in sd R OA and NPL, providing further evidence that bank reform is positively associated with bank stability. In Panel B of Table 3.4, we test whether our findings are consistent under alternative measures of bank competition. In columns (1) and (2), we first use the H-statistic as an alternative measure and find that it is positively and statistically significantly related to the Z-score, which confirms that competition as measured by the H-statistic 9 Yeyati
and Micco (2007) find a positive link between bank risk (as measured by the Z-score) and competition (as captured by the H-statistic) based on a sample of commercial banks from eight Latin American countries between 1993 and 2002. Schaeck et al. (2009) use the data from 31 systemic banking crises in 45 countries in 1980–2005 and find that competition reduces the likelihood of a crisis. Anginer et al. (2@014a) use a sample of publicly traded banks from 63 countries between 1997 and 2009 and find a positive relationship between competition and systemic stability.
68
3 Banking Sector Reform, Competition, and Bank Stability
Table 3.3 Bank reform, competition and stability: basic analysis Variables
(1)
(2)
(3)
(4)
(5)
(6)
Lerner index
−0.314**
−0.335***
−0.335***
−0.659***
−0.648***
−0.644***
(−2.34)
(−2.69)
(−2.69)
(−2.65)
(−2.76)
(−2.76)
Bank reform index
0.0726*
0.217*
(1.85)
(1.72)
Bank-specific variables log TA
0.0342
0.0366
0.118
0.0934
(0.90)
(0.95)
(0.97)
(0.77)
1.170***
1.172***
1.208***
1.220***
(15.38)
(15.38)
(6.00)
(6.11)
log LLP
−0.180***
−0.180***
−0.243***
−0.239***
(−6.84)
(−6.84)
(−4.46)
(−4.48)
log DPS
−0.0433
−0.0425
0.0444
0.0431
(−0.73)
(−0.72)
(0.33)
(0.32)
−0.887
−0.833
1.179
1.276
(−0.77)
(−0.73)
(0.56)
(0.60) −0.0182
log EQ
FA Country control variables GDP growth
0.0322***
0.0142*
0.0147*
0.00329
−0.0168
(3.92)
(1.88)
(1.95)
(0.21)
(−1.07)
(−1.15)
Inflation
−0.00421
−0.00675*
−0.00702*
−0.0152**
−0.0155**
−0.0169***
(−1.06)
(−1.81)
(−1.86)
(−2.27)
(−2.58)
(−2.79)
Crisis
−0.0402
0.00486
0.0102
−0.203
−0.243
−0.233
(−0.43)
(0.06)
(0.12)
(−1.21)
(−1.59)
(−1.53)
2.216***
−2.029**
−2.265**
(9.26)
(−2.36)
(−2.45)
Year fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
Bank fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
N
4445
4445
4445
1387
1387
1387
Adjusted R-squared
0.0248
0.159
0.160
0.293
0.148
0.151
F-statistic of first stage
12.11
15.31
14.98
Hansen’s J statistic
8.363
6.694
7.036
Constant
(continued)
3.6 Empirical Analysis
69
Table 3.3 (continued) Variables
(1)
(2)
(3)
Hansen’s J statistic (p-value) Estimation method
FE
FE
FE
(4)
(5)
(6)
0.193
0.213
0.350
IV
IV
IV
This table examines the impact of bank reform and competition on bank stability. The dependent variable is the logarithm of Z-score as defined in Eq. (3.1). Our sample period is 1998–2016. Lerner index is estimated based on Eq. (3.2) and employed as a measure of bank competitive indicator. Bank reform index is a bank reform indicator. Bank-level controls include log TA, log TA * log TA, log EQ, log LLP, log DPS, and FA. Country control variables include GDP growth, and Inflation. All variables are defined in Table 3.1. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. The Hansen’s J test evaluates the joint validity of instruments used. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively.
increases bank stability. We also use the concentration ratio (i.e., the HHI) as another measure of competition, but find no significant impact on the Z-score. This is consistent with the findings of previous studies (e.g., Anginer et al., 2014a; Claessens & Laeven, 2004) that the level of competition is not necessarily related to market structure.
3.6.2 Impact of Bank Regulation on the Relationship Between Competition and Stability The results of our basic analysis confirm that competition and bank reform increase bank stability in transition countries. Further, studies of the causes of the global financial crisis argue that deregulation and excessive competition are the main factors that lead to financial system fragility (e.g., Brunnermeier, 2009; Llewellyn, 2008; Milne, 2009). Moreover, previous analyses of the relationship between bank regulation and competition have also highlighted the importance of the former in determining a bank’s competitive conditions (Angelini & Cetorelli, 2003; Claessens & Laeven, 2004; Demirguc-Kunt et al., 2004). In this section, we extend our analysis to examine whether the relationship between bank competition and stability varies considering the difference in national bank regulations. In Table 3.5, we investigate the impact of the bank regulatory variables on risk. Both the bank-specific variables and the country-level control variables are included as explanatory variables but not reported for brevity. We test the impact of the following bank regulation variables: Activity restriction, Capital regulatory index, Declaring insolvency power, Diversification index, Private monitoring index, Financial statement transparency, and Deposit insurance.
70
3 Banking Sector Reform, Competition, and Bank Stability
Table 3.4 Bank reform, competition and stability: alternative measures Panel A: Alternative measures of bank stability (3)
Variables
(1) (2) sd R OA sd R OE
NPL
(4) (5) (6) sd R OA sd R OE NPL
Lerner index
0.566**
5.843***
1.885**
0.751*
8.721**
(2.29)
(2.94)
(2.42)
(1.66)
(2.57)
(2.73)
Bank reform index
−0.106*
−0.0238
−1.301**
−0.419**
−1.083
−2.057**
(−1.93)
(−0.03)
(−2.35)
(−2.03)
(−0.58)
(−2.18)
Constant
8.352***
86.66***
30.82***
5.893***
(5.22)
(4.87)
(3.58)
Bank-specific variables
Yes
Yes
Yes
Yes
Yes
Yes
Country control variables
Yes
Yes
Yes
Yes
Yes
Yes
Year fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
Bank fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
N
4367
4438
3492
1351
1387
1306
Adjusted R-squared
0.342
0.707
0.233
0.510
0.727
0.253
F-statistic of first stage
12.73
14.40
10.67
Hansen’s J statistic
5.492
5.302
5.86
Hansen’s J statistic (p-value)
0.482
0.506
0.293
IV
IV
IV
Estimation method
FE
FE
FE
Panel B: Alternative measures of bank competition (1)
(2)
(3)
(4)
Variables
log Zscore log Zscore log Zscore log Zscore
H-statistic
0.00581**
0.0110**
(2.28) HHI Bank reform index Constant
(2.33) 0.301
−0.464
(0.92)
(−0.35)
0.0718**
0.0714
0.196*
0.197**
(2.15)
(1.23)
(1.74)
(2.54)
−3.345**
−2.644***
(−2.31)
(−2.59)
Bank-specific variables
Yes
Yes
Yes
Yes
Country control variables
Yes
Yes
Yes
Yes
Year fixed effects
Yes
Yes
Yes
Yes (continued)
3.6 Empirical Analysis
71
Table 3.4 (continued) Panel A: Alternative measures of bank stability (1)
(2)
(3)
(4)
Bank fixed effects
Yes
Yes
Yes
Yes
N
4042
4445
1297
1387
Adjusted R-squared
0.157
0.158
0.141
0.141
F-statistic of first stage
12.94
14.39
Hansen’s J statistic
9.019
6.835
Hansen’s J statistic (p-value)
0.173
0.336
IV
IV
Estimation method
FE
FE
(5)
(6)
This table examines the impact of bank reform and competition on bank stability. Our sample period is 1998–2016. Bank Reform Index is a bank reform indicator. Bank-level controls include log TA, log EQ, log LLP, log DPS, and FA. Country control variables include GDP growth, Inflation, and Crisis. All variables are defined in Table 3.1. Bank-level control variables and country-level variables are also included in the regressions but not reported for brevity. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. The Hansen’s J test evaluates the joint validity of instruments used. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
The results for Activity restriction indicate that activity restrictions increase bank stability, as they reduce the potential channels of credit risk contagion (Anginer & Demirguc-Kunt, 2014). More stringent capital requirements can provide banks with a higher capital buffer to absorb losses, minimize the risk contagion among banks, and provide more incentives for investors to monitor the bank’s risk-taking (Allen et al., 2011; Repullo, 2004). Thus, higher capital regulatory stringency can reduce the probability of bank insolvency and increase bank stability, with the results for Capital regulatory index providing empirical support for this conclusion. Further, supervisory authorities that have greater power to declare a deeply troubled bank insolvent can reduce the risk contagion, while explicit guidelines for asset diversification can help banks increase the efficiency of asset management, reduce the potential risk introduced by non-performing loans, and diversify potential risks. The results for Declaring insolvency power and Diversification index confirm that if the supervisory authority in a country has greater power to declare insolvency and provides more explicit guidelines for diversification, the banking system would tend to be more stable. We also expect more private monitoring to be positively correlated with bank stability, as this provides more incentives for the private monitoring of firms. The results in columns (5) and (10) are consistent with our expectation. Considering the impact of external governance, we expect better financial statement transparency to be associated with lower bank fragility because information asymmetry may serve as a channel through which risks and shocks can be propagated in the banking sector (Hong & Stein, 2003). Moreover, better financial statement transparency
Constant
Deposit insurance
Financial statement transparency
Private monitoring index
Diversification index
Declaring insolvency power
Capital regulatory index
Activity restriction
(8)
(1.59)
(1.67)
0.120*
(−2.78) (1.22)
0.136
(−2.47)
(9)
(10)
(11)
(12)
(13)
(−3.39)
(−3.01)
(−3.21)
(−3.71)
(−3.77)
(−3.30)
(−3.73) (continued)
(−1.88)
−3.246*** −2.911*** −2.963*** −2.214*** −3.970*** −4.195*** −2.240***
−0.152*
(1.24)
0.138
(−2.64)
(−0.20)
(2.02)
(14) −0.522***
−0.0238
0.249**
(1.39)
(3.46)
(1.49)
0.243
(−2.56)
0.136
0.232***
(2.99)
(2.15)
(1.63)
0.185
(−2.65)
0.146***
0.124**
(2.20)
(1.69)
(1.20)
0.133
(−2.64)
0.178**
0.1000*
(0.99)
(2.07)
(1.50)
0.167
(−2.41)
0.0590
0.00183**
(2.08)
(1.46)
0.162
(−2.72)
0.00188**
(2.41)
(1.73)
0.185
(−2.83)
0.0913**
(1.31)
0.131*
(−2.75)
(2.32)
(1.78)
0.0915
(7)
0.0697**
Bank regulation variables
(1.57)
(1.20)
0.115*
(−2.37)
(6)
(−2.78)
0.106
(−2.91)
0.0835
(5)
(−2.84)
Bank reform index
(4) −0.368*** −0.375*** −0.506*** −0.369*** −0.486** −0.537*** −0.472** −0.532*** −0.518*** −0.612**
(3)
Lerner index
(2)
(1)
−0.376*** −0.386*** −0.316**
Variables
Table 3.5 Relationship between bank competition, reform, regulation and stability
72 3 Banking Sector Reform, Competition, and Bank Stability
FE
3980
FE
0.157
3712
FE
0.153
FE
0.172
2521
Yes
FE
0.154
3252
Yes
Yes
0.137
1868
Yes
Yes
Yes
IV
0.454
5.436
12.32
0.134
1871
Yes
Yes
Yes
Yes
(9)
IV
0.245
1.562
11.76
0.122
1827
Yes
Yes
Yes
Yes
(10)
IV
0.816
7.021
12.43
0.135
1871
Yes
Yes
Yes
Yes
(11)
IV
0.135
2.619
12.36
0.146
1807
Yes
Yes
Yes
Yes
(12)
IV
0.624
8.219
11.37
0.150
1441
Yes
Yes
Yes
Yes
(13)
IV
0.420
3.901
8.959
0.134
1870
Yes
Yes
Yes
Yes
(14)
This table examines the impact of bank reform and competition on bank stability. The dependent variable is the logarithm of Z-score as defined in Eq. (3.1). Our sample period is 1998–2016. Lerner index is estimated based on Eq. (3.2) and employed as a measure of bank competitive indicator. Bank reform index is a bank reform. Bank-level controls include log TA, log EQ, log LLP, log DPS, and FA. Country control variables include GDP growth, Inflation, and Crisis. Bank regulation variables include Activity restriction, Capital regulatory index, Declaring insolvency power, Diversification index, Private monitoring index, Financial statement transparency, and Deposit insurance. All variables are defined in Table 3.1. Bank-level control variables and country-level variables are also included in the regressions but not reported for brevity. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. The Hansen’s J test evaluates the joint validity of instruments used. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
FE
0.137
3854
Yes
Yes
Yes
Yes
(8)
IV
FE
0.151
3886
Yes
Yes
Yes
Yes
(7)
Estimation method
0.154
Adjusted R-squared
Yes
Yes
Yes
Yes
(6)
0.355
3874
N
Yes
Yes
Yes
Yes
(5)
Hansen’s J statistic (p-value)
Yes
Bank fixed effects
Yes
Yes
Yes
(4)
3.660
Yes
Year fixed effects
Yes
Yes
(3)
Hansen’s J statistic
Yes
Country Control variables
Yes
(2)
12.76
Yes
Bank-specific variables
F-statistic of first stage
(1)
Variables
Table 3.5 (continued)
3.6 Empirical Analysis 73
74
3 Banking Sector Reform, Competition, and Bank Stability
can significantly lower the information asymmetry between a bank’s stakeholders. The coefficient of Financial statement transparency in the IV estimation is significantly positive, confirming that better financial statement transparency increases bank stability, which is consistent with our expectation. Deposit insurance is negatively correlated with the Z-score, which is consistent with the findings of DeLong and Saunders (2011) and Fu et al. (2014), confirming that deposit insurance coverage makes depositors less likely to enforce market discipline and intensifies moral hazard problems. Table 3.6 examines the effect of bank regulation on the relationship between bank competition and stability, after controlling for the impact of bank reform. Columns (1) and (9) in Table 3.6 include the interaction term between the bank reform index and Lerner index and test whether the relationship between competition and stability varies when considering banking sector reform. The coefficients of the interaction term are significantly negative, which indicates that the effect of bank competition in increasing bank stability is more pronounced when transition countries experienced higher bank reforms. Columns (2)–(8) and (10)–(16) in Table 3.6 present the results of the interaction terms between the bank regulation variables and Lerner index, after controlling for the impact of bank reform. The coefficients of the interaction terms for Activity restrictions and Diversification index are significantly positive (columns (2) and (9) and columns (5) and (13), respectively), indicating that the effect of competition in reducing bank risk is less pronounced if the bank is exposed to higher activity restrictions and provided with more explicit guidelines for diversification. The negative coefficients of the interaction term for Capital regulatory index in columns (3) and (11) provide evidence that the benefit of bank competition in reducing bank instability is greater in a banking system that has more stringent capital requirements.
3.6.3 Robustness Checks We also perform robustness checks based on different specifications and subsamples from our main models.10 First, following Koetter et al. (2012), we employ the efficiency-adjusted Lerner index as an alternative measure of bank competition. Our main findings are consistent (see columns (1) and (2) of Table 3.7). Second, we introduce country FE into the regression to control for the heterogeneity between countries instead of bank FE, while year FE are also considered. The results remain unchanged (see columns (3) and (4) of Table 3.7). Third, we adopt different sample selection criteria. Columns (5) and (6) of Table 3.7 report the results including only countries with more than 100 observations and columns (7) and (8) report the findings excluding banks in Russia. The results show that our findings are consistent under these alternative sample selection criteria. 10 For brevity, we only report the results for the FE estimations. The results for the IV estimations are consistent with those findings and are available upon request.
Diversification index * Lerner index
Diversification index
Declaring insolvency power * Lerner index
Declaring insolvency power
Capital regulatory index * Lerner index
Capital regulatory index
Activity restriction * Lerner index
Activity restriction
Lerner index * Bank reform index
(1.81)
0.0677*
(1.88)
0.0673*
(−2.04)
−0.00319**
(0.01)
0.000297
(0.36)
(−0.13)
(−2.27)
(2.51)
(0.03)
0.00527
(0.69)
0.0527
(0.39)
0.0439
(2.24)
(−1.03) 0.0993**
(−1.74)
(4) −0.486
0.0862** 0.0424
(0.75)
(3) −0.532*
−0.0298** −0.0152
(2.09)
(−2.34) 0.0525
(−1.80) 0.0637**
Lerner index
Bank reform index
(2) −0.801**
(1) −0.268*
Variables
Table 3.6 The impact of bank reform and regulation on stability: through the channel of competition (5)
0.425*
(0.39)
0.0449
(0.17)
0.0204
(1.03)
0.0731
(−2.12)
−1.049**
(6)
(0.58)
0.0724
(1.47)
0.112
(−2.29)
−0.252**
(7)
(0.13)
0.0259
(1.22)
0.145
(−1.93)
−1.774*
(8)
(continued)
(0.41)
0.0486
(1.32)
0.0971
(−0.73)
−0.380
3.6 Empirical Analysis 75
Yes Yes Yes Yes 4313 0.161
Country control variables
Year fixed effects
Bank fixed effects
N
Adjusted R-squared
0.154
3765
Yes
Yes
Yes
Yes
0.150
3777
Yes
Yes
Yes
Yes
(−2.85)
(−3.23)
(−2.26)
(3)
−4.918*** −4.468***
(2)
−3.277**
(1)
Bank-specific variables
Constant
Deposit insurance * Lerner index
Deposit insurance
Financial statement transparency * Lerner index
Financial statement transparency
Private monitoring index * Lerner index
Private monitoring index
Variables
Table 3.6 (continued) (1.74)
(5)
(−0.44)
−0.0531
(2.69)
0.170***
(6)
(1.02)
0.228
(0.93)
0.104
(7)
(−0.48)
−0.186
(0.06)
0.0105
(8)
0.136
3732
Yes
Yes
Yes
Yes
(−3.49)
0.157
3870
Yes
Yes
Yes
Yes
(−2.72)
0.153
3620
Yes
Yes
Yes
Yes
(−3.43)
0.175
2434
Yes
Yes
Yes
Yes
(−3.09)
(continued)
0.154
3252
Yes
Yes
Yes
Yes
(−3.36)
−3.234*** −3.878*** −5.741*** −6.481*** −4.662***
(4)
76 3 Banking Sector Reform, Competition, and Bank Stability
Declaring insolvency power
Capital regulatory index * Lerner index
Capital regulatory index
Activity restriction * Lerner index
Activity restriction
Lerner index * Bank reform index (−0.72)
(−2.12)
(2.60)
0.199***
(1.03)
0.0475
(1.07) −0.127
(1.27) −0.0215**
(−1.89)
−0.103*
(0.52)
0.0214
(−0.16)
−0.0280
(1.39)
(0.28)
0.272
(11)
0.154
FE
(3)
0.120
(−1.62)
(−1.84) 0.142
−1.353
−0.463*
Lerner index
Bank reform index
(10)
FE
(2)
(9)
FE
(1)
Variables
Estimation method
Hansen’s J statistic (p-value)
Hansen’s J statistic
F-statistic of first stage
Variables
Table 3.6 (continued)
(0.69)
0.0824
(0.31)
0.0519
(1.53)
0.169
(−1.99)
−0.738**
(12)
FE
(4)
(−0.33)
−0.0568
(1.16)
0.128
(−1.74)
−1.338*
(13)
FE
(5)
(0.02)
0.00349
(1.73)
0.186*
(−1.34)
−1.782
(14)
FE
(6)
(−0.07)
−0.0188
(1.43)
0.233
(−2.32)
−2.041**
(15)
FE
(7)
(continued)
(0.01)
0.00165
(1.24)
0.138
(−0.17)
−0.120
(16)
FE
(8)
3.6 Empirical Analysis 77
Yes Yes
Country control variables
(9)
Bank-specific variables
Constant
Deposit insurance * Lerner index
Deposit insurance
Financial statement transparency * Lerner index
Financial statement transparency
Private monitoring index * Lerner index
Private monitoring index
Diversification index * Lerner index
Diversification index
Declaring insolvency power * Lerner index
Variables
Table 3.6 (continued)
Yes
Yes
(10)
Yes
Yes
(11)
(12)
Yes
Yes
(0.25)
0.0704
Yes
Yes
(1.94)
0.683*
(−0.30)
−0.0447
(13)
Yes
Yes
(0.97)
0.173
(2.21)
0.190**
(14)
Yes
Yes
(0.95)
0.292
(1.27)
0.169
(15)
(continued)
Yes
Yes
(−0.86)
−0.445
(−0.24)
−0.0577
(16)
78 3 Banking Sector Reform, Competition, and Bank Stability
Yes 1871 0.134 14.45 4.742 0.315 IV
Year fixed effects
Bank fixed effects
N
Adjusted R-squared
F-statistic of first stage
Hansen’s J statistic
Hansen’s J statistic (p-value)
Estimation method
(10)
IV
0.424
3.867
12.52
0.138
1868
Yes
Yes
(11)
IV
0.208
5.882
11.68
0.134
1871
Yes
Yes
(12)
IV
0.831
1.478
11.18
0.122
1827
Yes
Yes
(13)
IV
0.0963
7.875
11.83
0.139
1871
Yes
Yes
(14)
IV
0.559
2.992
11.95
0.146
1807
Yes
Yes
(15)
IV
0.0579
9.133
11.13
0.151
1441
Yes
Yes
(16)
IV
0.424
3.868
11.70
0.134
1870
Yes
Yes
This table examines the impact of bank reform and competition on bank stability. The dependent variable is the logarithm of Z-score as defined in Eq. (3.1). Our sample period is 1998–2016. Lerner index is estimated based on Eq. (3.2) and employed as a measure of bank competitive indicator. Bank reform index is a bank reform indicator. Bank-level controls include log TA, log TA * log TA, log EQ, log LLP, log DPS, and FA. Country control variables include GDP growth, Inflation, and Crisis. Bank regulation variables include Activity restriction, Capital regulatory index, Declaring insolvency power, Diversification index, Private monitoring index, Financial statement transparency, and Deposit insurance. All variables are defined in Table 3.1. Bank-level control variables and country-level variables are also included in the regressions but not reported for brevity. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. The Hansen’s J test evaluates the joint validity of instruments used. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
(9) Yes
Variables
Table 3.6 (continued)
3.6 Empirical Analysis 79
Bank reform index * foreign dummy
Lerner index * foreign dummy
Lerner index * bank reform index * Commercial dummy
Bank reform index * commercial dummy
Lerner index * commercial dummy
Commercial dummy
Lerner index * bank reform index
Bank reform index
Lerner index
Efficiency-adjusted Lerner index * Bank reform index
(−2.59)
0.545*** (3.25)
(1.84)
(−2.72)
−0.965***
0.125*
(−2.19)
Efficiency-adjusted Lerner index
(2)
(1) −0.0510** −2.752***
Variables
Efficiency-adjusted Lerner index
(1.04)
0.0729
(−2.38)
(−2.40)
(0.43)
0.0433 −0.0603**
(0.46)
0.0462
(−1.72)
−0.0298**
(1.97)
0.0637***
(−1.98)
(−2.43)
(6)
−0.228**** −0.0590*
(5)
(−2.41)
(4)
Countries with more than 100 observations
−0.336** −0.268**
(3)
Country fixed effects
Table 3.7 Impact of bank competition and reform on bank stability: robustness checks
(continued)
(1.90)
0.124**
(−2.69)
−0.453***
(7)
Russia excluded
80 3 Banking Sector Reform, Competition, and Bank Stability
Yes No 3738 0.138 FE
Country fixed effects
N
Adjusted R-squared
Estimation method
Yes
Year fixed effects
Bank fixed effects
Yes Yes
Country control variables
FE
0.142
3738
No
Yes
Yes
Yes
Yes
(−3.28)
FE
0.647
4440
Yes
No
Yes
Yes
Yes
(−1.93)
−3.339*** −2.046*
(3)
(−2.15)
(2)
FE
0.651
4313
Yes
No
Yes
Yes
Yes
(−1.88)
−3.056*
(4)
Country fixed effects
−2.051**
(1)
Efficiency-adjusted Lerner index
Bank-specific variables
Constant
Lerner index * bank reform index * EU entry
Bank reform index * EU entry
Lerner index * EU entry
EU entry
Lerner index * bank reform index * foreign dummy
Variables
Table 3.7 (continued)
FE
0.150
2567
No
Yes
Yes
Yes
Yes
(−1.44)
−2.283
(5)
FE
0.145
2524
No
Yes
Yes
Yes
Yes
(−1.65)
−2.650*
(6)
Countries with more than 100 observations
FE
0.181
2735
No
Yes
Yes
Yes
Yes
(continued)
(−2.04)
−2.196**
(7)
Russia excluded
3.6 Empirical Analysis 81
Lerner index * commercial dummy
Commercial dummy
Lerner index * Bank reform index
Bank reform index
Lerner index
Efficiency-adjusted Lerner index * Bank reform index
Efficiency-adjusted Lerner index
Variables
Table 3.7 (continued)
(2.40)
(0.44) 0.00626
2.936**
0.154
(1.76)
(−2.34)
(2.22)
(1.24) −0.304*
0.819**
0.0719
−0.0575**
(2.46)
0.123**
(−2.70)
(−0.16)
−0.336*** −0.260
(−1.76)
(10)
−0.302*
(9)
Russia excluded Commercial banks (8)
(12)
(2.25)
0.0726**
(−2.69)
(−2.02)
−0.0029**1
(0.53)
0.0335
(−1.67)
−0.335*** −0.233*
(11)
Foreign banks (14)
(1.56)
0.0886
(−2.62)
(continued)
(−0.15)
−0.0184
(0.97)
0.0621
(−0.66)
−0.327*** −0.234
(13)
EU entry
82 3 Banking Sector Reform, Competition, and Bank Stability
Bank reform index * EU entry
Lerner index * EU entry
EU entry
Lerner index * bank reform index * foreign dummy
Bank reform index * Foreign dummy
Lerner index * foreign dummy
Lerner index * bank reform index * commercial dummy
Bank reform index * commercial dummy
Variables
Table 3.7 (continued) (9)
(−0.57)
−0.351
(−2.12)
−0.791**
(0.00)
(10)
Russia excluded Commercial banks (8)
(11)
(−0.43)
−0.109
(1.28)
0.153
(−0.38)
−0.321
(12)
Foreign banks
(−0.02)
(continued)
0.0936
(−1.76)
−2.412*
−0.0111 (1.44)
(14)
0.196
(13)
EU entry
3.6 Empirical Analysis 83
2623 0.189 FE
N
Adjusted R-squared
Estimation method
FE
0.160
4445
No
Yes
Yes
Yes
Yes
(11)
FE
0.165
4313
No
Yes
Yes
Yes
Yes
(−3.30)
FE
0.160
4445
No
Yes
Yes
Yes
Yes
(−2.45)
FE
0.162
4313
No
Yes
Yes
Yes
Yes
(−2.28)
−3.291**
(12)
Foreign banks
−5.980*** −2.265**
(10)
(1.64)
0.641
(0.64)
(14)
FE
0.161
4445
No
Yes
Yes
Yes
Yes
(−2.71)
FE
0.163
4313
No
Yes
Yes
Yes
Yes
(−2.27)
−2.470*** −3.303**
(13)
EU entry
This table examines the impact of bank reform and competition on bank stability according to different estimation specifications and subsamples. The dependent variable is the logarithm of Z-score as defined in Eq. (3.1). Our sample period is 1998–2016. Lerner index is estimated based on Eq. (3.2) and employed as a measure of bank competitive indicator. Bank reform index is a bank reform indicator. Bank-level controls include log TA, log EQ, log LLP, log DPS, and FA. Country control variables include GDP growth, Inflation, and Crisis. All variables are defined in Table 3.1. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
Yes No
Country fixed effects
Yes
Year fixed effects
Bank fixed effects
Yes Yes
(−2.40)
(−1.68)
Country control variables
−2.437**
−3.074*
(9)
Russia excluded Commercial banks (8)
Bank-specific variables
Constant
Lerner index * bank reform index * EU entry
Variables
Table 3.7 (continued)
84 3 Banking Sector Reform, Competition, and Bank Stability
3.6 Empirical Analysis
85
We then examine the relationship when considering different types of banks. The results for commercial banks (columns (9) and (10)) show that they enjoy more stability than other types of banks and that the relationships among bank reform, competition, and stability do not significantly change, while the results for foreign banks (columns (11) and (12)) show no significant differences between foreign banks and domestic banks, although the former are regulated not only by the home country but also by host countries.11 Finally, we test whether transition countries’ decision to join the European Union (EU) significantly changes the relationships among bank reform, competition, and stability.12 We include the binary variable EU entry, which equals one after the country joined the EU, to examine the impact of EU entry. The results in columns (13) and (14) suggest that EU entry increases bank stability and that the positive relationship between bank competition and stability becomes more pronounced after joining the EU.13
3.7 Conclusion This study tests the influence of banking sector reform and competition on bank stability based on unbalanced data from 22 transition countries between 1998 and 2016. The initial results confirm the negative relationship between market power and bank stability and the positive relationship between bank reform and stability. These findings indicate that competition incentivizes banks to diversify risk and thus reduces bank instability and that banks experience an improvement in financial stability after banking sector reforms. In terms of bank regulations, both higher activity restrictions and more explicit guidelines for asset diversification increase bank stability, but this positive effect significantly weakens for banks with higher market power. More stringent capital requirements in combination with higher market power increase the risk of bank insolvency. The results also show that higher declaring insolvency power, private 11 Previous
studies such as De Haas and Van Lelyveld (2006), Havrylchyk and Jurzyk (2011), and Fang et al. (2014) argue that foreign banks are not significantly sensitive to the bank reforms in host countries but are more sensitive to the wishes of parent banks in home countries and that foreign banks’ lending criteria are highly associated with the parent country’s regulation rather than that of the host country (Ongena et al., 2013). These conclusions may explain our findings: foreign banks are not sensitive to bank reform and changes in competitive conditions in host countries as well as the relationships among bank reform, competition, and stability do not vary significantly for domestic banks or foreign banks. 12 During our sample period, some transition countries joined the EU including the Czech Republic, Hungary, Poland, Slovakia, and Slovenia (2004), Bulgaria and Romania (2007), and Croatia (2013). The banks in transition countries that joined the EU are subject to changes in the macroeconomic environment, financial markets, and bank regulation. 13 We are also concerned about how the bank reform changes affect bank stability. As a robustness check, we use the change in the bank reform indicator as an alternative measure of bank reform. The results are consistent with our main findings and are available upon request.
86
3 Banking Sector Reform, Competition, and Bank Stability
monitoring, and financial statement transparency enhance bank stability, whereas having a deposit insurance scheme is negatively related to financial soundness. Our findings suggest several policy implications for transition countries. First, the negative relationship between market power and stability confirms the importance of competitive conditions in the banking system, while the positive relationship between bank reform and stability confirms the importance of bank reforms such as liberalizing interest rates, increasing lending to private enterprises, and increasing the market share of private banks. Second, the findings that activity restrictions, capital regulation, official supervisory actions, private monitoring, and higher financial statement transparency are correlated with higher bank stability provide empirical support that an appropriate bank regulation framework is crucial for ensuring bank stability. Finally, while our results suggest that higher activity restrictions reduce bank fragility, increases in activity restrictions make the financial market less contestable, which makes the banking system less competitive. This finding emphasizes the importance for a regulatory framework to strike the right balance between increasing bank stability and avoiding potential competition.
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Fernández, A. I., González, F., & Suárez, N. (2013). How do bank competition, regulation, and institutions shape the real effect of banking crises? International evidence. Journal of International Money and Finance, 33, 19–40. Fu, X. M., Lin, Y. R., & Molyneux, P. (2014). Bank competition and financial stability in Asia Pacific. Journal of Banking & Finance, 38, 64–77. Fungáˇcová, Z., & Weill, L. (2013). Does competition influence bank failures? Economics of Transition, 21(2), 301–322. Grossman, R. S. (1992). Deposit insurance, regulation, and moral hazard in the thrift industry: Evidence from the 1930s. The American Economic Review, 800–821. Havrylchyk, O., & Jurzyk, E. (2011). Profitability of foreign banks in Central and Eastern Europe. Economics of Transition, 19(3), 443–472. https://doi.org/10.1111/j.1468-0351.2010.00406.x Hellmann, T. F., Murdock, K. C., & Stiglitz, J. E. (2000). Liberalization, moral hazard in banking, and prudential regulation: Are capital requirements enough? American Economic Review, 90(1), 147–165. Hong, H., & Stein, J. C. (2003). Differences of opinion, short-sales constraints, and market crashes. The Review of Financial Studies, 16(2), 487–525. Koetter, M., Kolari, J. W., & Spierdijk, L. (2012). Enjoying the quiet life under deregulation? Evidence from adjusted Lerner indices for US banks. Review of Economics and Statistics, 94(2), 462–480. Kopecky, K. J., & VanHoose, D. (2006). Capital regulation, heterogeneous monitoring costs, and aggregate loan quality. Journal of Banking & Finance, 30(8), 2235–2255. Laeven, L., & Levine, R. (2009). Bank governance, regulation and risk taking. Journal of Financial Economics, 93(2), 259–275. Laeven, L., & Valencia, F. (2010). Resolution of banking crises: The good, the bad and the ugly. International Monetary Fund Working Paper. Laeven, L., & Valencia, F. (2018). Systemic banking crises revisited. IMF Working Paper No. 206. International Monetary Fund. Leroy, A., & Lucotte, Y. (2017). Is there a competition-stability trade-off in European banking? Journal of International Financial Markets, Institutions and Money, 46, 199–215. Llewellyn, D. T. (2008). The Northern Rock crisis: A multi-dimensional problem waiting to happen. Journal of Financial Regulation and Compliance, 16(1), 35–58. Mare, D. S., Moreira, F., & Rossi, R. (2017). Nonstationary Z-Score measures. European Journal of Operational Research, 260(1), 348–358. Marinˇc, M. (2013). Banks and information technology: Marketability vs. relationships. Electronic Commerce Research, 13(1), 71–101. Matutes, C., & Vives, X. (1996). Competition for deposits, fragility, and insurance. Journal of Financial Intermediation, 5(2), 184–216. Milne, A. (2009). The fall of the house of credit: What went wrong in banking and what can be done to repair the damage? Cambridge University Press. Ongena, S., Popov, A., & Udell, G. F. (2013). “When the cat’s away the mice will play”: Does regulation at home affect bank risk-taking abroad? Journal of Financial Economics, 108(3), 727–750. Panzar, J. C., & Rosse, J. N. (1987). Testing for “monopoly” equilibrium. The Journal of Industrial Economics, 35(4), 443–456. Perotti, E. C. (1993). Bank lending in transition economies. Journal of Banking & Finance, 17(5), 1021–1032. Repullo, R. (2004). Capital requirements, market power, and risk-taking in banking. Journal of Financial Intermediation, 13(2), 156–182. Schaeck, K., & Cihák, M. (2014). Competition, efficiency, and stability in banking. Financial Management, 43(1), 215–241. Schaeck, K., & Cihák, M. (2008). How does competition affect efficiency and soundness in banking? New empirical evidence. European Central Bank Working Paper Series No. 932.
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Chapter 4
The Impact of Bank Regulation and Supervision on Competition
4.1 Overview This article empirically investigates the influence of bank regulation and supervision on the competitive landscape in banking systems. Using the information on 23 emerging economies from 1996 to 2016, we confirm that banking systems with fewer activity restrictions and (foreign) bank entry barriers are more competitive. Greater capital strictness and official supervision enhance competition in the banking industry. Our findings also highlight that greater explicit guidelines on asset diversification and deposit insurance coverage and lower private-sector monitoring are associated with more intensive bank competition. A further examination reveals that, during a bank crisis, the relationship between activity restrictions, entry barriers, diversification guidelines, and competition become more pronounced, and the positive effect of foreign bank limitations, capital strictness, official supervision, and private monitoring on competitive conditions become less effective. Finally, we divide our sample into foreign banks and domestic banks and find that foreign banks are more sensitive to official supervision and private monitoring, and less sensitive to activity restrictions, foreign bank limitations, and diversification guidelines.
4.2 Introduction Over the past two decades, because of the development of information technology, globalization, and deregulation, the banking system has undergone dramatic changes. Emerging economies have experienced considerable economic development and financial reforms, including financial liberalization, bank privatization, widening access to foreign banks, and restructuring national banking systems, which aimed to reshape competitive conditions in the banking sector. For instance, the This chapter was published in Emerging Markets Finance and Trade. © Shanghai Jiao Tong University Press 2021 S. Li, Financial Regulation and Bank Performance, Contributions to Finance and Accounting, https://doi.org/10.1007/978-981-16-3509-0_4
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Chinese government recapitalized the state-owned commercial banks and reduced the number of non-performing loans (NPLs) by injecting new capital; additional changes included interest rate liberalization, fewer activity restrictions, and privatization of the banks through selling shares on the market and introducing minority foreign ownership stakes (Hasan et al., 2009). In India, the deregulation of the banking system includes interest rate liberalization, the removal of bank entry restrictions, private ownership restrictions, and activity restrictions; these structural deregulations were aimed at promoting competition. Prudential norms on NPLs and capital requirements were also implemented to strengthen financial stability (Zhao et al., 2010). These changes decreased profitability in traditional bank activities and led to massive mergers and acquisitions (M&A) among financial institutions in emerging economies. While changes in the financial market are the main driving forces for bank consolidation in developed economies, financial supervisory authorities play an important role in the bank consolidation process in emerging economies (Gelos & Roldós, 2004). The overall differences in economic development, bank consolidation, and regulation between developed economies and emerging economies created distinctive features in competitive conditions in the banking industry in emerging economies. Following the recent global financial crisis, policy makers reshaped bank regulations substantially, especially in emerging economies. The goal of this study is to examine the impact of bank regulation and supervision on competitive conditions in the banking sector in emerging economies and examine whether the unique characteristics in emerging markets shed light on the relationship between bank regulation and competition from different perspectives. Using a sample of 1629 banks in 23 emerging economies between 1996 and 2016, this study employs the Panzar and Rosse (1987) methodology and constructs the competitive variable H-statistic as a measure of competition in the banking system. We also compute the Lerner index (see also Coccorese, 2009; Koetter et al., 2012) and the Boone indicator (2008) as alternative measures of competition. We investigate how competition evolved under different types of bank regulation and supervision. As our sample period covers several banking crises in some emerging economies as well as the recent global financial crisis, we also examine whether the relationship between bank regulation and competition changed during the banking crisis. Considering the different roles played by domestic banks and foreign banks, we also study whether the impact of bank regulation on foreign banks exhibits different patterns. Our analysis shows that banking systems with higher concentration and fewer activity restrictions and entry barriers are more competitive. We also find that reducing foreign bank limitations and increasing capital strictness and official supervisory power also enhances competition in the banking sector. The results also provide evidence that competition in banking systems with fewer government-owned banks and fewer diversification guidelines tends to be more intensive. The results also confirm that higher private monitoring of banks and deposit insurance coverage significantly contribute to an increase in bank competition.
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93
During a banking crisis, the impact of activity restrictions, entry barriers, and diversification guidelines on competitive conditions become more effective, whereas foreign bank limitations, capital strictness, supervisory power, and private monitoring become less effective. To further investigate whether the relationship between bank regulation and competition varies according to the type of ownership, we divide the sample banks into two groups: foreign banks and domestic banks. Our findings suggest that foreign banks are more sensitive to official supervisory power and private monitoring and less sensitive to activity restrictions, foreign bank limitations, and diversification guidelines. Our analysis extends previous studies in several respects. First, we focus our analysis on 23 emerging economies. In emerging economies, cross-border M&As are the main reason for consolidation in the banking system, and financial authorities are deeply involved in the banking system and serve an important role in its restructuring. These factors indicate that bank consolidation and competitive conditions in emerging economies exhibit different characteristics. During our sample period, financial reforms have been implemented across the emerging economies, including financial liberalization, bank privatization, widening access to foreign banks, and restructuring the national banking systems, so as to reshape competitiveness in the banking sector. The unique institutional setting of the emerging economies can provide further insight into previous research. We also employ three different measures of competition (H-statistic, Lerner index, and Boone indicator) and incorporate bank regulatory and supervisory factors to examine the impact of bank regulation and supervision on competition in emerging economies, while controlling for a variety of bank-level and country-level characteristics that may affect bank competition. The rest of the paper is structured as follows. The third section provides a review of the previous studies on competition and then summarizes the literature on bank regulation and competition. The fourth section mainly addresses the estimation of competition variables, sample selection, data, and research methodology. The fifth section offers empirical analysis and robustness checks, and last section draws the final conclusions.
4.3 Literature Review 4.3.1 Previous Studies on Competition The previous literature includes empirical studies that measure industrial competition using structural and non-structural approaches. The structural approach is based on the traditional structure-conduct-performance (SCP) paradigm and links concentration, competition, and firm performance. SCP assumes that the market structure, which is reflected in the level of market concentration, affects firm behavior and then in turn determines firm performance (Bain, 1951). The problem is that the SCP
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4 The Impact of Bank Regulation and Supervision on Competition
analysis does not explicitly incorporate the effect of regulation and examine whether regulatory changes increase or decrease the relationship between market structure and competitive behavior in the banking system. One non-structural approach to measuring competition is the new empirical industrial organization (NEIO) approach. Unlike the SCP paradigm, which tries to determine competition from the market structure in a given industry, the NEIO models directly analyze firm conduct to detect the market power of firms. The NEIO models rely on a comparative statics analysis, as in the Panzar and Rosse (1987) model. The PR model measures market power using the H-statistic, which is calculated as the sum of revenue elasticities with respect to input prices. It measures how much a change in factor prices affects the firm’s equilibrium revenue. The PR model has been widely used to measure competition in the banking industry. Vesala (1995) investigates how deregulation in the 1980s affected competition among Finnish banks. Coccorese (2004, 2009) analyzes competitive conditions in the Italian banking industry. Matthews et al. (2007) and Maudos and Solís (2011) employ both the PR model and the Lerner index to analyze competition in the British and Mexican banking industries, respectively. These findings mostly indicate that banks operate under monopolistic competition. An alternative non-structural technique to the PR model is to estimate a parameter that directly measures firms’ competitive behavior from information on firm costs and demand. For example, the Lerner index is a relative markup of the price over marginal cost and measures firm market power (Lerner, 1934). The higher the markup is, the greater the market power. The Lerner index ranges from 0 in the case of perfect competition to 1 in the case of monopoly. A number of studies (e.g., Bikker & Haaf, 2002) show empirically that the H-statistic and the Lerner index are negatively correlated. That is, the relative price–cost markup (smaller Lerner index) decreases with higher competition (higher H-statistic). The Lerner index is widely used to estimate competition in the banking sector. Coccorese (2009) points out that the Lerner index is a true reflection of the bank’s degree of market power. Angelini and Cetorelli (2003) assess the behavior of Italian regional banks and find that deregulation fostered a reduction in the price-costs margin. Fu et al. (2014) use the Lerner index as a measure of bank competition and investigate the influence of bank competition on individual bank fragility, and Anginer et al. (2014a) use both the H-statistic and the Lerner index to measure bank competition and find a robust negative relationship between bank competition and systemic risk. Based on the hypothesis that more efficient banks are rewarded more in more competitive markets, Boone (2008) developed a new indicator. The basic intuition underlying the Boone indicator is that competition improves efficiency in bank performance with regard to profitability and market share and weakens the performance of inefficient banks. The Boone indicator has two major advantages compared to other measures: (1) it is based on strong theoretical foundations and captures competition due to both a fall in entry barriers and to more aggressive behavior on the part of incumbents, and (2) it captures the dynamics and non-price strategy in the market, while the other measures of competition are based on static price competition. The Boone indicator has been used in considerable literature in recent banking studies,
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including Delis (2012), Schaeck and Cihák (2014), Khan et al. (2017), Clark, et al. (2018). The empirical evidence on bank competition in emerging market economies is growing. For example, Gelos and Roldós (2004) examine the evolution of the market structure in the banking system in emerging markets based on the PR model and confirm that markets have not become less competitive there. Jeon et al. (2011) show that an increase in foreign bank penetration enhances competition in these host countries’ banking sector, and the positive foreign bank penetration and banking competition link has a spillover effect from foreign banks to their domestic counterparts. Apergis (2015) employs the PR methodology and assesses competition across the banking systems in emerging market economies, while emphasizing the impact of the recent financial crisis on the degree of competition in these banking systems. Other papers examine bank competitive conditions focusing on one or a few emerging economies: Tan (2016) and Tan and Floros (2018) investigate the relationship between bank competition, risk, and profitability based on evidence in the Chinese banking industry; Soedarmono et al. (2011, 2013) and Khan et al. (2017) examine bank competition based on emerging markets in Asia.
4.3.2 Bank Regulation and Effects on Bank Competition The traditional theory indicates that bank deregulation is positively associated with efficiency because of a reduction in regulatory costs imposed on the banking system. Based on both theoretical and empirical evidence, Keeley (1990) confirms that deregulation reduces bank market power in the US market, and Dick (2006) suggests that higher loan loss provisions after the deregulation. Matutes and Vives (2000) study the links between competition for deposits and risk-taking incentives and conclude that the welfare performance of the market and the appropriateness of alternative regulatory measures depend on the degree of rivalry and the deposit insurance regime. Hellmann et al. (2000) analyze the relationship between competition for deposits and capital regulation in a dynamic framework in which banks choose their asset risk privately and compete for deposits and find that capital requirement regulation is not an optimal choice for controlling risk-taking incentives. A number of studies have investigated the effect of regulations and factors that presumably are related to competition in the banking system. Based on a survey of bank regulation and supervision in the banking system, Barth et al. (2004) find that higher entry barriers reduce bank efficiency and lead to an increase in interest rate margins and personnel expenses, which empirical confirms that entry restrictions reduce bank competition. Using a cross-country sample, Claessens et al. (2001) examine the impact of foreign banks on domestic banking and confirm that the introduction of foreign banks increases efficiency at domestic banks. In a cross-country study based on bank financial data in 72 countries, DemirgucKunt et al. (2004) confirm that tighter bank entry restrictions and activity restrictions are negatively related to banking efficiency, especially at foreign banks. Gelos and
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Roldós (2004) examine the evolution of market structure in emerging markets and argue that lower entry barriers mitigated a decline in competition driven by consolidation. Claessens and Laeven (2004) investigate competitive conditions across 50 banking systems, finding that systems with greater foreign bank entry and few entry and activity restrictions are more competitive. Delis et al. (2011) examine the relationship between bank productivity and regulatory framework in 22 transition countries and find that private monitoring and activity restrictions have a significant impact on productivity. Only a few recent studies provide direct findings on the relationship between banking regulation and competition by focusing only on emerging economies. For example, Beck et al. (2005) analyze the impact of liquidation, federalization, privatization, and reconstruction on the performance of state banks in Brazil; Fu and Heffernan (2009) investigate the effect of reforms in China’s banking sector on market structure and performance; Brissimis et al. (2008) examine the impact of banking sector reform on bank performance in ten emerging economies in Europe; Zhao et al. (2010) evaluate the impact of financial sector reforms on cost structure, ownership, and competition in the Indian banking system; Tompson (2004) gives a detailed analysis of banking reform in Russia; and Williams and Nguyen (2005) and Noman et al. (2018) examine the impact of financial liberalization, bank restructuring, and bank regulation on bank performance in countries in Southeast Asia.
4.4 Estimation Methodology and Data 4.4.1 Estimation of Competition Variables (1)
Panzar-Rosse model
In our analysis, we first employ the PR model to construct the H-statistic to measure market power. Panzar and Rosse (1987) show that the H-statistic, which is equal to or less than zero, indicates a collusive or joint monopoly equilibrium or monopolistic competition without the threat of entry; a value between zero and one means monopolistic competition, and a value equal to one indicates perfect competition. Based on Demirguc-Kunt et al. (2004) and Anginer et al. (2014b), we employ the following reduced-form revenue equation and estimate the H-statistic for each country in each year1 : ln OPIit = α +
3 j=1
1 Bikker
βj ln wjit +
πk Controlskit + αi + εit
(4.1)
k
et al. (2012) indicate that a scaled revenue function leads to a significant upward bias and incorrectly measures the degree of competition, so in our analysis we employ the unscaled revenue equation to reduce the estimation bias.
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where OPI it is the operating income (as a measure of revenue).2 We define w1 as the ratio of annual interest expenses to total funding and use it as a proxy for the average funding rate; w2 is the ratio of personnel expenses to total assets used as an proxy for personnel expenses; and w3 is the ratio of other non-interest expenses to fixed assets used as a proxy for the price of physical capital. We also add a set of control variables: Customer Loan, which is defined as the ratio of customer loans to total assets to control for credit risk; NEA, which is defined as the ratio of other nonearning assets to total assets to control for asset composition; Customer Deposit, which is defined as the ratio of customer deposits to the sum of customer deposits and short-term funding to capture the funding structure of the bank; and Equity Ratio, which is defined as the ratio of equity to total assets to account for bank leverage. As in Coccorese (2009), all bank-specific factors that have potential effects on the level of operating income but are not included in Eq. (4.1) are captured through the insertion of dummy variables associated with banks (denoted by αi ). We winsorize all variables at the 1st and 99th percentile levels to reduce the influence of outliers. The H-statistic, which is defined as the sum of the elasticities of revenues with respect to input prices, is then given by β1 + β2 + β3 . As we use unscaled revenue in Eq. (4.1), one potential caveat is that the scale differences across banks may introduce heteroskedastic standard errors into the coefficients, which in turn inflates the H-statistic. In our analysis, we estimate the unscaled PR revenue model using pooled feasible generalized least squares (FGLS) to address the heteroskedasticity problem and clustered standard errors to account for general heteroskedasticity and cross-sectional correlation in the model errors (Arellano, 1987). An important feature of the H-statistic is that the PR model must be based on firms that operate in a long-run equilibrium (Panzar & Rosse, 1987). We follow the analysis of Bikker et al. (2012) and employ an equilibrium return on equity (ROE) test to check whether banks operate in a long-run equilibrium. By using ROE instead of operating income (OPI) as an independent variable in Eq. (4.1), the H-statistic under the ROE test equals zero if the banking system operates in a long-run equilibrium. (2)
Lerner index
We also employ the Lerner index as an alternative measure of competition and measure banks’ competitive behavior based on information on costs and demand. The Lerner index is calculated as follows: Lerner Indexit = (Pit − MCit )/Pit
(4.2)
In our analysis, Pit is the output price proxied by the ratio of total revenue to total assets, and MC it is the marginal cost.3 The marginal cost is derived from the total 2 For
a robustness check, we also use total revenue as another measure of revenue, and the findings are consistent. 3 Based on earlier studies, we define the output price P as the ratio of total revenues to total assets; it see also Angelini and Cetorelli (2003) and Fu et al. (2014).
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cost function—that is: MCit =
TCit (α1 + α2 ln Qit + α9 ln w1it + α10 ln w 2it + α11 ln w3it ) Qit
(4.3)
where the translog total cost function is α 2 (ln Qit )2 + α3 ln w1it 2 α α 6 7 + α5 ln w3it + (ln w1it )2 + (ln w2it )2 2 2
ln TCit = α0 + α1 ln Qit +
+ α4 ln w2it α 8 + (ln w3it )2 +α9 ln w1it ∗ ln Qit + α10 ln w2it ∗ ln Qit 2 +α11 ln w3it ∗ ln Qit +α12 ln w1it ∗ ln w2it + α13 ln w2it ∗ ln w3it + α14 ln w3it ∗ ln w1it + αi + εit
(4.4)
TC it denotes the total operating expenses, and Qit represents output, measured by total assets; w1 , w2 , and w3 represent the bank’s input prices as defined previously in the PR model. Following Koetter et al. (2012) and Fu et al. (2014), we use the stochastic cost frontier analysis to estimate Eq. (4.4) for each country in each year. (3)
Boone indicator
Finally, we also use Boone indicator as defined in Boone (2008). The Boone indicator evaluates the relationship between bank competition and efficiency, which indicates that banks with higher efficiency also perform better and have higher profits. The idea behind the Boone indicator is that as the relationship between efficiency and profits increases, so does the degree of competition. The Boone model is defined as follows: ln Profitit = α + β ln MCit + εit
(4.5)
where Profit it is the profit before tax of bank i in year t, MCit is the marginal cost as defined in Eq. (4.3). The Boone indicator β is negative and decreases with increases in competition. The log–log specification is employed to deal with potential heteroskedasticity.
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4.4.2 Data Sources, Sample Selection, and Variable Definitions (1)
Data Sources
Based on the International Monetary Fund’s definition of emerging economies, we focus our analysis on 23 emerging markets.4 We obtained the data from different sources. The financial data for each bank is collected from Bankscope. To avoid double counting, we use the consolidated financial information on each bank if available and unconsolidated reports otherwise. We obtained the bank regulation and supervision data from Barth et al. (2013), and the country economy development variables come from the World Bank Development Indicator Database. (2)
Sample selection
Based on a complete sample of banks obtained from Bankscope, we apply the following selection criteria: (1) we delete observations in which data on one of the variables employed in estimating the H-statistic, Lerner index, and Boone indicator are missing; (2) for the estimation of competition measures, we set the minimum number of observations to 20 for each year in each country, thus we delete countries with less than 20 bank-year observations; (3) we also delete observations in which the data on one of the bank-level and country-level control variables are missing. We use unbalanced data between 1996 and 2016, and our sample consists of commercial banks, saving banks, co-op banks, and bank holding companies. Our analysis has a total of 1629 banks and a total of 12,856 bank-year observations, with the largest number of observations in 2010 (914) and the lowest number in 1996 (236),5,6 All the data are inflation adjusted and expressed in USD. (3)
Variable Definitions
As we are interested in the impact of bank regulation and supervision on competition in the banking industry, we collect information of bank regulation and supervision from Barth et al. (2013) and define a set of variables to measure bank regulation and supervision in the banking system from different perspectives. 4 See detailed information on emerging economies in World Economic Outlook: Adjusting to Lower
Commodity Prices, International Monetary Fund, October 2015. our database, banks in Bangladesh and South Africa changed their accounting standard from local GAAP to international accounting standards during 2005–2006. In order to include as long a sample period as possible, we also include all bank observations (including those before the implementation of international accounting standards). Thus, for those two countries, we have two different accounting standards in our sample period: local GAAP (before 2005 or 2006) and international accounting standards (after 2005 or 2006). We use a fixed-effect model and include the year dummy variables to control for the effect of the changes in accounting standards. 6 In our sample period, especially during the global financial crisis in 2008–2009, some banks were bailed out. As information on the bailed-out banks in our sample is not available, we include both bank fixed-effect and year-fixed effects to avoid potential bias introduced by the bailed-out banks. 5 In
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4 The Impact of Bank Regulation and Supervision on Competition
We define Bank concentration as the degree of concentration of deposits at the five largest banks in a given country and Government-owned banks as the extent to which the banking system’s assets are government owned, to examine the impact of market structure on competition in a given country. To indicate restrictions on bank activity in a given country, we define Activity restriction as a bank’s ability to engage in securities, insurance, and real estate activities; it ranges from 0 to 12, and a higher score indicates more restrictions on banks engaging in such activities. To measure the regulations on bank competition, we define two variables: Foreign bank limitation, which measures whether foreign banks may own domestic banks and whether foreign banks may enter a country’s banking industry, in which lower values indicate greater strictness; and Entry barriers, which indicates that various types of legal documentation are required to obtain a banking license, in which higher values indicate more documentation. We use Capital regulatory to indicate the capital regulatory strictness, in which a higher value indicates greater strictness. Official supervisory power and Diversification index measure official actions by supervisors, and Private monitoring index is used to as a proxy for private monitoring. We also employ Deposit insurance ratio, which is defined as the size of the deposit insurance fund relative to total bank assets, as a measure of the deposit insurance coverage in a given country. We use several variables to control for bank-specific time-varying effects on competition. We define TCD as the ratio of total customer deposits to total assets to control for the deposit size, LLP as the ratio of loan loss provision to gross loans to control for loan quality, NIIC as the ratio of non-interest income to total operating income, ROA as a proxy for profitability, and log (TA), defined as the logarithm of gross total assets, to control for bank size. As country control variables, we include GDP per capita, GDP growth, and Inflation in a country to account for economic cycles. We also add Interest rate and Market capitalization to GDP to control for changes in monetary policy and capital market size in a given country. In our analysis, competition variables, including the H-statistic and the Lerner index, are estimated for each country in each year, and bank-level financial data and country-level control variables are also collected for each calendar year. The bank regulation and supervision information comes from Barth et al. (2013); however, their surveys were conducted in 1999, 2002, 2006, and 2011. Following Anginer et al. (2014a), we employ the previous survey data until new survey data become available to match the bank regulation and supervision variables with bank-specific variables and country control variables.7 Table 4.1 summarizes the definitions of the variables and data sources employed in this analysis.
7 Specifically, the survey data conducted in 1999 for 1996–2001, the survey data conducted in 2002
for 2002–2005, the survey data conducted in 2006 for 2006–2010, and the survey data conducted in 2011 for 2011–2016.
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Table 4.1 Variable definition and data sources Variables
Definitions
Data sources
Dependent variables H-statistic
H-statistic is calculated as the sum of the Own calculation elasticity of revenue with respect to three input prices for each country at each year between 1996 and 2016 based on the Panzar and Rosse (1987) model
Lerner index
Lerner index is equal to the difference between asset price and marginal cost, normalized by asset price
Own calculation
ln profit
Logarithm of the profit before tax in $ thousand
Bankscope
TCD
The ratio of total customer deposits to total assets
Bankscope
LLP
The ratio of loan loss provision to gross loans
Bankscope
NIIC
Non-interest income divided by total operating income
Bankscope
Bank control variables
ROA
Net income divided by total assets
Bankscope
log TA
Logarithm of total asset in $ thousand
Bankscope
Bank crisis
A dummy variable that equals one if the Laeven and Valencia (2010) country is going through a systemic crisis in a given year, and zero otherwise
Foreign dummy
A binary variable which equals one if the Bankscope bank is a foreign subsidiary and zero otherwise
Country control variables Market capitalization to GDP
Stock market capitalization to GDP
World Bank Development Indicator Database
GDP per capita
GDP per capital in $ thousand
World Bank Development Indicator Database
GDP growth
Annual growth rate of GDP at market prices based on constant local currency
World Bank Development Indicator Database
Inflation
Inflation rate
World Bank Development Indicator Database
Interest rate
The interest rate charged by banks on loans to prime customers
World Bank Development Indicator Database
Regulation and supervision variables Bank concentration
The degree of concentration of deposits in the 5 largest banks
Barth et al. (2013)
Government-owned banks
The extent to which the banking system’s assets are government owned
Barth et al. (2013) (continued)
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4 The Impact of Bank Regulation and Supervision on Competition
Table 4.1 (continued) Variables
Definitions
Activity restriction
A variable measures a bank’s ability to Barth et al. (2013) engage in securities, insurance, and real estate activities. The ranges from 0 to 12, and a higher score indicates more restrictions on banks to engage in such activities
Data sources
Foreign bank limitation
A variable measures whether foreign Barth et al. (2013) banks may own domestic banks and whether foreign banks may enter a country’s banking industry. The variable ranges from 0 to 4, and lower values indicate greater stringency
Entry barriers
A variable which indicates that whether Barth et al. (2013) various types of legal submissions are required to obtain a banking license. The variable ranges from 0 to 8, and higher values indicate greater stringency
Capital regulatory index
A variable that captures both the overall capital stringency and the initial capital stringency based on answers to eight questions. It ranges from zero to eight, with a higher value indicating higher capital stringency
Barth et al. (2013)
Official supervisory power
A variable that ranges from zero to fourteen, with fourteen indicating the highest power of the supervisory authorities
Barth et al. (2013)
Diversification index
A variable that ranges from zero to two, with higher values indicating more diversification
Barth et al. (2013)
Private monitoring index
A variable that measures whether there incentives/ability for the private monitoring of firm, with higher values indicating more private monitoring
Barth et al. (2013)
Deposit insurance ratio
The size of the deposit insurance fund relative to total bank assets
Barth et al. (2013)
This table reports the definitions and data sources of the variables included in our analysis
4.4.3 Statistical Results We provide the results in Panel A of Table 4.2, beginning with the competition variables. The average H-statistic is 0.575, ranging from 0.165 to 0.816, and the Lerner index has a mean of 0.225 with a standard deviation of 0.374.
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Table 4.2 Summary statistics Panel A: Data statistics Variable
N
Mean
STD
Min
Max
P25
Median
H-statistic
12,856 0.575
0.534
0.165
0.816
0.269
0.527
Lerner index
8792
0.374
0.192
0.901
0.287
0.258
ln Profit
20,782 10.389 3.757
0.943
18.265
1.647
9.733
TCD
12,856 0.535
0.256
0.031
0.816
0.335
0.613
LLP
12,856 0.016
0.018
0.000
0.057
0.002
0.009
NIIC
12,856 0.311
0.233
0.045
0.882
0.122
0.260
ROA
12,856 0.014
0.013
−0.003
0.043
0.005
0.011
log TA
12,856 14.862 2.660
10.284
19.537
12.831 14.581
Bank crisis
12,856 0.081
0.273
0
1
0
0
Foreign dummy
12,856 0.179
0.383
0
1
0
0
0.225
Market capitalization to GDP 12,856 62.929 53.275 0.850 (%)
321.984 26.753 46.601
GDP per capita ($ thousand)
12,856 5.280
3.587
0.395
16.007
2.400
4.596
GDP growth (%)
12,856 4.580
4.172
−13.127 14.231
3.005
5.094
Inflation (%)
12,856 8.320
8.824
−5.016
3.525
6.859 12.134
75.271
Interest rate (%)
12,856 18.469 15.716 3.536
67.083
7.470
Bank concentration (%)
10,677 56.683 18.385 4.000
93.000
52.000 57.000
Government-owned banks (%)
11,333 30.787 23.439 0.000
75.000
11.000 32.000
Activity restriction
12,714 7.702
2.013
4
12
6
8
Foreign bank limitation
12,693 3.670
0.674
0
4
3
4
Entry barriers
12,856 7.511
0.905
3
8
7
8
Capital regulatory index
12,645 6.455
2.263
2
10
5
6
Official supervisory power
12,856 11.578 2.356
4
16
10
12
Diversification index
11,559 1.269
0.757
0
2
1
1
Private monitoring index
12,465 8.565
1.239
6
11
8
9
Deposit insurance ratio
7441
0.325
0
1
0
0
0.120
Sample distribution by year Year
Frequency
Percentage
Country
Frequency
1996
236
1.84
Argentina
819
Percentage 6.37
1997
263
2.05
Bangladesh
201
1.56
1998
419
3.26
Brazil
1,490
11.59
1999
476
3.70
Bulgaria
203
1.58
2000
574
4.46
Chile
384
2.99
2001
537
4.18
China
1,307
10.17
2002
520
4.04
Colombia
354
2.75 (continued)
104
4 The Impact of Bank Regulation and Supervision on Competition
Table 4.2 (continued) Sample distribution by year 2003
482
3.75
Hungary
2004
608
4.73
India
853
6.64
2005
710
5.52
Indonesia
897
6.98
2006
741
5.76
Malaysia
975
7.58
2007
755
5.87
Mexico
164
1.28
2008
763
5.93
Pakistan
435
3.38
2009
817
6.36
Peru
346
2.69
2010
914
7.11
Philippines
468
3.64
2011
884
6.88
Poland
470
3.66
2012
743
5.78
Romania
247
1.92
2013
666
5.18
Russia
1,247
9.70
2014
591
4.60
South Africa
532
4.14
2015
585
4.55
Thailand
359
2.79
2016
572
4.45
Turkey
451
3.51
Ukraine
120
0.93
Venezuela
234
1.82
Total
12,856
100
Total
12,856
100
300
2.33
This table reports summary statistics of the variables for the full sample, and sample distribution according to year and country. Our sample period iss 2000–2016. All data are inflation-adjusted and bank-specific variables are winsorized at the 1st and 99th percentile level to reduce the influence of outliers
Then we report the results for bank-specific variables. The average ratio of customer deposits to total assets (TCD) is 54%, and LLP has an average of approximately 1.6% of gross loans. NIIC accounts for roughly 31.1% of the total operating income, and the average ROA is 1.4%. The average log TA value is 14.86, but the size of the banks in our sample varies substantially. This implies that any analysis needs to account for the size effect. In our sample, 17.9% of the banks are foreign owned. We include five country-level variables (Market capitalization to GDP, GDP per capita, GDP growth, Inflation, and Interest rates) to control for the changes of stock market and macroeconomic factors. The average market capitalization to GDP ratio is 62.9, GDP per capita is $5,280, GDP growth is 4.58%, inflation is 8.32%, and interest rate is 18.47%. Finally, we report descriptive statistics of bank regulation and supervision variables. The average for bank concentration is 56.68%, government-owned banks is 30.79%, activity restrictions 7.7, foreign bank limitations 3.67, entry barriers 7.51, capital regulatory index 6.46, official supervisory power 11.58, diversification index 1.27, private monitoring index 8.57, and deposit insurance coverage ratio is 12%.
4.4 Estimation Methodology and Data
105
In Panel B of Table 4.2, we report the sample distribution by calendar year and by country. The number of banks ranges from 236 to 914, with the largest number in 2010 and the lowest number in 1996.
4.4.4 Determinants of Bank Competition To examine the impact of bank regulation and supervision on competition, we regress the competition variables on a set of bank-specific variables, country variables, and bank regulation and supervision variables. The regression model is as follows: Competitionit = β0 +
N
βn bank_controlsnit
n=1
+
J
αj country_controlsjkt + γ bank_regulationkt
j=1
+ νi + μt + εi,t
(4.6)
The dependent variable Competition is one of the bank competition variables: the H-statistic or the Lerner index. bank_controls are a set of bank-specific control variables: TCD, LLP, NIIC, ROA, and log TA. country_controls are the country-level control variables: Market capitalization to GDP, GDP per capita, GDP growth, Inflation, and Interest rate. bank_regulation are bank regulation and supervision variables: Bank concentration, Government-owned banks, Activity restriction, Foreign bank limitation, Entry barriers, Capital regulatory index, Official supervisory power, Diversification index, Private monitoring index, and Deposit insurance ratio. ε is an error term. Standard errors are heteroskedasticity consistent and are clustered at the bank level. We follow Gormley and Matsa (2014) in employing bank-specific fixed effects to control for unobserved time-invariant heterogeneity across banks that might affect competition. Regressions include year-fixed effects to control for macroeconomic factors and monetary policy that may vary over time.
4.5 Empirical Results 4.5.1 Bank Regulation, Supervision, and the H-Statistic In this section, we investigate how bank regulation and supervision affect competition in the banking industry from emerging markets, after controlling for the effects of bank-level and country-level characteristics. As the H-statistic and the Lerner index with standard errors are generated from the first-stage regressions as defined in the
106
4 The Impact of Bank Regulation and Supervision on Competition
third section, to increase the accuracy of our second-stage estimation, we follow Doidge et al. (2006) and Chue and Cook (2008) and weigh each observation by the inverse of the standard error of the H-statistic and the Lerner index for each country in each year obtained at the first stage. With this procedure, the H-statistic and the Lerner index of observations that are estimated more precisely in the first-stage regressions have a higher weight in the second-stage analysis. We first examine the impact of bank regulation and supervision on the H-statistic, and the results are reported in Table 4.3. Considering the impact of market structure on competition, the result in column 1 in Table 4.3 indicates that bank concentration is positively and statistically correlated with the H-statistic, which indicates that banks in a more concentrated system are exposed to a higher level of competition. This positive relationship between the H-statistic, which measures bank competition based on the new empirical industrial organization (NEIO) approach, and bank concentration, which defines market structure based on the structure-conduct-performance (SCP) paradigm, suggests that the level of competition is not necessarily related to market structure (see also Fernández De Guevara et al., 2005; Ryan et al., 2014). Two possible explanations exist for this finding: first, the banking sector is often observed to be simultaneously concentrated and competitive, and so concentration may be a poor proxy for underlying market power; moreover, a more serious issue is that market structure and concentration may proxy for a whole range of conduct that determines bank and market characteristics, including average bank size, bank complexity in terms of product variety and activities, the ease of information flow in the market, and the overall size of the market. We also include the degree of government bank ownership, to measure the level of banking system’s assets owned by the government. The result indicates that higher government bank ownership seems to reduce competition in the banking industry. Column 2 in Table 4.3 tests the impact that regulations on bank activity have on competition. Higher Activity restriction indicates more restrictions on banks engaging in securities, insurance, and real estate activities, and the result indicates that higher activity restrictions result in lower competition. This confirms that, although activity restrictions reduce the potential risks in the banking industry (Anginer & Demirguc-Kunt, 2014), but this also reduces competition in the banking system. The positively significant coefficient of Foreign bank limitation and negatively significant coefficient of Entry barriers confirm that less foreign bank entry or ownership limitations and lower banking entry requirements positively affect competition in the banking system. These findings suggest that lower foreign bank limitations and banking entry requirements increase competition in the banking system. To examine the impact of capital regulation, we include the variable Capital regulatory index. A higher Capital regulatory index indicates greater strictness, and our results provide evidence that competition in the banking system is more intense if capital strictness is more pronounced in the country. Previous studies (e.g., Agoraki et al., 2011; Chortareas et al., 2012) indicate that strengthening capital restrictions can improve efficient bank operations, which in turn enhances competition among banks.
(1)
(2.89)
−0.0243
(−1.34)
−0.0235
(−1.29)
(−1.95)
(−1.94)
(2.88)
−0.0105*
−0.0104*
0.407***
(1.14)
(1.18)
0.397***
0.0118
0.0122
0.264***
(3.67)
0.261***
Interest rate
Inflation
GDP growth
GDP per capita
Market capitalization
(2)
(3.62)
(−1.17)
−0.0210
(2.79)
0.377***
(−1.94)
−0.0103*
(1.19)
0.0123
(3.36)
0.239***
(3)
(−1.29)
−0.0229
(2.99)
0.423***
(−1.96)
−0.0105**
(1.08)
0.0109
(3.50)
0.245***
(4)
(−1.48)
−0.0266
(2.71)
0.366***
(−1.88)
−0.00993*
(1.25)
0.0130
(3.64)
0.259***
(5)
(−1.75)
−0.0305*
(2.85)
0.386***
(−1.97)
−0.0109**
(1.22)
0.0129
(3.81)
0.273***
(6)
(−1.69)
−0.0302*
(3.26)
0.462***
(−1.83)
−0.00994*
(1.23)
0.0128
(3.67)
0.261***
(7)
(−1.87)
−0.0323*
(2.97)
0.430***
(−1.91)
−0.0102*
(1.36)
0.0146
(3.59)
0.261***
(8)
(−0.85)
−0.0158
(3.69)
0.493***
(−1.89)
−0.00917*
(1.27)
0.0102
(1.84)
0.144*
(9)
0.0224***
(11.05)
(9.89)
(4.86)
(4.73)
0.0218***
0.00763***
(5.32)
(4.71)
0.00709***
0.0106***
(7.58)
(7.92)
0.00993***
0.0679***
(−17.00)
0.0694***
(−16.96)
(10.25)
0.0221***
(4.37)
0.00666***
(4.50)
0.00955***
(8.68)
0.0734***
(−17.24)
(11.96)
0.0243***
(5.86)
0.00844***
(6.93)
0.0133***
(9.73)
0.0739***
(−17.72)
(9.16)
0.0200***
(4.72)
0.00691***
(3.69)
0.00721***
(8.11)
0.0740***
(−16.75)
(9.78)
0.0222***
(4.13)
0.00585***
(4.59)
0.00967***
(7.45)
0.0679***
(−15.33)
(10.17)
0.0212***
(5.52)
0.00837***
(5.53)
0.0120***
(7.80)
0.0641***
(−17.48)
(9.41)
0.0220***
(3.81)
0.00551***
(4.20)
0.00865***
(7.81)
0.0772***
(−14.78)
(continued)
(17.39)
0.0446***
(2.32)
0.00681**
(16.03)
0.0280***
(32.20)
0.161***
(−15.73)
−0.00784*** −0.00794*** −0.00772*** −0.00831*** −0.00766*** −0.00741*** −0.00822*** −0.00728*** −0.0124***
Country control variables
log TA
ROA
NIIC
LLP
TCD
Bank control variables
Variables
Table 4.3 Impact of bank regulation and supervision on H-statistic
4.5 Empirical Results 107
(1)
Deposit insurance ratio
Private monitoring index
Diversification index
Official supervisory power
Capital regulatory index
Entry barriers
Foreign bank limitation
Activity restriction
Government-owned −0.00522*** banks (−4.29)
(13.35)
Bank concentration 0.00371***
Regulation and supervision variables
Variables
Table 4.3 (continued)
(−2.22)
−0.0161**
(2)
(3.11)
0.0528***
(3)
(−10.00)
−0.0937***
(4)
(3.69)
0.0336***
(5)
(4.49)
0.0360***
(6)
(−6.68)
−0.137***
(7)
(4.74)
0.0827***
(8)
(continued)
(1.83)
0.105*
(9)
108 4 The Impact of Bank Regulation and Supervision on Competition
9365
0.186
Number of observations
R-squared
0.130
12,714
Yes
0.131
12,693
Yes
Yes
(0.17)
0.0435
(3)
0.134
12,856
Yes
Yes
(3.32)
0.911***
(4)
0.130
12,645
Yes
Yes
0.139
12,856
Yes
Yes
(−0.33)
−0.0929
−0.0345 (−0.13)
(6)
(5)
0.141
11,559
Yes
Yes
(2.54)
0.649**
(7)
0.168
12,465
Yes
Yes
(−1.66)
−0.571*
(8)
0.292
7441
Yes
Yes
(−1.97)
−0.583**
(9)
The dependent variable is H-statistic which is estimated based on Panzar and Rosse (1987) model as defined in Eq. (4.1). Our sample period is 1996–2016. Bank-level controls include TCD, LLP, NIIC, ROA, and log TA. Country control variables include Market capitalization, GDP per capita, GDP growth, Inflation, and Interest rate. Regulation and supervision variables include Bank concentration, Government-owned banks, Activity restriction, Foreign bank limitation, Entry barriers, Capital regulatory index, Official supervisory power, Diversification index, Private monitoring index, and Deposit insurance ratio. All variables are defined in Table 4.1. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
Yes
Year fixed effects
Yes
(1.36)
Yes
(0.99)
Bank fixed effects
0.369
0.250
Constant
(2)
(1)
Variables
Table 4.3 (continued)
4.5 Empirical Results 109
110
4 The Impact of Bank Regulation and Supervision on Competition
Results for official supervisory power are in columns 6 and 7 in Table 4.3. We find that the cross-country difference in bank competition can be explained by the differences in official supervisory power and explicit guidelines for asset diversification, in which higher official supervisory power increases competition and greater explicit guidelines for asset diversification reduce competition. Next we explore the relationship between private monitoring and bank competition. If the banks have greater incentives or ability to engage in private monitoring, then we would expect that that a higher private monitoring index is associated with higher competition. The result for private monitoring is in column 8 in Table 4.3. Consistent with our expectations, a higher private monitoring index significantly increases bank competition in a given country. Finally, we test the impact of deposit insurance on bank competition. While deposit insurance provides banks with a guarantee when they fail and prevents banks runs, it also leads to moral hazard and excessive risk-taking (Anginer et al., 2014b), which may in turn enhance competition among banks. In column 9 in Table 4.3, we include the variable Deposit insurance ratio, which is defined as the size of the deposit insurance fund relative to total bank assets, to examine the impact of deposit insurance on competition in the banking system. The positive and significant coefficient of Deposit insurance ratio confirms that deposit insurance increases competition in the banking system. Among bank-level control variables, we find that customer deposits (TCD) and profitability (ROA) are positively and significantly related to bank competition in all specifications, and higher non-interest income (NIIC) indicates lower competition in the banking system. Among country-level variables, we find that the development of the stock market has a negatively significant impact on competitiveness in the banking system, and higher GDP per capita, GDP growth, inflation, and interest rate are statistically and positively significant. This suggests that in emerging economies, the development of the economy and the stock market show the same patterns and are the main factors driving competition in the banking system.
4.5.2 Alternative Measures of Competition We also consider whether our results are robust across different measures of competition, and the regression results are in Table 4.4. First, we use the Lerner index as a measure of competition. The results in Panel A in Table 4.4 show that Activity restriction is positively and significantly related to the Lerner index, indicating that higher activity restrictions reduce bank competition; Foreign bank limitation is negatively and significantly related to the Lerner index, which suggests that lower foreign bank limitations lead to higher competition in the banking system. The negatively significant coefficients of Capital regulatory index and Official supervisory power provide evidence that higher capital strictness and supervisory power result in higher competition among banks in a given country. Diversification index is positively and statistically significantly related to the Lerner index, which confirms that greater
(1)
(2)
(3)
(4)
Deposit insurance ratio
Private monitoring index
Diversification index
Official supervisory power
Capital regulatory index
Entry barriers
Foreign bank limitation
Activity restriction
Government-owned 0.00212 banks (1.52)
(1.15)
Bank concentration 0.000498
(1.91)
0.00735*
(−1.85)
−0.0188*
(−0.50)
−0.00534
Panel A: Impact of bank regulation and supervision on Lerner index
Variable
(−2.29)
−0.0140**
(5)
(−1.95)
−0.00555*
(6)
(2.43)
0.0257**
(7)
(−3.25)
−0.0239***
(8)
(−2.56)
−0.188**
(9)
Table 4.4 Impact of bank regulation and supervision on competition. Alternative measure of competition: Lerner index and Boone indicator
(continued)
(10)
4.5 Empirical Results 111
6456
0.201
Number of observations
R-squared
0.199
8773
Yes
0.193
8816
Yes
Yes
(1.93)
0.456*
(3)
0.195
8907
Yes
Yes
(2.36)
0.556**
(4)
0.201
8725
Yes
Yes
(1.72)
0.413*
(5)
Entry barriers * ln MC
Foreign bank limitation * ln MC
Activity restriction * ln MC
Government-owned banks * ln MC
Bank concentration * ln MC
ln MC
(12.69)
0.00527***
(−3.71)
(12.00)
0.0423***
(−0.08)
−0.00179***
(−9.91)
(−11.47)
(3) −0.00242
(2)
−0.145*** −0.368***
(1)
(−12.83)
−0.182***
(6.17)
0.302***
(4)
(6)
0.195
8907
Yes
Yes
(2.47)
0.574**
(6)
(0.86)
0.0118
(−7.57)
(−5.60)
−0.146*** −0.150***
(5)
Panel B: Estimation of bank regulation and supervision affecting the Boone indicator
Yes
Bank and Year fixed effects
Yes
(2.46)
Yes
(2.23)
Bank and country control variables
0.567**
0.525**
Constant
(2)
(1)
Variable
Table 4.4 (continued)
(2.81)
0.0941***
(7)
0.198
8218
Yes
Yes
(2.68)
0.608***
(7)
(−11.67)
−0.242***
(8)
0.200
8631
Yes
Yes
(2.95)
0.706***
(8)
(12.91)
0.538***
(9)
0.275
5361
Yes
Yes
(0.10)
0.0290
(9)
(continued)
(−12.42)
−0.210***
(10)
(10)
112 4 The Impact of Bank Regulation and Supervision on Competition
17,059
20,782
131.5
Number of observations
Chi-squared
465.4
20,033
Yes
(204.44)
9.486***
(3)
490.7
20,701
Yes
(203.31)
9.361***
(4)
253.9
20,782
Yes
(329.33)
9.996***
(5)
(6)
1037.4
19,971
Yes
(228.95)
9.267***
(−15.10)
−0.0447***
3513.0
20,599
Yes
(190.54)
8.394***
(−34.98)
−0.0858***
(7)
339.4
19,828
Yes
(219.38)
9.449***
(2.40)
0.0242**
(8)
962.1
19,898
(211.14)
9.340***
(−22.95)
−0.119***
(9)
200.7
15,483
Yes
(201.58)
9.556***
(−1.17)
−0.285
(10)
The dependent variables are Lerner index and log of profit in Panel A and Panel B, respectively. Our sample period is 1996–2016. Bank-level controls include TCD, LLP, NIIC, ROA, and log TA. Country control variables include Market capitalization, GDP per capita, GDP growth, Inflation, and Interest rate. Regulation and supervision variables include Bank concentration, Government-owned banks, Activity restriction, Foreign bank limitation, Entry barriers, Capital regulatory index, Official supervisory power, Diversification index, Private monitoring index, and Deposit insurance ratio. All variables are defined in Table 4.1. Bank-level control variables and country-level variables are also included in the regressions but not reported for brevity. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
524.5
Yes
9.601***
(190.89)
9.581***
(2)
(157.27)
(1)
Bank and year fixed Yes effects
Constant
Deposit insurance ratio * ln MC
Private monitoring index * ln MC
Diversification index * ln MC
Official supervisory power * ln MC
Capital regulatory index * ln MC
Variable
Table 4.4 (continued)
4.5 Empirical Results 113
114
4 The Impact of Bank Regulation and Supervision on Competition
explicit guidelines for asset diversification increase the market power of banks and thus reduces competition in the banking system. We also find that Private monitoring index and Deposit insurance ratio are negatively and significantly associated with the Lerner index. The negative relationship indicates that a higher private monitoring index and deposit insurance coverage reduce the market power of banks and increase bank competition. For a robustness check, we also use the Boone indicator as another alternative measure of competition to see the effect on it of bank regulations and supervision. We include the interaction terms of marginal cost (lnMC) and bank regulation and supervision variables bank_regulation to analyze the change in the Boone indicator due to interaction with bank_regulation (captured in the coefficient of the interaction term). The regression results of the Boone indicator are reported in Panel B in Table 4.4. As expected, the regression in column 1 in Panel B shows that marginal cost is negatively and significantly associated with profit. We find that the interaction term of bank concentration and marginal cost, Bank concentration * ln MC, is negatively and significantly associated with profit. This indicates that the negative Boone indicator is lower for a banking industry with higher bank concentration and provides empirical support that competition is higher in banking systems with higher concentration. The interaction term between Government-owned banks and marginal cost is positively and significantly related to profit. This indicates that the Boone indicator is lower for government-owned banks than for other banks. The interaction terms for Activity restriction and Diversification index are positively and significantly related to profit. Hence, higher activity restrictions and greater explicit guidelines for asset diversification lead to a decrease in bank competition. The coefficients of the interaction terms for Foreign bank limitation and Capital regulatory index are significantly negative, which further confirms that higher foreign bank limitations and greater capital strictness corresponds with an increase in bank competition. The interaction terms Official supervisory power and Private monitoring index are negatively and significantly related to profit. This confirms that Official supervisory power and Private monitoring index are negatively and significantly related to the Boone indicator. Hence, higher official supervisory power and private monitoring are associated with higher competition in the banking industry.
4.5.3 Impact of the Financial Crisis Following the recent global financial crisis, policy makers reshaped bank regulation substantially. We are concerned with how the crisis affects competition in the banking system and whether the relationship between bank regulation and competition shows a different pattern during the crisis. In this section, we use bank-crisis information for individual countries from a database compiled by Laeven and Valencia (2018) and include interaction terms between country-level regulation variables and Bank Crisis
4.5 Empirical Results
115
as additional explanatory variables. Bank Crisis is a dummy variable that equals one if the country is going through a systemic crisis in a given year, and zero otherwise. The results with the bank crisis interactions are in Table 4.5. The coefficient on the interaction variable for bank concentration is negative (column 1 in Table 4.5), suggesting that competition in a more highly concentrated banking system is lower during a bank crisis; in banking systems with more government-owned banks, competition among banks is much higher during a bank crisis. We also see that the negative relationship between Activity restrictions and bank competition is more pronounced during a bank crisis, and the results of Entry barriers and Diversification index have similar findings, indicating that the effect of Activity restrictions, Entry barriers, and Diversification index in reducing bank competition is more pronounced during a bank crisis. The coefficients of the interaction terms for Foreign bank limitation, Capital regulatory index, Official supervisory power, Private monitoring index, and Deposit insurance ratio are significantly negative, indicating that a reduction in foreign bank limitations and increase in capital strictness, supervisory power, and private monitoring are more effective in enhancing bank competition in normal times than during a bank crisis. Our findings confirm that during a bank crisis, the impact of activity restrictions, entry barriers, and diversification guidelines on competitive conditions become more effective while foreign bank limitations, capital strictness, supervisory power, and private monitoring become less effective.
4.5.4 Further Tests: Foreign Banks Versus Domestic Banks In this subsection, we further investigate whether the relationship between bank regulation and competition differs between domestic banks and foreign banks. Foreign banks have better profitability performance than domestic banks in developing countries (Claessens et al., 2001), and foreign banks are subject to regulations not only in the host country but also in the parent country. De Haas and Van Lelyveld (2006) and Ongena et al. (2013) also find that foreign banks are more sensitive to the strictness of parent-country regulation, rather than to the institutional conditions in the host countries, and the impact of institutional reforms can have a less significant impact on foreign banks (Fang et al., 2014). To test these matters, we introduce a binary variable Foreign dummy, which equals one if the bank is a foreign subsidiary and zero otherwise. The results, including the interaction terms between bank regulation variables and Foreign dummy, are in Table 4.6. The results of Activity restriction, Foreign bank limitation, and Diversification index indicate that the changes in competition in the banking industry with activity restrictions, foreign bank limitations, and diversification guidelines are more pronounced at domestic banks than at foreign banks, and the impact of official supervisory power and private monitoring on competition are greater for foreign banks than domestic banks. Our findings suggest that foreign banks are more sensitive
(1)
(2)
(10.94)
0.00321***
Entry barriers * bank crisis
Entry barriers
Foreign bank limitation * bank crisis
Foreign bank limitation
Activity restriction * bank crisis
Activity restriction
Government-owned banks 0.00802*** * bank crisis (3.94)
(−5.41)
Government-owned banks −0.00628***
Bank concentration * bank −0.00988*** crisis (−8.92)
Bank concentration
(−6.18)
−0.0288***
(−2.17)
−0.0157**
Bank crisis, regulation, and supervision variables
Variables
(−3.15)
−0.0354***
(2.81)
0.0470***
(3)
Table 4.5 Bank regulation, supervision, bank crisis, and H-statistic
(−5.11)
−0.0227***
(−9.77)
−0.0937***
(4)
(5)
(6)
(7)
(8)
(continued)
(9)
116 4 The Impact of Bank Regulation and Supervision on Competition
Deposit insurance ratio * bank crisis
Deposit insurance ratio
Private monitoring index * bank crisis
Private monitoring index
Diversification index * bank crisis
Diversification index
Official supervisory power * bank crisis
Official supervisory power
Capital regulatory index * bank crisis
Capital regulatory index
Variables
Table 4.5 (continued)
(1)
(2)
(3)
(4)
(5)
(−4.16)
−0.0196***
(3.61)
0.0330***
(−4.08)
−0.0146***
(4.37)
0.0352***
(6)
(−6.96)
−0.198***
(−7.21)
−0.152***
(7)
(−5.75)
−0.0220***
(4.78)
0.0829***
(8)
(continued)
(−6.56)
−0.195***
(1.95)
0.113*
(9)
4.5 Empirical Results 117
9365
0.191
Number of observations
R-squared
0.135
12,714
Yes
(3)
0.134
12,693
Yes
Yes
(0.13)
0.0325
(4)
0.136
12,856
Yes
Yes
(3.23)
0.882***
0.133
12,645
Yes
Yes
(−0.23)
0.141
12,856
Yes
Yes
(−0.36)
(6) −0.101
(5) −0.0636
(7)
0.141
11,559
Yes
Yes
(2.72)
0.685***
(8)
0.172
12,465
Yes
Yes
(−1.81)
−0.617*
(9)
0.292
7441
Yes
Yes
(−1.85)
−0.549*
The dependent variable is H-statistic which is estimated based on Panzar and Rosse (1987) model as defined in Eq. (4.1). Our sample period is 1996–2016. Bank-level controls include TCD, LLP, NIIC, ROA, and log TA. Country control variables include Market capitalization, GDP per capita, GDP growth, Inflation, and Interest rate. Regulation and supervision variables include Bank concentration, Government-owned banks, Activity restriction, Foreign bank limitation, Entry barriers, Capital regulatory index, Official supervisory power, Diversification index, Private monitoring index, and Deposit insurance ratio. All variables are defined in Table 4.1. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
Yes
Bank and year fixed effects
Yes
(1.13)
Yes
(0.89)
Bank and country control variables
0.225
Constant
(2)
0.305
(1)
Variables
Table 4.5 (continued)
118 4 The Impact of Bank Regulation and Supervision on Competition
(1)
(−3.53)
−0.00495***
(0.23)
0.0000592
(12.55)
0.00370***
Entry barriers * foreign dummy
Entry barriers
Foreign bank limitation * foreign dummy
Foreign bank limitation
Activity restriction * foreign dummy
Activity restriction
Government-owned banks * foreign −0.000912 dummy (−0.48)
Government-owned banks
Bank concentration * foreign dummy
Bank concentration
Bank crisis, regulation, and supervision variables
Variables
(1.98)
0.0215**
(−2.78)
−0.0232***
(2)
(−3.28)
−0.108***
(3.75)
0.0701***
(3)
Table 4.6 Bank regulation, supervision, foreign ownership, and H-statistic
(−1.11)
−0.0358
(−9.16)
−0.0918***
(4)
(5)
(6)
(7)
(8)
(continued)
(9)
4.5 Empirical Results 119
Deposit insurance ratio * foreign dummy
Deposit insurance ratio
Private monitoring index * foreign dummy
Private monitoring index
Diversification index * foreign dummy
Diversification index
Official supervisory power * foreign dummy
Official supervisory power
Capital regulatory index * foreign dummy
Capital regulatory index
Variables
Table 4.6 (continued)
(1)
(2)
(3)
(4)
(5)
(0.05)
0.000396
(3.59)
0.0335***
(2.47)
0.0455**
(3.02)
0.0263***
(6)
(2.80)
0.0765***
(−7.35)
−0.150***
(7)
(2.30)
0.0754**
(3.86)
0.0690***
(8)
(continued)
(0.04)
0.000341
(1.83)
0.105*
(9)
120 4 The Impact of Bank Regulation and Supervision on Competition
9365
0.186
Number of observations
R-squared
0.130
12,714
Yes
(3)
0.132
12,693
Yes
Yes
(0.15)
0.0385
(4)
0.134
12,856
Yes
Yes
(3.45)
0.941***
0.130
12,645
Yes
Yes
(−0.13)
0.140
12,856
Yes
Yes
(−0.38)
(6) −0.107
(5) −0.0347
(7)
0.142
11,559
Yes
Yes
(2.47)
0.635**
(8)
0.168
12,465
Yes
Yes
(−1.68)
−0.574*
(9)
0.292
7441
Yes
Yes
(−1.97)
−0.583**
The dependent variable is H-statistic which is estimated based on Panzar and Rosse (1987) model as defined in Eq. (4.1). Our sample period is 1996–2016. Bank-level controls include TCD, LLP, NIIC, ROA, and log TA. Country control variables include Market capitalization, GDP per capita, GDP growth, Inflation, and Interest rate. Regulation and supervision variables include Bank concentration, Government-owned banks, Activity restriction, Foreign bank limitation, Entry barriers, Capital regulatory index, Official supervisory power, Diversification index, Private monitoring index, and Deposit insurance ratio. All variables are defined in Table 4.1. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
Yes
Bank and year fixed effects
(1.48) Yes
(0.96)
Yes
Bank and country control variables
0.404
0.244
Constant
(2)
(1)
Variables
Table 4.6 (continued)
4.5 Empirical Results 121
122
4 The Impact of Bank Regulation and Supervision on Competition
to official supervisory power and private monitoring and less sensitive to activity restrictions, foreign bank limitations, and diversification guidelines than domestic banks.
4.5.5 Robustness Checks To assess the reliability of our results, in this section we conduct a series of robustness checks with an alternative econometric model and different subsamples. We consider an alternative regression specification to assess whether our findings are consistent under different estimations. Specifically, instead of controlling for bank-fixed effects, we include country-fixed effects in the regression to control for country differences and year-fixed effects to control for time-varying effects. The results are in Panel A in Table 4.7. The relationships between bank regulation variables and H-statistics are consistent with our findings, except that Governmentowned banks and Deposit insurance ratio become insignificant. Thus, the results confirm that our results on bank regulation and competition are robust to alternative regression specifications. As a robustness check, we also performed our analysis across different subsamples. First, we replicate the analysis on the subsample limited to commercial banks. The result in Panel B in Table 4.7 shows that our main findings still hold for commercial banks. Second, as our sample countries cover the BRICS (Brazil, Russia, India, China, South Africa) countries, it is therefore interesting and warranted to perform our analysis on subsamples of banks in the BRICS countries and check whether the impact of bank regulation on competition changes in these five biggest and most rapidly growing emerging economies. The results in Panel C in Table 4.7 show that the findings of bank regulation variables are consistent with our previous conclusions. Third, Bonin et al. (2005) show that data from Bankscope suffer from several problems and are less accurate, especially for transition countries among the former Soviet republics. To check that our results are not driven by potential data problems, we perform an analysis on the subsample without banks in the former Soviet republics (see Panel D in Table 4.7). The results were largely unchanged. Based on the subsample analysis, we can conclude that our results are robust based on alternative sample selection criteria.
4.6 Conclusion This study examines the influence of bank regulation and supervision on competitive conditions using cross-country data from 23 emerging economies between 1996 and 2016. Both structural and nonstructural approaches are employed to measure competition in the banking system. The main results indicate that banking systems with higher concentration and fewer activity restrictions and entry barriers are more
(15.33)
0.00414***
Deposit insurance ratio
Private monitoring index
Diversification index
Official supervisory power
Capital regulatory index
Entry barriers
Foreign bank limitation
Activity restriction
(−1.44)
Government-owned banks −0.00168
Bank concentration
Panel A: Alternative specification
(1)
(−2.48)
−0.0148**
(2)
(4.72)
0.0757***
(3)
(−12.45)
−0.0987***
(4)
Table 4.7 Impact of bank regulation and supervision on competition: robustness check
(4.43)
0.0377***
(5)
(4.88)
0.0347***
(6)
(−8.25)
−0.153***
(7)
(5.18)
0.0781***
(8)
(continued)
(0.48)
0.0236
(9)
4.6 Conclusion 123
0.307
R-squared
(11.07)
Official supervisory power
Capital regulatory index
Entry barriers
Foreign bank limitation
Activity restriction
(−1.65)
Government-owned banks −0.00226*
Bank concentration
0.00346***
9365
Number of observations
Panel B: Commercial banks
Yes
Country and year fixed effects
(−2.61)
−0.0201***
0.194
12,714
Yes
Yes
(−1.61)
(−2.87)
Yes
(2)
−0.192
(1)
−0.281***
Bank and country control variables
Constant
Table 4.7 (continued) (3)
(3.46)
0.0684***
0.193
12,693
Yes
Yes
(−5.15)
−0.571***
(4)
(−11.00)
−0.109***
0.199
12,856
Yes
Yes
(3.30)
0.333***
(5)
(2.73)
0.0316***
0.194
12,645
Yes
Yes
(−4.76)
−0.704***
(6)
(2.57)
0.0261**
0.201
12,856
Yes
Yes
(−4.25)
−0.559***
(7)
0.267
11,559
Yes
Yes
(0.68)
0.0737
(8)
0.237
12,465
Yes
Yes
(−4.92)
−1.123***
(9)
(continued)
0.335
7441
Yes
Yes
(−7.07)
−1.084***
124 4 The Impact of Bank Regulation and Supervision on Competition
0.196
R-squared
(1.71)
Activity restriction
(−15.33)
Government-owned banks −0.0961***
Bank concentration
0.000580*
5712
Number of observations
Panel C: BRICS
Yes
Bank and year fixed effects
(−4.74)
−0.0838***
0.168
8100
Yes
Yes
(2.33)
Yes
0.667**
(1.92)
(2)
0.521*
(1)
Bank and country control variables
Constant
Deposit insurance ratio
Private monitoring index
Diversification index
Table 4.7 (continued)
0.170
8112
Yes
Yes
(0.89)
0.236
(3)
0.168
8236
Yes
Yes
(4.42)
1.265***
(4)
0.168
8043
Yes
Yes
(0.80)
0.246
(5)
0.173
8236
Yes
Yes
(0.80)
0.257
(6)
(7)
0.129
7378
Yes
Yes
(3.21)
0.875***
(−4.87)
−0.122***
0.183
7934
Yes
Yes
(−0.12)
−0.0517
(2.28)
0.0547**
(8)
(continued)
0.334
4391
Yes
Yes
(−2.35)
−0.731**
(0.46)
0.0305
(9)
4.6 Conclusion 125
Yes
3947
0.684
Bank and year fixed effects
Number of observations
R-squared
0.550
5429
Yes
Yes
(18.68)
(15.66)
Yes
6.853***
(2)
6.139***
(1)
Bank and country control variables
Constant
Deposit insurance ratio
Private monitoring index
Diversification index
Official supervisory power
Capital regulatory index
Entry barriers
Foreign bank limitation
Table 4.7 (continued) (3)
0.545
5429
Yes
Yes
(13.97)
6.004***
(4.74)
0.216***
0.543
5429
Yes
Yes
(15.91)
6.326***
(−4.74)
−0.0775***
(4)
0.539
5429
Yes
Yes
(14.35)
6.067***
(4.74)
0.0368***
(5)
0.539
5429
Yes
Yes
(11.58)
5.587***
(4.74)
0.0366***
(6)
0.578
4343
Yes
Yes
(5.66)
4.111***
(−4.74)
−0.195***
(7)
0.538
5429
Yes
Yes
(18.89)
8.230***
(4.74)
0.129***
(8)
(continued)
0.805
3044
Yes
Yes
(−217.35)
−128.5***
(−4.14)
0.442***
(9)
126 4 The Impact of Bank Regulation and Supervision on Competition
(1)
(2)
(9.29)
0.00375***
Deposit insurance ratio
Private monitoring index
Diversification index
Official supervisory power
Capital regulatory index
Entry barriers
Foreign bank limitation
Activity restriction
(−1.52)
Government-owned banks −0.00474
Bank concentration
(−2.69)
−0.0194***
Panel D: Subsample without transition countries
Table 4.7 (continued)
(3.09)
0.0510***
(3)
(−10.54)
−0.0971***
(4)
(3.40)
0.0347***
(5)
(4.94)
0.0397***
(6)
(−6.80)
−0.144***
(7)
(5.18)
0.0916***
(8)
(continued)
(5.36)
0.271***
(9)
4.6 Conclusion 127
8810
0.197
Number of observations
R-squared
0.136
11,967
Yes
(3)
0.137
11,946
Yes
Yes
(0.18)
0.0522
(4)
0.139
12,109
Yes
Yes
(3.10)
0.979***
0.136
11,898
Yes
Yes
(−0.34)
0.145
12,109
Yes
Yes
(0.48)
(6) 0.145
(5) −0.115
(7)
0.153
10,812
Yes
Yes
(2.09)
0.607**
(8)
0.172
11,718
Yes
Yes
(−0.81)
−0.289
(9)
0.322
6756
Yes
Yes
(3.35)
0.955***
The dependent variable is H-statistic which is estimated based on Eq. (4.1). Our sample period is 1996–2016. Bank-level controls include TCD, LLP, NIIC, ROA, and log TA. Country control variables include Market capitalization, GDP per capita, GDP growth, Inflation, and Interest rate. Regulation and supervision variables include Bank concentration, Government-owned banks, Activity restriction, Foreign bank limitation, Entry barriers, Capital regulatory index, Official supervisory power, Diversification index, Private monitoring index, and Deposit insurance ratio. All variables are defined in Table 4.1. Bank-level control variables and country-level variables are also included in the regressions but not reported for brevity. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the bank level. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
Yes
Bank and year fixed effects
Yes
(1.37)
Yes
(0.96)
(2)
0.425
(1)
0.284
Bank and country control variables
Constant
Table 4.7 (continued)
128 4 The Impact of Bank Regulation and Supervision on Competition
4.6 Conclusion
129
competitive. The results also confirm that lowering foreign bank limitations and increasing capital strictness and official supervisory power also enhances competition in the banking sector. Our evidence also highlights that banking systems with fewer government-owned banks and diversification guidelines and higher private monitoring of banks and deposit insurance coverage tend to more competitive. In addition, our analyses indicate that during a bank crisis the relationship between activity restrictions, entry barriers, diversification guidelines, and competition is intensified while the positive effect of foreign bank limitation, capital stringency, official supervisory power, and private monitoring on competitive condition diminishes. Moreover, by examining the impact of bank regulation and supervision on foreign vs. domestic banks, our findings show that foreign banks are more sensitive to official supervisory power and private monitoring and less sensitive to activity restrictions, foreign bank limitations, and diversification guidelines. Our findings raise several important policy implications for financial regulators in emerging economies. First, to increase competition in the banking system, regulators need to be more cautious about evaluating and approving consolidation between banks in order to prevent excessive concentration in the banking system at the country level. Financial authorities should also reduce policy interventions in the banking system. Second, to improve the efficiency of capital allocation in the banking system, regulators also should seek to reduce activity restrictions and lower (foreign) bank entry barriers. Third, a certain level of capital strictness and official supervisory power should be maintained to reduce bank risk-taking behavior and enhance effective risk management, and this is more important for foreign banks that seek to take advantage of differences in regulations between the parent country and host countries. Finally, deposit insurance fosters competition among banks, and increasing deposit insurance coverage should be encouraged to promote competition and efficiency in the banking system. This research has a number of limitations. For instance, the data on bank regulation were obtained from the database in Barth et al. (2013), which only covers data until 2011. After 2010, new regulations and supervision were designed to respond to the global financial crisis (Claessens & Kodres, 2014), which, in turn, could reflect different findings across our sample countries. During our sample period, especially during the global financial crisis, some banks were bailed out by their government. Therefore, this raises concern regarding potential bias introduced by the bailed-out banks. Therefore, future research should consider the changes in bank regulation and supervision in emerging economies in recent years, and detailed information on the bailed-out banks in emerging economies also should be collected and examined in future studies.
130
4 The Impact of Bank Regulation and Supervision on Competition
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Delis, M. D. (2012). Bank competition, financial reform, and institutions: The importance of being developed. Journal of Development Economics, 97(2), 450–465. Delis, M. D., Molyneux, P., & Pasiouras, F. (2011). Regulations and productivity growth in banking: Evidence from transition economies. Journal of Money, Credit and Banking, 43(4), 735–764. Demirguc-Kunt, A., Laeven, L., & Levine, R. (2004). Regulations, market structure, institutions, and the cost of financial intermediation. Journal of Money, Credit and Banking, 36(3), 593–622. Dick, A. A. (2006). Nationwide branching and it’s impact on market structure, quality, and bank performance. The Journal of Business, 79(2), 567–592. Doidge, C., Griffin, J., & Williamson, R. (2006). Measuring the economic importance of exchange rate exposure. Journal of Empirical Finance, 13(4), 550–576. Fang, Y., Hasan, I., & Marton, K. (2014). Institutional development and bank stability: Evidence from transition countries. Journal of Banking & Finance, 39, 160–176. Fernández De Guevara, J., Maudos, J., & Perez, F. (2005). Market power in European banking sectors. Journal of Financial Services Research, 27(2), 109–137. Fu, X., & Heffernan, S. (2009). The effects of reform on China’s bank structure and performance. Journal of Banking & Finance, 33(1), 39–52. Fu, X. M., Lin, Y. R., & Molyneux, P. (2014). Bank competition and financial stability in Asia Pacific. Journal of Banking & Finance, 38, 64–77. Gelos, R. G., & Roldós, J. (2004). Consolidation and market structure in emerging market banking systems. Emerging Markets Review, 5(1), 39–59. Gormley, T. A., & Matsa, D. A. (2014). Common errors: How to (and not to) control for unobserved heterogeneity. Review of Financial Studies, 27(2), 617–661. Hasan, I., Wachtel, P., & Zhou, M. (2009). Institutional development, financial deepening and economic growth: Evidence from China. Journal of Banking & Finance, 33(1), 157–170. Hellmann, T. F., Murdock, K. C., & Stiglitz, J. E. (2000). Liberalization, moral hazard in banking, and prudential regulation: Are capital requirements enough? American Economic Review, 90(1), 147–165. Jeon, B. N., Olivero, M. P., & Wu, J. (2011). Do foreign banks increase competition? Evidence from emerging Asian and Latin American banking markets. Journal of Banking & Finance, 35(4), 856–875. Keeley, M. C. (1990). Deposit insurance, risk, and market power in banking. The American Economic Review, 80(5), 1183–1200. Khan, H. H., Kutan, A. M., Ahmad, R. B., & Gee, C. S. (2017). Does higher bank concentration reduce the level of competition in the banking industry? Further evidence from South East Asian economies. International Review of Economics & Finance, 52, 91–106. Koetter, M., Kolari, J. W., & Spierdijk, L. (2012). Enjoying the quiet life under deregulation? Evidence from adjusted Lerner indices for US banks. Review of Economics and Statistics, 94(2), 462–480. Laeven, L., & Valencia, F. (2010). Resolution of banking crises: the good, the bad and the ugly. International Monetary Fund Working Paper, WP/10/146. Laeven, L., & Valencia, F. (2018). Systemic Banking Crises Revisited. IMF Working Paper No. 206. International Monetary Fund. Lerner, A. P. (1934). The concept of monopoly and the measurement of monopoly power. The Review of Economic Studies, 1(3), 157–175. Matthews, K., Murinde, V., & Zhao, T. (2007). Competitive conditions among the major British banks. Journal of Banking & Finance, 31(7), 2025–2042. Matutes, C., & Vives, X. (2000). Imperfect competition, risk taking, and regulation in banking. European Economic Review, 44(1), 1–34. Maudos, J., & Solís, L. (2011). Deregulation, liberalization and consolidation of the Mexican banking system: Effects on competition. Journal of International Money and Finance, 30(2), 337–353.
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Noman, A. H. M., Gee, C. S., & Isa, C. R. (2018). Does bank regulation matter on the relationship between competition and financial stability? Evidence from Southeast Asian countries. PacificBasin Finance Journal, 48, 144–161. Ongena, S., Popov, A., & Udell, G. F. (2013). “When the cat’s away the mice will play”: Does regulation at home affect bank risk-taking abroad? Journal of Financial Economics, 108(3), 727–750. Panzar, J. C., & Rosse, J. N. (1987). Testing for “monopoly” equilibrium. The Journal of Industrial Economics, 35(4), 443–456. Ryan, R. M., O’Toole, C. M., & McCann, F. (2014). Does bank market power affect SME financing constraints? Journal of Banking and Finance, 49, 495–505. https://doi.org/10.1016/j.jbankfin. 2013.12.024. Schaeck, K., & Cihák, M. (2014). Competition, efficiency, and stability in banking. Financial Management, 43(1), 215–241. Soedarmono, W., Machrouh, F., & Tarazi, A. (2011). Bank market power, economic growth and financial stability: Evidence from Asian banks. Journal of Asian Economics, 22(6), 460–470. Soedarmono, W., Machrouh, F., & Tarazi, A. (2013). Bank competition, crisis and risk taking: Evidence from emerging markets in Asia. Journal of International Financial Markets, Institutions and Money, 23, 196–221. Tan, Y. (2016). The impacts of risk and competition on bank profitability in China. Journal of International Financial Markets, Institutions and Money, 40, 85–110. Tan, Y., & Floros, C. (2018). Risk, competition and efficiency in banking: Evidence from China. Global Finance Journal, 35, 223–236. Tompson, W. (2004). Banking reform in Russia: Problems and prospects. OECD Economics Department Working Papers, No. 410, OECD publishing. Vesala, J. (1995). Testing for competition in banking: Behavioral evidence from Finland. Bank of Finland Studies Working Paper. Bank of Finland. Helsinki. Williams, J., & Nguyen, N. (2005). Financial liberalisation, crisis, and restructuring: A comparative study of bank performance and bank governance in South East Asia. Journal of Banking & Finance, 29(8), 2119–2154. Zhao, T., Casu, B., & Ferrari, A. (2010). The impact of regulatory reforms on cost structure, ownership and competition in Indian banking. Journal of Banking & Finance, 34(1), 246–254.
Chapter 5
Bank Competition, Regulation, and Efficiency
5.1 Overview This study investigates the impact of bank competition and regulation on bank efficiency in the Asia–Pacific region during 2001–2016. The result reveals that market power is positively related to bank efficiency. We also find that stringent activity restrictions, strong official supervisory power, and low capital requirements are associated with high bank efficiency. Furthermore, market power has a stronger efficiency-increasing effect in a banking system characterized by the activity restrictions, supervisory power, and capital requirements described above. Foreign banks operating under increased activity restrictions in a host country with strong official supervisory power have relatively high efficiency.
5.2 Introduction Banks, a special type of enterprise highly regulated by the government, constitute an important part of the financial system. Since the 1980s, the global financial market has become increasingly integrated, and both the developed and developing countries have embarked on financial liberalization. In the process of financial liberalization, deregulation, and increased integration, banks have expanded their services and engaged in greater risk-taking activities to improve their productive efficiency (Luo et al., 2016). With increased competition following financial liberalization and deregulation, banks have to improve their efficiency (Andrie¸s & C˘apraru, 2014; Schaeck & Cihák, 2008). In this paper, we examine how bank competition and regulation affect bank efficiency, using a sample of 1261 banks across 28 Asia Pacific countries for the period This chapter is co-authored with Xiaolin Li and was published in Asia-Pacific Journal of Accounting & Economics. © Shanghai Jiao Tong University Press 2021 S. Li, Financial Regulation and Bank Performance, Contributions to Finance and Accounting, https://doi.org/10.1007/978-981-16-3509-0_5
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2001–2016. We employ a stochastic frontier analysis (SFA) model to estimate bank efficiency. Our findings confirm that market power is positively related to bank efficiency. Increased activity restrictions, strong official supervisory power, and low capital requirements are associated with high bank efficiency. Furthermore, market power has a stronger efficiency-increasing effect in a banking system characterized by the activity restrictions, supervisory power, and capital requirements described above. Foreign banks operating under strict activity restrictions in a host country with strong official supervisory power are highly efficient. We choose the Asia–Pacific region for this sample study, considering the limited research on the above relationship so far in this part of the world.1 In addition, since the 1990s, governments across the region have implemented financial reforms, including financial liberalization, bank privatization, widening access to foreign banks, and restructuring of the national banking systems, to enhance the competitive strength of the banking sector. For instance, the Chinese government has recapitalized state-owned commercial banks and replaced non-performing loans by injecting new capital, in addition to undertaking interest rate liberalization, reducing activity restrictions, and privatization of banks by selling shares on the market and introducing minority foreign ownership stakes (Fang et al., 2019; Hasan et al., 2009). In India, the deregulation of the banking system includes interest rate liberalization and removal of bank entry restrictions, private ownership restrictions, and activity restrictions aimed at promoting competition. Prudential norms on non-performing loans and capital requirements are also implemented to strengthen financial stability (Zhao et al., 2010). The banking sector in Asia–Pacific region underwent a restructuring process after the 1997–1998 Asian financial crisis and the recent global financial crisis (Noman et al., 2018; Williams & Nguyen, 2005). Our study contributes to the literature in several ways. First, we focus on the Asia– Pacific region. Since the 1990s, the development of information technology alongside financial reforms have reshaped the banking systems in the Asia–Pacific region (see Das & Ghosh, 2006; Koetter et al., 2012; Marinˇc, 2013). Technology development and financial reforms have facilitated financial market development, increased the market share of private credit, reduced the financing cost of private credit (Bae & Goyal, 2009; Djankov et al., 2007), and encouraged banks to undertake risky but more value-added activities (John et al., 2008; Laeven & Levine, 2009). Second, we employ SFA to estimate bank efficiency and evaluate the impact of competition and regulation on bank efficiency while accounting for bank-specific characteristics, market structure, financial development, and macroeconomic factors that may impact bank performance. We use an alternative definition of competition, perform different robustness analyses, and use different estimation methods for different subsamples. 1 Previous
studies examine the national banking systems in the Asia–Pacific region from different perspectives. Soedarmono et al. (2011) and Liu et al. (2012) analyze the competition and financial stability relationship based on a sample of 12 Asian and 4 South East Asian countries. Fu et al. (2014) consider the tradeoff between competition and financial stability based on 14 Asia–Pacific countries, whereas Noman et al. (2018) examine the impact of bank regulation on the relationship between bank competition and financial stability based on five South East Asian countries.
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135
Third, our analysis enhances previous studies by evaluating the impact of bank regulation on the relationship between competition and efficiency, which could provide implications for policymakers. The rest of the paper is structured as follows. Section 5.3 reviews the related literature. Section 5.4 presents the variable definitions and research design. Section 5.5 provides an empirical analysis and robustness checks. Section 5.6 concludes the paper.
5.3 Related Literature Review 5.3.1 The Impact of Competition on Bank Efficiency Under the traditional competition-efficiency hypothesis, competition improves resource allocation and transfers profit to more efficient firms and, thus, increases the efficiency of firms (e.g., Olley & Pakes, 1996; Stiroh, 2000; Tirole, 1988). As competition increases, banks are forced to specialize, providing services at lower prices and minimizing costs (Zarutskie, 2013). Competition also forces banks to improve lending efficiency and reduce credit risk (Dick & Lehnert, 2010). Schaeck and Cihák (2008) examine how competition among banks impacts efficiency, and indicate that competition has a positive effect on cost and profit efficiency. Efficiency improvement would lead to bank expansion, growth in market share, and in turn a more concentrated market (Andrie¸s & C˘apraru, 2014). However, under the alternative competition-inefficiency hypothesis, greater competition leads to higher inefficiency. In a more competitive banking system, banks may respond to an increased proportion of low-quality borrowers by reducing their lending standards and expanding credit, and borrower-specific information becomes more dispersed, as each bank is informed about a smaller pool of borrowers (Dell’Ariccia & Marquez, 2006). This reduces banks’ screening ability, increasing information asymmetry between banks and borrowers, and creating inefficiency as more low-quality borrowers obtain financing (Marquez, 2002). Weill (2004) and Casu and Girardone (2009a, b) test the effect of competition on bank efficiency from the EU and confirm a negative relationship. Maudos and Fernández de Guevara (2007) and Koetter et al. (2012) analyze the relationship between market power in loan and deposit markets and efficiency and confirm a positive relationship between market power and cost X-efficiency, and reject the so-called quiet life hypothesis proposed by Berger and Hannan (1998).
5.3.2 Literature Review on Bank Regulation and Bank Efficiency The traditional theory also confirms that bank regulation affects bank efficiency. Banking regulation is a combination of supervisory and restrictive policies aiming
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to both protect the banking sector from excessive risk-taking, and minimize moral hazard (Barth et al., 2004). In this section, we review the literature pertaining to regulations that influence bank competition and efficiency, focusing on activity restrictions, capital requirements, and supervisor power. These are the main concerns of regulators and supervisors for policy implications designed to promote competition and efficiency in the banking industry.2 Activity restrictions refer to regulatory restrictions on a bank’s ability to engage in securities, insurance, and real estate activities. These are the key determinants of the scope of a bank’s operations, which may influence both bank competition and efficiency (Barth et al. 2013a, b). Given the influence of activity restrictions, the proponents claim that activity restrictions prevent the formation of large banks and complex structures, which are difficult to monitor or discipline. By forcing banks to focus only on their specializations, activity restrictions would lead to improved bank efficiency. Pasiouras et al. (2009) provide empirical support for the proponents and show that activity restrictions improve profit efficiency. However, bank activity restrictions also prevent banks from diversifying income sources and achieving economies of scope and scale, which in turn hinder the bank’s efficiency and performance (Claessens & Klingebiel, 2001). Chortareas et al. (2012) and Barth et al. (2013a, b) empirically confirm that tighter activity restrictions are negatively associated with bank efficiency. Capital requirements indicate the minimum capital requirements that bank needs to hold against its risk-weighted assets, and are considered as a regulatory tool for building a capital buffer and improving financial stability (Laeven & Levine, 2009). Concerning the theories of capital requirements, Allen et al. (2011) identified a positive relationship between capital requirements and bank efficiency, as capital requirements incentivize bank managers to improve risk management and force bank investors to control risk-taking activities. In support of this theoretical view, Ben Naceur and Kandil (2009) and Chortareas et al. (2012) confirm that stringent capital requirements lead to improved bank efficiency. On the other hand, some studies, including Pasiouras et al. (2009), argue that because of the agency cost from the shareholder–debtholder relationship, higher capital requirements may hinder bank efficiency. The importance of supervisory power has been stressed for maintaining a stable and well-performing banking system (Basel Committee on Banking Supervision, 2012), but there are conflicting views about the importance of supervisory power. Some theoretical studies (e.g., Beck et al., 2006) suggest that supervisors require higher power to ensure their independence, and more resources to prevent banks from engaging in undesirable activities and taking excessive risks. Following this view, higher official supervisory power leads to improved bank efficiency. Conversely, other analyses claim that higher supervisory power leads to higher corruption and larger unofficial economies (Djankov et al., 2002), which significantly contributed to deepening several recent systemic banking crises (Quintyn & Taylor, 2003). Given this view, banks are forced to make sub-optimal decisions under higher supervisory 2 See
Barth et al. (2004) for the definitions and measurement of bank regulation variables.
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137
power, which in turn leads to reduced bank efficiency. Empirical analyses on supervisory power indicate conflicting views, similar to those in the theoretical studies. Pasiouras et al. (2009) and Chortareas et al. (2012) confirm that supervisory power is positively associated with bank efficiency, while Barth et al. (2004) and DemirgüçKunt and Detragiache (2011) show that greater supervisory power leads to higher risks and has a weak effect on bank efficiency. In sum, previous studies show that bank regulation might influence bank efficiency in different ways. However, it is yet to be empirically examined how regulations influence bank efficiency in a competitive environment in a disaggregated manner, especially in the Asia–Pacific region.
5.4 Variables Definition and Research Design 5.4.1 Bank Efficiency Estimation and Main Variables Definition (1)
Estimation of bank efficiency
We use stochastic frontier analysis (SFA) to estimate bank profit and cost efficiency, following Bonin et al. (2005) and Luo et al. (2016). The SFA model is defined as follows: TPit ln W3it
TCit W3it
2 Qmit Wmit = α0 + + α1m ln β1m ln W3it W3it m=1 m=1 2 2 Qmit Q1it Q2it 1 ln α2m ln + α3 ln + 2 m=1 W3it W3it W3it 2 2 Wmit Wmit W2it 1 ln β2m ln + β3 ln + 2 m=1 W3it W3it W3it 2 2 Qmit Wnit ln + α41 ln(EQit ) γmn ln + W3it W3it m=1 n=1 α52 2 α42 T + (ln(EQit ))2 + α51 Tt + 2 2 t 2 Qmit Wmit μm ln + πm ln + Tt W3it W3it m=1 2 Qmit Wmit ρm ln + τm ln + ln EQit W3it W3it m=1 2
+ α6 Tt ln EQit + uit + νit
(5.1)
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In Eq. (5.1), TPit (TCit ) indicates the total profit (total cost) of bank i in year t. Qm indicates the bank output variables, which are measured by (1) the ratio of interest income to gross loans (denoted by Q1 ) and (2) the ratio of non-interest income to other earning assets (denoted by Q2 ). Wn indicates input price variables, including (1) the ratio of interest expenses to total deposits (denoted by W1 ), (2) the ratio of other operating expenses to fixed assets (denoted by W2 ), and (3) the price of labor, which is defined as the ratio of personnel expenses to total assets (denoted by W3 ). To control for scale bias and potential heteroskedasticity, the dependent variables (TPit and TCit ), input prices (W1 and W2 ), and output variables (Q1 and Q2 ) are normalized by the price of labor (W3 ). Following Pasiouras et al. (2009), we also include the variable ln(EQ), which is defined as the logarithm of equity as a quasi-fixed input to control for the risk preference difference between banks. As our sample period covers 2001–2016, in order to control for the impact of technological development on bank efficiency, we introduce a time trend variable, T . The error term in the SFA model is decomposed into two parts: uit and νit . The error term uit indicates the profit (cost) inefficiency and is distributed as a truncated normal variable, and νit indicates random error and is distributed as a normal variable.3 The inefficiency for each bank is defined as Profit Ineffit = exp(uit ) for profit inefficiency and Cost Ineffit = exp(−uit ) for cost inefficiency. (2)
Measure of bank competition
In our analysis, to measure the competition in banking industry, we estimate the Lerner index and evaluate banks’ competitive behavior. The Lerner index is defined as follows: Lerner Indexit = (Pit − MCit )/Pit
(5.2)
The Lerner index is a relative mark-up of price over marginal cost and measures firm market power (Lerner, 1934). The higher the mark-up, the greater is the market power. The Lerner index ranges from 0 in the case of perfect competition to 1 in the case of monopoly. In our analysis, Pit indicates output price, which is measured by the ratio of total revenue to total assets for bank i in year t, and MCit indicates the marginal cost of bank i in year t.4 MCit is defined as in Eq. (5.3): MCit =
TCit (β1 + β2 lnQ3it + β9 lnW1it + β10 lnW2it + β11 lnW3it ) Q3it
(5.3)
The translog total cost function is
3 See
the detailed explanation of the SFA model in Battese and Coelli (1995). it as the ratio of total revenues to total assets. See also Koetter et al. (2012) and Fu et al. (2014).
4 Based on earlier studies, we define the output price P
5.4 Variables Definition and Research Design
139
3 β2 β2+m lnWmit (lnQ3it )2 + 2 m=1 3 3 1 2 + β5+m (lnWmit ) + lnQ3it β8+m lnWmit 2 m=1 m=1
lnTCit = β0 + β1 lnQ3it +
+ β12 (lnW1it )(lnW2it ) + β13 (lnW1it )(lnW3it ) + β14 (lnW2it )(lnW3it ) + εit
(5.4)
where TCit is defined as the total cost of bank i in year t. W1 , W2 , and W3 are input price variables as defined in Sect. 5.4.1. Q3 is the output variable, which is a proxy for total assets. Equation (5.4) is estimated based on the stochastic cost frontier analysis for each country in each year (see also Koetter et al., 2012; Fu et al., 2014). We alternatively employ the recent competition measure, Boone indicator, defined in Boone (2001, 2008). The Boone indicator captures the link between firm performance and efficiency; firms with higher efficiency have superior performance, which results in higher profits. The hypothesis of the Boone indicator is that the relationship between efficiency and profits is increasing in the degree of competition. The Boone model is defined as follows: ln TPit = α + β ln MCit + εit
(5.5)
where TPit indicates the total profit, MCit is the marginal cost of bank i in year t, and β is defined as the Boone indicator. The Boone indicator β is negative and decreasing in the level of competition. We use the log–log specification to deal with potential heteroskedasticity. (3)
Bank regulatory variables
Given the impact of bank regulations on bank efficiency, we employ three indexbased measures to control for country-specific differences in banking regulations, following Chortareas et al. (2012), Barth et al. (2013a, b), and Luo et al. (2016). The variable Activity restrictions is related to bank activity restrictions and measures the degree to which banks are allowed to engage in securities, insurance, and real estate activities. A higher value indicates greater restrictions. The variable Capital stringency index measures both initial and overall capital stringency and estimates whether the capital requirements reflect certain risk elements and whether allowances are made for certain market value losses before the minimum capital adequacy is determined. The variable Official supervisory power indicates whether the supervisory authorities have the authority to take specific actions to prevent and correct problems, with a higher value indicating greater powers of supervision.
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(4)
5 Bank Competition, Regulation, and Efficiency
Control variables
Bank-specific variables: We use a set of bank-specific variables to control for timevarying effects on bank performance. The probability of bank insolvency is proxied by Z-score (e.g., Demirgüç-Kunt & Huizinga, 2010; Laeven & Levine, 2009), which indicates the number of standard deviations by which returns would have to fall from the mean to wipe out all equity in the bank. A higher Z-score indicates a lower probability of bank failure.5 We define the variable TCD as the ratio of total customer deposits to total assets to control for the funding of the bank, LLP as the ratio of loan loss provision to gross loans to control for the loan quality, FA as the ratio of fixed assets to total assets to measure bank asset structure, and log (TA) as the logarithm of total assets to control for bank size. To control for the difference introduced by banks’ specializations, we also define two dummy variables, Commercial dummy and Savings dummy, which take the value of one for a commercial bank and a savings bank, respectively. Country control variables: We first consider the effect of market structure and define the variable Bank concentration, which indicates the degree of concentration of deposits in the five largest banks in a given country, and the variable Governmentowned banks, which measures the extent to which the banking system’s assets are government owned, to examine the impact of market structure on bank efficiency in a given country. During our sample period, most economies experienced financial reforms. Thus, we include different measures to account for the financial development of the banking system in the Asia–Pacific region. We first include the variable Financial openness, an index defined by Chinn and Ito (2008).6 The normalized index Financial openness ranges from 0 to 1, with a higher value indicating a higher level of financial openness. Second, we include the variable Financial freedom, which is obtained from the annual report of the Heritage Foundation and indicates the financial freedom of a given country. Financial freedom ranges from 0 to 100 and measures the extent of government regulation of financial services, the extent of state intervention in banks and other financial services, the difficulty of opening and operating financial services firms, and government influence on the allocation of credit.7 A higher value of Financial freedom indicates greater financial freedom. 5 The Z-score is calculated as follows: Z-score = (ROA
it +ETAit )/sd (ROA)it , where ROAit is defined as return on assets for bank i at year t, ETAit is the ratio of equity to assets, and sd (ROA) indicates the standard deviation of ROA. We employ a three-year rolling window to calculate sd (ROA) instead of the whole sample period, allowing for the time variation of sd (ROA). See Demirgüç-Kunt and Huizinga (2010) for a detailed discussion. 6 The Financial openness index is constructed according to information on four categories: (1) the presence of multiple exchange rates, (2) restrictions on current account transactions, (3) restrictions on capital account transactions, and (4) the requirement to surrender export proceeds. Information on each category is provided in the Annual Report on Exchange Arrangements and Exchange Restrictions, published by the International Monetary Fund (IMF); a category is assigned 1 if the restriction does not exist and 0 otherwise. 7 For detailed information about the index of economic freedom, defined by the Heritage Foundation, see https://www.heritage.org/index/about.
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Finally, we include GDP growth and Inflation at country level to account for the impact of macroeconomic factors, and define the variable Crisis, which equals one if the country experienced a systemic financial crisis in a given year, to control for the impact of financial crisis on bank efficiency. Table 5.1 summarizes the definition and data sources of variables employed in this analysis.
5.4.2 Model Specification Based on the profit (cost) inefficiency estimated in Eq. (5.1), we investigate the determinants of profit (cost) inefficiency of banks. The model is specified as follows: Ineffit = α0 + β1 Compeitionit + γRegulationjt + × Bank_controlsit + × Country_controlsjt + αi + ϕt + εit
(5.6)
where Ineff it represents the profit (cost) inefficiency for bank i at year t estimated based on Eq. (5.1), Compeitionit is the competition indicator, which is a proxy for the Lerner index, or Boone indicator, and Regulationjt denotes one of the bank regulatory characteristics of country j, including Activity restrictions, Capital stringency index, and Official supervisory power. Bank_controlsit indicates bank-specific variables, including Z-score, TCD, LLP, FA, log TA, Commercial dummy and Savings dummy. Country_controlsjt is a matrix of country-level control variables, including Bank concentration, Government-owned banks, Financial openness, Financial freedom; GDP growth, Inflation, and Crisis. Both bank fixed effects (αi ) and year fixed effects (ϕt ) are considered to control for unobserved time-invariant heterogeneity across banks, as well as macroeconomic factors and monetary policy, which may vary over time. We use the method proposed by Vogelsang (2012), who extend Driscoll and Kraay’s (1998) study with bank fixed effects to a setting with bank and year fixed effects, to estimate standard errors and hypothesis tests robust to heteroskedasticity, serial correlation, and spatial correlation.
5.4.3 Data Selection Our analysis focuses on 28 countries from Asia Pacific between 2001 and 2016. The financial data of each bank are collected from Bankscope. To avoid double counting, we use the consolidated financial information of each bank, if available, and unconsolidated reports otherwise. Data on financial openness are obtained from
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5 Bank Competition, Regulation, and Efficiency
Table 5.1 Variables definition and data sources Variables
Definitions
Data sources
Variables for efficiency estimation TP
Total profit of bank in a given year, expressed in $1000
Bankscope
TC
Total cost of bank in a given year, expressed in $1000
Bankscope
Q1
The ratio of interest income to gross loans
Bankscope
Q2
The ratio of non-interest income Bankscope to other earning assets
W1
The ratio of interest expense to total deposits
Bankscope
W2
The ratio of other operating expenses to fixed assets
Bankscope
W3
The ratio of personnel expense to total assets
Bankscope
ln EQ
The logarithm of equity capital Bankscope of banking institution in a given year
T
The time trend variable
Self-definition
Lerner index
Lerner index is equal to the difference between asset price and marginal cost, normalized by asset price
Own calculation
Boone indicator
A measure of degree of competition, calculated as the elasticity of profits to marginal costs
Own calculation
Competition indicators
Bank-specific variables Z-score
Calculated as the sum of ROA Bankscope and equity-to-assets divided by the standard deviation of ROA of each bank over past 3 years
TCD
The ratio of total customer deposits to total assets
Bankscope
LLP
The ratio of loan loss provision to gross loans
Bankscope
FA
The ratio of fixed assets to total assets
Bankscope
log TA
The logarithm of total asset in $ Bankscope 1000 (continued)
5.4 Variables Definition and Research Design
143
Table 5.1 (continued) Variables
Definitions
Data sources
Foreign dummy
Takes a value of 1 if the bank is a foreign-owned bank, and 0 otherwise
Bankscope
Commercial dummy
Takes a value of 1 if the bank is a commercial bank and 0 otherwise
Bankscope
Saving dummy
Takes a value of 1 if the bank is a saving bank and 0 otherwise
Bankscope
The degree of concentration of deposits in the 5 largest banks
Barth et al. (2013a)
Market structure variables Bank concentration
Government-owned banks The extent to which the banking Barth et al. (2013a) system’s assets are government owned Financial development variables Financial openness
The Chinn-Ito index measures the degree of capital account openness. Construction is based on transactions from the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER)
Chinn and Ito (2008). This paper uses the 2015 version of the database, available from: http:// web.pdx.edu/ ito/Chinn-Ito website.htm
Financial freedom
Financial freedom component of the Heritage index of Economic Freedom
The Heritage Foundation. Available from: http://www.her itage.org/index/
Country control variables GDP growth
Annual growth rate of GDP at World Bank Development market prices based on constant Indicator Database local currency
Inflation
Inflation rate
Crisis
A binary variable which equals Laeven and Valencia (2018) one if the country was experiencing a systemic crisis in a given year, and zero otherwise
World Bank Development Indicator Database
Bank regulatory variables Activity restrictions
A variable measures a bank’s ability to engage in securities, insurance, and real estate activities. The ranges from 0 to 12, and a higher score indicates more restrictions on banks to engage in such activities
Barth et al. (2013a)
(continued)
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5 Bank Competition, Regulation, and Efficiency
Table 5.1 (continued) Variables
Definitions
Capital regulatory index
A variable that captures both the Barth et al. (2013a) overall capital stringency and the initial capital stringency based on answers to eight questions. It ranges from 0 to 8, with a higher value indicating higher capital stringency
Data sources
Official supervisory power A variable indicates whether the Barth et al. (2013a) supervisory authorities have the authority to take specific actions to prevent and correct problems. It ranges from 0 to 14, with fourteen indicating the highest power of the supervisory authorities This table reports the definitions and data sources of the variables included in our analysis
Chinn and Ito (2008), and we use the 2015 version of the database.8 Data on the financial freedom index are collected from the annual report of the Heritage Foundation. We obtain the market structure variables and bank regulation data from Barth et al. (2013a, b),9 the country economy development variables are collected from the World Bank Development Indicator Database, and information on bank crises is obtained from Laeven and Valencia (2018). Based on the complete sample of banks obtained from Bankscope, we apply the following selection criteria: (1) we delete observations for which data on one of the variables employed in estimating profit (cost) inefficiency are missing; (2) we delete observations for which data on one of the variables employed in estimating the Lerner index are missing; (3) to estimate the Z-score, we employ the mean of return on assets (ROA) and equity to total asset ratio (ETA) and the standard deviation of ROA (sd (ROA)) over three years, retaining only those banks that have at least three consecutive years’ observations; (4) we delete the observations for which data on one of the bank-specific and country-level control variables are missing or drop out of the 5th and 95th percentiles of bank-specific variables.
8 As the information of financial openness for 2016 is not available, we use interpolation and extrap-
olation techniques, following Goetz et al. (2016), to fill in the missing observation of financial openness of 2016 for each country. 9 Information on bank regulation is provided by Barth et al. (2013a, b), but the surveys were conducted in 1999, 2002, 2006, and 2011. Following Anginer et al. (2014), we employ the previous survey data until the new survey data become available for matching the bank regulation and supervision variables with bank-specific variables and country control variables. Surveys were conducted in 1999 for the years 1998–2001, in 2002 for the years 2002–2005, in 2006 for the years 2006–2010, and in 2011 for the years 2011–2016.
5.4 Variables Definition and Research Design
145
We finally have 1261 banks and 9193 bank-year observations between 2000 and 2016. Our sample consists of commercial, savings, and co-operative banks from 28 Asia–Pacific countries. The data are inflation adjusted and expressed in USD.
5.4.4 Summary Statistics We first report the summary of profit inefficiency and cost inefficiency for 28 Asia– Pacific economies between 2000 and 2016 by country (Panel A), by year (Panel B), and group difference analysis (Panel C) in Table 5.2. Considering bank efficiency by country, we find that on average, banks in Korea have the highest profit and cost efficiency. Comparing profit and cost inefficiency by year, we confirm an increasing trend for both profit and cost efficiency from 2000 to 2016. Profit and cost efficiencies during the global financial crisis (2008–2010) are slightly higher than before the global financial crisis (2005–2007), but the difference is insignificant. We then provide the summary statistics in Table 5.3. Profit inefficiency ranges from 0.238 to 0.632, with an average of 0.467; cost inefficiency has a mean of 0.492 with a standard deviation of 0.120, and varies from 0.249 to 0.662. The mean of the Lerner index is 0.685 with a standard deviation 0.589, and the average Boone indicator is − 1.286, ranging from −10.39 to −0.166. For the bank regulation variables, an average country in our sample has activity restrictions of 8.02, a capital regulatory index of 6.81, and official supervisory power of 11.56.
5.5 Empirical Results 5.5.1 Basic Results We examine the impact of competition and regulation on bank efficiency, and Table 5.4 reports the basic results. The dependent variables, profit inefficiency and cost inefficiency, are reported in columns (1)–(5) and columns (6)–(10), respectively. The negative and statistically significant coefficients of the Lerner index for both profit and cost inefficiency in different specifications indicate that increased market power is associated with higher bank efficiency, which is consistent with the competitioninefficiency view of Maudos and Fernández de Guevara (2007). In examining the role of regulation on bank efficiency, first, the coefficient of Activity restrictions in columns (2) and (7) is negative and significant, suggesting that activity restrictions appear to be an effective regulatory tool to improve bank efficiency, supporting the findings of Pasiouras et al. (2009) and Djalilov and Piesse (2019). This is because strict bank activity restrictions drive banks to acquire greater expertise and specialization, and in turn become more profitable and cost efficient in specific market segments.
146
5 Bank Competition, Regulation, and Efficiency
Table 5.2 Summary of profit inefficiency and cost inefficiency according to countries and years Panel A: Mean by country Country
Profit inefficiency
Cost inefficiency
Observations
Australia
0.453
0.469
316
Azerbaijan
0.561
0.585
128
Bangladesh
0.504
0.526
406
Bhutan
0.536
0.562
13
Cambodia
0.533
0.562
64
China (Mainland)
0.383
0.409
740
Fiji
0.628
0.659
6
Hong Kong SAR (China)
0.451
0.479
310
India
0.465
0.485
882
Indonesia
0.485
0.506
674
Japan
0.467
0.500
2825
Kazakhstan
0.512
0.537
253
Korea
0.260
0.272
57
Kyrgyzstan
0.580
0.609
56
Malaysia
0.448
0.469
702
Maldives
0.310
0.340
12
Nepal
0.504
0.525
216
New Zealand
0.475
0.495
148
Pakistan
0.502
0.526
383
Papua new Guinea
0.506
0.551
9
Philippines
0.492
0.521
326
Samoa
0.599
0.634
6
Singapore
0.370
0.388
61
Sir Lanka
0.497
0.518
266
Tajikistan
0.601
0.625
40
Thailand
0.440
0.465
277
Tonga
0.614
0.646
7
Turkmenistan
0.538
0.561
10
Panel B: Mean by year Year
Profit inefficiency
Cost inefficiency
Observations
2001
0.541
0.566
348
2002
0.542
0.570
353
2003
0.540
0.570
340
2004
0.543
0.573
456 (continued)
5.5 Empirical Results
147
Table 5.2 (continued) Panel A: Mean by country Country
Profit inefficiency
Cost inefficiency
Observations
2005
0.542
0.572
489
2006
0.533
0.563
510
2007
0.524
0.554
538
2008
0.522
0.551
557
2009
0.524
0.552
556
2010
0.523
0.550
600
2011
0.516
0.544
648
2012
0.510
0.537
678
2013
0.511
0.537
664
2014
0.311
0.329
779
2015
0.306
0.323
874
2016
0.302
0.318
803
Total
0.467
0.492
9193
Panel C: Group difference Periods
Before the global financial crisis (2005–2007)
During the global financial crisis (2008–2010)
Group difference (t-statistic)
Group mean (Profit inefficiency)
0.532
0.523
0.009 (1.06)
Group mean (Cost inefficiency)
0.562
0.551
0.011 (1.35)
This table gives a summary of the mean of profit inefficiency and cost inefficiency according to country and year, and group difference analysis
Second, the positive coefficient of Capital regulatory index in columns (3) and (8) suggests that higher capital requirements are associated with lower bank efficiency in the Asia–Pacific region. With higher bank capital requirements, bank owners may pursue a costly financing policy and prioritize equity over deposits (Djalilov & Piesse, 2019), which in turn reduces the banks’ incentives to screen and monitor lending when equity capital becomes more expensive to raise than deposits, ultimately leading to higher risks and lower efficiency (Barth et al., 2004). Finally, columns (4) and (9) show that the effect of official supervisory power is negative and significant, suggesting that higher official supervisory power increases bank efficiency. This finding supports the argument that powerful official supervision drives banks to improve corporate governance, reduce risk-taking activities, and improve banks’ functioning, which potentially leads to higher efficiency (Beck et al., 2006). For the bank and country control variables, the negative and significant coefficient of Z-score is significantly negative for both profit and cost inefficiency, suggesting that
148
5 Bank Competition, Regulation, and Efficiency
Table 5.3 Summary statistics Variables
N
Mean
Standard Min deviation
Max
P10
Median P90
Dependent variables Profit inefficiency
9193
0.467
0.115
0.238
0.632
0.279
0.497
0.600
Cost inefficiency
9193
0.492
0.120
0.249
0.662
0.294
0.524
0.627
−0.078 0.636
1.527
Competition indicators Lerner index
9193
0.685
0.589
−0.258 2.024
Boone indicator
9045 −1.286
1.935
−10.39 −0.166 −4.123 −0.731 −0.271
Bank-specific variables Z-score
9193
3.630
3.518
−0.515 13.216
0.299
2.622
8.921
TCD
9193
0.759
0.171
0.195
0.951
0.500
0.803
0.933
LLP
9193
0.007
0.008
−0.001 0.031
0.000
0.005
0.019
FA
9193
0.014
0.011
0.001
0.044
0.003
0.011
0.029
log TA
9193
16.969
3.927
2.966
28.865
12.615
16.025
22.978
Foreign dummy
9193
0.143
0.350
0
1
0
0
1
Commercial dummy
9193
0.478
0.500
0
1
0
0
1
Saving dummy
9193
0.010
0.098
0
1
0
0
0
Country control variables Bank concentration 9193
54.173 13.801
36
100
40
52
76
Government-owned 9193 banks
22.904 27.927
0
96
0
3
69
Financial openness
9193
46.811 17.086
10
90
30
50
70
Financial freedom
9193
54.173 13.801
36
100
40
52
76
GDP growth
9193
4.281
3.619
−5.417 34.500
0.336
4.220
8.480
Inflation
9193
3.729
5.100
−18.93 28.164
−1.101 2.463
8.984
Crisis
9193
0.022
0.148
0
1
0
0
0
Activity restrictions 7192
8.018
1.950
3
12
5
8
10
Capital regulatory index
7220
6.809
2.238
2
10
4
7
10
Official supervisory 7358 power
11.556
1.960
6
16
9
12
14
Regulatory variables
This table reports summary statistics of the variables for the full sample. Our sample period is 2001–2016. All data are inflation-adjusted, and bank-specific variables are winsorized at the 5th and 95th percentile level to reduce the influence of outliers
Bank concentration
(5)
(−1.56)
(2.16)
(1.12)
(0.63)
0.00861**
(−4.14)
0.00351
0.0000501
(0.34)
0.000212***
(2.65)
(2.04)
0.000265**
(2.76)
0.00872***
(0.62)
0.00157
(−108.86)
(−110.34)
0.00156
(−303.55)
−0.00433***
(4.16) −0.0288***
(4.69)
−0.0288***
(7.85)
−0.0298***
(2.47) 0.252***
(2.75)
0.0906**
(5.11)
0.0221***
(−2.73)
0.271***
0.289***
(3.68)
(5.00)
0.101***
(7.89)
0.108***
0.0211***
0.0241***
(−2.76)
(2.64)
0.000207***
(3.38)
0.0102***
(1.39)
0.00197
(−177.43)
−0.0288***
(6.97)
0.264***
(2.55)
0.0730**
(6.70)
0.0205***
(−2.79)
(0.65)
0.0000636
(2.70)
0.00821***
(0.92)
0.00132
(−176.55)
−0.0289***
(6.73)
0.258***
(3.44)
0.101***
(6.69)
0.0211***
(−2.77)
(4.39)
0.000352***
(4.65)
0.00787***
(−0.74)
−0.000751
(−323.94)
−0.0312***
(9.77)
0.336***
(4.78)
0.133***
(9.74)
0.0284***
(−1.96)
(1.31)
0.000184
(5.54)
0.0122***
(1.31)
0.00317
(−120.43)
−0.0304***
(6.26)
0.323***
(3.24)
0.112***
(6.40)
0.0266***
(−0.98)
(−4.89)
(7) −0.00389
−0.0000696
(−1.51)
−0.00217***
(6)
(3.22)
0.000396***
(6.28)
0.0123***
(1.29)
0.00317
(−118.08)
−0.0304***
(5.65)
0.304***
(2.93)
0.103***
(6.38)
0.0275***
(−0.91)
−0.0000655
(−5.10)
−0.00365***
(8)
Dependent variable: cost inefficiency
(−3.61)
(−7.51)
−0.000796*** −0.000279
(4)
−0.000222*** −0.000203*** −0.000199*** −0.000201*** −0.000200*** −0.000116*
Country control variables
Saving dummy
Commercial dummy
log TA
FA
LLP
TCD
Z-score
(−1.70)
(−3.43)
(−5.25)
Bank-specific variables
−0.000334*** −0.000562*
−0.00252***
Lerner index
(3)
(2)
(1)
Variables
Dependent variable: profit inefficiency
Table 5.4 Impact of bank competition and regulation and bank efficiency (9)
(4.55)
0.000356***
(7.39)
0.0130***
(2.67)
0.00366***
(−192.78)
−0.0304***
(8.84)
0.314***
(3.32)
0.0891***
(8.68)
0.0255***
(−0.93)
−0.0000641
(−1.28)
−0.00348
(10)
(continued)
(2.17)
0.000207**
(6.68)
0.0118***
(2.14)
0.00299**
(−192.19)
−0.0305***
(8.91)
0.312***
(4.10)
0.111***
(8.83)
0.0267***
(−0.98)
−0.0000677
(−7.99)
−0.00392***
5.5 Empirical Results 149
(5.44)
(−8.48)
Official supervisory power
Capital regulatory index
Activity restrictions
(−5.45)
(−1.55)
(6.03)
(8.65)
0.0647***
(3.10)
0.000187***
(5)
(−0.38) (−1.35)
−0.00225
(−4.12)
(−2.38)
(2.66) −0.000826***
(1.78) −0.000451**
0.000633***
0.000550*
(−1.73)
(−0.55)
(−0.94) −0.0000248
−0.000459*
(−1.21)
(−0.59) −0.000490
(−8.45) −0.0000844
(−2.12)
(−1.62)
(−0.68)
(−1.05) −0.00132
−0.0000351
(−1.68)
−0.0000764
(−1.78)
(−9.00) −0.000149*
(−5.62) −0.000188*
−0.000763**
(−0.93)
−0.00171
(−4.69)
−0.000674
−0.000294*** −0.0000702
(−4.34)
−0.000386*** −0.000166
Bank regulation variables
Crisis
Inflation
GDP growth
(4.21)
0.0394***
(3.35)
0.000156***
(4)
(0.33)
0.00165
(2.28)
0.0000950**
(6)
(2.78)
0.0241***
(1.40)
0.0000982
(7)
(1.99)
0.0198**
(2.05)
0.000167**
(8)
Dependent variable: cost inefficiency (9)
(1.68)
0.0103*
(2.98)
0.000125***
(10)
(5.10)
0.0353***
(3.29)
0.000175***
(−6.63) (0.31)
0.0000313
(−1.31)
−0.00224
(−4.05)
(−2.02)
−0.000672**
(−0.42)
−0.00562
(−0.14)
−0.000241*** −0.0000101
(−2.06)
−0.000175**
(−10.85)
(1.99)
0.000574**
(−0.98)
−0.00528
(−0.24)
−0.0000167
(0.12)
0.0000114
(−6.75)
(−0.84)
−0.000152
(−1.38)
−0.00343
(−0.10)
−0.00000592
(0.42)
0.0000357
(−10.56)
(continued)
(−3.27)
−0.000602***
(2.70)
0.000604***
(−1.84)
−0.000458*
(−1.49)
−0.00610
(0.48)
0.0000295
(1.21)
0.000102
(−10.07)
−0.000223*** −0.000225*** −0.000235*** −0.000249*** −0.000244*** −0.000282*** −0.000268*** −0.000279*** −0.000284*** −0.000282***
(4.20)
Financial freedom
0.0471***
(1.72)
(1.14)
0.0525***
0.0220***
Financial openness
0.000164*
(3)
0.0000944
(2)
Dependent variable: profit inefficiency
(1)
Government−owned 0.000118** banks (2.54)
Variables
Table 5.4 (continued)
150 5 Bank Competition, Regulation, and Efficiency
Yes
9081
0.976
Year fixed effects
N
R−squared
(2.49)
0.972
7039
Yes
Yes
(5.44)
Yes
Bank fixed effects
0.487***
0.715***
Constant
(2)
(1)
Variables
0.972
7220
Yes
Yes
(6.30)
0.159***
(3)
Dependent variable: profit inefficiency
Table 5.4 (continued)
0.972
7208
Yes
Yes
(3.23)
0.192***
(4)
0.972
7039
Yes
Yes
(6.06)
0.355***
(5)
0.981
9081
Yes
Yes
(9.65)
0.599***
(6)
0.978
7039
Yes
Yes
(4.12)
0.436***
(7)
0.978
7220
Yes
Yes
(0.84)
0.871
(8)
Dependent variable: cost inefficiency (9)
0.978
7208
Yes
Yes
(7.23)
0.341***
(10)
0.978
7039
Yes
Yes
(1.23)
0.682
5.5 Empirical Results 151
152
5 Bank Competition, Regulation, and Efficiency
higher bank stability corresponds to higher bank efficiency. We find that increasing banks’ customer deposits, loan loss provisions, and fixed assets decreases profit and cost efficiency. The significantly negative coefficient of log TA indicates that larger banks enjoy higher efficiency compared with small banks. Commercial banks are more efficient than saving banks. Considering the impact of market structure, our results show that higher bank concentration and more government-owned banks are associated with lower bank efficiency.10 Higher financial openness leads to lower bank efficiency and higher financial freedom is related to higher efficiency. This table examines the impact of competition and regulation on bank efficiency. The dependent variables are Profit inefficiency and Cost inefficiency in columns (1)– (5) and columns (6)–(10), respectively as defined in Eq. (5.1). Our sample period is 2001–2016. Bank-level controls include Z-score, TCD, LLP, FA, log TA, Commercial dummy, and Saving dummy. Country control variables include Bank concentration, Government-owned banks, Financial openness, Financial freedom, GDP growth, Inflation, and Crisis. Bank regulation variables include Activity restrictions, Capital regulatory index, Official supervisory power. All variables are defined in Table 5.1. Both bank and year fixed effects are included and reported t-statistics (in parentheses) are based on standard errors that are estimated based on Vogelsang (2012) and robust to heteroskedasticity, serial correlation, and spatial correlation. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
5.5.2 Impact of Bank Regulation on the Relationship Between Competition and Efficiency The results of our basic analysis in Sect. 5.5.1 indicate that both market power and bank regulation have an impact on bank efficiency. Previous studies confirm that bank regulation is also an important factor in determining banks’ competitive conditions (e.g., Claessens & Laeven, 2004; Demirguc-Kunt et al., 2004; Li, 2019) and has a significant effect on the relationship between competition and bank performance (e.g., Agoraki et al., 2011; Noman et al., 2018). Thus, in this subsection, we test whether bank regulation affects the relationship between competition and efficiency in our sample, and the results are reported in Table 5.5. The coefficient of the interaction term of Activity restrictions and Lerner index remains negative and significant, suggesting that increased activity restrictions strengthen the positive effect of market power on bank efficiency, probably because tighter activity restrictions result in lower competition in the banking system (Anginer
10 The positive coefficient of Bank concentration is not contradictory to the main conclusion that increased market power (measured by Lerner index) is associated with higher bank efficiency, as the degree of concentration is not necessarily related to the degree of competition, and competition and concentration can coexist (Mamatzakis et al., 2005). See Claessens and Laeven (2004), Beck et al. (2006), and Olivero et al. (2011) for the detail discussion.
Government-owned banks
Bank concentration
Saving dummy
Commercial dummy
log TA
FA
LLP
TCD
(−4.21)
−0.00653*** (−0.71)
−0.00101
(6)
(2.99)
(1.88)
(3.08)
0.000169***
(0.65)
0.000100*
0.000256***
(2.84)
(2.73)
0.0000626
0.00872***
0.00869***
(0.99)
(−177.02)
(−175.21)
(1.14)
−0.0288***
−0.0288***
0.00143
(6.71)
(6.96)
0.00164
0.256***
0.265***
(3.26)
(3.40)
(6.97)
0.0953***
(6.69)
0.0996***
0.0220***
0.0212***
(−2.77)
(3.54)
0.000167***
(2.70)
0.000214***
(3.33)
0.00985***
(1.44)
0.00204
(−177.55)
−0.0288***
(6.93)
0.264***
(2.52)
0.0724**
(6.66)
0.0205***
(−2.80)
(3.00)
0.000174***
(0.61)
0.0000565
(1.67)
0.00496*
(−0.32)
(2.19)
0.000102**
(2.04)
0.000192**
(6.77)
0.0123***
(2.30)
(−190.15) 0.00322**
(−32.34)
−0.0304***
(9.15)
0.319***
(4.10)
0.112***
(8.75)
0.0266***
(−0.98)
−0.000443
−0.0229***
(7.92)
0.304***
(2.67)
0.0770***
(7.69)
0.0236***
(−3.00)
(3.49)
0.000174***
(4.61)
0.000381***
(6.83)
0.0123***
(2.12)
0.00294**
(−193.38)
−0.0304***
(8.91)
0.310***
(4.06)
0.110***
(9.05)
0.0274***
(−0.96)
(−3.73)
−0.0107***
(5)
(3.27)
0.000138***
(4.62)
0.000364***
(7.30)
0.0126***
(2.73)
0.00374***
(−192.88)
−0.0304***
(8.79)
0.313***
(3.28)
0.0884***
(8.62)
0.0255***
(−0.94)
−0.0000649
(−4.22)
−0.00969***
(7)
Dependent variable: cost inefficiency
−0.0000667
(−1.88)
−0.00450*
(4)
(−2.78)
(−1.35)
(−2.16)
(3)
−0.000201*** −0.000200*** −0.000202*** −0.000214*** −0.0000682
−0.00356
−0.00363**
Lerner index
Z-score
(2)
(1)
Variables
Dependent variable: profit inefficiency
Table 5.5 Impact of bank regulation on the relationship between competition and efficiency
(continued)
(3.58)
0.000185***
(1.93)
0.000172*
(4.76)
0.00792***
(0.69)
0.000916
(−35.15)
−0.0235***
(11.04)
0.384***
(3.34)
0.0894***
(10.02)
0.0291***
(−1.24)
−0.0000845
(−4.97)
−0.0132***
(8)
5.5 Empirical Results 153
Official supervisory power
Capital regulatory index * Lerner index
Capital regulatory index
(4.01)
0.0252***
−3.245 (−0.93)
(5) (3.03)
0.0198***
(6) (1.71)
0.0104*
(7)
Dependent variable: cost inefficiency (4)
(−1.28)
(−9.75) (0.36)
(3.31)
(2.08)
0.000455*
0.000718***
0.000448**
(−0.98)
(−1.74)
(−1.73)
(−1.49) −0.000339*
(−0.67) −0.000402*
−0.000421
(−1.10)
−0.00567
(−0.29)
−0.0000176
−0.000206
(−1.19)
−0.00618
(0.89)
−0.000308
−0.0000338
(−0.32)
−0.000288
(−3.65) 0.0000638
(1.00)
(−1.47)
(−0.89)
(−0.52)
(−5.84) −0.000390*** 0.0000309
0.000263
−0.00140
−0.00179
−0.000386
(−1.37)
(−1.25)
−0.0000312
(−1.65)
(−2.15)
−0.0000816
(−1.85)
−0.0000815
(−9.22) −0.000147*
(−8.17)
−0.000193**
(−8.05)
−0.000167*
Activities restrictions −0.000509** * Lerner index (−2.39)
Activity restrictions
Crisis
Inflation
GDP growth
(6.05)
0.0395***
(3) (0.06)
0.210
(8)
(3.50)
0.000697***
(0.51)
0.000128
(−0.81)
−0.00540
(−0.44)
−0.0000248
(0.05)
0.00000428
(−9.94)
0.000337
(−1.18)
−0.00319
(−0.02)
−0.00000136
(0.46)
0.0000387
(−10.89)
(continued)
0.000574**
(4.61)
0.000923***
(−1.33)
−0.000390
(−1.33)
−0.000284
(−0.51)
−0.000147
(−0.76)
−0.00801
(−0.08)
−0.0000053
(−1.54)
−0.000152
(−6.31)
−0.000232*** −0.000231*** −0.000260*** −0.000210*** −0.000272*** −0.000273*** −0.000297*** −0.0002***
(6.62)
(7.99)
Financial freedom
0.0471***
0.0541***
Financial openness
(2)
Dependent variable: profit inefficiency
(1)
Variables
Table 5.5 (continued)
154 5 Bank Competition, Regulation, and Efficiency
Yes
Yes
7039
0.972
N
R-squared
0.972
7066
Yes
Yes
(4.19)
(7.13)
Year fixed effects
2.951***
0.356***
(2)
(−0.97)
(−2.28)
0.972
7208
Yes
Yes
(4.51)
0.973
7039
Yes
Yes
(5.99)
0.492***
−0.000461** 0.944***
(1.69) −0.00110
(−0.13)
(4)
(3)
Dependent variable: profit inefficiency
(1)
Bank fixed effects
Constant
Official supervisory power * Lerner index
Variables
Table 5.5 (continued)
0.978
7039
Yes
Yes
(6.99)
0.746***
(5)
0.978
7066
Yes
Yes
(5.43)
0.395***
(6) (2.40)
(8)
0.978
7208
Yes
Yes
(5.21)
0.738***
(−2.81)
0.979
7039
Yes
Yes
(3.27)
0.329***
(−5.57)
−0.000541*** −0.0011***
(1.37)
(7)
Dependent variable: cost inefficiency
5.5 Empirical Results 155
156
5 Bank Competition, Regulation, and Efficiency
et al., 2014; Li, 2019). The coefficient of the interaction term for Capital regulatory index and Lerner index remains positive and significant, demonstrating that the market power effect in increasing bank efficiency is less pronounced if the bank is exposed to stringent capital requirements. The interaction term of Official supervisory power and Lerner index suggests implications similar to those of activity restrictions; the negative coefficient of the interaction term provides evidence that strong supervisory power of bank regulators intensifies the market power effect in increasing bank efficiency. This table examines the impact of bank regulation on the relationship between competition and bank efficiency. The dependent variables are Profit inefficiency and Cost inefficiency in columns (1)–(4) and columns (5)–(8), respectively as defined in Eq. (5.1). Our sample period is 2001–2016. Bank-level controls include Z-score, TCD, LLP, FA, log TA, Commercial dummy, and Saving dummy. Country control variables include Bank concentration, Government-owned banks, Financial openness, Financial freedom, GDP growth, Inflation, and Crisis. Bank regulation variables include Activity restrictions, Capital regulatory index, Official supervisory power. All variables are defined in Table 5.1. Both bank and year fixed effects are included and reported t-statistics (in parentheses) are based on standard errors that are estimated based on Vogelsang (2012) and robust to heteroskedasticity, serial correlation, and spatial correlation. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
5.5.3 Further Test: Foreign Banks Versus Domestic Banks Some previous studies indicate that foreign banks and domestic banks have different patterns: foreign banks earn more profit than domestic banks (Claessens et al., 2001), and are more sensitive to strict parent country regulation than to the institutional conditions in host countries (e.g., De Haas & Van Lelyveld, 2006; Ongena et al., 2013). In this subsection, we further test whether the determinants of efficiency have significantly changed for foreign banks compared to domestic banks.11 For the market power effect in increasing profit and cost efficiency, the results in Table 5.6 show no significant difference between foreign and domestic banks. The coefficients of interaction terms for Activity restrictions and Official supervisory power confirm that foreign banks operating in a host country with higher activity restrictions and official supervisory power have higher bank efficiency. This is probably because foreign banks outperform domestic banks in terms of profitability and cost efficiency by exploiting their advanced technologies, a highly skilled labor force, and better risk management practices in host countries with higher activity restrictions and official supervisory power (Haque & Brown, 2017).
11 We
also include the dummy variable Foreign dummy in the regression, but it is dropped from the model owing to collinearity.
Government-owned banks
Bank concentration
Saving dummy
Commercial dummy
log TA
FA
LLP
TCD
(1.77)
(2.53)
(0.53) 0.0000949*
(2.67) 0.000117**
0.0000503
(2.71)
(1.13) 0.000215***
0.00862***
0.00356
(1.08)
(−4.10)
(−32.28)
(−175.36) 0.00156
(−303.87) −0.00429***
−0.0229***
−0.0288***
−0.0298***
(2.53)
0.000132**
(2.58)
0.000203***
(2.02)
0.00591**
(−0.22)
−0.000307
(7.66)
(7.09)
0.291***
0.271***
(2.11)
(7.86)
(3.40)
(3.73)
0.0609**
(7.93)
0.0244***
(−2.91)
−0.000209***
(−0.86)
−0.000503
(3)
0.290***
0.0998***
0.110***
(6.68)
(−2.81)
(−3.60) (7.90)
−0.000203***
−0.000221*** 0.0211***
(−0.38)
(−5.37)
0.0242***
−0.000228
−0.00280***
Lerner index
Z-score
(2)
(1)
Variables
Dependent variable: pinefficiency
Table 5.6 Impact of ownership on the relationship between bank competition, regulation and bank efficiency
(3.29)
0.000153***
(2.66)
0.000210***
(3.37)
0.0101***
(1.44)
0.00204
(−177.38)
−0.0288***
(6.92)
0.263***
(2.52)
0.0722**
(6.68)
0.0205***
(−2.78)
−0.000200***
(−1.47)
−0.000843
(4)
(2.84) (continued)
0.000170***
(0.75)
0.0000738
(2.69)
0.00822***
(0.96)
0.00139
(−178.10)
−0.0289***
(6.62)
0.255***
(3.39)
0.0990***
(6.51)
0.0208***
(−2.76)
−0.000199***
(−0.39)
−0.000234
(5)
5.5 Empirical Results 157
−0.000439*** (−4.13)
−0.000226*** (−7.94) −0.000168*
−0.000221*** (−8.38) −0.000381***
Capital regulatory index * foreign dummy
Capital regulatory index
Activities restrictions * foreign dummy
Activity restrictions
Foreign dummy * Lerner index
Crisis
Inflation
GDP growth
Financial freedom
(−0.81) −0.000461
(−1.06) −0.00168 (−0.78) −0.000547 (−0.41)
(−4.73) −0.000589 (−0.60) 0.00160 (1.29)
(continued)
0.000359
(2.50) 0.000241
(−0.60)
(−3.56)
(−2.06)
0.000632**
−0.00107***
−0.0000269** −0.000160
(−2.19)
(−2.69)
(−0.33)
−0.000441
(−1.39)
−0.00230
(−0.39)
−0.0000253
(−0.81)
−0.0000732
(−8.50)
−0.000242***
−0.000637**
(0.11)
0.000153
(−0.54)
−0.000485
(−0.58)
−0.0000345
(−1.70)
−0.000152*
(−9.02)
−0.000250***
(8.80)
0.0663***
(5)
−0.000748***
(−0.36)
−0.00577
(0.81)
0.0000529
(−1.86) −0.0000686
(−4.29) −0.000296***
(−5.76)
−0.000197***
(6.04)
0.0399***
(−1.00)
(7.76)
(4.29)
−3.495
0.0522***
0.0224***
(4)
(3)
Financial openness
(2)
Dependent variable: pinefficiency (1)
Variables
Table 5.6 (continued)
158 5 Bank Competition, Regulation, and Efficiency
9081 0.976
N
R-squared
0.972
7039
Yes
(8.74)
(−1.01)
(−1.96)
TCD (9.74)
−0.0000697
−0.000116* 0.0265***
(−7.66)
(−4.30)
0.0284***
−0.00415***
−0.00210***
Lerner index
Z-score
(7)
(6)
Variables
Dependent variable: cost inefficiency
Yes
Year fixed effects
Yes
(8.75)
(4.88) Yes
4.341***
1.378***
(2)
Dependent variable: pinefficiency (1)
Bank fixed effects
Constant
Official supervisory power * foreign dummy
Official supervisory power
Variables
Table 5.6 (continued)
(10.29)
0.0299***
(−1.15)
−0.0000787
(−1.22)
−0.00388
(8)
0.973
7066
Yes
Yes
(45.30)
0.752***
(0.51)
(3)
(8.66)
0.0254***
(−0.92)
−0.0000635
(−6.80)
−0.00357***
(9)
0.972
7208
Yes
Yes
(39.55)
(8.60) (continued)
0.0264***
(−0.97)
−0.0000671
(−7.54)
−0.00410***
(10)
0.972
7039
Yes
Yes
(62.16)
0.876***
(−2.22)
(−0.35) 0.874***
(−2.77) −0.00119**
(−1.54)
−0.000598***
(0.58)
(5)
−0.000541
−0.000321
(4)
5.5 Empirical Results 159
GDP growth
Financial freedom
Financial openness
Government-owned banks
Bank concentration
Saving dummy
Commercial dummy
log TA
−0.000213*** (−6.41)
−0.000270*** (−9.80) 0.0000265
−0.000281*** (−10.81) −0.000174**
−0.000213**
0.0000292
(−10.66)
−0.000288***
(1.74)
0.0108* (−0.01)
(3.79)
−0.0437
0.0236***
(0.35)
(2.89)
0.000122***
(4.59)
0.000359***
(7.40)
0.0129***
(2.74)
0.00377***
(−192.35)
−0.0304***
(8.76)
0.311***
(3.23)
0.0869***
(9)
(3.19)
0.000148***
(4.05)
0.000317***
(5.29)
0.00884***
(0.86)
0.00113
0.00175
(2.11)
(2.28)
(1.97) 0.0000988**
(4.39) 0.0000949**
0.000183**
(6.73)
(4.65) 0.000352***
0.0122***
0.00788***
(2.27)
(−0.73)
(−34.85)
(−190.28) 0.00318**
(−323.81) −0.000740
−0.0237***
−0.0304***
−0.0312***
(10.48)
(9.23)
0.366***
0.322***
(9.77)
(2.63)
0.0709***
(8)
0.336***
(4.06)
(4.77)
FA
0.111***
0.134***
LLP
(7)
Dependent variable: cost inefficiency (6)
Variables
Table 5.6 (continued)
(continued)
0.000112
(−10.20)
−0.000280***
(5.30)
0.0370***
(2.97)
0.000157***
(2.29)
0.000220**
(6.67)
0.0119***
(2.21)
0.00309**
(−193.78)
−0.0305***
(8.78)
0.308***
(4.00)
0.109***
(10)
160 5 Bank Competition, Regulation, and Efficiency
Official supervisory power * foreign dummy
Official supervisory power
Capital regulatory index * foreign dummy
Capital regulatory index
Activities restrictions * foreign dummy
Activity restrictions
Foreign dummy * Lerner index
Crisis
Inflation
Variables
Table 5.6 (continued)
−0.00108
−0.00132 (−1.12)
0.000450 (0.40)
−0.00141 (−0.73)
−0.000812** (−2.15)
(continued)
(−1.63)
(0.23)
(0.52) −0.000321 0.0000461
(0.31)
(2.63) 0.000312
0.000136
0.000620***
(−0.10)
(−1.78)
(−2.04) −0.0000250
(−2.37)
−0.000644**
(−0.95)
−0.00115
−0.00116*
(−0.49)
−0.000599
(−1.48)
−0.00612
(0.50)
0.0000307
(1.31)
(10)
(−2.51)
(−0.95)
(−0.77)
−0.00338
(−0.04)
−0.00000252
(0.35)
(9)
−0.0000153**
−0.000651**
(−1.25)
(−0.96)
(−1.33)
(−0.12) −0.00748
(−0.10) −0.00555
(−4.06)
(−2.14) −0.00000742
(0.31) −0.00000634
(−2.05) −0.000241*** −0.00226
(8)
(7)
Dependent variable: cost inefficiency (6)
5.5 Empirical Results 161
Yes 9081 0.981
N
R-squared
0.978
7039
Yes
Yes
0.979
7066
Yes
Yes
(43.28)
0.713***
(8)
0.978
7208
Yes
Yes
(9)
0.978
7039
Yes
Yes
(10)
This table examines the impact of bank regulation on the relationship between competition and bank efficiency. The dependent variables are Profit inefficiency and Cost inefficiency in columns (1)–(5) and columns (6)–(10), respectively as defined in Eq. (5.1). Our sample period is 2001–2016. Bank-level controls include Z-score, TCD, LLP, FA, log TA, Commercial dummy, and Saving dummy. Country control variables include Bank concentration, Government-owned banks, Financial openness, Financial freedom, GDP growth, Inflation, and Crisis. Bank regulation variables include Activity restrictions, Capital regulatory index, Official supervisory power. All variables are defined in Table 5.1. Both bank and year fixed effects are included and reported t-statistics (in parentheses) are based on standard errors that are estimated based on Vogelsang (2012) and robust to heteroskedasticity, serial correlation, and spatial correlation. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
Yes
Year fixed effects
(54.09)
(56.17)
Bank fixed effects
0.874***
0.964***
Constant
(7)
Dependent variable: cost inefficiency (6)
Variables
Table 5.6 (continued)
162 5 Bank Competition, Regulation, and Efficiency
5.5 Empirical Results
163
5.5.4 Robustness Checks To address the reliability of our findings, we conduct different robustness checks based on an alternative definition of competition, different estimation methods, and different subsamples. First, we use the Boone indicator as an alternative measure of bank competition. The Boone indicator directly captures the link between bank performance and efficiency and does not impose more stringent data requirements than the Lerner index does (Boone, 2008). The results using the Boone indicator as a measure of bank competition are reported in Table 5.7. While our main results are largely unchanged, the negative and statistically significant coefficient of the Boone indicator indicates that profit and cost inefficiencies increase with greater bank competition, which is consistent with the findings based on the Lerner index. Second, we also perform our analysis according to different estimation methods. We first introduce the country fixed effects in the regression to control for heterogeneity between countries instead of bank fixed effects while the year fixed effects are also considered. Then, we perform our basic analysis based on the instrumental variable (IV) analysis to control for a potential endogeneity and heteroskedasticity problem (see also Berger et al., 2009; Fu et al., 2014). The competitive indicator Lerner index is instrumented by lagged Lerner index, Restructuring power and Financial conglomerate restrictiveness. Restructuring power indicates whether the supervisory authorities have the power to restructure and reorganize a troubled bank, and Financial conglomerate restrictiveness indicates the restrictions on financial conglomeration of the financial system in a country, and measures the extent to which banks may own and control non-financial firms, non-financial firms may own and control banks, and non-bank financial firms may own and control banks; higher values are more restrictive. To obtain consistent and efficient estimates in the presence of non-independently and identically distributed errors, we use the GMM estimator in the IV estimation analysis and estimators are clustered at bank level to account for within-cluster correlation of the disturbances (see also Driscoll & Kraay, 1998).12 The country fixed effects estimation and IV regression results are presented in Table 5.8. For brevity, we report only the results of profit inefficiency.13 The results of the Lerner index and bank regulation variables under the different estimation methods are largely unchanged. This table examines the impact of competition and risk on bank efficiency. The dependent variable is Profit inefficiency as defined in Eq. (5.1). Our sample period is 2001–2016. Bank-level controls include Z-score, TCD, LLP, FA, log TA, Commercial 12 The instrumental variables are statistically significant in the first-stage analysis. In the first-stage regression, the Lerner index is negatively related to Restructuring power and Financial conglomerate restrictiveness at 1%. In addition, we employ different tests for weak identification and underidentification, such as the Kleibergen–Paap rk LM and Wald F statistic (Kleibergen & Paap, 2006) and the Cragg–Donald Wald F statistic (Hall & Peixe, 2000), and confirm that the instruments are valid. 13 The results of cost inefficiency under the country fixed effects estimation and IV regression are consistent with our main findings and are available upon request.
Government-owned banks
Bank concentration
Saving dummy
Commercial dummy
log TA
FA
LLP
TCD
(1.70)
(2.92)
(1.17)
0.000134*
0.000134***
(1.93)
(2.22)
0.000168
0.000188*
(1.18)
(−4.27)
0.00875**
(0.35)
−0.00435***
0.00359
(−109.98)
0.000892
(−305.92)
(1.96)
0.000180*
(1.63)
0.000268
(2.57)
0.00854**
(0.35)
0.000905
(−107.73)
−0.0290***
(4.17)
(4.45)
−0.0290***
(8.27)
−0.0299***
0.251***
(2.34)
0.0875**
(5.32)
0.0227***
(−2.56)
−0.000185**
0.254***
(2.44)
0.305***
(3.04)
(5.32)
0.0905**
0.0904***
(8.26)
(−2.50)
0.0221***
(−3.92)
0.0250***
(−6.03)
−0.000184**
(−8.41)
−0.000235***
(−0.95)
Z-score
−0.00129
−0.00126***
−0.00134***
Boone indicator
(3)
(2)
(1)
Variables
Dependent variable: profit inefficiency
Table 5.7 Alternative measure of bank competition
(3.81)
0.000176***
(1.54)
0.000149
(3.28)
0.00967***
(0.90)
0.00129
(−178.54)
−0.0290***
(6.79)
0.260***
(2.52)
0.0727**
(6.78)
0.0206***
(−2.71)
−0.000192***
(−7.84)
−0.00127***
(4)
(3.84)
0.000226***
(2.23)
0.000236**
(2.74)
0.00832***
(0.47)
0.000677
(−177.08)
−0.0290***
(6.35)
0.243***
(3.08)
0.0908***
(7.12)
0.0222***
(−2.56)
−0.000182**
(−1.62)
−0.00123
(5)
(2.76)
0.000117***
(4.76)
0.000435***
(5.14)
0.00849***
(−0.55)
−0.000545
(−327.53)
−0.0312***
(9.33)
0.320***
(5.05)
0.141***
(9.72)
0.0278***
(−1.19)
−0.0000687
(−6.09)
−0.000958***
(6)
(2.12)
0.000146**
(3.14)
0.000416***
(5.21)
0.0119***
(0.84)
0.00206
(−119.77)
−0.0306***
(5.46)
0.280***
(3.51)
0.124***
(6.49)
0.0263***
(0.03)
0.00000202
(−4.45)
−0.000940***
(7)
(2.40)
0.000193**
(3.46)
0.000517***
(5.67)
0.0116***
(0.82)
0.00207
(−116.41)
−0.0307***
(5.24)
0.276***
(3.31)
0.120***
(6.41)
0.0270***
(0.00)
4.03e-08
(−1.36)
−0.000965
(8)
Dependent variable: cost inefficiency (9)
(3.49)
0.000149***
(4.54)
0.000412***
(7.02)
0.0123***
(1.85)
0.00257*
(−193.66)
−0.0307***
(7.96)
0.284***
(4.07)
0.110***
(8.42)
0.0244***
(−0.02)
−0.00000110
(−6.09)
−0.000977***
(10)
(continued)
(4.31)
0.000229***
(5.08)
0.000502***
(6.42)
0.0115***
(1.36)
0.00191
(−193.15)
−0.0307***
(7.72)
0.270***
(4.46)
0.122***
(8.98)
0.0266***
(0.04)
0.00000244
(−1.82)
−0.000929*
164 5 Bank Competition, Regulation, and Efficiency
Bank fixed effects
Constant
Official supervisory power
Capital regulatory index
Activity restrictions
Crisis
Inflation
GDP growth
Financial freedom
(−1.06)
(1.03)
(3.48)
Yes
(3.53)
Yes
5.217***
4.431*** Yes
(2.76)
1.913*** (3.22) Yes
Yes
(3.46)
3.142***
(−3.46)
4.131***
(−2.68)
(3.22) −0.000701***
(1.72) −0.000505***
0.000765***
0.000574*
(−0.22)
(−1.85)
(−1.09)
−0.00172
(−0.43)
−0.0000280
(−0.14)
−0.0000128
(−9.05)
−0.000264***
(6.16)
0.0477***
(5)
−0.0000596
(−0.75)
−0.000673
(−0.60)
−0.0000364
(−0.33)
−0.0000290
(−8.71)
−0.000250***
(4.70)
0.0319***
(4)
−0.000305*
(−1.16)
(−1.03) −0.00127
(−1.02)
−0.00113
(−4.46)
0.00103
−0.0000767
(−0.64)
(−1.00)
−0.0000759
(−3.78)
(−5.49) −0.0000618
(−5.81)
−0.0000993
(−7.98)
−0.000344***
−0.000289***
(3.40) −0.000242***
(3.36)
−0.000245***
(2.30)
−0.000221***
(3) 0.0380***
0.0125**
Financial openness
(2)
0.0341***
(1)
Variables
Dependent variable: profit inefficiency
Table 5.7 (continued)
Yes
(2.12)
0.486**
(−1.17)
−0.00309
(−4.00)
−0.000242***
(−0.75)
−0.0000653
(−11.54)
−0.000318***
(0.41)
0.00213
(6)
Yes
(0.89)
1.148
(−2.23)
−0.0000771**
(−1.34)
−0.00564
(−0.99)
−0.0000686
(1.57)
0.000145
(−7.95)
−0.000329***
(1.43)
0.0134
(7)
Yes
(0.57)
0.725
(2.20)
0.000689**
(−0.60)
−0.00597
(−0.67)
−0.0000452
(2.26)
0.000199**
(−7.58)
−0.000330***
(2.17)
0.0220**
(8)
Dependent variable: cost inefficiency (9)
Yes
(1.19)
0.317
(−0.90)
−0.000159
(−0.76)
−0.00427
(−0.87)
−0.0000491
(2.63)
0.000213***
(−11.62)
−0.000327***
(1.85)
0.0119*
(10)
Yes (continued)
(1.42)
0.531
(−2.66)
−0.000495***
(3.68)
0.000822***
(−0.33)
−0.0000822
(−1.56)
−0.00621
(−0.36)
−0.0000214
(2.78)
0.000230***
(−12.03)
−0.000344***
(3.74)
0.0272***
5.5 Empirical Results 165
Yes
8936
0.977
Year fixed effects
N
R-squared
0.973
6938
Yes
(2)
0.973
7087
Yes
(3)
0.974
7074
Yes
(4)
0.973
6938
Yes
(5)
0.982
8936
Yes
(6)
0.979
6938
Yes
(7)
0.979
7087
Yes
(8)
Dependent variable: cost inefficiency (9)
0.979
7074
Yes
(10)
0.979
6938
Yes
This table examines the impact of competition and regulation on bank efficiency. The dependent variables are Profit inefficiency and Cost inefficiency in columns (1)–(5) and columns (6)–(10), respectively as defined in Eq. (5.1). Our sample period is 2001–2016. Bank-level controls include Z-score, TCD, LLP, FA, log TA, Commercial dummy, and Saving dummy. Country control variables include Bank concentration, Government-owned banks, Financial openness, Financial freedom, GDP growth, Inflation, and Crisis. Bank regulation variables include Activity restrictions, Capital regulatory index, Official supervisory power. All variables are defined in Table 5.1. Both bank and year fixed effects are included and reported t-statistics (in parentheses) are based on standard errors that are estimated based on Vogelsang (2012) and robust to heteroskedasticity, serial correlation, and spatial correlation. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
(1)
Variables
Dependent variable: profit inefficiency
Table 5.7 (continued)
166 5 Bank Competition, Regulation, and Efficiency
(−1.47)
−0.00178
(7)
(2.75)
0.00831***
(0.83)
0.00115
(−186.63)
−0.0289***
(6.60)
0.251***
(3.13)
0.0913***
(7.13)
0.0224***
(−2.80)
(3.26)
0.00973***
(1.13)
0.00155
(−188.61)
−0.0288***
(6.93)
0.263***
(2.56)
0.0732**
(6.87)
0.0209***
(−2.84)
(2.58)
0.00778***
(0.65)
0.000910
(−186.17)
−0.0290***
(6.72)
0.258***
(3.45)
0.101***
(6.85)
0.0215***
(−2.83)
(0.51)
0.00139
(1.35)
0.00205
(−169.65)
−0.0294***
(7.59)
0.326***
(0.20)
0.00627
(8.90)
0.0278***
(−1.58) 0.0273***
(0.43)
0.00119
(1.30)
0.00199
(−170.64)
−0.0295***
(7.84)
0.340***
(0.28)
0.00888
(8.72)
(0.50)
0.00137
(1.23)
0.00187
(−168.18)
−0.0294***
(7.55)
0.325***
(0.24)
0.00746
(8.89)
0.0280***
(−1.53)
−0.000110
(−2.00)
−0.00238**
(8)
(0.57)
0.00156
(1.19)
0.00181
(−170.16)
−0.0294***
(7.68)
0.328***
(0.05)
0.00165
(8.77)
0.0274***
(−1.63)
−0.000117
(−1.09)
−0.00130
(9)
(2.24)
0.00907**
(3.20)
0.00596***
(−90.97)
−0.0286***
(6.19)
0.426***
(−1.15)
−0.0522
(8.35)
0.0362***
(−3.86)
−0.000570***
(−2.51)
−0.0219**
(10)
(−8.51)
(−7.81) −0.000155* (−1.75)
(−2.22)
(−9.01)
−0.000197**
(−8.35) (−1.06)
−0.0000947
(−8.44)
(3.45)
0.000410***
(−5.20)
(3.65)
0.000433***
(−4.76)
(3.47)
0.000416***
(−5.32)
(3.27)
0.000391***
(−5.66)
(continued)
(−1.63)
−0.000528
(−0.32)
−0.000215*** −0.00022*** −0.000227*** −0.000240*** −0.000236*** −0.000160*** −0.000148*** −0.000165*** −0.000174*** −0.0000179
0.00812***
(2.58)
(0.79)
(−4.41)
0.00326
0.00110
−0.00455***
(1.04)
−0.0289***
(−187.65)
−0.0298***
(−319.29)
0.270***
(7.06)
0.287***
(7.81)
0.101***
(3.43)
0.108***
(3.67)
0.0215***
(6.86)
0.0245***
(8.07)
(−2.86)
−0.000125*
(−2.01)
−0.00235**
(6)
Panel B: IV estimation
(−1.76)
(−0.55)
−0.000298
(5)
−0.000225*** −0.00021*** −0.000202*** −0.000204*** −0.000204*** −0.000113
(−1.67)
−0.000827*
(4)
(−3.66)
Government-owned −0.000390*** −0.000168* banks (−4.39) (−1.86)
Bank concentration
Saving dummy
Commercial dummy
log TA
FA
LLP
TCD
Z-score
(−1.09)
−0.000349** −0.000586
(−2.44)
−0.00253***
(−5.26)
Lerner index
(3)
(2)
(1)
Variables
Panel A: Country fixed effects
Table 5.8 Robustness checks based on alternative estimation methods
5.5 Empirical Results 167
Country fixed effects
Constant
Official supervisory power
Capital regulatory index
Activity restrictions
Crisis
Inflation
GDP growth
Financial freedom −0.000586
−0.000827
−0.000298
−0.00235**
−0.00178
−0.0215
Yes
4.443***
(3.74)
2.347***
5.542*** Yes
(2.81) Yes
(7.72)
5.481*** Yes
(6.15)
2.020***
−0.000768*** (−3.85)
(−2.30)
(2.29) −0.000438**
0.000562** (2.43)
0.000497**
−0.000563** (−2.21)
(−0.68)
−0.00085***
(−1.08)
0.0209
(−3.57)
(5.72)
Yes
−0.0224 (−1.13)
No
(−0.90)
−0.0278
(−1.58)
No
(−2.47)
−0.000655**
(−1.52)
−0.0273
(−0.86)
(−2.83)
0.0215
(−2.84)
−0.0245
(−2.80)
(−0.87)
(−1.47) −0.000125*
(−2.01)
(−0.01)
−0.0000437
(−1.31)
−0.0000981
(7)
(−1.76)
(−0.55)
(−0.07)
−0.000247
(−1.73)
−0.000115*
(6)
Panel B: IV estimation
(−2.86)
(−1.57)
(−2.41)
−0.00230**
(−0.40)
−0.0000260
(5)
−0.000225*** −0.00021*** −0.000202*** −0.000204*** −0.000204*** −0.000113
(−1.09)
(−0.61)
−0.000543
(−0.65)
−0.0000387
(4)
(−3.66)
−0.000349
(−0.64)
−0.00253***
(−5.26)
(−1.41)
−0.00135
−0.00183*
(−1.94)
0.000589
(0.60)
−0.0000818 (−1.38)
(−1.02)
(3)
−0.000293*** −0.0000666
Financial openness
(2)
(−4.67)
(1)
Variables
Panel A: Country fixed effects
Table 5.8 (continued)
No
(0.41)
0.000131
(−0.89)
−0.0280
(−1.53)
−0.000110
(−2.00)
−0.00238**
(−0.03)
−0.000111
(−1.77)
−0.000118*
(8)
No
(−2.36)
−0.000489**
(−0.77)
−0.0274
(−1.63)
−0.000117
(−1.09)
−0.00130
(−0.10)
−0.000324
(−0.79)
−0.0000546
(9)
No (continued)
(−2.78)
−0.000731***
(1.24)
0.000465
(−2.96)
−0.00186***
(−1.35)
−0.0362
(−3.86)
−0.000570***
(2.51)
0.0219**
(1.02)
0.00167
(1.27)
0.000160
(10)
168 5 Bank Competition, Regulation, and Efficiency
0.972
−
Yes
9081
0.976
−
-
Year fixed effects
N
R-squared
Hansen J statistic
Hansen J statistic (p-value)
−
0.972
7066
Yes
No
(3)
−
0.973
7208
Yes
No
(4)
−
0.972
7039
Yes
No
(5)
0.111
3.385
0.979
4273
Yes
Yes
(6)
0.124
2.833
0.979
4249
Yes
Yes
(7)
Panel B: IV estimation
0.086
4.903
0.979
4273
Yes
Yes
(8)
0.280
2.545
0.979
4273
Yes
Yes
(9)
0.181
1.786
0.973
5812
Yes
Yes
(10)
This table examines the impact of competition and regulation on bank efficiency. The dependent variable is Profit inefficiency as defined in Eq. (5.1). Our sample period is 2001–2016. Bank-level controls include Z-score, TCD, LLP, FA, log TA, Commercial dummy, and Saving dummy. Country control variables include Bank concentration, Government-owned banks, Financial openness, Financial freedom, GDP growth, Inflation, and Crisis. Bank regulation variables include Activity restrictions, Capital regulatory index, Official supervisory power. All variables are defined in Table 5.1. Reported t-statistics (in parentheses) are based on standard errors that are heteroskedasticity consistent and clustered at the country level. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
7039
Yes
No
No
Bank fixed effects
(2)
(1)
Variables
Panel A: Country fixed effects
Table 5.8 (continued)
5.5 Empirical Results 169
170
5 Bank Competition, Regulation, and Efficiency
dummy, and Saving dummy. Country control variables include Bank concentration, Government-owned banks, Financial openness, Financial freedom, GDP growth, Inflation, and Crisis. Bank regulation variables include Activity restrictions, Capital regulatory index, Official supervisory power. All variables are defined in Table 5.1. Both bank and year fixed effects are included and reported t-statistics (in parentheses) are based on standard errors that are estimated based on Vogelsang (2012) and robust to heteroskedasticity, serial correlation, and spatial correlation. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively Finally, we performed our analysis on different subsamples: commercial banks and subsample of countries with more than 200 observations (see Table 5.9). Similar to Table 5.8, we report only the results of profit inefficiency for brevity.14 Our main findings are still consistent.
5.6 Conclusions This study investigates the impact of competition and regulation on bank efficiency based on international evidence in the Asia–Pacific region. Our sample consists of unbalanced financial data of 9193 observations from 1261 banks operating in 28 Asia–Pacific countries between 2001 and 2016. Our results indicate that market power is positively related to bank efficiency. The findings also highlight that stringent activity restrictions, strong official supervisory power, and low capital requirements are associated with increased bank efficiency. Furthermore, the market power effect in increasing profit and cost efficiency is more pronounced in a banking system characterized by the above activity restrictions, supervisory power, and capital requirements. Foreign banks operating under strict activity restrictions in a host country with strong official supervisory power have high profit and cost efficiency. Our findings highlight several important policy implications for financial regulators in Asia–Pacific region. First, in order to increase bank efficiency, regulators need to follow a more cautious approach to evaluating and approving bank consolidation to prevent excessive competition in the banking system at the country level. Second, a certain level of activity restrictions and official supervisory power should be maintained to improve bank efficiency, and this is more important for foreign banks that seek to take advantage of regulation differences between the parent country and host countries. Finally, high capital requirements reduce bank efficiency in the Asia–Pacific region, suggesting that policy makers need to identify the weaknesses in the present capital regulations and improve their effectiveness to enhance bank efficiency.
14 The results of cost inefficiency for commercial banks and subsample of countries with more than
200 observations have the similar findings and are available upon request.
Financial openness
Government-owned banks
Bank concentration
Saving dummy
Commercial dummy
log TA
FA
LLP
TCD
(−6.34)
−0.000366***
(−0.33)
(2.68)
0.129***
0.133***
(0.30)
−0.0000284
0.000196***
(2.65)
−
−
0.0000148
0.000106
−
−
0.000125***
−
−
−
(−30.57)
0.104***
(0.15)
0.00000752
(1.27)
−
−
−
(−31.38)
−0.0255***
−
−0.0263***
−0.0223***
(5.95)
0.306***
(−13.01)
(6.35)
(−0.18)
−0.00619
−
0.323***
(6.84)
(0.03)
(−0.94) 0.404***
(10.60) 0.00120
(11.14) −0.0327
(10.63)
(−6.83)
(−6.81) 0.0392***
−0.00038***
−0.000409***
(−2.15)
−0.00168**
(3)
0.0386***
(−3.07)
(−2.17)
0.0395***
−0.00570***
−0.00146**
Lerner index
Z-score
(2)
(1)
Variables
Panel A: Commercial banks
Table 5.9 Robustness checks based on different subsamples
0.115***
(3.17)
0.000152***
(1.60)
0.000121
−
−
−
−
(−32.24)
−0.0258***
(5.91)
0.324***
(−1.11)
−0.0395
(10.30)
0.0370***
(−6.48)
−0.000375***
(−0.03)
−0.0000835
(4)
0.0347
(−1.24) (continued)
−0.0000661
(−1.91)
−0.000173*
−
−
−
−
(−29.06)
−0.0233***
(6.39)
0.315***
(−0.31)
−0.0105
(10.94)
0.0377***
(−6.73)
−0.000364***
(−2.70)
−0.00834***
(5)
5.6 Conclusions 171
Official supervisory power * Lerner index
Official supervisory power
Capital regulatory index * Lerner index
Capital regulatory index
Activities restrictions * Lerner index
Activity restrictions
Crisis
Inflation
GDP growth
Financial freedom
Variables
Panel A: Commercial banks
Table 5.9 (continued)
(−2.10)
−0.000174 (−0.74)
(continued)
(2.21) −0.000486**
(−2.23)
0.000636**
(1.72)
(−0.13) −0.000663**
(−5.89) 0.000330*
(−2.79) −0.0000263
−0.00161***
(−4.21) −0.00066***
(−0.52)
−0.00100***
(−1.40)
(−1.53) −0.00976***
(−4.24)
(−1.26)
−0.00589
−0.000132
−0.00093***
(−0.58)
(−1.21)
−0.00663
(−1.75)
−0.000120*
(1.04)
(−0.73)
−0.00794
−0.00766
(−3.59)
−0.000248***
(4.14)
(2.00) 0.000111
−0.000217
(−1.97)
(−2.91)
(3.53)
(−1.97) 0.000390***
0.0000778**
(0.39)
(5)
(−1.79)
−0.000147**
−0.000217***
(−1.02) 0.000335***
−0.0000700**
(10.83)
(4)
−0.000511*
(5.21)
(0.58)
(−0.85) 0.000481***
(−1.96) 0.0000539
−0.0000359
(9.59)
(11.75) −0.0000300
(7.35)
(3)
(2)
−0.0000696*
(1)
172 5 Bank Competition, Regulation, and Efficiency
Yes 4342 0.740
N
R-squared
Commercial dummy
log TA
FA
LLP
TCD
(0.65)
(−5.61)
(−174.14)
(−172.62) 0.000945
(−345.61) −0.00550***
−0.0290***
−0.0289***
−0.0300***
(0.52)
0.000760
(6.80)
(6.75)
0.263***
0.259***
(3.82)
(7.68)
(3.68)
(3.63)
0.113***
(6.83)
0.0216***
(−2.89)
−0.000209***
(−1.41)
−0.00213
(8)
0.779
4005
Yes
Yes
(2.81)
5.542***
(3)
0.292***
0.109***
0.101***
(6.71)
(−2.86)
(−3.39) (7.33)
−0.000207***
−0.000205*** 0.0212***
(−2.10)
(−4.05)
0.0225***
−0.00356**
−0.00193***
Lerner index
Z-score
(7)
(6)
0.789
398z7
Yes
Yes
Variables
Panel B: Banks with more than 200 observations
Yes
Year fixed effects
(3.74)
(5.72)
Bank fixed effects
4.443***
2.347***
Constant
(2)
(1)
Variables
Panel A: Commercial banks
Table 5.9 (continued)
(0.91)
0.00130
(−175.05)
−0.0289***
(6.82)
0.264***
(3.30)
0.0961***
(6.14)
0.0190***
(−2.92)
−0.000210***
(−3.64)
−0.00869***
(9)
0.782
4094
Yes
Yes
(7.72)
5.481***
(4)
(−0.48) (continued)
−0.000677
(−32.55)
−0.0239***
(7.33)
0.283***
(2.87)
0.0841***
(7.52)
0.0232***
(−3.04)
−0.000216***
(−5.01)
−0.0144***
(10)
0.803
3987
Yes
Yes
(6.15)
2.020***
(5)
5.6 Conclusions 173
Activities restrictions * Lerner index
Activity restrictions
Crisis
Inflation
GDP growth
Financial freedom
Financial openness
Government-owned banks
−0.00144 (−1.53)
−0.000393 (−0.40)
(−0.72)
(−2.19)
(continued)
(−0.36)
−0.000110
(−1.59)
−0.00529
(0.73)
0.0000542
(−3.99)
−0.000511***
(−7.51)
−0.000275***
−0.000171
(−0.41)
−0.000370
(−1.42)
−0.0000887
(−1.13)
−0.000115
(−10.35)
−0.000297***
(−1.07)
−3.906
(3.38)
0.000195***
(1.80)
0.000183*
(1.62)
0.00486
(10)
(−1.06)
(−1.39)
−0.00132
(−2.68)
−0.000165***
(−8.85)
−0.000258***
(7.23)
0.0472***
(3.64)
0.000172***
(1.29)
0.000123
(2.94)
0.00879***
(9)
−0.000470**
−0.000320
(−1.99)
(−6.10)
(−2.16)
(−2.37) −0.000132**
−0.000245**
−0.000115 (−1.40)
(−9.58)
(−8.51)
−0.000344***
−0.000220**
−0.000277***
−0.000230***
(6.87)
(7.55)
(2.68) 0.0485***
(5.91)
(1.95) 0.0511***
(2.64)
0.000151***
(2.25)
0.000232**
(2.53)
0.00797**
(8)
0.0304***
0.000103*
(1.41)
(1.18) 0.000124***
0.000147
0.000114
(2.52)
(0.68)
Bank concentration
0.00817**
0.00202
Saving dummy
(7)
(6)
Variables
Panel B: Banks with more than 200 observations
Table 5.9 (continued)
174 5 Bank Competition, Regulation, and Efficiency
8644 0.979
N
R-squared
0.973
6772
Yes
(−0.17)
0.973
6772
Yes
Yes
(0.82)
0.356
(2.57)
(1.31)
0.974
6896
Yes
Yes
(1.54)
0.974
6772
Yes
Yes
(0.77)
0.675
(−6.52)
(−3.89) 0.2952
(2.35) −0.00146***
(−0.39) −0.000786***
0.000637**
0.000556**
0.000284 −0.000104
−0.0000544
(10)
(1.16)
(9)
0.000312
(8)
This table examines the impact of competition annd risk on bank efficiency. The dependent variable is Profit inefficiency as defined in Eq. (5.1). Our sample period is 2001–2016. Bank-level controls include Z-score, TCD, LLP, FA, log TA, Commercial dummy, and Saving dummy. Country control variables include Bank concentration, Government-owned banks, Financial openness, Financial freedom, GDP growth, Inflation, and Crisis. Bank regulation variables include Activity restrictions, Capital regulatory index, Official supervisory power. All variables are defined in Table 5.1. Both bank and year fixed effects are included and reported t-statistics (in parentheses) are based on standard errors that are estimated based on Vogelsang (2012) and robust to heteroskedasticity, serial correlation, and spatial correlation. Significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively
Yes
Year fixed effects
Yes
(4.473)
Yes
2.176***
(5.17)
(7)
5.146***
(6)
Bank fixed effects
Constant
Official supervisory power * Lerner index
Official supervisory power
Capital regulatory index * Lerner index
Capital regulatory index
Variables
Panel B: Banks with more than 200 observations
Table 5.9 (continued)
5.6 Conclusions 175
176
5 Bank Competition, Regulation, and Efficiency
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Chapter 6
Conclusion
6.1 Overview This chapter gives a general discussion on the main findings of this book. The primary objective of this study is to evaluate the impact of financial regulatory on bank performance in normal times and during the global financial crisis. This chapter briefly summarizes the findings in each chapter of the main findings and provides a systematic overview of them.
6.2 Summary of the Main Findings Chapter 2 empirically investigates how bank capital and competitive conditions affect bank risk-taking. Particularly, based on the Basel Accords, we classify bank capital based on capital quality: Tier 1 capital with higher loss-absorbing capacity and Tier 2 capital with lower loss-absorbing capacity. We then examine the effects of bank capital structure on bank risk-taking. We test whether bank risk-taking behaviour varies according to the quality of bank capital, and whether this relationship changes significantly in different competitive conditions and during the financial crisis. Using the unbalanced financial information of 7620 banks from 118 countries between 2001 and 2016, we find that banks with a higher Tier 1 ratio and a lower Tier 2 ratio exhibit lower risk-taking. Further, a bank that has higher market power in a banking system tends to reduce its risk-taking activities. The results also confirm that banks with higher profitability, more funding, and higher quality of bank assets are exposed to lower risk. During a financial crisis, banks exhibit a higher level of risk-taking than in normal times. Our findings also highlight that the negative relationship between the Tier 1 ratio and bank risk are more pronounced in more competitive conditions. During the financial crisis, Tier 1 capital acted as a stable funding source and reduced bank risk,
© Shanghai Jiao Tong University Press 2021 S. Li, Financial Regulation and Bank Performance, Contributions to Finance and Accounting, https://doi.org/10.1007/978-981-16-3509-0_6
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6 Conclusion
but the evidence on Tier 2 capital shows that a higher Tier 2 ratio results in a higher level of risk and increases bank instability. Chapter 3 investigates the impact of both bank reform and competition on stability by analyzing the empirical relationships among banking sector reform, competition, and stability in transition economies. By using unbalanced data on 1121 banks from 22 transition countries between 1998 and 2016, our findings confirm the negative relationship between market power and stability, supporting the view that competition increases bank stability. The results also confirm that bank reform increases banking stability, supporting the view that better financial development leads to a more stable banking system. Both higher activity restrictions and more explicit guidelines for asset diversification increase bank stability, but this positive effect significantly weakens for banks with higher market power. More stringent capital requirements in combination with higher market power increase the risk of bank insolvency. Declaring insolvency power, private monitoring, financial statement transparency, and deposit insurance have only a direct impact on bank stability. The goal of Chap. 4 is to examine the impact of bank regulation and supervision on competitive conditions in the banking sector in emerging economies and examine whether the unique characteristics in emerging markets shed light on the relationship between bank regulation and competition from different perspectives. Using a sample of 1629 banks in 23 emerging economies between 1996 and 2016, this study employs the Panzar and Rosse (1987) methodology and constructs the competitive variable H-statistic as a measure of competition in the banking system. We also compute the Lerner index (see also Coccorese, 2009; Koetter et al., 2012) and the Boone indicator (2008) as alternative measures of competition. We investigate how competition evolved under different types of bank regulation and supervision. As our sample period covers several banking crises in some emerging economies as well as the recent global financial crisis, we also examine whether the relationship between bank regulation and competition changed during the banking crisis. Considering the different roles played by domestic banks and foreign banks, we also study whether the impact of bank regulation on foreign banks exhibits different patterns. Our analysis shows that banking systems with higher concentration and fewer activity restrictions and entry barriers are more competitive. We also find that reducing foreign bank limitations and increasing capital strictness and official supervisory power also enhances competition in the banking sector. The results also provide evidence that competition in banking systems with fewer government-owned banks and fewer diversification guidelines tends to be more intensive. The results also confirm that higher private monitoring of banks and deposit insurance coverage significantly contribute to an increase in bank competition. During a banking crisis, the impact of activity restrictions, entry barriers, and diversification guidelines on competitive conditions become more effective, whereas foreign bank limitations, capital strictness, supervisory power, and private monitoring become less effective. To further investigate whether the relationship between bank regulation and competition varies according to the type of ownership, we divide the sample banks into two groups: foreign banks and domestic banks. Our findings suggest that foreign banks are more sensitive to official supervisory power and private
6.2 Summary of the Main Findings
181
monitoring and less sensitive to activity restrictions, foreign bank limitations, and diversification guidelines. Chapter 5 examines how bank competition and regulation affect bank efficiency, using a sample of 1261 banks across 28 Asia Pacific countries for the period 2001– 2016. We employ a stochastic frontier analysis (SFA) model to estimate bank efficiency. Our findings confirm that market power is positively related to bank efficiency. Increased activity restrictions, strong official supervisory power, and low capital requirements are associated with high bank efficiency. Furthermore, market power has a stronger efficiency-increasing effect in a banking system characterized by the activity restrictions, supervisory power, and capital requirements described above. Foreign banks operating under strict activity restrictions in a host country with strong official supervisory power are highly efficient.