Revisiting the impact of institutional quality on post-GFC bank risk-taking: Evidence from emerging countries

Journal Pre-proof Revisiting the impact of institutional quality on post-GFC bank risk-taking: Evidence from emerging countries

Ajim Uddin, Mohammad Ashraful Ferdous Chowdhury, Sanjay Deb Sajib, Mansur Masih PII:

S1566-0141(18)30259-0

DOI:

https://doi.org/10.1016/j.ememar.2019.100659

Reference:

EMEMAR 100659

To appear in:

Emerging Markets Review

Received date:

8 July 2018

Revised date:

16 March 2019

Accepted date:

3 November 2019

Please cite this article as: A. Uddin, M.A.F. Chowdhury, S.D. Sajib, et al., Revisiting the impact of institutional quality on post-GFC bank risk-taking: Evidence from emerging countries, Emerging Markets Review(2018), https://doi.org/10.1016/ j.ememar.2019.100659

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© 2018 Published by Elsevier.

Journal Pre-proof Revisiting the impact of institutional quality on post-GFC bank risk-taking: Evidence from emerging countries

Ajim Uddin1, Mohammad Ashraful Ferdous Chowdhury2, Sanjay Deb Sajib3, Mansur Masih4,* [email protected] 1

Martin Tuchman School of Management, New Jersey Institute of Technology, New Jersey, USA School of Management and Business Administration, Shahjalal University of Science and Technology, Sylhet, Bangladesh and Ph.D. scholar, INCEIF, Malaysia 3 Officer, Bangladesh Bank 4 Professor of Finance and Econometrics, INCEIF, Malaysia 2

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Corresponding author.

Abstract

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This is the first attempt to address the impact of institutional quality on post-GFC bank risk-taking behavior. This study is conducted on 730 banks from 19 emerging countries covering the period 2011– 2016. We used six indicators of good governance as a proxy for institutional quality. Both static panel and Dynamic GMM estimation are used to identify the impact of these variables on bank risk-taking; measured by Z-score. We evidenced that increasing government effectiveness, controlling corruption, and improving agents’ confidence and adherence to the rule of law reduce bank risk exposure and improve banks’ stability. Besides supporting the Z-score model, the robustness test using σ(NIM) also provides evidence of the impact of regulatory quality on reducing bank risk. Surprisingly, both models tend to indicate improving voice and accountability stimulate higher bank risk-taking in emerging countries. Furthermore, our study provides an interesting reconciliation to the major debate on the impact of size on bank risk.

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Keywords: Institutional quality; bank risk-taking; legal institutions; corruption.

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JEL Classification: G2, D73

Journal Pre-proof Abstract This is the first attempt to address the impact of institutional quality on post-GFC bank risk-taking behavior. This study is conducted on 730 banks from 19 emerging countries covering the period 2011– 2016. We used six indicators of good governance as a proxy for institutional quality. Both static panel and Dynamic GMM estimation are used to identify the impact of these variables on bank risk-taking; measured by Z-score. We evidenced that increasing government effectiveness, controlling corruption, and improving agents’ confidence and adherence to the rule of law reduce bank risk exposure and improve banks’ stability. Besides supporting the Z-score model, the robustness test using σ(NIM) also provides evidence of the impact of regulatory quality on reducing bank risk. Surprisingly, both models tend to indicate improving voice and accountability stimulate higher bank risk-taking in emerging countries. Furthermore, our study provides an interesting reconciliation to the major debate on the impact of size on bank risk.

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Keywords: Institutional quality; bank risk-taking; legal institutions; corruption.

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JEL Classification: G2, D73

1. Introduction

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The unfoldment of the 2007-2008 global financial crisis (GFC) has reshaped our understanding of financial institutions. GFC raises several questions about the operating activities of these institutions. The major causes like bank run in the wholesale market, excessive securitization, and unduly risky loans all pointing towards a major flaw in the business model of the most important financial institutions, commercial banks. Scholars identified aggressive risk-taking behavior by banks as the prime factor behind the GFC. Bank risk-taking is referred to as the uncertainty that arises from the operating activities and decision-making phenomena of the banks. It depends on various variables, including bank structures, competition, regulation and corporate governance (Agoraki et al., 2011; Anginer et al., 2016; Boyd and De Nicoló, 2005; Laeven and Levine, 2009; Wagner, 2009)

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Before the crisis, fierce competition and high-profit requirements had driven banks to engage in excessive risk-taking, making both banks and the financial system vulnerable to shocks (Rajan, 2006). In that period banks made excessive investments in mortgage lending and securitization activities. High short-run return on mortgage and securitization influenced investors to heavily invest in these instruments while ignoring the associated risks. To meet the demand, banks also responded by issuing more debt to creditors and trenching risky securities. In addition to these, the prevailing notion of always appreciating house price creates a false sense of security. But when the housing bubble outburst, banks were forced to write down several hundred billion dollars in bad loans caused by mortgage delinquencies. Low credit quality and highly exposed liquidity risk of banks coupled with bank run and lack of access to the central banks’ funds, instantly evaporated market liquidity and left banks with fire sales of assets as their only option. Several major banks were compelled to write off their market capitalization in the stock market. The cost of excessive risk-taking by banks had to be borne by the economy; only in the USA the household wealth loss was $19.2 trillion (The US Department of the Treasury, 2012) Risk-taking behavior of banks had always been an important issue to regulators. GFC reignite the debate over the optimal level of risk that can ensures banks’ stability without hurting their profitability. After GFC several preventive measures and regulatory requirements had been implemented to overcome the crisis and prevent any such future occurrence. However, how banks are doing in this post-crisis era is still unknown. Research is needed to answer questions like, is there any fundamental change in banks’ risk-taking behavior? Are banks more responsible and vigilant in their risk-taking behavior in the postcrisis era? Are banks in all countries showing a similar trend in their risk-taking behavior or the country’s political and institutional quality has any bearing on banks’ risk-taking behavior? This study is an effort to answer the last question.

Journal Pre-proof The idea of the impact of institutional quality on bank risk-taking is still novel. Although numerous studies were conducted to determine the effect of institutional quality on the economic and financial development of a country, little attention has been paid to study the impact of these factors on the risktaking behavior of the banking industry. Institutions greatly influence the culture and behavior of a society. Bankers as part of the society are not free from such influence. Political science and economic literature suggest a positive relationship between institutional quality and economic development. Sound institutions ensure efficient economic system by implementing adequate financial regulatory and supervision framework (Gazdar and Cherif, 2015). Strong property rights and an effective legal system will eventually result in the overall economic and financial development (Voghouei et al., 2011). The rule of law and absence of corruption ensure accountability and stability in the financial sector. Higher institutional quality within a financial system provides better financial liberalization (Chinn and Ito, 2006). From these threads of literature, we hypothesize the existence of a relation between institution quality and bank risk-taking.

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Recently Ashraf (2017) found that political institution - an element of institutional quality- has a major impact on bank risk taking. Our study is an augmentation of his study. In addition to the political institution, we include other institutional quality variables to explain the risk-taking behavior of banks. We analyzed the impact of all institutional quality on banks’ risk-taking and their credit granting mentality while controlling for bank-specific, country-specific, and industry-specific factors. This paper aims to identify how important is the institutional quality for banks’ risk-taking behavior. We further investigate whether more stable and better institutional quality encourage banks to take more risk or do they work as a check and balance to reduce bank risk exposure. While most of the previous studies are based on pre-GFC data, our paper is mainly focused on the post-GFC data. This provides us valuable insight into the situation of the post-GFC banking industry. This study only includes countries from the emerging markets. The exclusion of developed countries was influenced by their very much identical performance in good governance indicators. We also have the reasons to believe that, banking regulations in emerging countries are slack and therefore their decision making will be more influenced by their institutional quality.

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The rationale behind this study is twofold. First, it contributes to banking literature by addressing one very important but unexplored area of banking business - the relationship between a country’s institutional quality and its banks’ risk-taking behavior. To the best of our knowledge, no previous study comprehensively evaluates the impact of institutional quality on bank-risk taking behavior. This study also answers some imperative questions about the post-GFC banks’ business model. The role of banks’ excessive risk-taking behind GFC and the lack of related after-crisis literature warrant the need for this study. It sheds lights on current bank-risk taking behavior in emerging countries as well as the driving forces behind such behavior. Secondly, the findings of this study have significant implications for both government and regulatory authorities. Most emerging countries suffered from low institutional quality. Widespread corruption and political instability are quite common in these countries. The findings tend to suggest that government and regulatory authorities need to recognize and take preventive measures against the impact of institutional quality on bank risk-taking. Besides supporting most of the established literature as a novel contribution, our study evidenced strong statistical and economic importance of institutional quality in bank risk-taking behavior. Effective government system, control of corruption, and the rule of law all were found to have a negative relation with bank risk-taking. The findings indicated that improving institutional quality through better policy measures can significantly reduce bank risk. The structure of the remainder of this paper is as follows. This introductory section is followed by a review of the literature in section two. Section three discussed the data and variables used in this study. Section four presents the methodology and econometric models. Section five discusses the empirical results as well as the results of the robustness test. Finally, section six includes concluding remarks and future policy implications. 2. Literature Review Bank risk-taking behavior is a well-studied phenomenon. Academicians find it as an important policy issue for the overall banking sector stability. Over the years many theoretical and empirical studies

Journal Pre-proof examined the risk-taking behavior of banks. Our paper builds on the strand of literature examining bank risk-taking behavior in an international setting. Historically, researchers in identifying factors affecting bank risk-taking were mainly concerned about two set of factors; banks’ internal factors such as size (González, 2005; Saunders et al., 1990), loan-loss provision, bank capitalization (Laeven and Levine, 2009), and environmental factors such as, competition (Beck et al., 2013; Boyd and De Nicoló, 2005), bank regulation (Klomp and Haan, 2012), deposit insurance (Demirgüç-Kunt and Detragiache, 2002) and activity restriction (Barth et al., 2004) etc.

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Saunders et al. (1990) assessed the relationship between bank size and bank risk-taking behavior and found a negative correlation between bank size and bank risk-taking. In contrast, González (2005) in his assessment shows a significant positive relationship between these two. Laeven et al. (2014) examined the relationship during GFC and found risk-taking behavior of banks increases proportionally with bank size (Laeven et al., 2016). Besides these key theoretical aspects, several empirical studies also conducted on this topic. Demsetz and Strahan (1997) used data from 150 publicly traded bank holding companies from 1980-1993 to examine the impact of bank size on bank risk-taking and found undiversifiable risk increased with the growing size of the bank. Iannotta et al. (2007) analyzed risk-taking data of 180 large banks of European countries and found large banks are generally better capitalized. In contrast, in a study on 270 commercial banks across 48 countries, Laeven and Levine (2009) found large banks are exposed to higher risk as they possess low capital ratio than smaller banks. Capital requirements is another crucial determinant for bank risk-taking. Previous literature shows a mixed conclusion about its influence. Bolt and Tieman (2004) found strict capital requirement lead banks to adopt stricter credit policy. Laeven and Levine (2009) also support their idea and argued, stringent capital requirement enhances bank stability. In contrast, Delis and Staikouras (2011) showed a negative relationship between bank capitalization and bank risk-taking.

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Several empirical literature conclude that banks with large market power have a lower probability of default. More competition in the banking industry enhance financial stability and decrease borrower credit risk (Boyd and De Nicoló, 2005). Some find a positive linear relationship between higher competition and banking sector stability (Barth et al., 2004; Schaeck et al., 2009) while other proposed a nonlinear relationship between competition and bank risk-taking (Martinez-Miera and Repullo, 2010). Deposit insurance is a regulatory measure to protect the depositor of the banks. While it plays a significant role in maintaining public confidence in the financial system, it comes with an unintended consequence. It encourages banks to take excessive risks. Most of the studies found that there is a negative relationship between explicit deposit insurance and bank risk-taking (Anginer et al., 2016; Demirgüç-Kunt and Detragiache, 2002; Hoque et al., 2015). Explicit deposit insurance reduces market discipline and eventually encourages banks to take excessive risks (Angkinand and Wihlborg, 2010).

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Klomp and Haan (2012), assessed the impact of bank regulation and supervision on bank risktaking behavior. They found that stricter regulation and oversight reduces banking risk. Measures like capital regulation, supervisory control, liquidity regulation, and activity restrictions reduce bank risk level (Klomp and Haan, 2012). Few studies examine the effect of core principles for effective bank supervision implemented by the Basel Committee on Banking Supervision (BCPs). Demirgüç-Kunt et al. (2008) initially find a positive relationship between overall financial soundness of the banks and the BCPs compliance. But in a follow-up study in 2011 with 3,000 banks from 86 countries, they found better compliance with BCPs not always result in financial soundness. This result is consistent with the result of Marston (2014) who uses a sample of 25 countries to examine the relationship between the overall index of BCBS compliance with non-performing loans (NPLs) and loan spreads. The result from these studies indicates that BCPs compliance is not a significant determinant factor of financial soundness of a bank. Mixed findings also exist regarding the relationship between activity restrictions and bank risktaking. Barth et al. (2004) argued there is a negative relation between activity restrictions and bank stability. Lower activity restrictions and allowing banks to diversify their income across several sources enhance stability and eventually reduce bank risk level (Barth et al., 2004). In contrast, Klomp and Haan (2012) shows a positive relationship between activity restrictions and bank stability. They argued higher activity restrictions reduces banks liquidity risk and market risk which in turn enhance banks stability.

Journal Pre-proof Apart from these bank-specific and macroeconomic variables, in a recent study Ashraf (2017) tried to evaluate the impact of political institutions on bank risk-taking. According to him, sound political institutions stimulate bank risk-taking behavior. This is consistent with the hypotheses that better political institutions increase banks’ risk by boosting the credit market competition from alternative sources of finance. In addition, Houston et al. (2010) found bank risk-taking is higher in countries with strong creditor rights. Chen et al. (2015) assessed the impact of corruption on bank risk-taking. They collected data from 1200 banks from 35 emerging countries for the period 2000-2012. They found bank risk-taking behavior is positively related to corruption; the higher the level of corruption in a country the higher level of risk banks are taking in that country. In addition, they also examined the interactive effect of corruption on the monetary policy’s risk-taking channel and found that there is a notable impact of monetary policy on bank risk-taking with a high level of corruption (Chen et al., 2015). Studies also proved that corruption impact banking stability of a country. A higher level of corruption disrupts the lending and investment decision of the banks which finally destabilize the total banking industry (Barry et al., 2016; Toader et al., 2018).

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To the best of our knowledge except for these selected studies, no other study tried to evaluate the impact of institutional quality or good governance on bank risk-taking behavior. However, literature in economics and other areas of finance strongly suggest Institutional quality plays a pivotal role in the development of an efficient and effective financial system. Socio-economic factors like corruption, property rights, and political stability influence the financial development of a country (Uddin et al., 2017a). Gazdar and Cherif (2015) argued institutions play a vital role in the overall performance of a financial market. They examined the relationship between institutional quality and financial development and found a positive correlation between these two factors. Besides, Voghouei et al. (2011) argued that institutional quality and political stability stimulate the development of the financial sector. Politically connected firms able to obtain more loans from banks but end up with a higher default rate (Khwaja and Mian, 2005). Firms with a connection to politicians have better access to long-term bank loans and require less collateral (Charumilind et al., 2006). Park (2012) found nonperforming loans is higher in countries with high corruption. Likewise, Barth et al. (2009) found illegal practices of the lending process through financing less efficient project is very costly for the economy. The practice of extortion and bribery can increase the volatility of the economy and hinder the entrepreneurship development of the country (Barth et al., 2009).

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A financial system becomes efficient when its institutions become sound and effective. The efficiency of a financial system, in turn, influences the risk-taking behavior of the organizations that operate in that system. The proven significant influence of various institutional quality variables on financial system encouraged us to believe the existence of a relationship between institutional quality and bank risk-taking. Although there are a few studies about the relationship between bank risk-taking and some stand-alone institutional quality variables, there is no comprehensive study that tests the impact of all the institutional variables on bank risk-taking. In this study, we include all the indicator of good governance along with the bank-specific and country-specific control variables and identify how these institutional quality variables affect bank risk-taking in emerging countries. 3. Data and Variables The dataset consists of the annual data of 730 banks from 19 countries. In addition to bank-level data, the country-specific data of each country also been collected and used in this study. We gathered the bank-specific data from the Orbis Bank Focus of Bureau Van Dijk database. The country-level data used in this study can divide into two groups: macroeconomic variables and institutional quality variables. We collected Macroeconomic data from the World Bank and FRED Saint Luis database (Fed, 2018). The worldwide governess indicators (WGI) variables are used as a proxy to measure institution quality. WGI report on six broad dimensions of governance; voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rules of law, and control of corruption. We collected WGI data from the World WGI database (World Bank, 2018). 3.1. Sample The study consists of bank holding companies and commercial banks from 19 emerging countries. The countries are selected based on the International Monetary Fund (IMF) emerging countries list as of

Journal Pre-proof 2015. As we believe the market was still recovering in 2010, we include data from the time period of 2011 to 2016. From 23 countries on the list, some countries are excluded based on the missing institutional quality data or unavailability of other country-specific or industry level data. We collect the income statement and balance sheet data for each bank from Orbis Bank Focus database. Not all banks from these 19 countries are included in our study. The sample selection involves removing small banks and banks with missing necessary accounting data. From all banks, first banks with asset less than 500 million USD are removed. Later bank with less than four years of valid data is also removed. Finally, to avoid the outlier effect, we conduct a 99 percent winsorization for all remaining banklevel data. Table 1 reports the countries included in our study, as well as the number of banks, mean Zscore, mean σ(NIM), and the mean of institutional quality variables for each country.

Political Stability

Regulatory Quality

Rule of Law

Voice

-0.116 -0.755 -0.139 0.201 -0.748 -0.058 -0.166 -0.413 -1.159 -0.259 0.981 -1.054 -0.741 0.102 0.899 -0.296 0.076

0.093 -1.293 -0.199 -0.546 -1.509 -1.124 -0.548 -1.090 -2.135 -0.184 0.125 -1.999 -2.573 -1.089 1.075 -0.903 -0.464

-0.844 -0.885 -0.024 -0.264 -0.663 -0.405 -0.212 -1.406 -1.199 -0.228 0.682 -0.773 -0.667 -0.074 0.666 -0.394 0.052

-0.658 -0.775 -0.065 -0.430 -0.537 -0.066 -0.469 -0.928 -1.469 -0.567 0.501 -1.084 -0.833 -0.416 0.862 -0.772 0.179

0.366 -0.444 0.468 -1.633 -1.091 0.421 0.096 -1.545 -1.081 -1.196 -0.397 -0.559 -0.766 0.068 -1.076 -1.038 -1.858

-0.441 -0.887 -0.189 -0.363 -0.637 -0.441 -0.561 -0.715 -1.294 -0.887 0.229 -1.160 -0.933 -0.505 0.996 -0.975 0.002

0.344 0.286

-0.089 -1.082

0.331 0.234

0.128 -0.134

0.619 -0.683

-0.017 -0.397

30 24 46 153 26 64 61 7 6 27 53 19 21 23 12 97 15

2.281 0.598 3.016 0.553 0.770 0.376 0.946 2.400 1.570 1.419 0.375 1.025 0.731 0.417 0.407 1.452 0.534

3.224 3.212 3.244 4.007 3.085 3.487 3.618 2.315 3.456 2.592 3.907 2.650 3.539 3.576 4.079 2.464 4.467

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0.915 0.397

4.265 4.014

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Government Effectiveness

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Argentina Bangladesh Brazil China Egypt, AR. India Indonesia Iran, IR. Iraq Kazakhstan Malaysia Nigeria Pakistan Philippines Qatar Russian F. Saudi Arabia South Africa Thailand

No. of Banks

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Table 1. Number of Banks and Mean Statistics of Each Country Corruption

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3.2. Measurement of Bank Risk Taking

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Following established literature (Ashraf et al., 2016; Beck et al., 2013; Houston et al., 2010; Laeven and Levine, 2009) bank risk-taking is measured by Z-score. Z-score is calculated as the return on asset (ROA) plus annual equity to total asset ratio (CAR) divided by the standard deviation of return on asset before loan loss provision. That is, Z-score = (ROA+CAR)/σ(ROA). Z-score indicates the number of standard deviations a banks return must fall from its mean value to deplete all shareholders’ equity. It is also a measure of banks stability. The higher the Z-score of a bank the more stable the bank is and therefore less probability of that bank to go bankrupt (Uddin et al., 2017b) As Z-score is a highly skewed measure (Beck et al., 2013), we use natural logarithm of 1+Z-score to smooth the value. From now on, for the rest of the paper Z-score refer to the ln(1+Z). The use of logged value also avoids truncation of the Z-score to zero. As logged Z-score is widely accepted and unproblematic bank insolvency risk measure (Lepetit and Strobel, 2015) we used this as the dependent variable in our study. In addition to Z-score for robustness test, we also use the volatility of net interest income, σ(NIM) as the dependent variable. σ(NIM) equal the standard deviation of net interest margin of a bank and is also a widely accepted and used measure of bank insolvency risk (Ashraf et al., 2016; Ashraf, 2017). A higher value of σ(NIM) indicates increasing banks earnings volatility and therefore higher bank risk. 3.3. Measurement of Institutional Quality

Journal Pre-proof Institutional changes determine the way society evolves and the direction of economic performance (Lott and North, 2006). In recent years, in addition to fundamental macroeconomic variables, institutional quality variables received increasing attention as a contributor to long-run economic development. Several studies have provided conclusive evidence of the impact of institutional factors on the economic growth of a country (Acemoglu et al., 2002; Eicher and Leukert, 2009; Knack and Keefer, 1995). Being the heart of the economic system banking sector are yet to receive its due attention for the impact of institutions on its performance.

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Institution, as North defined “rules of the game in a society, or, more formally…the humanly devised constraints that shape human interaction. In consequence, they structure incentives in human exchange, whether political, social or economic”(Lott and North, 2006) .Institution significantly influence the culture and behavior of a society, and the behavior of the banking industry is no way out of this influence. Although many studies are conducted to determine the impact of institutional quality on the economic and financial development of a country, little attention has been paid to analyze the impact of these factors on the performance of the banking industry and how they shape the participants' behaviors of such industry. Recently Ashraf (2017) tried to analyze the impact of political institutions on bank risk-taking behavior. By capturing government constraint, he concludes that better political institutions encourage banks to take more risk. Our study tries to augment his study to include other institutional quality variables in addition to government and political constraint.

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World Bank WGI is used to measure the institutional quality of a country. WGI summarizes the views on the governance quality of a country from a large number of enterprises, citizen, and experts. WGI captures six key dimensions of governance. These are voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption. It combines data from 30 different data sources and updates annually (World Bank, 2018).

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Voice and accountability reflect the ability of people of a country to select their government, express their opinion, freedom of association, and free media. Political stability and absence of violence measure the likelihood of no violence, terrorism, and stability of the government within a country. Government effectiveness reflects the quality of public services, quality of policy formulation and implementation and the extent by which these are free from political pressure. Regulatory quality indicates the ability of a country’s government to formulate and implement sound regulatory policies to effectively regulate and promote private sector development. The rule of law reflects the confidence of agents on the law and the degree by which they abide by the law. It also includes a country's quality of contract enforcement, property right, level of crime and violence, and effectiveness of its law enforcement agencies. Finally, the control of corruption reflects the level of corruption in a country. All these institutional quality variables range from approximately -2.5 (weak) to 2.5 (strong) governance performance (World Bank, 2018).

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3.4. Bank Level Control Variables

To control the bank-specific characteristics three bank-level variables are used in this study. These include total assets, loan loss provision divided by total asset, and noninterest income divided by total income. The logarithm of the total asset is used as the proxy measure of size. The logarithm is used to account the skewness of the variable. Literature suggests size can have a positive or a negative effect on bank stability. The loan loss provision divided by total asset measure the credit risk of the bank and it is expected to have a negative impact on bank Z-score. Final bank-specific variable; annual noninterest income over annual total income reflect the percentage of total income that is generated by non-interest related activities. It also expected to have a negative relation with Z-score. 3.5. Country Level Control Variables Six country-level control variables are used in this study to account the impact of a country's macroeconomic development and industry structure on its banks. Among these GDP per capita, inflation, and real interest rate are used to account the broader macroeconomic environment, and bank concentration, deposit insurance, and capital to asset ratio are used to control the overall banking industry characteristic of a country. For GDP we use natural logarithm of annual gross domestic product per capita measured in current US dollars. Inflation denotes the percentage change in the annual average consumer price index. Real interest rate removes the inflationary expectation from the interest rate. Real

Journal Pre-proof interest rate is used to measure cross country and over time variation in macroeconomic condition. Data for these variables are collected from the World Bank database. Bank Concentration refers to the total asset of the three largest banks operating in a country divided by total assets of all banks operating in that country and calculated annually for each country. It analyzes the impact of industry structure on bank risk-taking. Deposit insurance is a dummy variable, and it equals to 1 if a country has implemented explicit deposit insurance and 0 otherwise. While excessive depositor insurance is expected to prevent depositor runs but it is also a source of moral hazard, as banks become relaxed in their lending activities. The expected coefficient of deposit insurance is negative. Capital to asset ratio measures a country’s bank capital and reserves to its total assets. Capital and reserve include all owners’ funds, retained earnings, general and special reserves, provisions, and valuation adjustment. It is used as a proxy for regulatory capital requirements for the banking industry of a country.

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4. Methodology 4.1. Pooled Panel

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Since the dataset consists of a panel data from 2011–2016, following the commonly established methodology pooled panel methodology is applied to this study as a base model. The pooled panel OLS considers the cross-country variation in institutional quality variables. By estimating the impact of institutional quality on bank risk taking it helps us understand the change in the probability of bank default over time for every change in institutional quality variables (Ashraf, 2017). 𝑘 𝑙 𝑚 𝑍_𝑠𝑐𝑜𝑟𝑒𝑖,𝑗,𝑡 = 𝑎𝑖 + ∑𝑘𝑘=1 𝛽𝑘 𝑋𝑖,𝑗,𝑡 + ∑𝑙𝑙=1 𝛽𝑙 𝑋𝑗,𝑡 + ∑𝑚 𝑚=1 𝛽𝑚 𝑋𝑗,𝑡 + 𝜀𝑖,𝑗,𝑡

(1)

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Here, i, j and t subscripts represent bank, country and year, respectively. 𝑎𝑖 is the constant term. The dependent variable, Z-score, is used as a proxy for bank risk-taking, where higher values of Z-score 𝑘 represent a lower probability of bank default and vice versa. 𝑋𝑖,𝑗,𝑡 are the bank-level control variables. 𝑙 These include total assets, loan loss provisions/total assets, and noninterest income/total income. 𝑋𝑗,𝑡 is the country-level control variables. These include banking industry level variables- bank concentration, capital to asset ratio, and explicit deposit insurance as well as macroeconomic variables -log of GDP per capita, real interest rate, and inflation. Finally, the main focus of this study, the institutional quality 𝑚 variables are denoted by 𝑋𝑗,𝑡 . These include voice and accountability, political stability no violence, government effectiveness, regulatory quality, rule of law, and control of corruption. 𝜀𝑖,𝑗,𝑡 is the error term. Heteroskedastic-robust standard errors are used to estimate p-values in regressions. 4.2. Dynamic Generalized Method of Moments (GMM)

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For the analysis of banking sector panel data, fixed and random effects models are generally used. However, there is a probability that the impact of one-year performance can affect the subsequent year's performance (Athanasoglou et al., 2008). This impact of lagged dependent variable also resulted in a difficulty in the models especially when the time period (T) is shorter than the number of observations (N) (Beggs and Nerlove, 1988). To address this issue, the difference Generalized Methods of Moments (GMM) model was developed by Arellano and Bond (1991) by differencing all regressors and employing GMM. Arellano and Bover (1995) argued that difference GMM includes lagged levels as well as lagged differences. The underlying assumption of the GMM - the first differences of instrumental variables are uncorrelated with the fixed effects- allows the model to introduce more instruments and improve its efficiency. Roodman (2009) argues that both difference and system GMM estimators are suitable for studies that involve ‘small T, large N’ panels; where independent variables are not strictly exogenous; and heteroscedasticity and autocorrelation exist among individual samples, in this study, banks. However, the problem of serious finite sample biases might arise with difference GMM if the instruments used have near unit root properties. That’s why Bond (2003) suggests for System GMM as it has notably smaller finite sample bias and much greater precision when estimating autoregressive parameters using persistent series. In addition, the system GMM controls for unobserved heterogeneity and the persistence

Journal Pre-proof of the dependent variable. The following formula for GMM proposed by Athanasoglou et al. (2008) is used to conduct the empirical analysis: 𝑘 𝑙 𝑚 𝑍_𝑠𝑐𝑜𝑟𝑒𝑖,𝑗,𝑡 = 𝑎𝑖 + 𝛿 𝑍_𝑠𝑐𝑜𝑟𝑒𝑖,𝑗,𝑡−1 + ∑𝑘𝑘=1 𝛽𝑘 𝑋𝑖,𝑗,𝑡 + ∑𝑙𝑙=1 𝛽𝑙 𝑋𝑗,𝑡 + ∑𝑚 𝑚=1 𝛽𝑚 𝑋𝑗,𝑡 + 𝑣𝑖,𝑗,𝑡

(2)

And 𝑣𝑖,𝑗,𝑡 = 𝑣𝑖,𝑗 + 𝜀𝑖,𝑗,𝑡

(3)

Here, 𝑍_𝑠𝑐𝑜𝑟𝑒𝑖,𝑗,𝑡−1 is the lag value of dependent variable. 𝑣𝑖,𝑗,𝑡 is the disturbance term, with 𝑢𝑖,𝑗 is the unobserved bank-specific effect and the 𝜀𝑖,𝑗,𝑡 idiosyncratic error. This is a one-way component regression model, where 𝑢𝑖,𝑗 ~ IIN (0, ϭv²) and independent of 𝜀𝑖,𝑗,𝑡 ~(0, ϭu²). 4.3. Robustness Test

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Although Z-score is the most acceptable and widely used measure of bank risk, an alternative measure will further corroborate the findings from Z-score. Therefore, for robustness test, we use σ(NIM) as a second measure of bank risk-taking. σ(NIM) equals the standard deviation of annual net interest margin. It helps to measure the lending risk of a bank. It is also directly associated with the loan quality and earning capacity of a bank. For this, some authors even argued it is a better measure than Z-score for lower quality loans (Ashraf, 2017). The base model for Pooled Panel analysis using σ(NIM) is 𝑘 𝑙 𝑚 𝜎(𝑁𝐼𝑀)𝑖,𝑗,𝑡 = 𝑎𝑖 + ∑𝑘𝑘=1 𝛽𝑘 𝑋𝑖,𝑗,𝑡 + ∑𝑙𝑙=1 𝛽𝑙 𝑋𝑗,𝑡 + ∑𝑚 𝑚=1 𝛽𝑚 𝑋𝑗,𝑡 + 𝜀𝑖,𝑗,𝑡

(4)

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And 𝑣𝑖,𝑗,𝑡 = 𝑣𝑖,𝑗 + 𝜀𝑖,𝑗,𝑡

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5. Empirical Results and Discussions 5.1. Summary Statistics

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The descriptive statistics of the variables are presented in table 1 and table 2. Where table 2 reports global statistics, table 1 reports country-level mean statistics. It includes mean Z-score, mean σ(NIM), and the mean of institutional quality variables for each country. The country-level mean statistics signify the cross-country variation in our sample group. It also reveals the close relationship between our two measures of bank risk. A closer look will reveal that countries with higher σ(NIM) also demonstrate lower Z-score i.e. Iran, Kazakhstan, Russian Federation. On an average most emerging countries have a Zscore above 3 with the lowest in Iran (2.315) and the highest in Saudi Arabia (4.467). Although the mean scores of institutional quality variables vary across countries and among variables, an easily discernable pattern also exists. Countries like Egypt, Iraq, Pakistan has a consistent low score, indicating the adverse impact of ongoing war and political instability on these countries over the last decade. Overall, Qatar, South Africa, and Saudi Arabia outperform other emerging countries in institutional quality measures, indicating the presence of political stability and the rule of law in these countries. Table 2. Descriptive Statistics Variable ROAA ROE Z-Score NIM σ(NIM) LTA LLPTA NITI Capital to Asset Ratio

N 4380 4380 4254 4380 4380 4192 4254 4185 4254

Mean 1.234 11.201 3.469 4.058 0.978 15.688 0.011 1.418 9.265

Median 1.122 11.980 3.564 3.215 0.548 15.677 0.005 0.826 8.921

Std. Dev 2.125 11.447 1.021 4.043 1.353 2.013 0.029 3.098 2.319

Minimum

Maximum

-19.721 -42.153 -2.734 -15.362 0.047 4.550 -0.036 -14.797 5.317

21.722 40.322 6.483 71.272 14.699 21.718 1.042 23.633 14.798

Lower Quartile 0.684 6.470 2.872 2.266 0.310 14.474 0.002 0.395 7.156

Upper Quartile 1.770 17.066 4.138 4.996 1.069 16.847 0.011 1.584 11.091

Journal Pre-proof 4380

45.760

43.660

12.226

26.751

94.982

41.111

47.863

4380

8.707

8.920

0.855

6.728

11.392

8.154

9.295

4287 4371 4380 4380

5.735 4.856 -0.474 -0.036

5.411 3.918 -0.450 -0.043

3.773 8.186 0.411 0.471

-0.900 -17.374 -1.396 -1.264

39.266 53.543 1.111 1.115

2.628 2.218 -0.808 -0.259

7.690 6.375 -0.304 0.243

4380 4380

-0.697 -0.239

-0.593 -0.283

0.639 0.418

-2.817 -1.521

1.224 0.838

-0.998 -0.416

-0.378 -0.100

4380 4380

-0.363 -0.643

-0.422 -0.765

0.421 0.793

-1.701 -1.907

0.959 0.654

-0.692 -1.266

-0.080 0.154

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Bank Concentration Log GDP Per Capita Inflation Real Interest Corruption Govt. Effectiveness Political Stability Regulatory Quality Rule of Law Voice and Accountability

5.2. Institutional Quality and Bank Risk Taking

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The impact of institutional quality variables along with bank-specific and macro-environmental control variables on Z-score are reported in table 3. The second and third column of the table report the regression output from pooled panel analysis; pooled OLS and random effects model respectively. We analyzed both fixed effects and random effects model but based on the Hausman test we only report the random effects result. Hausman test results are also consistent with our primary objective as we wanted to identify the effect of variables on bank risk-taking both over time and across countries. Column four reports the result from dynamic GMM analysis. Following Arellano and Bond (1991) we only report system GMM, for its increased efficiency when the panel units (number of banks) are large and time periods are moderately small. System GMM corrects any bias for standard GMM by using additional moment restriction and makes lagged first differences as instruments in the level equation (Arellano and Bover, 1995; Roodman, 2009). Both first order and second order autocorrelation follow the Arellano and Bond (1991) specification for GMM consistency as we have autocorrelation in the first order [AR(1)] but no autocorrelation in the levels [AR(2)]. Both the Sargan test and Hansen test result is insignificant implying that the instruments as a group are exogenous. Therefore, we believe our dynamic GMM model is consistent and robust for the analysis.

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Table 3. Institutional Quality and Bank Risk Taking Pooled OLS

Random Effects

Lag Z-score LTA

Dynamic GMM 0.689*** (0.071)

-0.110*** (0.005)

-0.184*** (0.015)

-0.138*** (0.010)

LLPTA

-6.812*** (0.954)

-8.322*** (0.427)

-9.601*** (1.312)

NITI

0.005* (0.003)

0.006*** (0.002)

0.002*** (0.000)

Bank Concentration

0.002 (0.002)

0.001 (0.001)

0.001 (0.000)

Deposits Insurance

-0.564*** (0.072)

-0.919*** (0.084)

-0.159** (0.061)

Capital to Asset Ratio

0.036*** (0.009)

0.044*** (0.005)

0.053*** (0.010)

Log GDP

-0.171*** (0.039)

-0.048*** (0.014)

-0.093*** (0.000)

Journal Pre-proof -0.040*** (0.007)

-0.011*** (0.002)

-0.003*** (0.000)

Real Interest

0.006** (0.002)

0.002** (0.001)

0.007*** (0.001)

Voice

-0.083* (0.042)

-0.137*** (0.037)

-0.069** (0.031)

Political Stability

0.050 (0.052)

0.036 (0.028)

0.013 (0.037)

Govt. Effectiveness

0.249*** (0.095)

0.105** (0.045)

0.120** (0.062)

Regulatory Quality

-0.014 (0.122)

0.073 (0.057)

0.097** (0.046)

Control of Corruption

0.244*** (0.051)

0.348*** (0.054)

0.198** (0.080)

Rule of Law

0.212*** (0.027)

0.212*** (0.063)

0.090*** (0.022)

Constant

5.601*** (0.356)

5.658*** (0.364)

1.757*** (0.464)

R-squared

0.337

0.000 0.924 0.211 0.854

Values in parentheses indicate standard deviation. A higher value of Z-score indicates higher stability and lower bank risk. ***, **, * denotes statistical significance at 1%, 5%, and 10% level, respectively. The Hausman test statistics: Prob>chi2 = 0.563

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Sargan test (p-value) Hansen test (p-value)

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The finding of previous literature about the impact of bank-specific variables on bank-risk also holds for the banks in emerging countries. As expected, we found size and loan loss provision have a negative effect, and net interest income has a positive effect on bank risk-taking. Loan loss to total asset ratio were found statistically and economically significant to bank risk in all three models. Although the magnitude varies, the large negative coefficients indicate a substantial increase in bank risk for an increase in the loan loss provision ratio. The impact of size on bank risk-taking is found significant in the random effects model (0.11), pooled panel (0.18), and GMM model (0.13) at 1% level of significance. Supporting the findings of Laeven et al. (2016) and Laeven and Levine (2009), the negative coefficient indicates that larger banks take a higher level of risk. Non-interest income ratio on bank risk-taking also were found statistically significant. Although the size of the coefficient is small, the sign indicates that in emerging countries bank Z-score improves as non-interest income ratio increases. In addition to that, the dynamic GMM model also proved the lagged impact of Z-score on current Z-score. The lag coefficient is 0.7 at 1 percent significant level. This indicates a substantial portion of the current year’s bank risk comes from the previous year’s bank risk. Among country-level control variables, except for bank concentration, all other variables affect bank risk-taking in some magnitude. No significant relation between bank concentration and bank risk Indicate that the structure of the banking industry does not influence banks risk-taking mentality. The dummy variable explicit deposit insurance has a very significant relation with Bank risk. According to the random effect model, the presence of deposit insurance can increase banks Z-score of that country by approximately 1 point. The findings support our initial hypothesis plus previous literature on this topic. Government explicit deposit insurance encourages banks to take more risk by giving them a sense of assurance that, the government will bail them out during distress. The regulatory capital requirement measured by capital to asset ratio has a significant positive relation to bank Z-score. The result is consistent in all three-model implying that bank stability increases with a country’s overall capital to asset

Journal Pre-proof ratio requirement. GDP and inflation have a negative relation with bank stability. Aggressive loan distribution might be one probable reason for a growing economy to increase bank risk. As the economy is growing banks find more and more project to be financially feasible for loan approval, and therefore engage themselves in aggressive loan disbursement. The subprime mortgages before GFC is a prime example of this behavior. The final control variable, the real interest rate also has a positive impact on bank risk-taking. However, the magnitude is small, for dynamic GMM it is 0.007 at 1% significant level. The positive relation indicates that, along with real interest rate banks asking interest rate also goes up, resulting in a lesser number of financially feasible projects to accept. In such a situation banks become conservative and therefore take less risk.

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Our study provides significant insight into the primary concern of our research, the impact of institutional quality variables on bank-risk taking. We found conclusive evidence about the effects of voice and accountability, government effectiveness, corruption, and rule of law on bank risk. According to the pooled panel, an effective government system (0.25), a reduction in corruption (0.24), and the existence of rule of law (0.21) can increase bank stability by improving bank Z-score. The result is also consistent in the random effects and system GMM model. Although in GMM the coefficient estimation is smaller than pooled panel estimation, all three variables, government effectiveness (0.12), control of corruption (0.20) and rule of law (0.09) are still significant at 1% level for significance. The impact of these three institutional quality variables on reducing bank risk is consistent with our initial assumptions. An effective government system and the rule of law not only ensure individual rights and institutional sovereignty but also facilitate fair and efficient distribution of wealth -- a prime notion behind an efficient economic system. Banks as a central piece of any country’s economic system, therefore, are very much affected by that country’s government system and the rule of law. In addition, corruption can significantly jeopardize the banking operation of a country. In countries with rampant corruption, bankers are more likely to be influenced by unethical means and accept loans that have a high probability of default. Similar results are also evident in Toader et al. (2018) and Barry et al. (2016). In both studies, the authors argued a higher level of corruption disrupts the lending and investment decision of the banks which eventually destabilized the total banking industry. Such corrupt and illegal practices on the lending process through financing less efficient project is also very costly for the economy (Barth et al., 2009). This significant positive impact of these institutional quality variables also has some important implications. Banks do maintain a lower level of risk in countries with better governance. Governance and rule of law ensure check and balance in the banking system. It promotes accountability in bankers’ actions as there is no way of getting out from an imprudent decision resulting in less aggressive lending and lower risk-taking.

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With reference to voice and accountability, it is expected that a higher level of information availability through media independence can reduce bank cost and increases the performance of the overall industry. However, too much transparency may be harmful as well. Interestingly we found voice and accountability has a substantial negative impact on bank risk-taking. The coefficient is 0.14 for random effects and 0.07 for dynamic GMM at 1% and 5% significant level respectively. Similar to our findings, Horváth and Vaško (2016) found higher transparency about financial imbalances and accompanying risks escalated the crisis in the last economic downturn in 2007-2008. Nevertheless, further explorative studies are necessary to develop proper understanding behind this eccentric behavior. Although our study provides conclusive evidence of the impact of government effectiveness, corruption, rule of law, and voice and accountability on bank risk-taking we do not find any relationship between political stability and bank risk-taking in our initial model. The insignificant impact of political stability is partially caused by the inclusion of GDP in our model. Political stability ensures positive GDP growth of a country (Aisen and Veiga, 2013; Feng, 1997). To test the second order impact, we conducted an additional test removing GDP and find the evidence of a negative relationship between political stability and bank Z-score. At 1 percent level of significance, both the random effects model (0.41) and Dynamic GMM (0.28) showed that a stable political situation increases bank-risk taking behavior. This result is also consistent with the findings of Ashraf (2017), who found sound political institutions stimulate bank risk-taking behavior. This excessive risk-taking is the result of higher credit market competition and aggressive loan distribution during a sound political environment or a booming economy. Finally, the impact of regulatory quality only found significant in dynamic GMM model (0.09) at 5 percent level of significance. Although GMM is the most sophisticated model in our study because of the insignificant relation in pooled panel and random effect model, we are unable to make a strong claim about the

Journal Pre-proof existence of a substantial relationship between a country’s government ability to formulate and implement effective regulatory policy and the risk-taking behavior of the banks of that country. Our results clearly find that the overall impact of institutional development is positive for the stability of the banking sector. The possible implication of this outcome is that institutional quality can strengthen the bank regulation and supervision, efficient lending practices, lowering moral hazard, and improved loan repayment system.

5.3. Robustness Test

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Table 4 reports the result of the robustness test using σ(NIM) as a measure of bank risk. We conducted all three pooled OLS, random effects, and dynamic GMM for σ(NIM) and like table 3 the results are reported in column two, three, and four respectively. The rationale behind using σ(NIM) model is that it is a more direct measure of a bank’s lending risk. Through robustness analysis, σ(NIM) model provides additional support to the findings of our Z-score model. In bank-specific control variables σ(NIM) model also proves that loan loss provision increases bank risk whereas net non-interest income ratio decreases bank risk. The results are economically and statistically significant across models. However, like the ongoing controversy in established literature, we found conflicting result about the impact of bank size on bank risk-taking. Where the Z-score model (section 5.2) indicates bank-risk falls as bank size increases, the σ(NIM) model here shows the opposite. Following the findings of Saunders et al. (1990) and Iannotta et al. (2007) in σ(NIM) model, we found that bank risk increases with bank size. The result is consistent in all three analysis at 1% level of significance.

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Although at first, the results might seem inconsistent, our finding provides a useful reconciliation for this controversial topic. This seemingly contradictory result occurs mainly for two reasons: nondiversifiable risk and off-balance sheet activities. Large banks have more diversification opportunities. Through diversification large banks can stabilize their earnings over the years, resulting in a lower σ(NIM). Therefore, when we measure bank risk via σ(NIM), we can see larger banks have a lower risk. However, undiversifiable risk increases with bank size (Demsetz and Strahan, 1997). But σ(NIM) is unable to reflect those undiversified risk. Luckily those undiversified risk and their impact can be captured by σ(ROA). Another reason for this opposing result is banks’ increasing use of off-balance sheet activities and their associated risk (Angbazo, 1997). Today banks are involved in a number of highly risky off-balance sheet activities like securitized loans, operating lease, etc.. These off-balance sheet activities are a major source of revenue for large banks. But for small banks, the opportunities for involving in such off-balance sheet activities are limited (Hassan et al., 1994; Hou et al., 2014). As the name suggests, off-balance sheet items do not appear in the balance sheet, while σ(NIM) only considers net interest income from the balance sheet. As a result, σ(NIM) is unable to reflect these off-balance sheet activities and their associated risk. However, as a holistic measure, Z-score reflects both balance sheet and off-balance sheet items and their associated risk. Therefore, when we measure bank risk via Z-score, we can see bank-risk increases with size reflecting both undiversifiable risk and the risk associated with off-balance sheet activities. Table 4. Robustness Test Using σ(NIM)

Variables

Pooled OLS

Random Effects

Lag σ(NIM)

Dynamic GMM 0.660*** (0.193)

LTA

-0.132*** (0.033)

-0.182*** (0.059)

-0.123*** (0.031)

LLPTA

4.471*** (1.119)

2.25*** (0.804)

3.883** (1.681)

NITI

-0.127*** (0.020)

-0.071*** (0.010)

-0.077*** (0.032)

Bank Concentration

0.019* (0.010)

0.010* (0.005)

0.065*** (0.012)

Journal Pre-proof 0.814*** (0.287)

0.606*** (0.157)

0.340** (0.164)

Capital to Asset Ratio

0.177*** (0.035)

0.147*** (0.029)

0.082* (0.056)

Log GDP

1.153*** (0.159)

1.007*** (0.155)

0.153*** (0.021)

Inflation

0.036 (0.029)

0.019 (0.015)

0.071*** (0.022)

Real Interest

0.032*** (0.010)

0.023*** (0.007)

0.069*** (0.025)

Voice

1.835*** (0.168)

1.539*** (0.188)

0.280 (0.026)

Political Stability

0.135 (0.201)

0.587*** (0.198)

0.447*** (0.131)

Govt. Effectiveness

-0.875** (0.378)

-0.667*** (0.266)

-0.545** (0.272)

Regulatory Quality

-1.055** (0.491)

-0.662** (0.322)

-0.408 (0.492)

Control of Corruption

0.249 (0.599)

-0.757** (0.328)

-0.780** (0.361)

Rule of Law

-1.845*** (0.507)

-1.389*** (0.366)

-0.565*** (0.101)

Constant

-5.548*** (1.413)

-2.844* (1.725)

1.551 (1.482)

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0.000 0.318

Sargan test (p-value)

0.033

Hansen test (p-value)

0.108

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Values in parentheses indicate standard deviation. A higher value of σ(NIM) indicates increasing banks earnings volatility and therefore higher bank risk. ***, **, * denotes statistical significance at 1%, 5%, and 10% level, respectively.

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In country-level control variables σ(NIM) model corroborates the findings of the Z-score model for deposit insurance rate, GDP, and inflation -- further confirming our claim that explicit deposit insurance and fast-growing economy encourage banks to take higher risk. However, we observe interesting result about the impact of bank concentration and bank capital requirement ratio on bank-risk. Though using the Z-score model we were unable to find any relation between bank concentration and bank risk-taking, here in σ(NIM) model we found bank concentration has a positive impact on bank risk. Low profitability of smaller banks in concentrated banking industry might be a reason for this. The more direct relation of profitability with NIM makes this relation visible in σ(NIM) model. We also found contradictory evidence about the impact of capital to asset ratio and real interest rate. Unlike Z-score (section 5.2), here we found an increase in regulatory capital requirement or an increase in real interest rate increases bankrisk. The finding is justified, as net interest margin is more of an indicator of bank income variability rather than the capital requirement. As regulators impose more capital requirement, banks need to free up more assets to accommodate additional capital requirement -- resulting in less fund for loan and investment and less profit opportunity. Similarly, an increase in the real interest rate means an increase in both the discount rate and prime rate-- resulting in a higher cost of funds as well as fewer investment opportunities.

Journal Pre-proof The robustness test using σ(NIM) as a measure of bank risk strongly supports our original hypothesis- low institutional quality increases bank risk-taking behavior. In addition to supporting most of the findings of Z-score, σ(NIM) provides additional evidence about the effect of political stability and rule of law on bank risk. Previously in section 5.2, we were unable to make conclusive evidence of the effect of these two. Here, the random effects analysis supports that effective government system (-0.667), rule of law (-1.389), and control of corruption (-0.757) decrease bank risk. This finding is also true for GMM. The negative impact of voice and accountability also holds in robustness test in pooled panel estimation. Interestingly, unlike Z-score, we found evidence of the impact of both political stability and regulatory quality on bank risk-taking. In both pooled panel and dynamic GMM we found political stability is significant at 1 percent significant level. This finding is also consistent with the second order impact of political stability on Z-score when we remove GDP from the model. Finally, at 5 percent significant level, regulatory quality is also found significant in pooled panel analysis. The negative coefficient indicates that better regulatory quality increases the risk-taking approach of the banking industry of a country. However, no relation in dynamic GMM limits us to qualify our claim by some degree.

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Both Z-score and σ(NIM) are acceptable and widely used measures of bank risk. Z-score measures ROA and CAR in relation to the variability on ROA, whereas σ(NIM) measures earning variability. Some visible contradictory findings may be the result of the inherent difference between the two measures. However, these contradictions do not affect the broad reliability of our findings. From our original analysis and confirmed by the robustness test, we found the institutional quality of a country significantly affects the risk level of banks operating in that country. We found strong evidence that government effectiveness, control of corruption, and rule of law reduce bank risk. Further analysis proved better regulatory quality also helps reduce bank risk. Among good governance measures, political stability and voice and accountability somehow encourage bank risk. This is the result of aggressive business practice by banks during a stable political environment (Ashraf, 2017; Houston et al., 2010). Although voice and accountability have a different estimation, overall the study tends to suggest that better institutional quality influences banks in taking a lower risk. Institutional quality works as a check and balance for a country’s banking sector. Therefore, improving institutional quality can reduce bank risk, ensure stability in the banking sector and overall economy --- resulting in a more robust economy and a lower probability of financial crisis like GFC. 6. Conclusions and Policy Implications

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Bank-risk taking behavior is a well-studied phenomenon. The 2007–2009 GFC has challenged our conventional knowledge about bank risk-taking behavior and urged the importance of new research in this area. As a response to this newfound need, this study analyzed the post-GFC risk-taking behavior of 730 banks from 19 emerging countries. This study provides some significant insight into one relatively untouched area of banking literature, the effect of institutional quality on bank risk-taking behavior. From the established literature it is proven that bank-specific factors coupled with some industry and country level factors significantly affect a bank’s risk exposure at a given time. In addition to that, the effect of culture and institutional quality on a country’s social and economic development is also well established. This study tries to combine these two areas of literature and intends to find how institutional quality affects the behavior of micro-level financial institutions--- more precisely the risk-taking behavior of banks. The empirical analysis of this study supports most of the earlier literature in bank risk-taking. Bankspecific variables - total asset, loan loss provision to total asset ratio, and net income to total income- are found most important determinants of bank risk-taking. Our study also provides valuable insights to reconcile the ongoing dispute about the effect of bank size on bank risk-taking. Because of undiversified risk and off-balance sheet items, when risk is measured by Z-score, increasing bank size increases bank risk and when risk is measured by σ(NIM), increasing bank size reduces bank risk. Country-level variables like GDP, real interest rate, and explicit deposit insurance also significantly influence bank risktaking behavior. However, unlike other studies, we do not see any conclusive evidence to support the idea that, banking industry concentration affects bank risk-taking behavior of a country. While this study supports most of the established literature about bank risk-taking, the novel contribution of this study is the role played by the institutional quality. Our study found strong statistical

Journal Pre-proof and economic importance of most institutional quality variables in bank risk-taking decision. Both pooled panel and dynamic GMM analysis proved that government effectiveness, corruption, and the rule of law of a country significantly influence the risk-taking behavior of that country’s banks. The results of our original Z-score model were also mostly corroborated by a robustness test using a second measure of bank risk, σ(NIM). High corruption, ineffective government, and absence of law and order, encourage banks to engage in highly aggressive loan disbursement activity. Low institutional quality increases credit market competition, create adverse selection problems, and induce moral hazards in the banking sector. Corruption induced by bribery and unethical influence provide incentives for bankers to approve loans without much credit evaluation. Poor law enforcement and insufficient regulation reduce the cost of default for debtors. Insufficient legal actions also provide bankers and debtors jail-free pass by assuring the ways to get out from fraud charges. It also creates the expectation of government bailouts. Therefore, in countries with low institutional qualities, bankers are more prone to accepting a high and sometimes unjustified risk.

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The findings of this study have significant implications for the governments, regulators as well as individual banks. At first, regulators and banks need to recognize the impact of political institutions on banks operating activities. Initiatives should be taken to detach banks’ decision-making system from the influence of the country’s institutional culture. Given the interdependency of a country’s culture, socioeconomic structure, institutional quality, and banks’ operating environment, this won’t be easy. Most emerging countries suffer from widespread corruption and political instability. Corruption also leads to low regulatory quality and poor law enforcement system. Therefore, the most implementable strategy will be following a more robust risk measurement criteria for countries with weak institutional quality. The regulatory authorities especially central bank needs to play the most crucial role. They need to recognize the differences of operating environments between developed and emerging countries and modify the international requirement’s (i.e., BASEL requirement’s, CAMEL rating) as necessary and where appropriate develop their own risk measures and penalty procedures. Secondly, ensuring the rule of law and regulatory quality government can not only improve the risk-taking behavior of banks but also provide stability and integrity to the overall economy. Explicit deposit insurance initially developed to safeguard the depositors and the economy during a financial crisis, proved to induce moral hazards. Therefore, higher capital requirement or regulation to control bank risk-taking would be a better solution than deposit insurance. Finally, bank as an institution also needs to come forward to maintain a balanced risk profile. Identifying the risk tolerance level, prioritizing stability over profitability, and aligning the idea of wealth maximization in the corporate mission can reduce a bank’s overall risk. Effective Internal control system and periodic internal audit can work as a check and balance that governance system in the first place failed to provide. It will ensure accountability and responsibility for the bankers’ activities.

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Although this study found a significant and substantial impact of institutional quality on bank-risk taking, the first-order effect of political stability is not conclusive. Additional study is required to explain the second-order effect we evidenced in our study. In addition, the positive impact of voice and accountability on increasing bank risk also warrants further investigation. This study only considers 19 emerging countries; therefore, the next step will be testing whether these findings also hold for other developing and even developed countries.

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addresses the impact of institutional quality on post-GFC bank risk-taking behavior both static panel and Dynamic GMM methods are used increased government effectiveness, rule of law, controlling corruption reduce bank risk robustness test using σ(NIM) mostly supports the Z-score model both models indicate improving voice and accountability enhance bank risk-taking

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