How does institutional development shape bank risk-taking incentives in the context of financial openness?

Pacific-Basin Finance Journal 58 (2019) 101209

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How does institutional development shape bank risk-taking incentives in the context of financial openness? ⁎

Duy Tung Bui , Thi Mai Hoai Bui

T

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University of Economics Ho Chi Minh City, School of Public Finance, 279 Nguyen Tri Phuong, Ward 5, District 10, Ho Chi Minh City, Viet Nam

A R T IC LE I N F O

ABS TRA CT

JEL codes: G21 F36 C23

This paper investigates the role of institutional development on the relationship between financial openness and bank risk-taking behavior. In particular, we investigate how institutional improvement can change the impact of financial openness on bank stability. Using a panel of 37 emerging markets and 21 advanced economies over the period 2000–2015, the results show that opening the financial market affects bank risk-taking behaviors differently, depending on the degree of institutional development. Empirical evidence also supports the competition-fragility view in the case of developing countries, while it favors the competition-stability hypothesis in developed economies.

Keywords: Financial openness Institutional development Bank risk-taking

1. Introduction Financial openness is still a controversial topic, particularly in emerging markets. These countries are in the progress of setting up a sufficient legal framework to lower barriers and make their financial markets more attractive to global investment (Das, 2004). For example, removing financial barriers imposed on lending markets allows banks to diversify their portfolio significantly and substantially reduces their exposure to risk. However, financial markets' authorities and supervisors would have more difficulties in supervising the banking systems because of unconventionally diversified operations, while regulations and institutions have not yet been improved. As a consequence, financial openness may increase the financial system's risks and incentives for taking risks, as argued by the competition- fragility hypothesis (Hellmann et al., 2000; Repullo, 2004). On the contrary, previous literature also shows that financial openness can reduce bank risks, as improved regulation quality on financial disclosure and transparency could prevent the moral hazard problem (Fang et al., 2014). Hence, the relationship between financial openness and bank risk-taking behavior is still unclear. Furthermore, previous literature has shown that bank behaviors change according to the levels of institutional development (Haselmann, 2006). Thus, reforming the banking systems in emerging economies can have different results, in term of quantity and quality, with respect to their developed counterparts. In this paper, we argue that financial openness can force domestic banks in developing countries to behave more prudently. However, this positive effect can only happen with reliable legal systems, market discipline, and transparency. In other words, the impact of financial openness on bank risk-taking behavior depends on the development of institutions. The paper contributes to the existing literature in the following aspects. We show how institutional factors affect the linkage between financial openness and bank risk-taking behavior. For example, opening the domestic financial market can reduce overall bank risks through diversification. However, diversifying investment portfolios can be complicated in weak institutions since certain

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Corresponding authors. E-mail addresses: [email protected] (D.T. Bui), [email protected] (T.M.H. Bui).

https://doi.org/10.1016/j.pacfin.2019.101209 Received 20 March 2019; Received in revised form 18 September 2019; Accepted 18 September 2019 Available online 30 October 2019 0927-538X/ © 2019 Elsevier B.V. All rights reserved.

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powerful political forces can redirect the funds into their projects. Moreover, we highlight the importance of controlling for the endogenous determination between financial stability and bank risk. Last but not least, our findings also have substantial implications for both developing and advanced countries. The empirical results imply that these countries should put more effort into opening the financial market. Capital account openness in developing countries is at very low levels; thus these markets may not yet benefit the positive effect of globalization. On the other hand, governments in advanced markets should be aware of the moral hazard problem. The results imply that this issue can be more severe with high institutional development. The paper relates to the existing empirical literature that investigates the effect of institutions, regulations, supervision (see Agoraki et al., 2011; Beck et al., 2013; Chen et al., 2015; Fang et al., 2014; Park, 2012 among many others), and economic openness (for example, Ashraf, 2018; Ashraf et al., 2017; Berger et al., 2009; Cubillas and González, 2014) on bank risk-taking behavior. Similar to the previous study, we first examine the effect of institutions and financial openness on bank stability separately. Then, filling a gap in the previous literature, we allow the interactions between financial openness and various indicators of institutional development. These interaction terms will explain how the impact of financial openness on bank stability changes with respect to the improvement of institutions. Furthermore, we investigate the underlying relationships with and without controlling for the competition channel. The rest of the paper is organized as follows. Section 2 provides a brief review of the literature on the issue and develops the primary hypotheses. Section 3 introduces the data and variables. Section 4 presents the empirical models and discussion of the results. Section 5 concludes the study. 2. Hypothesis development In the previous literature, there are different views explaining the impact of financial openness on bank risk, for instance, the “competition-fragility” hypothesis (Hellmann et al., 2000; Repullo, 2004), the “competition-stability” hypothesis (Boyd and Nicoló, 2005), the “diversification hypothesis”, the market risk hypothesis (Berger et al., 2017). According to these points of view, financial openness can have a positive or negative impact on bank risk. On the one hand, opening the financial sector can induce competition in the banking systems. In a highly competitive market, banks compete to gain as much market shares as possible and thus, become less prudent (“competition-fragility”). On the other hand, financial openness can help reduce bank risks through the “competitionstability” view, which is opposite to the previous “competition-fragility” hypothesis. Moreover, institutions or regulatory framework can influence the effect of financial openness on bank risk-taking. This intertwined relationship has not been examined before, but several theoretical underpinnings in the literature support interaction between openness and institutional development. According to the “competition-fragility” view, when the financial market becomes more open, the level of competition would increase. Eventually, banks operating in highly competitive markets would have their profit margins, as well as their charter values decreased. As a result, banks compete with each other to give out more loans to offset the lesser profit margins. This hypothesis argues that banks tend to lower their credit standards to get more customer and gain as much market shares as possible, thus reducing the portfolio quality in the process. Furthermore, the probability that a non-performing loans crisis occurs increases with the level of financial openness, as income volatility and uncertainty are more probable in highly open markets (Ashraf et al., 2017). Besides, the removal of financial barriers allows banks to expand their operations to international markets and take part in unusual activities. These foreign operations can become a source of risks, depending on the expertise of domestic banks in such markets and activities (Cubillas and González, 2014). This argument is in line with the market risk hypothesis (Berger et al., 2017). International banks are vulnerable to negative shocks generated by risky foreign assets. Because different market fundamentals can cause this type of risk, it can only be mitigated through extreme diversification. On the contrary, other studies argue that financial openness can help stabilize the banking system (Berger et al., 2017; Boyd and Nicoló, 2005). The first argument relies on the fact that opening the financial market allows more foreign capitals to flow into the domestic market. Thus, the banking system can take advantage of these funds to increase their liquidity and diversify their investments into various projects. This line of reasoning is similar to the “diversification hypothesis” in the study of Berger et al. (2017). Multinational banks can become less susceptible to risk by diversifying their investments. The level of diversification depends on the correlations among cross-country assets. On top of that, the “competition-stability” view challenges the “competition-fragility” hypothesis by arguing that high concentration creates more risks to the banking system. This is because banks with dominant market power tend to charge their clients with a high interest rate. The customers thus have more incentives to seek riskier investments to offset the high interest. As a result, low bank market power lessens the pressure on the interest rates and reduces the moral hazard problem of customers. However, there is evidence that these two main streams are not necessarily mutually exclusive. In other words, a nonlinear relationship may exist. On the one hand, La Porta et al. (1998) suggest that institutions, such as contracts and legal enforcement, help the financial market to function well. On the other hand, weak legal frameworks and poor-quality institutions prevent the financial markets from functioning at its potential level (Levine et al., 2000). This point of view is in line with the sand the wheel effect of corruption. Corruption can increase the cost of lending, which discourages firms from seeking bank loans and makes borrowers more susceptible to default risk. Firms in developing countries consider corruption as the main hindrance in accessing bank credit. However, corruption can also have an opposite effect, which is called the grease the wheel effect. In this line of thought, corruption may act as a catalyst for getting bank loans because larger firms can pay higher bribes and thus, they can access bank credits easier (Chen et al., 2015). Thus, we argue that institutional development affects how well financial markets function, thus influence the link between financial openness and bank risk. Furthermore, financial openness requires emerging market governments to carry out reforms in domestic regulations, such as 2

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improving the law and its enforcement. For instance, according to “competition-fragility” view, financial openness increases bank risktaking because it induces banks to seek more customer to compensate for the lower profit margins. We can reason that with a poorquality institution, or when the level of corruption is high, banks can easily find more customers through bribery and political influence. Besides, bank managers are more likely to accept bribery to compensate for their lower salary in highly competitive markets. Thus, we can expect that risk-taking behavior is more severe with high corruption. A weak regulatory framework can be a source of bank risk-taking due to low financial transparency and bank governance (Fang et al., 2014). In the context of financial openness, the problem generated by weak regulations would also be relevant because foreign banks, when operating in less rigorous supervisory systems than their home country, are more likely to lend with lower credit standards (Ongena et al., 2013). Furthermore, in an open financial system with strong regulation and legal system, banks can operate at their full potential and would have more chances to implement economies of scale and scope. Intuitively, financial institutions would shrink their operations in countries where regulation is weak, and property rights are not guaranteed. Thus, as long as the legal system is qualified, banks can diversify their income flows and become less vulnerable to risk. On the contrary, with low institutional development, the diversification possibilities are much more narrow. For instance, it is common in the literature that domestic banks can diversify their investment portfolios in multiple cross-border markets when the financial system is highly integrated into the international market (Ashraf et al., 2017; Fang et al., 2014). However, the mechanism may work differently, depending on institutional development. Previous studies have shown that, in a weak regulatory framework, powerful political forces can redirect the available funds in the banking system to their projects (Bui, 2018). This phenomenon may be the source of financial instability and poor investment quality (Aizenman et al., 2015). First, it reduces the diversification capacities of domestic banks since the funds are more clustered. Second, there would be fewer funds for the domestic market, which leads to a higher interest rate and more severe moral hazard problem and hence, more risk-taking behavior. As a consequence, we can postulate our hypothesis: Main hypothesis: Financial openness increases bank risk-taking in poor-quality institutions; however, this effect is mitigated through the improvement of institutions. 3. Data and variables 3.1. Bank risk-taking behavior We measure our primary dependent variable, bank risk-taking behavior, using the bank Z-score. This score is defined as the ratio between the average return on assets plus the capital asset ratio and the standard deviation of return on assets.

Z‐score =

ROA + CAR σ (ROA)

where ROA is the average annual return on assets, CAR is the ratio of a bank's capital by its total assets. σ(ROA) is the standard deviation of ROA. The Z-score indicates the probability of default of a banking system. Banks with higher Z-scores would be considered more stable, compared to those with lower scores. This is because the Z-score measures the capability of a banking system (capitalization and net income) with respect to the corresponding risk (fluctuations in incomes). Z-score has an advantage over other proxies of bank risk since its range lies in the domain of real numbers (Lepetit and Strobel, 2015). This variable can be frequently seen in recent studies (Ashraf, 2018; Ashraf et al., 2017; Berger et al., 2017). 3.2. Financial openness We use the Chinn-Ito Index (or KAOPEN), a de jure measure of financial openness, developed by Chinn and Ito (2006, 2008). This index measures a country's degree of capital account openness using the IMF's Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER). The authors codify some restrictions on cross-border financial transactions (in particular, capital account transactions, current account transactions, the existence of multiple exchange rates, and the regulations on the surrender of export proceeds) and construct the KAOPEN index from the dummy variables representing these restrictions. To be more specific, they build the index from the first standardized principal component of the codified dummies. The higher the value of the index, the more integrated the country is into cross-border capital transactions. 3.3. Institutional development The Worldwide Governance Indicators build six aggregate measures on the traditions and institutions of a country. They take into account how governments are selected, monitored and replaced; the extent that a government can formulate and implement sound policies; the perception of the population and the institutions that influence economic and social relationships (Kaufmann et al., 2011). The database includes six indicators: Government effectiveness, Regulatory quality, Voice and accountability, Political stability, Rule of law, and Control of corruption. Each indicator's value ranges from −2.5 to 2.5; higher scores mean better institutional development. Table 1 provides a detailed description of these variables. Besides, Fig. 1 visualizes six indicators of institutional development with respect to the bank's Z-score. Overall, there are two distinct clusters1: One for the developing markets and one for the developed countries. In general, advanced markets achieve high scores for institutional development and find themselves clustered at the right part of the graph. On the other hand, institutional development in developing economies is more divergent, ranging 3

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Table 1 Definition of institutions variables. Source: Kaufmann et al. (2011) Name

Meaning

Value

Government effectiveness

The quality of public services, the quality of the civil service and the degree of its independence from political pressures, the credibility of the government's commitment to such policies The extent that a government can formulate and implement sound policies and regulations that permit and promote private sector development The quality of contract enforcement, property rights, the police, and the courts, the likelihood of crime and violence The extent that public authority can have private gain, both petty and grand bribery The ability of the citizens to vote and select their government, freedom of expression, freedom of association and free media The likelihood that the government will be destabilized by unconstitutional or violent uprising

[−2.5,2.5]

4 3 0

0

1

1

Z-score 2

Z-score 2

3

4

Z-score and Institutions

-1

0 1 2 Government Effectiveness, L

-2

Advanced Market

-1 0 1 Regulatory Quality, L Dev. Market

2

Advanced Market

3 0

0

1

1

Z-score 2

Z-score 2

3

4

4

Dev. Market

3

-2

-1

0 1 Rule of Law, L

-2

-1 0 1 Voice and Accountability, L Dev. Market

2

Advanced Market

3 0

1

Z-score 2

3

4

Advanced Market

4

Dev. Market

2

Z-score 2

Political stability

1

Rule of law Control of corruption Voice and accountability

0

Regulatory quality

-3

-2

-1 0 1 Political Stability, L

Dev. Market

2

-2

Advanced Market

-1 0 1 2 Control of Corruption, L Dev. Market

Fig. 1. Z-score and institutions.

4

3

Advanced Market

[−2.5,2.5] [−2.5,2.5] [−2.5,2.5] [−2.5,2.5] [−2.5,2.5]

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Table 2 Summarized statistics. Mean

Z-score Financial openness Liquid assets Bank capital to assets Bank return on asset GDP growth Bank concentration Lerner Index Bank assets Financial Freedom Index Government Effectiveness Regulatory Quality Rule of Law Voice and Accountability Political Stability Control of Corruption

S.D

Min

All

Adv

Dev

All

Adv

Dev

All

Adv

11.60 1.04 0.32 9.11 1.02 0. 04 0.63 0.18 0.77 58.59 0.60 0.58 0. 44 0. 42 0. 09 0. 45

14.37 2.29 0.36 6.30 0.54 0.02 0.73 0.17 1.24 71.70 1.65 1.49 1.57 1.22 0.81 1.69

10.03 0.29 0.30 10.74 1.30 0.04 0.57 0.18 0.51 51.06 0.00 0.05 −0.20 −0.03 −0.31 −0.25

6.27 1.48 0. 16 3.96 1.24 0. 04 0. 19 1.59 0. 49 18.56 0.96 0.92 1.04 0. 89 0.95 1.09

6.11 0.27 0.18 2.10 0.61 0.02 0.18 0.71 0.40 14.30 0.42 0.33 0.39 0.42 0.61 0.54

5.80 1.41 0.15 3.87 1.41 0.04 0.16 1.93 0.30 16.41 0.61 0.71 0.69 0.75 0.86 0.59

1.40 −1.91 0.05 1.49 −6.70 −0.15 0.20 −44.64 0.06 10.00 −1.23 −1.88 −2.03 −1.77 −2.81 −1.43

3.50 1.07 0. 07 2.70 −3.48 −0.08 0.21 −8.66 0. 44 30.00 0.20 0.53 0.28 −0.39 −1.63 −0.03

Max Dev

All

1.40 40.04 −1.91 2.36 0.05 1.25 1.49 30.50 −6.70 7.88 −0.15 0.34 0.20 1.00 −44.64 1.08 0.06 2.57 10.00 90.00 −1.23 2.44 −1.88 2.26 −2.03 2.10 −1.77 1.80 −2.81 1.76 −1.43 2.47

Adv

Dev

37.14 2.36 1.25 13.60 2.47 0. 15 1.00 1.08 2.57 90.00 2.44 2.26 2.10 1.80 1.76 2.47

40.04 2.36 0. 88 30.50 7. 88 0. 34 1.00 0. 72 1.53 90.00 1.28 1.54 1.43 1.29 1.12 1.59

Note: Adv, Dev denotes advanced countries and developing countries, respectively. Source: Author's calculation.

from negative scores to positive middle scores.

3.4. Other control variables We also introduce a set of control variables to capture both banks specific factors and banking industry preference, as well as macroeconomic indicators. Bank-level control variables included in the model are bank liquid assets (The ratio of the value of liquid assets (easily converted to cash) to short-term funding plus total deposits), bank equity to total asset, bank return on assets after tax, and total bank assets. We use the assets of the three largest commercial banks as a share of total commercial banking assets as a measurement for banking concentration, a control variable for the banking industry of a country. For macroeconomic indicators, we use the GDP growth rate.

3.5. Data The data in this study is taken from various sources. All the banking data are collected from the Global Financial Development (GFD) database of the World Bank. The GFD calculates these variables from underlying unconsolidated banking-level data from Bankscope. We collect institutional data from the Worldwide Governance Indicators database. For financial openness index, data is compiled from Chinn and Ito (2008). We consider a panel of 58 countries from 2000 to 2014. Our panel consists of 37 emerging and developing countries: Argentina, Armenia, Belarus, Bolivia, Brazil, Bulgaria, Chile, China, Colombia, Costa Rica, Croatia, Czech Republic, Ecuador, Egypt, Georgia, Ghana, India, Indonesia, Kazakhstan, Korea, Latvia, Lithuania, Malaysia, Mexico, Nigeria, Oman, Pakistan, Peru, the Philippines, Poland, Romania, Russia, Serbia, South Africa, and Thailand, Ukraine, Venezuela; and 21 advanced economies: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Hong Kong SAR, Israel, Italy, Japan, the Netherlands, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the United Kingdom, and the United States. Table 2 reports the summary statistics of the variables used in the study. For each statistics, we report the value for the whole panel, the panel of advanced economies and the panel of developing countries, respectively. There are differences between developed and developing markets. For instance, the mean of Z-score in advanced economies (14.37) is higher than those of the developing world (10.03). The mean of KAOPEN index in industrial economies (2.29) is nearly eight times the mean of developing countries (0.29). Likewise, all of the measures of institutional development in high-income economies are larger than those of middle-income and low-income markets. However, emerging and developing countries have enormous potential for development. For instance, the group's average GDP growth in the period is double that of advanced markets. Similar patterns can be observed for bank return on asset. Table 3 shows the correlation matrix between variables. There is a positive correlation between bank Z-score and financial openness. Likewise, institutional development and bank Z-score are also positively associated.

1

For the Political stability indicator; the two clusters are not distinctly separate. 5

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Table 3 Correlation matrix. zscore

zscore Financial openness Liquid assets Bank capital to assets Bank return on asset GDP growth Bank concentration Total asset

1.00 0.24⁎⁎⁎ −0.06⁎ −0.11⁎⁎⁎ 0.04 0.01 0.08⁎⁎ 0.21⁎⁎⁎

zscore

zscore Government Effectiveness Regulatory Quality Rule of Law Voice and Accountability Political Stability Control of Corruption

1.00 0.25⁎⁎⁎ 0.23⁎⁎⁎ 0.22⁎⁎⁎ 0.06⁎ 0.01 0.24⁎⁎⁎

Financial openness

1.00 0.21⁎⁎⁎ −0.33⁎⁎⁎ −0.24⁎⁎⁎ −0.26⁎⁎⁎ 0.36⁎⁎⁎ 0.45⁎⁎⁎

Liquid assets

1.00 −0.00 0.11⁎⁎⁎ 0.02 0.18⁎⁎⁎ 0.01

Bank capital to assets

Bank return on asset

1.00 0.34⁎⁎⁎ 0.23⁎⁎⁎ −0.26⁎⁎⁎ −0.57⁎⁎⁎

1.00 0.41⁎⁎⁎ −0.09⁎⁎⁎ −0.38⁎⁎⁎

Government effectiveness

Regulatory quality

1.00 0.94⁎⁎⁎ 0.97⁎⁎⁎ 0.78⁎⁎⁎ 0.74⁎⁎⁎ 0.96⁎⁎⁎

1.00 0.95⁎⁎⁎ 0.79⁎⁎⁎ 0.73⁎⁎⁎ 0.92⁎⁎⁎

Rule of law

1.00 0.82⁎⁎⁎ 0.77⁎⁎⁎ 0.96⁎⁎⁎

GDP growth

1.00 −0.08⁎⁎ −0.33⁎⁎⁎

Bank concentration

1.00 0.30⁎⁎⁎

Total asset

1.00

Voice and accountability

Political stability

Control of corruption

1.00 0.64⁎⁎⁎ 0.80⁎⁎⁎

1.00 0.76⁎⁎⁎

1.00

t statistics in parentheses. ⁎ p < 0.10. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01.

4. Empirical analysis 4.1. Baseline estimations First, we estimate a baseline linear model to examine the effects of financial openness and institutional development on bank risktaking, proxied by the Z-score. The empirical model is written as: m

Z‐SCOREit = β0 + γt + μi +

∑ βj Xj,it + α1 KAOPENit + α2 INSTIit + ϵit j=1

(1)

Our dependent variable Z-score has high skewness (Ashraf, 2018). As a consequence, we take the natural logarithm of Z-score as our dependent variable. The first coefficient of interest is α1, which is the marginal effect of financial openness on Z-score. Following previous literature, we use the first lag since these measures are reported the situation at the end of a year. The second coefficient of interest is α2, which captures the impact of institutional development on bank risk-taking. Similarly, we consider its first lag in the model. Furthermore, our model considers both country fixed effect (μi) and time effect (γt), as well as a variety of macroeconomic, bank industry, and bank-specific factors in the vector X. The linear panel model is estimated using feasible generalized least squared, which controls for the presence of AR(1) correlation within panels and heteroskedasticity across panels. In particular, the AR(1) correlation coefficient within panels can be unique. In this section, we do not allow for interaction between financial openness and institutional development. The estimation results of Eq. (1) for all the countries are reported in Table 4. The general empirical finding is that better institutional development leads to more bank stability in our data. In particular, three over six indicators of institutional development show a significant positive impact on bank stability. The results are consistent with previous studies (Chen et al., 2015). For instance, the findings are in line with the sand the wheel hypothesis of corruption, rather than the grease the wheel effect. In countries with a high corruption level, banks are more exposed to risk because the business environment is considered riskier in those markets. In this line of argument, firms with strong political connections can access banking credit more easily, but their probability of default is also higher (Khwaja and Mian, 2005). In another study, Park (2012) concludes that countries with higher corruption are faced with the increasing non-performing loans problems. Government effectiveness quantifies the level of independence from political pressures of an economy. With higher effectiveness, there is less likely that available funds will be redirected to private investment projects of powerful political forces. Therefore, banks can have more opportunities to diversify their portfolios and reduce risk. The voice and accountability indicator measures the extent of freedom of expression, freedom of association, and free media. According to the baseline results, it contributes positively to banking system stability. This finding implies that asymmetric information is less severe in a country where information censorship is minimal. As a consequence, banks will have more information to choose their investments and control risk. In general, these empirical results imply that the stability of banking systems decreases as the institutional risk increases. From the baseline regressions, the impacts of financial openness on bank stability are significantly positive. The results are in line with Berger et al. (2017). Opening the domestic financial market allows more funds to flow into the country. According to the 6

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Table 4 Baseline linear estimation with financial openness and institutional variables, dependent variable: Logarithm of Z-score.

L.Liquid assets L.Bank capital to assets L.Bank return on asset L.GDP growth L.Bank concentration L.Total asset L.Financial openness L.Government Effectiveness

(1)

(2)

(3)

(4)

(5)

(6)

Z-score

Z-score

Z-score

Z-score

Z-score

Z-score

0.354⁎⁎⁎ (4.06) 0.072⁎⁎⁎ (15.39) 0.023⁎⁎ (2.28) 0.985⁎⁎⁎ (3.30) 0.011⁎⁎⁎ (11.97) 0.693⁎⁎⁎ (11.07) 0.034⁎⁎ (2.40) 0.082⁎⁎ (2.42)

0.345⁎⁎⁎ (3.91) 0.070⁎⁎⁎ (15.17) 0.020⁎⁎ (2.01) 1.027⁎⁎⁎ (3.40) 0.011⁎⁎⁎ (12.83) 0.721⁎⁎⁎ (11.81) 0.035⁎⁎ (2.16)

0.329⁎⁎⁎ (3.70) 0.070⁎⁎⁎ (14.29) 0.021⁎⁎ (2.08) 1.046⁎⁎⁎ (3.48) 0.012⁎⁎⁎ (12.90) 0.730⁎⁎⁎ (11.79) 0.046⁎⁎⁎ (2.98)

0.346⁎⁎⁎ (4.01) 0.072⁎⁎⁎ (15.99) 0.022⁎⁎ (2.23) 0.983⁎⁎⁎ (3.31) 0.011⁎⁎⁎ (11.85) 0.714⁎⁎⁎ (12.33) 0.033⁎⁎ (2.39)

0.289⁎⁎⁎ (3.07) 0.063⁎⁎⁎ (12.54) 0.022⁎⁎ (2.09) 1.380⁎⁎⁎ (4.52) 0.013⁎⁎⁎ (15.26) 0.686⁎⁎⁎ (14.31) 0.117⁎⁎⁎ (7.75)

0.332⁎⁎⁎ (3.77) 0.074⁎⁎⁎ (15.14) 0.019⁎ (1.86) 0.981⁎⁎⁎ (3.30) 0.011⁎⁎⁎ (12.28) 0.701⁎⁎⁎ (11.67) 0.038⁎⁎⁎ (2.59)

L.Regulatory Quality

0.041 (1.35)

L.Rule of Law

0.017 (0.55) 0.113⁎⁎⁎ (3.64)

L.Voice and Accountability

−0.205⁎⁎⁎ (−7.98)

L.Political Stability

0.063⁎⁎ (2.37)

L.Control of Corruption Chi-squared Observations

13,680.592 695

13,048.960 695

12,612.310 695

14,218.264 695

12,138.369 695

13,267.997 695

t statistics in parentheses. ⁎ p < 0.10. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01.

diversification hypothesis, banks can take this opportunity to increase their liquidity and diversify their portfolios. In the next section, we will further examine the impact of financial openness on financial stability using different techniques. For macroeconomic factors, GDP growth can contribute to less bank risk-taking, which is consistent with previous literature (Agoraki et al., 2011; Chen et al., 2015; Fang et al., 2014). Economic expansion may bring more profitable opportunities to banks and increase their retained earnings, which, in turn, boost bank equity. For bank-level determinants, the estimated coefficients of bank liquid assets are significantly positive in all specifications, which implies that higher liquidity is associated with stronger bank stability. This result is in line with the liquidity requirement of the Basel (Chen et al., 2015). Moreover, previous studies show that bank default and contagion could originate from a low aggregate liquidity level (Diamond and Rajan, 2005). Higher bank capital also implies less bank risk-taking, since it increases bank buffer against income volatility. Bank size contributes positively to bank stability. Larger banks have higher market power and better-diversified portfolios than smaller banks and thus can have fewer risks. Besides, under Basel II larger banks can have a competitive advantage over smaller banks and push them to take more risks (Hakenes and Schnabel, 2011). For bank-industry control variable, we find significant positive coefficients of bank concentration across six specifications. This result implies that higher bank concentration, or less competition, leads to less bank risk-taking. Therefore, it is consistent with the competition-fragility view (Hellmann et al., 2000; Repullo, 2004), rather than the competition-stability hypothesis (Boyd and Nicoló, 2005). 4.2. Interaction between financial openness and institutional development In this section, we further investigate the impact of financial openness and institutional development by adding an interaction term. We argue that the impact of financial openness on bank risk-taking may depend on the degree of institutional development. For instance, financial openness can reduce overall bank risk through diversification. Domestic banks can invest their funds in other cross-border markets. However, the extent of diversification may be different across countries, depending on their institutions. For example, in countries with poor institutional development, certain powerful political forces can redirect the funds into their investment projects, which narrows the diversification opportunities for banks. To this end, we modify Eq. (1) by adding an interaction term between financial openness and institutions: 7

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Table 5 Baseline nonlinear estimation with interaction between financial openness and institutional variables, dependent variable: Logarithm of Z-score.

L.Liquid assets L.Bank capital to assets L.Bank return on asset L.GDP growth L.Bank concentration L.Total asset L.Financial openness L.Government Effectiveness L.Financial openness × L.Government Effectiveness

(1)

(2)

(3)

(4)

(5)

(6)

Z-score

Z-score

Z-score

Z-score

Z-score

Z-score

0.366⁎⁎⁎ (4.16) 0.073⁎⁎⁎ (15.39) 0.024⁎⁎ (2.41) 1.032⁎⁎⁎ (3.40) 0.011⁎⁎⁎ (11.92) 0.684⁎⁎⁎ (10.95) 0.042⁎⁎⁎ (2.97) 0.102⁎⁎ (2.53) −0.014 (−0.84)

0.339⁎⁎⁎ (3.83) 0.071⁎⁎⁎ (15.09) 0.019⁎ (1.86) 1.104⁎⁎⁎ (3.59) 0.011⁎⁎⁎ (12.42) 0.718⁎⁎⁎ (11.71) 0.036⁎⁎ (2.23)

0.307⁎⁎⁎ (3.43) 0.070⁎⁎⁎ (14.30) 0.020⁎⁎ (1.99) 1.073⁎⁎⁎ (3.57) 0.011⁎⁎⁎ (12.44) 0.716⁎⁎⁎ (11.44) 0.053⁎⁎⁎ (3.33)

0.341⁎⁎⁎ (3.93) 0.073⁎⁎⁎ (15.92) 0.024⁎⁎ (2.48) 0.979⁎⁎⁎ (3.26) 0.011⁎⁎⁎ (11.93) 0.709⁎⁎⁎ (11.78) 0.034⁎⁎ (2.40)

0.264⁎⁎⁎ (2.85) 0.064⁎⁎⁎ (12.94) 0.026⁎⁎ (2.47) 1.336⁎⁎⁎ (4.42) 0.012⁎⁎⁎ (13.31) 0.697⁎⁎⁎ (13.86) 0.140⁎⁎⁎ (8.42)

0.311⁎⁎⁎ (3.51) 0.074⁎⁎⁎ (15.04) 0.017⁎ (1.66) 0.992⁎⁎⁎ (3.34) 0.011⁎⁎⁎ (11.85) 0.690⁎⁎⁎ (11.30) 0.042⁎⁎⁎ (2.74)

L.Regulatory Quality

0.033 (0.97) 0.012 (0.71)

L.Financial openness × L.Regulatory Quality

−0.032 (−0.83) 0.041⁎⁎ (2.17)

L.Rule of Law L.Financial openness × L.Rule of Law

0.090⁎⁎ (2.43) 0.010 (0.48)

L.Voice and Accountability L.Financial openness × L.Voice and Accountability

−0.232⁎⁎⁎ (−8.34) 0.068⁎⁎⁎ (4.74)

L.Political Stability L.Financial openness × L.Political Stability L.Control of Corruption

0.008 (0.21) 0.037⁎ (1.89)

L.Financial openness × L.Control of Corruption Chi-squared Observations

13,688.615 695

12,501.633 695

12,578.737 695

14,880.941 695

11,752.725 695

12,764.182 695

t statistics in parentheses. ⁎ p < 0.10. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01. m

Z‐SCOREit = β0 + γt + μi +

∑ βj Xj,it + α1 KAOPENit + α2 INSTIit j=1

+ α3 KAOPENit × INSTIit + ϵit

(2)

In this model, the coefficient of interest is α3, which measures how the impact of financial openness on bank stability changes with respect to the institutional variable. Table 5 reports the estimation results of Eq. (2). Overall, the impact and significance of the control variables remain robust and consistent with previous results. For the impact of institutional development on bank risk-taking, we also observe similar results for Government Effectiveness and Voice and Accountability measures, in which better institution is related to higher bank stability. The coefficients of the financial openness variable are significant across all models. The estimated coefficient of the interaction term between financial openness and political stability is positive, which implies that the impact of financial openness on bank stability would increase as the level of political stability improves. Likewise, improvements in the rule of law and control of corruption contribute positively to the effect of financial openness on bank stability. The results are not anticipated since we expect a consistently significant estimate of α3. As previously discussed, we believe that financial openness can affect bank risk-taking via several transmission channels. Furthermore, our results strongly support the competition-fragility hypothesis with statistically significant coefficients across all specification. This finding implies that bank 8

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Table 6 First-stage regression of (2), dependent variable: Bank concentration.

L.Liquid assets L.Bank capital to assets L.Bank return on asset L.GDP growth L.Total asset L.Financial openness L.Government Effectiveness

(1)

(2)

(3)

(4)

(5)

(6)

0.205⁎⁎⁎ (8.29) 0.031⁎⁎⁎ (25.30) 0.003 (1.06) 0.234⁎⁎ (2.57) 0.225⁎⁎⁎ (12.84) 0.042⁎⁎⁎ (7.83) 0.080⁎⁎⁎ (6.87)

0.210⁎⁎⁎ (8.22) 0.029⁎⁎⁎ (24.17) 0.004 (1.22) 0.233⁎⁎⁎ (2.61) 0.288⁎⁎⁎ (18.57) 0.047⁎⁎⁎ (8.54)

0.220⁎⁎⁎ (8.84) 0.031⁎⁎⁎ (24.45) 0.004 (1.34) 0.222⁎⁎ (2.45) 0.237⁎⁎⁎ (13.37) 0.043⁎⁎⁎ (7.67)

0.204⁎⁎⁎ (8.18) 0.029⁎⁎⁎ (25.21) 0.003 (0.90) 0.213⁎⁎ (2.44) 0.265⁎⁎⁎ (16.77) 0.046⁎⁎⁎ (8.54)

0.215⁎⁎⁎ (8.52) 0.031⁎⁎⁎ (27.43) 0.002 (0.76) 0.249⁎⁎⁎ (2.70) 0.281⁎⁎⁎ (22.45) 0.047⁎⁎⁎ (9.65)

0.202⁎⁎⁎ (8.14) 0.032⁎⁎⁎ (25.07) 0.003 (0.97) 0.236⁎⁎⁎ (2.58) 0.235⁎⁎⁎ (14.57) 0.041⁎⁎⁎ (8.21)

0.032⁎⁎⁎ (2.82)

L.Regulatory Quality

0.066⁎⁎⁎ (5.85)

L.Rule of Law

0.057⁎⁎⁎ (4.79)

L.Voice and Accountability

0.032⁎⁎⁎ (5.11)

L.Political Stability

0.066⁎⁎⁎ (7.35)

L.Control of Corruption Chi-squared Observations

12,748.601 700

10,990.771 700

12,609.701 700

10,643.062 700

13,859.593 700

12,575.003 700

t statistics in parentheses. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01.

competition can be an essential transmission channel of financial openness to bank risk-taking. To this end, one should consider the endogenous determination between financial stability and competition. In other words, we need to separate the effect of financial openness on bank competition to extract its impact on bank risk-taking. 4.3. Transmitting channel through bank competition In the next empirical setup, we examine whether the effect of financial openness, institutional development on bank risk-taking is dependent on bank competition. Following Pellegrini and Gerlagh (2004), we first estimate the first-stage regression, where bank concentration is regressed with various bank-specific and macroeconomic determinants, as well as financial openness and institutional variables. Then in a second stage regression, we re-estimate Eq. (2) with bank concentration replaced by the results in the first stage regression. If the impact of financial openness and institutional development passes through the competition channel, the first stage regression will allow us to control for this channel. Table 6 shows the first-stage regressions of (2) with bank concentration as the dependent variable. Financial openness contributes positively to bank concentration in all the first-stage regressions. Then, higher bank concentration leads to better bank stability, as prior findings demonstrate. These results favor the competition-fragility hypothesis. At this stage, we observe that the effect of financial openness on risk-taking behavior passes through the competition channel. The second-stage regression will allow us to investigate whether this effect goes through other mechanisms. If competition is the only channel, the coefficients of financial openness in the second-stage regression will be insignificant. We report the second stage estimation results in Table 7. After controlling for the competition channel, the estimated coefficients of financial openness and its interaction term with institutions are statistically significant in four specifications: Regulatory quality, Rule of law, Political stability, Control of corruption. Furthermore, these interaction terms are all positive. It implies that the impact of financial openness on financial stability would increase with higher levels of institutional developments.2 For instance, the interaction term with the regulatory quality index is significantly positive. This index measures the extent that a 2

For example, from Table 7 the impact of financial openness (KAOPEN) on banks Z-score using the Rule of law (RL) indicator is: ∂ ln(Zscore ) = 0.14 + 0.073 × RL ∂KAOPEN

Hence, the total impact is larger with higher scores of the Rule of law indicator. 9

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Table 7 Estimation results after controlling for bank competition channel, dependent variable: Logarithm of Z-score.

L.Liquid assets L.Bank capital to assets L.Bank return on asset L.GDP growth L.Total asset

∼ C L.Financial openness L.Government Effectiveness L.Financial openness × L.Government Effectiveness

(1)

(2)

(3)

(4)

(5)

(6)

Z-score

Z-score

Z-score

Z-score

Z-score

Z-score

0.644⁎⁎⁎ (6.51) 0.103⁎⁎⁎ (22.51) 0.042⁎⁎⁎ (3.67) 1.843⁎⁎⁎ (5.26) 0.951⁎⁎⁎ (15.90) 0.668⁎⁎⁎ (6.90) 0.099⁎⁎⁎ (6.16) 0.143⁎⁎⁎ (3.35) −0.011 (−0.61)

0.572⁎⁎⁎ (5.80) 0.101⁎⁎⁎ (22.37) 0.044⁎⁎⁎ (3.62) 1.932⁎⁎⁎ (5.58) 1.048⁎⁎⁎ (18.99) 0.687⁎⁎⁎ (7.13) 0.117⁎⁎⁎ (6.08)

0.534⁎⁎⁎ (5.43) 0.100⁎⁎⁎ (21.21) 0.044⁎⁎⁎ (3.65) 1.951⁎⁎⁎ (5.66) 0.997⁎⁎⁎ (16.92) 0.694⁎⁎⁎ (7.29) 0.140⁎⁎⁎ (7.62)

0.591⁎⁎⁎ (6.03) 0.104⁎⁎⁎ (22.51) 0.042⁎⁎⁎ (3.48) 1.969⁎⁎⁎ (5.63) 1.047⁎⁎⁎ (20.52) 0.719⁎⁎⁎ (7.68) 0.109⁎⁎⁎ (6.95)

0.537⁎⁎⁎ (5.57) 0.096⁎⁎⁎ (22.00) 0.057⁎⁎⁎ (4.98) 2.243⁎⁎⁎ (7.04) 0.996⁎⁎⁎ (22.80) 0.674⁎⁎⁎ (7.62) 0.218⁎⁎⁎ (14.31)

0.565⁎⁎⁎ (5.77) 0.105⁎⁎⁎ (22.31) 0.037⁎⁎⁎ (3.16) 1.823⁎⁎⁎ (5.33) 0.967⁎⁎⁎ (17.10) 0.647⁎⁎⁎ (6.65) 0.113⁎⁎⁎ (6.43)

−0.037 (−1.03) 0.048⁎⁎⁎ (2.73)

L.Regulatory Quality L.Financial openness × L.Regulatory Quality

−0.077⁎ (−1.94) 0.073⁎⁎⁎ (3.83)

L.Rule of Law L.Financial openness × L.Rule of Law L.Voice and Accountability

0.052 (1.22) 0.009 (0.42)

L.Financial openness × L.Voice and Accountability

−0.286⁎⁎⁎ (−11.06) 0.089⁎⁎⁎ (6.54)

L.Political Stability L.Financial openness × L.Political Stability

−0.021 (−0.53) 0.063⁎⁎⁎ (3.27)

L.Control of Corruption L.Financial openness × L.Control of Corruption Chi-squared Observations

12,759.187 634

13,750.326 634

13,308.077 634

14,012.783 634

14,465.531 634

14,629.674 634

t statistics in parentheses. ∼ C represents bank concentration after the first stage regression. ⁎ p < 0.10. ⁎⁎⁎ p < 0.01.

government can formulate and implement sound policies and regulations that permit and promote private sector development. The empirical result suggests that the positive effect of regulatory quality on the relationship between financial openness and bank stability may stem from the strength of the private sector. On the one hand, highly open economies tend to suffer income volatility and unpredictable (Ashraf et al., 2017). Thus, on the demand-side, sound policies are necessary to provide the domestic business sector a buffer against adverse global shocks. As a consequence, the private sector is less susceptible to risks and has more capacity to repay its debts. On the other hand, sound policies also strengthen the banking sector. For example, the diversification hypothesis works when banks become less restrictive in an open economy, which allows them to follow the economy of scale and scope (Fang et al., 2014). Likewise, the interaction term with the rule of law index is also significantly positive. The impact of financial openness on bank stability would increase as the quality of contract enforcement, property rights, the police, and the courts improve. Better law enforcement can help banks protect their assets and reduce the moral hazard problems of their clients. We also note that the interaction term between political stability and financial openness is significantly positive in Table 7 and in other specifications. Higher political stability can improve the impact of financial openness on bank stability, which can be referred to the disciplining effect. In the context of financial openness, enormous amount of capital can be mobilized around the world on such short notice thanks to the ability of trading 24 h a day. This possibly massive capital escape thus makes the financial market the supreme judge of government's policies (Garrett and Mitchell, 2001). Hence, the government would have strong incentives to maintain a stable political environment to attract more foreign investment. The interaction term with control of corruption is also 10

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Table 8 Estimation results for emerging markets, dependent variable: Logarithm of Z-score.

L.Liquid assets L.Bank capital to assets L.Bank return on asset L.GDP growth L.Total asset

∼ C L.Financial openness L.Government Effectiveness L.Financial openness × L.Government Effectiveness

(1)

(2)

(3)

(4)

(5)

(6)

Z-score

Z-score

Z-score

Z-score

Z-score

Z-score

1.423⁎⁎⁎ (9.14) 0.066⁎⁎⁎ (10.85) 0.036⁎⁎⁎ (3.24) 1.835⁎⁎⁎ (5.07) 1.254⁎⁎⁎ (13.41) 0.010⁎⁎⁎ (9.90) 0.083⁎⁎⁎ (5.17) −0.050 (−0.90) −0.082⁎⁎⁎ (−2.91)

1.370⁎⁎⁎ (8.75) 0.066⁎⁎⁎ (11.71) 0.035⁎⁎⁎ (3.06) 1.852⁎⁎⁎ (5.24) 1.312⁎⁎⁎ (15.25) 0.008⁎⁎⁎ (6.69) 0.085⁎⁎⁎ (4.00)

1.340⁎⁎⁎ (8.93) 0.060⁎⁎⁎ (10.10) 0.039⁎⁎⁎ (3.34) 1.884⁎⁎⁎ (5.43) 1.369⁎⁎⁎ (16.16) 0.009⁎⁎⁎ (8.18) 0.094⁎⁎⁎ (4.91)

1.349⁎⁎⁎ (8.22) 0.067⁎⁎⁎ (11.40) 0.031⁎⁎⁎ (2.66) 1.762⁎⁎⁎ (4.81) 1.310⁎⁎⁎ (16.96) 0.009⁎⁎⁎ (7.95) 0.065⁎⁎⁎ (3.62)

1.406⁎⁎⁎ (9.63) 0.061⁎⁎⁎ (10.73) 0.047⁎⁎⁎ (4.32) 1.978⁎⁎⁎ (6.04) 1.292⁎⁎⁎ (15.60) 0.008⁎⁎⁎ (7.29) 0.153⁎⁎⁎ (9.58)

1.427⁎⁎⁎ (9.79) 0.068⁎⁎⁎ (11.82) 0.028⁎⁎⁎ (2.59) 1.769⁎⁎⁎ (5.09) 1.251⁎⁎⁎ (15.86) 0.007⁎⁎⁎ (6.16) 0.073⁎⁎⁎ (4.01)

−0.078 (−1.63) 0.030 (1.25)

L.Regulatory Quality L.Financial openness × L.Regulatory Quality

−0.193⁎⁎⁎ (−3.56) 0.005 (0.17)

L.Rule of Law L.Financial openness × L.Rule of Law L.Voice and Accountability

0.037 (0.81) −0.018 (−0.67)

L.Financial openness × L.Voice and Accountability

−0.255⁎⁎⁎ (−8.54) 0.084⁎⁎⁎ (5.05)

L.Political Stability L.Financial openness × L.Political Stability

−0.016 (−0.32) 0.078⁎⁎⁎ (3.13)

L.Control of Corruption L.Financial openness × L.Control of Corruption Chi-squared Observations

7624.545 395

6244.303 395

7057.475 395

5911.770 395

8218.721 395

7299.497 395

t statistics in parentheses. ⁎⁎⁎ p < 0.01.

significantly positive, which implies that the sand the wheel effect would increase the positive impact of financial openness on bank stability. Moreover, these findings show that competition is not the sole transmitting channel. Banks in more liberalized markets with better institutional development would be free from extractive political forces and have more freedom to operate. They have better opportunities to implement economies of scale and scope, diversify their income, and be less vulnerable to risk (Fang et al., 2014), which can be attributed to the diversification hypothesis. 4.4. Differences between advanced and developing markets As shown in the summary statistics in Table 2, there are differences between the advanced world and the developing world. For example, the average KAOPEN index for developed economies is 2.29, while it is only 0.29 in the developing markets. Besides, the average institutional score of the former is always higher than the latter in every aspect. Because of these dissimilarities, the effect of financial openness and institutional development on bank risk-taking can be different among the two groups. In this section, we examine whether the empirical findings depend on the status of a country. In particular, to estimate these potential differences, we divide the original panel into two sub-panels: one for advanced economies and the other one for developing economies. Then, we re-estimated Eq. (2) for each of these two sub-samples, controlling for the endogenous determination of financial openness and bank competition. Table 8 reports the second-stage regression results for the developing sub-sample, while Table 9 shows the second-stage estimation results for the developed sub-sample. 11

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Table 9 Estimation results for advanced markets, dependent variable: Logarithm of Z-score.

L.Liquid assets L.Bank capital to assets L.Bank return on asset L.GDP growth L.Total asset

∼ C L.Financial openness L.Government Effectiveness L.Financial openness × L.Government Effectiveness

(1)

(2)

(3)

(4)

(5)

(6)

Z-score

Z-score

Z-score

Z-score

Z-score

Z-score

−0.272⁎⁎⁎ (−2.82) 0.045⁎⁎⁎ (4.25) −0.021 (−0.75) 0.354 (0.47) −0.402⁎⁎⁎ (−6.31) −0.005⁎⁎⁎ (−4.41) 1.219⁎⁎⁎ (18.42) 1.623⁎⁎⁎ (15.25) −0.700⁎⁎⁎ (−14.30)

−0.165⁎ (−1.82) 0.057⁎⁎⁎ (5.29) −0.006 (−0.21) 0.238 (0.31) −0.341⁎⁎⁎ (−5.67) −0.004⁎⁎⁎ (−3.73) 1.221⁎⁎⁎ (19.12)

−0.222⁎⁎ (−2.25) 0.049⁎⁎⁎ (4.31) −0.013 (−0.47) 0.586 (0.78) −0.392⁎⁎⁎ (−6.04) −0.004⁎⁎⁎ (−3.49) 1.298⁎⁎⁎ (19.05)

−0.119 (−1.31) 0.036⁎⁎⁎ (3.38) −0.015 (−0.59) −0.045 (−0.07) −0.423⁎⁎⁎ (−6.76) −0.004⁎⁎⁎ (−3.15) 1.435⁎⁎⁎ (22.93)

−0.081 (−0.82) 0.065⁎⁎⁎ (5.43) −0.007 (−0.26) 0.276 (0.40) −0.175⁎⁎ (−2.29) −0.002 (−1.47) 1.134⁎⁎⁎ (17.10)

−0.214⁎⁎ (−2.25) 0.045⁎⁎⁎ (4.25) −0.016 (−0.58) 0.441 (0.59) −0.378⁎⁎⁎ (−5.82) −0.005⁎⁎⁎ (−4.42) 1.247⁎⁎⁎ (20.28)

1.606⁎⁎⁎ (14.70) −0.748⁎⁎⁎ (−13.78)

L.Regulatory Quality L.Financial openness × L.Regulatory Quality

1.587⁎⁎⁎ (14.37) −0.749⁎⁎⁎ (−13.99)

L.Rule of Law L.Financial openness × L.Rule of Law

2.104⁎⁎⁎ (12.93) −1.051⁎⁎⁎ (−14.03)

L.Voice and Accountability L.Financial openness × L.Voice and Accountability L.Political Stability

0.113 (0.21) −0.173 (−0.74)

L.Financial openness × L.Political Stability

1.423⁎⁎⁎ (16.09) −0.643⁎⁎⁎ (−15.78)

L.Control of Corruption L.Financial openness × L.Control of Corruption Chi-squared Observations

16,103.887 239

15,776.726 239

13,446.729 239

15,104.629 239

9170.005 239

15,644.868 239

t statistics in parentheses. ⁎ p < 0.10. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01.

We observe several changes in the empirical results regarding the control variables in our models. For instance, higher bank liquidity is still associated with higher financial stability in developing countries, but this effect is not present in the case of advanced economies. Likewise, the positive effect of higher economic growth on bank stability disappears for the developed markets. For the competition hypothesis of bank risk-taking, the empirical results for the two groups are dissimilar. The results for developing markets support the competition-fragility hypothesis, while the findings for industrial economies are consistent with the competition-stability view. The effect of financial openness on bank stability is positively significant for both developing and advanced economies. However, in the case of advanced markets, the positive effect of capital account openness on bank Z-score has a larger magnitude than that of developing and emerging markets. The developing world is at low levels of capital account liberalization and may not yet benefit the full positive effect of financial openness. Regarding the interaction terms between financial openness and institutional reforms, we find significantly positive effects in the models with political stability and control of corruption for the developing panel. As a consequence, although the initial effect is small, these developing countries can still benefit as they improve their political stability and control of corruption. However, the interaction term with government effectiveness is significantly negative. This result suggests that, as the government becomes more effective, the banking system becomes riskier when financial openness increases. The finding is similar to the case of the developed sample reported in Table 9. We will argue below that improvements in government effectiveness may create perverse incentives for banks and increase the moral hazard problem, which leads to higher bank risks. 12

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Table 10 Robustness test for emerging markets using financial freedom, dependent variable: Logarithm of Z-score.

L.Liquid assets L.Bank capital to assets L.Bank return on asset L.GDP growth L.Total asset

∼ C L.Financial freedom L.Government Effectiveness L.Financial freedom × L.Government Effectiveness

(1)

(2)

(3)

(4)

(5)

(6)

Z-score

Z-score

Z-score

Z-score

Z-score

Z-score

0.832⁎⁎⁎ (6.16) 0.048⁎⁎⁎ (8.34) 0.023⁎⁎ (2.46) 1.407⁎⁎⁎ (4.60) 1.111⁎⁎⁎ (11.36) 0.007⁎⁎⁎ (6.89) 1.154⁎⁎⁎ (10.93) 0.139 (1.17) −0.663⁎⁎⁎ (−3.34)

0.935⁎⁎⁎ (7.22) 0.045⁎⁎⁎ (8.29) 0.017⁎ (1.94) 1.270⁎⁎⁎ (4.45) 1.206⁎⁎⁎ (14.48) 0.005⁎⁎⁎ (4.66) 1.274⁎⁎⁎ (11.65)

0.665⁎⁎⁎ (5.15) 0.044⁎⁎⁎ (7.77) 0.021⁎⁎ (2.36) 1.278⁎⁎⁎ (4.42) 1.181⁎⁎⁎ (13.92) 0.007⁎⁎⁎ (7.31) 1.189⁎⁎⁎ (10.85)

1.031⁎⁎⁎ (7.27) 0.048⁎⁎⁎ (8.00) 0.020⁎⁎ (2.22) 1.310⁎⁎⁎ (4.10) 1.064⁎⁎⁎ (13.31) 0.006⁎⁎⁎ (4.99) 1.037⁎⁎⁎ (8.53)

0.945⁎⁎⁎ (6.96) 0.048⁎⁎⁎ (8.39) 0.019⁎⁎ (2.09) 1.267⁎⁎⁎ (4.58) 1.089⁎⁎⁎ (11.71) 0.005⁎⁎⁎ (4.26) 1.045⁎⁎⁎ (9.36)

0.890⁎⁎⁎ (6.86) 0.049⁎⁎⁎ (8.61) 0.018⁎⁎ (2.15) 1.270⁎⁎⁎ (4.37) 1.010⁎⁎⁎ (12.14) 0.006⁎⁎⁎ (5.58) 1.095⁎⁎⁎ (9.53)

−0.004 (−0.05) −0.506⁎⁎⁎ (−2.85)

L.Regulatory Quality L.Financial freedom × L.Regulatory Quality

−0.446⁎⁎⁎ (−4.27) 0.259 (1.44)

L.Rule of Law L.Financial freedom × L.Rule of Law

−0.047 (−0.43) −0.089 (−0.49)

L.Voice and Accountability L.Financial freedom × L.Voice and Accountability

−0.455⁎⁎⁎ (−6.42) 0.456⁎⁎⁎ (3.28)

L.Political Stability L.Financial freedom × L.Political Stability

−0.439⁎⁎⁎ (−3.73) 0.639⁎⁎⁎ (3.52)

L.Control of Corruption L.Financial freedom × L.Control of Corruption Chi-squared Observations

8403.320 413

9107.061 413

11,221.059 413

6753.512 413

5858.588 413

10,694.365 413

t statistics in parentheses. ⁎ p < 0.10. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01.

For the developed sub-sample, we find significantly negative estimated coefficients for the interaction terms, except for the model interacting with political stability. These results suggest that, although the initial positive effect of financial openness on bank stability is substantial, it will become smaller as the institutions score become larger. In other words, in a highly open financial market, better institutions may induce banks to take more risk. This is a reverse effect of institutional development, which can steam from the moral hazard problem. Fig. 1 shows that, except for the political stability measure, the distinction between institutional development of developed markets and that of developing markets is evident. Institutional scores of advanced economies are clustered at higher levels, compared to those of developing countries. In fact, these scores are very close to the highest possible value, which is 2.5. Large and interconnected financial institutions can take excessive risks since they believe the government will bail them out in case of failure to avoid financial turmoils. Governments in countries with better institutions and regulations have a stronger capability and higher incentives to bail out banks as their economy would lose more otherwise, which aggravate the moral hazard problem. Cubillas et al. (2012) show that the adverse consequence of a banking crisis is more severe in countries where bank regulation, supervision, and institutions are strong. In another study, the effect of the too-big-to-fail hypothesis is stronger in economies with better public governance (Cubillas et al., 2017).

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Table 11 Robustness test for advanced markets using financial freedom, dependent variable: Logarithm of Z-score.

L.Liquid assets L.Bank capital to assets L.Bank return on asset L.GDP growth L.Total asset

∼ C L.Financial freedom L.Government Effectiveness L.Financial freedom × L.Government Effectiveness

(1)

(2)

(3)

(4)

(5)

(6)

Z-score

Z-score

Z-score

Z-score

Z-score

Z-score

−0.195⁎⁎ (−2.24) 0.065⁎⁎⁎ (6.60) 0.009 (0.32) 0.141 (0.20) −0.129⁎⁎ (−2.21) −0.003⁎⁎⁎ (−3.13) 3.990⁎⁎⁎ (17.72) 1.522⁎⁎⁎ (15.90) −2.415⁎⁎⁎ (−16.22)

−0.244⁎⁎⁎ (−2.71) 0.061⁎⁎⁎ (6.06) 0.054⁎ (1.91) 0.716 (1.00) −0.167⁎⁎⁎ (−3.11) −0.005⁎⁎⁎ (−5.14) 3.702⁎⁎⁎ (17.26)

−0.177⁎ (−1.95) 0.059⁎⁎⁎ (5.41) 0.028 (1.00) 0.667 (0.91) −0.161⁎⁎⁎ (−2.73) −0.004⁎⁎⁎ (−3.70) 4.171⁎⁎⁎ (17.86)

−0.125 (−1.30) 0.048⁎⁎⁎ (3.95) −0.018 (−0.63) 0.172 (0.21) −0.304⁎⁎⁎ (−3.98) −0.005⁎⁎⁎ (−3.33) 3.848⁎⁎⁎ (12.81)

−0.074 (−0.68) 0.119⁎⁎⁎ (10.27) −0.007 (−0.23) 0.961 (1.19) 0.104 (1.45) 0.002 (1.12) 2.573⁎⁎⁎ (13.17)

−0.155⁎ (−1.69) 0.060⁎⁎⁎ (5.97) 0.009 (0.33) 1.173 (1.60) −0.083 (−1.33) −0.004⁎⁎⁎ (−3.64) 4.011⁎⁎⁎ (18.07)

1.897⁎⁎⁎ (18.41) −2.755⁎⁎⁎ (−20.26)

L.Regulatory Quality L.Financial freedom × L.Regulatory Quality

1.634⁎⁎⁎ (15.90) −2.676⁎⁎⁎ (−19.15)

L.Rule of Law L.Financial freedom × L.Rule of Law

1.957⁎⁎⁎ (14.69) −2.827⁎⁎⁎ (−13.97)

L.Voice and Accountability L.Financial freedom × L.Voice and Accountability

1.481⁎⁎⁎ (7.59) −2.303⁎⁎⁎ (−8.69)

L.Political Stability L.Financial freedom × L.Political Stability

1.498⁎⁎⁎ (14.70) −2.386⁎⁎⁎ (−16.94)

L.Control of Corruption L.Financial freedom × L.Control of Corruption Chi-squared Observations

19,348.901 239

23,320.025 239

16,940.048 239

10,249.005 239

12,450.134 239

16,747.891 239

t statistics in parentheses. ⁎ p < 0.10. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01.

4.5. Robustness test In this section, we use another proxy of financial openness to check the robustness of our findings. First, we replace the first measure of financial openness by the financial freedom index, which is similar to Luo et al. (2016). The Heritage Foundation publishes this index annually as a component of the Economic Freedom Index. This variable assesses the total degree of financial freedom of an economy including five aspects: the extent of government regulation of financial services, state's shares in banks and other financial institutions, the level of financial and capital market development, the extent of government influence in allocating credit and openness to foreign competition. The maximum value is 100, which means negligible government interference, and the minimum value is 0, which translates into financial repression. Tables 10 and 11 report the results for developing and advanced countries, respectively. Overall, the findings are similar to prior settings. Another robustness test uses the Lerner index in place of bank concentration to proxy for the competition in the banking sector, which is also used in the study of Jiménez et al. (2013). This index measures market power in the banking market. It is calculated as a ratio of marginal costs (obtained from an estimated translog cost function with respect to output) relative to output prices (total bank revenue over assets). The Global Financial Development database calculates this index using banks data from Bankscope. In general, controlling for competition using another index does not change the main results of the paper for developed and developing economies.3 14

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The estimated coefficients of Political Stability are significantly negative in most empirical specifications. This index captures the likelihood that a government will be overthrown, such as violence steaming from political motivation and terrorism. The finding implies that the moral hazard effect is stronger with this indicator than other ones. Better government stability may stimulate risktaking behavior in the banking system because they believe that the current government would be ready to help them in crisis. If the authority let the banks fail, they risk the ongoing stability because a usurper can take the opportunity to destabilize and overthrow the current government. Thus, governments with better political stability would have more incentive to protect banks and worsen the moral hazard problem. 5. Conclusion We examine how institutions can change the effect of financial openness on bank risk-taking behavior. In the paper, we estimate various empirical specifications, with the main explanatory variables being financial openness, proxied by the KAOPEN index, and a variety of institutional indicators, as well as the interacting terms between these variables. The empirical models are then estimated using a panel of 37 emerging and developing markets, and 21 advanced economies, for a total of 58 countries for the period 2000–2015. The empirical results indicate that higher financial openness and better institutional quality contribute to stronger financial stability, proxied by bank Z-score. After controlling for the competition-risk channel, the results show that the effect of financial openness on bank risk-taking depends on institutional development. A better institution helps countries to reap more benefits from financial openness. However, we only find this positive effect for the whole panel and for developing markets. As a country moves from very low levels of institutional development to higher levels, improvement in institutions enhances the positive impact of financial openness on bank stability. However, when the degree of institutions is very high, the moral hazard problem can appear and induces banks to take more risk as the financial sector becomes more open. Besides, the bank-competition relationship in developing countries favors the competition-fragility view, while in developed countries, it is consistent with the competition-stability hypothesis. These findings can have several policy implications. Financial openness can lead to higher lending cost, with respect to the competition-fragility view, which hurts bank stability. However, this effect is different between good-quality and poor-quality institution. Thus, countries with poor development of institution should pay attention to the adverse consequences of financial openness. Emerging countries should develop and improve discipline, transparency, and regulation to reap the benefits of opening the financial sector. Furthermore, countries with better institutions should be aware of the moral hazard problem, especially for large and interconnected financial institutions. Acknowledgments The authors would like to thank the editors and two anonymous reviewers of this journal for their very constructive comments that contributed to the improvement of the paper. We are solely responsible for any errors that might yet remain. This research is funded by the University of Economics Ho Chi Minh City, Vietnam. References Agoraki, M.-E.K., Delis, M.D., Pasiouras, F., 2011. Regulations, competition and bank risk-taking in transition countries. J. Financ. Stab. 7 (1), 38–48. Aizenman, J., Jinjarak, Y., Park, D., 2015. Financial development and output growth in developing Asia and Latin America: a comparative sectoral analysis. In: Working Paper 20917. 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