Basel Compliance and Financial Stability
Evidence from Islamic Banks
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund
  • | 2 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund
  • | 3 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

The paper provides robust evidence that compliance with Basel Core Principles (BCPs) has a strong positive effect on the Z-score of conventional banks, albeit less pronounced on the Zscore of Islamic banks. Using a sample of banks operating in 19 developing countries, the results appear to be driven by capital ratios, a component of Z-score for the two types of banks. Even though smaller on Islamic banks, individual chapters of BCPs also suggest a positive effect on the stability of conventional banks. The findings support the effective role of BCP standards in improving bank stability, whose important implications led to the Islamic Financial Services Board (IFSB) publication of new recommendations in 2015 to bring BCP standards in line with the Core Principles for Islamic Finance Regulation (CPIFRs) standards. Our findings suggest that because Islamic banks are benchmarked closely to BCPs, the implementation of CPFIRs should also positively affect their stability.

Abstract

The paper provides robust evidence that compliance with Basel Core Principles (BCPs) has a strong positive effect on the Z-score of conventional banks, albeit less pronounced on the Zscore of Islamic banks. Using a sample of banks operating in 19 developing countries, the results appear to be driven by capital ratios, a component of Z-score for the two types of banks. Even though smaller on Islamic banks, individual chapters of BCPs also suggest a positive effect on the stability of conventional banks. The findings support the effective role of BCP standards in improving bank stability, whose important implications led to the Islamic Financial Services Board (IFSB) publication of new recommendations in 2015 to bring BCP standards in line with the Core Principles for Islamic Finance Regulation (CPIFRs) standards. Our findings suggest that because Islamic banks are benchmarked closely to BCPs, the implementation of CPFIRs should also positively affect their stability.

I. Introduction

In this study, we examine whether compliance with Basel Core Principles (BCPs) for effective banking supervision affects bank stability and risk taking by comparing conventional and Islamic banks. While Demirgüç-Kunt and Detragiache (2011) and Ayadi et al. (2016) used a large and heterogeneous sample of banks around the world, this paper not only extends the analysis to cover Islamic banks, as compared to conventional banks, but also focuses on banks operating mainly in developing and emerging countries.

BCPs were introduced in 1997 by the Basel Committee on Banking and Supervision (BCBS) and several surveys have been conducted by the IMF and the World Bank to assess the quality of banking regulation and supervision worldwide. These principles were initially created as a pilot project for 12 advanced countries but rapidly became the global standard for banking regulation. One important drawback with BCPs is that they do not take into account the specificities of certain types of banks, such as Islamic banks.2

In 2015, the Islamic Financial Services Board (IFSB),3 an international regulatory organization with a main objective of promoting the development and the stability of the Islamic financial industry, published a set of guidelines called Core Principles for Islamic Finance Regulation (CPIFR). These guidelines are built on BCBS standards and have been extended to deal with the specificities of Islamic banks.

Within these guidelines, some of the CPIFRs remained unchanged between CPIFRs and BCPs, some of them were amended, while other CPIFRs are completely new. Because CPIFR guidelines were published in 2015, Islamic banks were expected to implement them in January 2016 or later (IFSB, 2015). Accordingly, data on Islamic banks compliance with CPIFRs are not available at this stage. Yet, because some of the CPIFRs are similar to conventional bank BCPs, our study focuses on available BCPs and examines whether the adoption of current BCPs affects the stability of Islamic banks. This could also enable us to derive some important policy implications regarding the expected effects of CPIFRs on Islamic banks financial soundness.

To do this, we use an initial sample of 761 conventional and Islamic banks in 19 countries covering the period from 1999–2013. In contrast to Demirgüç-Kunt and Detragiache (2011), our findings suggest that BCP compliance index is positively associated with the stability of conventional banks in at least five out of seven individual chapters at the 1 percent level. The effect remains positive but less pronounced for Islamic banks, where three out of seven chapters are significantly positive at the 5 percent level or more. A deeper examination of the components of the dependent variable (i.e. bank Z-score) shows that results are mainly driven by bank capital ratios. The findings indicate that adherence to international regulatory standards improves the stability of the two bank types through incentives to hold higher capital ratios. The results hold when considering bank financial characteristics, macroeconomics, and institutional environment. The findings also remain unaffected across different subsamples and after applying alternative risk and stability measures, an instrumental variable approach (IV), a Heckman estimation technique to address endogeneity and selection bias, and the Propensity Score Matching (PSM) technique to reduce any bias in sample size.

This study contributes to the literature on both conventional and Islamic banks in at least three important ways: First, we highlight a strong positive impact of the BCP index on the stability of conventional banks, while the impact is positive albeit less effective on the stability of Islamic banks. This could provide regulatory organizations such as the BIS and the IFSB with initial empirical evidence to support the effective role of BCP standards in improving bank stability. Since BCPs are also effective in improving the stability of Islamic banks, the findings suggest that CPFIRs should also positively affect the stability of Islamic banks, as they are benchmarked closely to BCPs. Yet, an open question remains on whether BCP standards should be amended to cover for some specificities of Islamic banks. An argument in favor may find support in a more stable financial system found in countries where the two bank types operate.

Second, we show that regulatory compliance enhances bank stability through two main channels: (i) prudent investment decisions by avoiding risky activities, reflected in lower return on assets and lower volatility of returns; (ii) strong willingness of banks in developing countries to be recognized and more integrated in the global financial system, reflected in their strong solvency ratios.

Finally, we add to the comparative literature on conventional and Islamic banks (Abedifar et al. 2013; Beck et al. 2013; Mollah and Zaman, 2015; Mollah et al. 2016; Bitar et al. 2017b) by exploring the regulatory determinants of bank stability and finding compelling evidence of relative similarity between the two bank types.

The rest of the paper is structured as follows: Section II briefly reviews the literature. Section III describes the sample, the empirical approach, and variable definitions. Section IV presents the main results, while Section V reports the robustness checks. The last section concludes.

II. Literature Review

Literature examining the effect of banking regulation on the risk and the stability of the financial system does not provide a specific set of indicators that can be used to proxy for banking regulation. While some studies refer to accounting and market ratios such as regulatory capital, liquidity, and leverage measures, other studies are based on questionnaires and surveys performed by governments and international regulatory organizations. These studies often report inconclusive and contradictory results.

Barth et al. (2004, 2006, and 2008) are among the first to examine the effect of banking regulation and supervision on bank performance and stability using international data. Their findings suggest that strong monitoring of markets and the private sector is an important factor in promoting performance and stability of the financial sector. Focusing on corporate governance, Leaven and Levine (2009) use different proxies of banking regulation and supervision (capital requirements, capital stringency, activity restrictions, and deposit insurance) with bank ownership structure. They conclude that regulation increases bank risk-taking when a bank has an ultimate owner, while the opposite occurs when a bank is widely held. Klomp and de Haan (2012) ask whether banking regulation has an homogeneous effect on bank risk. Using a sample of 200 banks from 21 OECD countries, their findings show that banking regulation is more effective in improving safety for riskier banks thus suggesting that the effect of regulation is not uniform and depends on bank risk profile. Klomp and de Haan (2014) further investigate the association between banking regulation and risk by taking into consideration the level of development of a country’s institutional environment. Using a sample of 400 banks from 70 developing and emerging countries, their findings indicate that the positive effect of banking regulation and supervision on bank risk is supported in countries with a better institutional environment.

In recent literature, Doumpos et al. (2015) use a large sample of 1700 commercial banks operating in 90 countries over the period 2000–11 to study the effect of three indexes of regulation (central bank independence, central bank involvement in prudential regulation, and supervisory unification) on bank stability. Depending on bank size and the country’s official supervisory power, their results yield a positive and significant association with bank Z-score, especially in periods of crisis. Finally, using a sample from 19 EU countries covering the 1999–2011 period, Carretta et al. (2015) focus on the culture of banking supervision (proxied by the Hofstede’s cultural dimensions) to assess the stability of banks. Their findings suggest that a greater supervisory culture based on collectivism and avoidance of uncertainty is positively linked to bank Z-score. Accordingly, they highlighted the importance of cultural dimensions in the success of banking regulation by the Banking Union at the European Central Bank (ECB).

However, one important shortcoming in these studies is that they evaluate the effectiveness of banking regulation and supervision based on what is mentioned on the books rather than on actual implementation (Demirgüç-Kunt and Detragiache, 2011; Ayadi et al. 2016). In addition, actual reporting on the soundness of banking-sector laws and regulation often lacks true assessment, especially in low-income countries, which could exacerbate the variation between what books report and what is being practiced (Demirgüç-Kunt and Detragiache, 2011).

Another stream of literature adheres to the Basel Core Principles (BCP) index for effective banking supervision as an alternative measure to questionnaires and surveys reported above. Developed by the World Bank and the IMF under the Basel Core Financial Sector Assessment Program (FSAP), the BCP index is considered a unique source of information that represents the quality of supervision and regulation in countries around the globe. Demirgüç-Kunt and Detragiache (2011) argue that assessments by the FSAP are more effective for two reasons: First, the BCP index reflects the actual implementation of different factors that represent banking regulation and supervision. Second, assessments are based on an explicit and standardized methodology and are conducted by experienced international assessors with broad country experience.4

Several studies have employed the BCP index to proxy for compliance with banking regulation and supervision and to examine its effect on the performance and stability of the banking system. Sundararajan et al. (2001) examine the association between BCP compliance and bank soundness, using a sample of banks in 25 countries. Their findings highlight the importance of other bank-level and macroeconomic factors and conclude that the implementation of international standards is not sufficient in itself to ensure financial soundness. Das et al. (2005) find that countries with higher compliance with BCP resist more macroeconomic pressures. Podpiera (2006) also investigates the effect of BCPs on bank performance using a sample of banks from advanced, emerging, and developing countries. He finds that banks in countries with higher compliance with BCP have lower non-performing loans and interest margins. In a related context, Cihak and Tieman (2008) show that BCP compliance is positively and strongly associated with a country’s sound governance and higher GDP per capita, while the effect is less significant when replacing the BCP index with on-the-book regulatory measures.

More recently, Demirgüç-Kunt and Detragiache (2011) investigate the association between compliance with Basel Core Principles and banks’ financial stability. Employing an overall index of 25 Basel principles and a sample of international banks, the authors find no evidence of a significant relationship between compliance with Basel rules and a bank’s Z-score. Finally, Ayadi et al. (2016) extend the work of Demirgüç-Kunt and Detragiache (2011) by focusing on bank efficiency. Their results also show no association between BCPs and efficiency. However, when examining the effect of each chapter, they only find a negative impact between Chapter 4 (methods of ongoing supervision) and bank efficiency. Table 1 resumes the available literature on BCP studies.

Table 1

Overview of Basel Core Principles studies in conventional banking

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Because BCP compliance chapters are designed to promote the stability and the financial soundness of conventional banks, the likelihood of affecting the stability of their Islamic counterparts should be irrelevant or, at best, circumstantially slim. This might be expected if Islamic banks have different balance sheets and different financial products compared to conventional banks. The literature, however, offers different opinions on whether Islamic banks share the same financial characteristics as conventional banks. Scholars offer different opinions mainly because the current business model of Islamic banks presents substantial discrepancies between Sharia’a ideals and bank practices (Khan, 2010). One would expect that under Sharia’a law Profit Loss Sharing (PLS) instruments such as Musharaka and Moudharaba—as a core of Islamic banking and finance—dominate Islamic banks’ practices. Yet, unsurprisingly, non-PLS mark-up mode of finance such as Murabaha and Ijara predominate. Mark-up financing techniques are considered less Sharia’a compliant and a benchmark for conventional banks’ activities, suggesting the existence of similarities between the two bank types (Abedifar et al. 2013; Beck et al. 2013; Mollah and Zaman, 2015).

Recently, the IFSB published new guidelines on CPIFRs (IFSB, 2015) based on the Core Principles for Effective Banking Supervision (BCPs) created by the Basel Committee on Banking and Supervision (BCBS). According to the IFSB, the proposed guidelines aim to “build on the standards adopted by relevant conventional standards […] and to adapt or supplement them only to the extent necessary to deal with the specificities of Islamic finance” (p.2, IFSB, 2015). A detailed description of CPIFRs is presented in Appendix A.1.

CPIFR guidelines are different than BCP guidelines in at least three main areas: First, IAHs are treated more like investors than depositors, which impacts capital adequacy ratios, the results of the relevant risk weighting methodology, and the role of regulatory authorities on capital treatment, policies regarding the smoothing mechanism, and the bank exposure to displaced commercial risk.

Second, the Rate of Return (ROR) risk differs in that it depends on market conditions and on competition with conventional banks. The ROR might lead to the use of bank reserves or to DCR if an Islamic bank absorbs any losses (partially or entirely), if reducing its share of profits yields a shortcoming in the returns payable to IAHs, or through a donation from the shareholders share of income.

Finally, regulatory authorities ensure that Islamic banks possess an effective Sharia’a governance system to examine the compliance of Islamic banks activities, investments, and products with Islamic law. These differences might influence the way that the BCP index affects the stability of Islamic banks compared to conventional counterparts. While CPIFRs take into consideration these differences, an empirical investigation that examines their effect on the stability of Islamic banks is not possible now because the implementation of CPIFRs started only recently in 2016. We thus use the BCP index and argue that this index should have a similar effect to the CPIFR index for two reasons: First, according to IFSB (2015), seven principles in the CPIFR guidelines are kept the same, seventeen principles are amended, and only one principle is replaced. The main difference resides in four new CPIFR principles related to the specificities of Islamic banks that have not been considered in the BCP guidelines. Second, the literature often argues that the two bank types are not very different in terms of business orientation (Beck et al. 2013), stability and interest (financing) margins (Abedifar et al. 2013), profitability (Mollah and Zaman, 2015), and liquidity (Bitar et al. 2017b).

III. Data and Methodology

A. Sample Construction

In order to investigate the effect of Basel Core Principles on the stability and risk of conventional and Islamic banks, we compiled data from three main sources: (i) the IMF and the World Bank Basel Core Financial Sector assessment Program (FSAP) database, which contains detailed information on country evaluation and compliance with the Basel Core Principles for effective bank supervision (BCP) during 1999–2012; (2) the World Bank’s World Development Indicators (WDI) and World Governance Indicators (WGI) for macroeconomic and governance variables; and (3) the Bankscope Database provided by Bureau van Dijk and Fitch Ratings for accounting data.

In the selection of bank-level data, we recover financial information from 1999 to 2013 in 33 countries where both bank types exist. A bank is excluded from the sample if it does not have at least three continuous observations. Our sample includes 651 (110) conventional (Islamic) banks. In contrast to Ayadi et al. (2016), our study focuses on a broad sample of listed and unlisted banks—rather than only publicly listed banks—to avoid missing observations, given that most of Islamic banks are unlisted.

We then match bank-level information with country-level information for control of variation in a country’s macroeconomic and regulatory conditions. After checking the FSAP database, we find 28 countries that reported information on their compliance with BCP and where the two bank types exist. We also exclude countries such as Algeria, Bosnia, Brunei, Cayman Islands, Iraq, Iran, Qatar, Senegal, Sudan, and Yemen because of missing information on some of the BCP chapters. Our final sample is reduced to banks operating in 19 countries5 and characterised by the homogeneity that results from including banks in countries that have similar financial characteristics and macroeconomic conditions. Some of these countries include only a few Islamic banks, while other have a large number of conventional banks.

Because BCP chapters are collected in three different waves (1999, 2005 and 2012) and because our sample is constrained by the number of observations, we decided to match the data for different chapters as follows: (i) the 1999 wave data is used for the period 1999–2004, (ii) the 2005 wave data is used for the period 2005–11, and (iii) the 2012 wave data is used for the period 2012–13. However, some countries have witnessed two assessment waves. For instance, Saudi Arabia reports its BCP compliance in 2004 and 2011. Thus, the 2009 wave data is used for the period 2004–10 while the 2011 wave data is used for the period 2011–13.

B. Empirical Approach and Definition of Variables

The main dependent variable we use to evaluate bank stability is Z-score, and the main independent variable is the country’s BCP compliance index. We follow Mollah and Zaman (2015) and Bitar et al. (2016) and use random-effect, GLS regressions to examine the effect of BCP compliance on bank financial stability. We prefer the GLS technique, instead of other estimation techniques, for two reasons: First, regression models such as OLS ignore the panel structure of our data. Second, our Islamic bank dummy is time-invariant and cannot be estimated using a fixed-effect methodology. Accordingly, we use the following baseline regression equations:

Stabilityijt=α+β1×BCPjt+β2×Bankcontrolijt1+βt×Conutrycontroljt+Σt=1Tμt×Timet+εij      (1)
Stabilityijt=α+β1×Islamici+β2×BCPit+β3×BCPjt×Islamici+β4×Bankcontrolijt1+β5×Countrycontrolit+Σt=1Tμt×Timet+εij      (2)

where Stabilityijt represents the natural logarithm of Z-score of bank i in country j at time t. BCPjt is the Basel Core Principles compliance index for country j in time t (if a country has reported its BCP compliance more than once). Bank_controlijt–1 is a vector of bank-level control variables. Country_controljt is a vector of country-level control variables. Timet represents year fixed effects while εij is a random disturbance, assumed to be normally distributed with zero mean and constant variance, εit~iid N(0, σ2). In Eq. 2, Islamici is a dummy taking the value of one for Islamic banks and zero for conventional banks. Finally, an interaction term is introduced between Islamic and BCP compliance to investigate whether a country’s compliance with BCP affects the stability of Islamic banks differently from how it affects their conventional counterparts.

The Z-score is defined as ([return on average assets + equity/assets]/[standard deviation of the return on average assets] over (t, t-3). Demirgüç-Kunt and Detragiache (2011) interpret the Z-score as the number of standard deviations by which bank earnings would have to decrease to deteriorate the entire bank equity base. In the regression analysis, we focus on using the natural logarithm of Z-score (LnZ-score) to minimize the effects of higher values that could result from outliers. In our robustness tests, we follow Bitar et al. (2017b) and use loan-loss reserves to gross loans (LLRGLP), loan-loss provision to total loans (LLPTLP), nonperforming loans to gross loans (NPLGLP), and volatility of net-interest margin (SD NIM) to examine the impact of the BCP compliance index on the stability and risk of the two bank types.

Our main independent variable is the BCP compliance index derived from the IMF and the World Bank Basel Core Financial Sector Assessment Program (FSAP) database. This study extends the work of Demirgüç-Kunt and Detragiache (2011) and Ayadi et al. (2016) by comparing the effect of the BCP compliance index with the stability of Islamic and conventional banks mainly located in developing countries. The literature does not provide a standard measure of banking regulation and supervision. As explained and shown in the literature review, empirical studies often use surveys on banking regulation (Barth et al., 2004, 2006, 2008) to account for the institutional environment and to examine the effect of a wide range of regulatory and supervisory variables on bank financial soundness. The literature also uses accounting and market measures to examine the effect of holding higher capital, liquidity, and leverage ratios on bank financial soundness (Demirgüç-Kunt et al., 2013; Anginer and Demirgüç-Kunt, 2014; Vazquez and Federico, 2015; Bitar et al., 2016; Bitar et al., 2017a). Despite the plethora of research on banking regulation and supervision, BCP compliance index is rarely used in conventional banking literature and, to the best of our knowledge, has never been used in an Islamic banking context. The BCP index is based on 25 principles that are considered the best measures to capture compliance with banking regulation and supervision. These elements are classified into seven chapters as follows: (Ch. 1) Preconditions for Effective Banking Supervision; (Ch. 2) Licensing and Structure; (Ch. 3) Prudential Regulation and Requirements; (Ch. 4) Methods of Ongoing Supervision; (Ch. 5) Information Requirements; (Ch. 6) Formal Powers of Supervisors; (Ch. 7) Cross-Border Banking. The definition of the different elements used to construct these chapters are reported in Appendix A.2.

We follow Demirgüç-Kunt and Detragiache (2011) and Ayadi et al. (2016) and use aggregate and disaggregate approaches to distinguish between different chapters and to examine their effect on bank stability. Each of the 25 elements that constitute the BCP compliance index is evaluated based on the following four-point scale: (i) noncompliant; (ii) materially noncompliant; (iii) largely compliant; and (iv) compliant. We grade each point by assigning a numerical value (from one for noncompliant to four for compliant). The overall index of BCP compliance is then calculated as the average sum of the seven chapters.

We further allow for factors that may influence the relationship between BCP and bank stability by including two vectors: Bank_controlijt–1 is the vector of bank portfolio characteristics. It measures for bank size proxied by the natural logarithm of total assets (Inta)—which may arguably increase (Stiroh, 2004; Houston et al., 2010) or decrease bank stability and risk (Demirgüç-Kunt and Huizinga, 2010; Schaeck and Cihák, 2012; Beck et al., 2013)—and by growth rate of total assets (gtap) to allow for the expansion of a bank’s balance sheet during the current year (compared to the previous year). Abedifar et al. (2013) employ this ratio as a proxy for bank growth and development strategies. As they expand and develop, banks might be further exposed to information asymmetry, since a considerable increase in bank activities may result in weaker screening standards and lower monitoring of investments. We also include the cost to income ratio (cirp) to allow for any cross-bank differences in terms of inefficiency, where higher values reflect managerial inadequacies and thus a tendency for banks to take more risk (Chortareas et al., 2012; Abedifar et al. 2013; Beck et al., 2013). In addition, we use noninterest income to total operating income to allow for bank business model and activity diversification. Finally, we use the ratio of liquid assets to deposit and short-term funding to assess the sensitivity to bank runs, where banks with more liquid assets face lower bankruptcy costs, less information asymmetry, and are more capable of raising equity (Horváth et al., 2014; Belkhir et al., 2016).

Country_controljt is the vector of three macroeconomic and institutional variables commonly used in the stability literature (Houston et al., 2010; Demirgüç-Kunt and Detragiache, 2011; Schaeck and Cihák, 2012; Abedifar et al. 2013; Lee and Hsieh, 2013). It includes the GDP growth rate (gdpg) to allow for any potential cyclical behavior of regulation under Basel requirements, the inflation rate (inf) to capture a country’s general financial conditions, the oil rent to GDP (oil), the gas rent to GDP (gas), and mineral rent to GDP (mineral)6 as complementary measures to allow for differences between economies, especially because many countries in our sample are rich in natural resources.

Finally, we employ the world governance index as an additional measure to allow for a country’s political and institutional quality. This index is computed as the average of six governance dimensions (i.e., voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption).

In regression equations, all variables are winsorized at the 1 percent and 99 percent levels to mitigate the effect of outliers. We follow Beck et al. (2013) and Anginer and Demirgüç-Kunt (2014) and cluster at the bank level, instead of the country level, for two reasons: First, our sample includes some countries with a much larger number of observations than others. Second, as we have 19 countries, clustering at a country level might create biased results.

C. Descriptive Statistics

Table 2 reports descriptive statistics for the samples of conventional and Islamic banks. Panels A and B present the mean, the median, and the standard deviation for the bank-level dependent and independent variables, while Panel C presents the summary statistics for our key independent variable (i.e. BCP compliance index), the seven chapters, and the rest of the macroeconomic and institutional-environment control variables. Table 2, Panel D presents the BCP compliance mean for each country and the relative year of assessment.

Table 2

Descriptive statistics

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In Panels A and B, we perform Wilks’ lambda test (λ),7 Wilcoxon-Mann-Whitney test (Wilc), and the univariate analysis of variance test (F) for equality of means for each financial ratio. Results of the statistics tests are presented in the three last columns of Table 2 and suggest that conventional banks are significantly different from Islamic banks when using all the financial ratios (except the ratio of loan loss reserves to gross loans). The three tests indicate that the standard deviation of net-interest margins have the highest likelihood of separation between the two bank types, while the ratio of loan loss reserves to gross loans has the lowest. Finally, we note that in our main dependent variable (i.e. Z-score) there is a clear separation between the two bank types, as reported by the three tests as well.

In Panel C, the mean of the BCP compliance index (BCP index) is 84.95 percent, a much higher percentage than in Demirgüç-Kunt and Detragiache (2011) and Ayadi et al. (2016) studies. This percentage is likely being driven by the inclusion of a large set of banks from emerging and developing countries. Ayadi et al. (2016) argue that the BCP index is much lower in the United States and in other developed countries compared to developing countries. For instance, if we examine the upper 10 percent of BCP index distribution in Panel D, we find that the BCP index is the highest in Saudi Arabia (97.66 percent), followed by the UK (94.22 percent), then Malaysia (91.73 percent), and United Arab Emirates (90.71 percent). Three out of these four countries are developing ones. These findings suggest that banks in developing countries are moving toward global financial convergence through their compliance with BCPs and international regulation. Finally, Panel C presents the number of conventional and Islamic banks in each country. For conventional banks, the sample is dominated by banks from the United Kingdom and Bahrain for Islamic banks. We also notice that for the studied period, the number of available observations is rather weak and the percentage of reported observations (N obs. percent) is higher for conventional banks (58.4 percent) than for Islamic ones (52.1 percent).

IV. Empirical Results

In Table 3, Panel A, we present regression results examining the effect of the BCP index on bank stability using Eqs. 1 and 2. Models 1–4 report the results for conventional banks, Models 5–8 report the results for Islamic banks, and Models 9–2 report the results for the full sample. We also present the results for Z-score component for each sample after allowing for bank and country-level variables. These components include the ratio of return on average assets (ROAA), the standard deviation of ROAA (SDROAA), and the ratio of equity to assets (TETA). The Wald Chi2 tests are highly significant for all models, and the R-squares are relatively high, suggesting that the models are representative and fit with the GLS random effect regression justified in the previous section. We find that the BCP compliance index has a positive and significant effect on the stability of conventional banks (at the 1 percent level), Islamic banks (at the 5 percent level), and the full sample (at the 1 percent level). Economically, the estimated coefficients on BCP compliance in Models 1, 5, and 9 vary between 0.015 and 0.017, indicating that a one-unit increase in the BCP compliance index is associated with an increase of nearly two percentage points in the Z-score. In contrast to Demirgüç-Kunt and Detragiache (2011), our results indicate that the Z-score is higher for conventional and Islamic banks in countries with higher BCP compliance, suggesting sounder banking institutional settings. Demirgüç-Kunt and Detragiache (2011) and Ayadi et al. (2016) use a large and heterogeneous set of banks in countries with different regulatory regimes and different macroeconomic and institutional conditions, which could explain their limited findings. This study mainly focuses on countries where both Islamic and conventional banks operate with similar financial, economic, and institutional conditions.

Table 3

BCP compliance and bank stability: Islamic vs. conventional banks

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Notes: Standard errors are clustered at the bank level and are reported in parentheses below their coefficient estimates.

Statistical significance at the 10% level.

Statistical significance at the 5% level.

Statistical significance at the 1% level.

In addition, the sample mainly includes banks from developing countries, whereas Demirgüç-Kunt and Detragiache (2011) and Ayadi et al. (2016) samples are dominated by banks from developed countries.

To better understand what drives the positive association between BCP compliance and bank stability, we now focus on the components of Z-score to investigate whether such a significant impact is attributable to the effect of the BCP index on return on average assets, the volatility of returns, or bank capitalization. Table 3, Models 2 and 3 report a negative impact of BCP compliance on conventional banks’ profits (at the 1 percent level) and volatility of returns (at the 10 percent level), while, in Model 4, the association with capitalization is significantly positive (at the 1 percent level). For Islamic banks, the results appear insignificant except in Model 8, where the association between BCP compliance and bank capital is positive (at the 10 percent level). The results for the full sample report bear similar findings, although the coefficient estimate for the ratio of return on average assets becomes insignificant. In addition, Models 9–12 show that Islamic banks are not significantly different from conventional banks in terms of profits, volatility of returns, and capitalization. Finally, while our findings suggest that higher BCP compliance has a significantly positive effect on the stability of conventional banks ([αBCP] is positive and significant), we do not find any significant impact of BCP compliance on the stability of Islamic banks in Panel B ([αBCPinter] is not statistically significant), expect in Model 12, where the findings suggest that BCP compliance has a significantly positive effect on the capital ratios of Islamic banks at the 5 percent level.

Together, the findings suggest that BCP compliance is the main factor driving the Z-score of the two bank types through incentives to hold higher capital ratios in a strong regulatory environment that discourages excessive risk taking, which is inversely correlated with higher profits and volatile earnings. Findings concerning the capital ratio are consistent with newly emerged literature shedding light on the importance of institutional and regulatory factors as important determinants of bank capital decisions. For instance, Jayaraman and Thakor (2013) find that creditor protection can play a primordial role in incentivizing conventional banks to increase their capital ratios.

With regards to bank-level control variables, we find that bank size and Z-score are negatively correlated, due to the negative effect of bank size on capital for both bank types (Abedifar et al., 2013; Beck et al., 2013). We also find that bank growth of total assets is negatively associated with Z-score, reflecting weak screening standards and less monitoring incentives, especially because regulatory authorities are more flexible with large banks in term of capital requirements, which also explains the negative effect of growth of total assets ratio on bank capital. The cost to income ratio is negatively associated with bank Z-score, suggesting that managerial inadequacies reduce bank profitability and increase risk (Chortareas et al., 2012; Abedifar et al. 2013; Beck et al., 2013). With respect to Islamic banks, the effect of bank-level control variables is less pronounced, likely because of the contradictory signs between different components of Z-score. For instance, the liquidity ratios have a negative effect on bank profits and a positive effect on bank capital, which explains the insignificant effect on Z-score. For country-level control variables, we find that banks are more stable in countries with better GDP growth, higher mineral rents, lower gas rents, and lower inflation. The positive effect of GDP and mineral rents is mainly driven by ROAA, while the negative effect of gas rents and inflation is driven by the SDROAA.

V. Robustness Checks

A. BCP Index Components

To shed further light on the main results in Table 3, we now examine the impact of the seven chapters of BCP compliance on bank stability: Chapter 1. Preconditions for Effective Banking Supervision, Chapter 2. Licensing and Structure, Chapter 3. Prudential Regulations and Requirements, Chapter 4. Methods of Ongoing Supervision, Chapter 5. Information Requirements, Chapter 6. Formal Powers of Supervisors, and Chapter 7. Cross-Border Banking. While Demirgüç-Kunt and Detragiache (2011) and Ayadi et al. (2016) examine the effect of the seven chapters in a single regression model, in this study, we separately introduce each chapter and examine its effect on bank stability, taking into consideration the same bank and country-level control variables mentioned above. By doing so, we mitigate the effect of multicollinearity between different chapters and bank stability. For comparison purposes, we also report the effect of all the chapters on bank Z-score.

Results are presented in Table 4, Panel A and show important findings. First, the chapters reported in Models 2–7 have a significantly positive effect on conventional bank stability (at the 10 percent level or better). Chapter 2 on licencing and structure and Chapter 7 on cross-border banking present the most pronounced effect on conventional banks’ Z-score, while preconditions for effective banking supervision (Chapter 1) has the least pronounced effect. For Islamic banks, we also find important evidence of positive and significant association chapters reported in Models 10, 12, and 13 and Z-score. Chapter 2 on licensing and structure is, again, the chapter that has the most pronounced effect on Islamic banks’ Z-score, while Chapter 5 on information requirement is the chapter with the less pronounced effect. Second, if we compare the results after including all chapters in Models 8, 16, and 24, the findings become less pronounced for both conventional and Islamic banks—similarly to those reported by Demirgüç-Kunt and Detragiache (2011) and Ayadi et al. (2016), thus confirming our expectations regarding the problem of multicollinearity between different chapters, as well as the insignificant effect on bank stability. Finally, the results for the full sample resemble those reported separately for each sample. Specifically, our findings in Models 17–23 continue to suggest that higher BCP compliance has a significantly positive effect on the stability of conventional banks in Models 18–23 (αBCP chapters are positive and significant) and on the stability of Islamic banks in Panel B, Models 18, 20, and 21 ([αBCP chapters+αinter] are positive and significant).

Table 4

BCP compliance and bank stability: Individual chapters

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Notes: Standard errors are clustered at the bank level and are reported in parentheses below their coefficient estimates.

Statistical significance at the 10% level.

Statistical significance at the 5% level.

Statistical significance at the 1% level.

Table 4.

BCP

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compliance and bank stability: Individual chaptersNotes: Standard errors are clustered at the bank level and are reported in parentheses below their coefficient estimates.

Statistical significance at the 10% level.

Statistical significance at the 5% level.

Statistical significance at the 1% level.

B. Subsamples

We examine the robustness of previous results by exploring whether the relationship between BCP compliance and bank stability changes if we alter the sample composition to exclude regions (such as the Gulf Cooperation Council [GCC], the South East Asia [SEA], and the Middle East and North Africa [MENA]), the United Kingdom, listed and unlisted banks, and periods of different economic cycles (such as the periods before (1999–2006), during (2007–09), and after (2010–13 the financial crisis), as well as groups of countries and banks, depending on their stability, institutional environment, and efficiency scores.

Results are presented in Table 5, Panel A.1 for subsampling by regions. We find that the association between BCP compliance and conventional banks Z-score is significantly positive. This association is robust to the exclusion of banks in the GCC region, the SEA region, and the MENA region. Economically, the estimated coefficients on BCP compliance in Models 1, 4, and 7 vary between 0.008 and 0.015, indicating that a one-unit increase in the BCP compliance index is associated with an increase in the Z-score that varies between three quarters of a percentage point (when excluding conventional banks in the SEA region) and one-and-a-half percentage points (when excluding conventional banks in the MENA region). These findings suggest that the effect of BCP compliance on conventional banks stability is strongest in the SEA region, followed by conventional banks in the GCC countries, and by conventional banks in the MENA region, these latter reporting the weakest effect. For Islamic banks, the association between BCP compliance and conventional banks Z-score is marginally positive when excluding banks in the MENA region. However, the results become insignificant when excluding Islamic banks in the GCC and the SEA regions, suggesting that the positive association is mainly driven by those two regions. Finally, we do not find any significant impact of BCP compliance on the stability of Islamic banks in Panel A.2 ([ α BCPinter] is not statistically significant).

Table 5

BCP compliance and bank stability: alternative samples

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Notes: Standard errors are clustered at the bank level and are reported in parentheses below their coefficient estimates.

Statistical significance at the 10% level.

Statistical significance at the 5% level.

Statistical significance at the 1% level.