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Analyzing the Topography of Financial Regulation

Abstract

This study assesses the topography of financial regulation,

supervisory styles, and performance of banking systems across

the world. We gain insights by comparing regulatory and

supervisory practices and their impact on banking system

performance before and after the global crisis. The study

illustrates the differences in regulation/supervision among

crisis, non-crisis, and BRICS countries. Even as capital ratios

increased, bank governance and resolution regimes were

strengthened private sector incentives to monitor banks

deteriorated. The results show that the crisis-countries had

weaker regulatory and supervisory frameworks than those in

emerging countries during the crisis period. BRICS countries as

a distinct block have demonstrated uniqueness in their

regulatory/supervisory styles that are neither similar to those in

the crisis-countries nor to those in the non-crisis countries.

Keywords: central banks, banking regulation, capital

adequacy, regulation, risk, supervision, governance, crisis

JEL Classification: E58, G18, G20, G21, G32, G38, L51, O16

Article type: Research Paper

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1. Introduction

Financial regulation and supervision have been the subject of much recent debate and

attention due to the devastating effects of the global financial crisis. As the role of the

banking sector is undoubtedly indispensable in the process of financial intermediation and

thereby in achieving faster economic growth and sustainable development, a prudent

regulatory environment can not only facilitate performance of the banking systems but also

ensure financial stability. At the heart of bank regulation is a deep-seated concern that the

social and economic costs of such systemic crises are enormous. Financial crises are of

unique concern because they often have real effects on economic growth and employment.

As such, the goal of prudential regulation should be to ensure the financial stability of the

overall financial system. The recent global financial crisis has revealed the inadequacies in

supervisory enforcement and market discipline, underscoring the importance of combining

strong, timely, and anticipatory supervisory enforcement with better use of market discipline.

The crisis has triggered a healthy debate on approaches to regulation and supervision among

regulators, policymakers, and academics, leading to multiple proposals for further reforms.

As financial regulators around the world endeavor to decide how best to reform bank

regulation and supervision, an essential input ought to be a thorough understanding of what

other countries do and, eventually, of the implications of these choices.

At the global level, Barth et al., (2004) study the effect of a wide range of regulatory

and supervisory measures on banking stability and performance. Demirguc-Kunt et al.,

(2008) and Pasiouras et al., (2006) examine the effect of financial regulation and supervision

on the overall soundness of banks, as measured by credit ratings. Likewise, other studies have

pointed to weaknesses in regulatory/supervisory systems as one of the factors leading to the

crisis (Demirguc-Kunt and Detragiache, 2002; Gonzalez, 2005; Beck et al., 2006; Laeven and

Levine, 2010; Levine, 2010 and Barth et al., 2012). It is widely believed that the epicenter of

the crisis was in the developed countries, but the contagion was even to the emerging and

developing economies. This underscores the need to examine the recent state of bank

regulation and supervision in a wide range of countries and to compare them to the pre-crisis

situation. The following issues have received comparatively little attention in the financial

regulation and supervision topography: (1) To unravel the bank regulation, governance, and

supervisory styles of banking systems around the world. (2) To figure out the topography of

the regulatory and supervisory frameworks (3) To explore the bank regulation/supervisory

environment during the pre-crisis, crisis, and post-crisis periods. (4) To know how the 2

supervisory styles differed during these periods in the select group of countries: advanced

economies, emerging economies, and BRICS economies, and (5) To know what were the

factors that significantly affected the performance of banking systems in these groups of

economies during these periods.

Notwithstanding the high degree of interest in the topic and extensive work on the

global regulatory framework, there is a need to examine the information on the regulatory

and supervisory approaches pursued across countries and the changes brought about by the

crisis. This entails finding answers to three essential questions: First, what was the

topography of the regulatory and supervisory frameworks of the countries that were directly

hit by the global financial crisis? Second, how did they differ from those of the BRICS

countries? Third, what lessons can be drawn for strengthening the regulatory structures of

these countries? Furthermore, it is also desirable to trace how the national regulatory and

supervisory practices had changed since the previous BRSS in response to the global

financial crisis. In addition, an analysis of the relationship between bank performance and

stability with differences in bank regulations and supervision in BRICS countries and that of

the advanced countries merits attention. The originality of this study lies in its unique

approach to assessing the bank regulation and supervision styles around the world and their

impact on banking system profitability as it employs a robust database. Further, this study

provides not only a general assessment but also for BRICS and emerging economies.

Regulatory agencies around the world would greatly benefit from systematic evidence on the

relationship between bank performance and regulatory/supervisory systems.

The remainder of the paper is organized as follows: Section 2 presents the review of

the related literature on bank regulation, supervision, and efficiency; Section 3 describes the

data employed for the analysis and the methodological design, including the econometric

approaches; Section 4 presents the results and the related discussion; Section 5 concludes.

2. Related Literature

Banks are considered fragile as they have high leverage ratios, fractional reserves, and

high potential for a run. Great care is required in regulating banks, as they are sensitive and

fragile (Freixas and Rochet, 1997). The twin goals of bank regulation and supervision are

stability and efficiency of the financial system and these often appear to pull in opposing

directions. This has led to a raging debate on the nature and extent of the trade-off between 3

the two. Demirguc-Kunt and Detragiache (2002) and Beck et al., (2006) studied the effect of

regulations on banking crises, and Pasiouras et al., (2006) and Demirguc-Kunt et al., (2008)

have examined the effect of banking regulation on the overall soundness of banks. While

Barth et al., (2004) studied the effect of a broad range of regulatory and supervisory measures

on bank stability at the international level, Gonzalez (2005) and Laeven and Levine (2009)

examined the banks' risk-taking behavior.

Available literature on bank governance and regulation can be analyzed broadly under

two strands. First, exploring the unsystematic risk with internal variables as its potential

determinants (Brewer et al., 1996; Berger and DeYoung, 1997) and second, surveying the

systematic risk due to the negative externalities in the financial markets, regulations, and

macroeconomic conditions (Demirguc-Kunt, 1989; Hassan et al., 1994). Both streams offer

evidence of substantial correlations among the internal determinants, externalities, and the

bank risk. However, reviewing the banking regulation from the microeconomic perspective,

authors such as Santos (2000), Freixas and Santomero (2002) observe that regulation is not at

its optimal level.

Hanson et al., (2011) categorize banking regulation as micro-prudential and macro-

prudential, and observe that micro-prudential regulation is one in which regulation itself is a

partial equilibrium in its conception. Micro-prudential regulation is aimed at preventing the

costly miscarriage of individual financial institutions while the macro-prudential approach is

one that recognizes the general equilibrium effects and strives to safeguard the financial

system in its entirety. Bank regulation intends not only fosters investor protection but also

enhances the efficiency of capital allocation to raise the efficacy of financial markets.

Especially in emerging markets, the measurement used more often for regulating the banking

industry includes reserve requirements, suspension of convertibility, deposit insurance, and

capital adequacy requirements (Eichberger and Harper, 1997).

Emphasizing the need for regulation to safeguard banking stability, Swamy (2013)

observes that ensuring overall macroeconomic balance, enhancing the macro-prudential

functioning of institutions and markets, and reinforcing micro-prudential institutional

soundness through regulation and supervision need to be regularly done. A more detailed

debate of the formative papers on banking regulation can be obtained in Dewatripont and

Tirole (1993), and Freixas and Rochet (1997).4

Beliefs like the banks are “too big to fail” or “too big to discipline” often give rise to

the reasoning that the banks wield considerable economic power and consequently, political

clout, which leads to aggressive risk-taking behavior. Over a period of years, banks have

grown horizontally as well as vertically to such a complex extent that they are posing

difficulties in monitoring too. The “originate to distribute” (OTD) strategy quite obviously

allows the global systemically important financial institutions (G-SIFIs) to originate risky

loans and package them into asset-backed securities (ABS) with structured tranches and

subsequently repackage them further as collateralized debt obligations (CDOs) in upper-level

securitizations. Though in the short run, the OTD strategy is quite attractive and convincing,

in practical effect, in the long run, credit default swaps (CDS) and the synthetic CDOs

engineered by G-SIFIs have resulted in multiple bets in high-risk loans (Wilmarth, 2010).

Given the theoretical setting, there is a need to study the regulatory impact on the top five

banks in the banking systems during the crisis period.

Conventional approaches to bank regulation underscore the positive features of capital

adequacy requirements (Dewatripont and Tirole, 1994). Quite a few notable theoretical

considerations are observed in understanding the risk-taking behavior of the banks. Risk-

taking is an effect of the cause, such as the “conflict of interest” that may arise when banks

diversify their activities (such as insurance underwriting, real estate investment, securities

underwriting, etc.) as they may dump such securities on ill-informed investors in order to

help firms with outstanding loans (John et al., 1994). It is the factor of moral hazard1 that

induces the risk-taking behavior of the banks (Demirguc-Kunt and Detragiache, 2002), as this

would lead the banks to have more opportunities to engage themselves in a wide range of

activities (Boyd et al., 2005).

The ownership structure and management behavior influence the risk-taking behavior

of the banks. It is widely held that bank risk2 is dependent on each bank’s ownership structure

as standard agency theories advocate that bank risk-taking is influenced by ownership

structure (Jensen and Meckling, 1976; John et al., 2008). Further, Galai and Masulis (1976)

have found that diversified owners in the case of limited liability firms have incentives to 1 Merton (1977) was the first to quantify the moral hazard issue by relating the value of deposit insurance with that of a put option on the FDIC. In this regard, Pennacchi (2005) has evoked significant concerns of moral hazard as that induces the banks to invest in off-balance sheet portfolios with high systematic risk. Likewise, Bhattacharya et al., (1998) too have held the view that government deposit insurance affects the behaviors of banks.

2Walid and Eric (2010) have established a causal relationship between degree of internationalization and performance, but find that the nature of this relationship varies by bank, and also depends upon the riskiness associated with each bank's foreign asset exposures.

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increase the banks’ risk-taking tendency as they collect funds from depositors and

bondholders. Correspondingly, Jensen and Meckling (1976) and Demsetz and Lehn (1985)

observe that managers with ‘private benefits of control’ over banks tend to resort to less risk-

taking. In a detailed study of banking firms, providing evidence that stockholder-controlled

banks embrace more risks than managerially controlled banks, Saunders, et al., (1990) have

observed that management stock ownership induces their risk-taking behavior. Further, John

et al., (2000) in their seminal study of the theory of bank regulation and management

compensation argue for a towering role in managerial compensation structures in bank

regulation. Against this backdrop, it is essential to study the impact of the regulatory

environment on the ownership structures during the crisis period.

Banks experience risk due to the macroeconomic outlook as a slowdown in economic

growth is tied to high inflation, soaring interest rates, and a depreciating currency (Demirguc-

Kunt and Detragiache, 1998). On the other hand, Taylor (2009) and Yellen (2009) underscore

the viewpoint that a free flow monetary policy leads to excess liquidity and consequent low-

interest rates, resulting in a burst of financial engineering and innovation, which further

amplifies and accelerates the consequences of excess liquidity and rapid credit expansion,

ultimately giving rise to asset bubbles. Suggesting how the relationship between integration

and synchronization depends on the type of shocks hitting the world economy, Kalemli‐Ozcan et al., (2013) show that shocks to global banks played an important role in triggering

and spreading the global financial crisis.

The profit-seeking behavior of banks is at the core of the Minskyan model of financial

instability. In an uncertain decision-making environment, the rational profit-seeking behavior

of banks pushes them to pursue risk-taking financial practices that give rise to a state of

escalating financial fragility. According to Yellen (2009), asset price bubbles are at the heart

of Minsky’s viewpoint on how financial meltdowns occur. It is the imperfectness of financial

markets, and more particularly, the information asymmetries, which is the source of financial

instability or a crisis as is established in Mishkin’s approach. According to Mishkin, an

upsurge in information asymmetry causes ex-ante a compounding risk of adverse selection.

As has been observed in the recent past, the perverse incentives to managers that exist in the

banking industry persuade them to take on too much risk, which leads to crises (Davidson,

2010).

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The foregoing theoretical framework entails undertaking a thorough examination of

the bank regulation/supervisory environment during the crisis period and figuring out what

was the topography of the regulatory and supervisory frameworks of the countries that were

directly hit by the global financial crisis vis-a-vis that of countries that were not directly

affected. In addition, it would be desirable to examine the regulatory environment in the case

of the BRICS countries and find out whether they were quite different. In addition, an

analysis of the relationship between bank performance and stability with differences in bank

regulations and supervision in BRICS countries and that of the advanced countries merits

attention. What are the lessons that can be drawn to strengthen the regulatory structures of

these countries? Furthermore, there is need to trace how the national regulatory and

supervisory practices have changed and the kind of inferences that could be drawn to build

the regulatory literature in this domain?

3. Data and Methodology

We source the data from the World Bank’s Bank Regulation and Supervision Survey

(BRSS) data collected under its research program on Financial Institutions and Regulation.

The BRSS, carried out by the World Bank, is a unique source of comparable worldwide data

on the bank regulatory/supervisory styles of the banking systems around the world. Including

the current version of the survey database updated in 2012, and the earlier surveys released in

2001, 2003, and 2007, in all, four databases are explored in the analysis of this study. The

2012 survey3 database provides information on bank regulation and supervision for 143

jurisdictions. It covers data since 2008 and is therefore quite useful in scrutinizing the state of

bank regulation and supervision in the focus countries of this study and comparing it to the

pre-crisis situation. For the analysis, we consider 30 countries that are significant in terms of

their geo-economic position, exposure to the crisis, and the nature of their banking &

financial systems. These include fifteen countries directly affected by the crisis (systemic and

borderline cases) and fifteen of those indirectly affected by contagion. Amongst them are the

BRICS countries for a differentiated focus of the study. In all, the thirty countries considered

under this study cover more than 75 percent of global banking. We have classified the crisis

3 The World Bank’s BRSS survey of 2011-12 provides data for the years 2008, 2009, and 2010 for 143 countries, of which 37 are advanced and 106 are emerging and developing economies, and provides a balanced representation of countries in terms of level of income and population size. In terms of topical coverage, the survey is quite comprehensive, providing unique and valuable information on a wide range of issues related to bank regulation and supervision. It contains over 270 questions, some with sub-questions covering about 630 features of bank regulation and supervision.

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countries using the database developed by Laeven and Valencia (2010)4. Table 1 lists the

countries included in this study.

[Table 1 is about here]

Not all the responses to the BRSS questionnaire have been considered for analysis

due to issues of comparability. We have considered only those significant responses on

questions that cover topics on which consistent cross-country data are already available,

easily comparable, and widely acceptable. We choose our study variables from the available

versions of World Bank’s BRSS (i.e. released in 2000, 2003, 2007, and 2012). The bank

regulation and supervision variables are detailed in Table 2.


Comparing the responses to the previously mentioned BRSSs and attributing the

changes observed to the crisis is debatable as we cannot be sure that the changes observed

were indeed caused by the crisis. However, to probe the changes that were directly related to

the crisis, the BRSS 2012 includes questions that explicitly request regulators to identify

reforms introduced in response to the crisis.

We examine the relationship between the bank regulation and supervision variables

and the bank performance outcomes. This analysis contributes uniquely to the bank

regulation, supervision, and performance literature by investigating the impact of bank

regulatory and supervisory approaches on bank performance during the pre-crisis, post-crisis,

and crisis periods as none of the studies focuses on this issue. We estimate the regulatory

impact on banking performance during the pre-crisis, crisis, and post-crisis periods by

regressing each of the three important outcome variables (viz. after tax return, non-interest

income, and domestic credit provided by the financial sector) on various supervisory and

regulatory indicators. We use ordinary least square regressions to examine the relationships

between bank performance outcomes and bank regulation and supervision variables. To

estimate the impact of the regulatory variables on performance / (in)efficiency while

controlling for other country-specific characteristics, we provide the specification of the

model as follows:4Laeven and Valencia (2010) provide a new database of systemic banking crises for the period 1970-2009, building on earlier work by Caprio et al., (2005), Laeven and Valencia (2008), and Reinhart and Rogoff (2009). The update makes several improvements to the earlier database, including an improved definition of systemic banking crisis, the inclusion of crisis ending dates, and a broader coverage of crisis management policies. The database is the most up-to-date banking crisis database available. Table 1 in the paper provides the classification of countries for systemic banking crises, 2007-2009.

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P=∫ {(regulatory variables)+(economic variables)} Eq (1)

P=∫¿¿

We write the Eq (1) as

Pi=α+β ( X i+Y i )+∈ Eq (2)

where Рi is the banking system performance variable (such as after-tax return – atr, non-

interest income – nii, domestic credit provided by the financial sector as a percent of GDP –

dcfs); “X” is the bank regulation/supervisory variable, “Y” is the macroeconomic variable to

control for the macroeconomic environment, and “ε” is the error term. We follow white

heteroskedasticity-consistent procedures, estimating with the standard deviation weights.

Further, substituting the Eq 2 with the bank regulation/supervisory variables:

atr i=α +β1arbcar i+ β2 atfbi+β3 npli+β 4rer i+β5 osei+β6 gdpgr i+β7 infli+∈(Eq 3)

niii=α+ β1 arbcari+β2 npli+β3 rer i+β4 rers i+ β5 fbai+β6 fcl i+β7 nff i+β8 gdpgr i+β9 infli+∈ (Eq 4)

dcfsi=α+β1 arbcari+β2 atfbi+ β3 npli+β4 rer i+ β5 fbai+β6 fcli+β7 nff i+β8 gdpgr i+β9 infli+∈(Eq 5)

We present the results of the econometric analysis in the ensuing section. Our first

dependent variable – after-tax return on equity of the commercial banking system relates

largely to the overall (fund-based as well as non-fund based) activities of the banks (atr) and

acts as a general indicator of the financial institution’s performance. The second dependent

variable – percent of the commercial banking system's total gross income that was in the form

of non-interest income (nii) is related to the non-fund based activities of the banks and

generally indicates the efficiency of the financial institution’s ability to generate revenue

without the use of costly funds. The third dependent variable – domestic credit by the

financial sector as a percent of GDP (dcfs) – indicates the loan output of the financial

institutions.

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We examine the relationships between bank performance outcomes as described and

bank regulation and supervision variables during the pre-crisis period, post-crisis period, and

the crisis period. In order to elicit the relationship in the case of BRICS countries and

emerging countries, we introduce dummies d1 and d2 respectively. Though La Porta et al.,

(1998) observe that legal origin helps account for cross-country differences in financial

development, Barth et al., (2004) find that the results do not depend on including these

controls. Accordingly, we do not bring in the legal origin controls into our model. We believe

there are two methodological limitations to this analysis. One is that we conduct pure cross-

country regressions because information on regulations and supervisory practices is available

at particular points in time. The limitation with this approach is that it is challenging to

control fully for potential simultaneity bias as banking-sector outcomes may influence

regulations and supervisory practices. The other limitation is that only aggregate measures of

bank performance as available in the BRSS have been used. However, our cross-country

study provides a unique assessment of the relationships between banking systems’

performance and the regulation/supervision of banks of select geo-financially important 30

countries (including advanced and emerging) around the world. Further, we employ annual

growth of gross domestic product (gdpgr) and Inflation (infl) as the control variables. The

data for these two control variables and the third dependent variable dcfs is sourced from

World Development Indicators 2014 of World Bank database.

We perform multivariate regression analyses to understand the banking sector

outcomes and regulation/supervision employing a wide range of bank regulation/supervision

indicators. First, we use ordinary least squares regressions to observe the relationships

between bank outcomes and bank regulation and supervision. In these regressions, we regress

each of the three outcome variables (after-tax return on equity in the commercial banking

system, percent of the commercial banking system's total gross income that was in the form

of non-interest income, and domestic credit provided by the financial sector as a percent of

GDP) on various supervisory and regulatory indicators. To estimate the relationship between

performance and regulatory/supervisory practices, we need to control for (i) exogenous

determinants of bank performance and (ii) potential endogeneity of bank regulations and

supervisory practices. We do this in two steps. First, we use existing theory and evidence to

identify the exogenous determinants of banking sector development and include these

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determinants as control variables in the bank performance regulation/supervision regressions.

Second, we choose exogenous determinants of bank regulation and supervision and use them

as instrumental variables. By doing this, we provide an empirical assessment of whether

simultaneity bias is driving the results. As La Porta et al., (1998) observe, legal origin helps

account for cross-country differences in financial development; we include emerging

markets’ origin dummy and BRICS dummy variables as exogenous control variables.

4. Results and Discussion

Pre-Crisis Period:

The descriptive of variables in the pre-crisis period analysis is presented in Table 3.

We present the results of the analysis of banking systems’ performance and

regulation/supervision during the pre-crisis period in Table 4. The significant positive

relationship of assets held by the top five largest banks suggests the predominance of these

banks in their banking systems. In line with the theory, non-performing loans were of concern

for bank profitability as is suggested by the level of significance and the negative sign. On

expected lines, the real estate activities of banks negatively influenced the banking systems,

indicating the failure of macro-prudential regulation (Jalilian et al., 2007). During the pre-

crisis period, allowing the banks to engage in real estate activities impeded their profitability.

Supporting the theory of bank regulation from macroeconomic perspectives (Rochet, 2002),

banks owning 100% of the equity in any nonfinancial firm was positively associated with

after-tax income. The on-site examinations per bank in the BRICS countries were found to be

positively associated with after-tax income at a 5 percent significance level. Further, the

significance of the dummy variable for emerging economies suggests that the banking

supervision was different and more effective than in advanced economies.



How different the regulatory impact on the after-tax income of the banking systems in

the BRICS countries during the pre-crisis period was? is analyzed using the interaction

dummies in the multivariate regressions (Table 5). The positive relationship of assets held by

the top five largest banks suggests the dominance of these banks in BRICS’ banking systems,

though with a 10 percent significance level. The distinctness of the regulatory environment in

the BRICS countries can be seen from the fact that banks owning 100% of the equity in any

nonfinancial firm were positively associated with after-tax income at a 5 percent level of

significance. The on-site examinations per bank in the BRICS countries were found to be 11

positively associated with after-tax income. The power of supervisory agencies to suspend

the directors’ decision to distribute dividends had a positive impact on after-tax income in the

BRICS’ banking systems. Banks engaging in real estate activities during the pre-crisis period

in the BRICS countries were negatively associated with after-tax income.


We analyze the distinctiveness of regulatory impacts on the after-tax income of

banking systems in emerging economies during the pre-crisis period (Table 6). The positive

relationship of assets held by the top five largest banks suggests the dominance of these

banks in the banking systems of emerging economies, though without econometric

significance. The distinctness of the regulatory environment in the emerging economies can

be seen from the fact that banks owning 100% of the equity in any nonfinancial firm were

positively associated with after-tax income, though without econometric significance. The

on-site examinations per bank in the BRICS countries were found to be positively associated

with after-tax income. The power of supervisory agencies to suspend the directors’ decision

to distribute dividends had a negative impact on after-tax income in the emerging economies’

banking systems. Banks engaging in real estate activities during the pre-crisis period in the

emerging economies were negatively associated with an after-tax income similar to that in

BRICS economies.


We analyze the regulatory impact on the non-interest income of banking systems in

BRICS economies during the pre-crisis period (Table 7). The significant positive relationship

of assets held by the top five largest banks suggests the dominance of these banks in their

banking systems. The distinctness of the regulatory environment in the BRICS countries can


positively associated with after-tax income at a 5 percent level of significance. The power of

supervisory agencies to suspend the directors’ decision to distribute dividends had a positive

impact on after-tax income in the BRICS banking systems. Banks engaging in real estate

activities during the pre-crisis period in the BRICS countries were positively associated with

non-interest income at a 5 percent level of significance. On-site examinations were positively

associated with non-interest-income at a 5 percent level of significance. Banks engaging in

securities activities positively influenced the non-interest income of the banking systems at a

5 percent significance level.


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We analyze the regulatory impact on the non-interest income of banking systems in

emerging economies during the pre-crisis period (Table 8). The power of supervisory

agencies to suspend the directors’ decision to distribute dividends had a negative impact on

after-tax income in the emerging economies’ banking systems at a 5 percent level of

significance. On-site examinations were positively associated with non-interest-income,

though not with econometric significance. Banks engaging in securities activities positively

influenced the non-interest income of the banking systems at a 5 percent significance level.


How was the regulatory impact on domestic credit by the financial sector in BRICS

countries during the pre-crisis period analyzed (Table 9)? We observe a significant positive

relationship of assets held by the top five largest banks, which suggests the dominance of

these banks in their banking systems. The distinctness of the regulatory environment in the

BRICS countries can be seen from the fact that banks owning 100% of the equity in any

nonfinancial firm were positively associated with after-tax income at a 5 percent level of

significance. The power of supervisory agencies to suspend the directors’ decision to

distribute dividends had a positive impact. Banks engaging in real estate activities during the

pre-crisis period were as negatively associated, though without econometric significance. On-

site examinations were positively associated at a 5 percent level of significance. Banks’

engaging in securities activities had a positive influence at a 5 percent significance level.


We analyze the regulatory impact of the financial sector on domestic credit in

emerging economies during the pre-crisis period (Table 10). We observe a negative

relationship of assets held by the top five largest banks to domestic credit. The power of

supervisory agencies to suspend the directors’ decision to distribute dividends had a negative

impact. On-site examinations were positively associated, though not with econometric

significance. Banks engaging in securities activities positively influenced though not with

econometric significance.


Crisis Period:

We present the description of variables for the analysis during the crisis period in

Table 11. The results of the multivariate regression analysis of the banking systems’

performance and regulation/supervision during the crisis period are presented in Table 12.

The negative association of the assets held by the top five largest banks suggests the 13

predominance of these banks in their banking systems. In line with the theory, non-

performing loans were affecting negatively, at a 5 percent level of significance. The on-site

examinations were negatively associated with after-tax income. During the crisis period, the

total foreign-owned bank assets in the domestic banking system were positively influenced

non-interest income at a 10 percent level of significance. The commercial banking system’s

liabilities that were foreign-currency denominated were positively influencing the domestic

credit in the banking systems at a 5 percent level of significance. However, banks owning

100 percent of the equity in the nonfinancial firm negatively affected the domestic credit in

the banking systems at a 1 percent level of significance.



We analyze the regulatory impact on the after-tax income of banking systems in

BRICS countries during the crisis period (Table 13). The significant positive relationship of

assets held by the top five largest banks suggests the dominance of these banks in their

banking systems. The distinctness of the regulatory environment in the BRICS countries can


positively associated with after-tax income. Banks engaging in real estate activities were

positively associated with after-tax income. Banks’ engagement in securities activities

negatively affected the non-interest income of the banking systems at a 5 percent significance

level. The ratio of non-performing loans to total gross loans negatively affected after-tax

income at a 5 percent level of significance. The banks’ engagement in insurance activities

negatively affected their after-tax income.


We show in Table 14 the results of the analysis of the regulatory impact on the after-

tax income of banking systems in emerging economies during the crisis period. The negative

relationship of assets held by the top five largest banks suggests the significant reduction in

the after-tax income of these banks, causing a decelerating impact on the banking systems of

emerging economies. The banks’ engagement in insurance activities negatively affected their

after-tax income. The ratio of non-performing loans to total gross loans negatively affected

after-tax income at a 5 percent level of significance. The banks’ engagement in real estate

activities negatively affected after-tax-income. The banks’ engagement in securities activities

negatively affected the after-tax income.


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We present the results of the analysis of the regulatory impact on the non-interest

income of banking systems in BRICS economies during the crisis period in Table 15. The

negative relationship of assets held by the top five largest banks suggests the significant

reduction in the non-interest income of these banks, causing a decelerating impact on the

banking systems of emerging economies. The banks’ engagement in insurance activities

negatively affected their after-tax income. Their engagement in real estate activities

negatively affected non-interest income. The banks’ engagement in securities activities

negatively affected the non-interest income.


We report in Table 16 the results of the analysis of the regulatory impact on the non-

interest income of banking systems in emerging economies during the crisis period. The

positive relationship of assets held by the top five largest banks suggests the regaining of the

business by these banks in not having a decelerating impact on the emerging economy

banking systems. The banks’ engagement in insurance activities positively affected their

after-tax income. The ratio of non-performing loans to total gross loans did not negatively

affect non-interest income. However, the banks’ engagement in real estate activities

negatively affected non-interest income. Further, the banks’ engagement in securities

activities negatively affected the non-interest income.


Table 17 reports the results of the analysis of the regulatory impact on domestic credit

by the financial sector in BRICS economies during the crisis period. The negative


domestic credit in these banking systems. The power of supervisory agencies to suspend the

directors’ decision to distribute dividends had a negative impact on domestic credit. The

banks’ engagement in insurance activities positively affected their domestic credit. Their

engagement in real estate activities negatively affected domestic credit. The ratio of non-

performing loans to total gross loans did not negatively affect domestic credit at a 10 percent

level of significance. However, the banks’ engagement in real estate activities positively

affected domestic credit. Their engagement in securities activities did not have a negative

impact on domestic credit.


We present in Table 18 the results of the analysis of the regulatory impact on

domestic credit by the financial sector in emerging economies during the crisis period. The

negative relationship of assets held by the top five largest banks suggests the significant 15

reduction in the domestic credit in these banking systems. The power of supervisory agencies

to suspend the directors’ decision to distribute dividends had a negative impact on domestic

credit. Banks’ engagement in insurance activities positively affected their domestic credit.

Banks engagement in real estate activities was negatively affecting domestic credit. The ratio

of non-performing loans to total gross loans was not negatively affecting domestic credit at

10 percent level of significance. However, the banks’ engagement in real estate activities was

positively affecting domestic credit. Banks’ engagement in securities activities was not

having a negative impact on the domestic credit.


Post-Crisis Period:

Table 19 reports the description of variables for analysis during the post-crisis period.

The multivariate regression analysis for the post-crisis period shows interesting results (Table

20). The positive association of the assets held by the top five largest banks suggests the

predominance of these banks in their banking systems. In line with the theory, non-

performing loans had a negative effect on all the three dependent variables. The on-site

examinations positively affected after-tax income and domestic credit. However, banks

owning 100 percent of the equity in the nonfinancial firm negatively affected the domestic

credit in the banking systems. The power of supervisory agencies to suspend the directors’

decision to distribute dividends had a negative impact on non-interest income. The banks’

engagement in insurance activities positively affected their domestic credit. The general

provisions to total gross loans positively affected after-tax income.



Table 21 reports the results of the analysis of the regulatory impact on the after-tax

income of banking systems in BRICS economies during the post-crisis period. The positive

relationship of assets held by the top five largest banks suggests the significant improvement

in after-tax income in these banking systems. The power of supervisory agencies to suspend

the directors’ decision to distribute dividends had a positive impact. The banks’ engagement

in insurance activities positively affected their after-tax income. The ratio of non-performing

loans to total gross loans did not significantly affect after-tax income. However, the banks’

engagement in real estate activities did not negatively affect the after-tax income. The banks’

engagement in securities activities did not have a negative impact on the after-tax income.


16

Table 22 reports the results of the analysis of the regulatory impact on the after-tax

income of banking systems in emerging economies during the post-crisis period. The positive

relationship of assets held by the top five largest banks suggests the significant improvement

in the after-tax income in these banking systems. The power of supervisory agencies to

suspend the directors’ decision to distribute dividends had a positive impact. Banks’

engagement in insurance activities positively affected their after-tax income. The ratio of

non-performing loans to total gross loans was not significantly affecting after-tax-income.

However, Banks engagement in real estate activities was not negatively affecting after-tax-

income. Banks’ engagement in securities activities was not having a negative impact on the

after-tax income.


The results of the analysis of the regulatory impact on the non-interest income of

banking systems in BRICS economies during the post-crisis period are presented in Table 23.

The positive relationship of assets held by the top five largest banks suggests the significant

improvement in the non-interest income of these banks, causing an accelerating impact on the

banking systems in emerging economies. The power of supervisory agencies to suspend the

directors’ decision to distribute dividends had a positive impact. The banks’ engagement in

insurance activities positively affected their non-interest income. Their engagement in real

estate activities negatively affected non-interest income. The banks’ engagement in securities

activities negatively affected the non-interest income at a 10 percent level of significance.


Table 24 presents the results of the analysis of the regulatory impact on the non-

interest income of banking systems in emerging economies during the post-crisis period. The

positive relationship of assets held by the top five largest banks suggests the significant

improvement in the non-interest income of these banks, causing an accelerating impact on the

banking systems of emerging economies. The power of supervisory agencies to suspend the

directors’ decision to distribute dividends had a positive impact. The banks’ engagement in

insurance activities negatively affected their non-interest income. Their engagement in real

estate activities did not negatively affect non-interest income. However, banks owning 100

percent of the equity in nonfinancial firms negatively affected the non-interest income in the

banking systems. The banks’ engagement in securities activities negatively affected the non-

interest income.


17

Table 25 presents the results of the analysis of the regulatory impact on domestic

credit by the financial sector in BRICS countries during the post-crisis period. The negative


the domestic credit in these banking systems. The power of supervisory agencies to suspend

the directors’ decision to distribute dividends had a negative impact on domestic credit. The

banks’ engagement in insurance activities negatively affected their domestic credit. Banks

owning 100 percent of the equity in nonfinancial firms negatively affected the domestic

credit in the banking systems. The ratio of non-performing loans to total gross loans did not

negatively affect non-interest income at a 10 percent level of significance. The banks’

engagement in real estate activities negatively affected domestic credit. The banks’

engagement in securities activities did not have a negative impact on the domestic credit.


Table 26 presents the results of the analysis of the regulatory impact on domestic

credit by the financial sector in emerging economies during the post-crisis period. The

negative relationship of assets held by the top five largest banks suggests a significant

reduction in the domestic credit in these banking systems. The power of supervisory agencies

to suspend the directors’ decision to distribute dividends had a negative impact on domestic

credit at a 5 percent level of significance. The banks’ engagement in insurance activities

negatively affected their domestic credit. Banks owning 100 percent of the equity in

nonfinancial firms negatively affected the domestic credit in the banking systems. The ratio

of non-performing loans to total gross loans did not negatively affect domestic credit. The

banks’ engagement in real estate activities negatively affected domestic credit. Their

engagement in securities activities did not have a negative effect on domestic credit.


On expected lines, non-performing loans had a substantial negative impact on the

performance of financial institutions. The significant negative relationship of assets held by

the top five largest banks with the after-tax return on equity establishes that the assets of these

banks deteriorated substantially, causing negative returns to these banking systems and

resulting in increasing losses during the crisis period. Allowing banks to own 100% of the

equity in any nonfinancial firm had a significant negative impact on the banking systems

during the crisis period. It is interesting to find the positive impact of the banking systems’

liabilities denominated in foreign currency as evidenced by the fact that most of the advanced

banking systems failed during the crisis period and were able to find some solace. The

significance of the dummy variable for emerging economies and BRICS economies suggests 18

that the banking supervision was different and more effective than in the advanced economies

during the crisis period.

We find that the significance of actual risk-based capital indicates that with an average

of 12.91 percent, it had a positive impact on profitability. The actual risk-based capital ratio

being found positively significant only in BRICS countries confirms the hypothesis of a

positive link between capital requirements and bank profitability, particularly in the post-

crisis period5. The significant positive relationship of assets held by the top five largest banks

in all the three performance variables during the post-crisis period confirms that these are

vigorously recovering from their worst performance experienced during the crisis period. The

power of supervisors to control dividend distribution is found to have an adverse impact on

bank profitability, suggesting that supervisory agencies have obligated by the crisis period

experience, vigorously exercised their powers in regulating the bank directors’ powers to

distribute dividends. In line with the theoretical arguments (Boudriga et al., 2009), non-

performing loans continued to be of concern during the post-crisis period as well for bank

profitability as is suggested by the level of significance and the negative sign. Further, the

model is negatively significant in the case of emerging economies.

To sum up, in terms of structural topography, while government ownership of banks

has surged during the crisis period in the crisis and BRICS countries, there was a substantial

decrease in the assets of foreign banks in the crisis, non-crisis, and BRICS countries.

However, foreign-owned bank assets were found to have substantially increased only in the

crisis countries. There was substantial capitalization of banks not only in the advanced

countries of the crisis and non-crisis groups but also in BRICS countries which led to the

belief that there was indeed a spillover effect of the crisis on the BRICS countries. Therefore,

there is scope to reason that BRICS countries took lessons from the crisis and geared up to

strengthen their banking systems. Though these asset classification norms were already in

place before the crisis, either their implementation was flawed or the supervisory agencies

were not passionately enforcing them. General provisions against loans drastically went up in

the non-crisis emerging countries, leading us to conclude that these countries have taken a

cue from the crisis and initiated the required changes to place necessary firewalls against

bank failures.

5 Too little capital increases the danger of bank failure whilst excessive capital imposes unnecessary costs on banks and their customers and may reduce the efficiency of the banking system (Barth et al., 2004; 2006)

19

During the crisis period, the swift regulatory action is evident in the curbing/stopping of

banks from wholly owning nonfinancial firms, particularly in advanced and emerging

countries. Likewise, emerging and BRICS countries too have taken measures to curb/stop the

banks from actively involving themselves in securities activities. We find no significant

change in the depositor protection/guarantee measures, suggesting that the crisis did not

instigate substantial changes in this direction. Public sector claims sharply swelled only

among the crisis (advanced) and crisis-countries, suggesting that governments lent

substantially to bail out these banks. While the supervisory powers to control dividend

distribution strengthened substantially in the crisis countries only on-site examinations were

considerably better in the non-crisis countries than in the crisis countries. Interestingly, the

results suggest that BRICS countries did not undergo any substantial supervisory

transformation in this regard.

5. Conclusion

Our results offer interesting insights about the bank regulatory/supervisory styles,

illustrate the differences in regulation between crisis, non-crisis, and BRICS countries, and

highlight the ways in which bank regulation and supervision have changed during the crisis

period. Drawing on the analysis, we conclude that the world experienced different styles of

regulatory/supervisory styles in dealing with the crisis. Crisis-countries were not only laid

back in the treatment of bad loans and loan losses, they were also deficient in regulating the

capital requirements, constituting greater provisions, or in suspending bonuses or withholding

management fees. Even though crisis countries had robust information disclosure

requirements, the incentives for the private sector to monitor banks’ risks were weaker and

hence were able to aid in better risk management. On the contrary, emerging economies did

fare better, partly because of structural reasons and partly because their policies worked in

their favor. The soundness of domestic financial sectors also improved in emerging countries,

mostly due to better regulation and supervision, and prudent practices. Perhaps for the first

time in recent decades, the domestic financial systems of many emerging countries did not

amplify the shocks from the crisis. The crisis countries had weaker regulatory and

supervisory frameworks compared to those in emerging countries during the crisis. BRICS

countries as a distinct block have demonstrated uniqueness in the regulatory/supervisory

styles which are neither similar to the crisis-countries nor to the non-crisis countries. Their

regulatory practices have greatly evolved and hence could sustain the onslaught of the crisis 20

remarkably with relatively less damage and faster recovery. Overall, the

regulatory/supervisory styles are evolving and there have not been swift changes only due to

the crisis, except for some noteworthy developments, particularly in the area of capital

adequacy, asset classification approaches, controlling the managements in dividend

distribution and management fees, and allowing banks to take up related activities like

owning nonfinancial firms, dealing in securities and insurance businesses, etc. Although these

changes are encouraging, there is still scope for further reforming the regulatory and

supervisory structures as well as policies and practices.

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Table 1: List of countries covered in the study

Sl. No. Crisis-countries Countries indirectly affected by crisisAdvanced Emerging Advanced Emerging

1 Cyprus Argentina2 Denmark Australia 3 France Brazil4 Germany Canada5 Greece China6 Ireland Egypt7 Italy India8 Netherlands Indonesia9 Poland Kuwait10 Portugal Malaysia11 Russia Mexico

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http://www.frbsf.org/news/speeches/2009/0416.html

12 Spain New Zealand13 Switzerland Philippines14 United Kingdom South Africa15 United States Thailand

Notes: Countries of systemic cases with systemic banking crises are in bold font and the remaining with borderline cases are in regular font. Laeven and Valencia (2010) define systemic banking crises as cases where at least three of the listed interventions took place, and borderline cases are those that almost met their definition of a systemic crisis. Our classification of countries into advanced and emerging economies is influenced by the World Economic Outlook April 2011 of IMF (Table 4.1: Economy groupings). BRICS Countries (as per World Economic Outlook Database, April 2013, IMF) are in italic.

Table 2: Bank regulation/supervision variables Variables Symbol

1 Existence of an asset classification system under which banks have to report the quality of their loans and advances using a common regulatory scale acs

2 Actual risk-based capital ratio of the banking system arbcar3 Percent of total assets held by the five largest banks atfb4 Applications for commercial banking licenses from domestic entities: Accepted bl

5 Coverage of total deposits of participating commercial banks under protection/guarantee schemes dg

6 Power of supervisory agencies to suspend the directors’ decision to distribute dividends div

7 Effective tax rate on the aggregate commercial banking system's pre-tax income etr

8 Percent of the total foreign-owned bank assets in domestic banking system held in branches as opposed to other juridical forms (e.g. subsidiaries) fba

9 Percent of the commercial banking system’s liabilities that was foreign-currency denominated fcl

10 Ratio of general provisions to total gross loans gpr11 A bank can engage in insurance activities ins12 A bank may own 100% of the equity in any nonfinancial firm nff13 Ratio of non-performing loans (gross of provisions) to total gross loans npl14 Aggregate operating costs to assets ratio for the commercial banking system oc15 Onsite examinations per bank that were performed in the last 5 years os16 Minimum provisioning required as loans become Sub-Standard Assets pssa17 A bank can engage in real estate activities reest18 Percent of total bank assets that were residential real estate loans rer19 Percentage of residential real estate loans that were securitized rers20 Statutory corporate tax rate on domestic bank income sct21 A bank can engage in securities activities sec22 Ratio of specific provisions to gross non-performing loans spr

Table 3: Descriptive of Variables – Pre-Crisis PeriodMean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera

acs 0.73 1.00 1.00 0.00 0.45 -1.06 2.11 6.55arbcar 0.13 0.13 0.20 0.05 0.03 -0.28 4.51 3.25asset 0.91 0.98 1.00 0.08 0.18 -3.75 17.48 288.11atfb 0.65 0.67 0.90 0.18 0.17 -0.57 2.99 1.64Atr 0.04 0.12 0.21 -1.17 0.24 -4.24 21.58 521.56Bl 40.43 4.00 551.00 0.00 120.94 3.85 16.70 216.15car 0 0 0 0 0 2 5 16dg 0 1 1 0 0 0 3 0div 1 1 1 0 1 0 1 5etr 0.26 0.28 0.35 0.10 0.07 -0.87 2.68 2.37fba 0.29 0.21 0.96 0.06 0.23 1.69 4.96 17.86fcl 0.18 0.18 0.64 0.00 0.13 1.51 6.85 23.89ins 0.37 0.00 1.00 0.00 0.49 0.55 1.31 5.12

24

nff 0.23 0.00 1.00 0.00 0.43 1.26 2.59 8.16nii 0.29 0.31 0.61 -0.47 0.20 -1.70 8.18 46.40npl 0.04 0.03 0.15 0.01 0.03 2.98 13.99 188.91oc 0.03 0.02 0.11 0.01 0.02 2.38 8.05 54.28os 2.63 2.00 5.00 0.00 1.68 0.29 1.72 1.80pssa 0.23 0.20 1.00 0.00 0.23 2.48 8.47 38.59reest 0.13 0.00 1.00 0.00 0.35 2.16 5.65 32.07rer 0.12 0.11 0.38 0.00 0.10 0.74 2.83 2.41sec 0.53 1.00 1.00 0.00 0.51 -0.13 1.02 5.00spr 0.57 0.46 1.89 0.00 0.45 1.37 4.78 11.97

Table 4: Banking systems’ performance and regulation/supervision – pre-crisis periodWe run three regressions for each dependent variable (after-tax return – atr, non-interest income – nii, and domestic credit by the financial sector - dcfs) using White heteroscedasticity-consistent standard errors and covariance. The first is the basic model without introducing any dummy. In the second, we introduce the dummy d 1 for BRICS, and in the third; we introduce the dummy d2 for emerging economies.

Dependent variable: atr Dependent variable: nii Dependent variable: dcfs(1) (2) (3) (4) (5) (6) (7) (8) (9)

Regulatory/Supervisory variablesarbcar 0.67 0.77 0.48 0.71 0.86 0.82 -2.79 -2.71 -1.79

(0.45) (0.45) (0.6) (0.93) (1.07) (1.37) (1.8) (1.7) (2.4)atfb 0.2*** 0.2*** 0.2*** 0.14 0.14 0.15 0.53 0.51 0.61

(0.05) (0.05) (0.05) (1.10) (0.10) (0.12) (0.47) (0.50) (0.50)npl -0.21 -0.11 -0.27 -0.56 -0.38 -0.52 1.70 1.80 2.30

(0.25) (0.25) (0.24) (0.50) (0.62) (0.64) (1.88) (1.96) (1.87)ins 0.05*** 0.05*** 0.05*** 0.28* 0.28* 0.24

(0.01) (0.01) (0.01) (0.13) (0.13) (0.13)reest -0.05*** -0.05*** -0.05** -0.5*** -0.6*** -0.5***

(0.01) (0.02) (0.02) (0.17) (0.2) (0.18)spr -0.02 -0.03 -0.02 0.1** 0.15 0.17**

(0.02) (0.02) (0.02) (0.07) (0.09) (0.07)div -0.1*** -0.1*** -0.1***

(0.04) (0.04) (0.04)nff 0.1*** 0.09* 0.1*** 0.38** 0.38** 0.41***

(0.03) (0.04) (0.03) (0.15) (0.15) (0.14)sec 0.07 0.06 0.07

(0.04) (0.04) (0.04)os 0.09* 0.09 0.08

(0.04) (0.05) (0.04)Control Variables

gdpgr 0.02*** 0.01*** 0.01 0.001 0.003 0.003 0.10** 0.11 0.07(0.004) (0.01) (0.01) (0.01) (0.02) (0.02) (0.04) (0.06) (0.05)

infl 0.004 0.004 0.004 -0.001 -0.001 -0.001 -0.05** -0.05** -0.06**(0.002) (0.002) (0.003) (0.005) (0.006) (0.006) (0.02) (0.02) (0.02)

Dummy Variablesd1 0.03 0.05 0.04

(0.02) (0.06) (0.21)d2 0.03 -0.02 -0.18

(0.04) (0.09) (0.26)Intercept -0.2*** -0.2*** -0.19* 0.08 0.09 0.06 5.09*** 5.1*** 4.85***

(0.07) (0.08) (0.10) (0.12) (0.12) (0.21) (0.48) (0.54) (0.5)R-squared 0.73 0.75 0.74 0.62 0.62 0.62 0.89 0.89 0.90

Note: We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.

25

Table 5: Regulatory impact on after-tax income of banking systems in BRICS countries – Pre-Crisis PeriodThis table reports the results of the regressions run with the dependent variable – after-tax return ( atr) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d1 for BRICS are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)arbcar 0.67 0.74 0.72 0.45 0.63 0.67 0.75* 0.66 0.67 0.61

(0.47) (1.66) (0.46) (1.02) (0.48) (0.47) (0.42) (0.47) (0.47) (0.45)atfb 0.21*** 0.20*** 0.21*** 0.21*** 0.21*** 0.22*** 0.22*** 0.22*** 0.22*** 0.19***

(0.05) (3.94) (0.05) (0.06) (0.06) (0.05) (0.05) (0.05) (0.06) (0.06)npl -0.14 -0.13 -0.14 -0.07 -0.23 -0.19 -0.13 -0.14 -0.18 -0.23

(0.26) (-0.5) (0.25) (0.27) (0.28) (0.27) (0.28) (0.24) (0.27) (0.23)ins 0.05*** 0.05*** 0.05*** 0.04* 0.05*** 0.05*** 0.05*** 0.05*** 0.05*** 0.05***

(0.01) (3.59) (0.01) (0.02) (0.01) (0.01) (0.01) (0.01) (0.02) (0.01)reest -0.05*** -0.05*** -0.05*** -0.04 -0.04* -0.05*** -0.05** -0.05*** -0.05*** -0.05***

(0.02) (-3.0) (0.02) (0.03) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02)spr -0.02 -0.02 -0.02 -0.01 -0.01 -0.02 -0.02 -0.02 -0.02 -0.01

(0.03) (-1.0) (0.02) (0.05) (0.03) (0.03) (0.02) (0.02) (0.03) (0.02)Control Variablesgdpgr 0.02*** 0.01** 0.02*** 0.02** 0.02*** 0.02*** 0.01** 0.02*** 0.02*** 0.02***

(.005) (2.73) (0.0) (0.01) (0.01) (0.0) (0.01) (0.0) (0.0) (0.0)infl 0.004 0.004 0.004 0.003 0.005 0.004 0.005 0.004 0.004 0.004

(.003) (1.62) (.003) (.003) (.004) (.003) (.003) (.003) (.003) (.003) Interaction Variables

(arbcar) x (d1) 0.17(0.15)

(atfb)x (d1) 0.05* (2.01) (npl) x (d1) 0.83

(0.58)(os) x (d1) 0.01

(0.01)(reest) x (d1) -0.03

(0.05)(spr) x (d1) 0.00

(0.02)(div) x (d1) 0.03

(0.02)(sec) x (d1) 0.03

(0.02)(ins) x (d1) 0.01

26

(0.02)(nff) x (d1) 0.07 **

(0.03)Intercept -0.22 ** -0.22 ** -0.23 ** -0.20 -0.22 ** -0.23 ** -0.24 *** -0.23*** -0.23 ** -0.20 **

(.08) (.08) (.08) (0.1) (.09) (.08) (.08) (.08) (.08) (.08)R-squared 0.74 0.75 0.74 0.70 0.74 0.73 0.74 0.74 0.73 0.76Adj R2 0.59 0.61 0.60 0.43 0.59 0.58 0.59 0.60 0.58 0.62

Table 6: Regulatory impact on after-tax income of banking systems in emerging economies – Pre-Crisis PeriodThis table reports the results of the regressions run with the dependent variable – after-tax return ( atr) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d2 for emerging countries are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)arbcar 0.37 0.47 0.67 0.65 0.53 0.65 0.19 0.68 0.73 0.66

(.81) (.52) (.48) (.47) (.48) (.52) (1.0) (.45) (.43) (.47)atfb 0.21 *** 0.20*** 0.22*** 0.21*** 0.19*** 0.22*** 0.21*** 0.20*** 0.22*** 0.22***

(.05) (.05) (.05) (.06) (.05) (.05) (.06) (.06) (.05) (.05)npl -0.25 -0.28 -0.20 -0.17 -0.28 -0.26 -0.17 -0.22 -0.21 -0.24

(.24) (.23) (.25) (0.3) (.23) (.62) (.26) (.27) (.26) (.25)ins 0.05 *** 0.05*** 0.05*** 0.06** 0.05*** 0.05*** 0.05* 0.05*** 0.05*** 0.05***

(.01) (.01) (.01) (.03) (.01) (.01) (.02) (.01) (.01) (.01)reest -0.05 ** -0.04** -0.05*** -0.05** -0.05** -0.05*** -0.04 -0.03 -0.05*** -0.05***

(.02) (.02) (.02) (.02) (.02) (.02) (.03) (.02) (.01) (.02)spr -0.02 -0.03 -0.02 -0.01 -0.01 -0.02 0.00 -0.01 0.00 -0.02

0.02 0.02 0.02 0.04 0.02 0.02 0.05 0.02 0.05 0.02 Control Variablesgdpgr 0.01 * 0.01* 0.02*** 0.02*** 0.02*** 0.02** 0.02* 0.02*** 0.02*** 0.02***

(.01) (.01) (.01) (.005) (.005) (.01) (.01) (.005) (.005) (.005)Infl 0.004 0.005 0.004 0.004 0.005 0.004 0.003 0.004 0.004 0.003

(.003) (.003) (.003) (.003) (.003) (.003) (.003) (.003) (.003) (.003)Interaction Variables(arbcar) x (d2) 0.18

(.29)(atfb) x (d2) 0.05

27

(.04)(div) x (d2) -0.003

(.02)(ins) x (d2) -0.01

(.04)(nff) x (d2) 0.04

(.03)(npl) x (d2) 0.06

(.59)(os) x (d2) 0.01

(.01)(reest) x (d2) -0.04

(.03)(spr) x (d2) -0.02

(.04)(sec) x (d2) 0.02

(.02)Intercept -0.19 -0.18 -0.23 -0.23 -0.20 -0.23 -0.18 -0.23 -0.25 -0.22

(.12) (0.1) (.09) (.02) (.08) (.08) (0.1) (.03) (.14) (.08)R-squared 0.74 0.75 0.73 0.73 0.76 0.73 0.70 0.74 0.73 0.74Adj R2 0.59 0.61 0.58 0.58 0.62 0.58 0.42 0.60 0.58 0.59

Table 7: Regulatory impact on non-interest income of banking systems in BRICS economies – Pre-Crisis PeriodThis table reports the results of the regressions run with the dependent variable – non-interest income (nii) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d1 for BRICS are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.arbcar 0.89 1.10 0.88 1.05 0.56 1.20 2.36** 1.48 0.99 0.81

(1.1) (1.29) (1.7) (1.2) (1.3) (1.2) (1.0) (1.3) (1.1) (1.2)atfb 0.14 0.07 0.17 0.25* 0.06 0.11 0.18* 0.28* 0.17 0.16

(0.1) (.13) (.15) (.14) (.14) (0.1) (.09) (.14) (0.1) (0.1)npl 0.39 0.17 -0.15 0.35 -0.27 0.29 0.03 0.11 0.53 0.23

(.69) (.69) (1.0) (0.8) (.67) (.69) (.56) (.73) (.71) (0.7)ins -0.01 0.01 0.01 -0.02 0.02 -0.003 -0.03 0.01 -0.01 -0.02

(.05) (.05) (.07) (.06) (.05) (.05) (.03) (.05) (.05) (.05)reest -0.001 0.02 0.01 -0.002 0.02 -0.01 0.06* -0.08 -0.01 0.01

(.09) (.09) (.12) (0.1) (0.1) (.08) (.03) (.09) (.09) (.09)

28

spr 0.03 0.07 0.10 0.05 0.13 0.05 0.11* 0.05 -0.03 0.08(.06) (.07) (.13) (.09) (.10) (.07) (.06) (0.1) (.05) (.07)

Control Variablesgdpgr -0.01 -0.02* -0.01 -0.01 -0.01 -0.02 * -0.01 -0.003 -0.02 -0.01

(.01) (.01) (.01) (.01) (.01) (.01) (.01) (.01) (.01) (.01)infl -0.003 0.001 0.001 0.004 -0.001 -0.001 -0.004 -0.008 0.003 -0.001

(.01) (.01) (.01) (.01) (.01) (.01) (.004) (.01) (.01) (.01)Interaction Variables(arbcar) x (d1) 1.47***

(.39)(atfb) x (d1) 0.25***

(.08)(div) x (d1) 0.02

(.18)(ins) x (d1) 0.18

(.11)(nff) x (d1) 0.20**

(.07)(npl) x (d1) 5.69 ***

(1.7)(os) x (d1) 0.04**

(.01)(reest) x (d1) 0.35**

(.13)(spr) x (d1) 0.20***

(.05)(sec) x (d1) 0.16**

(.06)Intercept 0.14 0.16 0.09 0.03 0.18 0.12 -0.06 -0.03 0.14 0.12

(.15) (.18) (.21) (.18) (.19) (.16) (.12) (.19) (.16) (.16)R-squared 0.51 0.44 0.29 0.41 0.37 0.49 0.75 0.45 0.51 0.45Adj R-sq 0.22 0.11 -0.13 0.05 -0.01 0.19 0.50 0.12 0.21 0.13

Table 8: Regulatory impact on non-interest income of banking systems in emerging economies – Pre-Crisis PeriodThis table reports the results of the regressions with the dependent variable – non-interest income (nii) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d2 for emerging countries are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.arbcar 0.94 0.85 1.30 0.78 0.41 0.91 1.68 * 0.82 1.04 0.80

(2.3) (1.8) (1.0) (1.4) (1.5) (1.7) (0.8) (1.4) (1.6) (1.0)29

atfb 0.17 0.17 0.17 0.16 0.10 0.17 0.24 * 0.24 * 0.17 0.17(.15) (.15) (.11) (.17) (.15) (.15) (.12) (.13) (.15) (.11)

npl -0.18 -0.19 0.001 -0.13 -0.34 0.06 -0.19 -0.14 -0.21 -0.50(.83) (.82) (.69) (.89) (0.7) (1.9) (.58) (.68) (.76) (.65)

ins 0.01 0.01 0.04 0.02 0.02 0.02 -0.03 0.003 0.02 -0.01(.05) (.05) (.05) (.06) (.05) (.04) (.04) (.05) (.04) (.05)

reest 0.01 0.01 -0.03 0.01 0.02 0.005 0.09 -0.09 -0.002 0.02(0.1) (0.1) (.09) (.11) (0.1) (0.1) (.06) (0.1) (0.1) (0.1)

spr 0.10 0.10 0.14 * 0.11 0.12 0.10 0.15 ** 0.08 0.15 0.10(0.1) (0.1) (.07) (0.1) (.11) (0.1) (.06) (0.1) (.12) (.07)

Control Variablesgdpgr -0.01 -0.01 0.01 -0.01 -0.01 0.00 -0.001 -0.01 -0.003 -0.01

(.02) (.02) (.01) (.01) (.01) (.02) (.01) (.01) (.01) (.01)infl 0.001 0.001 -0.005 0.001 0.002 0.001 -0.005 0.004 0.001 -0.007

(.01) (.01) (.01) (.01) (.01) (.01) (.01) (.01) (.01) (.01)Interaction Variables(arbcar) x (d2) -0.06

(.85)(atfb) x (d2) -0.01

(.19)(div) x (d2) -0.16 **

(.06) (ins) x (d2) -0.02

(.09)(nff) x (d2) 0.10

(0.1)(npl) x (d2) -0.29

(1.8)(os) x (d2) 0.003

(.02)(reest) x (d2) 0.20

(.16)(spr) x (d2) -0.06

(.14)(sec) x (d2) 0.14 **

(.07)Intercept 0.07 0.08 -0.02 0.09 0.17 0.06 -0.05 0.07 0.03 0.13

(.31) (.28) (.15) (0.2) (.21) (.28) (0.1) (.19) (.25) (.16)R-squared 0.29 0.29 0.47 0.29 0.33 0.29 0.68 0.37 0.30 0.43Adj R-sq -0.13 -0.13 0.15 -0.13 -0.07 -0.13 0.36 0.00 -0.12 0.09

30

Table 9: Regulatory impact on domestic credit by financial sector in BRICS countries – Pre-Crisis PeriodThis table reports the results of the regressions with the dependent variable – domestic credit (dc) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d1 for BRICS are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.arbcar -3.3 -2.1 -1.4 -3.2 -3.7 -2.3 8.5 -3.1 -3.3 -3.2

(4.1) (3.9) (4.0) (4.3) (4.1) (4.1) (4.7) (4.3) (4.3) (3.5)atfb 1.0 * 0.8 * 1.1 * 1.2 * 0.9 * 1.0 * 0.7 1.1 * 1.1 * 1.1 **

(0.5) (.44) (.59) (.59) (.48) (.49) (.41) (.58) (.53) (.46)npl 4.8 ** 4.7 ** 5.0 ** 3.9 3.2 4.6 * 3.0 3.5 4.0 * 6.2 *

(2.2) (2.2) (2.2) (2.5) (2.2) (2.2) (2.1) (2.4) (2.2) (2.1)ins 0.0 0.1 0.0 0.1 0.1 0.1 0.3 0.1 0.1 0.0

(.24) (.22) (.25) (.26) (.23) (.23) (.22) (.23) (.24) (.22)reest -0.4 * -0.4 * -0.3 -0.4 * -0.4 -0.4 * -0.7 ** -0.4 -0.4 * -0.5 **

(.21) (.20) (.25) (.23) (.24) (.22) (.23) (.26) (.23) (.19)spr -0.7 ** -0.7 ** -0.7 * -0.6 -0.5 -0.6 * -0.7 *** -0.5 -0.6 -1.0 ***

(0.3) (.29) (.38) (.41) (.38) (.31) (.21) (0.4) (.35) (.18)Control Variablesgdpgr 0.0 -0.1 -0.1 0.0 0.0 -0.1 -0.1 ** 0.0 0.0 -0.1

(.05) (.05) (.07) (.05) (.05) (.05) (.06) (.05) (.05) (.05)infl -0.1 ** -0.1 ** 0.0 -0.1 * -0.1 * -0.1 ** -0.1 * -0.1 ** -0.1 * 0.0 **

(.02) (.02) (.03) (.03) (.03) (.02) (.03) (.03) (.03) (.02) Interaction Variables

(arbcar) x (d1) 3.68 *(2.0)

(atfb) x (d1) 0.90 **(.42)

(div) x (d1) 0.64(.71)

(ins) x (d1) 0.16(.55)

(nff) x (d1) 0.45 **(.18)

(npl) x (d1) 14.38 *

31

(7.4)(os) x (d1) 0.15 **

(.05)(reest) x (d1) 0.16

(.34)(sec) x (d1) 0.23

(.34)(spr) x (d1) 0.75 ***

(.18)intercept 5.14 *** 5.21 *** 4.88 *** 4.96 *** 5.18 *** 5.08 *** 4.30 *** 4.95 *** 5.03 *** 5.25 ***

(.73) (.64) (.79) (.81) (0.7) (0.7) (.62) (.79) (.78) (.67)R-sq 0.74 0.76 0.72 0.69 0.70 0.73 0.85 0.69 0.70 0.80Adj R-sq 0.59 0.62 0.56 0.52 0.54 0.58 0.72 0.52 0.53 0.69

Table 10: Regulatory impact on domestic credit by financial sector in emerging economies – Pre-Crisis PeriodThis table reports the results of the regressions with the dependent variable – domestic credit ( dc) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d2 for emerging countries are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.arbcar 5.4 -1.5 -2.6 -4.1 -4.1 -0.9 5.0 -3.2 -3.4 -0.4

(5.1) (4.8) (4.7) (3.7) (4.3) (4.2) (5.2) (4.1) (4.2) (4.7)atfb 1.2 * 1.3 * 1.1 ** 0.9 1.0 * 1.3 * 0.8 1.0 1.1 * 1.2 *

(.58) (.65) (.51) (.55) (.51) (.64) (0.5) (.57) (.54) (.61)npl 4.8 ** 4.1 * 3.8 5.6 * 2.9 11.1 1.8 3.2 3.0 3.1

(1.8) (2.3) (2.4) (2.6) (2.4) (7.1) (2.2) (2.1) (2.4) (2.1)ins 0.1 0.1 0.1 0.4 * 0.1 0.2 0.4 0.1 0.1 0.2

(.18) (.22) (.21) (.22) (.23) (.21) (.24) (.24) (.24) (.22)reest -0.5 *** -0.5 * -0.5 * -0.3 -0.4 -0.5 ** -0.6 ** -0.2 -0.4 -0.5 **

(.18) (.23) (.24) (.18) (.24) (.23) (.22) (.18) (.23) (.22)spr -0.4 -0.4 -0.4 -0.2 -0.5 -0.6 -0.5 ** -0.5 -0.5 0.1

(.32) (.35) (.33) (.33) (.38) (.34) (.21) (.37) (.35) (.53)Control Variables

gdpgr 0.1 0.01 0.01 0.01 0.01 0.0 -0.1 * 0.0 0.0 0.0(.05) (.07) (.06) (.04) (.05) (.05) (.06) (.05) (.05) (.06)

infl -0.1 * -0.1 * -0.1 * -0.1 * -0.1 * -0.1 * -0.1 * -0.1 * -0.1 * -0.1 *(.03) (.03) (.03) (.03) (.03) (.03) (.03) (.03) (.03) (.03)

32

Interaction Variables(arbcar) x (d2) -5.5 **

(2.4)(atfb) x (d2) -0.5

(.63)(div) x (d2) -0.3

(.29)(ins) x (d2) -0.8 **

(.32)(nff) x (d2) 0.26

(.21)(npl) x (d2) -8.9

(6.8)(os) x (d2) 0.06

(.09)(reest) x (d2) -0.4

(.34)(sec) x (d2) 0.17

(.33)(spr) x (d2) -0.7

(.55)intercept 3.68 *** 4.55 *** 4.80 *** 4.93 *** 5.18 *** 4.25 *** 4.51 *** 5.01 *** 5.05 *** 4.28 ***

(.82) (.98) (.81) (.69) (.77) (.98) (.77) (.75) (.78) (1.0)R-sq 0.77 0.71 0.71 0.75 0.70 0.72 0.82 0.70 0.70 0.72Adj R-sq 0.64 0.55 0.55 0.61 0.53 0.56 0.65 0.53 0.53 0.56

33

Table 11: Descriptive of Variables – Crisis PeriodMean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Obs

acs 0.67 1.00 1.00 0.00 0.48 -0.71 1.50 5.31 30arbcar 0.14 0.15 0.19 0.00 0.04 -2.16 9.93 83.24 30asset 0.79 0.97 1.00 0.00 0.36 -1.65 3.92 14.71 30atfb 0.61 0.66 0.92 0.00 0.23 -1.28 4.42 10.76 30atr 0.09 0.09 0.44 -0.54 0.15 -2.08 12.08 124.62 30bl 11.40 0.00 192.00 0.00 35.63 4.58 23.59 634.82 30car 0.08 0.08 0.12 0.00 0.03 -1.96 6.57 35.18 30dg 0.35 0.33 1.00 0.00 0.33 0.44 2.00 2.21 30div 0.79 1.00 1.00 0.00 0.41 -1.45 3.09 10.13 29etr 0.16 0.18 0.38 0.00 0.13 -0.08 1.45 3.04 30fba 0.22 0.18 0.95 0.00 0.24 1.68 5.17 19.94 30fcl 0.12 0.09 0.58 0.00 0.13 1.51 6.18 24.08 30ins 0.63 1.00 1.00 0.00 0.49 -0.55 1.31 5.12 30nff 0.20 0.00 1.00 0.00 0.41 1.50 3.25 11.33 30nii 0.30 0.31 0.54 0.00 0.17 -0.42 2.20 1.69 30npl 0.04 0.03 0.11 0.00 0.03 0.69 2.63 2.56 30oc 0.02 0.02 0.10 0.00 0.02 2.49 10.94 109.79 30os 4.44 4.26 18.00 0.00 3.97 1.77 6.81 27.07 24pssa 0.08 0.00 1.00 0.00 0.19 3.86 18.77 372.55 29reest 0.30 0.00 1.00 0.00 0.47 0.87 1.76 5.73 30rer 0.12 0.08 0.44 0.00 0.12 1.02 3.30 5.28 30sec 0.30 0.00 1.00 0.00 0.47 0.87 1.76 5.73 30spr 0.51 0.43 2.01 0.00 0.49 1.44 4.93 15.06 30

Table 12: Banking systems’ performance and regulation/supervision – crisis periodWe run three regressions for each dependent variable (after-tax return – atr, non-interest income – nii, and domestic credit by the financial sector - dcfs) using White heteroscedasticity-consistent standard errors and covariance. The first is the basic model without introducing any dummy. In the second, we introduce the dummy d 1 for BRICS, and in the third; we introduce the dummy d2 for emerging economies.


Regulatory/Supervisory variablesarbcar 1.89 2.45 1.71 0.44 0.46 0.56 -2.64 -4.48 -0.88

(1.51) (1.69) (1.48) (0.82) (0.86) (0.88) (4.00) (5.02) (3.26)atfb -2.49 -2.32 -2.52 1.96 1.61 1.40

(2.25) (2.27) (2.19) (2.25) (2.36) (1.87)npl -6.8** -6.4* -7.9** -0.20 -0.14 0.32 5.10 4.35 1.03

(3.28) (3.33) (3.53) (0.76) (0.79) (0.89) (4.42) (4.29) (4.94)rer 3.73 3.60 2.71 3.08 3.05 3.66 10.1*** 10.4*** 5.17

(3.17) (3.21) (2.81) (2.12) (2.18) (2.37) (2.9) (3.08) (4.45)os -0.01 -0.01 -0.01

(0.01) (0.01) (0.01)rers 2.32 2.34 2.41

(1.65) (1.70) (1.67)fba 2.9* 2.92 3.3* 6.66 7.31 2.65

(1.65) (1.70) (1.85) (4.74) (5.11) (4.47)fcl 1.52 1.45 1.86 11.3** 12.6*** 8.01**

(0.99) (1.06) (1.23) (4.1) (3.3) (3.3)nff -0.07 -0.10 -0.10 -0.7*** -0.7*** -0.4***

(0.06) (0.06) (0.08) (0.1) (0.1) (0.1)Control Variablesgdpgr -0.03 -0.02 -0.02 -0.01 0.01 -0.01 0.00 -0.01 0.01

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.02) (0.03) (0.02)infl 0.01 0.01* 0.01* 0.001 0.001 0.001 -0.05** -0.05** -0.04**

(0.01) (0.007) (0.006) (0.002) (0.002) (0.002) (0.02) (0.02) (0.02)Dummy Variablesd1 -0.07 -0.02 -0.20

(0.08) (0.04) (0.45)

34

d2 -0.14 -0.07 -0.5*(0.11) (0.06) (0.3)

Intercept 0.02 0.02 0.12 0.05 0.06 0.02 4.9*** 5.02*** 5.2***(0.06) (0.06) (0.11) (0.03) (0.03) (0.04) (0.17) (0.1) (0.2)

R-squared 0.35 0.35 0.38 0.48 0.48 0.50 0.64 0.65 0.73Note: We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.

35

Table 13: Regulatory impact on after-tax income of banking systems in BRICS countries – Crisis PeriodThis table reports the results of regressions with the dependent variable – after-tax return (atr) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d1 for BRICS are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level. arbcar 3.51* 3.00 2.86 3.03* 4.25* 4.58** 3.12 4.85**

(1.8) (1.7) (1.8) (1.6) (2.1) (2.2) (1.9) (2.3)atfb -5.5* -5.6* -5.4* -5.4* -5.4* -4.7 -5.2* -4.9

(2.8) (3.0) (2.8) (2.8) (2.9) (2.8) (2.8) (2.9)dg 0.29 0.32 0.38 0.36 0.16 0.36 0.34 0.29

(.34) (.31) (.28) (.29) (0.3) (0.3) (0.3) (.32)gpr -2.6 -2.8 -3.3* -3.2* -1.7 -3.2 -3.1 -2.6

(2.1) (1.9) (1.7) (1.7) (1.8) (1.9) (1.9) (2.0)nff -0.3* -0.28* -0.27* -0.27* -0.28** -0.2 -0.3** -0.2*

(.14) (.15) (.14) (.14) (.13) (.11) (.13) (.11)npl -10.3*** -10.5*** -10.3*** -10.3*** -10.6*** -9.61*** -10.5*** -9.7***

(3.2) (3.3) (3.2) (3.2) (3.2) (3.2) (3.1) (3.3)os -0.02 -0.02 -0.02 -0.02 -0.01 -0.01 -0.01 -.01

(.01) (.01) (.01) (.01) (.01) (.01) (.01) (.01)pssa -0.53** -0.53** -0.53** -0.53** -0.61** -0.51** -0.58** -0.5**

(.22) (.22) (.22) (.22) (.26) (.23) (.25) (.23)sec 0.08 0.09 0.10 0.09 0.08 0.03 0.11 0.02

(.07) (.06) (.06) (.07) (.05) (.05) (.07) (.05)spr -1.44* -1.41* -1.49* -1.51* -1.17* -1.47 -1.35* -1.3

(.79) (.76) (.79) (.77) (.68) (.86) (0.7) (0.8) Interaction Variables

(arbcar) x (d1) -1.74(3.4)

(atfb) x (d1) 2.59(4.5)

(ins) x (d1) -0.02(.07)

(nff) x (d1) 0.26(.19)

(npl) x (d1) -11.18**(4.6)

(reest) x (d1) 0.003(.21)

(sec) x (d1) -0.33*(.18)

(spr) x (d1) -0.3

36

(.51)intercept 0.23** 0.23** 0.23** 0.23** 0.21** 0.20* 0.23** 0.20*

(0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)R-sq 0.72 0.72 0.71 0.71 0.72 0.68 0.71 0.68Adj R-sq 0.54 0.55 0.54 0.54 0.55 0.48 0.54 0.49

Table 14: Regulatory impact on after-tax income of banking systems in emerging economies – Crisis PeriodThis table reports the results of the regressions with the dependent variable – after-tax return (atr) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d2 for emerging countries are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level. arbcar 3.07 2.96* 2.59 3.36* 4.42* 4.06* 4.11* 4.79**

(1.8) (1.6) (1.8) (1.7) (2.2) (2.2) (2.2) (2.1)atfb -5.4* -5.2* -5.3* -5.5* -4.9 -5.2* -4.9 -4.5*

(2.8) (2.8) (2.8) (2.7) (2.8) (2.8) (3.0) (2.3)dg 0.36 0.40 0.39 0.32 0.29 0.28 0.38 0.67*

(.31) (.28) (.29) (.28) (.29) (.29) (.29) (.34)gpr -3.2 -3.5* -3.6* -2.7 -2.7 -2.9 -3.2* -5.1**

(2.0) (1.8) (1.8) (1.8) (1.8) (1.8) (1.8) (2.3)nff -0.27* -0.27* -0.26* -0.34* -0.22** -0.19* -0.20* -0.17*

(.15) (.14) (.14) (.17) (0.1) (0.1) (0.1) (.09)npl -10.3*** -10.3*** -10.8*** -9.89*** -10.6*** -10.4*** -9.64*** -9.7***

(3.2) (3.1) (3.3) (2.8) (3.2) (3.3) (3.2) (2.6)os -0.02 -0.02 -0.02 -0.02 -0.01 -0.02 -0.01 -0.02

(.01) (.01) (.01) (.01) (.01) (.01) (.01) (.01)pssa -0.52** -0.52** -0.50** -0.51** -0.46* -0.58** -0.50** -0.45**

(.24) (.22) (.22) (0.2) (0.2) (0.2) (0.2) (0.1)sec 0.09 0.09 0.10 0.09 0.05 0.02 0.08 0.05

(.09) (.06) (.07) (.06) (.05) (.05) (.08) (.05)spr -1.50 -1.56** -1.46* -1.60** -1.28 -1.38* -1.45* -2.41*

(.87) (.77) (.75) (.72) (.76) (.75) (.77) (1.2)Interaction Variables(arbcar) x (d2) -0.34

(3.6)(atfb) x (d2) -0.67

(3.3)(ins) x (d2) -0.06

(.07)

37

(nff) x (d2) 0.20(.22)

(npl) x (d2) -5.68**(2.7)

(reest) x (d2) -0.11(.09)

(sec) x (d2) -0.10(.13)

(spr) x (d2) 1.16(.91)

intercept 0.23** 0.23** 0.25** 0.20** 0.21** 0.24** 0.20* 0.18**(0.1) (0.1) (0.1) (.08) (0.1) (0.1) (0.1) (.09)

R-sq 0.71 0.71 0.72 0.73 0.70 0.69 0.68 0.71Adj R-sq 0.54 0.54 0.55 0.57 0.52 0.51 0.49 0.54

Table 15: Regulatory impact on non-interest income of banking systems in BRICS economies – Crisis PeriodThis table reports the results of the regressions with the dependent variable – non-interest income (nii) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d1 for BRICS are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.acs 0.05 0.05 0.04 0.05 0.02 0.04 0.03 0.04

(0.06) (0.06) (0.05) (0.05) (0.05) (0.06) (0.05) (0.06)arbcar -0.8 -0.4 -0.35 -0.8 -0.2 -0.3 -0.8 -0.3

(1.2) (1.0) (0.9) (1.2) (1.01) (1.0) (1.0) (1.1)fba 2.65** 2.43** 2.45** 2.80** 2.67** 2.43** 2.89** 2.45

(1.2) (1.0) (1.1) (1.4) (1.1) (1.1) (1.2) (1.1)fcl 1.88 1.75 1.80 2.10 2.40* 1.79 2.25* 1.80

(1.1) (1.0) (1.1) (1.3) (1.2) (1.0) (1.1) (1.0)rer 3.40 3.24 3.24 3.40 3.08 3.21 3.37 3.22

(2.2) (2.2) (2.2) (2.2) (1.9) (2.2) (2.0) (2.2)rers 2.33 2.29 2.30 2.37 2.41* 2.30 2.42* 2.30

(1.3) (1.4) (1.4) (1.3) (1.3) (1.4) (1.3) (1.4)nff -0.1 -0.1 -0.08 -0.1 -0.1 -0.1 -0.1 -0.1

(0.06) (0.05) (0.05) (0.07) (0.06) (0.05) (0.07) (0.05)reest 0.03 0.03 0.03 0.03 0.06 0.03 0.06 0.03

(0.04) (0.04) (0.04) (0.05) (0.06) (0.04) (0.06) (0.04)os 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

38

Interaction Variables(arbcar) x (d1) 1.18

(1.53)(atfb) x (d1) -0.3

(1.53)(ins) x (d1) -0.003

(0.04)(nff) x (d1) 0.06

(0.09)(npl) x (d1) 4.83

(3.4)(reest) x (d1) -0.03

(0.06)(sec) x (d1) -0.1

(0.11)(spr) x (d1) -0.02

(0.17)intercept 0.07 0.06 0.06 0.07 0.05 0.06 0.06 0.06

(0.05) (0.05) (0.05) (0.05) (0.05) (0.05) (0.05) (0.05)R-sq 0.55 0.54 0.54 0.55 0.58 0.54 0.58 0.54Adj R-sq 0.31 0.30 0.30 0.31 0.36 0.30 0.36 0.30

Table 16: Regulatory impact on non-interest income of banking systems in emerging economies – Crisis PeriodThis table reports the results of the regressions with the dependent variable – non-interest income (nii) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d2 for emerging countries are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.acs 0.06 0.04 0.05 0.05 0.03 0.04 0.04 0.04

(0.06) (0.06) (0.06) (0.05) (0.05) (0.06) (0.05) (0.06)arbcar -1.09 -0.31 -0.28 -0.28 -0.29 -0.49 -0.38 -0.33

(1.55) (1.01) (0.98) (1.02) (0.99) (0.94) (0.99) (1.05)fba 2.73** 2.50** 2.40** 2.38** 2.70** 2.54** 2.53** 2.45**

(1.35) (1.14) (1.10) (1.11) (1.14) (1.13) (1.22) (1.11)fcl 1.85 1.81 1.71 1.75 2.14* 1.73 1.79 1.80

(1.08) (1.10) (1.09) (1.11) (1.11) (1.11) (1.09) (1.08)rer 3.58 3.25 3.27 3.29 2.97 3.11 3.26 3.22

(2.38) (2.21) (2.21) (2.18) (2.12) (2.19) (2.22) (2.22)rers 2.36 2.31 2.30 2.36 2.30 2.29 2.30 2.30

(1.38) (1.41) (1.41) (1.43) (1.35) (1.40) (1.39) (1.41)nff -0.10 -0.08 -0.08 -0.09 -0.09* -0.08 -0.08 -0.08

39

(0.07) (0.05) (0.05) (0.05) (0.05) (0.05) (0.05) (0.05)reest 0.03 0.03 0.02 0.03 0.05 0.06 0.03 0.03

(0.04) (0.04) (0.04) (0.04) (0.05) (0.07) (0.04) (0.04)os 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)Interaction Variables(arbcar) x (d2) 1.46

(1.97)(atfb) x (d2) 0.34

(0.99)(ins) x (d2) 0.02

(0.03)(nff) x (d2) 0.04

(0.06)(npl) x (d2) 2.19

(1.95)(reest) x (d2) -0.05

(0.07)(sec) x (d2) -0.01

(0.05)(spr) x (d2) -0.02

(0.12)intercept 0.06 0.06 0.05 0.05 0.06 0.06 0.06 0.06

(0.05) (0.05) (0.05) (0.05) (0.05) (0.05) (0.05) (0.05)R-sq 0.55 0.54 0.54 0.55 0.56 0.55 0.54 0.54Adj R-sq 0.32 0.30 0.30 0.31 0.33 0.31 0.30 0.30

Table 17: Regulatory impact on domestic credit by financial sector in BRICS economies – Crisis PeriodThis table reports the results of the regressions with the dependent variable – domestic credit ( dc) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d1 for BRICS are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.arbcar 7.30 8.67 10.35 ** 11.19 ** 8.78 10.15 ** 8.11 10.99 * 6.91

6.58 5.14 4.90 4.28 5.85 4.65 4.77 5.70 5.33atfb -1.28 -0.36 -0.21 -1.59 -1.59 0.48 -1.25 -0.88 0.08

5.98 6.02 6.19 5.88 6.40 6.43 5.95 6.52 5.60dg 0.06 0.14 0.33 -0.13 -0.12 0.44 0.07 -0.09 0.49

0.64 0.63 0.60 0.53 0.60 0.65 0.59 0.57 0.60gpr 3.38 2.84 1.79 5.07 4.81 1.02 3.49 4.80 0.27

4.83 4.96 4.56 3.88 4.62 4.86 4.35 4.45 4.4240

nff -0.56 * -0.47 * -0.27 -0.48 ** -0.54 * -0.25 -0.54 * -0.41 -0.45 *0.29 0.27 0.24 0.18 0.30 0.27 0.29 0.28 0.24

npl 7.26 8.12 10.45 ** 8.88 7.41 10.55 ** 7.84 8.73 * 8.705.58 5.79 4.28 5.35 5.03 4.08 5.69 4.29 5.78

os 0.04 0.05 0.02 0.06 * 0.04 0.02 0.04 0.04 0.040.03 0.04 0.03 0.03 0.04 0.03 0.03 0.03 0.03

pssa 0.37 0.40 0.65 * 0.54 0.37 0.65 ** 0.46 0.47 0.520.41 0.40 0.35 0.35 0.40 0.31 0.41 0.32 0.38

sec 0.14 0.08 -0.04 0.02 0.09 -0.07 0.08 -0.03 0.110.26 0.22 0.21 0.16 0.25 0.23 0.20 0.29 0.20

spr -0.69 -0.83 -1.09 -1.30 -0.44 -1.23 -1.04 -0.56 -1.611.28 1.22 1.18 1.01 1.25 1.07 1.20 1.17 1.07

Interaction Variables(arbcar) x (d1) 6.17

11.72(atfb) x (d1) -9.11

12.73(div) x (d1) -1.59 ***

0.500.74 ***

(ins) x (d1) 0.17(nff) x (d1) 0.08

0.58(npl) x (d1) -30.52 *

16.82(reest) x (d1) 0.94 *

0.45(sec) x (d1) 0.39

0.84(spr) x (d1) 2.87 **

1.30intercept 4.55 *** 4.52 *** 4.49 *** 4.47 *** 4.53 *** 4.48 *** 4.53 *** 4.48 *** 4.54 ***

0.25 0.24 0.22 0.22 0.24 0.21 0.24 0.22 0.23R-sq 0.40 0.40 0.51 0.56 0.39 0.46 0.43 0.40 0.47Adj R-sq 0.03 0.04 0.20 0.28 0.02 0.14 0.08 0.04 0.15

41

Table 18: Regulatory impact on domestic credit by financial sector in emerging economies – Crisis PeriodThis table reports the results of the regressions with the dependent variable – domestic credit (dc) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d2 for emerging countries are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.arbcar 9.37 * 9.40 * 8.61 10.58 * 9.05 9.80 * 9.39 * 12.32 ** 9.32 *

(5.27) (4.82) (5.11) (5.33) (5.61) (4.71) (5.19) (4.78) (4.97)atfb -1.39 1.52 -1.83 -1.65 -1.42 -1.12 -1.28 0.04 -1.31

(6.09) (5.43) (6.19) (6.48) (6.27) (6.38) (5.97) (5.07) (6.34)dg -0.13 0.40 0.04 -0.15 -0.11 0.12 -0.10 -0.25 -0.01

(0.65) (0.78) (0.58) (0.51) (0.58) (0.56) (0.62) (0.65) (0.81)gpr 4.89 0.61 3.31 5.59 4.69 3.10 4.74 5.16 4.09

(5.03) (5.55) (4.59) (3.94) (4.61) (4.56) (4.47) (4.88) (5.91)nff -0.51 -0.45 -0.42 -0.51 ** -0.51 * -0.39 -0.52 * -0.41 -0.51 *

(0.31) (0.30) (0.25) (0.24) (0.29 0.23 0.28 0.30 0.27npl 7.62 7.10 7.60 9.64 * 7.46 11.19 ** 7.87 7.77 7.82

(5.56) (5.57) (5.74) (5.16) (5.72) (4.19) (6.14) (4.61) (5.81)os 0.04 0.04 0.03 0.05 0.04 0.02 0.04 0.03 0.04

(0.03) (0.03) (0.03) (0.04) (0.03) (0.03) (0.03) (0.03) (0.03)pssa 0.39 0.48 0.39 0.29 0.37 0.20 0.40 0.35 0.40

(0.45) (0.41) (0.39) (0.48) (0.42) (0.45) (0.50) (0.35) (0.41)sec 0.06 0.04 0.06 0.05 0.07 -0.01 0.07 -0.27 0.07

(0.22) (0.19) (0.20) (0.20) (0.21) (0.22) (0.22) (0.24) (0.21)spr -0.40 -1.32 -0.86 -0.62 -0.40 -1.07 -0.45 -0.54 -0.78

(1.29) (1.48) (1.23) (1.12) (1.29) (1.08) (1.32) (1.02) (1.80)(arbcar) x (d2) -0.38

(11.3)(atfb) x (d2) -13.4

(12.4)(div) x (d2) -0.46

(0.59)(ins) x (d2) 0.21

(0.29)(nff) x (d2) -0.05

(0.54)(npl) x (d2) -19.2

(11.2)(reest) x (d2) 0.03

(0.32)(sec) x (d2) 0.66

(0.50)

42

(spr) x (d2) 0.44(2.09)

intercept 4.51 *** 4.53 *** 4.59 *** 4.43 *** 4.53 *** 4.49 *** 4.50 *** 4.49 *** 4.51 ***(0.24) (0.22) (0.25) (0.26) (0.27) (0.21) (0.28) (0.22) (0.24)

R-sq 0.39 0.44 0.42 0.41 0.39 0.46 0.39 0.46 0.39Adj R-sq 0.02 0.10 0.07 0.05 0.02 0.12 0.02 0.13 0.02

43

Table 19: Descriptive of variables – Post-Crisis PeriodMean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Obs

acs 0.77 1.00 1.00 0.00 0.43 -1.30 2.69 6.30 22arbcar 0.14 0.15 0.19 0.00 0.04 -2.20 9.01 50.90 22asset 0.76 0.91 1.00 0.00 0.38 -1.51 3.47 8.53 22atfb 0.60 0.65 0.92 0.00 0.24 -1.28 4.37 7.75 22atr 0.09 0.09 0.25 -0.09 0.07 -0.13 3.47 0.26 22bl 5.82 0.00 42.00 0.00 11.59 2.32 7.12 35.28 22car 0.08 0.08 0.12 0.00 0.03 -1.94 6.82 27.11 22dg 0.37 0.34 1.00 0.00 0.33 0.47 2.14 1.49 22div 0.73 1.00 1.00 0.00 0.46 -1.02 2.04 4.66 22etr 0.17 0.20 0.38 0.00 0.13 -0.22 1.61 1.94 22fba 0.21 0.18 0.85 0.00 0.20 1.71 6.02 19.07 22fcl 0.10 0.08 0.29 0.00 0.09 0.58 2.28 1.71 22ins 0.64 1.00 1.00 0.00 0.49 -0.57 1.32 3.76 22nff 0.23 0.00 1.00 0.00 0.43 1.30 2.69 6.30 22nii 0.33 0.36 0.54 0.00 0.16 -0.75 2.71 2.12 22npl 0.04 0.04 0.10 0.00 0.03 0.57 2.85 1.23 22oc 0.02 0.02 0.10 0.00 0.02 2.35 9.38 57.47 22os 4.25 3.51 18.00 0.00 4.09 1.89 6.92 27.27 22pssa 0.10 0.00 1.00 0.00 0.22 3.46 15.01 176.32 22reest 0.27 0.00 1.00 0.00 0.46 1.02 2.04 4.66 22rer 0.09 0.08 0.26 0.00 0.09 0.44 1.83 1.97 22sec 0.23 0.00 1.00 0.00 0.43 1.30 2.69 6.30 22spr 0.56 0.49 2.01 0.00 0.48 1.77 6.03 19.83 22

Table 20: Banking systems’ performance and regulation/supervision – post crisis periodWe run three regressions for each dependent variable (after-tax return – atr, non-interest income – nii, and domestic credit by the financial sector - dcfs) using White heteroscedasticity-consistent standard errors and covariance. The first is the basic model without introducing any dummy. In the second, we introduce the dummy d1 for BRICS, and in the third; we introduce the dummy d2 for emerging economies.


Regulatory/Supervisory variablesarbcar 0.39** 0.39** 0.39** 0.46 0.49 0.40

(0.16) (0.17) (0.17) (0.31) (0.39) (0.33)atfb 0.12*** 0.12*** 0.13*** 0.29*** 0.28** 0.31*** 1.45** 1.31* 0.69

(0.03) (0.03) (0.02) (0.10) (0.12) (0.1) (0.6) (0.6) (0.79)npl -0.46 -0.46 -0.49 -0.12 -0.07 -0.10 -8.79 -8.37 -6.59

(0.34) (0.35) (0.41) (1.14) (1.19) (1.12) (5.80) (5.9) (5.37)reest -0.02 -0.02 -0.02 -0.09** -0.09*** -0.1***

(0.02) (0.02) (0.03) (0.03) (0.03) (0.03)os 0.001* 0.001* 0.001* -0.01 -0.01 -0.01 0.03 0.02 0.04*

(0.001) (0.001) (0.001) (0.01) (0.01) (0.01) (0.02) (0.02) (0.01)gpr 0.1** 0.10** 0.10** -27.05 -22.04 -28.07

(0.03) (0.04) (0.04) (20.3) (23.1) (15.6)div -0.1*** -0.13*** -0.1***

(0.04) (0.04) (0.04)nff -0.26 -0.22 0.19

(0.23) (0.2) (0.32)pssa 0.12 0.20 0.26

(0.39) (0.44) (0.38)spr -1.0*** -0.92** -0.8***

(0.2) (0.3) (0.2)car 7.21 5.25 7.80

(4.70) (5.60) (4.88)Control Variablesgdpgr 0.01*** 0.01*** 0.01*** 0.02** 0.02*** 0.02 0.00 -0.02 0.04

(0.002) (0.002) (0.003) (0.008) (0.006) (0.01) (0.04) (0.05) (0.04)infl 0.001** 0.001** 0.001 -0.01** -0.01 -0.01** -0.1*** -0.1*** -0.06

44

(0.001) (0.001) (0.002) (0.004) (0.01) (0.005) (0.03) (0.03) (0.03)Dummy Variablesd1 0.01 0.02 0.30

(0.02) (0.11) (0.32)d2 0.01 0.05 -0.92**

(0.03) (0.05) (0.34)Intercept -0.07 -0.07 -0.07 0.20 0.20 0.21* 4.87*** 5.04*** 5.14***

(0.04) (0.04) (0.04) (0.11) (0.12) (0.11) (0.3) (0.3) (0.5)R-squared 0.81 0.81 0.81 0.73 0.73 0.74 0.78 0.79 0.84

Note: We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.

45

Table 21: Regulatory impact on after-tax income of banking systems in BRICS countries – Post-Crisis PeriodThis table reports the results of the regressions run with the dependent variable – after-tax return ( atr) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d1 for BRICS are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.arbcar 0.20 0.21 0.19 0.22 0.22 0.21 0.22 0.21 0.21

(.19) (.21) (.19) (.22) (.22) (.21) (.22) (.21) (0.2)atfb 0.05 0.04 0.06 0.04 0.04 0.04 0.04 0.04 0.05

(.04) (.04) (.04) (.04) (.04) (.04) (.04) (.04) (.04)npl -1.61 *** -1.62 *** -1.61 *** -1.63 *** -1.63 *** -1.63 *** -1.63 *** -1.61 *** -1.61 *** (.42) (.42) (.41) (.43) (.43) (.43) (.43) (.41) (.40) fba 0.07 * 0.07 * 0.07 0.07 * 0.07 * 0.08 * 0.07 * 0.07 * 0.07 * (.04) (.04) (.04) (.04) (.04) (.04) (.04) (.04) (.04) oc 1.92 *** 1.93 *** 1.95 *** 2.01 *** 2.01 *** 1.95 *** 2.00 *** 2.01 *** 1.94 *** (.39) (0.4) (.38) (0.4) (0.4) (0.4) (.41) (.38) (.38) reest -0.07 ** -0.07 ** -0.07 ** -0.08 ** -0.08 ** -0.07 ** -0.08 ** -0.08 ** -0.08 ** (.03) (.03) (.03) (.03) (.03) (.03) (.03) (.03) (.03) dg -0.03 -0.03 -0.04 -0.04 -0.04 -0.03 -0.04 -0.04 * -0.04 *

(.02) (.02) (.02) (.02) (.02) (.02) (.02) (.02) (.02) os -.002 -.002 -.002 -.001 -.001 -.001 -.001 -.001 -.002

(.002) (.002) (.003) (.002) (.002) (.002) (.002) (.004) (.004)Interaction Variables(arbcar) x (d1) 0.21 *

(.11)(atfb) x (d1) 0.04

(.02)(div) x (d1) 0.03

(.02)(ins) x (d1) 0.02

(.02)(nff) x (d1) 0.02

(.02)(npl) x (d1) 0.58

(0.4)(rer) x (d1) 0.07

(.07)(sec) x (d1) 0.01

(.05)(spr) x (d1) 0.02

(.03)intercept 0.08 *** 0.08 *** 0.08 ** 0.08 *** 0.08 *** 0.08 *** 0.08 *** 0.08 ** 0.08 ***

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(.03) (.03) (.03) (.03) (.03) (.03) (.03) (.03) (.03) R-sq 0.84 0.83 0.84 0.83 0.83 0.83 0.83 0.83 0.83Adj R-sq 0.74 0.73 0.73 0.72 0.72 0.73 0.72 0.72 0.72

Table 22: Regulatory impact on after-tax income of banking systems in emerging economies – Post-Crisis PeriodThis table reports the results of the regressions with the dependent variable – after-tax return (atr) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d2 for emerging countries are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.arbcar 0.02 0.14 0.26 0.21 0.18 0.16 0.20 0.17 0.21

(.26) (.17) (.15) (.21) (.19) (.18) (0.2) (.17) (.22)atfb 0.08 0.05 0.09 * 0.05 0.03 0.06 0.04 0.05 0.04

(.06) (.04) (.05) (.04) (.04) (.05) (.04) (.04) (.04)npl -1.50 *** -1.50 *** -1.47 *** -1.56 -1.56 *** -1.65 *** -1.69 *** -1.63 *** -1.58 *** (.46) (.42) (.43) (.48) (.44) (.44) (.45) (0.4) (.41) fba 0.04 0.03 0.03 0.07 0.06 0.04 0.05 0.08 ** 0.06

(.05) (.05) (.05) (.04) (.05) (.05) (.04) (.03) (.06)oc 1.86 *** 1.75 *** 1.66 *** 1.97 *** 2.10 *** 1.90 *** 1.99 *** 1.96 *** 2.00 *** (.44) (0.4) (0.4) (.41) (.35) (.42) (.38) (.32) (.35) reest -0.07 ** -0.07 ** -0.07 ** -0.08 *** -0.08 *** -0.07 * -0.08 ** -0.08 *** -0.08 ** (.03) (.03) (.03) (.03) (.03) (.04) (.03) (.03) (.03) dg -0.03 -0.03 -0.04 -0.04 * -0.04 * -0.03 -0.03 -0.04 -0.04 *

(.03) (.02) (.03) (.02) (.02) (.03) (.03) (.02) (.02) os -.001 -.002 -.002 -.001 -.001 -.001 -.001 -.002 -.001

(.002) (.002) (.002) (.002) (.002) (.002) (.002) (.003) (.003)Interaction Variablesarbcar) x (d2) 0.19

(.17)(atfb) x (d2) 0.05

(.04)(div) x (d2) 0.04

(.03)(ins) x (d2) 0.01

(.02)(nff) x (d2) 0.02

(.02)(npl) x (d2) 0.54

(.57)

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(rer) x (d2) 0.16(.16)

(sec) x (d2) 0.04(.04)

(spr) x (d2) 0.005(.02)

intercept 0.08 ** 0.08 *** 0.05 0.08 * 0.09 *** 0.08 ** 0.08 ** 0.09 *** 0.08 ** (.03) (.03) (.04) (.04) (.03) (.03) (.03) (.03) (.03) R-sq 0.84 0.85 0.86 0.83 0.84 0.84 0.84 0.85 0.83Adj R-sq 0.74 0.76 0.76 0.72 0.73 0.74 0.74 0.75 0.72

Table 23: Regulatory impact on non-interest income of banking systems in BRICS economies – Post-Crisis PeriodThis table reports the results of the regressions with the dependent variable – non-interest income (nii) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d1 for BRICS are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.acs -.14 ** -.14 ** -.13 ** -.14 ** -.14 ** -.14 ** -.14 ** -.11 ** -.12 ** (.06) (.06) (.06) (.06) (.06) (.06) (.06) (.04) (.05) arbcar 0.34 0.37 0.33 0.39 0.39 0.37 0.40 0.23 0.25

(.27) (.28) (.28) (.31) (.31) (.29) (0.3) (.23) (.24)atfb 0.27 *** 0.26 *** 0.28 *** 0.25 ** 0.25 ** 0.26 *** 0.25 ** 0.30 *** 0.29 *** (.07) (.08) (.07) (.09) (.09) (.08) (.09) (.06) (.06) div -.13 *** -.12 *** -.13 *** -.12 *** -.12 *** -.12 *** -.12 *** -.13 *** -.12 *** (.04) (.04) (.04) (.04) (.04) (.04) (.04) (.03) (.03) gpr -.82 *** -.82 *** -.82 *** -.83 *** -.83 *** -.83 *** -.83 *** -.68 *** -.73 *** (.11) (.11) (.12) (.13) (.13) (.12) (.13) (.09) (.09) sec 0.15 ** 0.16 ** 0.15 ** 0.16 ** 0.16 ** 0.16 ** 0.16 ** 0.13 *** 0.14 *** (.05) (.05) (.05) (.06) (.06) (.06) (.06) (.04) (.04) sct 0.86 *** 0.84 *** 0.88 *** 0.89 *** 0.89 *** 0.87 *** 0.88 *** 0.66 *** 0.71 *** (.21) (.19) (.22) (.23) (.23) (.21) (.22) (.17) (.17) os -.01 -.01 * -.01 -.01 -.01 -.01 -.01 -.02 ** -.02 **

(.01) (.01) (.01) (.01) (.01) (.01) (.01) (.01) (.01) reest -.04 -.04 -.04 -.04 -.04 -.04 -.04 -.07 ** -.06

(.04) (.04) (.04) (.04) (.04) (.04) (.04) (.03) (.04) Interaction Variables

(arbcar) x (d1) 0.25(.45)

(atfb) x (d1) 0.08

48

(.08)(div) x (d1) 0.02

(.09)(ins) x (d1) 0.04

(.05)(nff) x (d1) 0.04

(.05)(npl) x (d1) 0.94

(1.1)(rer) x (d1) 0.22

(.22)(sec) x (d1) 0.26 **

(.09)(spr) x (d1) 0.13 *

(.06)intercept 0.14 ** 0.14 ** 0.13 ** 0.13 ** 0.13 ** 0.13 ** 0.13 ** 0.20 *** 0.18 **

(.06) (.06) (.06) (.06) (.06) (.06) (.06) (.06) (.06)R-sq 0.89 0.90 0.89 0.89 0.89 0.89 0.89 0.93 0.92Adju R-sq 0.81 0.81 0.80 0.80 0.80 0.81 0.81 0.87 0.85

Table 24: Regulatory impact on non-interest income of banking systems in emerging economies – Post-Crisis PeriodThis table reports the results of the regressions with the dependent variable – non-interest income (nii) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d2 for emerging countries are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.acs -.13** -.13** -.13** -.13** -.14** -.13** -.13** -.14** -.12** 0.05 0.05 0.05 0.06 0.06 0.05 0.05 0.06 0.05 arbcar 0.06 0.18 0.28 0.37 0.36 0.18 0.32 0.40 0.04

0.31 0.20 0.22 0.29 0.29 0.23 0.27 0.31 0.24atfb 0.29*** 0.26*** 0.29*** 0.26*** 0.27*** 0.28*** 0.26*** 0.24** 0.26*** 0.06 0.08 0.07 0.08 0.08 0.07 0.08 0.09 0.08 div -.14*** -.14*** -.15*** -.13*** -.13*** -.14*** -.13*** -.13*** -.15*** 0.04 0.03 0.04 0.04 0.04 0.03 0.03 0.04 0.04 gpr -.81*** -.79*** -.77*** -.83*** -.82*** -.77*** -.79*** -.93*** -.76*** 0.11 0.11 0.13 0.12 0.12 0.10 0.11 0.16 0.09 sec 0.15** 0.15** 0.14** 0.15** 0.15** 0.14** 0.15** 0.20** 0.16** 0.06 0.06 0.05 0.05 0.05 0.06 0.06 0.07 0.06 sct 0.86*** 0.82*** 0.84*** 0.90*** 0.90*** 0.85*** 0.86*** 1.00*** 0.76***

49

0.22 0.21 0.24 0.24 0.24 0.21 0.23 0.28 0.18 os -.01 -.01 -.01 -.01 -.01 -.01 -.01 -.01 -.01

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01reest -.05 -.05 -.05 -.04 -.04 -.04 -.05 -.03 -.08

0.05 0.05 0.05 0.05 0.05 0.04 0.04 0.04 0.06Interaction Variables(arbcar) x (d2) 0.21

0.26(atfb) x (d2) 0.06

0.06(div) x (d2) 0.03

0.05(ins) x (d2) -.01

0.04(nff) x (d2) -.002

0.04(npl) x (d2) 0.98

0.56(rer) x (d2) 0.22

0.17(sec) x (d2) -.06

0.07(spr) x (d2) 0.05

0.04intercept 0.16 ** 0.17** 0.14** 0.13** 0.13* 0.14** 0.13** 0.11* 0.22** 0.07 0.08 0.06 0.06 0.07 0.05 0.05 0.06 0.10 R-sq 0.89 0.90 0.89 0.89 0.89 0.91 0.90 0.89 0.90Adj R-sq 0.81 0.81 0.80 0.80 0.80 0.83 0.82 0.80 0.82

Table 25: Regulatory impact on domestic credit by financial sector in BRICS countries – Post-Crisis PeriodThis table reports the results of the regressions with the dependent variable – domestic credit (dc) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d1 for BRICS are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.

acs -.02 -.01 -.03 .01 .01 .01 .01 -.02 -.04(.36) (.35) (.35) (.35) (.35) (.36) (.35) (.39) (.36)

arbcar -6.2* -6.3* -6.2 -6.6* -6.6 -6.4* -6.5* -6.3 -6.2 (3.5) (3.4) (3.8) (3.4) (3.4) (3.3) (3.4) (3.6) (3.6)atfb 1.22* 1.26** 1.21 1.33* 1.33 1.27* 1.32* 1.24* 1.22*

50

(.62) (0.6) (.76) (.63) (.63) (.59) (.63) (.65) (.67) div -.44 -.46 -.41 -.48 -.48 -.47 -.48 -.41 -.42

(.29) (.32) (.29) (.33) (.33) (.32) (.33) (.28) (.29)gpr 0.19 0.19 0.19 0.29 0.29 0.22 0.27 0.26 0.17

(.85) (.84) (.89) (.87) (.87) (.84) (.86) (1.1) (1.1)sec -.09 -.10 -.08 -.12 -.12 -.11 -.12 -.09 -.08

(.42) (.42) (.42) (.42) (.42) (.42) (.42) (.45) (.43)sct -1.2 -1.2 -1.3 -1.3 -1.3 -1.2 -1.3 -1.4 -1.3

(1.7) (1.7) (1.7) (1.6) (1.6) (1.7) (1.6) (2.3) (2.2)os 0.02 0.02 0.02 0.01 0.01 0.02 0.02 0.01 0.02

(.04) (.04) (.04) (.03) (.03) (.03) (.03) (.06) (.06)reest -.41 -.40 -.40 -.40 -.40 -.40 -.40 -.41 -.39

(0.5) (0.5) (.51) (0.5) (0.5) (0.5) (0.5) (0.54) (.51) Interaction Variables

(arbcar) x (d1) -1.0(2.4)

(atfb) x (d1) -.27(.48)

(div) x (d1) -.08(.54)

(ins) x (d1) -.31(.37)

(nff) x (d1) -.31(.37)

(npl) x (d1) -4.6(7.2)

(rer) x (d1) -1.1(1.4)

(sec) x (d1) .06(1.1)

(spr) x (d1) -.07(.65)

intercept 5.60*** 5.60*** 5.62*** 5.64*** 5.64*** 5.61*** 5.63*** 5.65*** 5.60*** (.69) (.69) (.69) (.67) (.67) (.68) (.67) (.83) (0.8) R-sq 0.51 0.51 0.50 0.51 0.51 0.51 0.51 0.50 0.50Adj R-sq 0.10 0.10 0.09 0.10 0.10 0.10 0.10 0.09 0.09

51

Table 26: Regulatory impact on domestic credit by financial sector in emerging economies – Post-Crisis PeriodThis table reports the results of the regressions with the dependent variable – domestic credit (dc) using White heteroscedasticity-consistent standard errors and covariance. The interaction variables for the dummy d2 for emerging countries are introduced each in all the models reported. We report the coefficients of regression and standard errors in parenthesis ( ). The levels of significance are indicated as * for 0.10 level, ** for 0.05 level and *** for 0.01 level.acs -.03 -.06 -.22 .09 -.02 -.10 -.08 .01 -.38

(.27) (.28) (.31) (.33) (.36) (.26) (.28) (.35) (.25)arbcar 1.4 -3.3 -3.5* -5.3* -6.3 -4.8 -6.2 -6.6* 0.07

(4.2) (3.3) (1.8) (2.8) (3.6) (3.7) (3.4) (3.6) (2.6)atfb 0.54 1.34** 0.43 1.01 1.26** 1.16* 1.30** 1.40 1.45***

(.63) (.49) (.54) (.58) (.57) (.56) (.58) (.89) (.44) div -.20 -.31 .11 -.40 -.41 -.33 -.43 -.45 -.05

(.33) (.32) (.24) (.31) (.28) (.26) (.25) (.29) (.26)gpr -0.2 -.42 -1.5 0.17 0.18 -0.3 0.03 0.91 -1.1**

(.78) (.81) (1.1) (.81) (0.8) (.78) (.76) (1.8) (.42) sec -0.1 -0.1 0.3 -0.1 -.07 -.02 -.08 -.39 -.17

(.36) (.37) (.41) (.34) (.39) (0.4) (.43) (.85) (.28)sct -0.5 -0.2 0.44 -1.0 -1.4 -1.0 -1.2 -2.1 1.35

(1.3) (1.6) (1.6) (1.6) (1.6) (1.4) (1.5) (2.6) (1.3)os 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.07*

(.03) (.03) (.03) (.03) (.03) (.03) (.03) (.04) (.04) reest -.16 -.16 -.02 -.16 -.39 -.39 -.34 -.43 0.44

(.42) (.44) (.43) (.46) (.47) (.48) (0.5) (.48) (.38) Interaction Variables

(arbcar) x (d2) -5.3**(2.3)

(atfb) x (d2) -.94(.56)

(div) x (d2) -1.0**(0.4)

(ins) x (d2) -.52(.36)

(nff) x (d2) -.06(.35)

(npl) x (d2) -8.7(4.9)

(rer) x (d2) -1.3(1.3)

(sec) x (d2) 0.42(1.2)

(spr) x (d2) -1.0***

52

(.29)Intercept 4.89*** 4.92*** 5.32*** 5.59*** 5.59*** 5.50*** 5.59*** 5.76*** 3.86*** (.59) (.64) (.36) (.57) (.68) (.63) (.67) (.77) (.64) R-sq 0.67 0.61 0.73 0.58 0.50 0.57 0.52 0.51 0.76Adj R-sq 0.39 0.29 0.50 0.24 0.09 0.21 0.13 0.10 0.56

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