› upload7282 › 824e156eff1afcaf1… · web viewanalyzing the topography of financial...
Post on 04-Jul-2020
0 Views
Preview:
TRANSCRIPT
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
1
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.
5
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).
6
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.
7
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.
[Table 2 is about here]
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.
8
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.
9
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
10
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.
[Table 3 is about here]
[Table 4 is about here]
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.
[Table 5 is about here]
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.
[Table 6 is about here]
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
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 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.
[Table 7 is about here]
12
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.
[Table 8 is about here]
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.
[Table 9 is about here]
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.
[Table 10 is about here]
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.
[Table 11 is about here]
[Table 12 is about here]
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
be seen from the fact that banks owning 100% of the equity in any nonfinancial firm were
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.
[Table 13 is about here]
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.
[Table 14 is about here]
14
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.
[Table 15 is about here]
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 16 is about here]
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
relationship of assets held by the top five largest banks suggests the significant reduction in
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.
[Table 17 is about here]
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.
[Table 18 is about here]
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 19 is about here]
[Table 20 is about here]
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.
[Table 21 is about here]
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.
[Table 22 is about here]
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 23 is about here]
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.
[Table 24 is about here]
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
relationship of assets held by the top five largest banks suggests the 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. 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 25 is about here]
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.
[Table 26 is about here]
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.
ReferencesBarth J, Caprio G and Levine R. (2004). Regulation and supervision: what works best?
Journal of Financial Intermediation 13: 205-248. Barth J, Caprio G and Levine R. (2008). Bank regulations are changing: for better or worse?
Comparative Economic Studies 50(4): 537-563. Barth J, Caprio G and Levine R. (2012). Guardians of Finance: Making Regulators Work for
Us. MIT Press. Beck T, Demirguc-Kunt A, and Levine R. (2006). Bank supervision and corruption in
lending. Journal of Monetary Economics 53: 2131–2163.Berger A N, Herring R J and Szegö G P. (1995). The role of capital in financial institutions.
Journal of Banking & Finance 19: 257–276.Berger A N and DeYoung R. (1997). Problem loans and cost efficiency in commercial banks.
Journal of Banking and Finance 21: 849-870.Besanko D and Kanatas G. (1996). The regulation of bank capital: Do capital standards
promote bank safety? Journal of Financial Intermediation, 5(4): 160–183.Bhattacharya S, Boot A W A and Thakor A V. (1998). The economics of bank regulation.
Journal of Money, Credit, and Banking 30(4): 745-770.Boudriga A, Taktak N B, Jellouli S. (2009). Banking supervision and Nonperforming loans: a
cross‐country analysis. Journal of Financial Economic Policy 1(4): 286-318Boyd J H, Kwak S and Smith B. (2005). The real output losses associated with modern
banking crises. Journal of Money, Credit and Banking 37: 977–99.Brewer E III, Jackson W E III and Mondschean T S. (1996). Risk, regulation, and S & L
diversification into non-traditional assets. Journal of Banking & Finance 20:723-744.Das U S, Quintyn M and Chenard K. (2004). Does regulatory governance matter for financial
system stability? An empirical analysis. IMF Working Paper No. 89, International Monetary Fund, Washington, DC.860
Davidson S. (2010). Bankers and scapegoats, in Suk-Joong Kim, Michael D. Mckenzie (ed)International Banking in the New Era: Post-Crisis Challenges and Opportunities. International Finance Review 11: 119-134
Demirguc-Kunt A and Detragiache E. (1998). Financial liberalization and financial fragility. Policy Research Working Paper Series 1917, The World Bank.
Demirguc-Kunt A and Detragiache, E. (2002). Does deposit insurance increase banking system stability? An empirical investigation. Journal of Monetary Economics, 49: 1373–1406.
Demirguc-Kunt A, Detragiache E and Tressel T. (2008). Banking on the principles:
21
compliance with Basel core principles and bank soundness. Journal of FinancialIntermediation, 17: 511–542.
Demsetz H and Lehn K. (1985). The structure of corporate ownership: causes and consequences. Journal of Political Economy 93: 1155-1177.
Dewatripont M and Tirole J. (1994). The Prudential Regulation of Banks. MIT Press.Djankov S, La Porta R, Lopez-de-Silanes F, and Shleifer A. (2002). The regulation of entry.
Quarterly Journal of Economics 117: 1–37.Eichberger J and Harper I R. (1997). Asymmetric information: contracts. Financial
Economics, 171-251Fernandez A I and Gonzalez F. (2005). How Accounting and Auditing Systems Can
Counteract Risk Shifting of Safety Nets in Banking: Some International Evidence. Journal of Financial Stability 1: 466-500.
Freixas X and Rochet J C. (1997). Microeconomics of Banking. MIT Press, Cambridge.Freixas X and Santomero A M. (2002). An overall perspective of banking regulation.
Working paper 02-1, Federal Reserve Bank of Philadelphia, 25.Galai D and Masulis R. (1976). The option-pricing model and the risk factor of stock.
Journal of Financial Economics 3: 53-81.Giannetti Mariassunta and Ongena Steven. (2012). Lending by example: Direct and indirect effects
of foreign banks in emerging markets. Journal of International Economics 86 (1): 167-180Gonzalez, F. (2005). Bank regulation and risk-taking incentives: An international comparison
of bank risk. Journal of Banking & Finance 29 (5): 1153-1184.Hale, Galina. (2012). Bank relationships, business cycles, and financial crises. Journal of
International Economics 88(2): 312–325Hanson S G, Kashyap A K and Jeremy C S. (2011). A macroprudential approach to financial
regulation. Journal of Economic Perspectives 25(1): 3-28.International Monetary Fund. (2010). A fair and substantial contribution by the financial
sector: final report for the G-20. Extracted from: www.imf.org/external/np/g20/pdf/062710b.pdfJensen M and Meckling W. (1976). Theory of the firm: managerial behavior, agency costs,
and ownership structure. Journal of Financial Economics 3: 305-360.John K, John T A and Saunders A. (1994). Universal banking and firm risk taking. Journal of
Banking & Finance 18: 307–323.John K, Saunders A, and Senbet LW. (2000). A theory of bank regulation and management
Compensation. Review of Financial Studies 13(1): 95-125John K L, Litov and Yeung B. (2008).Corporate governance and managerial risk taking:
theory and evidence. Journal of Finance 63(4): 1679-1728Jalilian H, Kirkpatrick C and Parker D. (2007). The impact of regulation on economic growth
in developing countries: a cross-country analysis. World Development 35(1): 87-103.Kalemli‐Ozcan S, Papaioannou E, and Fabrizio P. (2013). Global banks and Crisis
transmission. Journal of International Economics 89 (2): 495-510Keeley M C (1990). Deposit insurance, risk, and market power in banking. American
Economic Review 80 (5): 1183–1200.La Porta R, Lopez-de-Silanes F, Shleifer A, & Vishny R W. (1998). Law and finance.
Journal of Political Economy 106 (6): 1113–1155Levine R (2010). An autopsy of the U.S. Financial system: accident, suicide, or negligent
homicide. Journal of Financial Economic Policy 2(3): 196-213.Laeven L and Valencia F. (2010). Resolution of banking crises: the good, the bad, and the
ugly. IMF Working Paper 10/146Lau Lawrence. (2010). Financial regulation and supervision post the global financial crisis.
Working Paper No. 2. Institute of Global Economics and Finance, The Chinese University of Hong Kong.
22
Merton R C (1977). An analytic derivation of the cost of deposit insurance and loanguarantees. Journal of Banking & Finance 1: 3-11.
Mishkin F (1999a). Global financial instability: framework, events, issues. Journal ofEconomic Perspectives 13(4): 3–20.
Mishkin F (1999b). Lessons from the Asian crisis. Journal of International Money &Finance, 18: 709–723.
Pasiouras F, Gaganis C and Zopounidis C. (2006). The impact of bank regulations, supervision, market structure and bank characteristics on individual bank ratings: A cross-country analysis. Review of Quantitative Finance and Accounting 27: 403–438.
Pennacchi, G. (2006). Deposit insurance, bank regulation, and financial system risks. Journal of Monetary Economics 53(1): 1-30.
Pasiouras F, Tanna S, and Zopounidis C. (2009). The Impact of Banking Regulations on Banks’ Cost and Profit Efficiency: Cross-Country Evidence. International Review of Financial Analysis 18: 294-302.
Santos J A C (2000). Bank capital regulation in contemporary banking theory: a review of The literature. Working paper No 90, Bank for International Settlements, 32
Saunders A, Elizabeth S and Nickolaos G T. (1990). Ownership structure, deregulation, and bank risk taking. Journal of Finance 45: 643-654.
Shleifer A and Vishny R. (1998). The grabbing hand: government pathologies and their cures. Harvard University Press, Cambridge, MA.
Swamy Vighneswara. (2013). Banking system resilience and financial stability – an evidenceFrom Indian banking. Journal of International Business and Economy 14(1): 87-117
Taylor J (2009).The financial crisis and the policy responses: an empirical analysis of what Went wrong. Working Paper 14631, National Bureau of Economic Research.
VanHoose D (2007). Theories of Bank Behaviour under Capital Regulation. Journal of Banking and Finance 31: 3680-3697.
Yellen J L (2009). A Minsky meltdown: lessons for central bankers. FRBSF EconomicLetter 15, Federal Reserve Bank of San Francisco, New York, available at http://www.frbsf.org/news/speeches/2009/0416.html
Walid, Hejazi and Eric, Santor. (2010). Foreign asset risk exposure, DOI, and performance: An analysis of Canadian banks. Journal of International Business Studies 41: 845-860
Wilmarth (2010). Reforming Financial Regulation to Address the Too-Big-To-Fail Problem. Brooklyn Journal of International Law 35: 707-783.
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
23
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.
Dependent variable: atr Dependent variable: nii Dependent variable: dcfs(1) (2) (3) (4) (5) (6) (7) (8) (9)
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.
Dependent variable: atr Dependent variable: nii Dependent variable: dcfs(1) (2) (3) (4) (5) (6) (7) (8) (9)
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 ***
46
(.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)
47
(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
53
top related