Description
This paper aims to examine the association between regulatory capital and risk of Indian
commercial banks and the impacts of other relevant variables on them
Journal of Financial Economic Policy
Regulatory capital and risk of Indian banks: a simultaneous equation approach
Santi Gopal Maji Utpal Kumar De
Article information:
To cite this document:
Santi Gopal Maji Utpal Kumar De , (2015),"Regulatory capital and risk of Indian banks: a
simultaneous equation approach", J ournal of Financial Economic Policy, Vol. 7 Iss 2 pp. 140 - 156
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Regulatory capital and risk of
Indian banks: a simultaneous
equation approach
Santi Gopal Maji
Department of Commerce, North-Eastern Hill University,
Shillong, India, and
Utpal Kumar De
Department of Economics, North-Eastern Hill University, Shillong, India
Abstract
Purpose – This paper aims to examine the association between regulatory capital and risk of Indian
commercial banks and the impacts of other relevant variables on them.
Design/methodology/approach – The study is based on a secondary data set on Indian commercial
banks collected from “Capitaline Plus” corporate database and annual reports of the respective banks.
Total 41 major Indian banks (21 public and 20 private sector banks) are considered in this study. Here
absolute values of capital and risk are used as dependent variables along with some relevant bank
specifc explanatory variables in a system of a two-equation model. Based on the nature of
interrelationship and identifability of the equations, three-stage least squares (3SLS) technique is used
to estimate the relationship.
Findings – Risk and capital of Indian commercial banks are inversely associated. The infuence of
proftability on both capital and risk is signifcantly positive. Moreover, human capital effciency is
negatively associated with the undertaking of risk by the banks. In this respect, Indian private sector
banks are found to be more effcient in utilizing human capital for reducing credit risk.
Originality/value – It is the frst comparative study in India examining the relationship between
capital and risk of Indian public and private sector commercial banks covering both Basel I and II
periods. Further, the role of human resource in managing risk is considered as a relevant variable in this
study.
Keywords Banks, Credit, Government policy and regulation, Capital,
Financial risk and risk management, Multiple or simultaneous equation models
Paper type Research paper
1. Introduction
Regulatory capital has gained considerable attention after the implementation of Basel
I guidelines in 1988 to enhance fnancial soundness and competitive advantage in the
banking sector (Rime, 2001; Pennacchi, 2005). The voluntary adoption of Basel I, revised
framework in 2004 (Basel II) and further modifed guidelines in 2010 for promoting more
fexible banking system(Basel III) by a large number of countries have made the capital
adequacy ratio (CAR) a vital yardstick to access the solvency of the fnancial
institutions. The rationale for preserving adequate capital comes out of its indication of
suffcient fnancial resources at the disposal of a bank that provides cushion against
JEL classifcation – G20, G28, G32
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1757-6385.htm
JFEP
7,2
140
Received17 June 2014
Revised26 August 2014
Accepted5 November 2014
Journal of Financial Economic
Policy
Vol. 7 No. 2, 2015
pp. 140-156
©Emerald Group Publishing Limited
1757-6385
DOI 10.1108/JFEP-06-2014-0038
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failure. The option-pricing model states that an unregulated bank would take more risk
to increase the return to its shareholders (Benston et al., 1986; Keeley and Furlong, 1990).
On the other hand, the mean-variance framework says that the forced reduction in
leverage reduces the return of a bank when capital is relatively costly (Kim and
Santomero, 1988; Rochet, 1992). The introduction of risk-based capital standard as per
Basel accord may be a useful attempt to protect banks from failure (Rime, 2001).
However, the empirical studies provide controversial views regarding the preservation
of higher capital requirements to protect banks from vulnerabilities. Some researchers
(Besanko and Kanatas, 1996; Bichsel and Blum, 2004; Altunbas et al., 2007) argue that
bank capital fails to promote stability of a bank and to reduce its risk. There are others
(Berger and De Young, 1997; Jacques and Nigro, 1997; Agoraki et al., 2011) who provide
an alternative view establishing a negative association between capital and risk. In
contrast, Biekpe and Floquet (2008) and Van Roy (2008) fnd capital and risk to be
indifferent in most of the cases of cross-country analysis.
Although the regulatory pressure has gradually been increased over the years with
the aimof moving toward international best practices, relatively little attention has been
paid toward assessing the effectiveness of regulations in risk taking of banks,
specifcally in developing countries. Many researchers have tried to address the issue
during past two decades, but most of the studies are concentrated in USA and Europe
(Altunbas et al., 2007; Jacques and Nigro, 1997; Rime, 2001; Shrieves and Dahl, 1992;
Jokipii and Milne, 2011; Athanasoglou, 2011). In the context of Indian banking sector,
some researchers (Nag and Das, 2002; Rao, 2005; Murali and Subbukrishna, 2008; Pasha
et al., 2012) have highlighted the importance of regulatory framework in maintaining the
adequate level of bank capital to avoid vulnerabilities. Only a few studies have
examined the association between capital and risk (Nachane et al., 2000; Ghosh et al.,
2003; Das and Ghosh, 2004; Gupta and Meera, 2011; Maji and Dey, 2012). The purpose of
this paper is to fnd out the answer of a very pertinent question relating to the basic aim
of Basel accord in Indian context, i.e. do increases in CAR prompt banks to reduce risk
or to undertake more risk? Therefore, this paper tries to contribute further empirical
evidence relating to the capital-risk behavior of Indian banks in recent times.
The selection of Indian banks for this issue is of interest for several reasons. First,
non-recovery of loans and advances, which in turn results in high credit risk, was the
main reason for low performance of Indian banks as identifed by the Namasimham
Committee (1991). Hence, in the post-reform period, the Reserve Bank of India (RBI), as
well as the Government of India, has taken several steps to improve the fnancial
stability of the Indian banking system. Second, the regulatory pressure in terms of
maintaining minimumCAR is higher for Indian banks as compared to the international
standard. The RBI has raised the minimum CAR to 9 per cent as compared to the
internationally accepted level of 8 per cent in 1998 based on its mid-term review of
Monetary and Credit Policy. Subsequently, the CAR as per Basel II accord was also
higher as compared to the standard norm. Third, during the past two decades, Indian
commercial banking sector has faced several challenges in terms of competition
imposed by newly emerged private sector and foreign banks, technological
advancement, offering innovative products and services, etc., which ultimately
increases the risk of insolvency. Finally, as per the existing knowledge, so far, only two
empirical studies have been conducted to examine the association of capital and risk in
141
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Indian banking sector by using simultaneous equation approach (Nachane et al., 2000;
Das and Ghosh, 2004).
However, the present study differs from earlier two studies in respect of two
important aspects. First, this study covers the period of both Basel I and Basel II,
considering both public sector and private sector banks. Second, human capital
effciency (HCE) is taken as another determinant of bank risk. Human capital, which is
regarded as the heart of creating intellectual capital (Chen et al., 2005), represents the
combined knowledge, skill, experience, talent, attitude and effectiveness of employees of
an organization that are used to build unique competencies and improve business
performance (Cappelli and Crocker-Hefter, 1996; Bontis, 1998). The success of any
service sector like bank, which mainly depends upon intangibles rather than tangible
assets, is expected to be infuenced signifcantly by the effciency of the human
resources of the enterprise. Adequate empirical evidences are found to support this view
(Pulic, 2000; Goh, 2005; Mavridis, 2004; Mavridis and Kyrmizoglou, 2005; Yalama and
Coskun, 2007; Liu, 2009; Malik et al., 2012; Ghosh and Maji, 2012). All these studies have
investigated the impact of human capital and other components of intellectual capital on
banks’ fnancial performance. However, the importance of the effciency of bank
employees in managing credit risk has remained empirically unaddressed.
Indeed, the success in credit activities largely depends on the correct assessment and
interpretation of many internal and external factors at the time of granting loans as well
as throughout the credit period. As all these activities are performed by the bank
employees, their skill, knowledge, imaginative mind and experience are expected to play
a vital role in the matter of managing credit risk. Further, despite many regulatory
measures undertaken by the RBI to improve the solvency of Indian banks, a study
conducted by National Skill Development Corporation of India clearly points out the role
of human resources in developing well-defned credit evaluation policies, managing the
credit quality through adequate knowledge and focusing strongly on recovery and
collection efforts as the key success factors for banking and fnancial institutions[1]. The
inclusion of HCE as an explanatory variable in the model may, thus, provide another
dimension relating to the importance of intellectual capital in the management of bank
risk.
The rest of the paper is organized as follows. The next section includes a reviewof the
existing literature relating to the association between bank capital and risk. Section 3 is
devoted for data, variables and model used in this study. Section 4 presents the results
and followed by concluding remarks in Section 5.
2. Review of literature
A large number of studies indicate that banks in USA and Europe simultaneously
determine the capital and risk decisions (Shrieves and Dahl, 1992; Jacques and Nigro,
1997; Rime, 2001; Van Roy, 2008; Biekpe and Floquet, 2008; Godlewski, 2005; Jokipii and
Milne, 2011; Athanasoglou, 2011). To analyze the bank’s capital behavior, Shrieves and
Dahl (1992) have developed a simultaneous equation model and observed a positive
relationship between capital and risk in US banks. In contrast, Jacques and Nigro (1997),
using the same methodology, have found a signifcant negative relationship between
change in regulation of capital and risk level in US commercial banks. On the other hand,
Rime (2001) has analyzed the adjustments in capital and risk of Swiss banks and has
found that there is a regulatory pressure on Swiss banks that lead to a positive and
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signifcant impact on the ratio of capital to total assets, but no signifcant impact on
bank’s risk-taking behavior. In other words, Swiss banks improve their capital
adequacy by increasing their capital through retained earnings or equity issue and not
by decreasing their risk-taking behavior.
Another study conducted by Godlewski (2005) investigates the relationship between
bank capital and risk in emerging market economies. Applying simultaneous equation
framework of Shrieves and Dahl (1992), the study shows a similar result in case of the
US, the UK and other industrial economies. The study identifes the importance of the
regulatory, institutional and legal factors in running a healthy banking system. Biekpe
and Floquet (2008) have investigated the nature of relationship between capital and risk
exposures for 2,940 banks from 44 emerging market countries using the model of
Shrieves and Dahl (1992) with a partially adjusted framework. The study fnds no
statistically signifcant relationship between changes in capital and risk in the vast
majority of emerging markets banks, which is contrary to the results of erstwhile
empirical studies conducted in developed countries like the USA and the UK. However,
regarding the relationship between absolute level of capital and risk, the study
anticipates the association to be positive in the long run.
Altunbas et al. (2007) has done a study on the relationship between capital, risk and
effciency for a large sample of European banks between 1992 and 2000. The study
reveals that ineffcient European banks hold more capital and undertake less risk. Also,
a positive relationship between risk on the level of capital and liquidity is revealed,
which shows regulators’ preference for capital as a means of restricting risk-taking
activities. Van Roy (2008) on six G10 countries (Canada, France, Italy, Japan, the UKand
the USA) shows that there is no difference between the well-capitalized banks and
weakly capitalized banks in so far as the modifcation of the ratio of risk-weighted assets
to total assets is concerned. In contrast, Jokipii and Milne (2011) on US banking sector
observe a positive relationship between capital and risk for highly capitalized banks and
negative capital-buffer-risk relationship for banks with marginal CAR. It is also
observed that banks with higher liquidity can decrease their capital and increase their
levels of risk because the higher liquidity act as self-insurance for banks as against their
liquidity shocks. After the fnancial crisis of 2008, a study conducted by Athanasoglou
(2011) on banks’ choice of capital (both regulatory and equity) and risk in South-Eastern
European (SEE) observes a positive relationship between regulatory capital and risk,
but a inverse association between equity capital and risk. However, Lindguist (2004)
argues for an inverse relationship between capital and risk in case of Norwegian banks.
In India, the empirical investigation relating to the absolute and relative association
between bank capital and risk is very less. Nachane et al. (2000) have conducted a study
on Indian public sector banks for a period of only two years (1997-1999), using the model
developed by Shrieves and Dahl (1992) after segregating the selected banks into
well-capitalized and undercapitalized. The fndings of the study indicate that changes in
capital and risk are negatively related, which is consistent with the work of Jacques and
Nigro (1997). Using dynamic multivariate panel regression, Ghosh et al. (2003) have
examined the relationship between the requirement of capital and risk-taking behavior
of Indian public sector banks. The study fnds no conclusive evidence regarding risk
aversion among Indian banks and suggests for bank specifc regulatory capital ratio
based on risk profle to enhance stability. In an attempt to explore empirically the
infuence of terms of credit on non-reforming loans (NPLs) of Indian public sector banks,
143
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Ranjan and Dhal (2003) observe that the terms of credit have a signifcant on banks’
NPLs in the presence of bank size and other macroeconomic shocks. However, this is
outside of the scope of this study. In another study, Das and Ghosh (2004) have
investigated the relationship between capital and risk of Indian public sector banks
during the period from 1992-1993 to 1999-2000. The fndings of the study indicate that
capital and risk are negatively associated. Gupta and Meera (2011) have also observed
negative correlation coeffcient between CAR and non-performing assets of some select
Indian banks. The other study, however, indicates negative association of size and
capitalization with insolvency risk of Indian banks, although the relation between
capitalization and credit risk has remained inconclusive (Maji and Dey, 2012).
The outcomes of aforesaid studies indicate that the researchers have pointed out both
negative and positive association between bank capital and risk. It is important to note
that in the recent studies, a positive association between capital and risk is observed
more in case of developed countries, while in developing countries, the inverse or
insignifcant relation is more pronounced. Moreover, the researchers mostly have used
proftability as a factor to determine the bank capital, while its infuence on the
risk-taking behavior of the banks has not been examined. In reality, banks are found to
take more risk in case of higher proft margin, and they extend loan even without any
collateral security if they can charge very high margin of interest. There is, thus, a scope
to include proftability in the equation of risk also considering the risk–return
relationship in fnance literature. Again, in the knowledge-based economy, intellectual
capital is one of the important driving forces for service sector like banks that creates
signifcant gap between market value and book value (Edvinsson and Malone, 1997;
Clarke et al., 2011).
Plethora of empirical studies have used value added intellectual coeffcient (VAIC)
model developed by Pulic (2000) to measure HCE and have observed signifcant impact
of HCE on bank performance. Goh (2005) have found signifcant contribution of HCE in
value creation capability of both domestic and foreign banks in Malaysia. The fndings
of Mavridis (2004) conclude that Japanese banks have improved their performance
noticeably by utilizing the effciency of human capital. Similarly, in case of Greece
banking sector, Mavridis and Kyrmizoglou (2005) have observed positive association
between human capital and value added. Another study conducted by Yalama and
Coskun (2007) have seen greater infuence of human capital than physical capital on
proftability of banks in Turkey from 1995 to 2004.
In case of China’s listed commercial banks, Liu (2009) fnds signifcant positive
correlation between human capital and proftability. Using VAIC model Malik et al.
(2012) have observed HCE to be the main predictor of performance of Islamic banks in
Pakistan. Similar result is observed by Ghosh and Maji (2012) in the context of Indian
banking sector. This study also fnds signifcant contribution of HCE in enhancing
proftability of Indian banks during 2001 to 2010. Human capital can, thus, be
considered as a strong predictor of bank performance and the crux of achieving
sustainable competitive advantage.
3. Data, variables and models
3.1 Data
The study is based on secondary data on Indian commercial banks collected from
“Capitaline Plus” corporate data database and annual reports of the respective banks for
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a period of 14 years from 1998-1999 to 2011-2012. The RBI has increased the CAR from
international standard of 8- 9 per cent based on the mid-term review of Monetary and
Credit Policy during the period of 1998-1999. The study period, thus, covers the revised
CAR of RBI during Basel I period and also the Basel II norms for Indian commercial
banks. Total 41 major Indian banks are considered here, of which 21 are public sector
banks and 20 are private sector banks.
3.2 Defnitions of variables and theoretical relationship
3.2.1 Defnitions of capital and risk
3.2.1.1 Capital (CAR). Two alternative measures of bank capital are used in literature –
CAR and equity to assets ratio (Shrieves and Dahl, 1992; Jacques and Nigro, 1997; Rime,
2001). As CAR is the defnition of capital used by regulator, this measure is used in the
present study:
CAR is defined as:
(
Tier 1 capital ? Tier 2 capital
Risk ? weighted assets
)
? 100
3.2.1.2 Risk (RISK). Defning bank risk is very complicated, as the existing literature
suggests a number of alternatives, and all the measures have some limitations (Rime,
2001; Beck, 2008). In this study, we use the ratio of net non-performing loans to net loans.
This measure captures the credit risk of bank, which is the combined outcome of default
risk and exposure risk (Tang et al., 2009).
3.2.2 Variables affecting banks’ capital and risk
3.2.2.1 Proftability (PFT). The association between proftability and capital may be
positive or negative. If banks prefer to increase capital through retained earnings
instead of issuing equity to avoid negative signal to the market owing to information
asymmetry between insiders and outsiders, the proftability may have a positive effect
on bank capital. On the other hand, excessive regulatory pressure of maintaining
minimum capital may reduce the proft-earning capacity of bank. This may lead to a
negative association between proftability and capital. The association between
proftability and bank risk may be positive, as the higher proft induces to invest in more
risky project with a hope to earn higher return. Here, bank’s return on assets (ROA) is
used as the measure of proftability in this study.
3.2.2.2 Size (SIZE). Size is an important factor in the existing literature that affects
both capital and risk because large banks can have a better opportunity to diversity risk,
to invest in more proftable projects and to access the capital market. Athanasoglou et al.
(2008) apprehended the non-linear association of size with capital and risk. The natural
log of total assets is used here to measure bank size.
3.2.2.3 Net loans to total assets. The effect of net loans on capital may be positive. Net
loans as a share of total assets increase the possibility of higher default, and,
consequently, banks will increase their capital base. On the other hand, such association
may be negative if a bank can not improve its capital base in the event of high loss arises
fromthe non-recovery of loans. LNTAis used to capture its effect on capital and risk in
this study.
3.2.2.4 Human capital effciency (HCE). Human capital, an important component of
intellectual capital, is now considered as the principal driving force for the success of
any enterprise (Edvinsson and Malone, 1997; Chen et al., 2005). By utilizing human
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resources effciently in the credit-related activities, it is expected that a bank can reduce
the NPLs or, in other words, can lower the credit risk. The effciency of human capital is,
thus, expected to have a negative impact on banks’ credit risk.
HCE is calculated based on the VAIC model given by Pulic (2000). VAIC model is
based on the assumptions that a company creates its “added value” by utilizing both
physical capital and intellectual capital and the “added value” creation is the indicator of
overall effciency of the frm (Stahle et al., 2011). Value added (VA) in this model is
defned as the difference between output and input. Output is the total revenue
generated by a frmin a year, and input is the summation of all costs incurred by the frm
in generating revenue during the same period except employee costs (ECs) which are
treated as the value-creating entity (Tan et al., 2007 and Clarke et al., 2011). Algebraically
VA can be expresses as:
VA?NI ?T ?I ?D ?A?EC
Where NI is net income after tax, T is corporate tax, I is interest expense, D is
depreciation, Ais amortization and ECis the employee costs. VAICis the composite sum
of capital-used effciency, HCE and structural capital effciency (SCE).
According to this model, human capital (HC) is defned as overall EC and is
considered as an investment of the company for generating added value (Clarke et al.,
2011). HCE in this model is defned as: VA/HC. HCE indicates how much VA is created
by one monetary unit invested in human resources (Stahle et al., 2011).
3.3 Models
Existing theory suggests that banks’ capital and risk are interdependent. Most of the
researchers have used the model of Shrieves and Dahl (1992) considering the changes in
capital as a function of changes in risk and vice versa. Along with these, the relevant
explanatory variables have been used in the regression analysis. But the explanatory
power of the models in most of the cases is found to be very poor. Indeed, the theory of
optimal capital structure for banks is still in the evolving stage. Along with this, there is
gradual change in the regulatory pressure relating to the size of bank capital. Hence, it is
diffcult for a bank to fx a target level of capital or risk and partially adjust their capital
or risk accordingly, specifcally in the short run. Hence, in this study, we use the absolute
value of capital and risk and incorporate the lagged values of the dependent variable in
each equation. (Altunbas et al., 2007) and Biekpe and Floquet (2008) have also used the
absolute level of capital and risk.
As both the risk and capital adequacy are expected to be closely related with
both-way causation and are affected by a number of explanatory variables, a
two-equation simultaneous model in structural form would be appropriate to estimate
and examine the impact of various variables (Green, 2003). Thus, to examine the
relationship between capital and risk of Indian banks, following systemof two equation
model is used:
CAR
it
? ?
0
? ?
1
PFT
it
? ?
2
SIZE
it
? ?
3
NLTA
it
? ?
4
CAR_1
it
? ?
5
RISK
it
? ?
it
(1)
RISK
it
? ?
0
? ?
1
HCE
it
? ?
2
PFT
it
? ?
3
SIZE
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In this system, both CAR and Risk are endogenous, while the other variables are
considered to be exogenous variables. In equation (2), D is used as an explanatory
variable, which takes value 1 if the frm is large and 0 if it is medium or small. In terms
of total business (defned as the summation of deposits and advances), top 33 per cent of
the sample frms are categorized as the large frms. The justifcation of using this is that
apart from the impact of variation in size of frm as a continuous variable, there is
general difference in the behavior and capacity of very large and small frmin absorbing
risk. However, in case of equation (1), maintaining minimum CAR is mandatory as per
the RBI guidelines irrespective of the frmsize. It is also evident fromthe data relating to
the CAR of the select banks that there is no signifcant difference over the years. Thus,
D is not considered as an additional explanatory variable, as it may not improve the
explanatory power of the model.
As per the rule of order condition of identifcation, equation (1) is over-identifed;
however, equation (2) is exactly identifed. Hence, a generalized estimation of three-stage
least squares (3SLS) yields appropriate values of the coeffcients.
4. Results and discussion
4.1 Descriptive statistics
The descriptive statistics of the variables for selected public sector and private sector
banks in India are shown in Tables I and II, respectively. The Tables I and II reveal that
mean HCEof private sector banks (9.73) is much greater than that of public sector banks
Table I.
Descriptive statistics
for 21 major public
sector banks in India
(average from 1999
to 2012)
Variables N Minimum Maximum Mean SD
HCE 294 1.9 13.98 6.10 2.07
PFT 294 0.01 0.09 0.06 0.01
NIM 294 0.05 16.17 2.76 0.97
Ln SIZE 294 8.84 14.11 11.03 0.98
CAR 294 6.02 20.11 12.01 1.54
RISK 294 0.15 18.37 3.28 3.39
NLTA 294 0.04 0.68 0.51 0.11
Business 294 8,560.92 19,11,226.25 1,49,625.40 2,21,778.10
Source: Computed by the authors from the “Capitaline” database
Table II.
Descriptive statistics
for 20 major private
sector banks in India
(average from 1999
to 2012)
Variables N Minimum Maximum Mean SD
HCE 264 2.35 49.88 9.73 6.63
PFT 264 0.02 0.2 0.07 0.02
NIM 264 0.56 12.32 2.84 1.29
Ln SIZE 264 6.93 13.1 9.66 1.30
CAR 264 6.08 30.47 12.98 3.12
RISK 264 0 14.31 2.66 2.85
NLTA 264 0.26 0.74 0.52 0.09
Business 264 1,071.22 5,09,227.6 47,539.95 86,326.71
Source: Computed by the authors from the “Capitaline” database
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(6.10). It is an indication of better utilization of human capital in the value addition by
private sector banks. At the same time, the higher variability of HCE in private sector
may be an indicator of the incapability of some smaller private banks in utilizing the
human resources productively. Average PFTfor both bank groups is more or less same
and that may be due to the tough competitive environment in which banks have been
working. But the variability of PFTis higher for private sector banks, which is in parity
with the net interest margin. Data relating to bank size also indicate that average size of
public sector banks is much large, and its variability is also low as compared to private
sector banks. Public sectors banks normally get the patronage of the government; thus,
source of fund and deposit are not problems, as most of the socially relevant projects are
undertaken through them. These facilities are normally not available for public sector
banks.
To maintain adequate capital, both the bank groups, however, manage suffcient
CARas compared to the regulatory norms. But signifcant difference is observed in case
of credit risk (RISK). While mean RISK for private sector banks is 2.66, it is relatively
high for public sector banks (3.28). The variability of RISK is also very high for both
cases. Because of the compulsion, various social development schemes and
government-run projects are implemented through various public sector banks without
considering the potential risk involved in them. On many occasions, failure of the
projects enhances the risk of default.
In Table III, the mean values of the variables in three different time periods depict
some interesting picture. For instance, HCE in public sector banks shows an increasing
trend over the years, but for private sector banks, it has reduced from 14.62 in March
1999 to 6.98 in March 2006. One plausible reason for such decline may be the emergence
of some private sector banks, that, during this period, due to the lack of experience, could
not proftably utilize the human resources within a short span. Non-availability of
suffcient resources to be allocated for the human capital would be another possible
reason. Nevertheless, at the end of March 2012, HCE has increased to 8.95, which is
higher than that of public sector banks (8.25). Similarly, the credit risk of private sector
banks, which was very high in 1999 (7.84), has decreased to 1.25 in 2006 and has
increased slightly thereafter. In contrast, the credit risk of private sector banks shows a
declining trend over the periods. It is notable that the average regulatory capital (CAR)
was more than the required normfor both the bank groups. Bank size has increased for
Table III.
Changes in mean
values of various
banking measures in
India during 1999-
2012 (end March)
Variables
Public sector banks Private sector banks
1999 2006 2012 1999 2006 2012
HCE 4.75 4.78 8.25 14.62 6.98 8.95
PFT 0.07 0.05 0.06 0.09 0.05 0.07
NIM 3.48 2.97 2.40 2.84 2.93 2.59
Ln SIZE 10.04 11.00 12.14 8.22 9.74 10.92
CAR 11.13 12.08 11.72 13.27 11.56 13.10
RISK 7.84 1.25 1.49 5.95 1.46 0.61
NLTA 0.37 0.55 0.64 0.45 0.55 0.59
Source: Computed by the authors from the “Capitaline” database
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both during the study period. However, despite the differences in size, no signifcant
difference is found in case of earnings (ROA).
4.2 3-SLS regression results for all the banks
Table IV shows the 3SLS estimated results of the system of simultaneous equations (1)
and (2) for the combined data. In capital equation (1), the coeffcient of current profts
(PFT) is positive and signifcant at 1 per cent level. The observed positive impact of
proft on capital indicates that proftable frms have improved their capital base. This is
possible by retaining a part of proft or by issuing new shares, although the latter may
be interpreted as a signal of weakness. In the present context, the banks have improved
their capital base through retained earnings rather than by issuing new equity. Size of
the bank is insignifcantly but negatively associated with capital ratio. The implication
that can be drawn is that capital per unit of risk-weighted asset has remained unchanged
with the changes in size of the bank. The infuence of size on capital and risk is a
controversial issue in a number of literatures, indicating both positive and negative
association. However, the result of this study is consistent with the fndings of Das and
Ghosh (2004) who observe insignifcant negative association between size and capital in
Indian public sector banks.
On the other hand, LNTA and RISK have a signifcantly negative relationship with
CAR. This indicates that Indian banks have taken more risk by investing major funds in
loans with the aim of high margin. This is justifed by the observed signifcant positive
association between PFT and RISK in equation (2). The observed results are consistent
with the fndings of Das and Ghosh (2004) in Indian context, who argue that banks with
high risk should have higher expected income (or proft) and would try to increase the
capital base by investing a part of realized proft. Again, the observed signifcant
negative association between CAR and RISK, both in equations (1) and (2), is also
consistent with the fndings of earlier studies in the context of developing countries
Table IV.
Results of 3SLS
estimation of the
simultaneous
equation models of
CAR and risk for all
banks
Equation Variables Coeffcient SE Z statistics Adjusted R
2
1 Constant 6.0453 0.977 6.185* 0.588
PFT 38.859 5.235 7.422*
SIZE ?0.030 0.058 ?0.513
NLTA ?4.176 0.852 ?4.900*
CAR_1 0.580 0.028 20.27*
RISK ?0.291 0.031 ?9.257*
2 Constant ?1.355 1.435 ?0.944 0.778
HCE ?0.018 0.011 ?1.647***
PFT 23.572 4.174 5.647*
SIZE 0.004 0.127 0.034
? ?0.575 0.078 ?7.341*
CAR 0.828 0.017 48.49*
RISK_1 ?0.179 0.231 ?0.775
D1
Notes: Equation 1: Dependent variable – CAR, Instruments – cons, NIM, PFT, SIZE, NLTA, CAR_1,
RISK; Equation 2: Dependent variable – RISK, Instruments – constant, HCE, PFT, LLP, CAR, RISK_1,
D1; *and ***indicate that the coeffcient is signifcant at 1 per cent and 10 per cent level by two tailed test
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(Nachane et al., 2000; Lindguist, 2004; Das and Ghosh, 2004). This indicates that an
overcapitalized bank in terms of maintaining minimum risk-based capital ratio may
decrease the capital base by maintaining same level of risk or may increase the risk,
while an undercapitalized bank can increase the capital base to meet the regulatory
requirement or can reduce the portfolio risk. Moreover, the results are in line with the
fnding of Jacques and Nigro (1997) in the US banking sector and also with the
proposition of Repullo (2005) who predicts that higher capital implies lower risk.
However, signifcant positive infuence of lag CAR indicates that the current CAR
depends on the expansionary effect of past capital stock of the banks.
Results of equation (2) indicate that current risk is positively infuenced by past risk
(RISK_1). Thus, the risk-taking behavior of Indian banks also signifcantly depends on
their past experience, i.e. there is a lagged impact. HCE (as mentioned in the
methodology part) is considered to be an additional variable in risk equation
considering its possible impact on frm performance in the knowledge-based economy.
The coeffcient of HCE is found to be negative and signifcant. This indicates that
human resources in Indian banking sector as a whole helped to manage the
credit-related activities effciently and fnally to reduce the credit risk. Risk measured in
terms of proportion of non-performing assets is, however, negatively associated with the
change in CAR, which also follows reciprocally fromequation (1). On the other hand, the
study fails to fnd any signifcant impact of bank size (SIZE) on the credit risk.
The study, thus, indicates the inverse relation between risk and regulatory capital
when they are determined simultaneously, and the implication is that the banks are
relatively more caucus in taking risk even in the face of high proftability, the rate of
which is, however, almost stable across the banks due to some regulatory measures by
the central bank. This is also evident from the descriptive statistics of proftability.
Indeed, after the introduction of Basel norms in India, banks have gradually increased
the risk-based capital ratio, and once it is over capitalized, banks have tried to reduce the
credit risk. In this respect, human capital has played a crucial role. The negative
association between capital and risk, however, does not support the fndings of earlier
researchers in the context of developed countries (Shrieves and Dahl, 1992; Rime, 2001;
Altunbas et al., 2007; Van Roy, 2008; Athanasoglou, 2011) and also contradicts the
propositions of a positive relationship by buffer capital theory, managerial risk aversion
theory and bankruptcy cost avoidance theory. Finally, the coeffcient of dummy variable
D is negative but not signifcant. Here, the negative sign indicates that large banks
undertake less risk that may be because of their better opportunity to diversify risk as
compared to the small banks.
4.3 Comparative regression results of public sector and private sector banks
Separate regression results for public and private sector banks are shown in
Tables Vand VI, respectively. The results indicate that there is no difference in observed
sign of the coeffcients, but differences are observed in some cases about the signifcant
level of the coeffcients. As observed for all banks, PFT is positively and signifcantly
related to CAR and RISK for both public sector and private sector banks. Similarly, lag of
CARandlagof RISKhave signifcantlypositive infuence ontheir respective current values
for boththe bankgroups. The infuence of NLTAonCARis negative andsignifcant incase
of public sector banks, while suchassociationis not signifcant for private sector banks. The
possible reason is that Indian public sector banks have increased the share of loans in total
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assets, due to various social development schemes of the government over the periods by
maintaining almost equal CAR. On the other hand, private sector banks have maintained
almost same share of NLTAsince 2005 onward, as theyhave no suchcompulsion, or hardly
such schemes are implemented through them.
Table V.
Results of 3SLS
estimation of the
simultaneous
equation models of
CAR and risk for
public sector banks
Equation Variables Coeffcient SE Z statistics Adjusted R
2
1 Constant 7.622 1.387 5.492* 0.531
PFT 31.513 8.285 3.803*
SIZE 0.077 0.094 0.812
NLTA ?6.279 1.100 ?5.708*
CAR_1 0.478 0.044 10.720*
RISK ?0.305 0.035 ?8.582*
2 Constant ?14.094 16.504 ?0.854 0.846
HCE ?0.066 0.062 ?1.060
PFT 55.089 25.045 2.200**
SIZE 1.031 1.416 0.728
? ?0.429 0.130 3.303*
CAR 0.868 0.068 12.65*
RISK_1 ?1.249 1.989 ?0.627
D1
Notes: Equation 1: Dependent variable – CAR, Instruments – cons, NIM, PFT, SIZE, NLTA, CAR_1,
RISK; Equation 2: Dependent variable – RISK, Instruments – constant, HCE, PFT, LLP, CAR, RISK_1,
D1; *and **indicate that the coeffcient is signifcant at 1 per cent and 5 per cent level by two tailed test
Table VI.
Results of 3SLS
estimation of the
simultaneous
equation models of
CAR and risk for
private sector banks
Equation Variables Coeffcient SE Z statistics Adjusted R
2
1 Constant 2.020 1.796 1.125 0.607
PFT 33.088 7.271 4.511*
SIZE 0.147 0.117 1.255
NLTA ?1.884 1.566 ?1.203
CAR_1 0.669 0.041 16.401*
RISK ?0.137 0.063 ?2.186**
2 Constant ?20.463 7.460 ?2.743* 0.605
HCE ?0.066 0.029 ?2.278**
PFT 37.138 11.592 3.204*
SIZE 1.982 0.742 2.669*
? ?0.282 0.147 ?1.917***
CAR 1.021 0.086 11.740*
RISK_1 ?3.256 1.278 ?2.548**
D1
Notes: Equation 1: Dependent variable – CAR, Instruments – cons, NIM, PFT, SIZE, NLTA, CAR_1,
RISK; Equation 2: Dependent variable – RISK, Instruments – constant, HCE, PFT, LLP, CAR, RISK_1,
D1; *, **and ***indicate that the coeffcient is signifcant at 1, 5 and 10% levels, respectively, by
two-tailed test
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Another notable difference is found regarding the infuence of HCE on RISK.
Signifcant negative association is found in case of private sector banks. This indicates
that Indian private sector banks are more effcient in utilizing the human resources in
reducing the credit risk. Indeed, in private sector, the length of service as well as pay
structure of the employees, to a large extent, is related to the performance, while it is
permanent in nature for the public sector banks. Further, the major private sector banks
are new generation banks, and they have recruited specialized staffs for various
banking activities. Finally, the signifcant negative infuence of the dummy variable D
on risk in case of private sector banks indicate that large banks are more effcient in
reducing credit risk. Small and mediumbanks involve more risk, which is also justifed
by the positive infuence of SIZE as a continuous variable on RISK.
4.4 Discussion and policy implications
Management of risk has emerged as one of the core banking activities all over the world
since the banking crisis observed in different countries. The preservation of risk-based
capital as per the guidelines of Basel Committee on Banking Supervision is considered
as an important yardstick for measuring the solvency or fnancial stability of banks. But
the determination of the adequate percentage of risk-based capital ratio is still in the
evolving stage. In this situation, the most important is to access how effectively a bank
could utilize its regulatory capital to minimize the risk or in other words, improve its
fnancial soundness.
The fndings of the present study in the context of Indian banking sector would help
policy or decision-makers in different ways. First, the observed negative association
between bank regulatory capital and risk indicates that a bank can reduce its risk by
enhancing its capital base. Alternatively, if a bank can effciently manage its credit
activities by reducing NPAs, minimumcapital base may provide sound fnancial health.
This is evident in Indian banking sector during the period of 2002-2008. Second, the role
of human resource is very vital in the overall functioning of a fnancial institution in
general and in the management of credit risk in particular. In this respect, mention can
be made about the study of National Skill Development Corporation in India [1]. The
study clearly points out the importance of the skilled human resources and other
components of intellectual capital as key success factors for banking and fnancial
service sectors. The observed negative association between HCE and bank risk in the
present context is an added contribution of the paper that may be a valuable tool for
decision-makers. By utilizing the human resources more effciently, a bank can reduce
the credit risk in particular and can improve the overall fnancial strength in general.
This can be done by enhancing the skill and knowledge of the employees through
adequate training. Effciency of human capital can also be improved by providing
suffcient structural capital comprising all supportive infrastructures, working
environment, new process, strategy, technology, database, etc., as the effciency of
human capital is closely associated with the structural capital effciency (Wiig, 1999;
Bollen et al., 2005). Finally, proftability also infuences the bank risk and capital
positively, which is on the expected line. Higher proft potential entices the banks to take
more risk and at the same time more proft adds to the stock of existing capital. However,
in certain cases, the banks are found to be risk averter due to increased competition as
well as regulations.
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5. Concluding remarks
The present study is a modest attempt to examine the regulatory capital and risk-taking
behavior of Indian commercial banks from 1999 to 2012 using a simultaneous equation
approach. The study reveals a strong inverse relationship between risk and capital
adequacy ratio. Several earlier studies have observed signifcant infuence of size on risk
and capital. But here, we observe insignifcant association of size on risk and capital
except for the private sector banks in taking risk. Indeed, all banks have maintained the
minimum regulatory capital over the years. HCE is found to be an important factor in
managing credit risk of Indian banks. However, the infuence of this factor in particular
and the impact of intellectual capital in general have not received considerable attention
of the researchers in the context of managing bank risk. The observation of the present
study may encourage researchers to consider the role of intellectual capital in the
analysis of risk management.
Another important fnding of the study is the positive infuence of proftability on
both capital and risk. Fromthe analysis of mean values over the years, it is evident that
there is a signifcant declining trend of credit risk during the period of 2002-2008. The
CAR and proftability (ROA) during this period have also declined showing a
fuctuating trend over the years. But since 2009, the credit risk has either remained
constant or slightly increased, while the CAR has declined during the period of
2010-2012. Interestingly, the ROAhas slightly increased during the period of 2009-2012.
The observed behavior of risk, capital and proftability of Indian banks, thus, prompt us
to think about the interdependency of these three variables. Hence, two equation models
may be extended further to capture this complex issue.
Note
1. Human resource and skill requirements in the banking, fnancial services and insurance
sector (2022) – Areport by National Skill Development Corporation, www.nsdcindia.org/pdf/
bfsi.pdf
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About the authors
Santi Gopal Maji is currently an Assistant Professor of Commerce at North-Eastern Hill
University, India. His teaching and research interests are intellectual capital, risk management
and corporate fnance. He has written a number of research articles in renowned national and
international research journals. Santi Gopal Maji is the corresponding author and can be contacted
at: [email protected]
Utpal Kumar De is currently a Professor of economics at North-Eastern Hill University, India,
and is teaching for more than 20 years at post-graduate level. His primary interest of teaching and
research is environmental and resource economics, agricultural economics and also the
application of Econometrics.
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
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doc_536764019.pdf
This paper aims to examine the association between regulatory capital and risk of Indian
commercial banks and the impacts of other relevant variables on them
Journal of Financial Economic Policy
Regulatory capital and risk of Indian banks: a simultaneous equation approach
Santi Gopal Maji Utpal Kumar De
Article information:
To cite this document:
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simultaneous equation approach", J ournal of Financial Economic Policy, Vol. 7 Iss 2 pp. 140 - 156
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Regulatory capital and risk of
Indian banks: a simultaneous
equation approach
Santi Gopal Maji
Department of Commerce, North-Eastern Hill University,
Shillong, India, and
Utpal Kumar De
Department of Economics, North-Eastern Hill University, Shillong, India
Abstract
Purpose – This paper aims to examine the association between regulatory capital and risk of Indian
commercial banks and the impacts of other relevant variables on them.
Design/methodology/approach – The study is based on a secondary data set on Indian commercial
banks collected from “Capitaline Plus” corporate database and annual reports of the respective banks.
Total 41 major Indian banks (21 public and 20 private sector banks) are considered in this study. Here
absolute values of capital and risk are used as dependent variables along with some relevant bank
specifc explanatory variables in a system of a two-equation model. Based on the nature of
interrelationship and identifability of the equations, three-stage least squares (3SLS) technique is used
to estimate the relationship.
Findings – Risk and capital of Indian commercial banks are inversely associated. The infuence of
proftability on both capital and risk is signifcantly positive. Moreover, human capital effciency is
negatively associated with the undertaking of risk by the banks. In this respect, Indian private sector
banks are found to be more effcient in utilizing human capital for reducing credit risk.
Originality/value – It is the frst comparative study in India examining the relationship between
capital and risk of Indian public and private sector commercial banks covering both Basel I and II
periods. Further, the role of human resource in managing risk is considered as a relevant variable in this
study.
Keywords Banks, Credit, Government policy and regulation, Capital,
Financial risk and risk management, Multiple or simultaneous equation models
Paper type Research paper
1. Introduction
Regulatory capital has gained considerable attention after the implementation of Basel
I guidelines in 1988 to enhance fnancial soundness and competitive advantage in the
banking sector (Rime, 2001; Pennacchi, 2005). The voluntary adoption of Basel I, revised
framework in 2004 (Basel II) and further modifed guidelines in 2010 for promoting more
fexible banking system(Basel III) by a large number of countries have made the capital
adequacy ratio (CAR) a vital yardstick to access the solvency of the fnancial
institutions. The rationale for preserving adequate capital comes out of its indication of
suffcient fnancial resources at the disposal of a bank that provides cushion against
JEL classifcation – G20, G28, G32
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1757-6385.htm
JFEP
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140
Received17 June 2014
Revised26 August 2014
Accepted5 November 2014
Journal of Financial Economic
Policy
Vol. 7 No. 2, 2015
pp. 140-156
©Emerald Group Publishing Limited
1757-6385
DOI 10.1108/JFEP-06-2014-0038
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failure. The option-pricing model states that an unregulated bank would take more risk
to increase the return to its shareholders (Benston et al., 1986; Keeley and Furlong, 1990).
On the other hand, the mean-variance framework says that the forced reduction in
leverage reduces the return of a bank when capital is relatively costly (Kim and
Santomero, 1988; Rochet, 1992). The introduction of risk-based capital standard as per
Basel accord may be a useful attempt to protect banks from failure (Rime, 2001).
However, the empirical studies provide controversial views regarding the preservation
of higher capital requirements to protect banks from vulnerabilities. Some researchers
(Besanko and Kanatas, 1996; Bichsel and Blum, 2004; Altunbas et al., 2007) argue that
bank capital fails to promote stability of a bank and to reduce its risk. There are others
(Berger and De Young, 1997; Jacques and Nigro, 1997; Agoraki et al., 2011) who provide
an alternative view establishing a negative association between capital and risk. In
contrast, Biekpe and Floquet (2008) and Van Roy (2008) fnd capital and risk to be
indifferent in most of the cases of cross-country analysis.
Although the regulatory pressure has gradually been increased over the years with
the aimof moving toward international best practices, relatively little attention has been
paid toward assessing the effectiveness of regulations in risk taking of banks,
specifcally in developing countries. Many researchers have tried to address the issue
during past two decades, but most of the studies are concentrated in USA and Europe
(Altunbas et al., 2007; Jacques and Nigro, 1997; Rime, 2001; Shrieves and Dahl, 1992;
Jokipii and Milne, 2011; Athanasoglou, 2011). In the context of Indian banking sector,
some researchers (Nag and Das, 2002; Rao, 2005; Murali and Subbukrishna, 2008; Pasha
et al., 2012) have highlighted the importance of regulatory framework in maintaining the
adequate level of bank capital to avoid vulnerabilities. Only a few studies have
examined the association between capital and risk (Nachane et al., 2000; Ghosh et al.,
2003; Das and Ghosh, 2004; Gupta and Meera, 2011; Maji and Dey, 2012). The purpose of
this paper is to fnd out the answer of a very pertinent question relating to the basic aim
of Basel accord in Indian context, i.e. do increases in CAR prompt banks to reduce risk
or to undertake more risk? Therefore, this paper tries to contribute further empirical
evidence relating to the capital-risk behavior of Indian banks in recent times.
The selection of Indian banks for this issue is of interest for several reasons. First,
non-recovery of loans and advances, which in turn results in high credit risk, was the
main reason for low performance of Indian banks as identifed by the Namasimham
Committee (1991). Hence, in the post-reform period, the Reserve Bank of India (RBI), as
well as the Government of India, has taken several steps to improve the fnancial
stability of the Indian banking system. Second, the regulatory pressure in terms of
maintaining minimumCAR is higher for Indian banks as compared to the international
standard. The RBI has raised the minimum CAR to 9 per cent as compared to the
internationally accepted level of 8 per cent in 1998 based on its mid-term review of
Monetary and Credit Policy. Subsequently, the CAR as per Basel II accord was also
higher as compared to the standard norm. Third, during the past two decades, Indian
commercial banking sector has faced several challenges in terms of competition
imposed by newly emerged private sector and foreign banks, technological
advancement, offering innovative products and services, etc., which ultimately
increases the risk of insolvency. Finally, as per the existing knowledge, so far, only two
empirical studies have been conducted to examine the association of capital and risk in
141
Indian banks
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Indian banking sector by using simultaneous equation approach (Nachane et al., 2000;
Das and Ghosh, 2004).
However, the present study differs from earlier two studies in respect of two
important aspects. First, this study covers the period of both Basel I and Basel II,
considering both public sector and private sector banks. Second, human capital
effciency (HCE) is taken as another determinant of bank risk. Human capital, which is
regarded as the heart of creating intellectual capital (Chen et al., 2005), represents the
combined knowledge, skill, experience, talent, attitude and effectiveness of employees of
an organization that are used to build unique competencies and improve business
performance (Cappelli and Crocker-Hefter, 1996; Bontis, 1998). The success of any
service sector like bank, which mainly depends upon intangibles rather than tangible
assets, is expected to be infuenced signifcantly by the effciency of the human
resources of the enterprise. Adequate empirical evidences are found to support this view
(Pulic, 2000; Goh, 2005; Mavridis, 2004; Mavridis and Kyrmizoglou, 2005; Yalama and
Coskun, 2007; Liu, 2009; Malik et al., 2012; Ghosh and Maji, 2012). All these studies have
investigated the impact of human capital and other components of intellectual capital on
banks’ fnancial performance. However, the importance of the effciency of bank
employees in managing credit risk has remained empirically unaddressed.
Indeed, the success in credit activities largely depends on the correct assessment and
interpretation of many internal and external factors at the time of granting loans as well
as throughout the credit period. As all these activities are performed by the bank
employees, their skill, knowledge, imaginative mind and experience are expected to play
a vital role in the matter of managing credit risk. Further, despite many regulatory
measures undertaken by the RBI to improve the solvency of Indian banks, a study
conducted by National Skill Development Corporation of India clearly points out the role
of human resources in developing well-defned credit evaluation policies, managing the
credit quality through adequate knowledge and focusing strongly on recovery and
collection efforts as the key success factors for banking and fnancial institutions[1]. The
inclusion of HCE as an explanatory variable in the model may, thus, provide another
dimension relating to the importance of intellectual capital in the management of bank
risk.
The rest of the paper is organized as follows. The next section includes a reviewof the
existing literature relating to the association between bank capital and risk. Section 3 is
devoted for data, variables and model used in this study. Section 4 presents the results
and followed by concluding remarks in Section 5.
2. Review of literature
A large number of studies indicate that banks in USA and Europe simultaneously
determine the capital and risk decisions (Shrieves and Dahl, 1992; Jacques and Nigro,
1997; Rime, 2001; Van Roy, 2008; Biekpe and Floquet, 2008; Godlewski, 2005; Jokipii and
Milne, 2011; Athanasoglou, 2011). To analyze the bank’s capital behavior, Shrieves and
Dahl (1992) have developed a simultaneous equation model and observed a positive
relationship between capital and risk in US banks. In contrast, Jacques and Nigro (1997),
using the same methodology, have found a signifcant negative relationship between
change in regulation of capital and risk level in US commercial banks. On the other hand,
Rime (2001) has analyzed the adjustments in capital and risk of Swiss banks and has
found that there is a regulatory pressure on Swiss banks that lead to a positive and
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signifcant impact on the ratio of capital to total assets, but no signifcant impact on
bank’s risk-taking behavior. In other words, Swiss banks improve their capital
adequacy by increasing their capital through retained earnings or equity issue and not
by decreasing their risk-taking behavior.
Another study conducted by Godlewski (2005) investigates the relationship between
bank capital and risk in emerging market economies. Applying simultaneous equation
framework of Shrieves and Dahl (1992), the study shows a similar result in case of the
US, the UK and other industrial economies. The study identifes the importance of the
regulatory, institutional and legal factors in running a healthy banking system. Biekpe
and Floquet (2008) have investigated the nature of relationship between capital and risk
exposures for 2,940 banks from 44 emerging market countries using the model of
Shrieves and Dahl (1992) with a partially adjusted framework. The study fnds no
statistically signifcant relationship between changes in capital and risk in the vast
majority of emerging markets banks, which is contrary to the results of erstwhile
empirical studies conducted in developed countries like the USA and the UK. However,
regarding the relationship between absolute level of capital and risk, the study
anticipates the association to be positive in the long run.
Altunbas et al. (2007) has done a study on the relationship between capital, risk and
effciency for a large sample of European banks between 1992 and 2000. The study
reveals that ineffcient European banks hold more capital and undertake less risk. Also,
a positive relationship between risk on the level of capital and liquidity is revealed,
which shows regulators’ preference for capital as a means of restricting risk-taking
activities. Van Roy (2008) on six G10 countries (Canada, France, Italy, Japan, the UKand
the USA) shows that there is no difference between the well-capitalized banks and
weakly capitalized banks in so far as the modifcation of the ratio of risk-weighted assets
to total assets is concerned. In contrast, Jokipii and Milne (2011) on US banking sector
observe a positive relationship between capital and risk for highly capitalized banks and
negative capital-buffer-risk relationship for banks with marginal CAR. It is also
observed that banks with higher liquidity can decrease their capital and increase their
levels of risk because the higher liquidity act as self-insurance for banks as against their
liquidity shocks. After the fnancial crisis of 2008, a study conducted by Athanasoglou
(2011) on banks’ choice of capital (both regulatory and equity) and risk in South-Eastern
European (SEE) observes a positive relationship between regulatory capital and risk,
but a inverse association between equity capital and risk. However, Lindguist (2004)
argues for an inverse relationship between capital and risk in case of Norwegian banks.
In India, the empirical investigation relating to the absolute and relative association
between bank capital and risk is very less. Nachane et al. (2000) have conducted a study
on Indian public sector banks for a period of only two years (1997-1999), using the model
developed by Shrieves and Dahl (1992) after segregating the selected banks into
well-capitalized and undercapitalized. The fndings of the study indicate that changes in
capital and risk are negatively related, which is consistent with the work of Jacques and
Nigro (1997). Using dynamic multivariate panel regression, Ghosh et al. (2003) have
examined the relationship between the requirement of capital and risk-taking behavior
of Indian public sector banks. The study fnds no conclusive evidence regarding risk
aversion among Indian banks and suggests for bank specifc regulatory capital ratio
based on risk profle to enhance stability. In an attempt to explore empirically the
infuence of terms of credit on non-reforming loans (NPLs) of Indian public sector banks,
143
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Ranjan and Dhal (2003) observe that the terms of credit have a signifcant on banks’
NPLs in the presence of bank size and other macroeconomic shocks. However, this is
outside of the scope of this study. In another study, Das and Ghosh (2004) have
investigated the relationship between capital and risk of Indian public sector banks
during the period from 1992-1993 to 1999-2000. The fndings of the study indicate that
capital and risk are negatively associated. Gupta and Meera (2011) have also observed
negative correlation coeffcient between CAR and non-performing assets of some select
Indian banks. The other study, however, indicates negative association of size and
capitalization with insolvency risk of Indian banks, although the relation between
capitalization and credit risk has remained inconclusive (Maji and Dey, 2012).
The outcomes of aforesaid studies indicate that the researchers have pointed out both
negative and positive association between bank capital and risk. It is important to note
that in the recent studies, a positive association between capital and risk is observed
more in case of developed countries, while in developing countries, the inverse or
insignifcant relation is more pronounced. Moreover, the researchers mostly have used
proftability as a factor to determine the bank capital, while its infuence on the
risk-taking behavior of the banks has not been examined. In reality, banks are found to
take more risk in case of higher proft margin, and they extend loan even without any
collateral security if they can charge very high margin of interest. There is, thus, a scope
to include proftability in the equation of risk also considering the risk–return
relationship in fnance literature. Again, in the knowledge-based economy, intellectual
capital is one of the important driving forces for service sector like banks that creates
signifcant gap between market value and book value (Edvinsson and Malone, 1997;
Clarke et al., 2011).
Plethora of empirical studies have used value added intellectual coeffcient (VAIC)
model developed by Pulic (2000) to measure HCE and have observed signifcant impact
of HCE on bank performance. Goh (2005) have found signifcant contribution of HCE in
value creation capability of both domestic and foreign banks in Malaysia. The fndings
of Mavridis (2004) conclude that Japanese banks have improved their performance
noticeably by utilizing the effciency of human capital. Similarly, in case of Greece
banking sector, Mavridis and Kyrmizoglou (2005) have observed positive association
between human capital and value added. Another study conducted by Yalama and
Coskun (2007) have seen greater infuence of human capital than physical capital on
proftability of banks in Turkey from 1995 to 2004.
In case of China’s listed commercial banks, Liu (2009) fnds signifcant positive
correlation between human capital and proftability. Using VAIC model Malik et al.
(2012) have observed HCE to be the main predictor of performance of Islamic banks in
Pakistan. Similar result is observed by Ghosh and Maji (2012) in the context of Indian
banking sector. This study also fnds signifcant contribution of HCE in enhancing
proftability of Indian banks during 2001 to 2010. Human capital can, thus, be
considered as a strong predictor of bank performance and the crux of achieving
sustainable competitive advantage.
3. Data, variables and models
3.1 Data
The study is based on secondary data on Indian commercial banks collected from
“Capitaline Plus” corporate data database and annual reports of the respective banks for
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a period of 14 years from 1998-1999 to 2011-2012. The RBI has increased the CAR from
international standard of 8- 9 per cent based on the mid-term review of Monetary and
Credit Policy during the period of 1998-1999. The study period, thus, covers the revised
CAR of RBI during Basel I period and also the Basel II norms for Indian commercial
banks. Total 41 major Indian banks are considered here, of which 21 are public sector
banks and 20 are private sector banks.
3.2 Defnitions of variables and theoretical relationship
3.2.1 Defnitions of capital and risk
3.2.1.1 Capital (CAR). Two alternative measures of bank capital are used in literature –
CAR and equity to assets ratio (Shrieves and Dahl, 1992; Jacques and Nigro, 1997; Rime,
2001). As CAR is the defnition of capital used by regulator, this measure is used in the
present study:
CAR is defined as:
(
Tier 1 capital ? Tier 2 capital
Risk ? weighted assets
)
? 100
3.2.1.2 Risk (RISK). Defning bank risk is very complicated, as the existing literature
suggests a number of alternatives, and all the measures have some limitations (Rime,
2001; Beck, 2008). In this study, we use the ratio of net non-performing loans to net loans.
This measure captures the credit risk of bank, which is the combined outcome of default
risk and exposure risk (Tang et al., 2009).
3.2.2 Variables affecting banks’ capital and risk
3.2.2.1 Proftability (PFT). The association between proftability and capital may be
positive or negative. If banks prefer to increase capital through retained earnings
instead of issuing equity to avoid negative signal to the market owing to information
asymmetry between insiders and outsiders, the proftability may have a positive effect
on bank capital. On the other hand, excessive regulatory pressure of maintaining
minimum capital may reduce the proft-earning capacity of bank. This may lead to a
negative association between proftability and capital. The association between
proftability and bank risk may be positive, as the higher proft induces to invest in more
risky project with a hope to earn higher return. Here, bank’s return on assets (ROA) is
used as the measure of proftability in this study.
3.2.2.2 Size (SIZE). Size is an important factor in the existing literature that affects
both capital and risk because large banks can have a better opportunity to diversity risk,
to invest in more proftable projects and to access the capital market. Athanasoglou et al.
(2008) apprehended the non-linear association of size with capital and risk. The natural
log of total assets is used here to measure bank size.
3.2.2.3 Net loans to total assets. The effect of net loans on capital may be positive. Net
loans as a share of total assets increase the possibility of higher default, and,
consequently, banks will increase their capital base. On the other hand, such association
may be negative if a bank can not improve its capital base in the event of high loss arises
fromthe non-recovery of loans. LNTAis used to capture its effect on capital and risk in
this study.
3.2.2.4 Human capital effciency (HCE). Human capital, an important component of
intellectual capital, is now considered as the principal driving force for the success of
any enterprise (Edvinsson and Malone, 1997; Chen et al., 2005). By utilizing human
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resources effciently in the credit-related activities, it is expected that a bank can reduce
the NPLs or, in other words, can lower the credit risk. The effciency of human capital is,
thus, expected to have a negative impact on banks’ credit risk.
HCE is calculated based on the VAIC model given by Pulic (2000). VAIC model is
based on the assumptions that a company creates its “added value” by utilizing both
physical capital and intellectual capital and the “added value” creation is the indicator of
overall effciency of the frm (Stahle et al., 2011). Value added (VA) in this model is
defned as the difference between output and input. Output is the total revenue
generated by a frmin a year, and input is the summation of all costs incurred by the frm
in generating revenue during the same period except employee costs (ECs) which are
treated as the value-creating entity (Tan et al., 2007 and Clarke et al., 2011). Algebraically
VA can be expresses as:
VA?NI ?T ?I ?D ?A?EC
Where NI is net income after tax, T is corporate tax, I is interest expense, D is
depreciation, Ais amortization and ECis the employee costs. VAICis the composite sum
of capital-used effciency, HCE and structural capital effciency (SCE).
According to this model, human capital (HC) is defned as overall EC and is
considered as an investment of the company for generating added value (Clarke et al.,
2011). HCE in this model is defned as: VA/HC. HCE indicates how much VA is created
by one monetary unit invested in human resources (Stahle et al., 2011).
3.3 Models
Existing theory suggests that banks’ capital and risk are interdependent. Most of the
researchers have used the model of Shrieves and Dahl (1992) considering the changes in
capital as a function of changes in risk and vice versa. Along with these, the relevant
explanatory variables have been used in the regression analysis. But the explanatory
power of the models in most of the cases is found to be very poor. Indeed, the theory of
optimal capital structure for banks is still in the evolving stage. Along with this, there is
gradual change in the regulatory pressure relating to the size of bank capital. Hence, it is
diffcult for a bank to fx a target level of capital or risk and partially adjust their capital
or risk accordingly, specifcally in the short run. Hence, in this study, we use the absolute
value of capital and risk and incorporate the lagged values of the dependent variable in
each equation. (Altunbas et al., 2007) and Biekpe and Floquet (2008) have also used the
absolute level of capital and risk.
As both the risk and capital adequacy are expected to be closely related with
both-way causation and are affected by a number of explanatory variables, a
two-equation simultaneous model in structural form would be appropriate to estimate
and examine the impact of various variables (Green, 2003). Thus, to examine the
relationship between capital and risk of Indian banks, following systemof two equation
model is used:
CAR
it
? ?
0
? ?
1
PFT
it
? ?
2
SIZE
it
? ?
3
NLTA
it
? ?
4
CAR_1
it
? ?
5
RISK
it
? ?
it
(1)
RISK
it
? ?
0
? ?
1
HCE
it
? ?
2
PFT
it
? ?
3
SIZE
it
? ?
4
?CAR
it
? ?
5
RISK_1
it
? ?
6
D
it
? v
it
(2)
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In this system, both CAR and Risk are endogenous, while the other variables are
considered to be exogenous variables. In equation (2), D is used as an explanatory
variable, which takes value 1 if the frm is large and 0 if it is medium or small. In terms
of total business (defned as the summation of deposits and advances), top 33 per cent of
the sample frms are categorized as the large frms. The justifcation of using this is that
apart from the impact of variation in size of frm as a continuous variable, there is
general difference in the behavior and capacity of very large and small frmin absorbing
risk. However, in case of equation (1), maintaining minimum CAR is mandatory as per
the RBI guidelines irrespective of the frmsize. It is also evident fromthe data relating to
the CAR of the select banks that there is no signifcant difference over the years. Thus,
D is not considered as an additional explanatory variable, as it may not improve the
explanatory power of the model.
As per the rule of order condition of identifcation, equation (1) is over-identifed;
however, equation (2) is exactly identifed. Hence, a generalized estimation of three-stage
least squares (3SLS) yields appropriate values of the coeffcients.
4. Results and discussion
4.1 Descriptive statistics
The descriptive statistics of the variables for selected public sector and private sector
banks in India are shown in Tables I and II, respectively. The Tables I and II reveal that
mean HCEof private sector banks (9.73) is much greater than that of public sector banks
Table I.
Descriptive statistics
for 21 major public
sector banks in India
(average from 1999
to 2012)
Variables N Minimum Maximum Mean SD
HCE 294 1.9 13.98 6.10 2.07
PFT 294 0.01 0.09 0.06 0.01
NIM 294 0.05 16.17 2.76 0.97
Ln SIZE 294 8.84 14.11 11.03 0.98
CAR 294 6.02 20.11 12.01 1.54
RISK 294 0.15 18.37 3.28 3.39
NLTA 294 0.04 0.68 0.51 0.11
Business 294 8,560.92 19,11,226.25 1,49,625.40 2,21,778.10
Source: Computed by the authors from the “Capitaline” database
Table II.
Descriptive statistics
for 20 major private
sector banks in India
(average from 1999
to 2012)
Variables N Minimum Maximum Mean SD
HCE 264 2.35 49.88 9.73 6.63
PFT 264 0.02 0.2 0.07 0.02
NIM 264 0.56 12.32 2.84 1.29
Ln SIZE 264 6.93 13.1 9.66 1.30
CAR 264 6.08 30.47 12.98 3.12
RISK 264 0 14.31 2.66 2.85
NLTA 264 0.26 0.74 0.52 0.09
Business 264 1,071.22 5,09,227.6 47,539.95 86,326.71
Source: Computed by the authors from the “Capitaline” database
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(6.10). It is an indication of better utilization of human capital in the value addition by
private sector banks. At the same time, the higher variability of HCE in private sector
may be an indicator of the incapability of some smaller private banks in utilizing the
human resources productively. Average PFTfor both bank groups is more or less same
and that may be due to the tough competitive environment in which banks have been
working. But the variability of PFTis higher for private sector banks, which is in parity
with the net interest margin. Data relating to bank size also indicate that average size of
public sector banks is much large, and its variability is also low as compared to private
sector banks. Public sectors banks normally get the patronage of the government; thus,
source of fund and deposit are not problems, as most of the socially relevant projects are
undertaken through them. These facilities are normally not available for public sector
banks.
To maintain adequate capital, both the bank groups, however, manage suffcient
CARas compared to the regulatory norms. But signifcant difference is observed in case
of credit risk (RISK). While mean RISK for private sector banks is 2.66, it is relatively
high for public sector banks (3.28). The variability of RISK is also very high for both
cases. Because of the compulsion, various social development schemes and
government-run projects are implemented through various public sector banks without
considering the potential risk involved in them. On many occasions, failure of the
projects enhances the risk of default.
In Table III, the mean values of the variables in three different time periods depict
some interesting picture. For instance, HCE in public sector banks shows an increasing
trend over the years, but for private sector banks, it has reduced from 14.62 in March
1999 to 6.98 in March 2006. One plausible reason for such decline may be the emergence
of some private sector banks, that, during this period, due to the lack of experience, could
not proftably utilize the human resources within a short span. Non-availability of
suffcient resources to be allocated for the human capital would be another possible
reason. Nevertheless, at the end of March 2012, HCE has increased to 8.95, which is
higher than that of public sector banks (8.25). Similarly, the credit risk of private sector
banks, which was very high in 1999 (7.84), has decreased to 1.25 in 2006 and has
increased slightly thereafter. In contrast, the credit risk of private sector banks shows a
declining trend over the periods. It is notable that the average regulatory capital (CAR)
was more than the required normfor both the bank groups. Bank size has increased for
Table III.
Changes in mean
values of various
banking measures in
India during 1999-
2012 (end March)
Variables
Public sector banks Private sector banks
1999 2006 2012 1999 2006 2012
HCE 4.75 4.78 8.25 14.62 6.98 8.95
PFT 0.07 0.05 0.06 0.09 0.05 0.07
NIM 3.48 2.97 2.40 2.84 2.93 2.59
Ln SIZE 10.04 11.00 12.14 8.22 9.74 10.92
CAR 11.13 12.08 11.72 13.27 11.56 13.10
RISK 7.84 1.25 1.49 5.95 1.46 0.61
NLTA 0.37 0.55 0.64 0.45 0.55 0.59
Source: Computed by the authors from the “Capitaline” database
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both during the study period. However, despite the differences in size, no signifcant
difference is found in case of earnings (ROA).
4.2 3-SLS regression results for all the banks
Table IV shows the 3SLS estimated results of the system of simultaneous equations (1)
and (2) for the combined data. In capital equation (1), the coeffcient of current profts
(PFT) is positive and signifcant at 1 per cent level. The observed positive impact of
proft on capital indicates that proftable frms have improved their capital base. This is
possible by retaining a part of proft or by issuing new shares, although the latter may
be interpreted as a signal of weakness. In the present context, the banks have improved
their capital base through retained earnings rather than by issuing new equity. Size of
the bank is insignifcantly but negatively associated with capital ratio. The implication
that can be drawn is that capital per unit of risk-weighted asset has remained unchanged
with the changes in size of the bank. The infuence of size on capital and risk is a
controversial issue in a number of literatures, indicating both positive and negative
association. However, the result of this study is consistent with the fndings of Das and
Ghosh (2004) who observe insignifcant negative association between size and capital in
Indian public sector banks.
On the other hand, LNTA and RISK have a signifcantly negative relationship with
CAR. This indicates that Indian banks have taken more risk by investing major funds in
loans with the aim of high margin. This is justifed by the observed signifcant positive
association between PFT and RISK in equation (2). The observed results are consistent
with the fndings of Das and Ghosh (2004) in Indian context, who argue that banks with
high risk should have higher expected income (or proft) and would try to increase the
capital base by investing a part of realized proft. Again, the observed signifcant
negative association between CAR and RISK, both in equations (1) and (2), is also
consistent with the fndings of earlier studies in the context of developing countries
Table IV.
Results of 3SLS
estimation of the
simultaneous
equation models of
CAR and risk for all
banks
Equation Variables Coeffcient SE Z statistics Adjusted R
2
1 Constant 6.0453 0.977 6.185* 0.588
PFT 38.859 5.235 7.422*
SIZE ?0.030 0.058 ?0.513
NLTA ?4.176 0.852 ?4.900*
CAR_1 0.580 0.028 20.27*
RISK ?0.291 0.031 ?9.257*
2 Constant ?1.355 1.435 ?0.944 0.778
HCE ?0.018 0.011 ?1.647***
PFT 23.572 4.174 5.647*
SIZE 0.004 0.127 0.034
? ?0.575 0.078 ?7.341*
CAR 0.828 0.017 48.49*
RISK_1 ?0.179 0.231 ?0.775
D1
Notes: Equation 1: Dependent variable – CAR, Instruments – cons, NIM, PFT, SIZE, NLTA, CAR_1,
RISK; Equation 2: Dependent variable – RISK, Instruments – constant, HCE, PFT, LLP, CAR, RISK_1,
D1; *and ***indicate that the coeffcient is signifcant at 1 per cent and 10 per cent level by two tailed test
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(Nachane et al., 2000; Lindguist, 2004; Das and Ghosh, 2004). This indicates that an
overcapitalized bank in terms of maintaining minimum risk-based capital ratio may
decrease the capital base by maintaining same level of risk or may increase the risk,
while an undercapitalized bank can increase the capital base to meet the regulatory
requirement or can reduce the portfolio risk. Moreover, the results are in line with the
fnding of Jacques and Nigro (1997) in the US banking sector and also with the
proposition of Repullo (2005) who predicts that higher capital implies lower risk.
However, signifcant positive infuence of lag CAR indicates that the current CAR
depends on the expansionary effect of past capital stock of the banks.
Results of equation (2) indicate that current risk is positively infuenced by past risk
(RISK_1). Thus, the risk-taking behavior of Indian banks also signifcantly depends on
their past experience, i.e. there is a lagged impact. HCE (as mentioned in the
methodology part) is considered to be an additional variable in risk equation
considering its possible impact on frm performance in the knowledge-based economy.
The coeffcient of HCE is found to be negative and signifcant. This indicates that
human resources in Indian banking sector as a whole helped to manage the
credit-related activities effciently and fnally to reduce the credit risk. Risk measured in
terms of proportion of non-performing assets is, however, negatively associated with the
change in CAR, which also follows reciprocally fromequation (1). On the other hand, the
study fails to fnd any signifcant impact of bank size (SIZE) on the credit risk.
The study, thus, indicates the inverse relation between risk and regulatory capital
when they are determined simultaneously, and the implication is that the banks are
relatively more caucus in taking risk even in the face of high proftability, the rate of
which is, however, almost stable across the banks due to some regulatory measures by
the central bank. This is also evident from the descriptive statistics of proftability.
Indeed, after the introduction of Basel norms in India, banks have gradually increased
the risk-based capital ratio, and once it is over capitalized, banks have tried to reduce the
credit risk. In this respect, human capital has played a crucial role. The negative
association between capital and risk, however, does not support the fndings of earlier
researchers in the context of developed countries (Shrieves and Dahl, 1992; Rime, 2001;
Altunbas et al., 2007; Van Roy, 2008; Athanasoglou, 2011) and also contradicts the
propositions of a positive relationship by buffer capital theory, managerial risk aversion
theory and bankruptcy cost avoidance theory. Finally, the coeffcient of dummy variable
D is negative but not signifcant. Here, the negative sign indicates that large banks
undertake less risk that may be because of their better opportunity to diversify risk as
compared to the small banks.
4.3 Comparative regression results of public sector and private sector banks
Separate regression results for public and private sector banks are shown in
Tables Vand VI, respectively. The results indicate that there is no difference in observed
sign of the coeffcients, but differences are observed in some cases about the signifcant
level of the coeffcients. As observed for all banks, PFT is positively and signifcantly
related to CAR and RISK for both public sector and private sector banks. Similarly, lag of
CARandlagof RISKhave signifcantlypositive infuence ontheir respective current values
for boththe bankgroups. The infuence of NLTAonCARis negative andsignifcant incase
of public sector banks, while suchassociationis not signifcant for private sector banks. The
possible reason is that Indian public sector banks have increased the share of loans in total
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assets, due to various social development schemes of the government over the periods by
maintaining almost equal CAR. On the other hand, private sector banks have maintained
almost same share of NLTAsince 2005 onward, as theyhave no suchcompulsion, or hardly
such schemes are implemented through them.
Table V.
Results of 3SLS
estimation of the
simultaneous
equation models of
CAR and risk for
public sector banks
Equation Variables Coeffcient SE Z statistics Adjusted R
2
1 Constant 7.622 1.387 5.492* 0.531
PFT 31.513 8.285 3.803*
SIZE 0.077 0.094 0.812
NLTA ?6.279 1.100 ?5.708*
CAR_1 0.478 0.044 10.720*
RISK ?0.305 0.035 ?8.582*
2 Constant ?14.094 16.504 ?0.854 0.846
HCE ?0.066 0.062 ?1.060
PFT 55.089 25.045 2.200**
SIZE 1.031 1.416 0.728
? ?0.429 0.130 3.303*
CAR 0.868 0.068 12.65*
RISK_1 ?1.249 1.989 ?0.627
D1
Notes: Equation 1: Dependent variable – CAR, Instruments – cons, NIM, PFT, SIZE, NLTA, CAR_1,
RISK; Equation 2: Dependent variable – RISK, Instruments – constant, HCE, PFT, LLP, CAR, RISK_1,
D1; *and **indicate that the coeffcient is signifcant at 1 per cent and 5 per cent level by two tailed test
Table VI.
Results of 3SLS
estimation of the
simultaneous
equation models of
CAR and risk for
private sector banks
Equation Variables Coeffcient SE Z statistics Adjusted R
2
1 Constant 2.020 1.796 1.125 0.607
PFT 33.088 7.271 4.511*
SIZE 0.147 0.117 1.255
NLTA ?1.884 1.566 ?1.203
CAR_1 0.669 0.041 16.401*
RISK ?0.137 0.063 ?2.186**
2 Constant ?20.463 7.460 ?2.743* 0.605
HCE ?0.066 0.029 ?2.278**
PFT 37.138 11.592 3.204*
SIZE 1.982 0.742 2.669*
? ?0.282 0.147 ?1.917***
CAR 1.021 0.086 11.740*
RISK_1 ?3.256 1.278 ?2.548**
D1
Notes: Equation 1: Dependent variable – CAR, Instruments – cons, NIM, PFT, SIZE, NLTA, CAR_1,
RISK; Equation 2: Dependent variable – RISK, Instruments – constant, HCE, PFT, LLP, CAR, RISK_1,
D1; *, **and ***indicate that the coeffcient is signifcant at 1, 5 and 10% levels, respectively, by
two-tailed test
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Another notable difference is found regarding the infuence of HCE on RISK.
Signifcant negative association is found in case of private sector banks. This indicates
that Indian private sector banks are more effcient in utilizing the human resources in
reducing the credit risk. Indeed, in private sector, the length of service as well as pay
structure of the employees, to a large extent, is related to the performance, while it is
permanent in nature for the public sector banks. Further, the major private sector banks
are new generation banks, and they have recruited specialized staffs for various
banking activities. Finally, the signifcant negative infuence of the dummy variable D
on risk in case of private sector banks indicate that large banks are more effcient in
reducing credit risk. Small and mediumbanks involve more risk, which is also justifed
by the positive infuence of SIZE as a continuous variable on RISK.
4.4 Discussion and policy implications
Management of risk has emerged as one of the core banking activities all over the world
since the banking crisis observed in different countries. The preservation of risk-based
capital as per the guidelines of Basel Committee on Banking Supervision is considered
as an important yardstick for measuring the solvency or fnancial stability of banks. But
the determination of the adequate percentage of risk-based capital ratio is still in the
evolving stage. In this situation, the most important is to access how effectively a bank
could utilize its regulatory capital to minimize the risk or in other words, improve its
fnancial soundness.
The fndings of the present study in the context of Indian banking sector would help
policy or decision-makers in different ways. First, the observed negative association
between bank regulatory capital and risk indicates that a bank can reduce its risk by
enhancing its capital base. Alternatively, if a bank can effciently manage its credit
activities by reducing NPAs, minimumcapital base may provide sound fnancial health.
This is evident in Indian banking sector during the period of 2002-2008. Second, the role
of human resource is very vital in the overall functioning of a fnancial institution in
general and in the management of credit risk in particular. In this respect, mention can
be made about the study of National Skill Development Corporation in India [1]. The
study clearly points out the importance of the skilled human resources and other
components of intellectual capital as key success factors for banking and fnancial
service sectors. The observed negative association between HCE and bank risk in the
present context is an added contribution of the paper that may be a valuable tool for
decision-makers. By utilizing the human resources more effciently, a bank can reduce
the credit risk in particular and can improve the overall fnancial strength in general.
This can be done by enhancing the skill and knowledge of the employees through
adequate training. Effciency of human capital can also be improved by providing
suffcient structural capital comprising all supportive infrastructures, working
environment, new process, strategy, technology, database, etc., as the effciency of
human capital is closely associated with the structural capital effciency (Wiig, 1999;
Bollen et al., 2005). Finally, proftability also infuences the bank risk and capital
positively, which is on the expected line. Higher proft potential entices the banks to take
more risk and at the same time more proft adds to the stock of existing capital. However,
in certain cases, the banks are found to be risk averter due to increased competition as
well as regulations.
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5. Concluding remarks
The present study is a modest attempt to examine the regulatory capital and risk-taking
behavior of Indian commercial banks from 1999 to 2012 using a simultaneous equation
approach. The study reveals a strong inverse relationship between risk and capital
adequacy ratio. Several earlier studies have observed signifcant infuence of size on risk
and capital. But here, we observe insignifcant association of size on risk and capital
except for the private sector banks in taking risk. Indeed, all banks have maintained the
minimum regulatory capital over the years. HCE is found to be an important factor in
managing credit risk of Indian banks. However, the infuence of this factor in particular
and the impact of intellectual capital in general have not received considerable attention
of the researchers in the context of managing bank risk. The observation of the present
study may encourage researchers to consider the role of intellectual capital in the
analysis of risk management.
Another important fnding of the study is the positive infuence of proftability on
both capital and risk. Fromthe analysis of mean values over the years, it is evident that
there is a signifcant declining trend of credit risk during the period of 2002-2008. The
CAR and proftability (ROA) during this period have also declined showing a
fuctuating trend over the years. But since 2009, the credit risk has either remained
constant or slightly increased, while the CAR has declined during the period of
2010-2012. Interestingly, the ROAhas slightly increased during the period of 2009-2012.
The observed behavior of risk, capital and proftability of Indian banks, thus, prompt us
to think about the interdependency of these three variables. Hence, two equation models
may be extended further to capture this complex issue.
Note
1. Human resource and skill requirements in the banking, fnancial services and insurance
sector (2022) – Areport by National Skill Development Corporation, www.nsdcindia.org/pdf/
bfsi.pdf
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About the authors
Santi Gopal Maji is currently an Assistant Professor of Commerce at North-Eastern Hill
University, India. His teaching and research interests are intellectual capital, risk management
and corporate fnance. He has written a number of research articles in renowned national and
international research journals. Santi Gopal Maji is the corresponding author and can be contacted
at: [email protected]
Utpal Kumar De is currently a Professor of economics at North-Eastern Hill University, India,
and is teaching for more than 20 years at post-graduate level. His primary interest of teaching and
research is environmental and resource economics, agricultural economics and also the
application of Econometrics.
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doc_536764019.pdf