Global Journal of Management and Business Research
FinanceVolume 13 Issue 7 Version 1.0 Year 2013
Type: Double Blind Peer Reviewed International Research Journal
Publisher: Global Journals Inc. (USA)
Online ISSN: 2249-4588 & Print ISSN: 0975-5853

Risk, Efficiency and Return of PSBs in India
By Dr. Gian Kaur & Pardeep Kaur
Guru Nanak Dev University, Amritsar, India

Abstract - The present study is an attempt to evaluate the performance of public sector banks interms of credit risk, efficiency and their impact on the profitability of these banks. The study
covered the period from 2000 to 2010. With the help of panel data regression analysis the study
concludes that NPLs affect adversely the profitability of banks while risk aversion seems to be infavor of the public sector banks in India. Though significant progress has been made in NPLs
management, much still need to be done in order to improve the performance of scheduled
commercial banks. Keeping in view the gravity of credit risk, the study recommends steps,
towards the recovery of such loans, to be undertaken and enforced by the RBI.

Keywords : NPLs, roa, panel data regression, banks.
GJMBR-C Classification : JEL Code: C23,G-21

RiskEfficiencyandReturnofPSBsinIndia
Strictly as per the compliance and regulations of:© 2013. Dr. Gian Kaur & Pardeep Kaur. This is a research/review paper, distributed under the terms of the Creative Commons
Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial
use, distribution, and reproduction in any medium, provided the original work is properly cited.

Risk, Efficiency and Return of PSBs in India
Dr. Gian Kaur α & Pardeep Kaur σ

Keywords : NPLs, roa, panel data regression, banks.

MI.

introduction

ajor challenges for Asian banks include
macroeconomic stability in a number ofcountries continued NPLs problems, lack ofresilience at the bank level, increasing foreigncompetitors and demographic shifts which could lead
consumers away from traditional deposits. (Deborah
scholar, Vice-President of Moody’s Financial instituteand Sovereign Risk Group,2005).
Financial stability paves the way for sustained
and rapid economic growth. Among various indicatorsof financial stability, bank’s non-performing loans (NPLs)
assume critical Importance as they reflect the bank’s
assets quality, credit risk and efficiency in the allocationof their resources for productive purpose. The NPL isdefined as past due concept, taking into account either
non-payment of interest due, principal or both.
The recent global financial crisis surfaced insecond half of 2007 and in September, 2008 , America’s
one of the biggest investment bank-Lehman Brothers
collapsed and triggered a chain reaction of economic,
financial and psychological crisis engulfing the entire
globe.
The Global Financial Stability Report in
September 2011 has cautioned that for the first timesince October 2008, the risks to global financial stability
have increased, signaling a partial reversal in the
progress made over the past three years. Banking
systems in advanced economies have continued to beon uncertain grounds on account of a lack luster
economic revival and increasing sovereign credit
strains. The US banking system improved in terms of
Author α : Punjab School of Economics, Guru Nanak Dev University,
Amritsar. E-mail : giandevgain@yahoomail.com
Author σ : Assistant Professor, GNDU College, Vera.
E-mail : dsekhon290@gmail.com

Year 2013

credit growth and profitability in 2010 and now thequestion of sustenance of the same (see Table 4). The
banking system in the Euro Zone, as a whole, stands
vulnerable to mounting credit, market and funding risks
as a result of severe deterioration in public finances incertain European countries. Many of these banks require
recapitalization to cushion them from the risk ofsovereign defaults. The UK banking system too
continues to be beleaguered by high leverage and weakasset quality. In major emerging economies, credit
growth has been at relatively high levels and being 17
regarded as a cause of concern given the growing
inflationary pressures and increasing capital inflows (e.g
CD ratio has increased consistently from 48.36 in 2000
to 73.16 in 2010). Further, concerns are also being
expressed about the credit growth laying foundations fora weak asset quality in the years to come. On thepositive side, both advanced and emerging economies,
individually, and multi-laterally, have moved forward
towards strengthening macro-prudential oversight of
their banking systems. While it is important to keep up
efforts towards strengthening the banking systems fromwithin, it is also equally important to develop effective
solutions for containing fiscal and economic risks, whichat the present juncture threaten the stability of the global
banking system from without. All such solutions need to
be designed keeping in mind the larger interests of the
global economy (Report on Trend and Progress of
Banking in India, 2010-11, Global Banking Developments, and Chapter II).
The Indian financial sector continues to besound and resilient. Banks remain well capitalized andare not excessively leveraged. The Reserve Bank
remains vigilant in respect of the underlying trends in
asset quality, as well as exuberant credit growth inselect sectors and is working on a forward looking
provisioning framework (Financial Stability Report, 2011,
RBI Monthly Bulletin, December22, 2011)
In 2009, Financial Stability Development
Council (FSDC) was formulated to watch financial
stability of the Indian economy. The first Financial
Stability Report (FSR) was published in March 2010, tofocus on renewing the nature, magnitude andimplication of risks that have bearing on the macroeconomic environment, financial institutions, markets
and infrastructure. The fourth FSR published in
December 2011, has revealed that the Indian financial
system remains stable. Subsequently a Systemic Risk
Survey has been instituted during 2011 to review
financial system of the economy.

Global Journal of Management and Business Research ( C ) Volume XIII Issue VII Version I

Abstract - The present study is an attempt to evaluate theperformance of public sector banks in terms of credit risk,
efficiency and their impact on the profitability of these banks.
The study covered the period from 2000 to 2010. With the helpof panel data regression analysis the study concludes that
NPLs affect adversely the profitability of banks while risk
aversion seems to be in favor of the public sector banks in
India. Though significant progress has been made in NPLs
management, much still need to be done in order to improve
the performance of scheduled commercial banks. Keeping inview the gravity of credit risk, the study recommends steps,
towards the recovery of such loans, to be undertaken andenforced by the RBI.

© 2013 Global Journals Inc. (US)

Risk, Efficiency and Return of PSBs in India

Objectives

Year 2013

II.

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The study spans over the period 2000-2010 viz.
inclusive of the years (2008-2010) of Global Financial
Crisis. The empirical analysis endeavors to capture theimpact of the crisis on the Indian state owned banks-the
PSBs. The overall objective of the study is to examine
the impact of various indicators of banking sector
reforms viz. NPAs, CAR and indicators of banking
business viz. spread, business per employee andoperating expenses.
The study is organized into five sections.
Section I deals with a brief overview of the commercial
banks in India during the period under study. Section II
provides the brief overview of the literature related withthe determinants of profitability of banks at national aswell as international levels. Section III deals with scope,
database and methodology used for the study. Section
VI presents trends of the NPLs at the global level, inparticular for Asia and Specifically for India. Section V
gives the specification of the model to be evaluatedalong with the analysis of the results.

a) Overview of the Indian Banking Sector

Commercial banking constitutes the largest
segment of the Indian financial system. It consists ofstate owned or public sector banks, private banks under
Indian ownership and foreign banks. Among thesebanks, 27 PSBs dominate the commercial banking
sector, accounting for more than 90 percent of thebanking business in India.
Until the beginning of the 1990s, several
quantitative and functional restrictions are operative. The

banking sector was characterized by administeredinterest rates and large pre-emption of funds in the formof required reserves and directed credit. During 1991,
the CRR of commercial banks was at statutory
maximum of 15 percent of total of demand and time
deposits, SLR to be invested in government and otherapproved securities were as high as 38.5 percent. This
resulted total reserve requirement ratio as 53.5 percent.
Under the “social objectives” of credit to the preferred
sector, termed as “priority sector” banks were directed
to lend 40 % of their net credit to this sector. Post
nationalization period witnessed wide spread expansionof banking business in the country.
The initiation of banking sector reforms in the
country during the early 1990s was conditioned by the
analysis and recommendations of various committees.
According to the RBIs Publication, at the end of March
2010, Indian banking sector consisted of 27 Public
Sector Banks, 22 Private Sector Banks, 34 Foreign
Banks, and 84 Regional Rural Banks. Therefore, Indianbanking sector comprised 83 Scheduled commercial
banks, made up of a total of 65412 branches and941375 employees. The total assets of Indian banking
sector has reached Rs. 6025141, with an averageannual growth rate of 18.49 percent over the period from2000 until 2010. The public sector banks continue todominate the banking industry, in terms of lending andborrowing, and it has widely spread out branches, which
help greatly in pooling up of resources as well as inrevenue generation for credit creation. The profitability ofIndian banks has been shown in following table.

Table 1 : Operating profits of Indian Commercial Bank (Amount in Rs. Cores)
Year

Public sector Banks

Private Sector Banks

Foreign Banks

2000-01

13792.95

2848.943105.15

2001-02
2002-03

21676.5429715.24

4646.447238.69

3513.61
3727.85

2003-04
2004-05

39290.10
37413.18

8324.597673.584985.534597.44

2005-06
2006-0737967.2142268.18

9768.0713469.84

6658.449599.81

2007-08
2008-09

50307
66972

18881.42
24194.82

1404.7
20098

Source : Report on Trend and Progress of Banking in India, Various Issues, RBI.
It has been observed from the table that private
sector banks perform better in terms of operating profits
than Sector banks.

b) Review of Related Literature

Heron, Sudan (2004) investigated thedeterminants of profitability of Islamic banks. The study
found that internal factors such as liquidity, total
expenditures, funds invested in Islamic securities andthe percentage of the profit-sharing ratio between thebank and borrower are highly correlated. Further results© 2013 Global Journals Inc. (US)shows that interest rates, market share and size of thealso positively affect the profitability of banks.
Basher, M (2003) analyzed how bank characteristics and the overall financial environment affect theperformance of Islamic banks. He utilized bank level
data of Islamic banks across eight Middle Easterncountries between 1993 and 1998. The author usedinternal and external banking characteristics to predict
profitability and efficiency. The results indicate that highcapital-to-asset and loan-to-asset ratios lead to higher

Risk, Efficiency and Return of PSBs in India

Bitola and Vera (2006) made an attempt to identify the
key determinants of profitability of Public Sector Banksin India. This study is based on step-wise multivariate
regression model used on temporal data from 1991-92
to 2003-04.The study concluded that the variables noninterest income, operating expenses, provision andcontingencies and spread have significant relationshipwith net profits.
III.

Data Base and Methodology

Year 2013

The study is based on panel data for 26 PSBs
over the period 2000 to 2010. An advantage of using
panel data is that more observations on the explanatory
variables are available. This has the effect of helping toovercome the inherent multi co linearity, which probably
exists between the independent variables in OLS
estimation. The present study is based on secondary
data and all the required data has been culled from 19
Reserve Bank of India publications viz. Report on Trend
and progress of Banking in India, Statistical Tables
Relating to Banks in India and publications of Indian
Banks Association. It is well known that commercial
banks in India comprised public sector banks, private
sector banks and foreign banks. As the number ofprivate and foreign banks has changed over the time,
the scope of the present study is limited only to public
sector banks. The bank of Saurshatra and IDBI bank ltd.
has been omitted from the study3. The period is selectedaccording to the nature of subject and the availability ofdata. The variables selected for studying the impact on
profitability are NPA, CAR, Spread, business per
employee and operating expenses.
In panel regression model the cross section
analysis provides meaningful analysis of interlink ages
among economic and financial variables. The panel
data (also called longitudinal data) refer to data for ndifferent entities observed at n different time periods.
Here the entities are 26 PSBs in India over the period
2000 to 2010 i.e T=11 and so there are (26×11=286)
observations. PSBs-the entities are homogenous andnon-variant in the specific time period. As the nature of
the problem-the entities considered in this analysis are
the state controlled banks and so are assumed to be
structurally invariant. Pooling all the (26X11=286)
observations, the following function for profitability of
PSBs has been estimated:
Yit=α+βiXit+µit
Where Y=ROA- a measure of Profitability of banks
X’s – Explanatory Variables (See Table.2)
Where i=1 to 26, t=1 to 11,
3

IDBI ltd has started its operations after 2004 and Bank of
Saurshatra closes its operations due to merger of SBS
with its parent subsidiary. The inclusion of these banks madethe data unbalanced.
© 2013 Global Journals Inc. (US)Global Journal of Management and Business Research ( C ) Volume XIII Issue VII Version I

profitability. The results also indicate that foreign-ownedbanks are likely to be profitable. Everything remaining
equal, the regression results show that implicit andexplicit taxes affect the bank performance andprofitability negatively while favorable macro-economic
conditions impact performance measures positively.
Kosmidou et al. (2008) investigates the impactof bank-specific characteristics, macroeconomic conditions and financial market structure on UK owned
commercial banks’ profits, over the period 1995-2002.
The findings depict that the capital strength of thesebanks has a positive and dominant influence on theirprofitability, the other significant factors being efficiency
in expenses management and bank size. These bankspecific determinants are robust to the inclusion of
additional macroeconomic and financial market
measures of bank performance, which add little to the
explanatory power but nevertheless appear to havepositively influenced profitability.
Singh, R.K (2009) conducted a study to assess
whether selected bank-specific and macro-economic
determinants have significantly affected profitability ofIndian banks. The study concluded that most of theselected indicators significantly impact banks in India
and profitability of banks in India has risen significantly
over the years and the selected macroeconomic
determinants exert a significant impact on profitability ofbanks.
Flaming, Valentine et al. (2009) used a sample
of 389 banks in 41 SSA (Sub-Saharan Africa) countries
to study the determinants of bank profitability. They
found that apart from credit risk, higher returns on
assets are associated with larger bank size, activity
diversification, and private ownership. Bank returns areaffected by macroeconomic variables which suggestthat macroeconomic policies that promote low inflationand stable output growth do boost credit expansion.
Davydenko (2010) used a panel of individual banks’financial statements of Ukraine banks to study the
impact of various factors on the profitability of Ukraine
banks from 2005 to 2009. According to the empiricalresults, Ukrainian banks suffer from low quality of loans
and do not manage to extract considerable profits fromthe growing volume of deposits. This study shows thatthe difference in profitability patterns of banks withforeign capital versus exclusively domestically ownedbanks. The results also indicate that there is room forconsolidation of Ukrainian banks in order to benefit
economies of scale.
Ramlall, Indranarain (2009) analyzed the determinants of profitability for the Taiwanese banking
system and used bank-specific, industry-specific andmacroeconomic factors, under a quarterly dataset, forthe period 2002 to 2007. The study found that while
credit risk triggers a negative impact on profitability,
capital tends to consolidate profits. In general, results
imply that Taiwanese banking system is well-diversified.

Risk, Efficiency and Return of PSBs in India

Table 2 : Description of variables used in the regression analysis
Variable

Description

Hypothesized relationship with profitability

OE
SP
NPAs

Operating expenses
SpreadNon-performing assets as apercent to net advances
Business per employee
Capital Adequacy Ratio+_

Year 2013

BPE
CAR

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+
+

Following Panel Data Regression models with
Fixed Effect Approach have been estimated.
1. Assuming that the intercept and slope coefficientsare constant across time and space and the error
term captures difference over time and entities.
2. The slope coefficients are constant, but the intercept
varies over time
3. The intercept is constant, but the slope coefficient
for a specific variable varies over time
4. The intercept and the slope coefficient for a specificvariable vary over timea) Pre-Emption of Funds

Indian banking industry bears a special featurein credit deployment to different sectors. (1) Banking
sectors since independence (1947) to 1969, the year of
nationalization of private banks viz.bringing these banks
under state control, exhibited concentration of power.
Since 1969 and then in 1980, India/RBI-the central bankof India has control over 26 banks – termed as public
sector banks(PSBs). Since 1969, the norm of social
control was established under which PSBs have been
directed to load a specific proportion viz. 40 percent of
their net bank credit to the priority sector (PS). This
sector covers agriculture, small scale industries andother weaker sections of the society. Studies point out
that adverse impact of this variable on the profitability ofbanks in India. It is due to the fact that (i) rate of interest
On such loans is low and administered one and (ii) suchloans are proving to more risk. Figures indicate that tototal NPLs in India, the contribution of PS loans has
increased consistently for the study period (2000-2001).
It was 44.5 percent in 2000 and increased continuous to63.6 percent in 2008, though it moderated slightly in thefollowing years 2009(55.2 percent) and 2010(53.8
percent, Table 2).

As a policy variable to control credit RBI targets
lending power of the commercial banks in terms ofRequired Reserves by fixing the ratios –(i) Cash –reserve
ratio(CRR) –the minimum to be kept in the form ofreserve against net deposits and (ii) statutory Liquidity
Ratio (SLR) the minimum to be invested in government
securities.
These ratios touched the maximum viz. 53.5
percent over and above 40 percent of PS lending. And
so more than 90 percent of the lending capacity of the
Indian banks was pre empted and the profitability of thebanks dipped with, among other factors, due to preemption funds.
With introduction of banking sector reforms in
1991, these ratios (CRR and SLR) were aimed to be
reduced to their minimum levels of (3 percent for CRR)
and (25 percent for SLR). And so in a phased manner
they have been reduced to about 5 percent (CRR) andto its minimum level of 25 percent of SLRTrends in NPA
Table 3 provides data on the non-performing
assets (NPAs). The ratio of non-performing assets(NPAs) to total advances /assets highlights trends inquality of loan assets. A closer examination of the data
indicates declining trend in NPAs as percentage to total
assets in case of all bank groups. These trends are
signs of improvement in the quality of loan assets and a
decline in the credit risk exposure of banks at an
aggregate level.
NPAs effect on profitability of banks is twopronged. These assets block the bank funds fromputting in income earning assets on one hand and do
not contribute to the returns of the banks( in terms ofinterest income earned) on the other hand. Expected
sign of NPAs as determinant of profitability of banks,
hence is negative.

Table 3 : Gross NPAs of Public Sector Banks in India
Year

NPL/Assets

NPL( PS) as % of total assets

2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

14
12.4
11.1
9.4
7.8
5.4
3.9
2.8
2.3
2.1
2.3

44.5
.45.4
46.2
47.2
47.5
48.1
54.1
59.5
63.6
55.2
53.8

Source : RBI, Report on Trend and Progress of Banking inIndia (Various Issues).
© 2013 Global Journals Inc. (US)

Risk, Efficiency and Return of PSBs in India

Public and Private sector banks have started declining
after 2002 this showed the reverse trend in contrast to
the earlier years.

b) Cross-country trend of NPLs (Non-performing
Loans)

According to the global NPL Report 2004, byErnst and Young, the level of non-performing
loan isestimated at about US$ 1.3 trillion during 2003 of which
the Asian region accounts for about 77 percent of global
NPLs. Within Asia, Japan and China account for 49
percent of the global NPL and about 85 percent Asian
NPLs, while Taiwan, Thailand, Indonesian and Philippines together contribute about 5 percent of the global
NPLs. India alone accounts for 2 percent of the global
NPLs of the financial sector in the world.

Table 4 : Cross country Performance analysis of banks

21

2008

2009

2011*

1.4
(1.2)
0.9
(0.4)
2.7
(0.4)
2.6
(0.3)
0.3
1.5
(1.2)
4.6
(0.7)
4.5
(1.2)
0.9
(1.1)
0.8
3
(3.4)
2.5
(3.0)
2.5
(0.9)
6.2
(0.9)
2.7
(2.3)
4.1

2.9
(-0.1)
1.6(-0.4)
2.8
(0.0)
2.8
(-0.1)

5.4(-0.1)
3.5
(0.1)
3.6
(0.4)
3.3
(0.2)

3.8
(0.3)
3.5
(0.1)
-----

2.0
(0.4)
4.9
(0.3)
5.0
(0.2)
3.4
(0.8)
2.6
3.1
(1.5)
3.8
(1.8)
2.3
(1.0)
2.4
(1.0)
3.2
(1.4)
4.1

3.2
(0.4)
7.0
(0.2)
7.7
(-0.1)
5.1
(0.6)
9.0
4.2
(2.4)
9.7
(0.7)
2.3
(1.1)
1.6
(0.9)
3.1
(1.5)
3.3

6.9
(0.5)
11
(0.2)
13.4
(-0.3)
5.3
(0.5)
14.1
3.4
(3.3)
7.2
(2.3)
2.2
(1.2)
1.1
(1.0)
2.1
(1.6)
2.1

7.5

6.5
(1.5)
5.8

4.8
(1.5)
4.5

3.7
(1.2)
4.1

2.7
(1.8)
2.9

8.1

7.9

5.7

5.3

---

1.4
2.6

0.9

4.2

3.0

5.0

3.4

--

1.3

6.5

4.9

--

5.4

--

0.7

-4.5

0.7
3.5

Russia

5.6

2.4

India

3.3
(0.6)
7.1

Mexico

10.4
(0.8)
26.0
(0.1)
3.7

Indonesia

24.0

6.0

15.9

8.5

Philippins

26.5

Thaliand

15.7

United States
United Kingdom
FranceGermany
Portugal
Italy
GreeceSpain
Ireland
BrazilChina

Malaysia

2.0

Global Journal of Management and Business Research ( C ) Volume XIII Issue VII Version I

2007

0.8

2002

2006

(NPL/Gross Loans) in percent
Country↓→Year

Year 2013

All types of banks showed a declining trend ingross NPAs over the period under study but public
sector bank has higher ratio as compared to private
sector banks reason behind this is that PVT have asecured loan policy as compared to PSB. Even after
implementation of prudential norms in early nineties andserious concern raised by govt. about growing size of
NPAs, Public Sector Banks paid least attention to allthese warnings, which subsequently led to turning freshloans of banks into non-performing category. So, falling
ratio of NPAs in terms of advances is not a true indicatorof performance of PSBs in the field of NPAs. In fact,
growing size of gross NPAs in absolute form has beenreal cause of worry. However; there is a silver lining, on
account of the steps taken by the banks under the
Securitization Act? The gross as well as net NPAs of

Source : Report onGlobal Financial Stability, various issues,.IMF

Note : --- indicates lack of statistical data, * indicates data for the period 2011 varies from Quarter to quarter.
Figures in parenthesis indicates Return on assets or profitability of banks.
© 2013 Global Journals Inc. (US)Year 2013

Risk, Efficiency and Return of PSBs in India

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However, the trend in the NPL ratios of selected
countries has improved. In emerging countries likeThailand and Indonesia, structural reforms after the
Asian crisis had an immediate impact on the financial
sector inducing a sharp fall in NPLs. In Indonesia, theratio of NPL to total loans declined from 24 percent in2002 to 2.1 percent in 2011. For China, the figure stands
at 26 percent as against that of 1.1 percent in 2011andIndia bears the respective figures as 10.4 and 2.2.
Therefore, compared globally (see table 3), theperformance of Indian banks are now approachinginternational standards and they are among the better
performers in the emerging economics. The figures inthe table further indicate the direct and inverse
relationship between NPLs and return of the banking
assets.

c) Ratio of Intermediation Cost or Operating Cost to
Total Assets

Ratio of intermediation cost to total assets has
experienced gradual fall in the post reform period for allbank groups except for foreign banks. This variable isone of the indicators of a measure of efficiency ofbanks. Among the components of operating expenses
is the employee cost. The PSB’s cost on wages
declined following the voluntary retirement scheme in
the past 2001 period. However, the most of banks
towards information technology. Though adding tobasket of operating cost is still a step for increasing
efficiency of banks in India.

banking sector reforms.CRA can increase either byincreases in capital or decrease in risk weighted assets.
The latter can be decreased with investment in lesser
risk assets so i.e. it is least when assets are totally freei.e. risk weight is zero. Greater the CAR indicates greater
potential for investment in risk bearing assets, that yield
more income (2.5 percent) than risk free assets likegovernment securities in India. Hence impact of CAR on
banks profitability is hypothesized as positive. Overall,the CAR of SCBs India has improved significantly from
10.4 percent in 1996-1997 to 12.2 percent during 20052006. For PSBs, the figures are 11.8 and 12.2 for the
PSBs, the CAR has increase.

f)Findings

As discussed earlier, this study examines theimpact of credit risk and efficiency on the profitability ofIndian public sector banks. The results of the panel data
regression model with time dummies and fixed effectmodels I to V am reported in Tables A-E.
i. All Coefficients Constant Over TimeBy pooling the data on 26 entities over ‘11’
years with 286 observations, the OLS results obtained
are given in Table-5 and Table –6 as model -1 andmodel-2.
Model-1
Yit=α+β1BPEit+β2NPAit+β3SPit+β4OEit+µit
Model-2
Yit=α+β1BPEit+β2NPAit+β3SPit+β4OEit+ CARit+µitd) Spread

The variable spread (S), defined, as the excess
of interest income over interest expenses is an important
indicator of efficiency of banks. This ratio reflects thelocative efficiency of banks, the smaller figure indicating
higher efficiency. One of the objectives of banking
sector reforms was to lower the interest rates. In theprocess, the lending rates have tended to be sticky as
against the deposit rates. Over the time period the ratioof spread to total assets does not exhibit consistent
trend. It may be because of the fact that banks have got
freedom, in the post-reform regime, to determine thedeposit rates as well as lending rates. Moreover thebanks have diverted their investment portfolio to noninterest earning business/assets.
The variable spread(S) contributes to the
profitability of banks. Hence the relationship between
profitability of the banks and spread is hypothesized to
be positive.

e) Capital Adequacy Requirements: (CAR)

Maintenance of sufficient capital as apercentage of risk weighted assets, termed as CRA is
one of the requirements for banks under the norms of

Examination of results for model-1 and model-2
indicate that all the slope coefficients are statisticallysignificant with expected signs. Business Variables-BPE
and SPE exert positive significant influence on the
profitability of the banks, whereas the NPLs a measureof risk-put alarm for the banks as the variable bears
negative sign and is highly, significant at 10% level ofsignificance. The negative sign of OE (operating
expenditure), too indicate heavy expenditure on thebanks, contributing adversely to the profitability of thebanks. The variable CAR (Variable of banking sector
reforms as well as that of policy of the RBI), when added
to the model-1, gives positive impact and estimation ofmodel-2, indicates increase in R2. This shows that
capital position has improved in case of Indian banks,compensating the adverse impact of NPAs on ROA.
This may be the result of recapitalization of banks by thegovernment since inception of banking sector reforms in
1991.
ii. Time Effect: Intercept varies and slope coefficient
are invariant over time e.g Least Square Dummy
Variable (LSDV) model

Following model has been estimated:
ROAit=α1+α2Di2+α3Di3+------α11Di11+β1BPEit+β2NPAit+β3SPit+β4OEit+ β5CARit+µit© 2013 Global Journals Inc. (US)

Risk, Efficiency and Return of PSBs in India

Where Di2 =1 for the observation of year 2001
Di3 = 0 otherwise= 1 for year 2002
= 0 otherwise
Di11 = 1 for 2010
=0 otherwise
The results are described of this model 3 inTable 7. The coefficients of all the variables bear
expected signs with statistically significance as in model-1 and model-2. R2 increases, though slightly. The
coefficient of CAR is maintained but not significant. As
evident from the results, none of the time dummies turns
out to be significant statistically. It may be inferred thatthe profitability function, as specified, has not changed
much over the time under consideration.
iii. Intercept Constant but the slope coefficient ofvariable SP (spread-interest margin) varies overtime.

ROA it=α+β1BPEit+-------β5CARit+λ2Dt2SPit+ λ3Dt3SPit--------+ λ11D11SPit+µit

the banks has changed with diversification in its lending
and investing policies .Income from non-interest assets
has increased. However the value of R2 has increased
as compared to the results with fixed effects in Tables 5 23
and 6 (from .6339 to .6491).Such exercise was also
conducted with the other variables considered in themodel, but the effect was negligible.
iv. Intercept and slope coefficient of SP varies overtime

Following model has been estimated to meet the objective

ROAit = α1+α2Di2+--------+αi11Di11+β1BPEit+-------β5CARit+λ2Dt2SPit+λ3Dt3SPit--------+
λ11D11SPit+µit
Di2 =1 for 2001
= 0 otherwise
Di11=1 for 2010
=0 otherwise
Results of this model are produced in Table 9.
As observed R2 has increased from 0.6491 (with slope

dummies) and from 0.6339 (with intercept dummies) to
0.6543. However, the coefficients with differential
coefficients are not significant.

Table : 5 (Model-1)
Coefficient

Std. Error

t-ratiop-value

0.645858

0.171111

3.77450.00020***BPE

0.000786736

0.000318206

2.47240.01407**

NPA-0.0742188

0.00998933-7.4298<0.00001***

SP

0.299006

0.0522344

5.7243<0.00001***

OE-0.223042

0.0631938-3.5295

0.00049***

Constant

R-squared

0.629838

Adjusted R-squared

0.587906

Durbin-Watson

1.956952

Note : ***, **,* denotes the level of significance at 1%, 5% and 10% respectively.

Table : 6 (Model-2)
Explanatory
Variables
Constant
BPE
NPA

Coefficient

Std. Error

t-ratiop-value

0.594013
0.000781423-0.0726944

0.173270.000317092
0.00999509

3.42832.4643-7.27300.00071***0.01439**<0.00001***© 2013 Global Journals Inc. (US)Global Journal of Management and Business Research ( C ) Volume XIII Issue VII Version I

Here Di2 = 1 for the variable SP for 2001
= 0 otherwise
Di3 = 1 for SP for 2002
= otherwise
Di11 = 1 for SP for 2010
= 0 otherwise
Table 8 reproduces the estimates of the above
equation. It is observed that time has not put any
differential impact on this function. Over the period sincereforms in the banking industry, the portfolio behavior ofYear 2013

To examine this possibility, the model is outlined as:

Risk, Efficiency and Return of PSBs in India

SP
0.295177
0.0520988
5.6657<0.00001***
OE-0.222182
0.0629716-3.52830.00050***
0.00271258
1.6813
0.093938
CAR
0.00456061
0.633896
R-squared
0.590825
Adjusted Rsquared
Durbin-Watson
1.917549
Note : ***, **,* denotes the level of significance at 1%, 5% and 10% respectively.

Year 2013

Table 7 : (Model 3)

Global Journal of Management and Business Research ( C ) Volume XIII Issue VII Version I

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24

Coefficient

Std. Error

t-ratiop-valueConstant

0.577734

0.184121

3.13780.00191***BPE

0.00079358

0.000325073

2.44120.01535**

NPA-0.0716457

0.0102538-6.9872<0.00001***

SP

0.296358

0.0532307

5.5674<0.00001***

OE-0.224275

0.0640208-3.5032

0.00055***

CAR

0.00426848

0.00280748

1.52040.12970

Dt_2

0.0238977

0.0738895

0.32340.74665

Dt_3-0.0212538

0.0740296-0.28710.77428

Dt_4

0.0384069

0.0744328

0.51600.60632

Dt_5

0.0243112

0.0736787

0.33000.74171

Dt_6

0.00727716

0.0741539

0.09810.92190

Dt_7-0.0275667

0.0738994-0.3730

0.70945

Dt_8-0.0198867

0.0737216-0.2698

0.78758

Dt_9

0.0489137

0.0736818

0.66390.50741

Dt_10

0.034679

0.0742109

0.46730.64070

Dt_11

0.0606652

0.0739134

0.82080.41258

R-squared

0.638737

0.579755
Adjusted Rsquared
Durbin1.926033
WatsonNote : ***, **,* denotes the level of significance at 1%, 5% and 10% respectively.

Table 8 : (Model 4)
Coefficient

Std. Error

t-ratiop-value

0.519035

0.183508

2.82840.00506***

BPE

0.000920301

0.000347044

2.65180.00853***

NPA-0.072091

0.010068-7.1604<0.00001***

SP

0.228816

0.0934803

2.44770.01508**

Constant© 2013 Global Journals Inc. (US)OE

-0.196453

0.065468-3.00070.00297***Car

0.00423162

0.00270872

1.56220.11952DS2

0.0401762

0.122923

0.32680.74407

DS3
DS4
DS5
DS6

0.0674525
0.128407
0.131728
0.0293088

0.110539
0.11199
0.108709
0.106701

0.6102
1.1466
1.21170.27470.542280.252660.22677
0.78379DS7-0.046102

0.107531-0.4287

0.66849DS80.014729

0.108408

0.13590.89204DS9

0.140888

0.100175

1.40640.16086DS10

0.0847627

0.0689462

1.22940.22010

Risk, Efficiency and Return of PSBs in India

R-squared

0.649140

Adjusted R0.593515
squared
Durbin1.956471
WatsonNote : ***, **,* denotes the level of significance at 1%, 5% and 10% respectively.

Coefficient

Std. Error

t-ratiop-value

0.506674
0.00091133-0.0708054
0.217672-0.201454
0.00386168
0.0639291
0.0917677
0.144014
0.146597
0.0390757-0.036749
0.0256683
0.145768
0.0888224
0.0294251-0.0153676
0.0519968
0.0337804
0.0178665-0.0269238-0.0168655
0.0471006
0.041420.0660435
0.654339
0.582570

0.195739
0.000355586
0.0103329
0.0955086
0.0666011
0.00280905
0.127825
0.113834
0.114392
0.111218
0.108948
0.110073
0.110754
0.101928
0.0703147
0.0741739
0.0742978
0.075154
0.0744439
0.074710.074968
0.0741974
0.0751675
0.0743853
0.0742906

2.58852.5629-6.8524
2.2791-3.02481.37470.50010.8062
1.25891.31810.3587-0.33390.23181.43011.2632
0.3967-0.2068
0.69190.45380.2391-0.3591-0.2273
0.6266
0.55680.88900.01024**0.01100**<0.00001***
0.02356**
0.00276***0.17052
0.617450.42097
0.209290.188750.720170.73878
0.81692
0.154010.20776
0.69194
0.836310.489700.65041
0.811200.719810.82038
0.53152
0.578170.3749125

1.967788

Note : ***, **,* denotes the level of significance at 1%, 5% and 10% respectively. DS11 is omitted due to coli neatly.
IV.

Conclusion and Suggestion

Empirical results relating to ROAs of PSBs in
India as determined by different bank variables for theperiod 2000-2010 highlights that:
1. Effect of the policy variables and bank businesshave put significant impact on ROA, but
2. Over the time, the banks ‘performance has not been
affected by other structural variations in theeconomy-political,
technologicalor
globalupheavals etc. It indicates that Indian banking
industry is resilient to economic and other shocks.
Burden of NPLs on the financial institutions has
become a global phenomenon and so for Indian
Banking Industry. Though the NPLs as share of totalbank loans have decreased since inception of Banking
Sector Reforms (cross country comparison in thiscontext is in favor of India), the empirical results above
alarms for the negative impact of this variable on ROA.
The findings of this study suggest that NPLS, may betargeted seriously by the monetary policy. More vigilant
and strict policy towards recovery of bad loans isdemanded from the government and RBI.

References Références References
1. Badola,B.S and Rocha Verma 2006 “ Determinantsof Profitability of Banks in India-A Multivariate
Analysis” Delhi Business Review,Vol.7, No.2 pp.
2. Bashir, M 2003 “Determinants of Profitability ofIslamic Banks: Some Evidence from the Middle
East” Islamic Econometric Studies.
3. Davydenko, Anthnia 2010 “Determinants of Bank
Profitability in Ukraine” Undergraduate Economic
Review, Volume 7, Issue 1.
© 2013 Global Journals Inc. (US)Global Journal of Management and Business Research ( C ) Volume XIII Issue VII Version I

Constant
BPE
NPA
SP
OE
CAR
DS2
DS3
DS4
DS5
DS6
DS7
DS8
DS9
DS10
Dt_2
Dt_3
Dt_4
Dt_5
Dt_6
Dt_7
Dt_8
Dt_9
Dt_10
Dt_11
R-squared
Adjusted Rsquared
DurbinWatson

Year 2013

Table 9 : (Model 5)

Risk, Efficiency and Return of PSBs in India

4. Flamini Valentina et al. 2009 “The determinants ofCommercial Bank Profitability in Sub-SaharanAfrica” IMF Working Paper, 2009.
5. Kosmidou and Fotios Pasiouras 2008 “Determinants of Profitability of Domestic UK Commercial
Banks: Panel Evidence from the Period 1995-2002”

Applied Research Paper.

6. Ramlal, Indranarain 2009 “Bank Specific Industry –specific and Macroeconomic determinants ofProfitability in Taiwan Banking System: under panel
data estimation” International Research Journal ofYear 2013

Finance and Economics.

India, Various issues.

8. RBI, Monthly Bulletin, December 2011 .
9. Singh, R.K (2009) “Profitability Determinants ofBanks in India” International Journal of Global
Business, Volume.2 No.1, pp: 163-180.

Global Journal of Management and Business Research ( C ) Volume XIII Issue VII Version I

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7. RBI, Report on Trend and progress of Banking in© 2013 Global Journals Inc. (US)