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\title{Risk, Efficiency and Return of PSBs in India}
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             \author[1]{Yahoo! Mail  Services}

             \author[2]{Dr. Gian  Kaur}

             \affil[1]{  Guru Nanak Dev University, Amritsar}

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\date{\small \em Received: 10 December 2012 Accepted: 3 January 2013 Published: 15 January 2013}

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\begin{abstract}
        


The present study is an attempt to evaluate the performance 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 help of 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 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.

\end{abstract}


\keywords{NPLs, roa, panel data regression, banks.}

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\let\tabcellsep& 	 	 		 \par
introduction ajor challenges for Asian banks include macroeconomic stability in a number of countries continued NPLs problems, lack of resilience at the bank level, increasing foreign competitors and demographic shifts which could lead consumers away from traditional deposits. (Deborah scholar, Vice-President of Moody's Financial institute and Sovereign Risk Group,2005).\par
Financial stability paves the way for sustained and rapid economic growth. Among various indicators of 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 allocation of their resources for productive purpose. The NPL is defined as past due concept, taking into account either non-payment of interest due, principal or both.\par
The recent global financial crisis surfaced in second 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.\par
The Global Financial Stability Report in September 2011 has cautioned that for the first time since 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 be on uncertain grounds on account of a lack luster economic revival and increasing sovereign credit strains. The US banking system improved in terms of credit growth and profitability in 2010 and now the question of sustenance of the same (see Table \hyperref[tab_3]{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 in certain European countries. Many of these banks require recapitalization to cushion them from the risk of sovereign defaults. The UK banking system too continues to be beleaguered by high leverage and weak asset quality. In major emerging economies, credit growth has been at relatively high levels and being 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 for a weak asset quality in the years to come. On the positive 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 from within, it is also equally important to develop effective solutions for containing fiscal and economic risks, which at 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).\par
The Indian financial sector continues to be sound and resilient. Banks remain well capitalized and are not excessively leveraged. The Reserve Bank remains vigilant in respect of the underlying trends in asset quality, as well as exuberant credit growth in select sectors and is working on a forward looking provisioning framework (Financial Stability Report, 2011, RBI Monthly Bulletin, December22, 2011)\par
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, to focus on renewing the nature, magnitude and implication 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. 
\section[{II.}]{II.} 
\section[{Objectives}]{Objectives}\par
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 the impact 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 and operating expenses.\par
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 with the determinants of profitability of banks at national as well 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, in particular for Asia and Specifically for India. Section V gives the specification of the model to be evaluated along 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 of state owned or public sector banks, private banks under Indian ownership and foreign banks. Among these banks, 27 PSBs dominate the commercial banking sector, accounting for more than 90 percent of the banking business in India.\par
Until the beginning of the 1990s, several quantitative and functional restrictions are operative. The banking sector was characterized by administered interest rates and large pre-emption of funds in the form of 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 other approved 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 expansion of banking business in the country.\par
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, Indian banking sector comprised 83 Scheduled commercial banks, made up of a total of 65412 branches and 941375 employees. The total assets of Indian banking sector has reached Rs. 6025141, with an average annual growth rate of 18.49 percent over the period from 2000 until 2010. The public sector banks continue to dominate the banking industry, in terms of lending and borrowing, and it has widely spread out branches, which help greatly in pooling up of resources as well as in revenue generation for credit creation. The profitability of Indian banks has been shown in following table.  profitability. The results also indicate that foreign-owned banks are likely to be profitable. Everything remaining equal, the regression results show that implicit and explicit taxes affect the bank performance and profitability negatively while favorable macro-economic conditions impact performance measures positively.  {\ref Kosmidou et al. (2008)} investigates the impact of 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 these banks has a positive and dominant influence on their profitability, 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 have positively influenced profitability.\par
Singh, R.K (2009) conducted a study to assess whether selected bank-specific and macro-economic determinants have significantly affected profitability of Indian banks. The study concluded that most of the selected 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 of banks.\par
Flaming, Valentine et al. (  {\ref 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 are affected by macroeconomic variables which suggest that macroeconomic policies that promote low inflation and 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 empirical results, Ukrainian banks suffer from low quality of loans and do not manage to extract considerable profits from the growing volume of deposits. This study shows that the difference in profitability patterns of banks with foreign capital versus exclusively domestically owned banks. The results also indicate that there is room for consolidation of Ukrainian banks in order to benefit economies of scale.\par
Ramlall, Indranarain (2009) analyzed the determinants of profitability for the Taiwanese banking system and used bank-specific, industry-specific and macroeconomic factors, under a quarterly dataset, for the 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.\par
Bitola and Vera (2006) made an attempt to identify the key determinants of profitability of Public Sector Banks in 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 and contingencies and spread have significant relationship with net profits. 
\section[{III.}]{III.} 
\section[{Data Base and Methodology}]{Data Base and Methodology}\par
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 to overcome 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 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 of private 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 study 3 . The period is selected according to the nature of subject and the availability of data. The variables selected for studying the impact on profitability are NPA, CAR, Spread, business per employee and operating expenses.\par
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 n different 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 and non-variant in the specific time period. As the nature of 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 made the data unbalanced.\par
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:Y it =?+?iX it +µ it\par
Where Y=ROA-a measure of Profitability of banks X's -Explanatory Variables (See Table   Since 1969 and then in 1980, India/RBI-the central bank of 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 and other weaker sections of the society. Studies point out that adverse impact of this variable on the profitability of banks in India. It is due to the fact that (i) rate of interest On such loans is low and administered one and (ii) such loans are proving to more risk. Figures indicate that to total NPLs in India, the contribution of PS loans has increased consistently for the study period  {\ref (2000)} {\ref (2001)}. It was 44.5 percent in 2000 and increased continuous to 63.6 percent in 2008, though it moderated slightly in the following years 2009(55.2 percent) and 2010(53.8 percent, Table \hyperref[tab_1]{2}).\par
As a policy variable to control credit RBI targets lending power of the commercial banks in terms of Required Reserves by fixing the ratios -(i) Cash -reserve ratio(CRR) -the minimum to be kept in the form of reserve against net deposits and (ii) statutory Liquidity Ratio (SLR) the minimum to be invested in government securities.\par
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 the banks dipped with, among other factors, due to preemption funds.\par
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) and to its minimum level of 25 percent of SLRTrends in NPA Table \hyperref[tab_2]{3} provides data on the non-performing assets (NPAs). The ratio of non-performing assets (NPAs) to total advances /assets highlights trends in quality 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.\par
NPAs effect on profitability of banks is two pronged. These assets block the bank funds from putting in income earning assets on one hand and do not contribute to the returns of the banks( in terms of interest income earned) on the other hand. Expected sign of NPAs as determinant of profitability of banks, hence is negative.  
\section[{C}]{C}\par
All types of banks showed a declining trend in gross 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 a secured loan policy as compared to PSB. Even after implementation of prudential norms in early nineties and serious concern raised by govt. about growing size of NPAs, Public Sector Banks paid least attention to all these warnings, which subsequently led to turning fresh loans of banks into non-performing category. So, falling ratio of NPAs in terms of advances is not a true indicator of performance of PSBs in the field of NPAs. In fact, growing size of gross NPAs in absolute form has been real 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  However, the trend in the NPL ratios of selected countries has improved. In emerging countries like Thailand and Indonesia, structural reforms after the Asian crisis had an immediate impact on the financial sector inducing a sharp fall in NPLs. In Indonesia, the ratio of NPL to total loans declined from 24 percent in 2002 to 2.1 percent in 2011. For China, the figure stands at 26 percent as against that of 1.1 percent in 2011and India bears the respective figures as 10.4 and 2.2. Therefore, compared globally (see table \hyperref[tab_2]{3}), the performance of Indian banks are now approaching international standards and they are among the better performers in the emerging economics. The figures in the table further indicate the direct and inverse relationship between NPLs and return of the banking assets. 
\section[{c) Ratio of Intermediation Cost or Operating Cost to Total Assets}]{c) Ratio of Intermediation Cost or Operating Cost to Total Assets}\par
Ratio of intermediation cost to total assets has experienced gradual fall in the post reform period for all bank groups except for foreign banks. This variable is one of the indicators of a measure of efficiency of banks. 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 to basket of operating cost is still a step for increasing efficiency of banks in India. 
\section[{d) Spread}]{d) Spread}\par
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 the locative efficiency of banks, the smaller figure indicating higher efficiency. One of the objectives of banking sector reforms was to lower the interest rates. In the process, the lending rates have tended to be sticky as against the deposit rates. Over the time period the ratio of 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 the deposit rates as well as lending rates. Moreover the banks have diverted their investment portfolio to noninterest earning business/assets.\par
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 a percentage of risk weighted assets, termed as CRA is one of the requirements for banks under the norms of i.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 like government 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 2005-2006. For PSBs, the figures are 11.8 and 12.2 for the PSBs, the CAR has increase. 
\section[{f) Findings}]{f) Findings}\par
As discussed earlier, this study examines the impact of credit risk and efficiency on the profitability of Indian public sector banks. The results of the panel data regression model with time dummies and fixed effect models I to V am reported in Tables A-E. 
\section[{i. All Coefficients Constant Over Time}]{i. All Coefficients Constant Over Time}\par
By pooling the data on 26 entities over '11' years with 286 observations, the OLS results obtained are given in Examination of results for model-1 and model-2 indicate that all the slope coefficients are statistically significant with expected signs. Business Variables-BPE and SPE exert positive significant influence on the profitability of the banks, whereas the NPLs a measure of risk-put alarm for the banks as the variable bears negative sign and is highly, significant at 10\% level of significance. The negative sign of OE (operating expenditure), too indicate heavy expenditure on the© 2013 Global Journals Inc. (US)\par
banks, contributing adversely to the profitability of the banks. 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 of model-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 the government since inception of banking sector reforms in 1991.\par
ii. The results are described of this model 3 in Table  {\ref 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 that the profitability function, as specified, has not changed much over the time under consideration.\par
iii. Intercept Constant but the slope coefficient of variable SP (spread-interest margin) varies over time.\par
To examine this possibility, the model is outlined as:  \hyperref[tab_8]{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 since reforms in the banking industry, the portfolio behavior of 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 and 6 (from .6339 to .6491).Such exercise was also conducted with the other variables considered in the model, but the effect was negligible.\par
iv. Intercept and slope coefficient of SP varies over time Following model has been estimated to meet the objective    Note : ***, **,* denotes the level of significance at 1\%, 5\% and 10\% respectiomitted due to coli neatly.ROA it = ? 1 +? 2 Di 2 +--------+?i 11 Di 11 +? 1 BPEit+-------? 5 CAR it +? 2 D t2 SP it +? 3 Dt3SPit--------+\par
IV. 
\section[{Conclusion and Suggestion}]{Conclusion and Suggestion}\par
Empirical results relating to ROAs of PSBs in India as determined by different bank variables for the period 2000-2010 highlights that: 1. Effect of the policy variables and bank business have put significant impact on ROA, but 2. Over the time, the banks 'performance has not been affected by other structural variations in the economy-political, technological or global upheavals etc. It indicates that Indian banking industry is resilient to economic and other shocks.\par
Burden of NPLs on the financial institutions has become a global phenomenon and so for Indian Banking Industry. Though the NPLs as share of total bank loans have decreased since inception of Banking Sector Reforms (cross country comparison in this context is in favor of India), the empirical results above alarms for the negative impact of this variable on ROA. 
\section[{vely. DS11 is}]{vely. DS11 is}\par
The findings of this study suggest that NPLS, may be targeted seriously by the monetary policy. More vigilant and strict policy towards recovery of bad loans is demanded from the government and RBI.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
     \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.4457115009746589\textwidth}P{0.1408382066276803\textwidth}P{0.142495126705653\textwidth}P{0.12095516569200779\textwidth}}
Year\tabcellsep Public sector Banks\tabcellsep Private Sector Banks\tabcellsep Foreign Banks\\
2000-01\tabcellsep 13792.95\tabcellsep 2848.94\tabcellsep 3105.15\\
2001-02\tabcellsep 21676.54\tabcellsep 4646.44\tabcellsep 3513.61\\
2002-03\tabcellsep 29715.24\tabcellsep 7238.69\tabcellsep 3727.85\\
2003-04\tabcellsep 39290.10\tabcellsep 8324.59\tabcellsep 4985.53\\
2004-05\tabcellsep 37413.18\tabcellsep 7673.58\tabcellsep 4597.44\\
2005-06\tabcellsep 37967.21\tabcellsep 9768.07\tabcellsep 6658.44\\
2006-07\tabcellsep 42268.18\tabcellsep 13469.84\tabcellsep 9599.81\\
2007-08\tabcellsep 50307\tabcellsep 18881.42\tabcellsep 1404.7\\
2008-09\tabcellsep 66972\tabcellsep 24194.82\tabcellsep 20098\\
\multicolumn{4}{l}{Source : Report on Trend and Progress of Banking in India, Various Issues, RBI.}\\
\multicolumn{2}{l}{It has been observed from the table that private}\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{sector banks perform better in terms of operating profits}\tabcellsep \tabcellsep \\
than Sector banks.\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.6850101626016261\textwidth}P{0.10970528455284552\textwidth}P{0.05528455284552846\textwidth}}
Variable\tabcellsep Description\tabcellsep Hypothesized relationship with profitability\\
OE\tabcellsep Operating expenses\tabcellsep -\\
SP\tabcellsep Spread\tabcellsep +\\
NPAs\tabcellsep Non-performing assets as a\tabcellsep \textunderscore \\
\tabcellsep percent to net advances\tabcellsep \\
BPE\tabcellsep Business per employee\tabcellsep +\\
CAR\tabcellsep Capital Adequacy Ratio\tabcellsep +\\
\multicolumn{2}{l}{Following Panel Data Regression models with}\tabcellsep \\
\multicolumn{2}{l}{Fixed Effect Approach have been estimated.}\tabcellsep \\
\multicolumn{2}{l}{1. Assuming that the intercept and slope coefficients}\tabcellsep \\
\multicolumn{2}{l}{are constant across time and space and the error}\tabcellsep \\
\multicolumn{2}{l}{term captures difference over time and entities.}\tabcellsep \\
\multicolumn{2}{l}{2. The slope coefficients are constant, but the intercept}\tabcellsep \\
varies over time\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{3. The intercept is constant, but the slope coefficient}\tabcellsep \\
\multicolumn{2}{l}{for a specific variable varies over time}\tabcellsep \\
\multicolumn{2}{l}{4. The intercept and the slope coefficient for a specific}\tabcellsep \\
variable vary over time\tabcellsep \tabcellsep \\
a) Pre-Emption of Funds\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Indian banking industry bears a special feature}\tabcellsep \\
\multicolumn{2}{l}{in credit deployment to different sectors. (1) Banking}\tabcellsep \\
\multicolumn{2}{l}{sectors since independence (1947) to 1969, the year of}\tabcellsep \\
\multicolumn{2}{l}{nationalization of private banks viz.bringing these banks}\tabcellsep \\
\multicolumn{2}{l}{under state control, exhibited concentration of power.}\tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.34\textwidth}P{0.14806451612903226\textwidth}P{0.30709677419354836\textwidth}P{0.05483870967741935\textwidth}}
2013\tabcellsep \tabcellsep \\
ear\tabcellsep \tabcellsep \\
Y\tabcellsep \tabcellsep \\
Volume XIII Issue VII Version I\tabcellsep \tabcellsep \\
( )\tabcellsep \tabcellsep \\
Global Journal of Management and Business Research\tabcellsep Year 2000 2001 2002 2003 2004 2005\tabcellsep \multicolumn{2}{l}{NPL/Assets NPL( PS) as \% of total assets 14 44.5 12.4 .45.4 11.1 46.2 9.4 47.2 7.8 47.5 5.4 48.1}\\
\tabcellsep 2006\tabcellsep 3.9\tabcellsep 54.1\\
\tabcellsep 2007\tabcellsep 2.8\tabcellsep 59.5\\
\tabcellsep 2008\tabcellsep 2.3\tabcellsep 63.6\\
\tabcellsep 2009\tabcellsep 2.1\tabcellsep 55.2\\
\tabcellsep 2010\tabcellsep 2.3\tabcellsep 53.8\\
© 2013 Global Journals Inc. (US)\tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: Source : RBI, Report on Trend and Progress of Banking in India (Various Issues).]} 
\caption{\label{tab_2}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.17309442548350398\textwidth}P{0.06962457337883958\textwidth}P{0.06188850967007963\textwidth}P{0.13731513083048918\textwidth}P{0.13924914675767916\textwidth}P{0.1382821387940842\textwidth}P{0.13054607508532423\textwidth}}
\multicolumn{5}{l}{(NPL/Gross Loans) in percent}\tabcellsep \tabcellsep \\
Countryâ??" ?Year\tabcellsep 2002\tabcellsep 2006\tabcellsep 2007\tabcellsep 2008\tabcellsep 2009\tabcellsep 2011*\\
United States\tabcellsep 1.4\tabcellsep 0.8\tabcellsep 1.4 (1.2)\tabcellsep 2.9 (-0.1)\tabcellsep 5.4 (-0.1)\tabcellsep 3.8 (0.3)\\
United Kingdom\tabcellsep 2.6\tabcellsep 0.9\tabcellsep 0.9 (0.4)\tabcellsep 1.6 (-0.4)\tabcellsep 3.5 (0.1)\tabcellsep 3.5 (0.1)\\
France\tabcellsep 4.2\tabcellsep 3.0\tabcellsep 2.7 (0.4)\tabcellsep 2.8 (0.0)\tabcellsep 3.6 (0.4)\tabcellsep ----\\
\tabcellsep 5.0\tabcellsep 3.4\tabcellsep 2.6\tabcellsep 2.8\tabcellsep 3.3\tabcellsep --\\
Germany\tabcellsep \tabcellsep \tabcellsep (0.3)\tabcellsep (-0.1)\tabcellsep (0.2)\tabcellsep --\\
\tabcellsep \tabcellsep \tabcellsep 0.3\tabcellsep \tabcellsep \tabcellsep \\
Portugal\tabcellsep --\tabcellsep 1.3\tabcellsep 1.5 (1.2)\tabcellsep 2.0 (0.4)\tabcellsep 3.2 (0.4)\tabcellsep 6.9 (0.5)\\
Italy\tabcellsep 6.5\tabcellsep 4.9\tabcellsep 4.6 (0.7)\tabcellsep 4.9 (0.3)\tabcellsep 7.0 (0.2)\tabcellsep 11 (0.2)\\
Greece\tabcellsep --\tabcellsep 5.4\tabcellsep 4.5 (1.2)\tabcellsep 5.0 (0.2)\tabcellsep 7.7 (-0.1)\tabcellsep 13.4 (-0.3)\\
Spain\tabcellsep --\tabcellsep 0.7\tabcellsep 0.9 (1.1)\tabcellsep 3.4 (0.8)\tabcellsep 5.1 (0.6)\tabcellsep 5.3 (0.5)\\
Ireland\tabcellsep --\tabcellsep 0.7\tabcellsep 0.8\tabcellsep 2.6\tabcellsep 9.0\tabcellsep 14.1\\
Brazil\tabcellsep 4.5\tabcellsep 3.5\tabcellsep 3 (3.4)\tabcellsep 3.1 (1.5)\tabcellsep 4.2 (2.4)\tabcellsep 3.4 (3.3)\\
Russia\tabcellsep 5.6\tabcellsep 2.4\tabcellsep 2.5 (3.0)\tabcellsep 3.8 (1.8)\tabcellsep 9.7 (0.7)\tabcellsep 7.2 (2.3)\\
India\tabcellsep 10.4 (0.8)\tabcellsep 3.3 (0.6)\tabcellsep 2.5 (0.9)\tabcellsep 2.3 (1.0)\tabcellsep 2.3 (1.1)\tabcellsep 2.2 (1.2)\\
China\tabcellsep 26.0 (0.1)\tabcellsep 7.1\tabcellsep 6.2 (0.9)\tabcellsep 2.4 (1.0)\tabcellsep 1.6 (0.9)\tabcellsep 1.1 (1.0)\\
Mexico\tabcellsep 3.7\tabcellsep 2.0\tabcellsep 2.7 (2.3)\tabcellsep 3.2 (1.4)\tabcellsep 3.1 (1.5)\tabcellsep 2.1 (1.6)\\
Indonesia\tabcellsep 24.0\tabcellsep 6.0\tabcellsep 4.1\tabcellsep 4.1\tabcellsep 3.3\tabcellsep 2.1\\
Malaysia\tabcellsep 15.9\tabcellsep 8.5\tabcellsep 6.5 (1.5)\tabcellsep 4.8 (1.5)\tabcellsep 3.7 (1.2)\tabcellsep 2.7 (1.8)\\
Philippins\tabcellsep 26.5\tabcellsep 7.5\tabcellsep 5.8\tabcellsep 4.5\tabcellsep 4.1\tabcellsep 2.9\\
Thaliand\tabcellsep 15.7\tabcellsep 8.1\tabcellsep 7.9\tabcellsep 5.7\tabcellsep 5.3\tabcellsep ---\end{longtable} \par
  {\small\itshape [Note: Source : Report on Global 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 indicatesReturn on assets or profitability of banks.]} 
\caption{\label{tab_3}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
Model-2\end{longtable} \par
 
\caption{\label{tab_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.12931854199683043\textwidth}P{0.7206814580031695\textwidth}}
\tabcellsep =1 for the observation of year 2001\\
\tabcellsep = 0 otherwise\\
\tabcellsep = 1 for year 2002\\
\tabcellsep = 0 otherwise\\
\tabcellsep = 1 for 2010\\
\tabcellsep =0 otherwise\\
2013\tabcellsep \\
ear\tabcellsep \\
Y\tabcellsep \\
22\tabcellsep \\
Volume XIII Issue VII Version I\tabcellsep \\
( ) C\tabcellsep \\
Global Journal of Management and Business Research\tabcellsep Table : 5 (Model-1) Coefficient Std. Error 0.645858 0.171111 0.000786736 0.000318206 -0.0742188 0.00998933 0.299006 0.0522344 -0.223042 0.0631938 0.629838 0.587906 1.956952 Note : ***, **,* denotes the level of significance at 1\%, 5\% and 10\% respectively. t-ratio p-value Constant 3.7745 0.00020*** BPE 2.4724 0.01407** NPA -7.4298 <0.00001*** SP 5.7243 <0.00001*** OE -3.5295 0.00049*** R-squared Adjusted R-squared Durbin-Watson\end{longtable} \par
 
\caption{\label{tab_5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{:} \par 
\begin{longtable}{P{0.3097041847041847\textwidth}P{0.18704906204906205\textwidth}P{0.13614718614718613\textwidth}P{0.09383116883116882\textwidth}P{0.12326839826839826\textwidth}}
SP\tabcellsep 0.295177\tabcellsep 0.0520988\tabcellsep 5.6657\tabcellsep <0.00001***\\
OE\tabcellsep -0.222182\tabcellsep 0.0629716\tabcellsep -3.5283\tabcellsep 0.00050***\\
CAR\tabcellsep 0.00456061\tabcellsep 0.00271258\tabcellsep 1.6813\tabcellsep 0.093938\\
R-squared\tabcellsep 0.633896\tabcellsep \tabcellsep \tabcellsep \\
Adjusted R-\tabcellsep 0.590825\tabcellsep \tabcellsep \tabcellsep \\
squared\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Durbin-Watson\tabcellsep 1.917549\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{Note : ***, **,* denotes the level of significance at 1\%, 5\% and 10\% respectively.}\\
\tabcellsep \multicolumn{2}{l}{Table : 7 (Model 3)}\tabcellsep \tabcellsep \\
\tabcellsep Coefficient\tabcellsep Std. Error\tabcellsep t-ratio\tabcellsep p-value\\
Constant\tabcellsep 0.577734\tabcellsep 0.184121\tabcellsep 3.1378\tabcellsep 0.00191***\\
BPE\tabcellsep 0.00079358\tabcellsep 0.000325073\tabcellsep 2.4412\tabcellsep 0.01535**\\
NPA\tabcellsep -0.0716457\tabcellsep 0.0102538\tabcellsep -6.9872\tabcellsep <0.00001***\\
SP\tabcellsep 0.296358\tabcellsep 0.0532307\tabcellsep 5.5674\tabcellsep <0.00001***\\
OE\tabcellsep -0.224275\tabcellsep 0.0640208\tabcellsep -3.5032\tabcellsep 0.00055***\\
CAR\tabcellsep 0.00426848\tabcellsep 0.00280748\tabcellsep 1.5204\tabcellsep 0.12970\\
Dt\textunderscore 2\tabcellsep 0.0238977\tabcellsep 0.0738895\tabcellsep 0.3234\tabcellsep 0.74665\\
Dt\textunderscore 3\tabcellsep -0.0212538\tabcellsep 0.0740296\tabcellsep -0.2871\tabcellsep 0.77428\\
Dt\textunderscore 4\tabcellsep 0.0384069\tabcellsep 0.0744328\tabcellsep 0.5160\tabcellsep 0.60632\\
Dt\textunderscore 5\tabcellsep 0.0243112\tabcellsep 0.0736787\tabcellsep 0.3300\tabcellsep 0.74171\\
Dt\textunderscore 6\tabcellsep 0.00727716\tabcellsep 0.0741539\tabcellsep 0.0981\tabcellsep 0.92190\\
Dt\textunderscore 7\tabcellsep -0.0275667\tabcellsep 0.0738994\tabcellsep -0.3730\tabcellsep 0.70945\\
Dt\textunderscore 8\tabcellsep -0.0198867\tabcellsep 0.0737216\tabcellsep -0.2698\tabcellsep 0.78758\\
Dt\textunderscore 9\tabcellsep 0.0489137\tabcellsep 0.0736818\tabcellsep 0.6639\tabcellsep 0.50741\\
Dt\textunderscore 10\tabcellsep 0.034679\tabcellsep 0.0742109\tabcellsep 0.4673\tabcellsep 0.64070\\
Dt\textunderscore 11\tabcellsep 0.0606652\tabcellsep 0.0739134\tabcellsep 0.8208\tabcellsep 0.41258\\
R-squared\tabcellsep 0.638737\tabcellsep \tabcellsep \tabcellsep \\
Adjusted R-\tabcellsep 0.579755\tabcellsep \tabcellsep \tabcellsep \\
squared\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Durbin-\tabcellsep 1.926033\tabcellsep \tabcellsep \tabcellsep \\
Watson\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{Note : ***, **,* denotes the level of significance at 1\%, 5\% and 10\% respectively.}\\
\tabcellsep \tabcellsep 6 (Model-2)\tabcellsep \tabcellsep \\
Explanatory Variables\tabcellsep Coefficient\tabcellsep Std. Error\tabcellsep t-ratio\tabcellsep p-value\\
Constant\tabcellsep 0.594013\tabcellsep 0.17327\tabcellsep 3.4283\tabcellsep 0.00071***\\
BPE\tabcellsep \multicolumn{2}{l}{0.000781423 0.000317092}\tabcellsep 2.4643\tabcellsep 0.01439**\\
NPA\tabcellsep -0.0726944\tabcellsep 0.00999509\tabcellsep -7.2730\tabcellsep <0.00001***\end{longtable} \par
 
\caption{\label{tab_7}Table :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8} \par 
\begin{longtable}{P{0.14433962264150943\textwidth}P{0.06644204851752021\textwidth}P{0.17526954177897575\textwidth}P{0.18099730458221022\textwidth}P{0.12601078167115903\textwidth}P{0.15694070080862532\textwidth}}
2013\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
ear\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Y\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Volume XIII Issue VII Version I\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
( ) C\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Global Journal of Management and Business Research\tabcellsep Constant BPE NPA SP OE Car DS2 DS3 DS4 DS5\tabcellsep Coefficient 0.519035 0.000920301 -0.072091 0.228816 -0.196453 0.00423162 0.0401762 0.0674525 0.128407 0.131728\tabcellsep : (Model 4) Std. Error 0.183508 0.000347044 0.010068 0.0934803 0.065468 0.00270872 0.122923 0.110539 0.11199 0.108709\tabcellsep t-ratio 2.8284 2.6518 -7.1604 2.4477 -3.0007 1.5622 0.3268 0.6102 1.1466 1.2117\tabcellsep p-value 0.00506*** 0.00853*** <0.00001*** 0.01508** 0.00297*** 0.11952 0.74407 0.54228 0.25266 0.22677\\
\tabcellsep DS6\tabcellsep 0.0293088\tabcellsep 0.106701\tabcellsep 0.2747\tabcellsep 0.78379\\
\tabcellsep DS7\tabcellsep -0.046102\tabcellsep 0.107531\tabcellsep -0.4287\tabcellsep 0.66849\\
\tabcellsep DS8\tabcellsep 0.014729\tabcellsep 0.108408\tabcellsep 0.1359\tabcellsep 0.89204\\
\tabcellsep DS9\tabcellsep 0.140888\tabcellsep 0.100175\tabcellsep 1.4064\tabcellsep 0.16086\\
\tabcellsep DS10\tabcellsep 0.0847627\tabcellsep 0.0689462\tabcellsep 1.2294\tabcellsep 0.22010\\
© 2013 Global Journals Inc. (US)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_8}Table 8}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9} \par 
\begin{longtable}{P{0.21062610229276893\textwidth}P{0.19413580246913578\textwidth}P{0.1768959435626102\textwidth}P{0.12217813051146384\textwidth}P{0.14616402116402116\textwidth}}
\tabcellsep \tabcellsep : (Model 5)\tabcellsep \tabcellsep \\
\tabcellsep Coefficient\tabcellsep Std. Error\tabcellsep t-ratio\tabcellsep p-value\\
Constant\tabcellsep 0.506674\tabcellsep 0.195739\tabcellsep 2.5885\tabcellsep 0.01024**\\
BPE\tabcellsep 0.00091133\tabcellsep 0.000355586\tabcellsep 2.5629\tabcellsep 0.01100**\\
NPA\tabcellsep -0.0708054\tabcellsep 0.0103329\tabcellsep -6.8524\tabcellsep <0.00001***\\
SP\tabcellsep 0.217672\tabcellsep 0.0955086\tabcellsep 2.2791\tabcellsep 0.02356**\\
OE\tabcellsep -0.201454\tabcellsep 0.0666011\tabcellsep -3.0248\tabcellsep 0.00276***\\
CAR\tabcellsep 0.00386168\tabcellsep 0.00280905\tabcellsep 1.3747\tabcellsep 0.17052\\
DS2\tabcellsep 0.0639291\tabcellsep 0.127825\tabcellsep 0.5001\tabcellsep 0.61745\\
DS3\tabcellsep 0.0917677\tabcellsep 0.113834\tabcellsep 0.8062\tabcellsep 0.42097\\
DS4\tabcellsep 0.144014\tabcellsep 0.114392\tabcellsep 1.2589\tabcellsep 0.20929\\
DS5\tabcellsep 0.146597\tabcellsep 0.111218\tabcellsep 1.3181\tabcellsep 0.18875\\
DS6\tabcellsep 0.0390757\tabcellsep 0.108948\tabcellsep 0.3587\tabcellsep 0.72017\\
DS7\tabcellsep -0.036749\tabcellsep 0.110073\tabcellsep -0.3339\tabcellsep 0.73878\\
DS8\tabcellsep 0.0256683\tabcellsep 0.110754\tabcellsep 0.2318\tabcellsep 0.81692\\
DS9\tabcellsep 0.145768\tabcellsep 0.101928\tabcellsep 1.4301\tabcellsep 0.15401\\
DS10\tabcellsep 0.0888224\tabcellsep 0.0703147\tabcellsep 1.2632\tabcellsep 0.20776\\
Dt\textunderscore 2\tabcellsep 0.0294251\tabcellsep 0.0741739\tabcellsep 0.3967\tabcellsep 0.69194\\
Dt\textunderscore 3\tabcellsep -0.0153676\tabcellsep 0.0742978\tabcellsep -0.2068\tabcellsep 0.83631\\
Dt\textunderscore 4\tabcellsep 0.0519968\tabcellsep 0.075154\tabcellsep 0.6919\tabcellsep 0.48970\\
Dt\textunderscore 5\tabcellsep 0.0337804\tabcellsep 0.0744439\tabcellsep 0.4538\tabcellsep 0.65041\\
Dt\textunderscore 6\tabcellsep 0.0178665\tabcellsep 0.07471\tabcellsep 0.2391\tabcellsep 0.81120\\
Dt\textunderscore 7\tabcellsep -0.0269238\tabcellsep 0.074968\tabcellsep -0.3591\tabcellsep 0.71981\\
Dt\textunderscore 8\tabcellsep -0.0168655\tabcellsep 0.0741974\tabcellsep -0.2273\tabcellsep 0.82038\\
Dt\textunderscore 9\tabcellsep 0.0471006\tabcellsep 0.0751675\tabcellsep 0.6266\tabcellsep 0.53152\\
Dt\textunderscore 10\tabcellsep 0.04142\tabcellsep 0.0743853\tabcellsep 0.5568\tabcellsep 0.57817\\
Dt\textunderscore 11\tabcellsep 0.0660435\tabcellsep 0.0742906\tabcellsep 0.8890\tabcellsep 0.37491\\
R-squared\tabcellsep 0.654339\tabcellsep \tabcellsep \tabcellsep \\
Adjusted R-\tabcellsep 0.582570\tabcellsep \tabcellsep \tabcellsep \\
squared\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Durbin-\tabcellsep 1.967788\tabcellsep \tabcellsep \tabcellsep \\
Watson\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_9}Table 9}\end{figure}
 			\footnote{© 2013 Global Journals Inc. (US)} 			\footnote{Risk, Efficiency and Return of PSBs in India} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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\bibitem[Badola and Verma ()]{b0}\label{b0} 	 		‘Determinants of Profitability of Banks in India-A Multivariate Analysis’.  		 			B Badola 		,  		 			Rocha Verma 		.  	 	 		\textit{Delhi Business Review}  		2006. 7  (2) .  	 
\bibitem[Bashir ()]{b1}\label{b1} 	 		‘Determinants of Profitability of Islamic Banks: Some Evidence from the Middle East’.  		 			M Bashir 		.  	 	 		\textit{Islamic Econometric Studies}  		2003.  	 
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\end{document}
