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| Determinants of Rising Non-Performing Assets in Public Sector Banks: Panel Model Approach | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Paper Id :
16223 Submission Date :
2022-07-18 Acceptance Date :
2022-07-22 Publication Date :
2022-07-25
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| Abstract |
Indian banking system has always been applauded for its strong regulatory system. The regulatory system has safeguarded the financial system of India from the many biggest crises in the world such as the sub-prime mortgage crises in 2008, demonetization in 2016, COVID pandemic in 2019 and many more over the period. But the banking system in India is battling against a severe issue of rising NPAs since years. This has dismantled the system from within. NPAs beyond a limit would hamper the credit culture of banks which is a serious concern to overall financial strength of banks. For this purpose, the study examines the status of NPAs in India based on relevant statistics collected for the period of sixteen years, 2004-05 to 2019-20. Trend Analysis describes the changing pattern of NPAs in different commercial banks, namely, Public Sector Banks, Private sector Banks and Foreign Banks. Further, Panel Regression Analysis is carried out for determining the factors responsible for rise in the NPAs of public sector banks. The study shows that macro-economic factors such as inflation and country economic growth are the root-cause for the changing pattern of NPAs. While non-interest income and return on assets found to have significant effect on NPAs.
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| Keywords | Bank Lending, Non-Performing Assets, Inflation. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Introduction |
Indian banking sector is one of the mature system of the Indian financial system which is remarkably regulated by the Reserve Bank of India. The system has substantiated an unscathed approach towards the many biggest crises in the world for which it has always been praised in the global financial system. However, the fact cannot be ruled out that the banking system in India is battling against a severe issue over a long period of time which has dismantled the system from within. The non-performing loans also mentioned as the non-performing assets are one of the biggest internal banking issues that have impacted the credit culture of banks as well as its financial performance. The problem of continuous rise in non-performing loans in the system was being acknowledged by different policy makers soon after the reforms of financial sector in 1991 introduced on the recommendations of the Narasimham Committee (Singh, 2013).
The Narasimham Committee Report in 1991 defined NPAs as those assets (advances, bills discounted, overdrafts, cash credit etc.) for which the interest remains due for a period of four quarters (180 days). According to the guidelines issued by RBI in 2005, Non-performing assets are defined as the assets that have stopped giving income to the bank for more than 90 days. The banks nowadays are more profit-oriented and thereby, giving easy loans have become more fashionable. Without proper credit monitoring this can turn into a burden for the banks when the borrowers fail to pay back loan amount as well as the interest. This situation soon made the bankers and the policy makers realize that NPAs beyond a limit would hamper the credit culture of banks but is a serious concern to overall financial strength of banks.
Looking at the rising NPAs of banks over the years, the credit mechanism on the banks in India has been questioned by many authors in the studies. They have tried to test different credit variables that could affect the credit system of the banks. As giving loans is one of the core functions of the banks, all the efforts have been made to find out a mechanism that could give the system a strong credit mechanism and safeguard its financial strength. With this backdrop, the present study examines the status of NPAs in India based on relevant statistics collected for the period of sixteen years, 2004-05 to 2019-20. Section A studies the trend analysis of NPAs in Banking system of India to describe the changing pattern of NPAs in different commercial banks, namely, Public Sector Banks, Private sector Banks and Foreign Banks. Section B covers the Panel Regression Analysis which is carried out for determining the factors responsible for rise in the NPAs of public sector banks.
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| Objective of study | 1. To study the NPA trends in the Indian Banking sector.
2. To study the factors responsible for rising NPAs of Public sector banks in India. |
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| Review of Literature | The
literature reviewed for the study has provided numerous evidences in favour and
argument of factors related to NPAs. This provides an insight and direction to
the study to find out factors responsible for changing pattern of NPAs in the
selected period in a more comprehensive manner. The studies had highlighted on
various bank-specific as well as macro-economic variables affecting NPAs in
different banks. Dhal and Ranjan (2003) captured the effect of credit variables
in the presence of bank size induced risk preferences and macroeconomic
variables on the NPLs of banks in India and showed a significant relationship
between variables. Singh
(2013) based on the banker’s viewpoint presented a list of factors impeding the
performance of banks due to problem of NPAs. The study recommended various
measures such as credit appraisal techniques and presence of monitoring
department to curb the problem. Prasanna, Rana & Thenmozhi (2014) showed
that GNPA had more influence of macro-economic variables than the NNPA was
influenced as NNPA depends on NPA provisions. As the relationship had been
inverse, the banks could look forward for credit expansion during healthy
economic environment. Dhal and Misra (n.d.) highlighted the role of credit
variables (interest rate, collateral and maturity) in presence of macroeconomic
variables such as GDP growth in influencing the NPAs of the Indian Public
Sector Banks. Banerjee
and Murali (2015) used 15 years quarterly data of banks and demonstrated the
instrumental role of macro-economic variables such as exchange rate, interest
rate deposit, GDP and FIIs on the NPAs. According to Padhi and Patra (2016) the
major determinants of NNPAs were return on assets and capital adequacy ratio.
While the study highlighted that different bank behaves differently towards
changes in macroeconomics variable based on their practices and
regulations. Further, in the same years, Durafe and Singh (2016) examined
the pro-cyclical effect of NPA of public sector and private sector banks in
India. The study found the presence of pro-cyclical behavior of NPA of banks
where bank-specific factors played a significant role along with macroeconomic
factor. Laila (2017) studied different bank-specific and macroeconomic
variables of NPLs of banks with India with different ownership structure and
concluded that determinants vary with respect to ownership structure.
Therefore, no single set of variables exist to explain the changes in NPLs of
Banks. Kaur and Kumar (2018) performed the test on different public sector banks
during 2001-02 to 2013-14 to capture the effect of various bank-specific and
macro-economic factors on the NPAs. The study highlighted that the banks should
aim at having strong capital base along with sound profitability to curb the
rising NPAs. The economic environment of the country also played a significant
role in this. The literature covered a plethora of studies explaining the NPLs based on bank-specific factors and macroeconomic variables for banks with different ownership structure. However, it is also imperative to understand that no study is conducive to provide a single of factors in this regard. The present study investigates the NPAs status of scheduled commercial banks in India for the period 2004-05 to 2019-20. Further, the study examined the factors responsible for rising NPAs in Public Sector Banks as compared to other Banks during the period. The aim of the study is to identify the cause of rising NPAs in the public sector banks, being the crucial banking sector of the financial system of India. |
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| Methodology | The study examines the status of NPAs in the Indian Banking sector based on secondary data/sources. For this, the major source of data is the RBI reports and database and the World Bank database. Besides, the other relevant statistics are also collected using different other secondary sources such as journals (offline/online), government reports and websites, reference books, news reports, etc. |
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| Tools Used | For the purpose of assessing and comparing the NPA performance of SCBs and for examining the factors responsible for rising NPAs in Public sector banks, graphical presentation, trend analysis, compound annual growth rate (CAGR) and Panel Regression are the tools/techniques used in the study. The time period of the study is 2004-05 to 2019-20. The panel data regression model is designed to examine the various bank-specific and macro-economic factors as independent variables that impact GNPA as the dependent variables after allowing for unobserved heterogeneity in the model. Other tool and techniques used applied in the model are, namely, Augmented-dickey-fuller test or unit roots test for checking stationarity, Variance Inflation Factor (VIF test) for multi-collinearity, autocorrelation test, Heteroskedasticity test and Sargan Hansen test for selecting between fixed effects and random effects model. Using the tools and techniques the paper further presents the analysis and interpretation of the results. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Analysis | Section
A: Trend Analysis of NPAs in Scheduled Commercial Banks As
per the Graph 1b, it is witnessed that NPAs of Foreign Banks were on rise
during the period 2008 to 2011as compared to other banks. Compared to this
Private sector banks also depicted a small rise but this was not same for the
public sector banks. Further, when the foreign banks accounted for downfall in
the period 2012 to 2020, the public sector banks and the private sector banks
reported a rise with peak in 2018 and then started falling. Moreover, the most
evident pattern was witnessed in the public sector banks during this period.
NPAs of public sector banks reported to be the highest compared to Private
sector banks, Foreign Banks and scheduled commercial banks as a whole. Section
B: Determinants of Non-Performing Assets of Public Sector Bank: Panel Data
Estimation The
results above have shown the constant increase in the NPAs with highest NPA in
the year 2018, this has made it apparent that the rising NPAs are a serious
problem for the public sector banks. In order to curtail the problem, it is
crucial to understand the causal effect of different factors concerning NPAs.
In the study, further, the determinants of NPAs of public sector banks have
been examined with the help of Panel Regression Model. In the Panel Regression
Model, three model estimators have been prepared, namely, the Pooled OLS, the
Fixed effects (FE) and the Random Effect (RE). Further, the results of
Sargen-Hansen test would identify the best estimator of Panel Regression Model. Model Specification For
the model, the dependent variable used is Gross Non-Performing Assets (GNPA) of
the Public sector banks and the explanatory variables include the bank-specific
and macroeconomic variables as described in table . In
this model, the study considers: GNPA
= f (bank-specific factors, macroeconomic factors) The
regression models formulated are: GNPAit =
α + β1(NIM)it + β2(NONINT_TA)it + β3(ROA)it +
β4(CAR)it + β5(GDP_G)it + β6(INFL)it +
εit Where,
GNPA = Gross Non-Performing Assets NIM
= Net Interest Margin NONINT_TA
= Non-interest Income to Total Asset ROA
= Return on Asset CAR
= Capital Adequacy Ratio GDP_G
= GDP Growth Rate INFL
= Inflation i =
Bank, t = Year, α = intercept, β1--- β6 = Slope coefficient, ε = error
term Diagnostic Test The
required diagnostic tests are conducted in order to validate the model
specification. For this, the required assumptions of regression model are test
using different diagnostic tests. The results of the test are as follows: i. The
data is found to be stationary as the p-value is below 0.05 and thereby, the
study rejects the null hypothesis of unit root presence. ii.
For multi-collinearity testing, VIF values are below 10. Thus, there is no
presence of multi-collinearity in explanatory variables. iii. The
Breusch-Pagan/ Cook-Weisberg test are done to check for hetroskedascity. It
shows that the p-value is less than 0.05. The study failed to reject the null
hypothesis there is presence of hetroskedascity. This problem is dealt in
actual model.
chi2(7) = 181.70
Prob > chi2 = 0.0000 iv.
The Durbin Watson test for autocorrelation in Panel data showed no presence of
autocorrelation. Durbin-Watson
d-statistic(7, 176) = 1.732695. Descriptive Statistics Descriptive
Statistics presented below in Table 2 provide an insight over the
characteristics of data used in the study.
Source:
Computed As
per the table results, the mean value for variables, GNPA, NIM, NONINT_TA, ROA,
CAR, GDP_G and INFL are 6.388, 2.533, 0.999, 0.433, 12.379, 5.872 and 5.957
respectively. The standard deviation shows that there is not much variation in the
values. The minimum and maximum value denotes the lowest and highest value of
the data respectively. Panel Data Regression Analysis Results After
ensuring with the required assumptions and understanding the nature of data,
the study further presents the regression results in a panel data model form,
covering three estimates, namely, OLS estimates, fixed effect estimates and
random effect estimates in Table 3 for the model specified.
Source:
Computed (* significant at < 0.05 percent) The
Sargan-Hansen Test Statistics in Table 3 shows that the test value is 114.632
with p-value less than 0.05. Thereby, the study rejects the null hypothesis and
the results of fixed effect model are found to be appropriate for the model
specified. The
fixed effect regression estimates shows that the model has the F-statistic
value of 226.71 with the p-value less than 0.05 i.e., the model is correctly
fitted. Further, the within-R2 value is 0.8118 while the
between R2 value and overall R2 value are
0.7800 and 0.8088. This shows the model vary more over time than between the
company. The rho value of 0.095 indicates 9 per cent variation due to
difference in panels.
The
relationship studied with the respective variables shows that NONINT_TA and ROA
are the important bank-specific variables impacting GNPA and INFL is the
significant macro-economic determinant of GNPA. Further, NIM, CAR and GDP_G
failed to show any significant impact on GNPA. |
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| Findings | NPAs are the measure for the financial strength of a bank. Rising NPAs in the banks especially in the public sector banks have raised a serious concern for the Indian financial system. The time to time examination of determinants that may affect the NPAs of banks has become imperative. Many bank-specific and macro-economic variables have different impact over the NPAs. GDP and inflation are two important macro-economic variables that reflect economic situation of a country. The studies have shown that GDP has a negative relationship with NPAs (Kaur and Kumar (2018), Jayaraman, Makun & Sharma (2018)). When the economy is growing, the borrowers have sufficient funds to pay back the loan amount. Inflation as a determinant of NPAs has a positive impact on NPAs. High rate of inflation results in difficulty in the loan repayment as it impact capacity of borrower with limited wages and salary (Jayaraman, Makun & Sharma, 2018). However, in the study, the relationships are found to be contrary to the available studies. During the period 2004-05 to 2019-20, a rise in GDP led to positive increase in the NPAs while inflation showed a negative impact on the GNPA (in line with Laila (2017). However, the results were found to be insignificant with GDP but significant with inflation. Further, ROA as a proxy for bank performance showed negative and significant impact on GNPA while non-interest income to total assets as a proxy for diversification of business showed positive and significant impact on GNPA. This is in line with Basu et al (2019) and Das (2021). The results show that banks usually provide loans easily to borrowers with lenient credit policy during booming period. Excessive credit disposal led to more NPAs. Kaur and Kumar (2018) showed negative relationship between NPA and CAR. However, the study results are contrary to it. Capital adequacy ratio and net interest income is found to be positive but insignificant in the study. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Conclusion |
NPAs are the measure for the financial strength of a bank. Rising NPAs in the banks especially in the public sector banks have raised a serious concern for the Indian financial system. The time to time examination of determinants that may affect the NPAs of banks has become imperative. Many bank-specific and macro-economic variables have different impact over the NPAs. GDP and inflation are two important macro-economic variables that reflect economic situation of a country. The studies have shown that GDP has a negative relationship with NPAs (Kaur and Kumar (2018), Jayaraman, Makun & Sharma (2018)). When the economy is growing, the borrowers have sufficient funds to pay back the loan amount. Inflation as a determinant of NPAs has a positive impact on NPAs. High rate of inflation results in difficulty in the loan repayment as it impact capacity of borrower with limited wages and salary (Jayaraman, Makun & Sharma, 2018).
However, in the study, the relationships are found to be contrary to the available studies. During the period 2004-05 to 2019-20, a rise in GDP led to positive increase in the NPAs while inflation showed a negative impact on the GNPA (in line with Laila (2017). However, the results were found to be insignificant with GDP but significant with inflation. Further, ROA as a proxy for bank performance showed negative and significant impact on GNPA while non-interest income to total assets as a proxy for diversification of business showed positive and significant impact on GNPA. This is in line with Basu et al (2019) and Das (2021). The results show that banks usually provide loans easily to borrowers with lenient credit policy during booming period. Excessive credit disposal led to more NPAs. Kaur and Kumar (2018) showed negative relationship between NPA and CAR. However, the study results are contrary to it. Capital adequacy ratio and net interest income is found to be positive but insignificant in the study.
Based on the results, the study concludes that the bankers need to evaluate the credit variables in presence macro-economic variables as the credit mechanism study. Credit guidance and credit monitoring should be conducted on regular basis to ensure better credit delivery mechanism. This could curb the problem of rising NPAs in the banks in India. |
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