Abstract
The paper examines the role of bank-specific variables in explaining the dynamics of non-performing assets (NPAs) of Indian banks in a panel data framework over the post liberalisation period, 1995–2011. The results have been derived after controlling for macroeconomic factors like real GDP, inflation, exchange rate etc. Applying several variants of Generalized Method of Moments (GMM) technique in dynamic models, we find that that there is significant time persistence of NPAs in Indian banking system. We also find that larger banks are more prone to default than smaller banks. We find support for the ‘bad management hypothesis’ as we observe that an increase in profit level of the banks reduces NPAs in the next period. Lagged capital adequacy ratio as an important prudential indicator also significantly reduces current NPAs of banks. The paper also draws some important policy implications about NPA management.
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Notes
An NPA has been defined as a loan advance in respect of which payment of interest or repayment of instalment of principal or both remains unpaid for a certain period of time. At present, in Indian banking system an NPA is defined as an advance where payment of interest or repayment of instalment of principal (in case of term loans) remains unpaid for a period of one-quarter or more. In fact, Narasimham Committee (1998) as a part of the second phase of reforms of the banking sector, recommended the tightening of the asset classification and provisioning norms with an objective of moving towards international standard. Accordingly, the RBI has moved over to the one-quarter norm (90 days) since 2004. Net NPAs are obtained from gross NPAs after adjusting (i) balance in interest suspense account, (ii) claims received from credit guarantors and kept in suspense account, (iii) part payment received and kept in suspense account and total provisions (RBI 1997).
This period has been considered because in this period, a proper objective and transparent yardstick for the measurement of problem loans was introduced in Indian banking replacing the earlier ‘Health Code System’ (RBI 1999a).
Although the two-step estimator is asymptotically more efficient than the one-step estimator and relaxes the assumption of homoscedasticity, the efficiency gain is not that important even in the case of heteroscedastic errors (Judson and Owen 1999). Moreover, the two-step estimator imposes a bias in standard errors due to its dependence on estimated residuals from the one-step estimator which may lead to unreliable asymptotic statistical inference particularly in data samples with small cross section dimension (Bond and Windmeijer 2002; Windmeijer 2005).
These are appropriate instruments under the following additional assumption: although there may be correlation between the levels of the right-hand side variables and the bank-specific effect in Eq. (1), there is no correlation between the differences of these variables and the bank-specific effect. Given that lagged levels are used as instruments in the regression in differences, only the most recent difference is used as an instrument in the regression in levels.
Gross NPA reflects the quality of the loans made by banks. In contrast, net NPA reflects the actual burden of the bank. In Indian banking system, there is a time lag involved in the process of recovery and detailed safeguards are placed before the write-off of NPAs. As a result, banks even after making provisions for the advances considered irrecoverable continue to hold such advances, which are termed as gross NPA (RBI 1999a).
We also check the contemporaneous effect of all the macroeconomic variables.
Securitisation and Reconstruction of Financial Assets and Enforcement of Security Interest (SARFAESI) act provides for constitution of Asset Reconstruction Company (ARC) in order to remove NPAs from the balance sheets of the banks through the process of securitisation of assets. It is thought to be a unique mechanism for the settlement of dues and can be pursued without the intervention of courts. The ARC specialises in recovery and liquidation of assets. In India, both the Committee on Banking Sector Reforms (GOI 1998) and the Committee on Restructuring Weak Public Sector Banks (RBI 1999b) recommended the transfer of sticky assets of banks to the ARC.
Ghate et al. (2013) consider the period 1950–1991 as the pre-reform period and the period 1992–2010 as the post-reform period.
This is confirmed from the observed correlation of the variables.
Macroeconomic variables which are treated as strictly exogenous (with lag) are instrumented with their levels lagged by two or more periods. Lagged dependent variable is also instrumented similarly. However, bank-specific variables which are treated as predetermined (weakly exogenous), are instrumented using their levels lagged by one or more periods. The procedure requires no second-order correlation in the differenced equation. While the presence of first-order autocorrelation in the error terms does not imply inconsistency of the estimates, the presence of second-order autocorrelation makes estimates inconsistent (Arellano and Bond 1991).
In Indian banking system, there is a time lag involved in the process of recovery and detailed safeguards put in place before the write-off. As a result, banks even after making provisions for the advances considered irrecoverable, continue to hold such advances.
Typically, a decline in economic activity tends to affect non-performing loans with a time lag.
Indian economy is thought to be less open than other developing economies even though India has opened up its market since the beginning of the last decade (especially from July 1991) by lowering various tariff and non-tariff barriers, and liberalising investment policy. So, apart from the liberalisation policies towards more exports, depreciation of Indian rupee is thought to play a major role in boosting up exports and contributing to the reduction of loan default in banking system.
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Acknowledgments
We gratefully acknowledge the useful comments and suggestions from an anonymous referee of this journal, the editors and from the participants of the 13th Annual European Economics and Finance Society (EEFS) conference held in Thessaloniki, Greece during 12–15, June 2014. The first author also acknowledges financial support received under the ISIRD grant from the Indian Institute of Technology, Ropar. The usual disclaimer applies.
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Bardhan, S., Mukherjee, V. Bank-specific determinants of nonperforming assets of Indian banks. Int Econ Econ Policy 13, 483–498 (2016). https://doi.org/10.1007/s10368-016-0344-4
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DOI: https://doi.org/10.1007/s10368-016-0344-4