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Investigating the Determinants of Non-performing Assets: The Case of the Indian Banking Sector


Affiliations
1 Associate Professor, Department of Humanities and Management, Dr B R Ambedker National Institute of Technology, Jalandhar, Punjab, India
2 Research Scholar, IKG Punjab Technical University, Kapurthala, Punjab, India
     

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The purpose of this paper is to investigate significant macroeconomic and bank-specific determinants of non-performing assets (NPAs) in the Indian banking sector, based on data for a period of more than two decades (1997 to 2017). For the objective in hand, step-up confluence analysis is applied to panel data, with both fixed effects and random effects modelling; the latter has an advantage over the former, based on Hausman’s test. Findings of the study reveal that the significant macroeconomic variables explaining the NPAs include GDP growth rate, external debt, and FDI inflows. Furthermore, bank level determinants, viz. revenue efficiency, return on assets, and return on equity, indicate that better the quality of management, lower the NPAs. The findings of the study have far-reaching implications for banking regulation and policy, as efficiency and performance measures can be the paramount indicators for future management of NPAs. Moreover, the statistically significant macroeconomic variables can manage the effect of economic turbulences on the health of the Indian banking system.

Keywords

Non-performing Assets, Indian Banking Sector, Bank-specific and Macroeconomic Variables, Panel Data Estimation, Frisch Confluence Analysis
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  • Amemiya, T. (1971). The estimation of the variances in a variance-components model. International Economic Review, 1-13.
  • Arpa, M., Giulini, I., Ittner, A., & Pauer, F. (2001). The influence of macroeconomic developments on Austrian banks: Implications for banking supervision. BIS Papers, 1, 91-116.
  • Babouček, I., & Jančar, M. (2005). Effects of macroeconomic shocks to the quality of the aggregate loan portfolio, 22. Czech National Bank.
  • Baltagi, B. (2001). Econometric analysis of panel data (3rd ed.). John Wiley and Sons Ltd.
  • Barr, R. S., & Siems, T. (1994). Predicting bank failure using DEA to quantify management quality, Financial Industry Studies Working Paper 94-1. Federal Reserve Bank of Dallas.
  • Batra, S. (2003). Developing the Asian markets for non-performing assets: Developments in India. Paper Presented at the III Forum on Asian Insolvency Reform, Seoul, Korea.
  • Beck, R., Jakubik, P., & Piloiu, A. (2015). Key determinants of non-performing loans: New evidence from a global sample. Open Economies Review, 26(3), 525-550.
  • Berger, A. N., & DeYoung, R. (1997). Problem loans and cost efficiency in commercial banks. Journal of Banking & Finance, 21(6), 849-870.
  • Bester, H. (1985). Screening vs. rationing in credit markets with imperfect information. American Economic Review, 75, 850-855.
  • Bhowmick, G., & Banerjee, B. (2008). Non-performing assets in the priority sector advance of public sector banks during 1994-95 to 2005-06: An empirical analysis. Indian Accounting Review, 17(2), 75-82.
  • Blomstrom, M., Lipsey, R. E., & Zejan, M. (1992). What explains developing country growth? (No. w4132). National Bureau of Economic Research.
  • Bofondi, M., & Ropele, T. (2011). Macroeconomic determinants of bad loans: Evidence from Italian banks. Occasional papers No. 89, March, Banca d’Italia.
  • Boot, A. W. A., Thakor, A. V., & Udell, G. F. (1991). Secured lending and default risk: Equilibrium analysis, policy implications and empirical results. Economic Journal, 101, 458-472.
  • Brewer III, E., Deshmukh, S., & Opiela, T. P. (2014). Interest-rate uncertainty, derivatives usage, and loan growth in bank holding companies. Journal of Financial Stability, 15, 230-240.
  • Buncic, D., & Melecky, M. (2013). Macro-prudential stress testing of credit risk: A practical approach for policy makers. Journal of Financial Stability, 9(3), 347-370.
  • Calvo, G. A. (1996). Capital flows and macroeconomic management: Tequila lessons. International Journal of Finance & Economics, 1(3), 207-223.
  • Chan, Y. S., & Kanatas, G. (1985). Asymmetric valuation and the role of collateral in loan agreements. Journal of Money, Credit and Banking, 17, 85-95.
  • Chen, N.-K. (2001). Bank net worth, asset prices and economic activity. Journal of Monetary Economics, 48, 415-436.
  • Croissant, Y., & Millo, G. (2008). Panel data econometrics in R: The plm package. Journal of Statistical Software, 27(2), 1-43.
  • Dees, S. (1998). Foreign direct investment in China: Determinants and effects. Economics of Planning, 31(2-3), 175-194.
  • Demirgüç-Kunt, A., & Huizinga, H. (1999). Determinants of commercial bank interest margins and profitability: Some international evidence. The World Bank Economic Review, 13(2), 379-408.
  • Demirgüç-Kunt, A., & Detragiache, E. (1998). The determinants of banking crises in developing and developed countries. IMF Staff Papers, 45(1), 81-109.
  • De Mello Jr., L. R. (1997). Foreign direct investment in developing countries and growth: A selective survey. The Journal of Development Studies, 34(1), 1-34.
  • Dimitrios, A., Helen, L., & Mike, T. (2016). Determinants of non-performing loans: Evidence from Euro-area countries. Finance Research Letters, 18, 116-119.
  • Espinoza, R. A., & Prasad, A. (2010). Non-performing loans in the GCC banking system and their macroeconomic effects. (No. 10-224). International Monetary Fund.
  • Fofack, H. L. (2005). Non-performing loans in Sub-Saharan Africa: Causal analysis and macroeconomic implications. The World Bank.
  • Gambacorta, L. (2005). Inside the bank lending channel’. European Economic Review, 49(7), 1737-1759.
  • Gerlach, S., Peng, W., & Shu, C. (2005). Macroeconomic conditions and banking performance in Hong Kong SAR: A panel data study. BIS Papers, 22(2), 481-497.
  • Ghosh, A. (2014). Asset quality of banks: Evidence from India. Indian Institute of Banking and Finance, 1069-1090.
  • Ghosh, A. (2015). Banking-industry specific and regional economic determinants of non-performing loans: Evidence from US states. Journal of Financial Stability, 20, 93-104.
  • Hausman, J. A. (1978). Specification tests in Econometrics. Econometrica: Journal of the Econometric Society, 1251-1271.
  • Heid, F., & Kruger, U. (2011). Do capital buffers mitigate volatility of bank lending? A simulation study. Discussion Paper Series 2: Banking and Financial Studies No.03/2011.
  • Jackson, P., & Perraudin, V. (1999). The nature of credit risk: The effect of maturity, type of obligor, and country of domicile. Financial Stability Review, 128-140.
  • Jimenez, G., & Saurina, J. (2005). Credit cycles, credit risk, and prudential regulation. International Journal of Central Banking, 2, 65-98.
  • Keeton, W. R., & Morris, C. S. (1987). Why do banks’ loan losses differ? Economic Review, 72(5), 3-21.
  • Klein, N. (2013). Non-performing loans in CESEE: Determinants and impact on macroeconomic performance. IMF Working Paper, European Department, P/13/72(March).
  • Konstantakis, K. N., Michaelides, P. G., & Vouldis, A. T. (2016). Non-performing loans (NPLs) in a crisis economy: Long-run equilibrium analysis with a real time VEC model for Greece (2001-2015). Physica A: Statistical Mechanics and its Applications, 451, 149-161.
  • Laeven, L., & Valencia, F. (2008). Systemic banking crises: A new database. IMF Working Papers No. 08/224, International Monetary Fund.
  • Laeven, L., & Levine, R. (2009). Bank governance, regulation and risk taking. Journal of Financial Economics, 93(2), 259-275.
  • Lawrence, E. C. (1995). Consumer default and the life cycle model. Journal of Money, Credit and Banking, 27(4), 939-954.
  • Levin, A., Lin, C.-F., & Chu, C.-S. (2002). Unit root tests in panel data: Asymptotic and Finite-sample properties. Journal of Econometrics, 108, 1-24.
  • Lokare, S. M. (2014). Re-emerging stress in the asset quality of Indian Banks: Macro financial linkages. RBI Working Paper, WPS (DEPR); 03/2014, February.
  • Louzis, D. P., Vouldis, A. T., & Metaxas, V. L. (2012). Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios. Journal of Banking & Finance, 36(4), 1012-1027.
  • Maddala, G. S., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61(S1), 631-652.
  • Makri, V., Tsagkanos, A., & Bellas, A. (2014). Determinants of non-performing loans: The case of Eurozone. Panoeconomicus, 61(2), 193-206.
  • Matejašák, M., Černohorský, J., & Teplý, P. (2009). The impact of regulation of banks in the US and the EU-15 countries. E + M: Ekonomie a Management, 3, 58-69.
  • Miaou, S. P. (1990). A stepwise time series regression procedure for water demand model identification. Water Resources Research, 26(9), 1887-1897.
  • Michael, J. N., Vasanthi, G., & Selvaraju, R. (2006). Effect of non-performing assets on operational efficiency of Central-Cooperative banks. Indian Economic Panorama, 16(3), 33-39.
  • Nair-Reichert, U., & Weinhold, D. (2001). Causality tests for cross-country panels: A new look at FDI and economic growth in developing countries. Oxford Bulletin of Economics and Statistics, 63(2), 153-171.
  • Nerlove, M. (1971). Further evidence on the estimation of dynamic economic relations from a time-series of cross sections. Econometrica, 39(2), 359-82.
  • Nkusu, M. (2011). Nonperforming loans and macro-financial vulnerabilities in advanced economies. IMF Working Paper. Strategy, Policy, and Review Department, July.
  • Pain, D. (2003). The provisioning experience of the major UK banks: A small panel investigation. Working Paper No 177, Bank of England, London.
  • Patra, B., & Padhi, P. (2016). Determinants of nonperforming assets-bank-specific and macroeconomic factors: A panel data analysis of different group of commercial banks operating in India. Theoretical & Applied Economics, 23(4).
  • Pindyck, R. S., & Rubinfeld, D. L. (1985). Econometric models and economic forecasts. Tokyo, Japan: McGraw-Hil.
  • Podpiera, J., & Weill, L. (2008). Bad luck or bad management? Emerging banking market experience. Journal of Financial Stability, 4(2), 135-148.
  • Prasanna, P. K., Thenmozhi, M., & Rana, N. (2014). Determinants of non-performing advances in Indian banking system. Banks & Bank Systems, 9(2), 65-77.
  • Rajan, R. (1994). Why bank policies fluctuate: A theory and some evidence. Quarterly Journal of Economics, 109(2), 399-441.
  • Rajan, R., & Dhal, S. C. (2003). Non-performing loans and terms of credit of public sector banks in India: An empirical assessment. Reserve Bank of India Occasional Papers, 24(3), 81-121.
  • Rajaraman, I., & Vasishtha, G. (2002). Non-performing loans of PSU banks: Some panel results. Economic and Political Weekly, 429-435.
  • Reinhart, C., & Rogoff, K. (2010). From financial crash to debt crisis. NBER Working Paper 15795.
  • Rinaldi, L., & Sanchis-Arellano, A. (2006). Household debt sustainability: What explains household non-performing loans? An empirical analysis. ECB Working Paper.
  • Salas, V., & Saurina, J. (2002). Credit risk in two institutional regimes: Spanish commercial and savings banks. Journal of Financial Services Research, 22(3), 203-224.
  • Schildbach, J., Schneider, S., & AG, D. B. (2017). Large or small? How to measure bank size. Deutsche Bank Research.
  • Stijepović, R. (2014). Recovery and reduction of non-performing loans – Podgorica approach. Journal of Central Banking Theory and Practice, 3(3), 101-118.
  • Teixeira, J. C. A., Silva, F. J. F., Fernandes, A. V., & Alves, A. C. G. (2014). Banks’ capital, regulation and the financial crisis. North American Journal of Economics and Finance, 28, 33-58.
  • Tiwari, A. (2016). 5.80 lakh crore in bad loans: Let’s pray for India’s bleeding banks: A new IMF report states our public coffers are worse than the notoriously messy banking of China. DailyO. Retrieved from http://www. dailyo.in/business/bad-loan-waiver-public-sector-banks-imf-psus-npas-indian-economy/story/1/11122.html
  • Vallascas, F., & Hagendorff, J. (2013). The risk sensitivity of capital requirements: Evidence from an international sample of large banks. Review of Finance, 17(6), 1947-1988.
  • Wallace, T. D., & Hussain, A. (1969). The use of error components models in combining cross section with time series data. Econometrica, 37(1), 55-72.
  • Williams, J. (2004). Determining management behaviour in European banking. Journal of Banking & Finance, 28(10), 2427-2460.
  • Zhang, D., Cai, J., Dickinson, D. G., & Kutan, A. M. (2016). Non-performing loans, moral hazard and regulation of the Chinese commercial banking system. Journal of Banking & Finance, 63, 48-60.

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  • Investigating the Determinants of Non-performing Assets: The Case of the Indian Banking Sector

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Authors

Sonia Chawla
Associate Professor, Department of Humanities and Management, Dr B R Ambedker National Institute of Technology, Jalandhar, Punjab, India
Seema Rani
Research Scholar, IKG Punjab Technical University, Kapurthala, Punjab, India

Abstract


The purpose of this paper is to investigate significant macroeconomic and bank-specific determinants of non-performing assets (NPAs) in the Indian banking sector, based on data for a period of more than two decades (1997 to 2017). For the objective in hand, step-up confluence analysis is applied to panel data, with both fixed effects and random effects modelling; the latter has an advantage over the former, based on Hausman’s test. Findings of the study reveal that the significant macroeconomic variables explaining the NPAs include GDP growth rate, external debt, and FDI inflows. Furthermore, bank level determinants, viz. revenue efficiency, return on assets, and return on equity, indicate that better the quality of management, lower the NPAs. The findings of the study have far-reaching implications for banking regulation and policy, as efficiency and performance measures can be the paramount indicators for future management of NPAs. Moreover, the statistically significant macroeconomic variables can manage the effect of economic turbulences on the health of the Indian banking system.

Keywords


Non-performing Assets, Indian Banking Sector, Bank-specific and Macroeconomic Variables, Panel Data Estimation, Frisch Confluence Analysis

References