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Bank lending and loan quality: an emerging economy perspective

Abstract

This paper analyses how non-performing loans (NPLs) in the emerging economy of India behave through the cycle. We find that a one-percentage point increase in loan growth is associated with an increase in NPLs over total advances (NPL ratio) of 4.1% in the long run with the response being higher during expansionary phases. Furthermore, NPL ratios of banks are found to be sensitive to the interest rate environment and the overall growth of the economy. Notwithstanding differences in management and governance structures, there is a procyclical risk-taking response to credit growth in the case of both public and private banks with private banks being more reactive to changes in interest rate and business cycle conditions.

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Fig. 1

Source: Supervisory returns, RBI

Fig. 2

Source: Supervisory returns, RBI

Notes

  1. The earliest of the theoretical works relating to procyclicality in bad loans is by Minsky (1982). One of the pioneering attempts to empirically understand procyclicality of bad loans was made by Keeton (1999) and then later by Salas and Saurina (2002); see the discussion in Sect. 2 of this paper.

  2. Commercial banks account for around 64% of the total assets of the Indian financial system, RBI (2015). Apart from commercial banks, the various other components of the financial system are insurance companies (with a share of 14%), non-banking financial companies (9%), mutual funds (6%), cooperatives and Regional Rural Banks (4%) and others including pension funds (3%). Banks are net lenders for the non-banking segment, while they are net borrowers for mutual funds and insurance companies, thus linking them closely with the other constituents of the financial system (ibid.).

  3. Moreover, Dooley and Hutchison (2009) focused more on equity market indices and credit default swap spreads as indicators of disturbances in the equity and credit markets, respectively, and not analysed loan quality per se.

  4. Following the global financial crisis, certain special regulatory concessions and asset classification benefits were allowed to banks to salvage genuine projects. These special regulatory concessions ended in March 2015. Restructured loans relate to the loans covered under these concessions. Also, all the figures given here relate to the domestic operations of commercial banks.

  5. See Jimenez and Saurina (2006) for an extensive review of literature with regard to procyclical lending and risk-taking behaviour of financial institutions.

  6. One of the reasons for the bias in favour of advanced economies is the limited availability of granular default data for many emerging economies.

  7. Even before Keeton (1999), Keeton and Morris (1987 cited in Caporale et al. 2013) had used a linear regression to examine the impact of macroeconomic variables on credit losses for US banks between 1979 and 1985.

  8. Apart from the studies mentioned in the foregoing sentence, see Segoviano et al. (2006) and Berge and Boye (2007).

  9. Louzis et al. (2011) used the ratio of operating expenses to capture efficiency. They observed a positive impact of this variable on NPLs, upholding the ‘bad management’ hypothesis.

  10. Louzis et al. (2011) captured diversification through the ratio of non-interest income to total income and the size of a bank. They found that the share of non-interest income had a negative relation with NPLs upholding the ‘diversification’ hypothesis but found the impact of size to be not significant. Similarly, Jimenez and Saurina (2006) also found the impact of size on NPLs to be not significant for their sample of banks.

  11. Jimenez and Saurina (2006) found that higher the geographical concentration in loan portfolio, the higher was the NPL ratio. As regards collateralised loans, they found a higher proportion of collateralised loans to industry, but not to households, being risky and having a positive impact on NPLs.

  12. While the ‘bad management’ hypothesis would render a negative impact on NPLs as better profitability is expected to bring down NPLs in future, ‘procyclical credit policy’ (Louzis et al. 2011) would show a positive impact. Louzis et al. (2011) observed that banks with higher return on equity (RoE) had lower NPLs indicating that there was evidence in favour of the ‘bad management’ hypothesis but not in favour of the ‘procyclical credit policy’ hypothesis.

  13. However, he found a positive impact of credit growth on stressed loans (gross NPLs plus restructured loans).

  14. Net NPLs are taken as one of the triggers for placing a bank under the prompt corrective action framework of the Reserve Bank of India (RBI), as they reflect the stability of the bank. However, as the focus of this study is on risk-taking by a bank, gross NPLs are considered as a more appropriate measure. As discussed already, there was regulatory forbearance following the crisis. However, here we focus on gross NPLs of banks and not restructured loans, as gross NPLs reflect the realisation of risk that is represented in banks’ books, while restructured loans may flag a possible source of incipient stress.

  15. As per the NPL norms laid down by the Reserve Bank of India (RBI), any term loan that remains overdue for more than 90 days needs to be classified as an NPL. However, there are longer time periods fixed for classifying certain types of loans as NPLs, including agricultural loans (see RBI, “Master Circular—Prudential norms on Income Recognition, Asset Classification and Provisioning pertaining to Advances”, at www.rbi.org.in). Furthermore, infrastructural exposures, which figure prominently in the loan books of Indian banks, often have a different repayment schedule on account of their long gestation period. Hence, even if the standard norm of 90 days is applicable to these loans, they may take time to appear as NPLs in banks’ books.

  16. The Indian banking sector has three major segments arranged in order of their importance (measured in terms of their asset shares) in the banking system: public banks, domestic private and foreign private banks. Traditionally, the banking system was dominated by public banks with a negligible presence of domestic private and foreign banks. However, with the liberalisation of the banking system since the early-1990s focused on enhancing competition and consolidation, domestic private sector banks have rapidly increased their foothold through three rounds of fresh licensing as well as inorganic growth through mergers and acquisitions. Furthermore, with the intent of introducing financial innovations and global best practices into the Indian banking system, the entry of foreign banks was encouraged as part of the World Trade Organisation (WTO) commitments. Consequently, there has been a steady, although less dramatic as compared to domestic private sector banks, rise in the share of foreign banks in the total assets of the banking system. Hence, to capture the changing structure of the Indian banking system, the share of foreign banks has been used as a variable in this paper.

  17. We have used the Bombay Stock Exchange (BSE) industrial index. This is because all the commonly used stock indices capture stocks of the financial (banking) sector. However, in order to understand the impact of collateral valuation, it is essential to only consider the asset prices of the non-financial sector. While it is possible to use the index of housing prices also as a control, the data on housing prices in India are available only from 2007 onwards.

  18. See the classification of banks in RBI publications viz., Reports on Trend and Progress of Banking in India and Financial Stability Reports as well as in Das and Ghosh (2006).

  19. Foreign banks are issued a single bank license and are governed by the same Income Recognition, Asset Classification and Provisioning (IRACP) and capital adequacy norms as their domestic private and public counterparts. However, see RBI (2013b) for a discussion on the operations of foreign banks being limited to only branch mode and see Kashyap and Kumar (2013) on the preference among many foreign banks for business models that are skewed towards specialised banking services, including wholesale and investment banking.

  20. See Hahm et al. (2011) for an illustration of how non-core funding can be a source of instability for a bank.

  21. For geographical expansion, it may be useful to have an indicator of the expansion in under-banked geographical regions. However, bank-level data on regional/State-level branches are not publicly available. While bank-level data on rural/semi-urban/urban branches are available, they are not strictly comparable across years given the change in the classification of population centres. See Ramakumar and Chavan (2011) on the point about change in the classification of centres.

  22. Indian banks are subject to priority sector lending (PSL) norms since 1968. The major sectors included under PSL are agriculture (and allied activities), Micro and Small Enterprises (MSEs), Housing (primarily low cost housing up to a stipulated loan limit), education and export credit. Apart from these, loans given to Self-Help Groups and State-sponsored organisations for the disadvantaged social groups are also included under PSL. These sectors are both socially and economically important given their role in social redistribution and economic growth through employment generation. See “Master Circular-Priority Sector Lending-Targets and Classification”, www.rbi.org.in. If we were to rank these sectors in the order of their importance in total bank credit, the ranking would be agriculture (and allied activities) (average share of 11.8% from 2000 to 2014), MSEs (8.9%) and housing (8%) with the other segments accounting for the rest of the PSL; the shares are worked out taking data from the Database on Indian Economy, www.rbi.org.in.

  23. As already noted, any term loan that remains overdue for more than 90 days is classified as an NPL. There are three categories within NPLs, namely ‘sub-standard’, ‘doubtful’ and ‘loss’ assets. An asset is labelled as sub-standard if it remains an NPL for a period of 12 months. After completing 12 months in the NPL category, an asset is labelled as doubtful. Finally, a doubtful asset is downgraded to a loss asset when it is deemed uncollectible by the bank although it may not be fully written off from the bank’s book. See RBI “Master Circular-Prudential norms on Income Recognition, Asset Classification and Provisioning pertaining to Advances”, at www.rbi.org.in. Apart from the key change in 2004, there were two more changes within the NPL category in 2001 and 2005, when the period of classification of a sub-standard asset before it was downgraded to the doubtful category was reduced first to 18 months and then to 12 months, respectively. However, we have not controlled for this change as it does not involve an increase in NPLs but only an increase in provisioning given the change in the status of an asset that is already classified as an NPL.

  24. This follows from the DPD models developed by Arellano and Bond (1991) and Roodman (2006).

  25. See Roodman (2006) for the criteria for deciding the suitability of using the DPD methodology.

  26. One-step GMM estimators use weight matrices that are independent of estimated parameters. The two-step estimators weigh the moment conditions by a consistent estimate of their covariance matrix (Windmeijer 2005).

  27. The variables are tested for stationarity using the panel unit root method suggested by Levin, Lin & Chu and Im, Pesaran and Shin W-stat.

  28. The pair-wise correlation coefficients range between 0 and (±) 0.5 for our variables. This indicates a weak-to-moderate degree of correlation, see Jain et al. (2011).

  29. ‘Scheduled’ commercial banks are banks that are included in the Second Schedule of the RBI Act, 1934. At present, the majority of the commercial banks in India have a scheduled status with only three Local Area Banks (LABs) classified as non-scheduled, see Statistics Relating to Commercial Banks at a Glance in RBI (2014a).

  30. As majority of foreign banks in India are single-branch banks, the foreign banks that we selected for our analysis are the ones with at least two branches during the period of analysis.

  31. The data used in this section at the system-wide and bank group level are taken from the supervisory returns from the RBI and are available from 2001 onwards on a quarterly basis. However, these data are not available at the bank level. Hence, the empirical estimation in the subsequent section is based on bank-level data collected from annual accounts of banks.

  32. The null hypothesis of the AR(2) test is that the errors in the first-differenced equation exhibit no second-order serial correlation, while the null hypothesis of the Sargan test is that instruments are valid. Failure to reject the null hypotheses of both tests should give support to our estimations.

  33. The long run elasticity of NPL with respect to loan growth is given by the following equation: \( \frac{{\Delta {\text{NPL}}}}{{\overline{\text{NPL}} }} = \frac{\theta }{{1 + \widetilde{\text{NPL}} }} \) where \( \theta = \mathop \sum \nolimits_{k = 1}^{3} \hat{\beta }_{k} /\left( {1 - \hat{\alpha }} \right) \), \( \overline{\text{NPL}} = {\text{mean}}\left( {\text{NPL}} \right) \) and \( \widetilde{\text{NPL}} = {\text{mean}}({\text{NPL}}/\left( {1 - {\text{NPL}}} \right)) \).

  34. The NPL ratio is negatively correlated with the share of foreign banks (which controls for the evolving ownership structure in India’s banking system). This, however, does not imply a different procyclicality (correlation with credit growth) of foreign banks as compared to other banks.

  35. At the end of 2014, the share of retail loans was 22% for private banks and 14% for public banks. Housing held a share of 8% for public banks, while its share was 12% for private banks. Credit card receivables accounted for less than 1% of the total loan portfolio of public banks, while it was 2% of the loan portfolio of private banks. Within private banks, the share of credit card receivables for foreign banks was even higher at 4%; data taken from Basic Statistical Returns of Scheduled Commercial Banks in India at www.rbi.org.in.

  36. The regression in column II of Table 7 based on model (8) includes additional controls: (i) geographical expansion in the operations of banks, captured through branch growth; (ii) share of secured loans; (iii) share of priority sector loans. These variables are statistically not significant and are not reported explicitly in the table to save space.

  37. Brei and Gambacorta (2012) find that while stronger capitalisation sustains loan growth in normal times, banks during a crisis can turn additional capital into greater lending only once their capitalisation exceeds a critical threshold. This implies that recapitalisations may not translate into greater credit supply until bank balance sheets are sufficiently strengthened.

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Correspondence to Leonardo Gambacorta.

Additional information

We thank Claudio Borio, Romar Correa, Saibal Ghosh, Gabriel Jiménez, Richhild Moessner, Madhusudan Mohanty, Suresh Sundaresan and, in particular, two anonymous referees for useful comments and suggestions. Pallavi Chavan worked on this project while visiting the Bank for International Settlements under the Central Bank Research Fellowship programme. The views expressed in this paper are those of the authors only and do not reflect the views of the Reserve Bank of India or of the Bank for International Settlements.

Appendix A

Appendix A

See Table 8.

Table 8 Ratio of NPLs to gross loans—an international comparison.

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Chavan, P., Gambacorta, L. Bank lending and loan quality: an emerging economy perspective. Empir Econ 57, 1–29 (2019). https://doi.org/10.1007/s00181-018-1436-5

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Keywords

  • Procyclicality
  • Loan quality
  • Bank lending
  • Emerging economy
  • Bank ownership
  • Moral hazard

JEL Classification

  • E320
  • G210
  • G010