Money & Finance

Are banks responsive to credit demand shocks in rural India?

  • Blog Post Date 05 October, 2015
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Sankar De

Shiv Nadar University

sankar.de@snu.edu.in

The output of Kharif crops is estimated to decrease by about 2% this year due to deficient monsoon rains in some Indian states. How responsive are commercial banks to a credit demand shock in rural India? Analysing data on rainfall and agricultural credit during 1993-2010, this column finds that banks increase the supply of agricultural credit to farmers following a drought, but that the additional credit is directed towards existing customers.

How effectively does the commercial banking system respond to idiosyncratic shocks to income and consumption of rural households? Does it make extra credit available at a reasonable cost? While this is an enormously important question, our survey of the literature on rural financial markets finds few studies on the responsiveness of the financial intermediation system to credit demand shocks (De and Vij 2013). In contrast, local bilateral credit and insurance arrangements with landlords, moneylenders, family and friends, or group-based mutual savings and insurance arrangements such as rotating savings and credit associations (ROSCA) have received much attention in the literature (see, for example, Coate and Ravallion 1993, La Ferrara 2003, Townsend 1995, Genicot and Ray 2002). However, the risks to income and consumption that rural households face are typically correlated, as they often arise from common external shocks such as floods and famines, and the pool of savings is usually limited. As a result, local markets fail to offer adequate risk diversification opportunities and funds at a reasonable cost. Consequently, individuals and households in the rural sector are left facing considerable residual risk, with no option but to adopt costly and inefficient strategies to smooth income or consumption. A number of such strategies have been discussed in the existing literature, including scattering plots of cultivable land (between drought-prone and other areas) (McCloskey 1976, Townsend 1993), settling for a lower average return by opting for a more diversified mix of crops and non-farm production activities, adjustment of labour supply across time periods in response to shocks (Kochar 1999), labour bonding (Srinivasan 1989, Genicot 2002), and selling investment assets to smooth consumption (Rosenzweig and Wolpin 1993). Not surprisingly, the welfare implications of such strategies are typically very negative.

While the discussions in the existing literature are insightful and have advanced our understanding of the strategies, they rest on the implicit premise that risk diversification opportunities offered by the existing system of financial intermediation, namely the commercial banking system, are either very limited or altogether missing in the rural economy. However, this premise itself has remained largely unexamined1.

In an ongoing research project, Siddharth Vij and I attempt to redress this imbalance in the existing literature (De and Vij 2013). We examine the responsiveness of the commercial banking system in the rural economy of India to exogenous shocks to credit demand following a drought.

Rainfall, credit supply and farm output in India

Why did we choose this particular setting? Agriculture remains a major sector of the economy in India and other emerging countries. While it accounts for about 19% of the GDP (Gross Domestic Product) in India, the importance of the sector in the economy is actually much higher due to its role in job creation and poverty alleviation in the countryside. About two-thirds of the Indian population depend on agriculture for their livelihood. Rainfall and supply of credit are two key determinants of agricultural output in India and other emerging economies. There is substantial evidence in the existing academic literature as well as professional reports and government policy papers in India that rainfall is an important determinant of Indian farm output. For example, using rainfall and crop yield data on 272 districts over 32 years, Cole et al. (2009) report that, on average, a one-standard deviation2 increase in rainfall results in a 3-4% increase in the value of output3.

The importance of agricultural credit, the other key determinant of farm output in India, is inherently tied to the heavy dependence of agriculture on rainfall. Undoubtedly, poor rainfall causes idiosyncratic shocks to income and consumption of households in rural India, which in turn creates shocks to credit demand. Former Deputy Governor of the Reserve Bank of India (RBI) Rakesh Mohan has noted that this results in high cost of credit and pervasive rural indebtedness (RBI, 2006). His observation suggests lack of responsiveness of the commercial banking system to credit demand shocks; however, there has been no systematic study of this issue so far.

Responsiveness of banks to credit demand shocks in rural India

To motivate our approach, we present a simple model that links the responsiveness of commercial banks to exogenous credit demand shocks to the bankers’ incentive structure. The model incorporates a standard feature of the rural credit cycle and a few typical features of bankers’ incentives that have been documented by other researchers (Banerjee, Cole and Duflo 2005, Banerjee and Duflo 2008). Farmers seek bank credit for operating expenses (seeds, fertilisers, etc.) during the crop planting season. In a year of normal rainfall, they pay off their debt from the proceeds of the harvest. In a year of poor rainfall, their ability to pay off their current debt is diminished, and some of them default. But they still need a fresh loan for the next planting season. The bankers face a penalty if they recognise a bad loan, and prefer to bail out the defaulting farmers and give them fresh loans. In many cases, bailouts substitute the probability of a larger future default for the certainty of a smaller current default. But this is not so in the case of drought-driven defaults, because a year of drought is typically followed by a year of normal rainfall4.

The model offers several testable predictions. First, the volume of outstanding agricultural credit extended by banks increases following a year of poor rainfall, driven by those farmers who are unable to pay off their current loans but still get fresh loans. Second, the credit increase occurs in the intensive margin (the average size of the existing loans) rather than in the extensive margin (the number of loan accounts). Banks typically have more information about their current borrowers than new borrowers. Following a difficult year, their information set is more refined, and they are better able to target better farmers within their current pool of borrowers with more credit.

To test the hypotheses, we use extensive panel data5 on droughts and agricultural credit at the district level. Our data on rainfall and drought come from the Indian Meteorological Department (IMD). We use the Standardized Precipitation Index (SPI) as our primary measure of drought conditions6. We obtained SPI data for 458 Indian districts for the period 1993-2003 from a study by researchers associated with the IMD (Pai et al. 2010). We also construct an alternative measure of drought, using the percentage of normal (PN) rainfall method. In this method, the rainfall in a particular year is compared with the district’s long period average (LPA) rainfall. If the rainfall is less than 75% the LPA, it indicates a drought in the district. We were able to calculate the PN measures for 334 districts for the period 1993-2010. The SPI measure, available for a larger number of districts, and the PN measure, available for a longer period, yield more or less the same number of district-year observations for our tests (about 3,300). Our data on bank credit come from the RBI’s annual publication Basic Statistical Returns of Scheduled Commercial Banks (BSR). The publication provides data on the amount of credit outstanding, occupation-wise, at the end of the fiscal year in each district, as well as the number of accounts for which credit is outstanding. We have this data from 1993-94 to 2009-10. The data offer two types of variation at the district level which are important for our purpose: considerable cross-sectional variation between credit observations (across observations in the same time period) and time-series variation (across time periods) between occurrences of droughts at the district level. We exploit these variations in the data to identify the causal impact of unanticipated changes in the demand for farm credit (due to exogenous changes in rainfall) on the supply of farm credit by commercial banks.

Banks increase agricultural credit following a drought

The test results confirm our hypotheses. We find that banks increase agricultural credit following drought-affected years compared with normal years. The increase is of the order of 4–5% by the SPI measure of drought and 3-4% by the PN measure7. Further examination suggests that the observed increase in outstanding credit consists primarily of fresh loans to existing customers (rather than new customers), and not addition of overdue interest and other charges added to old loans. Interestingly, there is no significant difference in additional credit origination following a drought between districts that are drought-prone and districts that are not. On the whole, we find positive evidence on the role of the commercial banking system in the rural economy.

Conceivably, agricultural loans to farmers in drought-affected areas are likely to have high marginal returns, or at least higher marginal returns than during normal times. A drought typically depletes their savings, causing serious capital scarcity (Rozensweig and Wolpin 1993). Our finding that banks in rural India increase agricultural credit following a drought compared with non-drought years suggests that allocation of bank credit is not always sub-optimal.

Notes:

  1. In the introduction to the Handbook of Agricultural Economics (2007), Conning and Udry observe that “while these studies have advanced our understanding of local bilateral financial contracting and mutual insurance within poor communities, the study of financial intermediation has remained relatively neglected.”
  2. Standard deviation is a measure in statistics that quantifies the amount of variation or dispersion of a set of data values.
  3. The Financial Express, a major financial newspaper in India, carried the following item on August 24, 2009:
  4. Approximately 25% of the country is affected by drought and agricultural output is set to plummet this year. Lower income for rural workers will in turn be a huge drag on private consumption, an important driver of India´s economic expansion.” The drought being referred to affected 25% of the country and was associated with 29% below-normal rainfall during the busy Kharif season (June-September) in 2009. 
  5. In our sample, using one measure of drought, a district experiences a drought in two consecutive years in 15.3% of the cases, compared with average occurrence of drought in 11.3% of the cases. By a second measure of drought, the corresponding numbers are 15.3% and 17.5%.
  6. Panel data comprises observations on multiple cases (individuals, firms, countries etc.) at two or more time periods.
  7. The SPI is a drought index developed in McKee, Doesken and Kleist (1993). The deviation from the median rainfall over a long period is standardised to arrive at the index value for a particular year. A value of less than -1 indicates drought.
  8. The results are significant at the 1% level in each case.

Further Reading

  • Banerjee, AV, SA Cole and E Duflo (2005), ‘Bank Financing in India’, In Tseng, W and D Cowen (Eds.), India’s and China’s Recent Experience with Reform and Growth, Palgrave Macmillan.
  • Cole, Shawn A (2009), “Financial Development, Bank Ownership, and Growth. Or, Does Quantity Imply Quality?", Review of Economics and Statistics 91(1): 33-51.
  • Conning, J and C Udry (2007), ‘Rural Financial Markets in Developing Countries’, In Evenson, R and P Pingali (Eds.), Handbook of Agricultural Economics, Elsevier.
  • De, S and S Vij (2013), ‘Are Banks Responsive to Exogenous Shocks to Credit Demand in Rural Economies? District-level Evidence from India’, Working Paper, Indian School of Business. 
  • Genicot, Garance (2002), “Bonded Labor and Serfdom: A Paradox of Voluntary Choice”, Journal of Development Economics 67(1): 101-27.
  • Kochar, Anjini (1999), “Smoothing Consumption by Smoothing Income: Hours-of-Work Responses to Idiosyncratic Agricultural Shocks in Rural India”, Review of Economics and Statistics 81(1): 50-61.
  • McKee, TB, NJ Doesken and J Kleist (1993), ‘The relationship of drought frequency and duration to time scales’, 8th Conference on Applied Climatology, pp. 179-84.
  • Mohan, Rakesh (2006), “Agricultural Credit in India: Status, Issues and Future Agenda”, Economic and Political Weekly, 41(11): 1013-23.
  • Rosenzweig, Mark and Kenneth I Wolpin (1993), “Credit Market Constraints, Consumption Smoothing, and the Accumulation of Durable Production Assets in Low-income Countries: Investments in Bullocks in India”, Journal of Political Economy 101(2): 223-44.
  • Srinivasan, TN (1989), ‘On Choice among Creditors and Bonded Labour Contracts’, In Pranab Bardhan (Ed.), The Economic Theory of Agrarian Institutions, Oxford University Press.
  • Townsend, RM (1999), ‘The medieval village economy: A study of the Pareto mapping in general equilibrium models’, Frontiers of Economic Research series, Princeton University Press
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