What gives rise to the rural debt trap?


The Pan-Indian Debt and Investment Surveys (AIDIS), conducted by the National Statistical Office, are among the most important sources of nationally representative data on the rural credit market in India. Easy and quick access to formal sector credit allows households to invest in income-generating activities. In its absence, non-institutional sources can meet short-term consumption needs. The AIDIS report released this month reveals that non-institutional sources have a strong presence in the rural credit market, despite the high costs associated with their borrowing.

According to the report, the average debt per household in rural India is 59,748 rupees, almost half of the average debt per household in urban India. A key indicator of access to credit is the incidence of indebtedness (IOI) – the proportion of households with outstanding loans as of June 30 of the year in which the survey is conducted (2019 in this case). According to the latest AIDIS report, IOI is 35 percent in rural India – 17.8 percent of rural households are indebted to institutional credit agencies, 10.2 percent to non-institutional agencies and 7 percent from both. Dependence on institutional sources is often seen as a positive development, signifying a widening of financial inclusion, while dependence on non-institutional sources denotes vulnerability and backwardness.

The share of the debt of institutional lenders in the total outstanding debt in rural India is 66 percent compared to 87 percent in urban India. In non-institutionalized debt, professional and agricultural loan sharks remain the primary sources of credit. Continued dependence on informal credit indicates links between labor / input markets and the rural credit market. This is troubling, because the interest rate charged on 45 percent of institutional debt is between 10 and 15 percent, while on 44 percent of non-institutional debt, it is between 20 and 25 percent. .

To know how socio-economic inequalities shape household debt, we must question the purpose of the loan. Institutional credit is taken mainly for agricultural enterprises and housing in rural India. A significant portion of the debt from non-institutional sources is used for other household spending. Data indicates that better-off households have better access to formal sector credit and use it for more income-generating purposes. The richest 10% of rural households in terms of asset ownership spend almost two-thirds of their institutional debt and 40% of their non-institutional debt on agricultural / non-agricultural activities, while the poorest 10% spend half of their total debt. on household spending.

Access to institutional credit is largely determined by the ability of households to provide assets as collateral. The report shows that the richest 10 percent of households borrowed 80 percent of their total debt from institutional sources, while 50 percent borrowed about 53 percent of total debt from non-institutional sources. In addition, the debt-to-asset ratio (DAR) of the poorest 10 percent of households in rural India is 39, much higher than the estimated DAR of 2.6 for the richest 10 percent of households. This, together with larger borrowing from non-institutional sources, acts as a debt trap for households with fewer assets.

Access to credit is complicated by the interplay of social identities. The average asset ownership of listed caste and tribal households in rural areas is one-third of that of upper caste households. The low asset ownership of marginalized social groups restricts their access to institutional credit.

Lack of access to affordable credit is at the heart of rural distress. The lack of marketable collateral, the demand for credit for consumption and information constraints were the main reasons why a large part of the rural population was excluded from institutional financing. Credit policy must be reorganized to meet the consumption needs of the rural poor and to find alternative guarantees to integrate rural households into the institutional financing network.

The authors are researchers at the Center for the Study of Regional Development, JNU

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