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Does Microcredit Reach the Poor and Vulnerable? Evidence from Northern Bangladesh

Sajeda Amin
Population Council, One Dag Hammarskjold Plaza, New York NY 10017

Ashok S. Rai
Center for International Development, Harvard University, 79 JFK St., Cambridge MA 02138.

Giorgio Topa
Department of Economics, New York University, 269 Mercer St., New York, NY 10003

Abstract/ Executive Summary:

This paper evaluates whether microcredit programs such as the popular Grameen Bank reach the relatively poor and vulnerable in two Bangladeshi villages. It uses a unique panel dataset with monthly consumption and income data for 229 households before they received loans. We find that while microcredit is successful at reaching the poor, it is less successful at reaching the vulnerable. Our results also suggest that microcredit is unsuccessful at reaching the group most prone to destitution, the vulnerable poor. Our main contribution is to explicitly evaluate the targeting of an anti-poverty intervention using the e¢cient risk-sharing framework in Townsend (1994).

Keywords: Poverty, Vulnerability, Microcredit, Targeting, Risk Sharing, Grameen Bank

Location: Rajshahi district in northwest Bangladesh.

Start Date:

End Date:

Program Area: Socio-economic and Policy

Commodity: Micro-finance, Poverty

Objectives:

To explicitly evaluate the targeting of an anti-poverty intervention using the general equilibrium framework of risk-sharing in village economies.

Methodology:

The study uses transactions data collected over 12 months in two villages (called A and B to preserve anonymity) in the district of Rajshahi in northwest Bangladesh. Village A is primarily agricultural while village B is more diversified in its income sources: only 20 percent of the households in village A but over half the households in village B do not report agriculture or daily labor as their main occupation. Village B has several small shops, a marketplace (haat) that meets twice a week and attracts 200 vendors, and local government o¢ces. All major marketing activities for village A are held in marketplaces outside the village. Both villages grow three rice crops a year. In addition, village A grows betel leaf, a cash crop, and village B has several jointly owned mango orchards (Amin, 1998). Under commonly made assumptions (separability of consumption and leisure, common rates of time preference, additively separable preferences over time) e¢cient risk-sharing within a village implies that household consumption should move only with aggregate consumption and not with household income (Deaton, 1997, pages 372 - 383; and Townsend, 1994). We measure vulnerability based on this risk-sharing test. Instead of looking at specific smoothing mechanisms (such as loans, gifts, savings or asset sales), vulnerability is derived from household outcomes (consumption and income). In this section, we describe how we estimate a baseline vulnerability measure for each household and propose alternative vulnerability measures to study its robustness. We also report on the correlation of vulnerability with poverty and with measures of the variability of consumption. As one would perhaps expect, poorer households tend to be more vulnerable than richer households in our data. Vulnerability is significantly negatively correlated with consumption using the pooled data and in village B. This finding is stable across all the alternative vulnerability measures, except for CRRA measure where the correlation is negative but not significant. The same pattern emerges from a comparison of the average vulnerability of households below the poverty line with those above the poverty line. Households below the poverty line have significantly higher average vulnerability than those above the poverty line for the baseline and half the alternative vulnerability measures using the pooled data, and for the baseline and all but one of the alternative vulnerability measures in village B. Our measure of vulnerability is quite distinct from measures of consumption variability, such as the coe¢cient of variation (CV) of consumption or the variance of log consumption.15 The baseline and most alternative measures of vulnerability are not significantly correlated with either of the two measures of consumption variability. Though poor households tend to be more vulnerable than the rich, rich households have more variable consumption than the poor. Household consumption is positively correlated with the CV of consumption using the pooled data and in village A for food consumption. Households below the poverty line have a significantly lower CV of consumption than those above the poverty line in each village. Households below the poverty line also have a significantly lower variance of log consumption than households above the poverty line using the pooled data. In addition, while consumption variability may just capture di¤erences in risk aversion across households, our vulnerability measure is unbiased even when some households are more risk-averse than others, provided the degree of risk aversion is uncorrelated with shocks to household income (Section 3.2). Consequently, for the rest of the paper we will be concerned with vulnerability and not with consumption variability

Source of Information: CID Working Paper No. 28, December 2001

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Achievement/Findings:

This paper uses panel data from two Bangladeshi villages to test if microcredit reaches the poor and vulnerable. This analysis is possible due to the convenient timing of data collection. Households were extensively surveyed in 1991-1992 when microcredit programs had only a small presence in the study villages. Households were subsequently resurveyed in 1995 by which time microcredit programs had firmly established themselves. This paper studies the interaction between microcredit selection and the vulnerability of rural households. Vulnerability refers to the inability of households to insure against idiosyncratic risks, and it is distinct from measures of consumption variability. It provides an example of how an anti-poverty program which is successful at reaching the poor may exclude the most in need of assistance, the vulnerable poor. The forces that make some poor households vulnerable may also make them greater risks for microcredit providers, however. This may explain why microcredit programs are unsuccessful at reaching the vulnerable poor in these villages and suggests that subsidized credit may have limits as an anti-poverty strategy. Our vulnerability estimates for instance are based on monthly data collected over 12 months. It would be interesting to see if estimates of household vulnerability are stable from year to year, or if some households are more vulnerable to annual income shocks than to monthly shocks. It would also be useful to know if microcredit’s success at reaching the vulnerable poor depends on village characteristics, such as average village poverty or vulnerability. How does the process of forming borrower groups a¤ect the likelihood that the vulnerable poor join microcredit programs? Do relatively poor and vulnerable households had higher default rates after they join microcredit programs? We leave these questions for future research with more detailed data sets.

Knowledge Management: Report/Proceedings