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|HomePublicationsCatalogServing the Poorest of the Poor: The Poverty Impact of the Khushhali Bank’s Microfinance Lending in PakistanResearch Methodology and Data|
Research Methodology and Data
The nature of the Khushhali Bank’s operations lent itself to an impact assessment using prospective clients who have not yet accessed loans as a comparison, or control group. The bank is rapidly expanding into new villages and the number of active clients is increasing at a rate of approximately 20,000 clients every 3 months. Bank management and staff were willing to cooperate with surveyors in identifying new villages that had just received the service and within those villages identifying new clients, allowing them to be surveyed in the interim between their application and the approval to get a microloan and the actual disbursement of the money.
Using the approach of surveying prospective clients who have not yet accessed loans as a control group, impact can be estimated with a single equation:
where Yij, is a vector of outcome variables (see Appendix 9.1 [ PDF 72.9KB | 1 page ] for a detailed list of variables and summary statistics for each) Xij is a vector of household characteristics (see Appendix 9.2 [ PDF 72.6KB | 1 page ]), Vj represents village fixed effects, which control for observable and unobservable variables that may influence program placement, Mij is a membership dummy variable equal to 1 for any household that participates in the program and Tij is a measure of treatment: participation in the microfinance program.
The treatment variable is based on three alternative measures of participation in the
The hypothesis tested is whether participation in the microfinance program of the Khushhali Bank has a positive effect on various outcome measures. Support for the hypothesis requires that the estimated coefficient β3 on one of the treatment variables in (1) is statistically significantly positive. A statistically significantly positive coefficient estimate on one of the treatment variables indicates that the degree of participation in the program – either the length of time the client has participated, or how many loans he or she has taken out or the total value of those loans – has an impact.
In addition to the overall impacts of participation in the microfinance program, we examine whether there are any special impact for poorer borrowers. Defining Pij=1 if a household is in the bottom quintile of the population in terms of monthly per capita food consumption, we first control for the fact that these borrowers are likely to have lower overall measures of welfare by including the dummy in all regressions, and then look for differential impact by interacting that dummy variable with the treatment variables to see whether participation in the program has more impact for those borrowers.12
The hypothesis tested is whether participation in the microfinance program for very poor borrowers has a more positive effect on various outcome measures than it does for average borrowers. Support for the hypothesis requires that the estimated coefficient â6 in equation (1), the interaction of the treatment variables with a dummy variable indicating extremely poor borrowers, is statistically significantly positive. A finding of no special impact for these extremely poor borrowers does not mean that the program has no impact on their welfare, but rather that their impact does not differ from the impacts of the program overall.
Estimation of equation (1) above was carried out using primary data from 2,881 rural and urban households in Pakistan. A stratified random sample of 1,454 Khushhali Bank clients and future clients was drawn from 139 rural villages and 3 urban cities where Khushhali operates. A roughly equal number (1,427) of randomly selected non-clients from the same villages or settlements were also surveyed (see appendix 9.1 for details of the survey).
The Khushhali Bank’s mandate is to serve the poor, defined as persons who have a meager means of subsistence and whose total income during a year is less than the minimum taxable limit. Accordingly, Khushhali serves clients who are ‘poor’ and ‘very poor’ but not those who are ‘destitute’ (receiving zakat as discussed in chapter 8) or the ‘non-poor’, who receive enough income to pay income tax. Clients are screened by bank staff and classified into one of the above categories when they apply for the loan. The program also has an element of self-targeting in that participation in the program is voluntary and the loan product – uncollateralized micro-loans of between Rs 3,000- 30,000 – are designed to be attractive to poor clients. These are loans of approximately $50 - $500. Indeed, in the sample drawn for this study, more than 70% of the clients were below the official poverty line of the Government of Pakistan13. 20% of the sample, defined here as the ‘core poor’ or ‘poorest of the poor’, were subsisting on less than half of the caloric consumption defined by the Government of Pakistan as poor. Rough calculations of total consumption-expenditure indicate that the 70% of the sample defined as poor are living on approximately 87 cents per day and the bottom 20% of ‘core poor’ are living on less than 50 cents per day (at current exchange rates).
For most of the empirical analysis, ordinary least squares analysis (OLS) was applied in estimating equation (1). For regressions in which the outcome variable of interest was a yes/no dummy variable on qualitative information, logit estimation techniques were used.
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