Poverty Impact

One of the early and most widely cited of the poverty impact studies is Hulme and Mosley (1996). This employed a control group approach looking at the changes in income for households in villages with microfinance programs and changes for similar households in non-program areas. Programs in a number of countries are considered including the Grameen Bank in Bangladesh and the Bank Rakyat Indonesia (BRI). In general a positive impact is found on borrower incomes of the poor (1988-92) with on average an increase over the control groups ranging from 10-12% in Indonesia, to around 30% in Bangladesh and India (Hulme and Mosley 1996, table 8.1). Gains are larger for non-poor borrowers, however, and within the group the poor gains are negatively correlated with income. However, despite the breadth of the study and its use of control group techniques, it has been criticized for a possible ‘placement’ bias, whereby microfinance programs may be drawn to better placed villages, so that part of the advantage relative to the control group may be due to this more favorable location. Further, the quality and accuracy of some of the data, particularly in relation to the representative nature of the control groups, has been questioned (Morduch 1999:1600).

Another major early initiative that has provided some of the firmest empirical work were the surveys conducted in the 1990s by the Bangladesh Institute of Development Studies (BIDS) and the World Bank; these provided the data for several major analyses, such as Pitt and Khandker (1998). Khandker (1998) summarizes a number of different studies conducted in Bangladesh using the 1991/92 survey and focusing on three major microfinance programs, including the Grameen Bank and the Bangladesh Rural Advancement Committee (BRAC). Methodologically impact is assessed using a double-difference approach between eligible and ineligible households (with land holdings of more than half an acre making households ineligible) and between program and non-program villages. After controlling for other factors, such as various household characteristics, any remaining difference was attributed to the microfinance programs. The study draws a number of conclusions, but the main one is that the program had a positive effect on household consumption, which was significantly greater for female borrowers. On average, a loan of 100 taka to a female borrower, after it is repaid, allows a net consumption increases of 18 taka. In terms of poverty impact it is estimated that 5% of participant households are pulled above the poverty line annually.

Khandker (2003) follows up this earlier work by employing panel data. He uses the BIDS.World Bank survey conducted in 1998-99 that traced the same households from the 1991-92 survey.⁷ He finds apparently strong and positive results. Whilst borrowing by males appears to have no significant impact on consumption, that by females, who are the dominant client group, does have a positive impact. From this analysis a 100 taka loan to a female client leads to a 10.5 taka increase in consumption (compared with 18 taka in the earlier analysis). Allowing for the impact of higher consumption on poverty gives estimates of poverty impact. It is estimated that due to participation in microfinance programs moderate poverty among program participants decreased 8.5 percentage points over the period of seven years and extreme poverty dropped about 18 points over the same period.⁸ He also finds evidence of positive spillovers on non-program participants in the villages with the impact greater for those in extreme poverty. Poverty for non-participants is found to decline by 1 percentage point due to the programs whilst extreme poverty declines by nearly 5 percentage points. This impact is due solely to female borrowing.

The same data set has also been used to identify health impacts as opposed to income changes. Pitt et al. (2003) find that credit going to females has a large and significant impact in two out of three health measures for children. Male borrowing has no such effect. For example, a 10% increase in credit to females increases the arm circumference of daughters by 6.3%, the height of girls by 0.36 cm annually and of boys by 0.50 cms. The relations are stronger for daughters than sons. Hence in Bangladesh, micro credit and improved family health appear to be related.

These are strong and positive results and probably are the clearest evidence there is that micro finance is working in the way intended to bring sustained relief from poverty. However a couple of caveats are in order. First, the accuracy of the original results as presented in Pitt and Khandker (1998) has been disputed on the grounds that the eligibility criteria of low land holdings was not enforced strictly in practice. In a reworking of the results focusing on what are claimed to be more directly comparable households, no impact on consumption from participation in a program is found (Morduch 1999:1605).9 Second, in the BIDs-World Bank survey data, the ‘ultra poor’ (defined as those with less than 0.2 acres of land) form nearly 60% of participants and the likelihood of participation is strongly and negatively associated with level of land holding. Nonetheless, how much is borrowed depends principally on the entrepreneurship of households, so that the charge that the risk-averse very poor will benefit less has not been totally dispelled. Furthermore, the panel data reveal a relatively high dropout rate of around 30%, indicating that there were problems for many households.

There are examples of many other studies that are either inconclusive or provide less convincing results. Coleman (1999) and MkNelly et al. (1996) both focus on experiences with village banking in Thailand. Coleman (1999) utilizes data on villages that had participated in village bank microfinance schemes and those control villages that were designated as participants, but had not yet participated. This allows a double difference approach that compares the difference between income for participants and non-participants in program villages with the same difference in the control villages, where the programs were introduced later. From the results here, the poverty impact of the schemes appears highly dubious. Months of village bank membership have no impact on any asset or income variables and there is no evidence that village bank loans were directed to productive purposes. The small sizes of loans mean that they were largely used for consumption, but one of the reasons there is a weak poverty impact is that there was a tendency for wealthier households to self-select into village banks.

Coleman (2004) uses the same survey data but reconsiders the estimation strategy to control for self-selection. He argues that the village bank methodology, which relies on self-selection by loan size and monitoring by frequent meetings, may not reach the poorest. As many wealthy households tend to be on village bank committees, the failure to control for this leads to systematic biases. The regression results of Coleman (2004) indicate that there is a substantial difference between ordinary members and committee members of village banks. The impact of micro credits on ordinary members’ well being is either insignificantly different from zero or negative. On the contrary, the impact of microfinance programs on committee members’ measures of wealth, such as income, savings, productive expenses and labor time is positive, implying a form of program capture by the better-off in the village, even though this group may not be well-off by national standards. A similar result in terms of rationing micro credit in favor of better-off groups or members is found by Doung and Izumida (2002) in a study of six villages in Viet Nam. There whilst credit availability is linked with production and income, household economic position and prestige in a village plus the amount of credit applied for are the main determinants of how credit is allocated.

MkNelly et al. (1996) evaluated the Freedom from Hunger credit with education program in Thailand operated through village banks. The results show positive benefits, however, although non-participants in non-program villages are used as controls, there are problems in accepting the results. No statistical tests are reported, so one cannot judge whether differences between participants and non-participants are significant. There is also a potential measurement bias since the staff responsible for the program also did the interviewing.

Chen and Snodgrass (2001) examine the operations of the Self Employed Women’s Association (SEWA) bank in India, which provides low-income female clients in the informal sector with both saving and loan services. The study tests for the impact of these services by comparing the bank’s clients against a randomly selected control group in a similar geographic area. Two surveys were conducted two years apart. Average incomes rose over time for all groups—borrowers, savers and the control—although the increase was less for the latter. In terms of poverty incidence there was little overall change, although there was substantial ‘churning’, in that amongst the clients of SEWA there was quite a lot of movement above or below the poverty line. In interpreting these results Meyer (2002) argues that the evidence on the counterfactual, that is what would have happened to the clients in the absence of the services of SEWA, is not sufficiently strongly established to draw any firm conclusions on poverty impact.

The smoothing of consumption over time to protect the poor against adverse shocks is one of the principle objectives of micro credit. Using data again for Bangladesh, Amin et al. (2003) compute several measures of vulnerability.¹⁰ They find that the micro credit participants in the two villages covered are more likely to be below the poverty line than if they had been selected at random, so that the programs have reached the poor. However, the vulnerable are more likely to join a micro credit program in only one of the two villages. Further, for the vulnerable below the poverty line in one village, there is no evidence that there are more likely to be members of a program, and in the other village there is evidence that they have either chosen not to join or are actively excluded, presumably on the grounds that they are a poor credit risk. Hence the very poor and vulnerable do not appear to be reached.

More positive conclusions in terms of the ability of micro finance to reduce vulnerability are found for Indonesia by Gertler et al. (2003), who find that access to micro finance helps households to smooth consumption in the face of declines in health of adult family members. Having established an empirical relationship between health condition and consumption, the authors test for a relation between access to a financial institution and consumption shortfalls associated with ill health. Using geographic distance as a measure of access, they find that for households in an area with a BRI branch, health shocks have no effect on consumption.¹¹ This study does not differentiate within the group of the poor.

7. Technically the study is rigorous in employing a two stage instrumental variable approach along with a household fixed-effects method to control for possible endogeneity bias, particularly for the fixed unobserved characteristics of households (that is the more entrepreneurial amongst the poor are those who borrow and these may do better anyway).
8. Poverty is based on a daily calorie intake of 2,112 and extreme poverty on one of 1,739.
9. This debate, which in part centers around details of econometric estimation, has not been resolved. An unpublished paper by Pitt reworks the original analysis to address the concerns of Morduch and is said to confirm the original results (Khandker 2003, footnote 1).
10. Unlike the Khandker studies this data picks up households before they joined a micro credit scheme. Their vulnerability measure is broader than simply fluctuations in consumption.
11. Patten et al (2001) find evidence that the micro finance side of the Indonesian banking system performed much more robustly during the macro crises of the late 1990s than did the commercial banking sector.

The views expressed in this paper are the views of the author/s and do not necessarily reflect the views or policies of the Asian Development Bank Institute nor the Asian Development Bank. Names of countries or economies mentioned are chosen by the author/s, in the exercise of his/her/their academic freedom, and the Institute is in no way responsible for such usage.

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1. altafsamo
   (posted 27 March 2008 / 01:42:42 PM)
   
   I think micro finance if implemented in true spirit can do wonders but in countries like Pakistan the impact has not been so impressive although several studies has shown its positive impact. On the ground realities are very different and these need to be addressed by these multilateral donor agencies.
   Most of the loans are not properly utilized.
   Special emphasis is needed to be given on the capacity building of the prospective borrower. Let the borrower train to catch fish themselves by building capacity to utilize the borrowed amount for investment that will benefit them in long run. This will help decrease poverty.
2. muneeb seyal
   (posted 09 November 2007 / 08:42:14 PM)
   
   Well, I guess microfinance is doing a great work.
   Normally banks are not interested in providing funds to poor people.
   It's proved in history that poor are more honest than the rich.
3. Eric
   (posted 26 September 2007 / 10:35:57 PM)
   
   Whereas Micro finance holds the key to poverty education, it is not just the provision of the financial services that matters, information on where to invest and manage the business is equally needed.
4. Syed Shah
   (posted 22 April 2007 / 12:50:06 AM)
   
   Well I think micro finance can play a power full role in poverty alleviation, nature conservation and biodiversity conservation because it has the potential to diversify the source of income for the poor people and hence can result in reducing the pressure on exploitation of natural resources.