Poverty and Microfinance

Here we define poverty as an income (or more broadly welfare) level below a socially acceptable minimum and microfinance as one of a range of innovative financial arrangements designed attract the poor as either borrowers or savers. In terms of understanding poverty a simple distinction can be drawn within the group ‘the poor’ between the long-term or 'chronic poor' and those who temporarily fall into poverty as a result of adverse shocks, the 'transitory poor'. Within the chronic poor one can further distinguish between those who are either so physically or socially disadvantaged that without welfare support they will always remain in poverty (the 'destitute') and the larger group who are poor because of their lack of assets and opportunities. Furthermore within the non-destitute category one may distinguish by the depth of poverty (that is how far households are below the poverty line) with those significantly below it representing the 'core poor', sometimes categorized by the irregularity of their income. In some Latin American cases for example the core poor or destitute are taken to be those below 50% of the poverty line (although Latin American poverty lines are generally higher than in Asia)

In principle, microfinance can relate to the chronic (non-destitute) poor and to the transitory poor in different ways. The condition of poverty has been interpreted conventionally as one of lack of access by poor households to the assets necessary for a higher standard of income or welfare, whether assets are thought of as human (access to education), natural (access to land), physical (access to infrastructure), social (access to networks of obligations) or financial (access to credit) (World Bank 2000:34). Lack of access to credit is readily understandable in terms of the absence of collateral that the poor can offer conventional financial institutions, in addition to the various complexities and high costs involved in dealing with large numbers of small, often illiterate, borrowers. The poor have thus to rely on loans from either moneylenders at high interest rates or friends and family, whose supply of funds will be limited. Microfinance institutions attempt to overcome these barriers through innovative measures such as group lending and regular savings schemes, as well as the establishment of close links between poor clients and staff of the institutions concerned. The range of possible relationships and the mechanisms employed are very wide.

The case for microfinance as a mechanism for poverty reduction is simple. If access to credit can be improved, it is argued, the poor can finance productive activities that will allow income growth, provided there are no other binding constraints. This is a route out of poverty for the non-destitute chronic poor. For the transitory poor, who are vulnerable to fluctuations in income that bring them close to or below the poverty line, microfinance provides the possibility of credit at times of need and in some schemes the opportunity of regular savings by a household itself that can be drawn on. The avoidance of sharp declines in family expenditures by drawing on such credit or savings allows 'consumption smoothing.' In practice this distinction between the needs of the chronic and transitory poor for credit for 'promotional' (that is income creating) and 'protectional' (consumption smoothing) purposes, respectively, is over-simplified since the chronic poor will also have short term needs that have to be met, whether it is due to income shortfalls or unexpected expenditures like medical bills or social events like weddings or funerals. It is one of the most interesting generalizations to emerge from the micro finance and poverty literature that the poorest of the chronic poor (the core poor) will borrow essentially for protectional purposes given both the low and irregular nature of their income. This group, it is suggested, will be too risk averse to borrow for promotional measures (that is for investment in the future) and will therefore be only a very limited beneficiary of microfinance schemes (Hulme and Mosley 1996: 132).

The view that it is the less badly-off poor who benefit principally from microfinance has become highly influential and, for example, was repeated in the World Development Report on poverty (World Bank 2000:75). Apart from the risk aversion argument noted above a number of other explanations for this outcome have been put forward. A related issue refers to the interest rates charged to poor borrowers. Most microfinance schemes charge close to market-clearing interest rates (although these will often not be enough to ensure full cost-recovery given the high cost per loan of small-scale lending). It may be that, even setting aside the risk-aversion argument, such high rates are unaffordable to the core poor given their lack of complementary inputs; in other words, despite having a smaller amount of capital marginal returns to the core poor may be lower than for the better-off poor. If the core poor cannot afford high interest rates they will either not take up the service or take it up and get into financial difficulties. Also where group lending is used, the very poor may be excluded by other members of the group, because they are seen as a bad credit risk, jeopardizing the position of the group as a whole. Alternatively, where professional staff operate as loan officers, they may exclude the very poor from borrowing, again on grounds of repayment risk. In combination these factors, it is felt by many, explain the weakness of microfinance in reaching the core poor.⁵ The sector has responded in a number of cases by establishing special programs for the core or 'ultra poor'. The best known of these are in Bangladesh and involve the well-established institutions of BRAC and ASA. The programs essentially aim to provide a range of services, covering training, health provision and more general social development for the disadvantaged, as well as grants of assets or credits. The ultra poor are encouraged to build up a savings fund and to graduate to conventional microfinance programs. Other variants of this approach involve greater flexibility in repayment terms for the poorest (Fernando 2004).

Given the new trends in the sector and their possible effect in diluting the original poverty focus of MFIs, the question of their impact on the poor (and particularly the core poor) is clearly of great policy interest. It might be thought that if such institutions are designed to serve only poor clients and if repayment rates are high, no further detailed analysis is needed. Such a view is misleading for a number of reasons. First, there is no guarantee that only the poor will be served unless strong eligibility criteria (like land ownership) are enforced. Often the aim is to dissuade the non-poor by the inconvenience of frequent meetings or the stigma of being a member of a credit group of the poor. Such disincentives need not work and eligibility criteria, where they exist, may not be enforced. Second, high repayment rates may be due to social pressure within a group or family and may not reflect the capacity to repay (if for example loans from moneylenders have to be taken out to repay the microcredit). Third, even if the poor are genuinely served by MFIs as long a public funds are required to finance the MFI there is the issue of how cost-effective this means of reaching the poor is compared with alternatives. This requires a comparison of the cost of transferring the funds through a micro credit institution per unit of benefit received by the target group, as compared with the benefitcost ratio for alternative schemes for reaching the core poor, such as food subsidies, workfare, and integrated regional development initiatives. Such comparisons must take account of not just the administrative costs involved, but also the leakage rate (that is the benefits to the non-poor).

Hence for these reasons there is a strong case for attempting to assess both the depth of outreach of microfinance programs, the impact of access to microfinance services on the welfare of clients and the costs of achieving this impact.

On the first point, assessing the depth of outreach or access of the poor to microfinance programs, it is important to note from the outset that most MFIs probably do not consider their institutional mission to be serving the poorest of the poor. Particularly in Latin America, most MFIs report a broader agenda to provide financial services to poor communities or specific groups such as female entrepreneurs who would not otherwise have access. Among MFIs that report to the Mix Market, slightly less than half of those in Asia identified "specifically targeting very poor clients" as their institutional mission. In Latin America, the share is even smaller: only around 10%. Of the Latin American MFIs that claim to target very poor clients, only two use some sort of targeting tool to identify clients. In Asia, most of the MFIs that specifically target the very poor use some sort of targeting tool, such as a means test, participatory wealth ranking or a housing index to identify the target group.

For those MFIs that do explicitly aim to serve the poorest within their community, recent work on poverty outreach of MFIs has focused on constructing a poverty index that can be used to establish whether the target group is being reached. The Consultative Group to Assist the Poorest (CGAP) has developed a poverty assessment tool (PAT) that can be used to compare clients and non-clients of MFIs in the same community. This is based on the construction of a weighted index of poverty based on a range of indicators covering the human resources of households, characteristics of their dwellings, measures of food security and their assets. The different indicators are weighted by principal components analysis, which allows weights to differ between cases (Zeller et al 2001). The approach here is to sort a non-client sample into three equal groups (high, intermediate and low) on the basis of their poverty score. The poverty index scores at the cut-off points between the three groups then become a reference point for the client or participant sample and their distribution between the three categories can be compared with that of the non-clients. As the non-client groups are divided equally, any deviation from equal proportions amongst the clients signals a skew either for or against greater poverty outreach.⁶

The PAT is an outreach, as compared with an impact, assessment and therefore does not directly address the question of what impact the programs have on their clients. Conducting a rigorous impact assessment is challenging. It is not simply a case of looking at a group of borrowers, observing their income change after they took out micro loans and establishing who has risen above the poverty line. Accurate assessment requires a rigorous test of the counterfactual – that is how income (or whatever measure is used) with a microcredit compares with what it would be without it, with the only difference in both cases being the availability of credit. This requires empirically a control group identical in characteristics to the recipients of credit and engaged in the same productive activities, who have not received credit, and whose income (or other measure) can be traced through time to compare with that of the credit recipients.

A practitioner-friendly impact assessment toolkit is also available: the result of the Assessing the Impact of Microenterprise Services (AIMS) Project. This assessment tool has been used in longitudinal studies of the impact of programs in Peru (Mibanco), India (SEWA) and Zimbabwe (Zambuko Trust). This procedure looks at change over time and matches pairs of observations between borrowers and members of a control group, where each pair have similar starting values for the impact variable (like income or sales revenue) and other characteristics, like age, gender or sector of activity. Simplifying, this approach identifies impact as:

Impact = 1/n ∑ (Y_t+1 - Y_t )p

Where Y_t and Y_t+1 are an impact variable (like income) in period t and t +1 respectively, p refers to matched pairs of borrowers and non-borrowers, where there are n pairs. Thus impact can be rationalized as the average difference between matched pairs of program participants and control group.7 Where impact is greater than zero (and statistically significant) microfinance will have made a difference and once again initially poor and non-poor borrowers can be distinguished in the analysis. The weakness in the applications of approach to date is that researchers have only been able to control for observable characteristics.

Failure to account for unobservable characteristics may lead to biased measures of impact. Two key sources of bias can arise in empirical work that attempts to assess the impact of microcredit on poor households – selection bias and placement bias. The former arises where there are key differences between borrowers and non-borrowers that cannot be observed, measured and allowed for, with self-selection bias (that is where those with particular characteristics choose to participate in a program) a key problem. Hence whilst differences in education, age or gender can be controlled for statistically there can also be differences in attitude to risk or 'entrepreneurship', which will be basically unobservable. A bias will arise if there is an association between a decision to take a micro loan and these unobserved characteristics. Hence if the more entrepreneurial individuals are those who take out loans, growth in their income relative to income of those who have not taken out a loan may be due in part to the effect of the loan itself, but in part to their entrepreneurial ability. Attribution of all of the change to the loan will overstate its impact. Placement bias arises where loans go to locations or activities that are in some way favored, such as villages with better infrastructure or sectors with strong demand growth. Comparing income change for households in a superior location (or sector) who have a loan, with income change for similar households in another location (or sector), who have not taken out a loan, and attributing of all this to the loan will create an upward bias.

Best-practice approaches to resolving these problems employ a form of "difference-indifference" (two-stage least squares instrumental variables) analysis that compares participants and a similar control group and between locations or sectors with and without access to the program.⁸ One approach (as used for example by Pitt and Khandker (1998) on Bangladesh) is to use exogenous eligibility criteria for participation in a microfinance program (for example lack of land ownership) as a means of avoiding a self-selection bias. Placement bias is allowed for by comparing those who are eligible with those who are ineligible, both in villages that are covered by programs and those that are not. Hence the analysis based on a double difference can be simplified as follows

Impact = (Yep - Yip) - (Yen - Yin)

Where Y is change in an outcome measure (such as income) over the study period, e and i stand for eligible and ineligible households, respectively, and p and n stand for program and non-program villages, respectively. For microfinance to produce positive results Impact must be greater than zero. If poor and non-poor borrowers can be identified, there will be a quantification of poverty impact.

The chief problem with this approach is that many microfinance schemes do not use formal eligibility criteria and those that do may not always enforce them, creating a further source of error. An alternative where no formal criteria are set out but approvals for borrowing are known is to use as a control group those approved for loans who have not yet taken them up (for example as used by Mosley and Hulme (1996) in their country studies). This address the self-selection issue unless not taking up a loan reveals an aversion to risk and is correlated with subsequent outcomes.

A variant of this approach (as applied by Coleman (1999, 2004) for Thailand) draws on the fact that most microfinance activities start in a narrowly defined area and then expand their coverage to similar villages elsewhere or within urban centers. In the rural case, if the villages are similar and if the borrowers can choose to participate, then selfselecting participants in villages that have been identified for later inclusion in a program should provide an accurate control group for current borrowers in villages with a program. Here, again simplifying, this is equivalent to estimating impact as

Impact = (Y_Pt+1 - Y_Nt+1) - (Y_Pt - Y_Nt)

Where Y is as before, P and N stand for (self-selecting) participants and non-participants respectively, t stands for time a program has been operative in a particular village, so t + 1 covers the early and t the late entrant villages.

Here we examine some of the recent 'rigorous' studies on the impact of MFIs based on survey data that employ versions of these methodologies. We do not report the results of work based on more qualititative or participatory approaches.⁹ Table 2 [PDF 86KB | 5 pages] summarizes the results of the studies surveyed here for Asia and Table 3 [PDF 76KB | 2 pages] does the same for Latin America. In general it is perhaps not surprising that studies based on a rigorous counterfactual find much smaller gains from microfinance than simple unadjusted before and after type comparisons, which erroneously attribute all gains to micro credit. Also although the results are far from consistent, studies on Asia tend to report a stronger poverty impact from microfinance than do comparable work from Latin America.

III.1. Poverty Impact Studies - Asia

One of the early and most widely cited of the poverty impact studies is Hulme and Mosley (1996). This employs a control group approach looking at the changes in income for households in villages with microfinance programs and changes for similar households (for example, in terms of initial income, gender, education, and location) in non-program areas. As far as possible the control groups are drawn from households eligible for loans and who had been approved for loans by the institutions concerned, who had not yet received a loan. 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 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. 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). There also appears to be a basic problem with the data side of the case studies, since these are not based on a comparison between baseline data and that for a later survey year. Rather there is at least partial recourse to a recall approach for the earlier years of the period covered, as respondents are asked to estimate their income retrospectively. Finally the major conclusion of the study that there is a positive correlation of gains from microfinance with income, so that poorer borrowers gain proportionately less, has also been challenged on the grounds that their comparison of income changes for different categories of borrowers biases their results in favor of the conclusion. This follows since gains for different income groups are compared with the average for a control group, not with the change for comparable income categories within the control group; in other words gains to very poor borrowers are compared with average gains in the control group not to the gains to the very poor controls (Morduch 2003).

Another major early initiative that has provided some of the firmest empirical work were the surveys conducted in the 1990’s 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). As discussed above impact is assessed using a double-difference approach between eligible and ineligible households (with holdings of land 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 is 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. 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 micro finance 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.¹⁰ He also finds evidence of positive spillovers on nonprogram participants in the villages, with the impact greater for those in extreme poverty. Over the study period of seven years 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%. A 10% increase in female credit on average increases 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 microfinance 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).¹¹ 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 proportionately less has not been totally dispelled. Furthermore, the panel data reveals a relatively high dropout rate of around 30%, indicating that there may have been problems of repayment for many households.

For Asia, there are examples of 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 micro finance schemes and those control villages that were designated as participants, but had not yet participated. As noted above 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 size of loans means 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 better-off 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 substantial difference between ordinary members and committee members of village banks. The impact of microcredit on ordinary members' wellbeing 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 providing 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.¹² 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 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. This study does not differentiate within the group of the poor.

III.2. Poverty Impact Studies – Latin America

In Latin America in general the impact of microfinance on poverty has been less well documented both in a methodological sense and in terms of coverage in individual studies, which tend to be concentrated in a small number of countries, principally Bolivia and Peru. The overall impression, however, is that compared with Asia microfinance has reached less far down the income scale and that a significant proportion of borrowers are not in fact below the poverty line, although they may well have below average incomes. This is likely to be due at least in part to a greater commercial orientation with a focus on credit for urban micro-enterprises, with lower rural outreach in Latin America as compared with other regions. A typical requirement for access to credit from an MFI has been that the borrower should be the owner of a micro-enterprise, holding a national identification card and having at least six to twelve months experience in the economic activity for which the loan is to be used (Gulli and Berger 1999:26). It is perhaps not surprising that many of the poor do not meet these criteria.

For example, detailed evidence on the outreach of MFIs in Bolivia is provided by the survey reported in Navajas et al (2000), who use an index of basic needs fulfillment to classify borrowers into poor and non-poor groups. For the urban area of La Paz they find that of three MFIs, two tend to lend disproportionately to those above of the poverty line. For two of the three, the share of 'moderately poor' borrowers (at 29%) was lower than their share of the population (at 38%), although this was not the case for the third MFI, BancoSol (at 47%). However of the very poorest group the share of borrowers in all three institutions (at 2-5%) was well below their share in the population, reinforcing the view that MFIs have difficulty in reaching the very poor. When rural lending activities are also included there is a tendency for a skew in lending towards the 'threshold' group, defined as those just above the poverty line and the 'moderately' poor. Table 4 [PDF 77KB | 1 page] gives the ratio of the share of groups of borrowers by poverty class in the portfolio of the different MFIs to their share in total population. A figure above unity thus indicates a positive skew towards a particular poverty class and a figure below unity indicates the opposite.

In terms of institutional mix FIE, PRODEM and Sartawi are NGOs, whilst BancoSol and Caja Los Andes are regulated financial institutions. Table 4 shows that being an NGO (like FIE) is no guarantee of strong allocation of loans to the poor and that both regulated institutions had a superior distribution to FIE. However in turn the rural-based NGOs, PRODEM and Sartawi outperform BancoSol by this criteria.

This type of evidence on poverty outreach does not address the issue of how far incomes of poor borrowers have been affected. In the limited number of detailed poverty impact studies on Latin America, BancoSol of Bolivia remains by far the most studied institution. Hulme and Mosley (1996, table 4.1) look at a small sample of BancoSol borrowers. Using those approved borrowers who had not yet taken out a loan as a control they find an average annual increase in income of 28% for borrowers compared with an average of 14.5% for the control group. An estimated 8% of borrowers crossed the poverty line in 1992 alone. However in comparison with the MFIs from other countries in their study BancoSol has only a relatively small proportion of borrowers in the sample below the poverty line (29%) and average borrower household income from the sample was nearly five times the national poverty line, which is far higher than for any institution studied in other countries. BancoSol also showed the largest average absolute income increase for borrowers, and the proportionate increases were greater for the poor. Although the Hulme and Mosley study has a reasonable control group criteria (those approved borrowers who had not yet taken out a loan, but who might be expected to share the self-selection characteristics of current borrowers) it suffers from several problems; there is only a small sample of 36 borrowers; it is not clear that the control group matches borrowers exactly in terms of characteristics such as education, gender or sector of activity; and the sample is surveyed at a point in time so that retrospective income estimates are required to derive rates of change.

The last of these problems is addressed for BancoSol, but not the other Bolivian MFIs covered, in Mosley (2001), which resurveys the households covered earlier to obtain income data at two points in time. Mosley (2001) finds that for BancoSol borrowers resurveyed on average income growth was a little more than twice (214%) that of the control group; for the other three institutions the excess income growth for borrowers over the control group was between 132% and 158%. For poor borrowers (who were a minority of those surveyed) gains relative to the average for the control group were lower than for all borrowers, for example 151% in the case of BancoSol. Regression analysis relating income increase per household relative to the control group average to initial income shows a positive relationship, so that proportionate gains from borrowing rise with household income, although at a declining rate. There is a positive poverty impact, although given the fact that only a minority of borrowers (around one third) were poor at the starting point of the analysis in 1993, this is modest. Between 10%-20% of poor borrowers, varying between institutions, crossed the poverty line over the period studied as a result of microfinance.¹³ However when the core poor (those in 'extreme poverty' defined in Bolivia as those living on half the poverty line) are considered, it is clear that none of the MFIs studied are reaching them. From a sample of 200 borrowers over six years for four institutions, there is only one case of the removal of extreme poverty and hence this segment of the poor was not reached.

Dunn and Arbuckle (2001a, 2001b) use an analysis of covariance to examine loans to micro-enterprises for 305 households in Lima, Peru by Mibanco. The study draws on data at two points in time 1997 and 1999 and looks at changes in the borrowers relative to a control group of households who had not received a micro-enterprise loan. On average the borrower group appears to be around or slightly above the national poverty line, with approximately 30% below the national poverty line. As noted above, the procedure uses matched observations in the borrower and control groups that have the same starting values for performance variables, like net revenue, assets or employment, as well as the same values for 'moderating' variables, like gender of entrepreneur, sector of activity and location. Change in the performance variables for the matched observations over 1997-1999 are compared to establish if there are significant differences between the borrowers and the control group. The results suggest on average a significant difference in terms of enterprise revenue (roughly $1000 annually), fixed assets and employment creation (as much as nine extra days per month). These results are very substantial. The study however recognizes that it may be difficult to attribute all of these changes to the microcredit program of Mibanco, as the matching system used does not address adequately self-selection bias and the moderating variables used seem crude (for example, sector variables reported are 'commercial, service and industrial' rather than anything more precise such as industrial subsectors).

The poverty dimension of the study as reported in Dunn and Arbuckle (2001b) shows a positive poverty reduction effect. For households starting with the same poverty level, number of income sources and economically active members in 1997, on balance after net effects are allowed for by 1999 borrowers were 6% more likely to be above the poverty line than non-borrowers. There is the contrary result, however, that in the smaller group of new borrowers who took out a loan during 1997-1999, but not initially in 1997, new borrowers were 15% less likely to have moved out of poverty than the control group.¹⁴ The poor and non-poor appear to benefit almost equally in absolute terms, although there is evidence that the poorer borrowers were 20% more likely to liquidate assets in response to a financial shock.

Banegas et al (2002) look at the operations of two MFIs in Ecuador (Banco Solidario) and Bolivia (Caja los Andes) utilizing the CGAP poverty index noted above to establish outreach and a logit regression model (where being a client and taking a loan gives a dependent variable of 1.0 and being a non-client a dependent variable of zero) that links participation in a program with income changes and poverty scores. It is found that for both institutions taking a loan is associated with increases in income. However income change is measured not by the size of monetary values but by a simple scoring system (1 for income decrease, 2 for unchanged income and 3 for income increase). The relation with poverty varies since in the case of Banco Solidario lower poverty is associated with a greater probability of taking a loan and in the case of Caja los Andes with a higher probability. On the other hand Banco Solidario has a greater depth of outreach as 75% of its clients belonged to the lower and intermediate groups as defined by the CGAP poverty score, as compared with 48% for Caja los Andes. Again it seems therefore it is the better-off amongst the poor who are benefiting. Limitations of this analysis are the crudity of some of the indicators, for example for income change, and the way in which a control group of non-clients are selected; that is from households in the same locality that have micro-enterprises in the same sector as the borrowers and which have not had a loan from a formal sector institution. This simply ignores the issue of self-selection bias and does not control for factors like education and skills.

From a nutritional perspective MkNelly and Dunford (1999) look at the impact of Credit with Education loans to women in rural Bolivia. A relatively rigorous approach is applied by collecting data two years apart from a participant group and a control group, who would be offered the credit at the end of the study period. In addition amongst the participants a sub-group of those who joined during the course of the study, rather than immediately, is examined separately. Small loans were available in combination for training in health and nutrition, as well as micro-enterprise management topics. Roughly two-thirds of participants reported an increase in income over the study period and their net incomes in 1997 appeared far higher than the control group (perhaps casting some doubt on the representativeness of the latter). However on the key concern of the study, nutritional status (for example child height-for-age or weight-for-height measures), there is little evidence of any impact due to the program. The most positive result is that for households suffering 'food stress', participants are less likely to sell off animals and are more likely to take out loans as a coping strategy, than are non-participants.

In general, for Latin America the available studies suggest that MFIs, whilst they may be flourishing in commercial terms, and providing a valuable service to micro-enterprises often run by poor entrepreneurs, have relatively weak impact on those at the very bottom of the income distribution.

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