Methodological issues in microfinance impact assessment

An important methodological issue that arises in microfinance impact assessment studies is the question of how to demonstrate that microfinance interventions have led to change(s). According to Hulme (1997:3), an impact assessment study seeks to “...assess the difference in the values of key variables between the outcomes on ‘agents’ (e.g. individuals, enterprises, households, populations, policymakers, etc) that have experienced an intervention, against the values of those variables that would have occurred had there been no intervention”. This means that an impact assessment seeks to establish a plausible association between an intervention and changes experienced by “agents”. The characteristics of the “agents” prior to the intervention and their environments may intervene in the process and shape intervention outcomes (Sebstad et al., 1995, cited in Hulme, 1997). Then it becomes difficult to prove the impact of an intervention, or to conclude whether a given change can be entirely or necessarily attributed to an intervention.

In the literature, various research strategies have been adopted to assess the impact of microfinance interventions on clients and/or their households. One of the widely used research designs is a quasi-experimental research design, which compares programme

participants with non-programme participants on key variables that are expected to be affected by a given intervention (for example: income, decision-making) (see Hashemi et al., 1996; Pitt and Khandker, 1998, Osmani, 2007). This means that the population that has benefited from a given microfinance intervention is compared with the population that has not benefited from a similar intervention on common key variables of interests. The non- programme participants are used as a “control group” in order to simulate the situation that would prevail in the lives of borrowers if there had not been a microfinance intervention (Hulme, 1997). The “control group” is expected to have similar economic, physical and social environments, matching the treatment group or programme participants. This is because any structural difference in the contexts of the “control” group and “treatment” groups may lead to an overestimation or underestimation of the outcomes of a given intervention. In the microfinance literature, various problems are described related to selection biases, which include, a) finding a location that matches a treatment group, b) discerning the invisible attributes of a treatment group, c) a control group may be “contaminated” or may be in close contact with a control group, and d) fungibility of the loan fund - borrowers may transfer their loans to others or they may use the loan for “unintended” purposes (Hulme, 1997:13-14). Hence, a careful selection of the control

group is suggested to reduce the possibilities of any distortion that may arise from selection biases (Hulme, 1997; Mosley, 1997). The other alternative is to consider the accepted “clients- to-be” (pipeline clients)49, who live in the geographic areas of the treatment group50 as a “control group”. Accepted “clients-to-be” are assumed to provide a better comparison because they reside in the same locality with borrowers, and also because of their “motivation” to participate in the credit programme51. However, some of the issues related to selection bias discussed above may remain a problem because the two groups are more likely to be in close contact, as they share the same locality.

The other impact assessment approach in microfinance is related to qualitative methods of data collection. This qualitative method-based impact assessment focuses on the interpretation of the processes involved in participation in a given intervention and, based on that, provide information on the most plausible impact outcomes (Hulme and Mosley, 1996; Barnes and Sebstad, 2000). The approach is considered to be weak in addressing problems related to attribution and proving impact (Hulme and Mosley, 1996). Nevertheless, interpretations are often based on the perspectives of research groups (Barnes and Sebstad, 2000), who are in a better position to judge the impact an intervention has had on their lives and relationships.

This study does not claim to provide evidence about the actual impact of microfinance interventions on the intra-household division of labour and decision-making, but it seeks to provide evidence about the potential effects of microfinance interventions, by analyzing the pathways through which an impact is realized and by identifying the institutional contexts that may affect impact outcomes. The study employs different strategies to identify the potential effects of women’s access to microfinance services on the intra-household division of labour and decision-making. Membership duration in the credit programmes is

49 _{The terms “client- to-be” or pipeline client refers to an individual who is accepted to participate in the credit} programme but has not yet started participating or receiving financial services.

50 _{Other studies have looked at the impact of microfinance on women’s empowerment by comparing females} within male and female borrower households (such as Kabeer, 2001; Holvoet, 2005). Such an approach has its own drawback because credit might be used by individuals other than the borrower within the household. 51 _{This strategy also has another limitation, especially when the number of “clients-to-be” in a given area is} small for the study, compared to the treatment group, and when the research is longitudinal research (Hulme and Mosley 1996; Barnes and Sebstad, 2000).

Methodology and descriptive statistics

used to assess the potential effects of women’s borrowing on intra-household labour allocation and decision-making. The assumption is that the size and the number of loans that an individual accesses from a microfinance programme usually increases in tandem with an increase in the membership duration in the credit group/programme. This means that a longer membership duration in the credit programme entails larger sizes of loans, a higher number of loans, and more exposure compared to a short membership duration. The assumption is that accessing credit for relatively longer years, participation in credit and saving groups, and an overall attachment to MFI programmes will increase women’s bargaining power within the household. Hence, this distinction between short-time and long-time borrowers was intended to capture developments over time. Short-term borrowers were used as a “control group”, based on the assumption that microfinance programme interventions may not generate a meaningful impact on poor peoples lives in general and on gender relations in particular through one or two loans. Experts in the field of microfinance have also suggested that microfinance programme outcomes can be best captured after three or more years of a programme’s operation (Barnes and Sebsted, 2000). Hence, for the survey and in-depth interview data analysis I have used the length of membership duration in the credit group/programme to investigate the potential effects of microfinance interventions on intra-household labour allocation and decision-making. Moreover, I intend to provide evidence on the potential impact of microfinance to women by placing the testimonies of the in-depth interview participants in the larger institutional settings. Borrowers’ self-assessment about the impact, before and after the programme intervention, and their personal testimonies are used to establish a plausible association between participation in the programme and potential impact outcomes. This analysis is frequently supported by the survey data and the literature (see Kabeer, 1991, 2001). Furthermore, the inclusion of different groups such as ex-borrowers, spouses, and defaulters in the research is believed to provide an all-inclusive picture of the potential impact of microfinance programmes on gender relations.