The impact of microfinance on poverty alleviation has become a topic of intense debate in recent academic and policy literature. Originally touted as a means for poor people to escape poverty, more recent reports suggest that the impact is likely to be small and often mixed with such negative effects as overindebtedness, leading to illegal organ sales and suicides in extreme cases.<sup>1</sup> The research evidence also appears rather mixed. For example, Banerjee et al. (2015a) summarize the results of six randomized evaluations of microfinance and report “lack of evidence of transformative effects on the average borrower” while Bruhn and Love (2014) find significantly positive effects of introduction of microfinance-like product on income and labor market outcomes.

The key challenge of evaluating the impact of a microfinance program is to make sure any observed outcomes are due to the program itself and would not have occurred without the program. Thus, it is not sufficient to compare those with a microloan and those without it, because people who obtain a microloan may be fundamentally different from those that do not. Randomized Control Trials (RCTs) have increasingly become the preferred method of evaluation for many development economists (Duflo et al. 2008). However, an important limitation of RCT in evaluating microfinance effectiveness is that researchers cannot randomly assign the recipients to receive a microfinance loan for two main reasons. First, not everyone in a random treatment group would want to obtain a loan, which will result in a selective take-up. Second, the financial institution has to ensure the borrower’s creditworthiness and thus cannot allocate loans randomly. Both of these problems make it difficult for an RCT to evaluate the impact of microfinance on the individual level.<sup>2</sup> To avoid these problems, many recent RCTs evaluate the impact of the microfinance introduction to specific geographic areas.<sup>3</sup> In these studies, the microfinance is offered in some areas (villages, slums, towns), but not in others. Regardless of how many people actually take up the microfinance, and the take-up is usually quite low, the outcomes are compared across areas (i.e., a treated area is compared with a nontreated one). However, such studies can only produce the intention to treat estimates (ITT), which is the average impact of making microfinance available in an area (i.e., averaged over those who take it and whose who do not), or the Local Average Treatment Effect (LATE) if the Instrumental Variable (IV) estimator with random assignment as an instrument for take-up was used. Thus, the RCT cannot produce the estimate of the impact of microfinance on the individuals or households that actually take out the loans (i.e., the Average Treatment Effect on the Treated, or ATE). Depending on the policy question, different parameters will be of interest to policymakers. For example, if policymakers are interested in evaluating the average impact of credit introduction on the area as a whole, the ITT is the appropriate estimate. However, if policymakers want to know the impact of credit on individual borrowers, the ATE is the appropriate estimate.<sup>4</sup> The latter estimates can be produced by the Propensity Score Matching (PSM) method, which is employed in this paper.

> 1  As reported by BBC news, October 2013. http://www.bbc.com/news/world-asia-24128096?SThisFB

> 2. Occasionally, these problems can be addressed in the research design. For example, Karlan and Zinman (2010) avoid the second problem by using a marginally rejected pool of applicants, but they do not address the first one (i.e., the selection to demand a loan as they use a pool of loan applicants who revealed their demand for a loan by applying for one. Therefore, their results cannot be generalized to an average person. For further discussion see Banerjee et al. (2015a).

> 3. See Attanasio et al. (2015), Augsburg et al. (2015), Banerjee et al. (2015b), Beaman et al. (2014), Angelucci, Karlan, and Zinman (2015), Crepon et al. (2015), Tarozzi et al. (2015).

4. In the case of MFI loans, ITT usually measures the impact of microfinance _availability_ (i.e., in a village) on average individual or household outcomes, such as consumption or expenditure. However, because of low take-up, to 2