The PSM method creates a statistical comparison group of individuals without microfinance loans that has similar observable characteristics to the individuals with microfinance loans. While controlling on observables will reduce many of the significant differences between participants and nonparticipants, it cannot address the differences in unobservable characteristics such as the entrepreneurial spirit or “spunk” of the borrower. It is likely that such latent factors will affect the selection of people to obtain an MFI loan and the outcomes of interest such as poverty status, which will bias the results. Nevertheless, multiple studies that compared performance of PSM estimators relative to experimental results have argued that PSM can produce accurate estimates under certain conditions (see Heckman et al. 1997, 1998a, 1998b; Diaz and Handa 2006). As we discuss in the next section, our data satisfies all of these conditions. Moreover, the same authors argued that the bias due to unobservables is small relative to the bias due to observables. In addition, the PSM method has been used successfully to evaluate impact of different programs in a wide variety of settings (see Ravallion, 2008 for a survey). Most importantly, we argue that the unique set-up of our study that uses two different comparison groups (i.e., comparing MFI borrowers to those without any other loans and to those with other types of loans) allows us to evaluate the magnitude of the bias due to unobservable entrepreneurial spirit. We believe that, in this setting, PSM is an appropriate method to apply in an effort to evaluate microfinance effectiveness and has an important advantage of allowing a direct comparison of borrowers to nonborrowers.

Specifically, we apply the PSM method on the data collected by a recent randomized experiment by Banerjee et al. (2015b). Our study addresses three main questions. First, what are the main characteristics of microfinance borrowers relative to nonborrowers versus those borrowing from other sources (such as family and friends or moneylenders)? The knowledge of the borrowers’ characteristics is important for microfinance program targeting, especially in light of low microfinance take-up identified in many of the recent studies (see footnote 3). While MFI borrowers’ characteristics have been analyzed before (e.g., Crepon et al. 2015), to our knowledge this paper is the first to compare MFI borrowers to two distinct comparison groups: those who have no other loans versus those who borrow from other sources.

Second, what is the impact of microfinance on consumption and expenditures of average borrowers relative to nonborrowers, and is it the same or different relative to those borrowing from other informal sources? In other words, we can identify whether the impact of microfinance depends on the comparison group we use (i.e., those without any loans versus those with other types of loans). To our knowledge, such comparison has not been done in the previous literature.

Third, by comparing the magnitudes of estimates obtained using two comparison groups, we can shed some light on one source of biases commonly associated with microfinance borrowing arising due to unobservable entrepreneurial spirit (or talent/motivation/spunk, etc.). We argue that the bias due to unobservable entrepreneurial spirit is likely to be positive in comparing MFI borrowers to those without any other loans (since relative to this group, MFI borrowers are likely to be more entrepreneurial), but is likely to be negative while comparing new MFI borrowers to those borrowing from other sources (since those households were arguably more entrepreneurial

> increase power researchers often choose nonrandom sampling referred to as _convenience samples_ . This further clouds the interpretation of the results. For example, according to Banerjee et al. (2015), they estimate “the impact of microfinance becoming available in an area on likely clients,” which is “neither the effect on those who borrow nor the average effect on the neighborhood.” In contrast, LATE estimates the impact of credit on marginal households that take-up the credit when offered (i.e., those whose behavior is changed by the instrument, in this case increasing the availability of credit). In the case of heterogeneous impacts, the LATE estimator is not equal to the parameter of interest (i.e., ATE [see Ravallion, 2008]).

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