59), the male head of household, household composition, and dwelling. Thus, we believe that our dataset contains ample covariates.<sup>9</sup> There is very little previous work done on the selection into microfinance as most of the prior studies are more concerned with microfinance impact.<sup>10</sup> Second, the same survey instrument was used for participants and control group. Third, participants and control group come from the same local markets. To satisfy this requirement, we only use slums in which microfinance was introduced and compare microfinance users to nonusers. Thus, we believe that our rich data and setting provide solid justification for using PSM method.

While PSM cannot control for unobservable characteristics affecting program participation, several studies argued that such biases are likely to be small.<sup>11</sup> Unfortunately, most of the previous studies were done on the labor markets, and the biases could be different in the context of microfinance. However, our unique setting allows us to use two comparison groups: those with no other loans and those with other types of loans. As we argue in the introduction, these groups are likely to have opposite biases due to omitted entrepreneurial spirit. Comparing the estimates for these two groups allows us to place upper and lower bounds on true estimates and shed light on the potential magnitude of entrepreneurial spirit bias. Consistent with previous studies, our results show that such bias is likely to be small.

One of the advantages of the PSM is its semiparametric nature, which imposes fewer constraints on the functional form of the treatment model (i.e., it does not have to be linear), as well as fewer assumptions about the distribution of the error term relative to the regression-based models. We compare PSM performance with results obtained from several naïve regressions: (i) naïve regression run on the full sample; (ii) naïve regression run within common support; and (iii) naïve regression with inverse propensity score weighting. Our main results are produced using PSM methodology with nearest neighbor matching with replacement. This weighting minimizes bias because control units that are the closest to treatment units in terms of propensity scores can be used multiple times (see Dehejia and Wahba 2002).<sup>12</sup> The standard errors for PSM estimates are calculated using bootstrap simulation with 1,000 repetitions, which takes into account the fact that propensity scores are estimated.

Our data come from the randomized experiment of Banerjee et al. (2015b) and are described in more detail in their paper.<sup>13</sup> Here we provide only a brief description. In 2005, 52 of 104 poor

> 9. Ideally, the control variables are observed preprogram. Unfortunately, we do not have preprogram data. Therefore, we are careful in selecting control variables that are unlikely to be affected by the program. In an earlier version, we have included a richer set of control variables with similar results.

> 10. Crepon et al. (2015) also estimate the propensity to borrow. They use this model to increase the power of their randomized design by sampling households with a high propensity to borrow and to evaluate the existence of spillovers. They do not use PSM method to match participants to nonparticipants as we do here.

> 11. Heckman et al. (1997, 1998a, 1998b) argue that the bias coming from unobservable characteristics is small relative to the bias coming from the incorrect use of observable characteristics (i.e., comparing units outside of the common support). Glazerman et al. (2003) find that bias of nonexperimental estimates was lower when the comparison group was drawn from within the evaluation itself rather than from a national dataset and locally matched to the treatment population. Diaz and Handa (2006) argue that, in cases when the outcomes are measured using comparable surveys, the bias arising from PSM is negligible.

> 12. For sensitivity analysis, we also considered Kernel matching and Stratification matching which produce qualitatively similar results to the nearest neighbor method presented here (available on request).

> 13. We thank Esther Duflo for making the data generously available on her website.

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