impact of microcredit on individual level. Due to the more extensive dataset and a unique setting, our paper offers a number of significant contributions relative to Floro and Swain (2012).<sup>6</sup> The rest of the paper is organized as follows. Section I discusses PSM methodology, section II describes our data, section III presents our results, section IV contains a discussion and caveats, and section V concludes.

PSM constructs a statistical comparison group that is based on a model of the probability of participating in the program conditional on a set of observed characteristics. X. Ravallion (2003) refers to PSM as the “observational analog” to an experiment. An important assumption for validity of PSM is _conditional independence_ , which states that, given a set of observable covariates X that are not affected by treatment, potential outcomes Y are independent of treatment assignment T.<sup>7</sup> In other words, this condition implies that the uptake of the program is based entirely on observable characteristics, and hence the differences in outcomes between treated and controls can be attributed to the treatment. While this assumption is inherently untestable, it can be more credibly invoked if there are rich observable data on control variables (i.e., the X vector) that would allow one to control for as many of the relevant characteristics that can affect program participation, and the institutional setting in which the program takes place is well understood (Caliendo and Kopeinig 2008).

A number of studies have established that PSM can provide fairly accurate estimates under certain conditions.<sup>8</sup> They find that propensity score matching performs well if three conditions are met: (i) using a rich set of control variables; (ii) using the same survey instrument for treated and controls; and (iii) comparing participants and nonparticipants from the same local market.

Our data satisfies all three of these conditions. First, we have a rich set of control variables. As we describe below, the data come from detailed household surveys and provide ample individual and household characteristics, which we use to control for observable factors affecting participation in microfinance. Specifically, we use characteristics of eligible female (i.e., aged 18– study the impact of agricultural extension program in Peru, Chen et al. (2009) study the effects of the World Bankfinanced Southwest China Poverty Reduction Project.

6. First, we compare MFI borrowers to two distinct groups of controls: those without any loans and those with loans from other sources such as family and friends and money lenders. Second, we have a larger and richer dataset, which allows us to use a larger number of control variables in PSM estimation. Specifically, Floro and Swain’s (2012) control sample includes only 51 observations for nonparticipants, relative to nearly 700 participants. This implies that, on average, the same control observation has to be matched to nearly 14 treatment observations. In our sample, the treatment and control groups are much more balanced, which suggests likely smaller bias and variance. Third, Floro and Swain (2012) focus primarily on an indicator of vulnerability, which they measure as the variance of consumption and average food expenditures. We have a much wider set of outcomes, including purchases of durables, education, health expenditures, home repairs, and other expenditure categories, which allows for broader focus. Fourth, we use the data that has been used in an RCT evaluation, which allows us to compare the performance of these two methods, evaluate the presence of spillovers, and the magnitude of entrepreneurial bias. Finally, Floro and Swain (2012) study the impact of bank-connected Self Help Groups (SHG) rather than loans from a specialized microfinance institution.

7. The second identifying assumption is the presence of the common support, which can be tested and conditioned on. In essence, this means that treatment units have to be similar to control units in terms of observed characteristics. Rosenbaum and Rubin (1983) show that, under the two main assumptions: (i) conditional independence; and (ii) presence of a common support matching on _P_ ( _X_ ) is as good as matching on _X_ .

8. Heckman et al. (1997, 1998a, 1998b), Dehejia and Wahba (1999, 2002), Smith and Todd (2005), and Dehejia (2005) analyze performance of various matching schemes relative to experimental estimators.

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