nonentrepreneurial MFI compared with other loans). These results make sense since entrepreneurial MFI borrowers are more likely to invest in durable goods than nonentrepreneurial borrowers.

Looking at the temptation goods results, the results are mixed. Thus, comparing MFI borrowers to those without any loans, we still find large and significant expenditures on temptation goods in both groups (columns 1 and 2). However, comparing MFI borrowers to those with other loans, the temptation goods expenditures are close to zero and not significant in the entrepreneurial group (3% column 3), but about ten times as large and significant in the nonentrepreneurial group (39% column 4). The mixed results could be due to the volatile nature of temptation goods data, but, at a minimum, they show that relative to those with other loans, MFI does not increase temptation goods expenditures for entrepreneurial borrowers. Despite small sizes of entrepreneurial borrowers, these results are at least suggestive of the possibility that temptation and commitment problems are more present among the nonentrepreneurial borrowers. While these results do not represent conclusive evidence, such sample splits demonstrate that the PSM method has an edge in unpacking the heterogeneous impacts for various groups of borrowers, which is not possible with the intention to treat RCT designs that estimate the average for the treated versus nontreated areas.

# V. CONCLUSIONS

We employ the PSM method to evaluate the impact of microfinance on various expenditure categories. While we use the data from a recent RCT experiment (Banerjee et al. 2015b), our approach is able to answer a set of interesting and important questions unaddressed by the RCT design.

We contribute to existing literature on evaluation of the impact of microfinance in several important ways. First, we provide evidence on the impact of microfinance on an individual level, which is not possible using RCT designs that can only produce either ITT or LATE estimates (see footnote 3). While such parameters can be of interest in answering some policy questions (such as estimating the impact of introduction of microfinance on the total expenditures in the area), in other cases the policymakers would like to know the impact of microcredit on people who actually take it up (i.e., those that obtain a loan). Second, we describe characteristics of microfinance borrowers relative to those without loans and relative to those who also borrow from other sources. This is important for program targeting and allows for better understanding of factors influencing demand for microfinance. Third, we compare the outcomes of microfinance users to those without any loans and those who borrow from other sources. This allows us to evaluate the extent of the omitted variable bias due to entrepreneurial spirit and place upper and lower bounds on the true impacts. Thus, our paper serves as a complement to the recent emergence of RCT papers (cited in footnote 2) and shows how using PSM methodology can be useful in answering questions that RCTs are unable to address. Specifically, the PSM has an edge over the RCT in investigating the heterogeneous impacts, such as the entrepreneurial ability samples splits that we perform in this paper.

Finally, we believe that while the PSM method can be useful for investigating the impact of microfinance on poverty, the results should be treated with caution. Unobservables, such as entrepreneurial spirit and commitment problems, should be explicitly considered in the research design. Shedding more light on the extent of these biases is a fruitful avenue for future research.

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