neighborhoods in Hyderabad were randomly selected for the opening of a microfinance institution Spandana, which used the canonical group lending model and targeted women who may not necessarily be entrepreneurs. Spandana also targeted the “poor, but not the poorest of the poor” (Banerjee et al. 2015b). For our main analysis, we use data from the first wave of the household surveys, conducted about 15–18 months after Spandana openings in 52 neighborhoods where Spandana was opened to make sure our participants and nonparticipants come from the same local markets (which has been noted to improve PSM performance, as discussed in the previous section). Since the microfinance program was targeted to females in the range of 18–59 years old, the data was collected only on households that have at least one eligible woman in the household.

We have a total of 3,318 households in the main sample. We construct our outcome variables following Banerjee et al. (2015b) as monthly adult-equivalent expenditures, adjusted for inflation. Because the distributions of expenditures in rupees have significantly long right tails, we use log transformation on all expenditure variables. To ensure observations with zero expenditures are not dropped, we add one to all zero values before taking logs. Table 1, panel A reports summary statistics for the outcome variables. Specifically, we have data on total consumer expenditures, total nondurable expenditures, total durable expenditures, “temptation goods” (defined as meals outside of home, alcohol, and gambling), health and education (total education expenditures and education fees), expenditures on festivals, and home repairs (the questionnaire only asked to report home repairs above 500 Rs).<sup>14</sup> Table 1, panel B reports shares of expenditure categories as a percent of total expenditures. We first calculate shares of each category as a percent of total for each household and then report mean, median, etc., across all households. The average durable expenditures are only about 6%, while nondurable are 94%. Food is the largest category of nondurable expenditures at an average of about 39% of total. Health expenditures and temptation goods are, in contrast, fairly small categories (5–8% on average with even lower medians).

We have constructed a number of control variables to use in propensity score estimation. Our selection of controls is guided by the condition that they are unaffected by the MFI participation. Since Spandana was targeting women in the 18–59 year-old range, we select the oldest eligible woman in the household and include her characteristics such as age and education. The woman’s age, education, and whether she is a head of household are clearly not affected by the MFI borrowing. We also include male education using either the head of the household (if male) or the oldest male permanently residing in the household.<sup>15</sup> It is possible that MFI participation will affect some employment choices of female borrowers or their spouses (as also argued by Banerjee et al. 2015b). For example, self-employment can plausibly be affected by MFI. Thus, we cannot include indicators for female or male work, since these can be affected by MFI borrowings if one or both of them start their own business.

Household characteristics unlikely to be affected by MFI borrowing include the presence of dependents (defined as children under 13), the presence of young children (i.e., aged 0-2), and the number of eligible women in the households. The households with more eligible women are more likely to be Spandana borrowers. We include a dummy if there is only one eligible woman and a dummy for two eligible women while the omitted category is three or more.

> 14. In our estimation, we use variables defined as in Banerjee et al. (2015b), but we have also done robustness tests for some alternatively defined variables and find results to be unaffected.

> 15. To preserve the sample size, we replace observations with  missing values (i.e., those that answered “I don’t know” or refused to answer) with zero values and add dummies to capture the average impact of those with the missing education (for males and females). We do not report these dummies in regressions since their interpretation is unclear.

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