While propensity score matching controls for differences among participants and nonparticipants that are correlated with observable characteristics (those used to estimate propensity scores), there may still be some unobserved characteristics that influence program participation and outcomes. The panel nature of our data allows us to also control for time-invariant unobservable characteristics by taking the difference in the outcome of interest (for example expenditures) between 2004 and 2008 for each household before comparing between participants and non-participants ("difference-in-difference" or DID). Smith and Todd (2001) show that DID combined with propensity score matching yields the least biased results when compared to randomized control trial results.<sup>15</sup> The difference-in-difference approach assumes that expenditures and other outcomes of interest would have increased equally for participants and non-participants over the period in the absence of treatment; in other words, it assumes that outcomes display "equal trends in the absence of treatment".<sup>16</sup> If trends over time would have been different in the treatment and control groups, then the estimates of the impacts of the program would be biased. We test the validity of the "equal trends" hypothesis by looking at trends over time in the three years preceding the baseline survey (2000-2003) for a few variables for which recall data on the earlier situation were collected in 2004 and find that it holds.

Finally, following Abadie & Imbens (2009) and Stuart (2010) we use control variables in our regressions of DIDs over treatments for participants and matched non-participants to capture any residual effect and account for sampling design by using sampling weights in the regressions (as in Schuler, DuGoff, and Stuart, 2012). We control for female headship, indicators of educational status, caste, participation in village meetings in 2003, and participation the most important government programs (employment, education, housing, see Table 4) in 2008.

# **_5. Findings_**

## **_5.a Borrowing, assets, and expenditures_**

Since greater savings and increased access to loans are the key benefits participants report they obtained from IKP during the period under study (see Table 1), it is reassuring to find that women who participated in groups were able to borrow up to two-and-a-half times more than non-participants, with higher amounts for poorest women who had been in groups longer (on average nearly Rs. 6,500 more over the two years prior to the survey).<sup>17</sup> Overall household borrowing was higher for participating households, indicating greater access to funds rather than just a substitution of female for male loans. Households were able to save a little more: poorest mid- and late-joiners, as well as poorest Scheduled Tribe (ST) early participants, had about Rs. 1,800 more savings than non-participants (Table A.1 reports DIDs, standard errors, absolute percentage changes for participants over non-participants, and number of observations for all poverty category and lengths of exposure).

> 15 See also the discussion in Voss (2008).

> 16 Gertler, Martinez, Premand, Rawlings, and Vermeersch (2011).

> 17 Because of the focus of IKP on microfinance, we consider savings and loans received in the two years preceding the survey despite all the known issues about the quality of survey data on financial balances (for example, we are unable to calculate outstanding loans and debt because we do not have information on all loans or on amounts still due).

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