NGOs, savings funds and self-help groups) and from informal sources (including money lenders, relatives, friends, acquaintances and work), using reports by the household survey respondent.<sup>30</sup> Table 10 reports impacts on overall MFI borrowing, using the credit bureau data, while Table 11 considers impacts on the composition of the MF client’s borrowing portfolio at our partner MFI, using MFI administrative data. We have information on the interest rates charged on all the loan products offered by the partner MFI, but not for the other MFIs reported in the credit bureau data.<sup>31</sup> Thus, the analysis on the borrowing portfolio for the partner MFI could offer insights that would allow us to differentiate between which of two features – interest rate and loan label – could explain our findings. Interestingly, in both tables we observe a very different pattern for subsidy eligible households relative to that for subsidy ineligible households.

We start by discussing our findings for subsidy ineligible households. Table 10 reveals that the average subsidy ineligible MF client household did not increase its overall borrowing from MFIs, either from the implementing MFI or from other MFIs operating in the area. However, in Table 11 we observe a significant shift in MF loan composition: the increase in sanitation borrowing is accompanied by a reduction in the amount borrowed through education loans.<sup>32</sup> It could be possible that subsidy eligible households substitute away from other (more expensive) sources of (non-MFI) credit in response to the introduction of the sanitation loan. To assess whether this is the case, we consider impacts on formal borrowing from non-MFI sources (commercial banks, cooperatives, self-help groups, saving funds and NGOs) and informal borrowing (from relatives, friends, acquaintances and work).

Table 12 presents these findings. For each of these two aggregate outcomes, we consider three different specifications. In the first specification we use as the dependent variable the total loan amount obtained from the survey data as it is, without any corrections for possible mis-reporting on behalf of the respondent. Next, we add to the regression a control for whether or not we consider it likely that there is an inconsistency between the household respondent’s report on borrowing and household’s actual borrowing behaviour. To obtain an estimate of the household’s tendency to mis-report, we compare official administrative credit bureau data on uptake of MFI loans over the study period (yes or no) to the household respondent’s own report of MFI loan take up over the same period. We create an inconsistency indicator equal to one if we observe any inconsistency between the two measures and zero otherwise. We then add this indicator as a control to the regression models in Table 12 (columns 2-3 and columns 5-6). In addition, in columns 3 and 6, for households that report to have taken a positive loan amount (from other formal or informal sources, respectively) we use household’s specific discrepancy between its respondent’s report on MFI loan amounts and credit bureau data records on the same to construct a loan amount measure corrected for possible mis-reporting.<sup>33</sup> Overall, we do not find any impacts on borrowing from

> 30For the measures that rely on self-reported survey data, we conduct a set of robustness checks that take possible misreporting into account.

> 31This information is not reported in the credit bureau data. Information on the interest rates charged by other MFIs in the study area, obtained from their webpages after the endline survey, suggests that the interest rates charged by our partner MFI were similar or lower than those charged by other MFIs.

> 32This is a potentially disconcerting finding. To analyse the potential impact of this observed reduction on educational investments, we considered intervention impacts on school attendance and school attainment for school-aged children in our study sample. No statistically significant impacts are observed. However, since the study was not designed to look at such educational outcomes, we do not have sufficient statistical power to detect impacts on those sub-groups.

> 33Specifically, we divide the survey reported loan amount by the fraction of MFI loan amount as reported during endline

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