Table 10. We do not find any significant impacts on total borrowing. The estimated coefficient is small (less than 2% of the control mean) and in fact negative.

Table 10: Intervention impact on household borrowing - total, formal and informal sources

||(1)<br>Total|(2)<br>Formal|(3)<br>MFIs|(4)<br>Other formal|(5)<br>Informal|
|---|---|---|---|---|---|
|SL|-449.7|-88.33|341.7|-430.0|-361.4|
||(1844.0)|(1876.3)|(1533.8)|(1578.7)|(399.2)|
|Cluster-robust p-value|[0.8074]|[0.9625]|[0.8237]|[0.7853]|[0.3654]|
|Romano-Wolf p-value|[0.9710]|[0.9710]|[0.9710]|[0.9710]|[0.7812]|
|Strata FE|Yes|Yes|Yes|Yes|Yes|
|Interviewer FE|Yes|Yes|Yes|Yes|Yes|
|Household covariates|Yes|Yes|Yes|Yes|Yes|
|Ratio sample clients/GP size|Yes|Yes|Yes|Yes|Yes|
|Control mean|31744.3|29379.7|14969.7|14409.9|2364.6|
|N|2793|2793|2793|2793|2793|

_Notes_ : To remove the influence of outliers in the dependent variable, we drop households in the top 1 percent of the distribution of total borrowing. SL equals sanitation loan arm. Standard errors clustered at the village level are shown in round parentheses. *, **, *** indicate significance at the 10, 5 and 1 percent level, referring to Romano-Wolf p-values. These are obtained from a step-down procedure to adjust for multiple hypothesis testing performing 1000 bootstrap replications as proposed by Romano and Wolf (2005). Covariates: Toilet ownership at baseline, indicator for presence of a child aged 0 - 2 at baseline, ratio of number of sampled clients to village size. Amounts are in Indian Rupees (1 USD = Rs. 67.5). Data source: household survey.

Taking the null result on overall borrowing, combined with the finding that sanitation loans were taken up and every second one used to construct a new toilet, suggests that other investments (or consumption expenditures) must have been forgone as a result. While insignificant, coefficient estimates in columns 3-5 are supportive of this conclusion, suggesting a potential shift away from formal non-MFI borrowing as well as informal borrowing. The only positive coefficient observed, while also insignificant, is on MFI borrowing.

# **6.3.2 MFI borrowing**

We are able to use credit bureau data to look in more detail at micro-finance borrowing, with a more accurate measure compared to the household survey data. The data covers lending from all MFI providers in our study area over the study period. Table 11 considers impact estimates on total MFI borrowing (column (1)), borrowing from our implementing partner (column (2)) and borrowing from any other MFI (column (3)).<sup>49</sup> While we again find no significant impacts, the positive coefficients are in line with the previous evidence that the sanitation loan does not seem to have induced clients to borrow less from the implementing or any other MFI in the region.

An important caveat needs to be raised. Comparing levels of borrowing from MFIs using credit bureau data to the survey self-reported amounts reveals a significant under-reporting in our survey data. This finding is in line with other studies that have raised the concern of misreporting of borrowing data in surveys (Karlan and Zinman, 2008). Importantly for us, this misreporting does not differ by

> 49As discussed in Section 3.2.2, we do not have credit bureau data on all clients. Estimating the impacts of the intervention on this sub-sample yields similar estimates to those reported above. These are available on request.

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