Table 11: Intervention impact on household borrowing from MFIs (amount borrowed)

||(1)<br>Any MFI|(2)<br>Partner MFI|(3)<br>Other MFIs|
|---|---|---|---|
|SL|5180.6|3187.3|1993.3|
||(4157.4)|(3125.3)|(2422.5)|
|Cluster-robust p-value|[0.2128]|[0.3079]|[0.4107]|
|Romano-Wolf p-value|[0.3986]|[0.4885]|[0.4885]|
|Strata FE|Yes|Yes|Yes|
|Interviewer FE|Yes|Yes|Yes|
|Household covariates|Yes|Yes|Yes|
|Ratio sample clients/GP size|Yes|Yes|Yes|
|Control mean|85276.0|50289.4|34986.6|
|N|2496|2496|2496|

_Notes_ : Credit bureau information available for 2,496 households. SL equals sanitation loan treatment 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. Dependent variables come from credit bureau data.

treatment arm (see Online Appendix E). Having established that there was no switching away from MFI borrowing, we can further check whether households might have switched between loans offered by our implementing partner. If this was going on, we would expect a significant increase in sanitation loan amounts borrowed accompanied by a decrease in borrowing of other loan products. Results are presented in Table 12. We first confirm with administrative data from our implementing partner that amounts borrowed for sanitation increased significantly, in line with the impact on loan uptake. This is shown in column (1) of Table 12. Impact estimates on loan amounts taken for other loan products (columns (2) to (5)) are considerably smaller, insignificant, and - except for education loans - positive. The finding is robust to multiple hypothesis testing as indicated by the Romano-Wolf p-values.

These results confirm that the average client did not shift away from other loan products offered by our implementing partner, at least on average, following the introduction of sanitation loans.<sup>50</sup> This evidence is also in line with that presented in Section 6.1 which indicated that most households did not substitute away from the more expensive business loan to the cheaper sanitation loan, which would have been the case if the interest rate, but not the loan label, was the main driver of the observed intervention impacts.

> 50 Though we do not observe any impacts of the intervention on overall borrowing or borrowing composition, at least on average, this does not mean that overall borrowing could not have increased, and/or borrowing composition changed for some sub-groups. Indeed, the model indicates different effects for households by whether or not they are credit constrained and/or sensitive to loan labels. In line with this, Augsburg et al. [2019] find evidence of heterogeneous impacts on overall borrowing and borrowing composition by households’ eligibility for the SBM subsidy scheme.

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