Table 12: Intervention impact on uptake of loan products from the MFI (amount borrowed)

||(1)<br>Sanitation|(2)<br>Business|(3)<br>Education|(4)<br>Emergency|(5)<br>Consumption|
|---|---|---|---|---|---|
|SL|2651.8<sup>_∗∗∗_</sup>|969.0|-477.4|107.4|46.84|
||(527.7)|(2254.1)|(871.1)|(143.9)|(99.77)|
|Cluster-robust p-value|[0.0000]|[0.6673]|[0.5837]|[0.4553]|[0.6388]|
|Romano-Wolf p-value|[0.0000]|[0.8941]|[0.8941]|[0.8681]|[0.8941]|
|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|199.9|37871.2|8314.7|699.9|362.9|
|N|2821|2821|2821|2821|2821|

_Notes_ : 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. Dependent variables come from administrative data.

# **6.4 Non-sanitation investments and consumption**

We have established so far that sanitation loans were taken-up, and sanitation investments made by over half of the households that took a sanitation loan, but within the context of no change in overall household borrowing. We further provided some (weak) evidence that households seem to be switching away from non-MFI borrowing, both formal and informal towards borrowing from micro-finance lenders. The model indicates that an increase in sanitation loan take-up, and in sanitation investments, coupled with substitution out of other investments and other loans, and no change in overall borrowing would be consistent with the loan label mattering. To assess whether this is the case, we now consider intervention impacts on productive, non-sanitation investments.

Results are shown in Table 13. We start by looking at impacts on business ownership and closure. We consider impacts on the likelihood of the household owning any type of business (column 1), an agricultural business<sup>51</sup> (column 3) or whether it went through a business closure during the experiment. We do not detect any significant changes induced by the introduction of the sanitation loan product. We further check whether the intervention increased the likelihood of households making a large business investment and intervention impacts on reported profits. All estimated coefficients are insignificant, so that we conclude that, on average, sanitation loans were not used to make business investments that the household would otherwise not have made. It is worth highlighting that all estimated coefficients are however negative, suggesting some substitution out of these productive investments, which would be in line with the case highlighted in the model where households are sensitive to loan labels and the sanitation loan does not sufficiently relax credit constraints.<sup>52</sup>

> 51Agricultural business covers crop and animal husbandry.

> 52Triggered by the insignificant, but negative coefficient on MF education loans in Table 12, we also analyse education investments the client households might have forgone. We note that our survey was not designed to capture this possible externality, so that we only have crude indicators to work with, such as household members attending school or not. We do not find any significant shifts along these margins. Results are available upon request.

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