Table 8: Loan-to-toilet conversion

||(1)<br>HH re|(2)<br>port|(3)<br>Interviewer|(4)<br> observation|
|---|---|---|---|---|
||OLS|IV|OLS|IV|
|_Second stage_|||||
|Sanitation loan uptake|0.1630***|0.4753***|0.1371***|0.5005***|
||(0.0375)|(0.1558)|(0.0349)|(0.1562)|
|Strata FE|Yes|Yes|Yes|Yes|
|Interviewer FE|Yes|Yes|Yes|Yes|
|Household covariates|Yes|Yes|Yes|Yes|
|Ratio sample clients/GP size|Yes|Yes|Yes|Yes|
|r2|0.412|0.382|0.384|0.344|
|_First stage_|||||
|SL - First stage||0.1816***<br>(0.0356)||0.1816***<br>(0.0356)|
|F-stat||25.732||25.732|
|N|2821|2821|2821|2821|

_Notes_ : SL equals sanitation loan arm. Standard errors clustered at the village level are shown in parentheses. *, **, *** indicate significance at the 10, 5 and 1 percent level. 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. Strata and interviewer fixed effects included. Data source: household survey. Sanitation loan uptake comes from administrative data.

The second conclusion we can draw from this table is that on average, households do not report a switch away from any funding source for toilets (we note that the range of funding sources is in line with that reported at baseline). All estimated coefficients are positive, and except for one (’Other sources’), significantly so. This finding provides evidence that the loans did not displace funding for toilets households would have anyways built even in the absence of the intervention. Combined with evidence presented earlier, this implies that the remaining 50% of sanitation loans were for the most part likely used for non-sanitation investments and expenditures. This is consistent with the theoretical predictions from the model, which indicate that two features of the sanitation loan – additional credit supply and lower interest rate – would have made it attractive to finance investments beyond sanitation, and other consumption expenditures. The next section thus considers the intervention impacts on the household’s borrowing behaviour, and impacts on non-sanitation investments, specifically business investments.

# **6.3 Borrowing behaviour**

Next, we study impacts of the intervention on household borrowing. The theoretical model indicated that impacts on overall borrowing and the composition of household borrowing sources, combined with the impacts on sanitation loan uptake, sanitation investments and business investments, could be informative about whether the intervention impacts are driven by the loan label, or by a relaxation of credit constraints and/or the lower interest rate. In particular, the model predicts that overall borrowing should increase in all cases, _except_ when the label matters, and the credit constraint is not fully relaxed. In this case, households may substitute across loan products and investments following the introduction an impact estimate in line with increase in toilet ownership. Alternatively, we can run the regression on the sample for which we have reported data only, where we get an impact on ’Any source’ of 8.4ppts. Remaining coefficients are in line with those presented in Table 9.

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