Table 13: Intervention impact on business investments

||(1)<br>Business<br>ownership|(2)<br>Business<br>closed|(3)<br>Agricultural<br>business|(4)<br>Large<br>investment|(5)<br>Profts|
|---|---|---|---|---|---|
|SL|-0.0224|-0.0011|0.000681|-0.0175|-105.0|
||(0.0456)|(0.0071)|(0.0360)|(0.0191)|(1127.4)|
|Cluster-robust p-value|[0.6237]|[0.8736]|[0.9849]|[0.3601]|[0.9258]|
|Romano-Wolf p-value|[0.9630]|[0.9940]|[0.9940]|[0.7952]|[0.9940]|
|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|0.449|0.0286|0.235|0.143|7262.4|
|N|2821|2821|2821|2821|2764|

_Notes_ : To remove the influence of outliers, we drop households in the bottom and top 1 percent of the distribution of profits. 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.

Unfortunately, our data does not allow us to get a detailed enough picture on consumption expenditures over the study period. This could be the missing piece in the puzzle, given that existing evidence suggests that a significant proportion of micro-finance loans are used for consumption purposes (referencing the AEJ Applied studies) and households might also rely on micro-finance and informal borrowing sources to fund unexpected consumption expenditures following unanticipated shocks (Udry, 1994; Besley, 1995). In line, Table 10 suggested a switch away from these informal sources. The only data we have available is information on food and non-food expenditures (reported as two aggregated numbers) the week prior to the endline survey, rather than when the loans were taken. For completeness, we show impacts estimates on these outcomes in Table 14, showing impacts on levels with and without excluding the top 1% of the distribution.<sup>53</sup> We do not find any significant impacts of the intervention on these outcomes. Impacts on non-food expenditures in the week prior to the endline survey are significantly negative at the 10% significance level. This does however not survive multiple hypothesis testing. The insignificant average impact might be driven by the time period the survey covers when asking about consumption expenditures, measurement error, or, there may be significant impacts on these outcomes for specific sub-groups that are most likely to respond to specific loan features. For example, we would expect substitution away from other investments and loans to be concentrated among households who are still credit constrained, and sensitive to loan labels. Not all households in our sample may fit this description leading to non-significant impacts on average. Understanding what expenditures or investments were potentially not made due to the uptake of sanitation loans is an important subject to further study.

> 53We also estimate impacts on log and inverse hyperbolic transformation (since non-food expenditures are zero for 105 households) as well as on per household member. Results do not change.

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