Table 14: Intervention impact on consumption expenditures

||(1)<br>Food|(2)<br>Food (excl. outl.)|(3)<br>Non-food|(4)<br>Non-food (excl. outl.)|
|---|---|---|---|---|
|SL only|44.95|25.50|-30.38|-67.57|
||(36.11)|(17.96)|(60.83)|(37.78)|
|Cluster-robust p-value||[0.1559]||[0.0738]|
|Romano-Wolf p-value||[0.1648]||[0.1289]|
|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|
|controlmean|884.2|818.9|953.0|830.8|
|N|2821|2759|2821|2766|

_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). To remove the influence of outliers, we drop households in top 1 percent of the distribution in columns 2 and 4 (excl. outl.)). 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.

# **6.4.1 Ruling out alternative explanations**

Finally, we consider alternative explanations for the empirical findings. While we believe that the intervention would have improved sanitation investments through the provision of (labelled) credit, it is possible that the mere availability of a sanitation loan raised the salience of sanitation investments among the MFI’s clients. If this were the case, we would expect that clients in the treated communities would be more informed about the costs and benefits of safe sanitation; and may also have stronger beliefs about the benefits of safe sanitation. We consider whether this is the case by studying the effects of the intervention on the perceived costs and benefits of safe sanitation.

Specifically, each client was shown the picture of the same twin-pit toilet and asked the degree to which she agreed or disagreed with a series of statements capturing the costs and benefits – both monetary and non-monetary – of a typical household in their village using the toilet.<sup>54</sup><sup>_,_55</sup> We use polychoric principal components analysis to combine these variables into summary measures of the perceived costs and benefits, and consider intervention impacts on these variables. These are reported in Table 15. The table shows no significant impact of the intervention on perceived benefits or perceived costs of safe sanitation, indicating that our findings are not driven by the intervention raising the salience of sanitation investments only.

> 54A small number of clients, mainly in the control GPs, were shown the picture of another toilet. We drop these households from the analysis. Attanasio et al. [2018] shows that the sample is balanced between treatment and control for households shown the picture of the twin pit toilet.

> 55We chose to ask about a typical household rather than their own household in order to limit social desirability bias in their responses.

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