eligible households the estimate of impact of micro-credit on subsidy uptake is very similar to that of impact on toilet uptake. Just as the impact that we observed for toilet uptake in column 4, the observed impact on subsidy uptake is close to being significant at conventional statistical levels (p-value of 0.105). Similarity in impacts on toilet uptake and subsidy uptake for subsidy eligible households suggests that subsidy eligible households that constructed a new toilet were able to obtain a subsidy. Therefore, lack of access to the subsidy scheme is unlikely to have been the only barrier, if one at all, of sanitation investments for subsidy eligible households in our study area. Unsurprisingly, we do not observe any impacts on subsidy uptake for the subsidy ineligible group.<sup>23</sup>

# **5 How does micro-credit support the subsidy program?**

The findings thus far indicate that, first, both subsidy eligible and subsidy ineligible households took the sanitation loans. Second, this translated into a significant increase in toilet ownership and usage and a reduction in open defecation practice, mainly for the subsidy ineligible group, but to some extent also for the subsidy eligible group. Combined, these findings suggest that the sanitation micro-loan program was able to support the SBM program’s over-arching goal of reducing open defecation. An important question for policy design is _how_ the micro-credit program supported the subsidy program, which we address in this section.

## **5.1 Determinants of subsidy eligibility status**

We first investigate whether the micro-credit supported households who are vulnerable but are erroneously classified as ineligible for the SBM subsidy since they were recorded as having a toilet at SBM baseline.<sup>24</sup><sup>_,_25</sup> Comparing toilet ownership rates at SBM baseline and at survey baseline for VG households, we see in Table 8 that over half (83 out of 115) of vulnerable MF client households identified as owning a toilet in the 2012-2013 SBM data (making them ineligible for subsidy), did _not_ own a toilet according to our survey data 1-2 years later, in 2014.<sup>26</sup><sup>_,_27</sup> To examine which type of subsidy ineligible households the sanitation micro-credit supported we estimate a version of equation 1, now allowing for heterogeneity in impacts by three different sub-groups:

> 23In total, 3 subsidies were disbursed to subsidy ineligible MF clients in each of the treatment and control groups (6 in total).

> 24It is also possible that some toilets became dysfunctional. However, our survey would have captured this since households were asked to report on ownership of toilets regardless of whether they were functional.

> 25We note that also vulnerability status might change over time and might entail a degree of mis-classification. However, our survey data does not allow us to re-construct vulnerability status, which is why we take it as given and time-invariant in our analysis.

> 26Table 8 uses reconstructed baseline toilet ownership derived from endline reports of the date of toilet construction. Results are almost identical when using actual baseline toilet ownership observations using the reduced panel sample (results available on request).

> 27Our surveys asked households to report on ownership of any toilet, regardless of whether it was functioning. Thus, it is unlikely that we are missing out on households with a non-functioning toilet. It is possible that some households under-reported actual toilet ownership at our survey, in the hope of being considered eligible for a subsidy or any other form of support. Given the significant efforts that were spent in confirming toilet ownership status during our survey (for example through interviewer observations, random re-visits, random spot-checks, monitoring, flagging of inconsistent responses between survey baseline and survey endline, etc.) we expect this not to be the key driver behind these results. It is also possible that our matching process was not perfect. Results presented here, however, are restricted to the sample of matches for which a manual check confirmed that the match was most certainly correct (dropping one out of four matches). Imperfect matching is therefore unlikely to explain the big discrepancy observed in Table 8.

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