In Appendix A we examine the determinants of matching success and conclude that the matched sample is not representative of our study sample. Matched households are more likely to be larger and their heads are more likely to be male, older, educated and to work in agriculture. The fact that the likelihood of being matched is not random will affect our ability to generalise findings to the general population and will therefore need to be kept in mind when interpreting our findings. Notwithstanding the differences between the matched and unmatched sample, the matched sample is balanced across treatment and control arms, both in terms of their socio-economic characteristics and in terms of their SBM subsidy eligibility criteria. This can be seen in Tables 2 and 3.

Table 2 focuses on socio-economic characteristics of client households and informs us that the matched MF client sample is largely hindu (73%), with the remaining client households being either muslim or buddhist. The average household is reported to have 5 members, with a male head aged 47 years, who is on average able to read or write (around 70%), having had less than 6 years of formal education.

Turning to Table 3 we describe households’ official VG and subsidy eligibility status obtained from the SBM data. We find that 29% of the matched MF client sample (without a toilet at survey baseline) is officially classified as being BPL. An additional 51% is considered to be marginalised APL, so that overall 80% of MF client households without a toilet at survey baseline in our sample are officially categorised as VG. Given that we focus here on households without a toilet at survey baseline these VG households should in principle have all been eligible for a subsidy. However, the data reveal that 16% of VG households that do not have a toilet at the time of the survey baseline (which was conducted only a month after SBM was launched) are recorded to have one in the SBM data, classifying them officially subsidy in-eligible. We re-visit this matter in Section 5 where we discuss mechanisms of impacts of micro-credit. Overall in our sample of MF clients without a toilet at survey baseline, 64% was classified as being officially subsidy eligible.

We observe two notable imbalances between the treatment and control group, shown in Table 3. Matched MF client households in the sanitation credit treatment group are more likely to be small and marginal farmers and they are less likely to be a BPL household. Overall, however, MF client households in the treatment group are statistically speaking as likely as their counterparts in the control group to be categorised as SBM subsidy eligible. This means that even though we cannot generalise our findings to the population of all of our MF clients in Maharashtra (in light of the imbalances observed between the matched and unmatched sample), the results presented in this paper are internally consistent and strongly suggestive for what could be happening elsewhere.

# **3.4 Empirical Model**

The randomisation discussed above was designed to identify the causal effects of the introduction of microcredit for sanitation on outcomes of interest, in particular loan uptake, toilet ownership and sanitation behaviour. This analysis, reported in ACGMS 2019, reveals that, on average, sanitation loans were taken up in treatment areas and that a significant proportion were converted into new household toilets that would not have been constructed in the absence of the micro-credit program.

The main aim of this paper is to identify whether and, if so, how micro-credit is able to support the SBM subsidy program. In particular, we aim to test whether (i) the credit program enables those _ineligible_ for subsidies to construct a toilet, potentially by alleviating financial constraints; and/or (ii) 11