basis. This ensures not only good record keeping at the MFI level, but also provides the opportunity to access information on loans taken by sampled clients from all micro-finance providers in the study areas. We received the credit bureau reports for sample clients from a leading provider, used by the MFI partner when making decisions on loan applications. We obtained this information for around 88 percent of clients in our sample.<sup>14</sup> For more information on the source of the survey, MFI and credit bureau data we refer to ACGMS 2019.

Figure 2 provides an overview of the timeline of events, including the dates when the SBM data snapshots and survey data were collected, and how these relate to, the launch of the SBM program and the micro-credit intervention.

# **3.3 Matching result and sample description**

Linking our survey data to the SBM data was done by matching full names of household heads, their spouses and their fathers provided in our survey data to full names of household heads and their fathers available in the SBM data.<sup>15</sup> After using a computer algorithm based on a combination of exact and fuzzy matching - where for the latter we used a conservative threshold such that the probability of including erroneous matches was higher than the probability of excluding true matches - we carefully checked each match, dropped obviously wrong matches and flagged matches for which there was some uncertainty. Uncertain matches concern cases for example where the first and last name of the household head uniquely matched between datasets but where the first name of the father did not match. In our regression analysis we control for whether or not there was some uncertainty about the matching accuracy, which was the case for 25% of all matched observations. Appendix A explains the matching process in more detail.

We were able to uniquely match 1,312 client households to the SBM data, 47% of the 2,772 clients interviewed for our endline survey. These live in 75 of the 81 study GPs (78 of which we were able to identify in the SBM data). There are a number of possible explanations for the achieved match rate. First, the matching was based on the name and surname of the head of the household and his/her father’s name.<sup>16</sup> We encountered a number of duplicate name records in the SBM dataset, each of which had unique card identifiers and unique data on toilet ownership and subsidy uptake . This was the case for 7% of all SBM data observations in our study area. Since we would not be able to uniquely match these to our survey data, these observations were dropped from the SBM data sample before matching. Second, the survey data were collected at least 2-3 years after the SBM baseline. Households might have experienced a change in the head of household over this period, or have split or merged, undermining our ability to match them to the SBM data.

The matching result was balanced across treatment and control groups. Of the matched client households, 959 (73%) did not have a toilet at survey baseline. Our analysis focuses on this sample of households, as these are explicitly targeted by the SBM subsidy program (which does not cover toilet repairs or upgrade) and our objective is to assess the extent to which micro-credit can support SBM in its mission.

> 14It was not possible to obtain data for the remaining 12 percent of clients, since our partner MFI no longer had all the information required by the credit bureau in order to avail of these records.

> 15If household head had no spouse than the name of the eldest female household member was provided instead.

> 16In a minority of cases, where the father of the head of household was resident in the household during our endline survey, we were able to match based on three names - the head of household’s name, the head’s father’s name and the surname.

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