## 2004 census (see Figure A.1(c) for a map of section boundaries).<sup>11</sup>

The 254 clinics in the full sample fall into 205 sections. Figure 1(b) maps all sections included in the study with those include in the Ebola sub-sample in dark grey. Of the 205 sections, 45 sections include multiple clinics. In our primary analysis we restrict attention to the 160 sections that contain a single clinic and, thus, a unique treatment assignment. Of the 160, we retain 54 control sections, 46 CM, and 60 NFA. As a robustness check, we also analyze the data using a dose-response model, which uses the proportion of clinics in each section that receive either treatment (see Table E.11).

# **3.2 Data Collection**

## **3.2.1 Survey Data on Health Clinics, Health Services and Health Outcomes**

Baseline data collection took place in September 2011; endline surveys, in May and June of 2013 (see Figure 2 for a timeline). We rely on three survey instruments: first, surveys at each clinic, in which enumerators audited the staffing, cleanliness, drug stocks, and registers of clinics; second, surveys of leaders of each village regarding amenities, relations with the clinic, and community development; and third, household surveys which captured health and economic outcomes and health-seeking behavior among other outcomes.

We filed an analysis plan for the survey data at the AEA RCT registry on March 2017.<sup>12</sup> The analysis plan defines ten outcome families: (1) general utilization, (2) maternal utilization, (3) health outcomes, (4) satisfaction, (5) health service delivery, (6) clinic quality, (7) community development and political engagement, (8) contributions to clinics, (9) water and sanitation, and (10) economic outcomes. The subcomponents comprising each outcome family were specified in the analysis plan and are listed in Appendix D.2.

Each outcome family represents a set of variables aggregated using control group-standardized indices per Kling et al. (2007). To create an index of _K_ outcomes, we first reverse outcomes where necessary such _K_ that a higher value indicates better outcomes. We then compute _y_ � _i_ =<sup>1</sup> ∑ _<u>yik − μ</u>_ 0 _k_ , where _μ_ 0 _k_ and _σ_ 0 _k K_ � _σ_ 0 _k_ �

> 11 Our decision to aggregate cases to the section-level stems from two features of our geocoding procedure (see Section E.22). First, many villages in Sierra Leone do not have recorded names; when patients report their residences, they tend to name betterknown towns, rather than their village or hamlet. As relevant administrative units, sections typically feature a central or headquarter town. Using a larger administrative unit, we avoid measurement error that arises from attributing patients to larger towns that actually occur in the surrounding villages. Second, our geocoding procedure matches residences to lists of geolocated placenames. When we use smaller geographic units, these often contain few placenames that we can match patients’ residences to: 85 percent of census enumeration areas (which are just 7 square kilometers on average) contain one or zero placenames. By contrast, the average section contains 8 geolocated placenames; 94 percent of sections contain more than 1 placename.

> 12 AEARCTR-0002085: `https://www.socialscienceregistry.org/trials/2085` .

11