ELA assignment and their interaction, we require village level disruptions to be as good as random. Official WHO estimates show considerable variation in Ebola intensity across districts in Sierra Leone, but official data is unavailable at any finer level. The central map in Figure 5 shows WHO measured caseloads across the 14 districts, with the shading reflecting the quintile of caseloads for each. The highest intensity districts are Port Loko (close to, but not including the capital, Freetown), and Kailahun (where the initial outbreak occurred). Our study covers four districts: Port Loko, Kambia, Moyamba and Pujehun, where intensity levels are high, intermediate and low.

Given that WHO caseload data is unavailable at a lower level than by district, we use another approach to construct our village specific measure _Evd_ . We proceed in three steps.

First, we begin by noting that a body of earlier work has measured correlates of the local intensity of the Ebola crisis using spatiotemporal models to simulate the spread of the virus [Backer and Wallinga 2016, Maffioli 2017, Fluckinger _et al._ 2018]. These find that beyond covariates such as population, few observable characteristics of locations predict the severity of Ebola infection in those locations. Taken together this existing body of evidence suggests localized Ebola-related disruption might be as good as random.<sup>30</sup> Second, we use information from the village leader survey to construct a proxy for _Evd_ . We focus on dimensions that: (i) are credibly unaffected by ELA clubs operating in treated villages; (ii) have geographic variation _within_ district. Our disruption index combines data on the functioning of the nearest primary health unit (PHU) facility, and the nearest secondary school. Both serve multiple villages over a wide radius. For example, the lowest tier PHU facility typically serves a population of up to 5 _,_ 000 within a 3-mile radius covering 10 to 20 villages. In our evaluation sample, the average distance to the nearest PHU is 1 _._ 55 miles. Similarly, 83% of villages have no secondary school in them and the distance to the nearest one (conditional on not being in the village) is 4 _._ 91 miles. Hence the functioning of the nearest PHU and secondary school during the crisis is unlikely to be impacted by the presence of ELA clubs in treatment villages.<sup>31</sup>

> 30Fluckinger _et al._ [2018] construct a simulated measure of Ebola exposure at the district level. They simulate the spatiotemporal spread of the epidemic using a modified version of the simulation model in Backer and Wallinga [2016]. In such a simulation model, the outbreak is represented as a network of local epidemics that are interconnected across districts through a gravity model. Fluckinger _et al._ [2018] investigate the extent to which their simulation-derived predicted prevalence is correlated with time-invariant and time-varying district characteristics: they find that population predicts Ebola intensity. This is as expected given this is a key input into the gravity model. They do not find the intensity of the outbreak to correlate to distances to initial outbreaks, pre-epidemic public goods provision (schools, health clinics, police stations), the value of crop production per capita, the presence of minerals diamonds or gold, or changes in average rainfall, temperature and drought exposure between the preand post epidemic years. Maffioli [2017] reaches similar conclusions in the context of the outbreak in Liberia.

> 31PHUs comprise three tiers: community health posts (CHPs) community health centres (CHCs), and, maternal and child health posts (MCHPs). The MCHP is the first level of contact for patients. One or more MCH aides are posted at these centers serving a population up to 5000 within a 3-mile radius. The services provided include antenatal, delivery and postnatal care. At the next tier, the CHP is usually situated in a small town serving a population up to 10 _,_ 000 within a 5-mile radius. This has similar functions to the MCHP with added curative

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