As part of the village leaders survey, respondents were collectively asked to recall information from July 14 to September 15 on whether the PHU was closed, disrupted, and an overall PHU functioning score. They were also asked about whether the nearest secondary school opened on time (many openings were delayed from April 2015 but all schools would have re-opened when our endline survey began in February 2016). We combine components to construct a village specific disruption index, following Anderson [2008] in using the data covariance matrix to construct a weighted sum of components, giving less weight to components that have less variation across villages and that are more correlated with each other.<sup>32</sup> Panels A and B in Table 4 shows descriptive evidence on the components the index is constructed from. Each varies across villages, with there being substantial variation within and between districts (Column 2). Panel C shows the constructed disruption index, that by definition is standardized with mean zero and standard deviation one. To operationalize (1), we define a disruption dummy, _Evd_ , so that _Evd_ = 1 if the index is above its 75th percentile, and _Evd_ = 0 otherwise. We refer to these as high and low disruption villages, where 17% of villages are then defined to be high disruption locations.

To get a sense of what this classification means, low disruption villages never have their PHU ever closed, 18% have PHU disruptions, and their average PHU functioning score is 94. In contrast, 82% of high disruption villages have their PHU ever closed, all of them have the PHU ever disrupted and the PHU functioning score is 67. In low disruption locations, 88% of secondary schools opened on time, while this falls to 62% in high disruption locations.

Our _Evd_ construct is a proxy for all Ebola-related disruption experienced by village _v_ . Hence we do not interpret moving from a low to high disruption village as literally _only_ capturing the persistent impacts of having the nearest PHU more disrupted during the crisis, or having the nearest secondary school closed for a few months more. Rather we interpret the move from low to high disruption villages as measured by our index as proxying more widespread disruption in the economic lives of villagers far more generally, in terms of economic trade, labor market opportunities, and access to health care.

The outer district maps in Figure 5 show how the standardized disruption index varies over villages, by treatment and control. Three points are worth noting. First, the level of the disruption

> functions. Finally, the CHCs have preventive, promotive and curative functions with a catchment population up to 30 _,_ 000 within a 5-10 mile radius. These centres have inpatient care, as well as a laboratory and are managed by Community Health Officers and supported by nurses, midwives and technicians.

> 32More precisely, an Anderson [2008] index is constructed as follows. First define _yvk_ as the _k_ -th of _K_ Ebola measures for village _v_ where we switch the sign of _yvk_ so that higher values imply higher exposure. We then standardize _yvk_ , denoted _y_  ̃ _vk_ and define a summary index _s_  ̄ _v_ = **1**<sup>_′_</sup><sup>**ˆ**</sup> **Σ**<sup>_−_</sup><sup>**1**</sup> **1 1**<sup>_′_</sup><sup>**ˆ**</sup> **Σ**<sup>_−_</sup><sup>**1**</sup> _y_  ̃ _v_ , where **1** is a _K_ column � �� � vector, **Σ**<sup>**ˆ**</sup><sup>_−_</sup><sup>**1**</sup> is an estimate of the covariance matrix and _y_  ̃ _v_ is a _K_ column vector of all measures for village _v_ . We  ̄ then re-standardize the summary index _sv._

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