defined high disruption villages to be at the 75th or higher percentiles of our overall Ebola-related disruption index. Clearly, some judgement must be made about how to define high intensity, and the quantitative results will always be a function of exact choice made. To show the qualitative robustness of our core results to small alternations in this definition, we examine how the results vary using different thresholds of the disruption index. Figure A5 shows the results, by age cohort and for two summary outcomes: (i) hrs/wk spent in productive activities of learning, income generation or ELA clubs (Panel A); (ii) hrs/week spent with men (Panel B). The omitted category are villages in the first six deciles of the disruption index. The impacts on time in learning activities and time spent with men both vary depending on the intensity of disruption, where we show cutoffs at the 60th, 70th, 80th, 90th percentile of the index. Reassuringly, for both outcomes, we observe impacts of Ebola-related disruption across deciles, and offsetting impacts of ELA clubs, as the measured intensity of the Ebola outbreak varies.

A second facet of the disruption measure used is that it is defined in absolute terms, and so measures the most disrupted villages in any of the four districts included in our evaluation sample. As Figure 5 shows, this inevitably leads to most high-disruption villages being defined in Port Loko, the district with the highest incidence of health related Ebola disruption. For our research questions, it is the absolute level of Ebola related disruption that we are concerned with. However, this raises the concern of whether the impacts of higher versus low disruption instead pick up something specific to Port Loko relative to other districts. To probe this further we redefine the high disruption dummy to be district specific, so that villages in each district that lie in the highest quartile of the continuous disruption index are classified as high disruption. This is the final row of results shown for our main outcomes in Table A6: of the 12 significant coefficients in the baseline specifications, 9 remain statistically significant at conventional levels once we consider high disruption to be within-district rather than in absolute terms across the evaluation sample (with some point estimates being larger in absolute value than in the baseline specification).

As a final check, we reconstruct _Evd_ by additionally using information on the disruption of daily and periodic markets. This again uses data from the village leader survey, where we consider daily markets as disrupted or closed if reported as such by village leaders for at least one month between July 2014 and September 2015. However, a significant number of villages ( _n_ = 62) do not have a daily market. Hence we make the extreme assumption that both dummies for disruption and closure are equal to one in those cases. This gives the most conservative measure of disruptions and, if the assumption is incorrect, will bias estimated impacts of disruptions towards zero. Table A7 then shows statistics related to the components of this modified index: the two additional components related to markets shown in Panel C have considerable within district 39