**Table A7: Alternative Construction for the Ebola-Related Disruption Index**

**Standard deviations in brackets, standard errors in parentheses**

||**Mean**|**Between district**<br>**variation**<br>**(share of total)**|**Unconditional**<br>**coeff on ELA**|**Conditional**<br>**coeff on ELA**|**Conditional coeff on**<br>**ELA with distance**<br>**interaction**|**Conditional coeff on ELA**<br>**with distance interaction**<br>**and model selection**|
|---|---|---|---|---|---|---|
|**_A. Health Policy Components_**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|**PHU Ever Closed**|.140|254|.001|-.000|.046|.076|
||[.348]|.|(.049)|(.050)|(.075)|(.075)|
|**PHU Ever Disrupted**|.320|212|.000|-.006|-.077|-.069|
||[.468]|.|(.065)|(.065)|(.098)|(.096)|
|**PHU Functioning Score**|89.6|327|-1.19|-1.02|-1.00|-.342|
||[19.8]|.|(2.58)|(2.55)|(3.90)|(3.98)|
|**_B. School Policy Component_**|||||||
|**Nearest Secondary School Re-Opened on Time**|.825|132|.085|.106|.072|.080|
||[.381]|.|(.061)|(.059)|(.079)|(.081)|
|**_C. Economic Market Component_**|||||||
|**Daily Market Ever Disrupted**|.485|129|-.124|-.111|-.127|-.076|
||[.501]|.|(.076)|(.069)|(.134)|(.130)|
|**Daily Market Ever Closed**|.755|088|-.038|-.046|.053|.057|
||[.431]|.|(.067)|(.065)|(.132)|(.130)|
|**Periodic (Luma) Market Functioning Score**|6.60|179|.272|0.091|1.04|.124|
||[5.89]|.|(.806)|(.812)|(1.41)|(1.42)|
|**_D. Index_**|||||||
|**Disruption Index**|0|320|-.219|-.227|-.036|.127|
||[1]|.|(.146)|(.140)|(.309)|(.320)|
|**Disruption Dummy**|.250|215|-.066|-.084|-.133|-.079|
|[=1 if disruption index greater than 75th percentile]|[.434]|.|(.062)|(.062)|(.124)|(.129)|
|**N (villages)**|200|200|200|200|200|200|

**Notes:** All data comes from the Village Leaders Survey, collected in October 2015. Both PHUs and Daily Markets are considered as disrupted or closed if reported as such by village leaders for at least one month for the period between July 2014 and September 2015. For those villages that do not have a daily market (n=62), both dummies for disruption and closure are assumed as equal to one. The between group variation reported in Column 2 is computed through a one-way ANOVA analysis of the dependent variable across districts. For each measure of disruptions, Columns 3 through to 6 report estimated coefficients on assignment to treatment from regressions that use incrementally larger sets of village-level covariates. All regressions include district fixed effects and calculate robust standard errors. Column 3 reports the coefficient on ELA from a regression of the Ebola measure of interest on treatment assignment. The coefficient in Column 4 is obtained controlling for: number of dwellings, whether the village is a political stronghold (i.e. it is the residence of a chief), number of NGOs active pre-Ebola, average PPI score, share of Christians, distance from Freetown and distance from Kailahun (where the first Ebola case was recorded). In Column 5 distance from the nearest facility of interest (PHU in Panel A, Secondary School in Panel B, market structure in Panel C, all in panel D) and interaction between ELA assignment and these distances are added as regressors. None of the coefficients on the interaction between ELA and distance from each facility is statistically significant (not shown). Column 6 includes all regressors and interactions employed in Column 5, plus features selected by a penalized regression (Elastic Net) of the Policy index (dummy) on all village characteristics and their interactions. The Primary Health Unit functioning score is assessed on a monthly basis between July 2014 and September 2015, and later aggregated into an index ranging between 0 and 100. Secondary schools were considered as having re-opened on time if they were open in April 2015. The Disruption Index is constructed following Anderson [2008]. It aggregates variables in Panel A and B, and assigns higher values to communities more exposed to disruptions.