**Forced displacement indicators.** Our indicator of displacement status at the household level equals one if a household is an IDP household. Similarly, the displacement status indicator at the individual level equals one for individuals in IDP households. Indicators of displacement _experiences_ include a range of displacement characteristics, such as displacement date, number of times the household has been displaced, reason for displacement, and livelihood and education before displacement.

**Violence indicators.** Our indicator of exposure to violence equals one if an individual or household resides in an area where violent events occurred in the 12 months prior to the study. We prefer a binary indicator because there is a significant number of enumeration areas, in which no violent events occurred, it helps to deal with outliers with a high number of events, and it reduces issues of measurement error. For rural areas, we use an indicator of local violence at the LGA level, while for urban areas we use an indicator at the more fine-grained ward level. The reason is that the underlying contours of violence differ across rural and urban areas. The rural areas are very large compared to the urban areas and were characterized by relatively low levels of conflict intensity (compared to urban areas). Urban and peri-urban areas are much denser and were predominantly characterized by high-intensity violence that was highly concentrated spatially. We thus opted to choose the ward level in urban areas and the LGA level in rural areas as the appropriate administrative units of analysis.

# **3.3 Econometric specifications**

For our baseline estimates of additive and interactive impacts of gender and displacement status (discussed in Section 5), we use linear fixed-effect models that estimate the coefficients of interest, βand δ, by OLS regression: Here, Y is a coping behavior variable, D is the female indicator; E is the displacement status indicator, X is a flexible vector of control variables (including local fixed effects); and ɛ is the error term. In our main specifications, we cluster our standard errors at the ward level (53 clusters).

In conditional analyses, we focus on parsimonious specifications to avoid issues arising from the inclusion of “bad controls” (Angrist and Pischke, 2009, 2014). That is, our baseline models do not include variables that may themselves be outcomes of the gender or displacement variables, which would complicate the interpretation of estimated coefficients of interest due to endogeneity and spurious correlations. Similarly, we specify and test an additional set of models that estimate the main and interactive impacts of local violence shocks, where we distinguish rural and urban areas. As the number of clusters is below 50 for both the rural and urban analyses, we use HC2 standard errors for these regressions.

# **4 Data and descriptive statistics**

**Household survey data** . We analyze household survey data collected by the World Bank and collaborating partners between June and August 2018 (Pape and Kwenin, 2018). The sample is representative of the IDP population in North-east Nigeria and includes internally displaced persons and comparable host communities in the six north-eastern states of Nigeria. The survey

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