Depending on the flexible vector of control variables (X), we provide estimates of unconditional differences in means and check their robustness to regression adjustments in the form of additional covariates, fixed effects and adjusted standard errors.

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, we do not include variables that may themselves by outcomes of the gender or displacement variables, which would complicate the interpretation of estimated coefficients of interest due to endogeneity and spurious correlations. We carefully control for necessary covariates such as age and household size and include local fixed effects, whenever it is appropriate. For within-IDP analyses, fixed effects include enumeration area and pre-displacement district indicators, to partial out systematic differences due to the current environment and pre-displacement background. These specifications and checks help us to establish robust associations between key variables, but, due to their inherently endogenous nature, our results should not be interpreted as causal.

Specifically, we explicitly assume that the employment constraints emphasized by the framework above are outcomes of displacement and gender variables. Thus, we model relevant endowment, household, and institutional outcomes in separate regressions and do not include them in the headline regressions modeling employment outcomes.

## **2.3 Additional analyses**

To gain deeper insights into factors underpinning our main results, we conduct two types of additional analyses. First, we analyze the role of heterogeneous displacement characteristics and conflict exposure for employment outcomes among IDPs, and how these interact with being a woman (discussed in Section 5.5). To do so, we use similar specifications as for the main analyses as well as a simple probit model to specifically estimate how the impact of being female on employment varies with age at arrival in El Fasher, conditional on age today. Second, we analyze heterogeneity in employment outcomes based on an individuals’ age today, again using a simple probit model (discussed in Section 5.6).

# **3 Data**

**Household survey data** . In El Fasher, the World Bank in collaboration with several partners has collected unique survey data from May to July in 2018 that fulfill the ambitious requirements of our purposes (JIPS, 2019a; Pape and Scharma, 2019). The survey targeted two different groups of IDPs and two groups of comparable non-displaced populations in urban and peri-urban areas ( **Figure 1** ).

The total **survey sample** includes 18,533 individuals from 3,002 households:

- •996 IDP households (5,894 individuals) from the Abu Shouk area;

- •986 IDP households (5,960 individuals) from the El Salam area;

- •509 Non-IDP households (3,303 individuals) from adjacent peri-urban areas; and

- •511 Non-IDP households (3,376 individuals) from nearby urban areas.

The survey data set provides a comprehensive displacement profile of IDPs residing in the Abu Shouk and El Salam areas, including pre-displacement conditions, displacement history and outcomes, as well as detailed additional socio-economic data from both IDPs and non-IDPs.

Notably, a detailed household roster module offers for each member extensive information on sociodemographic indicators (including sex), health (for members aged 5 and over) and employment (for members age 15 and over). While most of this information was provided by one main respondent (a knowledgeable person aged 15 or over, whose sex is also known), an employment sub-module was to be answered by each adult in person, if they were available. The employment sub-module contains detailed information about current and pre-displacement employment outcomes.

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