**Employment.** Our main indicator of employment status equals one if an individual engaged in at least one of the following five forms of work in the past 7 days: paid labor for someone else, unpaid labor in an off-farm family business, non-agricultural self-employment, agricultural self-employment, and participation in unpaid training or workshops to foster skills. We also analyze binary indicators of these categories separately. Among workers, we also study employment at the intensive margin by analyzing the sectors and total number of activities an individual engages in, as well as hours of work per week and months of work per year.

To model proximate factors shaping employment outcomes, we build on the World Bank’s Africa Gender Innovations Lab’s (GIL’s) framework on gendered constraints to entrepreneurship, which emphasizes the role of endowments, the household and the local context (World Bank, 2019b).

We slightly adapt the original framework for our study and categorize factors shaping employment into three groups: 1) Endowments, such as education and health; 2) Household-level factors, such as gender and demographic composition; and 3) Institutions, defined broadly to include both formal rules and regulations as well as informal constraints to behavior, such as social norms (North, 1990), such as threats to safety and security; identification and documentation issues; access to local labor markets, good markets and trade, finance; and access to mechanisms for help with resolving issues of crime and other disputes.<sup>13</sup>

**Gender.** Our main gender indicator equals one if an individual is female. We also compute gender composition variables, such as the number of girls versus boys in the household and the number of female adults versus male adults.

**Displacement.** Our indicator of displacement status equals one if an individual is an IDP. Our indicators of displacement experiences include a range of displacement characteristics, such as length of a household’s displacement and individual age when the household arrived in El Fasher. We also compute indices of conflict exposure at the district level, including exposure to any form of collective violence, exposure to violence against civilians and exposure to fatalities. When comparing associations across variables that have different scales, we standardize all variables to zero mean and unit standard deviation.

# **2.2 Main analyses**

For our baseline estimates of separate and interactive impacts of gender and displacement status (discussed in Section 5.2), we use linear models that include both main terms and the interaction effect of interest (δ) is estimated by OLS regression: i = α i + β i + δ i ✕ i + γ' i + εi Here, Y is an employment variable, D is the female indicator; E is the displacement status YY DD EE DD EE XX Here, Y is an employment variable, D is the female indicator; E is the displacement status indicator, X is a flexible vector of control variables; and ɛ is the error term.

To take a closer look at separate impacts of gender among IDPs and displacement status (discussed in Sections 5.3 and 5.4), we specify linear models where the parameter of interest (η) is estimated by OLS regression: i = η i + γ' i + εi YY CC XX Here, Y is an employment variable, C is the female or displacement status indicator; X is a flexible vector of control variables; and ɛ is the error term.

13 We note that some institutional-level variables might also be regarded as ‘endowments’, such as access to land. To distinguish the two domains, endowments are focused on education and health outcomes in our study.

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