To construct the total exposure index at the district level in Egypt, we utilize several databases. Initially, we gather data on export value from the UNCOMTRADE database. In order to account for the demand generated in other sectors as a result of exports and calculate the overall exposure index, we incorporate the 2008 input-output (I-O) tables from Global Trade Analysis Project (GTAP).

We begin by computing the input-output coefficients from the GTAP I-O tables which capture the interdependencies between sectors in an economy. We match these coefficients with trade data from the United Nations Commodity Trade Statistics database (UNCOMTRADE) to compute the total export value for each sector, accounting for indirect changes in export demand through input-output linkages. Annex 2 of the study provides a detailed explanation of how these coefficients are computed and merged with UNCOMTRADE data.

The next step is to link these total export data with labor force surveys. To this effect, we utilize concordance tables available online which provide mappings between International Standard classification (ISIC) rev 3.1. codes and HS codes. By leveraging this concordance, we merge the microdata on labor force variables at the industry and area level in Egypt with total export data. Once the integrated labor and trade data is prepared, we are able to calculate the total trade exposure index based on districts, as previously explained. However, it is worth mentioning that all workers below the age of 15 from the sample were excluded from the analysis to ensure the accuracy and integrity of the findings.

# **4. Methodology**

The goal of the current empirical strategy is to understand the impact of rising export expansion on real wages, informality and female labor force participating, exploiting the cross-regional exposure to total exports in Egypt between 2007 and 2018.<sup>2</sup> To this effect, we consider the following simple linear regression model: is the dependent variable, 0is the intercept, 1 is the coefficient of our trade exposure variable, and aa 2 is the coefficient for the set control variables . is our main tt,tt+1 yyindependent variable, which stands for the area level total export exposure index, as defined in ββ ββ aa tt,tt+1 the previous section. We include time ββ _t_ levels of the dependent variable to control for possible KK<sup>′</sup> xx trends that are not related to the export shock. The size of the sample equals the number of areas (kizms and markaz) in Egypt.

A relevant issue we need to address is the potential endogeneity in the export exposure covariate. Since we observe changes in labor outcomes and exports simultaneously, we cannot identify which one is driving the other. To ensure truly exogeneity of our export exposure, we

> 2 The starting point for the analysis is the idea that the impact of a trade shock is differentiated across regions, depending on each district industry composition. A fundamental principle for this approach is the existence of segmented labor markets. Existing labor mobility barriers or rigidities (such as commuting costs or lack of transport infrastructure) allow us to observe variations in local labor market outcomes and, as a result, to estimate the effects of differentiated exposure to trade.