# **3 Data and Stylized Facts**

**Data** We combine multiple data sources to be able to empirically test the predictions of the theory present in this paper. Labor market indicators come from the Tunisian Government’s National Survey on Population and Employment (ENPE). Among other information, the ENPE reports individuals employment status by industry (if any), gender, and province for intermittent years between 2006 and 2016.

Trade data comes from two sources. Merchandise trade data comes from UNCOMTRADE while services trade numbers come from the WTO-OECD BaTiS database. We then used concordance tables to map HS and BOPS trade product codes onto ISIC industry codes, using concordance tables from WITS. Finally, we mapped ISIC industry codes onto Tunisian Economic Activity Nomenclature (NAT), which is a domestic economic activity classification based on NACE Revision 3. Additional details can be found in the Appendix.

We were then able to construct a provincial panel for labor market and trade indicators. Of primary concern, for each province, we can trace the evolution of employment by gender and sector as well as the provincial exposure to foreign demand shocks, which are defined later in this section.

**Stylized facts** Low female labor-force participation rates and high female unemployment rates characterize Tunisian labor markets. While Tunisia female labor force participation is higher than other Middle Eastern and Northern Africa (MENA) countries, it is about half of the rate of OECD countries and much lower than other developing regions such as Latin America. The gender gap in unemployment has increased substantially since the Arab spring, in 2011, and female unemployment rates have never converged back to pre-Arab spring levels. Sectoral employment is very segmented across gender lines and exports grew less in more femaleintensive sectors. In 2006, the female share of employment across different industries ranged anywhere from 1% to 75%. The most female-intensive industries are garments, social and cultural services, education, and banking and insurance. Importantly, industry contribution to export growth in the 2006-16 period is negatively correlated with initial female intensity. We plot these statistics in Figure 2.

<!-- Start of picture text --> 20%<br>Manufacture of textiles and textile products 75%<br>Social and cultural services 32%<br>Mfc of Mechanical and Electrical Products 29% 15%<br>Agriculture, hunting, and fisheries 28%<br>Manufacture of chemicals 22%<br>Manufacture of food products 20% 10%<br>Banking and Insurance 17%<br>Education and public administration 11%<br>5%<br>Other manufacture 11%<br>Public works and buildings 11%<br>Production and distribution of water 10% 0%<br>Mining 9%<br>Mfc of construction products, clay, and glass 8%<br>Transportation 7% -5%<br>Real estate and entrepreneurial services 5%<br>Hotels and Restaurants 1%<br>-10%<br>0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%<br>Female share in employment, 2006 Female share in employment, 2006<br>Export growth contribution, 2006-16<br><!-- End of picture text --> Figure 2: **Gender composition and contribution to export growth across industries** . The left-hand side panel shows the distribution of female intensity across industries at the beginning of the period (2006). The right-hand side panel shows that industries that contributed the most to aggregate export growth (2006-16) tended to be male-intensive in the base year (2006).

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