about 2 ppts per year. The LTGM also addresses the first concern by allowing for steady capital accumulation based on prevailing rates of investment as a share of GDP in each country.

In order to produce trajectories of adjustment, we calibrate the LTGM to each of the 19 countries in our MENA sample. For the 10 resource rich MENA countries (Algeria, Bahrain, the Islamic Republic of Iran, Iraq, Kuwait, Libya, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) we used the LTGM Natural Resource extension (LTGM-NR; Loayza et al. (2022)) rather than the standard LTGM since growth analysis using standard neoclassical models can be misleading. The LTGM-NR incorporates a resource sector (which does not use labor), and so takes into account the differential growth dynamics as well as reduced sensitivity of output to employment rates.<sup>15</sup> The details of the calibration are in Appendix Tables A2 and A3. In short, we calibrate key parameters like the labor share _β_ , the capital depreciation rate _δ_ , and the initial capital-to-output ratio based on Penn World Tables 10 data (with some exceptions). Future investment rates are based on IMF forecasts from the World Economic Outlook (WEO) until 2028 (their final year), which converge linearly to the historical average for each country by 2050. Total Factor Productivity (TFP) growth rates are chosen to replicate WEO GDP growth forecasts to 2028 (given other growth drivers) which then converge linearly to a cross-country average by 2050.<sup>16</sup> Future human capital growth comes from the LTGM-Human Capital Extension, with a few exceptions, which uses the same definition of human capital as the World Bank Human Capital Index (Kraay (2018)).<sup>17</sup> We assume that females and males have identical human capital, which is a reasonable assumption in most countries, but we relax this assumption in the next section.

Our analysis focuses on the _difference_ between the estimated growth path with and without higher female employment, and hence the specifics of the calibration for each country are differenced out.

**Results.** The increase in GDP per capita (GDPPC) from closing the gender employment gap in MENA countries by 2050 averages around 31% (median 34%) and as is in between the short run and long run GEGIs in Figure 2 (green bars). The relative sizes of the effects are shown in Figure 3.<sup>18</sup> The boost to growth is about similar in the long-run GEGI and 2050 LTGM in countries with the smallest gains like Qatar, but are around 30ppts larger in the countries with the largest gaps like Yemen. Across all countries, the gains are about 50% (not 50ppts) larger in the basic 15For the commodity sector: Capital shares in oil and natural gas sectors are set according to GTAP 2004-14 averages (the other factor is reserves, which generate rents). Initial gross exports come from UN Comtrade, initial reserves come from BP Energy (most recent observation), discoveries are set to a historic average from BP-Energy data) and export prices are set constant, based on World Bank Commodity forecasts from 2021.

> 16The cross-country average is based on potential for future structural transformation based on current share of employment in agriculture: 0 _._ 2% + 5 _× AgEmpShare_ (to a maximum of 1.5%).

> 17Specifically, human capital is based on Learning-adjusted years of schooling and health (stunting and adult survival rates). Human capital embedded in each age cohort, but only boosts productivity for cohorts of working age. Human capital data is taken from the components of the World Bank Human Capital Index, with historical years of schooling (for older generations) taken from the Barro-Lee educational dataset or Cohen-Soto-Leker when Barro-Lee is missing.

> 18We report the adjusted GEGI for ARE, BHR, KWT, OMN, QAT and SAU.

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