(especially OCED countries), it is substantially higher.<sup>35</sup> For example, for a labor share of 2/3 (orange dashed line in Figure 8), it takes about 15 years to reach the 20ppt threshold ( ≈ 90% of the gains), and with a labor share of 3⁄4 it only takes about 10 years (green line) to achieve 20 ppts ( ≈ 90% of the gains). The labor share mostly affects the size of direct effect.

Other parameters affect the speed of the indirect effect, though are less important quantitatively. Higher investment (purple line) and faster depreciation (dark blue) both marginally increase the speed of adjustment. A higher capital-to-output ratio slows the adjustment speed slightly.

**Summary.** In sum, a good rule of thumb is that about 1⁄2 to 2/3 of the gains to GDP happen roughly contemporaneously with the increase in female employment itself (the “direct effect” through higher labor), especially if the policy change is phased in over time. Remaining “indirect effect” (though induced capital accumulation) happens much more slowly, such that it takes 15-30 years after the change in employment for 90% of the total gains to be in place. Achieving the last few percentage points of the gains takes much longer.

# **6. Detailed comparison with the literature**

The three closest studies are McKinsey Global Institute (2015; henceforth MGI), Cuberes and Teignier (2016 henceforth CT), and Devadas and Kim (2020, henceforth DK). All of these studies try to estimate the gains to GDP of closing gender employment gaps in a broad cross-section of countries (including many developing countries). However, all three other papers also consider other mechanisms in addition to employment rates, and consequently have a broader perspective on the costs of gender gaps. However, these other mechanisms come at the expense of simplicity, transparency, a closed-form expression for the size of the impact on GDP and a substantially lower country coverage than the GEGI – especially for developing countries.

Despite this, the size of the gains are broadly similar, as is their cross-region correlation (I focus on regions rather than countries here given MGI do not report country-level estimates). Across regions, the correlation of the basic GEGI with broader measures in the three other papers are 95.8% for MGI, 95.9% for CT and 96.4% for DK. The average of the basic GEGI across regions (not countries) is 22%, which is slightly larger than DK (19%), but a little less than MGI (33%) and CT (26%). This is because of some of the other mechanisms in MGI and CT: stripping the other mechanisms out, the cross-region averages fall to 20% and 18% in MGI and CT respectively, which are slightly lower than the cross-region average in the basic GEGI. I now discuss each of these papers in turn.

**McKinsey Global Institute (2015)** provide one of the most high-profile estimates of the effect of closing gender gaps on GDP. Although these are reported for the year 2025, they are actually closer to “long run” measures reported here.<sup>36</sup> MGI includes three types of gender gaps: (i) employment rates (like in the basic GEGI); (ii) hours worked per employed person, and (iii) sectoral mix of employment by gender.

While this has the potential to provide more accurate estimates for countries with good data, it is also costly in terms of reduced country coverage and interpolated data for many developing countries. MGI states that it produces estimates for 95 countries, which is around half of the 185 countries for the basic GEGI, and 60% of the 35 There is a slight negative relationship between the basic GEGI and labor share, driven in part by oil rich countries in the Middle East that have both high GEGIs and low labor shares (due to oil). This slight negative relationship means that countries with higher basic GEGIs may be slightly slower to feel the benefits of higher female employment on GDP.

36 MGI project GDP forward to 2025 (from 2014) under a business-as-usual scenario, and then compares this to the GDP in 2025 with the 3 employment gaps above closed (participation hours and sectoral mix). While this is reported as GDP by 2025, in fact is closer to “long run GDP” as in this paper. First, closing such large gaps in many countries are implausible in such a short time and would cause substantial disruption to labor markets. Moreover, by assuming that sub-sectoral labor productivity is constant, they are effectively fixing the capital-to-labor ratio, which implies a substantial increase in required capital when female employment increases.

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