159 countries for the full GEGI. But this does not include the interpolation used to produce measures of hours worked and the sectoral mix (the parts not included in the basic GEGI). Only about half of countries in their sample (53/95) have data on hours worked, and so they interpolate these data for all the other 42 countries. They only use data on sub-sectorial labor productivity and employment by gender for around a quarter of countries in their sample (25/95), and so they interpolate this for other countries. As such, there are only 14 developing countries in their sample with complete data for all three types of gaps.<sup>37</sup> The basic GEGI is the most comparable with the GDP gains from closing labor force participation gaps in MGI (2015). While MGI does not report gaps for all countries, for the regions and selected large countries reported in their exhibits 6 and 7, the effects of closing gender gaps in participation rates on GDP line up quite closely with estimates from the basic GEGI (Figure 10) — and for MENA and SA (ex. India), the basic GEGI is slightly larger. When including the gains from closing gaps in hours and sectoral allocation, the GDP gains are unsurprisingly slightly larger in MGI on average (see Appendix Figure 5). These other two mechanisms are less important for the regions/countries with the largest gaps (such as India and MENA), but more important for the regions with lower participation-based gaps. However, as the hours and sectoral gaps are interpolated for many developing countries, those estimates may be less reliable – especially at the individual country level.

**Cuberes and Teignier (2016)** estimate the effects of gender gaps in both entrepreneurship and labor force participation on GDP, using a Lucas span-of-control type model of occupational choice.<sup>38</sup> Their estimates are available for 139 countries (33 OCED, 106 non-OECD), which is about 75% of the basic GEGI and 87% of the full GEGI (there is no interpolated data). CT define GDP losses relative to a higher benchmark without gender gaps—rather than as a fraction of the lower status quo for the GEGI (and MGI 2015)—so for comparability, CT’s gender gaps need to be scaled up by . I compare the basic GEGI at the country level with CT estimates of losses due to participation only (CT-Participation), which I calculate as their total losses (Variable 4 in Appendix Tables B1 and C1) 1/(1 −HHCC%) less losses only due to gender gaps in entrepreneurship losses (Variable 3 in Appendix Tables B1 and C1).

Figure 11 shows that the estimates of GDP losses from the basic GEGI are highly correlated with those from CT and for some countries they are almost identical (such as the Republic of Yemen, the Arab Republic of Egypt, Fiji and Tunisia). In general, the CT-Participation measure is about 10%-20% smaller than the basic GEGI.<sup>39</sup> Most of the differences are likely due to definitional difference between employment (for the GEGI) and participation (for CT2016), as well as different vintages of labor-market data. CT’s full measure—including entrepreneurship gaps in addition to participation gaps—is about 10ppts larger than the basic GEGI for low-GEGI countries, but almost identical for high-GEGI countries (Appendix Figure 6).<sup>40</sup>

**Devadas and Kim (2020)** use the World Bank Long Term Growth Model (as in Section 5) to calculate the effects on growth of closing gender gaps in labor force participation.<sup>41</sup> In addition to the standard neoclassical

37 The Appendix to MGI describes this group as the G20 plus Bangladesh, Morocco, Malaysia, the Netherlands, and Sweden. Of these, none are low income, four are lower-middle income (Bangladesh, India and Indonesia, Morocco) and ten are uppermiddle income (Malaysia, Argentina, China, Mexico, the Russian Federation, Mexico, Argentina, South Africa, Turkey, Brazil). 38 In this type of model, the more talented the manager is, the more productive are the labor and capital in his or her firm (and more talented managers run larger firms). Gender gaps in entrepreneurship result in GDP losses because many talented female managers are not able to become managers, resulting in losses in total factor productivity. 39 In part this due to 20 countries with no gender participation gaps in their sample ( in their notation), though it could also be due to a decreasing returns to scale production function. 40 As the full GEGI is fairly similar to the basic GEGI, so is the relationship with the MGI and CT2016 as discussed above. Appendix As the full GEGI is fairly similar to the basic GEGI, so is the relationship with the MGI and CT2016 as discussed above. Appendix λλ= 1 40 As the full GEGI is fairly similar to the basic GEGI, so is the relationship with the MGI and CT2016 as discussed above. Appendix Figure 7 compares the MGI measures with the full GEGI, with the most important difference being that the full GEGI is more similar to the MGI (LFP+hours+Sectors measure) for MENA and South Asia (ex-India). The full GEGI is plotted against the measures from Cuberes and Teignier (2016) in Appendix Figure 8, and has a slightly worse fit relative to the enterprise + participation measure.

41 DK2020 also consider the effects of closing gender gaps in human capital, but those are mostly small.

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