to technology adoption by female workers (Devadas and Kim 2020), the effects of greater female entrepreneurship via TFP (Cuberes and Teignier 2016), higher hours per female worker (McKinsey Global Institute 2015; henceforth MGI), or greater allocational efficiency of female labor across sectors or firms (MGI 2015; Bertay et al 2020) — see Cuberes and Teignier (2014) for a literature review. In addition, the GEGIs only capture gender gaps in employment—as their name suggests—rather than broader measures of gender gaps in terms of pay (Woden and De La Briere 2018) or education (Dollar and Gatti 1999, Devadas and Kim 2020).

There are relatively few other papers that try to estimate the effects of closing gender employment gaps on GDP for a large number of developing countries. The closest studies are Cuberes and Teignier (2016), MGI (2015) and Devadas and Kim (2020), which are discussed in detail in Section 6.<sup>3</sup> As mentioned above, the GEGIs are highly correlated with these other studies (>95%). Relative to the effect on GDP in those studies, the GEGIs are similar on average, or a little smaller because the other studies include other mechanisms beyond those considered in the GEGI. However, when focusing on comparable mechanisms, the GEGIs are similar or slightly larger.

The contribution of the GEGI to this literature is to provide a simple and transparent measure of the growth impacts of gender gaps that is available for almost every country. Simplicity and transparency are needed because alternatives do not have closed-form solutions for the size of the effect on GDP, and are not easy to calculate. In contrast, the GEGIs can be calculated easily in a spreadsheet from employment rates using closed-form expressions. Data availability is important because the above measures exclude many developing countries. For example, the sample size of the basic GEGI is double that of MGI (2015) and Devadas and Kim (2020), and 40% larger than Cuberes and Teignier (2016). The GEGIs are not intended to be the most accurate or comprehensive measure of gender employment gaps in countries with very good data, but rather to provide approximate estimates of GDP impacts in _every_ country, and the only estimates for many countries where the data used in other studies is missing.

The rest of this paper is organized as follows. Section 2 discusses the basic GEGI, Section 3 discusses the full GEGI, Section 4 explores differences between the basic and full GEGIs, Section 5 discusses the speed of adjustment of GDP toward the GEGI, Section 6 provides a quantitative comparison of the GEGIs with three other similar papers, Section 7 presents an application for Pacific Island countries and Section 8 concludes. Appendix Table 1 and an online spreadsheet [link] list the full and basic GEGIs for all countries, and the employment data from which they are constructed. Derivations are in Appendices 1-3. Appendix 4 provides adjusted GEGIs for eight countries with unbalanced male-female populations (Bahrain, Kuwait, Maldives, Nepal, Oman, Qatar, Saudi Arabia, and the UAE).

# **2. The basic Gender Employment Gap Index (GEGI)**

3 Goldman Sachs (2007) use a measure very similar to the basic GEGI to calculate GDP gains for 9 high income countries and the Euro area, though do not calculate it for a broader set of countries (and no developing countries). Unlike the GEGI, they claim that these gains are overstated due to “lower productivity, because it [higher employment] reduces the capital-to-labor ratio”, whereas the GEGIs are long run measures that include the effects of induced capital accumulation.

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