- The effect on GDP is in the long run (meaning a generation or so) because that is the amount of time

- required for physical capital to accumulate in response to higher employment rates (which will depend on financial market openness, domestic savings, etc.). More generally in standard Solow-Swan-style growth models, the effect of higher employment rates on economic growth has two parts:<sup>8</sup> 1. The _direct_ boost to GDP from more workers. This occurs relatively quickly, but is somewhere between half and three-quarters of the total gains, depending on the economy (the labor share of income ).

- 2. The _indirect_ boost to GDP, when higher female employment encourages capital accumulation. This occurs ββ

- relatively slowly, and typically accounts for between a quarter to half of the total gains (1- ).

- The “long-run” timeframe is needed to capture both direct and indirect effects. This is discussed more in Section 5. ββ

In more concrete terms, this means that the basic GEGI can be interpreted to answer two counterfactuals:

- Backward-looking: suppose a previous government had implemented policies (a generation ago) to bring female employment rates up to men’s, then GDPPC would be _GEGI%_ higher today than it is currently.

- Forward-looking: suppose the government enacted policies today to bring female employment rates up to men’s. Then GDPPC in the long-run (a generation or so from now) would be _GEGI%_ higher than otherwise.<sup>9</sup>

# **2.3 Data – Basic GEGI**

I calculate the GEGI using the employment-to-population ratio of 15-64-year-olds by gender, as in Equation 2. Employment data are available for around 185 countries. The data are taken from Pennings (2020), who in turn takes the survey data (not projections) from the ILO series “Employment-to-population by sex and age (%) – Annual”, Age (Youth,Adults): 15-64, using the latest period available<sup>10</sup> or from the World Bank JOIN database, which focuses on low income and middle-income countries.<sup>11</sup> In most cases, the ILO data are the most recent. For three countries (Burundi, Sierra Leone and Togo) the basic GEGI as calculated by Equation 2 is slightly negative, and so in these cases I round up the basic GEGI to zero so it is between 0 and 100%.

As discussed in Pennings (2020), in a number of developing countries employment rates change dramatically over time. Most likely this is due to changes in the definition of employment and whether it includes or excludes own-use production workers (subsistence agriculture) — though this is rarely properly documented.<sup>12</sup> Definitional changes in employment are less of an issue for the GEGI than for other measures, as we are interested in the relative gap between male and female employment rather than its absolute magnitude.

# **2.4 Results – Basic GEGI**

The Basic Gender Employment Gap Index (GEGI) is shown in Figure 1, plotted against income per capita (log scale). On average the GEGI is close to 19%, indicating that long-run GDP per capita would be almost 20% higher if the gender employment gap was closed. The basic GEGIs are listed in Appendix Table 1 for all 185 countries.

> 8 The GEGI does not include other potential benefits of higher female employment, like slower population growth.

> 9 Note that because the World Bank Human Capital Index (HCI) used in the construction of the full GEGI is forward looking only, the full GEGI presented in Section 3 only has the second interpretation.

> 10 Downloaded from https://www.ilo.org/shinyapps/bulkexplorer7/ on 13 Dec 2019. Tonga uses updated ILO data from 2018. 11 The JOIN employment rate is sometimes sourced from “Employment to Population Ratio, aged 15-64, (Column V) of the public JOIN spreadsheet (downloaded 21 October 2019, link), and is sometimes calculated from more recent microdata that were acquired through private correspondence.

> 12  These changes stem from definitional changes following the 19th International Conference of Labor Statistics (2013), though the implementation of those definitional changes is slow and not uniform across countries.

5