G , ( B B B ) and the basic GEGI on the X axis. One can see that most countries away from the origin fit in one of two groups. First, the only countries with a large better employment gap in _absolute_ terms, like Jordan, GG MM FF BBEE (LL −LL )/LL also have the G similar to the basic GEGI (and hence will have a similar basic and full GEGIs). The second group of countries is where the G is much larger than the basic GEGI—including Central African Republic discussed GG BBEE previously (on the bottom right of Figure 7). In these countries the better employment gap is relatively unimportant GG BBEE in absolute terms, because so few people in those countries work in better jobs. Expressed differently, Appendix Figure 1 shows that countries with a large difference between the G and the basic GEGI have less than half of employment in better jobs. GG BBEE Returning to the definition of the full GEGI (Equation 5) one can see that the where G is much larger than the basic GEGI, better employment is not very important as a share of GDP (low ).<sup>29</sup> Hence, any increase in GG BBEE female employment in the better-employment sector can only generate a modest improvement in GDP. One can ωω see this in Figure 8, where the difference between Better Employment Gap and the basic GEGI is negatively related to the weight of better employment in GDP .<sup>30</sup> In the case of Central African Republic (CAF), for example, the impact of closing the large Better Employment gap is shrunk down because better employment only contributes (ωω) 15% of GDP. This low GDP weight of better employment is a function of the share of employment in better jobs discussed above, as well as a low productivity premium of better jobs due to a low level of human capital. Specifically, Appendix Figure 2 shows that in most of the countries where G is much larger than the basic GEGI, the productivity boost from human capital is around twice that unskilled labor ( h= H ≈2 ), vs 4 times GG BBEE as productive in countries where the G and basic GEGI are similar.<sup>31</sup> EEM II/H II GG BBEE

# **5.  The Speed of Adjustment in GDP**

The GEGIs capture the size of the increase in GDPPC from closing gender employment gaps in the long run. But how long is the long run? And what fraction of the gains can be achieved in the short-to-medium term? The answer to these questions is complicated as it depends on modeling assumptions, parameters that vary countryby-country (and require data that are missing for the Pacific Islands), and the speed of the policy adjustment itself that generates the increase in female employment.

But to try to provide rough calculation, I calibrated the Long Term Growth Model (LTGM, Loayza and Pennings 2018) to the Philippines (based on Qian et al (2018), Ch 5),<sup>32</sup> and then simulated the effect on GDPPC of an immediate increase in female employment in 2020 that closed the gender gap.<sup>33</sup> This is similar to the exercise in the basic GEGI. The LTGM is an appropriate tool to answer this question as the production function in the LTGM is the same as that in the basic GEGI. In this calibration, the initial female employment rate is 46% and the initial male

> 29 According to the assumptions made.

30 Note that this weight depends on the number of workers of both genders in better employment, not the gender gap in better employment. 31 This assumes the human capital is evenly spread across the population. In reality, there is likely to be some positive sorting, such that people with higher human capital are more likely to be in better employment. 32 Most importantly, I assume that the labor share =0.5. The depreciation rate is 3.6%, capital-to-output ratio is 2.2, human capital growth rate is 0.5%, TFP growth rate is 1.5% and investment rate trends up from 26% of GDP initially to 30.1% by 2040. Population demographics are taken from the UN. Baseline GDP growth is initially about 5.5%, but slows to 4.5% by 2050. The ββ male LFP rate is assumed constant at 72%.

33 The Philippines was chosen because of the other gender work in that country and an existing LTGM calibration. However, its LTGM calibration is fairly similar to that in other developing countries, and so this exercise is likely informative for other countries. For example, the assume labor share of 0.5, is similar to the cross- country average in Penn World Tables 10.

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