highlighting women’s direct and indirect contribution to long-run economic growth (Galor & Weil, 1996; Klasen, 1999).

A range of modeling approaches have been used to quantify the economic loss caused by structural and systemic differences between men and women. Following the seminal work of Galor and Weil (1996), an overlapping generations framework by Agénor (2012) estimated the monetary valuation of a decline in the cost of childrearing (particularly sons), a decline in gender biases in the workplace, an increase in women’s bargaining power, and increased mothers’ time allocated to daughters. This model finds that policy regimes that increase public spending may contribute to increase in women’s productivity, women’s time allocation to work, and economic growth, particularly if investments were made to improve infrastructure (such as roads and access to water and sanitation). A simulation approach by Cavalcanti and Tavares (2007) examined differences in output and income by varying the degree of gender-based wage discrimination, taking as endogenous savings, fertility, and labor force participation rates. The authors found that a 50 percent increase in the gender wage gap led to a 35 percent decrease in income per capita, noting that up to 60 percent of the gap in output per capita between countries could be attributed to continued gender inequality. In Niger, a Computable General Equilibrium (CGE) model based on a LINKAGE model<sup>2</sup> was applied to estimate the economic opportunity from gender equality and to isolate the monetary impact of gender differences.<sup>3</sup> The study concludes that reduced gender gaps have the ability to increase GDP in 2030 by 12.6 percent under scenarios of reduced fertility and close to universal education, and by 8.9 percent if increased labor force participation and productivity are modeled (World Bank, 2019). Pennings (2022) constructs the Employment Gap Index (GEGI) that translates the inequalities in employment between men and women into the potential monetary gains (in GPD-percapita). On average across the countries studied,<sup>4</sup> the GEGIs estimate that long-run GDP per capita would be 19 percent larger if gender gaps could be closed by increasing female employment to that of men. With an application of a Lucas span-of-control model of occupational choice with heterogeneous entrepreneurial endowments, Cuberes and Teignier (2016) examine the impact of inequality between men and women in entrepreneurship and labor force participation on productivity and per-capita income, noting average income losses of 15 percent due to gender gaps.

Devadas and Kim (2020) use the World Bank Long-Term Growth Model (LTGM), which is built on a Solow-Swan growth model, to estimate the effect of increasing labor force participation and education for women on GDP growth. For the indirect impact of closing gender inequality, the study uses an extension of the LTGM-TFP model in which the total factor productivity (TFP) growth rate is a function of a TFP determinant index and initial TFP level. The authors find that the GDP-per-capita loss from gender gaps amounts to 7.9 percent in low-income countries, 21.3 percent in lower-middle income countries, 12.3 percent in upper-middle income countries and 6.4 percent in high-income countries. In a similar exercise, Wodon et al. (2020) employ a comprehensive and domain-specific framework to assess the economic opportunity associated with closing the gender gap by estimating the impact of inequalities using regression analyses or statistical differences. The authors highlight the importance of closing these gaps by separately estimating the impacts and economic costs of gender inequality across five domains:

> 2 See van der Mensbrugghe (2011) for a complete description.

> 3 In the case of Niger, costs calculated are related to earnings, population growth, under-five mortality and standing, and savings for education.

> 4 The study has a full variant that differs from the basic variant. Rather than using aggregate employment gaps, it uses a weighted average of employment gaps in “better employment” and “other” types of employment. The study uses 185 countries for the basic GEGI and 159 for the full GEGI.

4