human capital, the effect of adult height on worker productivity and wages, standard estimated values for production factor shares and the effect of schooling and height on earnings. Appendix 1 describes the parameter values that were used to calibrate the model and the sources from which these values were obtained. Several additional parameter values were used in the expanded version of CKW 2017 model.

Firstly, we parametrize the sectoral gender-specific friction and the gender-wage gap. This parametrization is drawn from a secondary-data analysis of the HIES 2014 from LISGIS (2015). The secondary source facilitates the estimation of earnings per unit worked (month, week and hour) for each employed person. We recognize that earnings measurement at the micro-level has a noisy and volatile nature<sup>15</sup> but it has the advantage of being detailed to the intended population of interest for the adoption at the macro-level in this study. We find that the sectoral friction is noticeable in the higher wages of the modern sector across gender. The wage of female workers in the modern sector is 72.2 percent higher than the wage of female workers in the traditional sector. The average wage of a male worker in the modern sector is 46.2 percent higher than the average wage of a male worker in the traditional sector. Likewise, we find that women have lower wages than men in both the modern and the traditional sector. The average wage of a male worker in the modern sector is 6.5 percent higher than the average wage of a female worker in the same sector. And the average wage of a male worker in the traditional sector is 25.5 percent higher than the average wage of a female worker in the traditional sector.<sup>16</sup> The modern-traditional divergence is substantially accentuated, particularly for women, and the gender-pay gap is observed in both the modern sector and the traditional sector to differing degrees.

Secondly, we use the adapted CKW 2017 model to estimate gender-specific contributions to output when we incorporate a series of convergences. This exercise seeks to inform policymakers of the potential monetary gain of the gender dividend. Namely, the modeling of these estimates is intended to demonstrate how interventions to close the gender gap within domains such as family planning, women’s educational attainment, returns to labor and the like can ultimately generate economic returns that would not occur under business-as-usual circumstances. For each selected input to production (e.g., educational attainment), we estimate the change in productive output when the existing gender gap within that factor is mechanically closed.

We run various exercises for each selected input to production.<sup>17</sup> First, we allow a complete convergence between men and women for each domain and which we refer to as the ideal case. We regulate the fertility to an instant-replacement rate for the ideal case, or a quantity necessary to ensure a net reproduction rate of 1.0 starting in the 2015-2020 period (United Nations, 2010). Second, we allow for partial convergences within selected domains through benchmarking, which allow a comparison to averages found either in the Sub-Saharan Africa region or in the OECD region. Some of the benchmarking with Sub-Saharan Africa and OECD regions are not estimated due to domain-specific constraints related to data availability or limited applicability of the convergence. For the sectoral difference, we further hypothesize a complete convergence with depreciation of modern-sector wages and appreciation of traditional-sector wages driven by the surplus of female workers contributing to modern-sector production and incurred by the triggered convergence. We conclude with a simultaneous convergence by allowing the

> 15 For instance, during an economic downturn (e.g., Ebola, COVID-19) low-skilled workers are the first to be discharged and suffer from temporary dismissals or breaks in employment. Since more affluent workers are the ones to remain in the workforce, the measurement of wages or earnings at that time may be biased upwards. Other types of biases exists, such as mis-reporting from respondents or seasonal fluctuations.

> 16 These estimates slightly differ from the wage gap discussed in the introduction since the latter condition on worker characteristics.

> 17 See Appendix 4 for the list of estimates modeled by category.

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