population share became insignificant in some specifications. The inclusion of R&D spending, which is available only for a much smaller sample, and urbanization also do not materially change the results.

Potential labor supply is defined as the product of the working-age population and the fitted value of age- and gender-specific regressions of labor force participation rates (lfpra,g,t) in percent on their structural determinants (Xa,g,t) and controlling for cohort effects, fixed effects, and the state of the business cycle—defined as the deviation of the logarithm of real GDP from the Hodrick-Prescott-filtered trend. The vector Xa,g,t includes gender-specific education outcomes (secondary and tertiary completion rates in percent of the population over the age of 25 and enrollment rates in percent of population of the age group that officially corresponds to the level of education, age-specific fertility rates (births per woman), and life expectancy (in years). These are interacted with a dummy variable Demde which takes the value of 1 for EMDEs. The vector Ca,g,t includes all the control variables:<sup>19</sup> lfpra,g,t = _α_ a,g + _β_ a,g Xa,g,t + _γ_ a,g Xa,g,t * Demde + _δ_ a,g Ca,g,t + _ε_ a,g,t .

Data on the working-age population comes from the UN Population Statistics Database. Data for age- and gender-specific labor force participation rates are available from Key Indicators of the Labor Market (KILM) of the ILO Population Statistics Database for 1990-2019, which is spliced by Labour Force Statistics of the OECD for 1960-2020 for 33 advanced economies and 16 EMDEs. This produces data for age- and gender-specific labor force participation rates for 1960-2020 for up to 38 advanced economies and 142 EMDEs.<sup>20</sup> Completion rates of secondary and tertiary education are from Barro and Lee (2013) and the World Bank’s World Development Indicators; age-specific fertility rate and life expectancy are from the UN’s World Population Projections database; gender-specific secondary and tertiary school enrollment rates are from the World Development Indicators. The regression sample includes up to 35 advanced economies and 133 EMDEs for 1987-2020.<sup>21</sup> The regression results are broadly in line with findings in the previous literature (table 7).

First, among teenage and younger women, fertility rates are associated with higher labor force participation as mothers are more likely to discontinue their education and 19 This approach combines those by Fallick and Pingle (2007) and Goldin (1994). For the United States, Fallick and Pingle (2007) estimate labor force participation by age group and gender as a function of cohort and age fixed effects as well as business cycle fluctuations. Goldin (1994) models aggregate labor force participation rates as a function of country-level variables such as female schooling. The regression used here incorporates both cohort effects and country-level variables modelling human capital and other factors driving labor force participation.

> 20 This is an unbalanced sample because some of the exogenous variables are not available for the full period for all countries. However, the regression results are robust to restricting the sample to the balanced panel with fully available data.

> 21 Since UN data for life expectancy is only available for five-year periods, historical life expectancy data from the World Developing Indicators database is used. For projection years or missing data, UN World Population Statistics are spliced with data from World Development Indicators database.

34