Data on female labor force participation and fertility are sourced from the World Bank’s World Development indicators, while information on female educational attainment comes from the BarroLee database. Controls – PPP-adjusted per capita income and overall trade openness – come from the Penn World Tables 6.3 (Heston et al., 2002).

Table 2 reports some summary statistics for the female content of exports for the OECD and nonOECD country groups. We can see that for the OECD, these averages are slightly lower, at about 0.25, and stable across decades. For the non-OECD countries, the female content of exports is higher, between 0.27 and 0.30, and, if anything, rising over time. Notably, the dispersion among the non-OECD countries is both much larger than among the OECD, and increasing over time. While the OECD sample, the standard deviation is stable at 0.03-0.04, for the non-OECD is rises monotonically from 0.08 to 0.12 between the 1960s and the 2000s.

Tables 3a to 3c report, for the different time periods, the countries with highest and lowest _FLNX_ values. Typically, countries with the highest values of female content of exports are those that export mostly textiles and wearing apparel, while countries with the lowest _FLNX_ are natural resource exporters. Equally important for our empirical strategy are changes over time. Tables 4a and 4b report the countries with the largest positive and negative changes in _FLNX_ between the 1960s and today. We can see that relative to the cross-sectional variation, the time variation is also considerable. Table 5 presents the summary statistics of the country-level variables of interest and controls. Our final dataset contains country-level variables on up to 146 countries.

# **4 Regression Results**

Table 6 reports the results of estimating the impact of the gender gap on comparative advantage in trade, i.e. eq. (21). The top panel presents the OLS results, the bottom panel the two-stage least squares results in which the interaction of _FLi_ with _GENDERc_ is instrumented with the interactions of _FLi_ with shares of Muslims and Christians in the population. All columns control include country and sector dummies. Columns 2, 4, and 6 in addition include the interaction terms means to capture Heckscher-Ohlin specialization forces, following the empirical model of Romalis (2004): capital intensity of sector _i_ is interacted with capital abundance of country _c_ , and skill intensity of sector _i_ is interacted with skill abundance of country _c_ .<sup>6</sup> We can see that the gender gap has a clear impact on trade flows, especially when the gender gap measures are instrumented with religious composition. The bottom of Panel B reports the first-stage results. The first-stage coefficients are highly statistically significant, and the instruments are comfortably strong in the econometric sense. The estimates are economically significant: moving from the 25th to the 75 percentile in a gender gap variable increases the export share in a sector at the 75th percentile

> 6Capital intensity is computed as the share of value added not going to labor compensation, and is built using the UNIDO database. Skill intensity is computed as the fraction of the total wage bill corresponding to workers with at least some college education in the US (originally obtained from Autor et al., 1998; and translated from the US Census Industrial Classification (CIC) into ISIC Rev. 3 industries using a correspondence built by the authors). Country-level skill and capital abundance are sourced from Hall and Jones (1999).

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