educational attainment is measured by the average years of schooling of females over 15 years of age. This variable measures women’s investment in human capital, which can be interpreted as making women more suitable for formal sector employment. Note, however, that this variable’s relationship to female labor supply is probably less straightforward, since staying in school longer actually _reduces_ one’s labor supply in the short to medium run. To summarize, these three outcome variables are intended to test the prediction of the model that when trade expands women’s employment opportunities, they will respond by raising their labor supply and investing in human capital (predictions 2a, 2b and 2c).

The controls include PPP-adjusted per capita income, overall trade openness, and, in the case of cross-sectional regressions, regional dummies. Our cross-sectional specifications are estimates on long-run averages for the period 1962-2007, while in the panel specifications the unit of time is a 5-year period, so all the variables are 5-year averages. The data span 1962 to 2007 in the best of cases, though not all variables are available for all time periods.

# **3 Data Sources and Summary Statistics**

The key indicator required for the analysis is the share of female workers in the total employment in each sector. We obtain this information from the UNIDO Industrial Statistics Database (INDSTAT4 2009). This database contains information on the total employment and female employment in each manufacturing sector for a large number of countries, starting in the late 1990s. The data are available at the 3-digit ISIC Revision 3 classification (61 distinct sectors). In order to construct the share of female workers in total employment in sector _i_ , _FLi_ , we take the mean of this value across the countries for which these data are available and relatively complete. The resulting sample includes eleven countries in each of the developed and developing sub-samples: Austria, Malta, Slovak Republic, Cyprus, Lithuania, Japan, United Kingdom, New Zealand, Korea, Italy, Ireland; and Indonesia, Turkey, Azerbaijan, Jordan, India, Philippines, Malaysia, Chile, Morocco, Egypt, Thailand. Table 1 reports the values of _FLi_ in our sample of sectors. It is clear that there is wide variation in the share of women in sectoral employment. While the mean is 27 percent, these values range from the high of 71 percent in Wearing Apparel and 62 percent in Knitted and Crocheted Fabrics to the low of 8 or 9 percent in Motor Vehicles, Bodies of Motor Vehicles, Building and Repairing of Ships, and Railway Locomotives.<sup>5</sup> The export shares _ωict_<sup>_X_are calculated based on the COMTRADE database, which contains bilateral</sup> trade data starting in 1962 in the SITC revision 1 and 2 classification. The trade data are then aggregated up to the ISIC Revision 3 classification using a concordance developed by the authors.

> 5One may be concerned that these values are very different across countries in general, and across developed and developing countries in particular. However, it turns out that the rankings of sectors are remarkably similar across countries. The values of _FLi_ computed on the OECD and non-OECD samples have a correlation of 0.89. Pooling all the countries together, the first principal component explains 77 percent of the cross-sectoral variation across countries, suggesting that rankings are very similar. We also experimented with taking alternative averages: medians instead of means across countries; and dropping outlier values of female shares in individual sectors. The results were very similar.

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