controlling for this trend leaves no variation that can be exploited to identify these coefficients.<sup>8</sup> Our main industry-level female intensity variable ( _FL_ ) is computed as the average share of female workers in each sector in a sample of some 20 developed and developing countries. We favor this measure because it is most representative of the female intensity of sectors in an average country. However, a drawback of our main measure is that it does not include non-manufacturing sectors. To check robustness of our results, we implement an alternative strategy, of constructing a measure of _FL_ based on data for a single country – the U.S.. We thus use the Labor Force Statistics database of the U.S. Bureau of Labor Statistics (BLS). Using data from the Current Population Survey, the BLS publishes “Women in the Labor Force: A Databook” on an annual basis since 2005. It contains information on total employment and the female share of employment in each industry covered by the Census. The data are available at the 4-digit U.S. Census 2007 classification (262 distinct sectors, including both manufacturing and non-manufacturing). 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 years for which the data on the female share of employment are available (2004-2009). The resulting sample includes 78 manufacturing and 15 non-manufacturing sectors. Appendix Table A1 reports the values of _FLi_ for the top and bottom 5 sectors according to U.S. data. These sectors are similar to what we find in our baseline measure: the least female-intensive sectors tend to be in heavy machinery, while the most female-intensive sectors in textiles and apparel. Appendix Table A1 presents the basic summary statistics of the U.S.-based _FL_ measure, breaking up the manufacturing and the non-manufacturing sectors.

While the U.S.-based alternative _FL_ measure has the advantage of extending the set of sectors to agriculture and mining, it has two important drawbacks. First, the data are compiled based on individual-level surveys rather than firm- or plant-level data, and thus relies on workers selfreporting their industry of occupation. Thus, if the number of individuals in the survey who report working in a particular sector is small, or if workers make mistakes in reporting their industry of employment, the data will be measured with error. And second, the U.S. is only one, very special country, and thus its values of of FL may not be representative of the average country’s experience.<sup>9</sup> Both of these considerations will raise the amount of measurement error on the right-hand side, leading to attenuation bias in the coefficients.

With these caveats in mind, Appendix Tables A3 through A5 replicate all of the regression estimates in the paper using the U.S.-based _FL_ indicator instead. We can see that by and large, the results using our main measure of gender gap – fertility – are robust. However, the other two outcome variables, that are marginally significant in our main results, are less so here.

> 8For these two outcome variables, adding country effects but no time effects produces marginally significant coefficients with the right sign, implying that the time effects are responsible for changing the sign of the coefficient.

> 9For our UNIDO-based measure, averaging of the share of female workers across a couple of dozen countries helps alleviate both of these problems.

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