28 country’s history.<sup>46</sup> Furthermore, since we run separate regressions for female and male workers, the cohort FE are gender-specific. See Web Appx. Section H for details.<sup>47</sup> Differences in the returns to experience are preserved if we include 10 industry FE and/or 10 occupation FE (Web Appx. Table A.6).<sup>48</sup> For the returns to education, the occupation FE disproportionately reduce the returns for women in richer nations, which might indicate selection. At the same time, the ability to enter better (i.e., higherreturn) occupations is part of the returns to education. In addition, overall patterns in the returns remain, and our simulation results should be largely unaffected.<sup>49</sup> A related concern is whether there are differences in self-employment between men and women, as there could be issues with how self-employed individuals calculate and self-report the amount of salary taken from the business. Using the I2D2 data, we find that women are globally less self-employed: The self-employment rate is 36, 18 and 6% for women in the LICs, MICs and HICs, respectively, vs. 44, 33 and 13% for men. If the salary of self-employed individuals includes some of the returns to capital, we may overestimate the returns to experience of men, and the gender gap in the returns. However, the returns are similar when excluding the self-employed (Web Appx. Table A.6).

Since women are more likely to be selected in contexts where they work less, Web Appx. Table A.6 shows that differences in the returns are preserved if we drop country-year-samples with disproportionately high non-employment rates for women, in particular samples with rates above the median (54%, which is also the mean) or 25th percentile (44%) in the sample.<sup>50</sup> Alternatively, we drop samples where the nonemployment rate is much higher for women than for men, in particular samples with rates above the mean (25%), median (22%) or the 25th percentile (13%) in the sample.

ends), 1954 (ends of racial segregation in schools), 1962 (Cuban Missile Crisis), 1964 (Civil Rights Act), 1968 (Martin Luther King assassinated), 1974 (Watergate), 1980 (Reagan), 1990 (Gulf War starts, Cold War ends), 1994 (Nafta), 2001 (9-11), and 2007 (Great Recession). Of course, blindly following Wikipedia-BBC leads us to include events that might not be important for workers. At the same time, it is better if we blindly follow their selection of important events rather than cherry-picking the events ourselves.

> 46However, with events FE, the gender gap in the returns to experience is similar across _all_ income groups. Nonetheless, the simulation results hold with return parameters estimated that way (not shown).

> 47Results hold if periods are based on other ages, controls for the country’s average per capita GDP growth rate for those years during which the individual was aged between 18 and 67 are added, or combinations of the decade FE, events FE and growth rate approaches are implemented (not shown).

48We follow the International Standard Industrial Classification (ISIC revision 3.1) and the International Standard Classification of Occupations (ISCO-88). See Web Appx. Section H for details.

49We cannot examine if, for a given industry/occupation, women self-select into low-return positions.

> 50For the population of 18-67 year-olds who are not attending school full-time, the _non-employment rate_ is equal to 100 - the percentage of the population that is currently working.