Many studies have shown that out-migration can be selective.<sup>27</sup> Thus, the set of individuals who decide to return to their countries of origin or leave the US for other destinations may not be a random sample from the initial distribution of the migrants. If the most (least) able members of a cohort decide to out-migrate, the improvement in occupational placement across the decade is understated (overstated). A differential selective effect of return migration by country of origin may affect the results obtained in the paper. Examining the effects of return migration is a challenging task when using the US Censuses as a data source mainly because different individuals are surveyed in different years.<sup>28</sup> Duleep and Regest (1997) suggest a method for testing the sensitivity of results based on US Census data to biases caused by sampling error and migration. The approach taken here is a very simple one and consists of calculating the rate of attrition for the skilled immigrants in a particular cohort over the decade that is closest to their arrival in the US. As the next step, the countries with the highest rates of return migration are identified and excluded them from the samples. Re-estimating equation (1) and examining the relationship between the new predicted occupational placements obtained after eliminating those countries from the sample, we find that the improvement patterns do not differ from the ones obtained before trimming the sample.

Another source of bias is related to the fact that the sample of people responding to the long-form questionnaires of the US Censuses is drawn anew each decade from the entire population of the country. Hence, the validity of the inferences stated in this paper may be affected by the comparison of different sets of individuals over time. Because real panel data is typically less easily available repeated cross-section datasets, researchers have determined the conditions under which the latter can produce unbiased estimates equivalent to the ones based on estimating genuine panels. The pseudo-panel methodology in which repeated cross-sections are treated as panels was introduced by Deaton (1985) and consists of estimating economic relationships based on cohort means rather than individual observations. This approach is based on the assumption that the cohort population means are genuine panels in that, at the population level, the groups contain the same individuals over time. Applying the pseudo-panel analysis to our data we find our results to be robust to the changes in the methodology.<sup>29</sup> The successful

> 27 Jasso and Rosenzweig (1988), Borjas (1989) , Borjas and Bratsberg (1996), Hu (2000), Lubotsky (2007) 28 All the detailed information available in the census databases comes from the answers to the long-form questionnaires, which are filled out by one in six persons residing in the US.  The sample changes from census to census, making it impossible to track the same individuals over time.

> 29 We first focus on the 1975 arrivals, and used Education Index 1 as a measure of occupational placement. We construct cohorts - in the sense implied by the pseudo-panel theory- by country, period of arrival (1970-1974; 1975-1979), and education level (unskilled, representing individuals with  less than five years

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