equivalent to 0.73 and 0.85 Canadian college graduates, respectively.

We revisit the wage e¤ect of immigration on average wages when the skill levels of immigrants and nationals are quality-adjusted. Di¤erences in the quality of education is likely to be a source of ’brain waste’(see Matoo et al, 2008). Hence, our adjustment consists in multiplying the number of college graduates originating from a given country by the relative productivity index computed for that country, and consider the remaining fraction as less educated workers. In the previous example a college graduate from Angola is considered as a combination of 0.73 college graduates and 0.27 non-college graduates in the rich country. This method has two main limitations. First, as our adjustment factor is based on Canadian data, it su¤ers from a selection bias. Indeed, Moroccan migrants to Canada are more than likely to have higher skills than Moroccan migrants to France. For this reason, we also provide a second simulation with a correction based on the squared of the Canadian index. Coming back to our example above, one college graduate immigrant from Angola or Portugal accounts for 0.51 or 0.72 units of highly skilled workers, respectively. Second, while our benchmark non-adjusted measure implies that immigrants’human capital is equivalent to that of natives (as if all migrants were trained in the host country), our adjusted measure implies that all immigrants were trained in their birth country. Reality is obviously somewhere in between. However our only objective here is to explore whether a correction for education quality can modify our predictions, hence an extremely negative assumption implies simply that our estimate is a lower bound of the wage e¤ect.

Our third correction in the accounting of foreign-born highly educated is based on the US wage of immigrants from each country of origin (in year 2000). We calculate the weekly wage of college educated immigrants from country j as a linear combination of the wages of US-born college educated and non college educated workers. We then use those weights to distribute a highly educated foreign-born between more and less educated US equivalents. With this method, for instance, a highly educated Vietnamese is considered as equivalent to 0.73 units of US college graduate and 0.27 units of US less educated. If foreign-born college educated migrants from a certain country are paid more than US born they are considered pure college equivalent.

Our fourth and ...nal correction is based on Mattoo et.al (2008) who calculate the probability that an immigrant from country j with tertiary education (completed at home prior to migration) obtains a skilled job in the US labor market. Using the data from 1990 and 2000 censuses and controlling for individual factors (such as age, experience and years since migration), they calculate these probabilities for hypothetical individuals identical in every respect except their origin country. They ...nd that migrants from South Asia perform very well while those from Eastern Europe and Latin America are placed in relatively unskilled jobs.

The results from all of the four corrections are reported in Figure 7. Some corrections imply smaller gains for host countries (especially for the US, Canada, Australia and Singapore) The largest change, relative to the baseline, is for the US where we now observe a loss of around 0.2% in average wages due to ten year of immigration, which is still a small percentage. The corrections do not change the overall picture and the a positive impact of immigration in most European countries is maintained. The limit of all corrections is that, for data limitations, they are all based on schooling quality and labor market assessment of immigrants in Canada or the US and not in European countries.

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