(i) all undocumented immigrants as measured in the early 2000’s are less educated and that (ii) they migrated to the destination country between 1990 and 2000. Our simulation uses the baseline set of elasticities of Table 3. Hence the solid line in Figure 6 corresponds to our baseline simulation depicted on Figure 1 Panel 1a. In the original scenario, the e¤ect of immigration is positive or nil in all EU countries. Adding illegal immigration (see dashed and dotted lines) slightly modi...es our conclusions only in countries where the proportion of illegal immigrants is very large (above 50 percent). Hence, Greece and Italy now su¤er small immigration costs (0.2 and 0.1%, respectively), while the e¤ect for the Netherlands turns to zero in the ’Upper Bound’scenario. The change is obviously driven by the adverse e¤ect on less educated non-migrants. Most of the countries however (such as UK, Belgium, France, Netherlands, Portugal, Spain and Sweden) maintain a positive wage e¤ect of immigration even under the most pessimistic scenario which is likely to largely overestimate the undocumented. In summary, only Italy and Greece su¤ered a (small) negative wage e¤ect from immigration even after adding the highest estimates of undocumented immigrants and counting all of them as less educated.

# 5.2 Accounting for the quality of education and skill downgrading

Simulations presented in Section 4 account for potential complementarity between natives and foreign workers in each skill category. Such imperfect substitutability emerge because immigrants have di¤erent motivations, culture-speci...c skills and limits compared to natives. Another source of heterogeneity between immigrants and natives arises from the absence of equivalence between national and foreign degrees both in terms of quality and subject coverage (such as the case in law degrees). In particular, highly educated immigrants trained in developing countries could be less productive in high-skill jobs than natives with similar educational degrees.

Evidence of such heterogeneity in the quality of education is provided by Coulombe and Tremblay (2009), who compare the skill intensity and schooling levels of Canadian immigrants and natives who were both submitted to standardized tests in literacy, math, and problem solving. These tests provide measures of pro...ciency that are comparable across countries and over time. On this basis, Coulombe and Tremblay estimate a ’skill-schooling gap’for each origin country, de...ned as the di¤erence between the mean years of schooling of the immigrant subgroup and the typical native with the same pro...ciency level. A positive skill-schooling gap of n years means that Canadian nationals with y years of schooling are as productive as immigrants with y+n years of schooling. The larger the skill-schooling gap, the lower is the quality of education in the country of origin. Simple bivariate OLS regressions show that the skill-schooling gap is a decreasing function of per capita income of the origin country. Their -0.10 point estimate of the slope coe¢ cient indicates that the skill-schooling gap is one year smaller when per capita income increases by US$10 000 in the origin country. Using this estimate and cross-country data on per capita income, we construct an indicator of skill-schooling gap for each origin country. Then, assuming that one year of schooling generates a productivity gain of 8 percent, we estimate the relative productivity of educated immigrants and natives in each country, with a benchmark value of one for workers trained in Canada (as well as workers trained in richer origin countries, i.e. the upper bound of this index is one). For example, college graduate immigrants from Angola and Portugal are 17