In this section, we report estimates of the Canadian born earnings equation (6) and nested versions of the immigrant earnings equation (7), from the most restricted to the least restricted. This allows for the examination of changes in coefficient estimates following the removal of restrictions. The estimated returns to years of schooling and work experience are reported in Table 2 for males and Table 3 for females. The other estimated coefficients are reported in Table B1 in Appendix B.
5.1 Base case: No control for human capital quality
The most restricted case of (7) is when it is assumed that the wage determination process of immigrants is exactly the same as that of Canadian born individuals; that is, when it is assumed that from a human capital point of view, the quality of one year of schooling or work experience acquired in an immigrant’s country of birth is the same as that acquired in Canada and that an immigrant’s country of birth does not have any influence on the wage determination process. This corresponds to equation (7) with all the θ’s and the γ vector set equal to zero. The results in the column labelled Model 1 in Table 2 and Table 3 show that if such an assumption was correct then the returns to human capital would significantly be lower for immigrants than for Canadian born individuals. For example, for males, the returns to years of schooling and years of work experience (evaluated at zero years of work experience) would respectively be 6.6 percent and 3.5 percent per year for male immigrants compared with 8.0 percent and 4.9 percent per year for their Canadian born counterparts. The results are similar for females.
5.2 Human capital acquired in Canada vs. Human capital acquired in birth country
Model 2 in Table 2 and Table 3 is a first step towards distinguishing between the quality of schooling and work experience acquired in Canada and that acquired in an immigrant’s country of birth. The difference between this model and Model 1 is that the returns to schooling and to work experience are now allowed to differ by a fixed quantity depending on whether schooling and work experience have been acquired in Canada or outside Canada. This model also allows for country of birth fixed effects. In other words, compared to Model 1, Model 2 relaxes the assumptions(θ2 = θ4 = θ7 = θ8 = γ = 0).
Model 2’s estimation results strongly support the notion that the quality of schooling and of work experience is perceived by Canadian employers to be lower if these qualifications have been acquired outside Canada. Looking at schooling first, we find from column (3) of Table 2 that all other things equal, a male immigrant earns 0.5 percent less per year of schooling if his schooling (including his highest diploma) has been acquired in his country of birth than if his schooling has been acquired in Canada (the equivalent figure for a female immigrant is 1.0 percent). What seems to be driving this result though is not so much whether schooling is acquired outside Canada, but whether the highest diploma is obtained in Canada. Indeed, this differential is reduced to only 0.1 percent if a male immigrant obtains his highest diploma in Canada instead of in his country of birth (the equivalent figure for a female immigrant is 0.3 percent).
The difference between the marginal return of one year of work experience acquired in Canada and that of one year of work experience acquired in an immigrant’s country of birth is even more pronounced: for males and females, the return to one year of work experience acquired in an immigrant’s country of birth is about 1.9 percent less than the return to one year of work experience acquired in Canada (or about two-third smaller in relative terms)10.
5.3 Relative GDP as a human capital quality indicator
Model 2 is not that useful for estimating the impact of human capital quality on the immigrant wage gap along the lines discussed in Section 2 as it pre-supposes that the effect of quality of schooling and work experience on immigrant earnings is the same for all countries of birth, which is clearly untenable, especially since results in international standardized literacy tests vary across countries. Model 3, Model 4 and Model 5 correct for that by introducing Relative GDP per capita as a human capital quality indicator11. Model 3 allows for human capital quality to affect earnings only directly (à la Borjas, 1987; Akbari, 1996 and Hanushek and Kimko, 2000) while Model 4 and Model 5 allow for human capital quality to affect earnings both directly and indirectly through the returns to years of schooling and years of work experience. Specifically, Model 3 corresponds to equation (7) but with the restriction (θ3 = θ5 = θ9 = θ10 = θ11 = θ12 = 0) while Model 4 and Model 5 correspond to equation (7) without restriction. Model 4 and Model 5 differ by the control variables that are included in the vector y. Model 4 includes province of residence, language skill and marital status. Model 5 adds to the above 9 occupation and 19 industry indicators. While incorporating occupation and industry variables in wage regressions is often frowned upon when performing Blinder-Oaxaca type decompositions (because of the possibility of endogenetiy between salary, occupation and industry), doing so could be useful in our case to test the robustness of our results12.
Looking at the coefficient estimates of Model 3 in Table 2 and Table 3, we find that the direct impact of Relative GDP per capita on immigrant wages is rather small (although statistically significant). Specifically, we find that an immigrant’s wage elasticity with respect to this variable is 0.042 for males (0.018 for females), which is actually smaller than the value of 0.116 found in Borjas (1987). This suggests, for example, that a male immigrant from a country whose GDP per capita level is 10 percent that of Canada (e.g., India) earns about 9.7 percent less than another immigrant who comes from a country whose per-capita GDP is comparable to that of Canada, but who is similar in all other respects. The equivalent figure for female immigrants is 4.6 percent.
While the results of Model 3 show that Relative GDP per capita has a positive direct impact on immigrants’ wages, we are concerned that Relative GDP per capita may be capturing more than a human capital quality effect. Indeed it could be capturing the effects of a host of other factors beside quality of schooling and of work experience, such as, for example, an immigrant self-selection effect (Borjas, 1987).
The coefficient estimates of Model 4 and Model 5 in Table 2 and Table 3 provide a more convincing argument that Relative GDP per capita is an appropriate indicator of human capital quality. Indeed, we find that the effects of Relative GDP per capita on immigrants’ earnings seem to be mostly operating through the Years of schooling and Years of work experience variables: the interaction effects of Relative GDP per capita are all highly statistically significant and of the expected signs (that is, they are positive). However, the direct impact of Relative GDP per capita is much smaller than in Model 3 and is actually statistically insignificant in Model 4, which suggest that there may not be a self-selection effect of the type discussed in Borjas (1987).
Overall, Model 4 and Model 5 estimate that human capital acquired in a rich country is valued significantly more than human capital acquired in a poor country. For male immigrants in Model 4, schooling acquired in a country whose GDP per capita is similar to that of Canada has a rate of return that is 1.6 percent per year higher than that acquired in a country whose GDP per capita is one-tenth that of Canada13. The equivalent figure for work experience is 0.8 percent14. The results for female immigrants are similar.
A puzzling result though concerns the returns to human capital acquired in Canada. While the return to work experience for immigrants (whether male or female) is roughly the same as that for Canadian born individuals if the work experience has been acquired in Canada, which is what we should expect, the return to schooling acquired in Canada in Model 4 is 1.0 percent per year lower for male and 2.0 percent per year lower for female immigrants than for their native counterparts. This may reflect Schaafsma and Sweetman’s (2001) contention that the outcome of education acquired by immigrants in Canada may be lower because of “acculturation”. In our case, the impact of acculturation would appear to be not only in terms of levels of attainment but also in terms of returns. More research is needed in that regard15.
Another interesting observation comes from comparing, for immigrants, the return to human capital acquired in Canada with that acquired in birth countries with GDP per capita similar to that of Canada. If the estimated returns to schooling and to work experience truly reflect the value of these skills (and are not reflective of other factors such as labor market discrimination), and if GDP per capita is a complete measure of human capital quality, then we should expect the rates of return on schooling and work experience acquired in Canada to be the same as those on schooling and work experience acquired in countries whose GDP per capita are similar to that of Canada. This is not, however, what we observe. The return per year of schooling acquired in a country whose GDP per capita is similar to that of Canada16 is about 0.8 percent lower for males (2.3 percent lower for females) than for schooling acquired in Canada, and the return to work experience is about 1.5 percent per year lower (1.8 percent compared with 3.3 percent for males and 1.3 percent compared with 2.8 percent for females)17.
Finally, we would be remiss if we did not mention that controlling for human capital quality significantly reduces the magnitude of the language skill coefficients in the wage regressions. For example, compared to only knowing English, the penalty for not knowing any official language for male immigrants goes from -35.7 percent in Model 1 to -14.9 percent in Model 4 (see Table B1 in Appendix B). The equivalent figures are respectively -24.2 percent and -9.4 percent for females. This suggests that the role of language skills in explaining the immigrant wage gap may not be as large as what has been estimated in previous studies—much of it may just have been reflecting lower schooling and work experience quality.
5.4 Results from the Blinder-Oaxaca decomposition
In this section, we look at the contribution of differences in human capital quality to the immigrant wage gap using the decomposition (8). Table 4 reports selected elements of that decomposition estimated using Model 1 (which does not accounts for human capital quality) and Model 4 (our preferred specification, which accounts for human capital quality while bypassing the possible endogeneity problems following the introduction of occupation and industry variables in the regression)18. As has been noted elsewhere (see, for example, Nadeau and Seckin, 2010), a key reason why Canadian immigrants earn less on average than natives is not because they have fewer years of schooling or fewer years of work experience than natives, but because their returns to schooling and work experience are much lower than those enjoyed by natives. Indeed, based on observed characteristics alone, Canadian male immigrants should earn 15.6 percent more than natives, but, mostly because of lower returns to schooling and work experience, in the end, they earn 4.1 percent less than natives (the equivalent figures are respectively 11.5 percent and 3.3 percent for female immigrants). It is also interesting to note that between the differential return to schooling and the differential return to work experience, it is the differential return to schooling that accounts for a larger share of the immigrant wage gap. In fact, the contribution of the differential return to schooling to the immigrant wage gap is about 35 percent larger than that of work experience for males, and about 150 percent larger for females.
A number of explanations have been proposed to account for the lower returns to schooling and work experience earned by immigrants compared to natives, including lower quality of skills and labor market discrimination. A key objective of this paper is to put a figure on the impact of the former. According to the Model 4 estimates in Table 4, lower human capital quality is by far the major reason why immigrants earn less than natives. In fact, we find that after controlling for quality, the share of the immigrant wage gap explained by the differential return to schooling drops by almost 25 percent for both males and females (from -0.184 to -0.141 for males and from -0.363 to -0.272 for females). The drop in the share of the immigrant wage gap explained by the differential return to work experience is even more dramatic: it is of about 56 percent for males (from -0.147 to -0.064) for males and of almost 72 percent for females (from -0.148 to -0.041).
Overall, we find that the lower quality of schooling and of work experience more than negates the endowment advantage that immigrants have in these areas. Indeed, based on observed years of schooling and observed years of work experience alone, male immigrants should earn 9.1 percent more than natives, but lower quality of schooling and work experience subtracts 15.3 percent from that (the equivalent figures are respectively 6.0 percent and 17.0 percent for female immigrants). Of the total human capital quality effect, 75 percent is accounted for by differences in GDP per capita for males and 50 percent for females.
It is also noteworthy that unlike other studies (see, for example, Aydemir and Skuterud, 2005; Nadeau and Seckin, 2010), we find that the role of language skills in explaining the immigrant wage gap is rather small. As a point of comparison, we find that the share of the immigrant wage gap explained by differences in human capital quality is more than 35 times that explained by language skills (both in terms of endowments and returns) for males and about nine times for females.
Finally, we observe that after controlling for human capital quality, the unexplained component of the wage gap (which is sometimes associated with labor market discrimination) is reduced by about 62 percent for male immigrants and virtually eliminated for female immigrants. This reinforces the point made in Bonikowska, Green and Riddell (2008) that what is sometimes blamed on labor market discrimination may just reflect lower human capita quality19.