Abstract
This paper studies the efficacy of immigrant selection based on skill requirements in the Canadian context. The point system results in a much higher skill level than would otherwise be achieved by family preferences. This positive selection is achieved by directly selecting higher-skilled principal applicants who are assessed by the point system and also indirectly through higher-skilled spouses. However, due to difficulties in transfer of foreign human capital, immigrants admitted for their skills do not necessarily perform better in the labor market and important factors used to assess admissibility have very limited power to predict short-term labor market success.
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Notes
Legal Immigration, Fiscal Year 2001, Annual Report, US Department of Justice (August 2002).
Migration Program Statistics, Department of Immigration and Multicultural Affairs, Australian Government.
UK recently adopted a point system similar to that of Canada while there are ongoing discussions for introducing a similar system throughout the European Union and the US.
See the Electronic supplementary material for details of the Canadian point system.
Jasso and Rosenzweig (1995) also point out that, in the US context where employment-based immigrants are nominated by employers, employers may screen for short-term productivity while family members may screen for the long term. In the Canadian context, employer screening is much less relevant since skill-based immigrants can apply on their own without a requirement of a job offer.
Aydemir (2006) discusses the role of economic opportunities and the immigration policy on resulting immigrant characteristics. Using a sample of immigrants to Canada, a positive selection is found at the application stage among individuals from UK but a negative selection among those from the US.
“Landing” refers to the process of permanent residency taking effect. For an individual residing outside Canada, this occurs when the individual arrives in Canada through a port of entry. Generally, all applications for permanent residence must be made through Canadian missions abroad. Exceptional cases that are allowed to apply and become permanent residents while residing in Canada and are not required to leave and re-enter the country for landing are excluded from the LSIC sampling frame. These exceptions are discussed in more detail in Section 5.
Those applying under family class may be sponsored either by immigrants or native-born Canadians, but the data does not provide this information.
As long as the applicant is eligible for sponsorship under family class, there is no incentive to apply as a skilled worker which requires passing strict selection criteria. Those immigrated as skilled workers although they had relatives in Canada were most likely ineligible for family sponsorship.
Applications for immigration under any visa type include an on paper assessment of the information provided, as well as an in-person interview regarding the application. Upon request, applicants are required to submit original or certified documents regarding their education levels. Also, during the interview process, visa officers assess stated language proficiencies (Source: Applying for permanent residence in Canada: A self-assessment guide for independent applicants, Citizenship and Immigration Canada).
The remaining group consists of individuals who responded that their skills are fairly well, poor, or cannot speak the language. Language abilities refer to those reported at the 6-month interview.
These national-origin groups referring to broad regions of origin are dictated by the sample size. The 11 national-origin groups are North America, Central and South America, Caribbean and Bermuda, Western and Northern Europe, Eastern Europe, Southern Europe, Africa, West and Central Asia and Middle East, Eastern Asia, Southeast Asia and Oceania, and Southern Asia.
For this decomposition, countries of origin with a minimum of 100 observations are selected and seven of them satisfy this restriction. The mean number of observations per country is 515 observations, and the mean number of family class immigrants and skilled worker immigrants used to calculate S ij are 142 and 326, respectively. The subsample of seven countries makes up 60% of all family class immigrants and 56% of all skilled worker immigrants. These seven countries are UK, Iran, China, Philippines, India, Pakistan, and Sri Lanka. Male and female immigrants are pooled together for sample size considerations.
Note that Borjas (1993) focuses on the stock of immigrants who arrived starting from 1960s until 1981, while this paper focuses on a cohort that arrived over 2000–2001. Over time, both the importance of education in determining eligibility in the point system and the source country distribution of immigrants have changed considerably. These differences may explain different conclusions regarding the role of the point system.
Among the 25- to 65-year-olds, about 66.6% of the respondents are principal applicants, 32.7% are spouses of principal applicants, and about 0.7% are dependants other than a spouse.
The sample size for spouses of PAs is very small for some gender and visa category groups. In order to boost the sample size for this group to reduce sampling error and to provide a more comprehensive picture of education within families, the questions in the survey about spousal education levels are utilized. Therefore, information in panel B of Table 4 is obtained either from a survey respondent who is a spouse of a PA reporting his/her own education level or from a survey respondent who is a PA providing information about his/her spouse. The survey does not report whether the spouse is an immigrant or a Canadian-born, thus, especially for the family class, there is a possibility that the spouse is Canadian-born. When I use only the education information about respondents but not their dependants, the results are very similar for all visa categories but the family class whose education levels become significantly lower. All qualitative results, however, remain the same. Also note that, while survey respondents report both their highest educational attainment and years of schooling, they were only asked about the highest educational attainment of spouse but not the years of schooling. I assume that mean years of schooling for a given level of educational attainment is the same between survey respondents and their spouses.
Asia is the only source region with large enough sample sizes where reliable comparisons can be made between skilled workers and family class immigrants by educational attainment.
Individuals with missing labor force or employment status or those who decline reporting their wages are dropped from the sample. This results in a total of 394 observations being dropped out of 7,319 observations.
Table A.2 in the electronic supplementary material presents the mean labor market outcomes for each visa class using the LSIC data, as well as similarly defined outcomes for the resident population using monthly labor force surveys, adjusted to reflect the demographic characteristics and geographic distribution of immigrants. Resident population outcomes are presented mainly to provide a benchmark to assess broad patterns in the economy.
Using the LSIC survey, it is possible to calculate “time since landing” (landing referring to officially becoming an immigrant) which may be different than time since arrival as some individuals may already be living in the country before adjusting their status. These potential differences are important for assessing the relative performance of immigrants across classes since both the fraction that adjusts status within a visa class and time spent prior to adjusting the status may vary across visa categories. Available evidence suggests that there are very few skilled-workers or business class immigrants that adjust status (less than 2%) while almost half of refugees do so. Refugees adjusting their status are the ones that applied for refugee status within Canada and survey’s sampling design excludes these individuals. Therefore, the small number of remaining individuals that adjust their status among skilled workers and business class is unlikely to bias the overall results. As a further check, multivariate models were re-estimated by excluding from the sample individuals who ever lived in Canada before becoming permanent residents. Results following this exclusion are very similar to those presented in the paper.
Population-averaged models allow individuals’ error terms to be correlated over time. However, the heterogeneity term is assumed to be independent of individual characteristics. The estimated within-group correlation was high, suggesting the use of models that allows for heterogeneity. Fixed effects estimation is not possible in the probit context and random effects estimation could not produce reliable estimates for probit models as the results were very sensitive to the number of quadrature points. Random effects models for earnings outcomes are estimated with very similar results to those reported here from population averaged models.
The language controls are entered as continuous variables from 1 to 4, 1 being the lowest and 4 the highest language ability. Entering language variables as dummy variable sets results in very similar results. An alternative specification also replaced years of schooling variable with dummy variables indicating highest level of educational attainment and results were very similar to those presented here. Given the interest in this paper on the importance of factors assessed by the point system in predicting future outcomes, language ability and schooling as reported in the 6-month interview are used regardless of when the outcomes are measured. All other controls are as of the time of the interview corresponding to the outcome.
The earnings outcomes presented are based on OLS regressions that do not correct for selection into the labor force. This selectivity may be important particularly for females. To correct for sample selectivity in panel data context, I implemented the maximum likelihood estimator proposed by Zabel (1992); however, the estimation could not be carried out as the log likelihood in this case is not globally concave and iterations occasionally break down in this estimation as noted by Limdep software manual (pp. E23–E27). Alternatively, to get a sense of possible biases for earnings outcome, I estimated earnings regressions for each cross-section and produced parameter estimates without correction for selectivity and with correction for selectivity using a Heckit procedure. The conclusions from this cross-sectional analysis is that (1) returns to human capital characteristics, such as schooling and experience, are not sensitive to sample selection and (2) unexplained differences between classes still remain and, in some cases, become larger, after the correction for sample selection.
Very similar results for the impact of schooling are also found in specifications estimated using only a single wave of data.
Interacting French-speaking abilities with a dummy for the French-speaking province of Quebec does not change these conclusions.
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Acknowledgements
I would like to thank the editor and two anonymous referees who provided valuable comments on earlier versions of the paper. I also benefited from comments received at presentations at Statistics Canada and CEA 2006 meetings. This paper represents the views of the author and does not necessarily reflect the opinion of any institution with which the author is affiliated.
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Responsible editor: Klaus F. Zimmermann
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Aydemir, A. Immigrant selection and short-term labor market outcomes by visa category. J Popul Econ 24, 451–475 (2011). https://doi.org/10.1007/s00148-009-0285-0
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DOI: https://doi.org/10.1007/s00148-009-0285-0