Essays on Skills and Labour Market Outcomes of Immigrants and the Canadian Born
Bibliographic record
Abstract
Inequalities in basic skills and labour market outcomes between immigrants (by admission category) and the Canadian-born, and the underrepresentation of women in the information and communication technology (ICT) sector, are examined using Statistics Canada’s 2012 Survey of Adult Skills, a product of the Organisation for Economic Cooperation and Development’s Programme for the International Assessment of Adult Competencies. Differences in basic ICT skills, and the rates of return to these skills in the Canadian labour market, between immigrants and Canadian non-immigrants, are the focus of the first chapter. Immigrants, especially men, are observed to be disproportionately employed in ICT industries and occupations. A measure of basic ICT skills is employed to document differences in skill levels and labour market earnings across immigration classes and categories of Canadians at birth. Adult immigrants, including those assessed by the points system, are found to have lower average ICT scores than Canadians at birth, although the rate of return to ICT skills is not statistically different between the two groups. Immigrants who arrived as children, and the Canadian-born children of immigrants, have similar outcomes to the children of Canadian-born parents. Chapter 2 explores differences in literacy and numeracy skills, and the economic returns to these skills, for immigrants to Canada in different admission classes and their Canadian-born counterparts. First, respondents are grouped into three broad categories – adult and young immigrants, and the Canadian-born. Then, these individuals are classified into nine population subgroups: adult economic immigrants, adult refugees, adult family reunification, other adult immigrants, adult temporary residents, young refugees, young non-refugee immigrants, and second- and third-generation Canadian-born individuals. The analysis suggests that both adult and young immigrants (those who arrived in Canada at age 13 or younger) do not perform as well on literacy and numeracy tests conducted in English or French as those born in Canada, although young immigrants have higher test scores than adult immigrants. Similar results are found for wages. Among immigrants, it is observed that economic immigrants tend to have the highest test scores and hourly wages, with refugees having the lowest. The wage returns to these basic skills are economically significant at the 25th, 50th, and 75th quantiles of log hourly wages and the Canadian labour market rewards immigrants and the Canadian-born equally for their literacy and numeracy skills. Chapter 3 explores why the proportion of women in Canada’s ICT sector is well below their percentages in other science, technology, engineering, and mathematics (STEM) fields. A measure of basic ICT skills is used to study the skills gap and differences in returns to these skills between men and women. After controlling for appropriate covariates, Canadian women on average score higher than their male counterparts in basic ICT skills. However, women with the same ICT test scores are less likely than men to be employed in ICT occupations. Hourly wages in ICT occupations are lower for women, but the earnings gap in these occupations is not higher than those in the general labour market. Given the current and projected shortages of ICT professionals, women represent a large, yet untapped, pool of talent for this sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".