Measuring labour market success: a comparison between immigrants and native-born Canadians using PIAAC
Bibliographic record
Abstract
Canadian society is characterised by a plurality of immigrants and Canadian migration policy and corresponding recognition approaches are strongly geared to economic criteria, qualifications and skills. This paper addresses the question how immigrants who have acquired their highest qualification outside Canada are able to use their foreign qualifications and skills in their current job. The analyses are conducted to verify the assumptions of human capital theory as well as the lack of transferability of human capital across country borders. To answer these questions a labour market success index is developed, which is used as a dependent variable in regression models. The results show that traditional operationalisations of human capital (years of education, years of work experience and skills) have a positive effect on individual labour market success. At the same time, being born abroad and having acquired one’s highest qualification abroad in comparison to Canada, especially in a Non-Western country, has negative effects on the overall labour market success of an individual. Detailed comparisons regarding different indicators of labour market success also prove these comparatively negative effects. The results demonstrate the limited explanatory power of human capital theory and the necessity to complement it with Bourdieu’s concepts of social and cultural capital.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".