A comparison of the Earnings of Immigrants in Canada, United States, Australia and Germany
Bibliographic record
Abstract
The legislation on employment equity is designed to protect the rights of all persons to equitable treatment in employment, but particularly those who belong to groups designated as disadvantaged. This paper tests the hypothesis that immigrants could be defined as such disadvantaged group. It investigates the earnings of immigrants relative to non-immigrants in four countries: Canada, the United States, Australia and Germany. This paper also addresses the question of the effects of gender, marital status, educational attainment, years since migration and country of origin as key explanatory factors on the earnings gap between different groups of immigrants. Although wages are only one aspect of labor market performance, comparisons based on wage rates are widely used to describe the labor-market disadvantages of paid employees in the designated groups. Section II briefly describes immigration policy in the four countries of interest and summarizes previous findings. Section III describes the data and discusses the definitions of the population of interest, the measure of earnings, and the taxonomy of the independent variables. Section IV presents the results in terms of descriptive statistics, Section V contains an analysis of the econometric results, and Section VI concludes the paper by discussion the interpretation one might place on these results. An appendix presents the sensitivity of results to changes in model specification and gives some technical details.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".