Human capital quality and the immigrant wage gap
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We propose a new methodology for analyzing determinants of the wage gap between immigrants and natives. A Mincerian regression framework is extended to include GDP per capita in an immigrant’s country of birth as a proxy for the quality of schooling and work experience acquired in that country. We find that Canadian immigrants’ returns to schooling and work experience significantly increase with the GDP per capita of their country of birth. The contribution of quality of schooling and work experience to the immigrant wage gap is also examined. Lower human capital quality completely negates the endowment advantage that immigrants have in the areas of schooling and work experience. Since data on GDP per capita are available for most countries over long periods, the proposed methodology can be applied to analyze immigrant wage gaps for a large set of countries for which common statistics on natives and immigrants are available. JEL codes J20, J24, J15, J61
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.
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 | 0.003 | 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 it