Labour market power, firm productivity, and the immigrant-native pay gap
Bibliographic record
Abstract
This paper examines the importance of labor market power and firm productivity for understanding the immigrant-native pay gap. Using matched employer-employee data from Canada, I estimate a wage-posting model that incorporates two-sided heterogeneity and strategic interactions in wage setting. In the model, firms mark down wages below the marginal revenue product of labor (MRPL), and the equilibrium immigrantnative pay gap arises from differences in wage markdowns and MRPL. The findings suggest that immigrants earn 77% of their MRPL on average, compared to 84% for natives. I also decompose the immigrant-native pay gap using counterfactual exercises that account for general equilibrium responses of workers and firms. The results of the counterfactuals suggest that (i) differences in labor supply curves contribute significantly to earnings inequality between immigrants and natives; (ii) immigrants tend to work at more productive firms, driven by their tendency to work in cities where firms are more productive on average; and (iii) heterogeneity in firm productivity magnifies the contribution of labor supply differences to the immigrant-native pay gap, highlighting the importance of interaction effects.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".