Bibliographic record
Abstract
My first chapter 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. The findings suggest that immigrants earn 77% of their marginal revenue product of labor on average, compared to 84% for natives. Additionally, immigrants tend to work at more productive firms, although they are less productive on average within the same firm. I use the model to conduct a novel decomposition of the immigrant-native pay gap in a general equilibrium framework and find that labor supply differences contribute significantly to the immigrant-native pay gap. My second chapter examines the impact of arguably exogenous labor supply shocks driven by the rapid growth in international college students in Canada from 2009 to 2019 on other workers and on firms. We find that the shocks had a small negative impact on total employment in local economies, as international students displaced some non-student workers. Interestingly, the main employment reductions are in firms that do not hire international students, implying that competition in product markets plays an important role in the adjustment of local economies to immigration shocks. Using firm births and deaths, we also find evidence of a shift toward firms that do not hire students and primarily employ part-time workers. My third chapter studies the labor market returns to permanent residency using an event study design that exploits variation in the timing of permanent visa receipt among temporary foreign workers. We find a sharp, immediate, and persistent increase of 6 percentage points in the probability of switching jobs following the transition to permanent residency. Additionally, we find an increase in labor earnings of 10-15 percent, with 20-40 percent of the gain attributable to a higher likelihood of employment at high-wage firms. To interpret these results, we develop a search-and-matching model featuring heterogeneous workers and firms, where permanent residents are treated the same as domestic natives, while temporary visa holders search separately within a segmented labor market.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".