Breaking barriers: The impacts of employer exposure to immigrants
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
We study how exposure of employers to immigrants, both at the market and at the individual firm level, mitigates immigrant-native disparities. We use administrative employee-employer matched data from Portugal, which provides a unique setting given that it experienced almost no immigration until the early 2000s followed by substantial immigration waves. Focusing on the evolution of market wages across successive immigration cohorts, we find that increased employer exposure to immigrant groups contributed substantially to the wage convergence between immigrants and natives over the last two decades. We also document that individual-level exposure of firms to immigrants appears to play an important role, influencing future hiring and remuneration of immigrants. Our results provide new insights into how barriers to hiring different worker groups shape economic inequality, with novel implications for integration policies.
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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 | 0.000 | 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