The Effect of Immigration on Wages: Exploiting Exogenous Variation at the National Level
Bibliographic record
Abstract
This paper estimates the effect of immigration on native wages at the national level taking into account the endogenous allocation of immigrants across skill cells. Time-varying exogenous variation across skill cells for a given country is provided by interactions of push factors, distance, and skill cell dummies: distance mitigates the effect of push factors more severely for less educated and middle experienced. Because the analysis focuses on the United States and Canada, I propose a two-stage approach (Sub-Sample 2SLS) that estimates the fiist stage regression with an augmented sample of destination countries, and the second stage equation with the restricted sub-sample of interest. I derive asymptotic results for this estimator, and suggest several applications beyond the current one. The empirical analysis indicates a substantial bias in estimated OLS wage elasticities to immigration. Sub-sample 2SLS estimates average - 1:2 and are very stable to the use of alternative instruments.
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How this classification was reachedexpand
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 | 0.015 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".