Bibliographic record
Abstract
Several papers have tested the empirical validity of the migration models proposed by Borjas (1987) and Borjas, Bronars, and Trejo (1992). However, to our knowledge, none has been able to disentangle the separate impact of observable and unobservable individual characteristics, and their respective returns across different locations, on an individual's decision to migrate. We build a model in which individuals sort, in part, on potential earnings - where earnings across different locations are a function of both observable and unobservable characteristics. We focus on the inter-provincial migration patterns of Canadian physicians. We choose this particular group for several reasons including the fact that they are paid on a fee-for-service basis. Since wage rates are exogenous, earning differentials are driven by differences in productivity. We then estimate a mixed conditional-logit model to determine the effects of individual and destination-specific characteristics (particularly earnings differentials) on physician location decisions. We find, among other things, that high-productivity physicians (based on unobservables) are more likely to migrate to provinces where the productivity premium is greater, while low-productivity physicians are more likely to migrate to areas where the productivity premium is lower. These results are consistent with a modified Borjas model of self-selection in migration based on both unobservables and observables.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".