The Effects of Inter-provincial Mobility on Individuals' Earnings: Panel Model Estimates for Canada
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents the findings of an empirical investigation of the effects of inter-provincial migration on individuals' earnings based on the newly available Longitudinal Administrative Database (LAD). The main results are based on a difference model which estimates the effects of mobility on (log) earnings which implicitly controls for initial earnings levels and other fixed effects, as well as other influences captured by the regressors included in the models. Inter-provincial mobility is found to be associated with statistically significant and in many cases quantitatively substantial changes in individuals' earnings, with these effects varying by age, sex, and province of origin. Pre- and post-move earnings profiles are also analysed, offering support for the validity of the difference model approach and indicating that movers are quickly integrated into local labour markets after their moves. Implications are discussed and possible directions for future research are suggested.
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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 | 0.002 | 0.018 |
| 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 | 0.002 |
| 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