Who Moves? A Panel Logit Model Analysis of Inter-provincial Migration in Canada
Bibliographic record
Abstract
This paper addresses the topic of inter-provincial migration in terms of the basic question: "who moves?" Panel logit models of the probability of moving from one year to the next are estimated using samples derived from the Longitudinal Administrative Database covering the period 1982-95. Explanatory variables include "environmental" factors, personal characteristics, labour market attributes, and a series of year variables. Separate models are estimated for eight age-sex groups. The major findings include that: i) migration rates have been inversely related to the size of the province, presumably capturing economic conditions, labour market scale effects, and pure geographical distance, while language has also played an important role; ii) residents of smaller cities, towns, and especially rural areas have been less likely to move than individuals in larger cities; iii) age, marriage, and the presence of children have been negatively related to mobility, for both men and women; iv) migration has been positively related to the provincial unemployment rate, the individuals' receipt of unemployment insurance (except Entry Men), having no market income (except for Entry Men and Entry Women), and the receipt of social assistance (especially for men); v) beyond the zero earnings point, migration has been positively related to earnings levels for prime aged men, but not for others, and these effects are generally small (holding other factors constant); vi) there were no dramatic shifts in migration rates over time, but men's rates dropped off a bit in the 1990s while women's rates (except for the Entry group) generally held steadier or rose slightly, indicating a divergence in trends along gender lines.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".