Migration and Job Search: Evidence from Canada1
Bibliographic record
Abstract
The job-search literature has tended to ignore the role of geographical mobility. This paper examines the association between migration and unemployment exit rate that has not previously been examined in the Canadian literature. By employing job search and human capital theories, this study investigates the search strategies of unemployed workers, allowing them to search simultaneously across several labor markets. The purpose of the paper is to investigate whether Canadian unemployed workers develop their search strategies by extending their job search efforts geographically and how this search behavior is affected by different individual and family characteristics and regional labor market conditions. This paper also examines the effect of Canadian Employment Insurance (EI) program on regional mobility and unemployment duration among unemployed. Owing to data limitations, very few studies in Canada about unemployment spells use duration modeling. This paper exploits the benefits of the up-to-date Survey of Labor and Income Dynamics (SLID) longitudinal data. Independent competing risks framework is used to examine the transition from unemployment to employment under different search strategies. Semi-parametric stratified Cox proportional hazard model and parametric log-logistic model with gamma distributed unobserved individual heterogeneity is applied. The results indicate that individual and family characteristics have a stronger effect on the transition rates than other factors like labor market conditions and previous-job-related characteristics do. There is evidence that current EI program affects search strategy of unemployed individuals and relatively more generous EI benefit would reduce the geographical mobility of the benefited workers.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".