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Record W2111760097 · doi:10.1177/0160017606286357

Internal Migration, Asymmetric Shocks, and Interprovincial Economic Adjustments in Canada

2006· article· en· W2111760097 on OpenAlexaffabout
Serge Coulombe

Bibliographic record

VenueInternational Regional Science Review · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEconomicsHuman capitalUnemploymentDifferential (mechanical device)Internal migrationProductivityConvergence (economics)Demographic economicsEconomic geographyNet migration ratePopulationMacroeconomicsEconomic growthDeveloping countryPopulation growth

Abstract

fetched live from OpenAlex

This article provides an empirical analysis of the role of labor mobility in the intranational (interprovincial) macroeconomic adjustment process in Canada. This analysis is based on a pooled time-series cross-section econometric setup of net migration flows across age groups between the ten Canadian provinces since 1977. The results indicate that interprovincial migration is driven by structural factors such as the long-run regional differential in unemployment rates, labor productivity, and the rural/urban differential structure of the provinces. Furthermore, it appears that interprovincial migration is not that sensitive to regional asymmetric shocks at the business cycle horizon. Finally, using a conditional convergence model of human capital, the author estimates that migration has a powerful effect on the redistribution of human capital across Canadian provinces. With the interprovincial migration process, human capital is redistributed from the more rural to the predominantly urban provinces and from the poor to the rich provinces.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.015
GPT teacher head0.227
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations81
Published2006
Admission routes2
Has abstractyes

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