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Record W4410580447 · doi:10.3138/cpp.2024-049

The Distributional Origins of the Canada-US GDP and Labour Productivity Gaps

2025· article· fr· W4410580447 on OpenAlexaffvenueabout
James MacGee, Joel Rodrigue

Bibliographic record

VenueCanadian Public Policy · 2025
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsBank of Canada
Fundersnot available
KeywordsProductivityEconomicsEconomic geographyLabour economicsDemographic economicsDevelopment economicsEconomic growth

Abstract

fetched live from OpenAlex

Entre 1960 et 2020, le produit intérieur brut (PIB) par adulte au Canada a varié entre 70 % et 90 % de celui des États-Unis. Cet écart persistant masque des différences de revenus relatifs importantes parmi les distributions des revenus au Canada et aux États-Unis. Nous observons qu'il existe de faibles différences de revenus moyens dans les centiles inférieurs, mais de forts écarts chez les personnes à revenu élevé, et que ces écarts sont plus substantiels chez les propriétaires d'entreprises et les universitaires. En utilisant les données de la World Inequality Database (base de données sur les inégalités dans le monde), nous constatons que le décile supérieur de la distribution des revenus représente les trois quarts de l'écart de PIB par adulte entre le Canada et les États-Unis et jusqu'à deux tiers de l'écart de productivité du travail mesuré. Nos travaux tendent à montrer que l'émigration sélective vers les États-Unis de travailleurs hautement qualifiés – phénomène communément appelé « fuite des cerveaux » – pourrait expliquer une partie importante des écarts observés sur les plans du PIB par adulte et de la productivité du travail.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2025
Admission routes3
Has abstractyes

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