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Productivity Disparities between American and Canadian High Tech Worker: The Human Capital and Knowledge spillovers/Disparites De Productivite Des Travailleurs Du Haut Savoir Canadiens et Americains : Le Capital Humain et Les Economies D'agglomeration Du Savoir

2009· article· fr· W284040088 sur OpenAlexvenueaboutno aff
Sylvie Arbour

Notice bibliographique

RevueCanadian Journal of Regional Science · 2009
Typearticle
Languefr
DomaineEconomics, Econometrics and Finance
ThématiqueRegional Economics and Spatial Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésProductivityHuman capitalMetropolitan areaExternalityWorkforceEconomicsLabour economicsKnowledge economyDemographic economicsEconomyEconomic growthGeography
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Abstract Following the first oil shock of 1973, there was a decline in the growth of productivity in Canada, as in the rest of the OECD countries. However, since the middle of the nineties, the productivity lag between the Canadian and United States economies has increased, mainly due to the earlier progress made in the United States regarding worker productivity. The assumption underlying our work is that the accumulation of human capital explains, at least in part, differences in productivity between the Canadian and American workers. The accumulation and diffusion of knowledge, induced by the exchange of information, ideas and findings among people, allow for an accumulation of what Beine, Docquier (2000) call a collectiveness skill, a skill that promotes increased productivity throughout the workforce. The knowledge obtained through such exchanges is not priced in the market, and is thus considered a positive externality, here called knowledge spillovers. We postulate that the accumulation and diffusion of knowledge, as well as the emergence of knowledge spillovers, is greatly increased in metropolitan areas characterized by a high population density, a high level of education, a high level of occupational specialization and finally, a location tied to other metropolitan areas where such knowledge spillovers occur. We also hypothesize that a lesser combination of these same characteristics in the local economy of Canadian metropolitan areas would partly explain the productivity gap between Canadian and American workers. This article focuses on two main objectives. As a first step, we aim to understand the microfoundafions of knowledge spillovers. We consider three theories that explain the mechanisms that lead knowledge spillovers to percolate. According to MAR (Marshall-Arrow-Romer) theory, the concentration of an industry in a city helps knowledge spillovers between firms and therefore the emergence of localization economies. By extrapolation, we assume in this article that workers benefit from localization economies, which would exist in area where occupational specialization is found. Another theory, by Lucas (1988), argues that the concentration of well-educated people in a city generates knowledge spillovers between firms and therefore the emergence of urbanization economies in that city. A third theory, following the work of Jacobs (1969) and Lucas (1988), considers that the majority of economic activities occurs in the cities. For Jacobs (1969) and Lucas (1988), the condtions offered by the cities improve the prospects towards the generation of new ideas and the promotion of agglomeration economies of urbanization. These economies would be the result of many levels of diversity in major urban centers, particularly in terms of labour force, infrastructure and specialized services for businesses. In a second step, we focus on geographic scope of human capital externalities. The question that we seek to answer is: what is the spatial scale at which human capital externalities take place? According to Baumont, Ertur et Le Gallo (2003) et Englmann, Walz (1995), knowledge spillovers may be associated not only with local spillovers, but also with global spillovers. By global spillovers we refer to knowledge spillovers of a given urban region that could benefit economic agents located in nearby urban regions. To test the three theories and whether or not knowledge spillovers are geographically bounded, we estimate a cross sectional econometric model, using data from censuses of United States and Canada and from the survey of occupational employment statistics (OES) in the United States. The object of the study covers 90 Metropolitan areas of 500,000 people and more in Canada and the United States, in 2001. The paper reaches two primary conclusions. First, though the average level of education intuitively appears to be an adequate indicator of the accumulation of human capital generating knowledge spillovers, our results suggest that the origin of these spillovers is rather found in the relative concentration of knowledge intensive occupations. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesÉtudes des sciences et des technologies
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,321
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,007
Communication savante0,0010,002
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,222
Écart entre enseignants0,193 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2009
Routes d'admission2
Résumé présentoui

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