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Record W284040088

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 on OpenAlexvenueaboutno aff
Sylvie Arbour

Bibliographic record

VenueCanadian Journal of Regional Science · 2009
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityHuman capitalMetropolitan areaExternalityWorkforceEconomicsLabour economicsKnowledge economyDemographic economicsEconomyEconomic growthGeography
DOInot available

Abstract

fetched live from 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. …

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.222
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; both teacher heads agree on what is shown here.

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

Citations0
Published2009
Admission routes2
Has abstractyes

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