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Record W2306422511 · doi:10.1111/caje.12372

Sources of Canadian economic growth

2019· article· en· W2306422511 on OpenAlexaffvenueabout
Samira Hasanzadeh, Hashmat Khan

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsCarleton University
Fundersnot available
KeywordsEconomicsEndogenous growth theoryGrowth rateHuman capitalPercentage pointGrowth accountingAnnual growth %Growth theorySteady state (chemistry)EconometricsMacroeconomicsMathematicsKeynesian economicsAgricultural economicsTotal factor productivityEconomic growthChemistryProductivity

Abstract

fetched live from OpenAlex

Abstract We apply modern ideas‐oriented growth accounting, based on the semi‐endogenous growth theory of Jones (2002), to compare the sources of Canadian and US economic growth between 1981–2014. This framework allows us to distinguish between transition dynamics and steady state growth as well as quantify their respective contributions. We find that the bulk of the 1.1 percentage points total average Canadian growth rate of output per hour has been due to transitional factors, mainly capital intensity and domestic human capital growth driven by educational attainment. The growth in excess ideas (total ideas growth minus steady state growth) has contributed a small share of 0.06 percentage points. Two features stand out in comparison to the US growth experience over the same period. First, over a full percentage point of the average US growth of 1.64% is due to excess ideas growth. Second, the “constant growth view” that reconciles large sources of transitional growth with relatively stable average growth is not supported in Canada. We estimate a relatively low elasticity of output with respect to world research effort as the reason behind the small share of R&D‐oriented sources of Canadian growth.

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)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.597

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0150.018
Science and technology studies0.0030.001
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.094
GPT teacher head0.157
Teacher spread0.063 · 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

Citations5
Published2019
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Economics/Revue canadienne d économiqueSame topicEconomic Growth and ProductivityFrench-language works237,207