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Record W2802683739 · doi:10.1111/caje.12330

Are factor biases and substitution identifiable? The Canadian evidence

2018· article· en· W2802683739 on OpenAlexaffvenueabout
Kenneth G. Stewart, Jiang Li

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsGlobal Affairs CanadaUniversity of Victoria
Fundersnot available
KeywordsEconomicsElasticity of substitutionSubstitution (logic)ImpossibilityEconometricsWelfare economicsMathematical economicsProduction (economics)MicroeconomicsPhilosophyPolitical science

Abstract

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Abstract Revised productivity accounts recently released by Statistics Canada are used to estimate a Klump–McAdam–Willman (KMW) normalized CES supply‐side system for the half‐century 1961–2012. The model permits distinct rates of factor‐augmenting technical change for capital and labour that distinguish between short‐term versus long‐term effects, as well as a non‐unitary elasticity of substitution and time‐varying factor shares. The advantage of the Canadian data for this purpose is that they provide a unified treatment of measurement issues that have had to be improvised in the US and European data used by previous researchers. In contrast to previous results, we find that an elasticity of substitution and distinct factor biases of technological progress are not well determined by the model. For the Canadian data, the KMW model does not appear to provide a framework that overcomes the classic Diamond–McFadden–Rodriguez non‐identification result. That impossibility theorem is manifested in our findings, not overcome by them. Résumé Est‐ce que les biais de facteurs et la substitution entre facteurs de production sont identifiables? Résultats pour le Canada . On utilise les données révisées sur la productivité récemment publiées pat Statistiques Canada pour calibrer un système d’offre normalisé à la Klump–McAdam–Willman (KMW) avec élasticité constante de substitution pour la période 1961‐2012. Le modèle permet un changement technique qui implique des taux distincts d’augmentation de productivité pour le travail et le capital qui distingue entre les effets à court et à long terme, ainsi qu’une élasticité de substitution non‐unitaire et des parts variables des facteurs dans le temps. L’avantage des données canadiennes pour ce genre d’analyse est que les problèmes de mesure ont été traités de manière uniforme alors que dans les données américaines et européennes, utilisées antérieurement par les chercheurs, le traitement des problèmes de mesure a été improvisé. Contrairement aux résultats antérieurs, on découvre qu’une élasticité de substitution et les biais du progrès technique en faveur de divers facteurs de production ne sont pas bien déterminés par le modèle. Pour les données canadiennes, le modèle KMW ne semble pas fournir un cadre de référence qui permette de surmonter le problème classique de non‐identification soulevé par les résultats de Diamond–McFadden–Rodriguez. Le théorème d’impossibilité reste entier dans les résultats obtenus et ne sont pas surmontés par es nouveaux résultats.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.314
GPT teacher head0.208
Teacher spread0.106 · 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.

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

Citations5
Published2018
Admission routes3
Has abstractyes

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