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Are factor biases and substitution identifiable? The Canadian evidence
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Are factor biases and substitution identifiable? The Canadian evidence

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  • Kenneth G. Stewart
  • Jiang Li

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.

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  • Kenneth G. Stewart & Jiang Li, 2018. "Are factor biases and substitution identifiable? The Canadian evidence," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(2), pages 528-548, May.
    • Handle: RePEc:wly:canjec:v:51:y:2018:i:2:p:528-548
    DOI: 10.1111/caje.12330
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    2. Samuele Ialenti & Guido Pialli, 2024. "The increase in the elasticity of substitution between capital and labour: a repeated cross-country investigation," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 33(3), pages 380-400, April.
    3. Michael Knoblach & Fabian Stöckl, 2020. "What Determines The Elasticity Of Substitution Between Capital And Labor? A Literature Review," Journal of Economic Surveys, Wiley Blackwell, vol. 34(4), pages 847-875, September.
    4. Daan Steenkamp, 2018. "Factor Substitution and Productivity in New Zealand," The Economic Record, The Economic Society of Australia, vol. 94(304), pages 64-79, March.
    5. Kemnitz, Alexander & Knoblach, Michael, 2020. "Endogenous sigma-augmenting technological change: An R&D-based approach," CEPIE Working Papers 02/20, Technische Universität Dresden, Center of Public and International Economics (CEPIE).
    6. Juan Qian & Ruibing Ji, 2022. "Impact of Energy-Biased Technological Progress on Inclusive Green Growth," Sustainability, MDPI, vol. 14(23), pages 1-24, December.

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