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The Effects of Biased Technological Changes on Total Factor Productivity: A Rejoinder and New Empirical Evidence

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  • Cristiano Antonelli

    (University of Torino
    BRICK)

  • Francesco Quatraro

    (University of Nice Sophia Antipolis
    GREDEG CNRS
    BRICK)

Abstract

The paper by Ji and Wang (2013) calls new attention on the analysis of the effects of the direction of technological change. The aim of this paper is to better articulate and test the theoretical arguments that the direction of technological changes has specific effects on the efficiency of the production process and to study the incentives and the processes that lead to its introduction. The decomposition of total factor productivity growth into the bias and the shift effects enables to articulate the hypothesis that the types of technological change whether more neutral or more biased reflect the variety of the innovation processes at work. The evidence of a large sample of European regions tests the hypothesis that regional innovations systems with a strong science base are better able to introduce neutral technological changes while regional innovation systems that rely more upon learning processes and tacit knowledge favor the introduction of directed technologies a form of meta-substitution that aims at exploiting the opportunities provided by the most intensive use of locally abundant factors.

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  • Cristiano Antonelli & Francesco Quatraro, 2014. "The Effects of Biased Technological Changes on Total Factor Productivity: A Rejoinder and New Empirical Evidence," GREDEG Working Papers 2014-01, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    • Handle: RePEc:gre:wpaper:2014-01
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    Cited by:

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    2. Michaël Assous & Roberto Lampa, 2014. "Lange's 1938 model: dynamics and the "optimum propensity to consume"," The European Journal of the History of Economic Thought, Taylor & Francis Journals, vol. 21(5), pages 871-898, October.
    3. Rui Gong & Yong-Qiu Wu & Feng-Wen Chen & Tai-Hua Yan, 2020. "Labor Costs, Market Environment and Green Technological Innovation: Evidence from High-Pollution Firms," IJERPH, MDPI, vol. 17(2), pages 1-20, January.
    4. T. Gries & R. Grundmann & I. Palnau & M. Redlin, 2017. "Innovations, growth and participation in advanced economies - a review of major concepts and findings," International Economics and Economic Policy, Springer, vol. 14(2), pages 293-351, April.
    5. Feder, Christophe & Kataishi, Rodrigo Ezequiel, 2017. "Decentralization in Heterogeneous Regions: A Biased Technological Change Approach," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201703, University of Turin.
    6. Chen, Yufen & Liu, Yanni, 2021. "How biased technological progress sustainably improve the energy efficiency: An empirical research of manufacturing industry in China," Energy, Elsevier, vol. 230(C).
    7. Antonelli, Cristiano & Feder, Christophe & Quatraro, Francesco, 2018. "Directed Technological Change and Technological Congruence: A New Framework for the Smart Specialization Strategy," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201805, University of Turin.
    8. Festré, Agnès, 2018. "Do people stand by their commitments? Evidence from a classroom experiment," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 76(C), pages 1-6.
    9. Frédéric Marty, 2014. "Towards an Economics of Convention-based Approach of the European Competition Policy," GREDEG Working Papers 2014-06, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    10. Valeria Costantini & Francesco Crespi, 2013. "Public policies for a sustainable energy sector: regulation, diversity and fostering of innovation," Journal of Evolutionary Economics, Springer, vol. 23(2), pages 401-429, April.
    11. Chen, Feng-Wen & Xu, Jingwei & Wang, Jiang & Li, Zhilong & Wu, Yongqiu, 2023. "Do rising labour costs promote technology upgrading? A novel theoretical hypothesis of an inverted U-shaped relationship," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 327-341.
    12. Yu Liu & Mingde Jia, 2023. "The Impact of Population Aging on Green Innovation: An Empirical Analysis Based on Inter-Provincial Data in China," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    13. Xinpeng Xu & Yaqin Zhang & Yanran Liao & Xiaopeng Fu, 2023. "Labor Protection, Enterprise Innovation, and Sustainable Development," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    14. Feder, Christophe, 2018. "The effects of disruptive innovations on productivity," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 186-193.
    15. Cristiano Antonelli, 2016. "A Schumpeterian growth model: wealth and directed technological change," The Journal of Technology Transfer, Springer, vol. 41(3), pages 395-406, June.
    16. Albert N. Link & John T. Scott, 2021. "Technological change in the production of new scientific knowledge: a second look," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 30(4), pages 371-381, May.
    17. Yizhong Wang & Linying Lv & Shanqiao Xia, 2022. "Initial public offering, corporate innovation and total factor productivity: Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(5), pages 4695-4726, December.
    18. Lin, Boqiang & Chen, Xing, 2020. "How technological progress affects input substitution and energy efficiency in China: A case of the non-ferrous metals industry," Energy, Elsevier, vol. 206(C).
    19. Zheng Li & Fengshuo Liu & Shuai Mi, 2022. "Can an increase in the minimum wage standard force enterprises to innovate? Evidence from China," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(8), pages 3807-3819, December.
    20. Antonelli, Cristiano & Gehringer, Agnieszka, 2017. "Technological change, rent and income inequalities: A Schumpeterian approach," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 85-98.

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    More about this item

    Keywords

    Biased Technological Change; Mobility; European Regions; GMM System; Transition Probability;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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