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Technological congruence and the economic complexity of technological change

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  • Antonelli, Cristiano

Abstract

Technological congruence is an emergent system property defined by the matching between the relative size of outputs’ elasticity with the relative abundance and cost of inputs in local factor markets. With given total costs, output is larger the larger is the output elasticity of the cheapest input. Technological congruence is a powerful tool that helps grasping the economic complexity of technological change with respect to the determinants of the direction of technological change and its effects in terms of growth accounting and specialization, both at the firm and the system level. Its appreciation stems directly from the advances of the economics of innovation in understanding the endogenous determinants of the introduction and diffusion of directed technological changes. Technological congruence is most relevant to influence the actual levels of total factor productivity of new technologies and – consequently – to shape the competitive advance of firms and countries.

Suggested Citation

  • Antonelli, Cristiano, 2016. "Technological congruence and the economic complexity of technological change," Structural Change and Economic Dynamics, Elsevier, vol. 38(C), pages 15-24.
    • Handle: RePEc:eee:streco:v:38:y:2016:i:c:p:15-24
    DOI: 10.1016/j.strueco.2015.11.008
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    Cited by:

    1. Christophe Feder, 2018. "Smart Specialization Strategy and Directed Technological Change," Economics Bulletin, AccessEcon, vol. 38(3), pages 1428-1437.
    2. He, Wenjian & Cheng, Yu & Lin, Ying & Zhang, Hongxiao, 2022. "Microeconomic effects of designating National Forest Cities: Evidence from China's publicly traded manufacturing companies," Forest Policy and Economics, Elsevier, vol. 136(C).
    3. 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.
    4. Qizheng Gao & Jianqing Zhang & Guo Chen, 2023. "Firm heterogeneity, biased technological change, and total factor productivity: Evidence from China," Journal of Productivity Analysis, Springer, vol. 60(2), pages 147-177, October.
    5. Juan Qian & Ruibing Ji, 2022. "Impact of Energy-Biased Technological Progress on Inclusive Green Growth," Sustainability, MDPI, vol. 14(23), pages 1-24, December.
    6. Pan, Xiongfeng & Yuan, Jiahong, 2024. "Global Value Chain, GVC、biased technological change and reginal total factor productivity differences," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    7. Anton Bondarev, 2021. "Games Without Winners: Catching-up with Asymmetric Spillovers," Dynamic Games and Applications, Springer, vol. 11(4), pages 670-703, December.
    8. Costa, Naijela Silveira da & Ferraz, Diogo & Moralles, Herick Fernando & Nascimento, Daisy do, 2022. "Economic complexity and human development: comparing standard and slack-based data envelopment analysis models," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), August.
    9. Antonelli, Cristiano & Scellato, Giuseppe, 2019. "Wage inequality and directed technological change: Implications for income distribution," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 59-65.
    10. Feder, Christophe, 2018. "The effects of disruptive innovations on productivity," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 186-193.
    11. 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.
    12. Ferraz, Diogo & Silveira, Naijela J. C. & Herick Fernando Moralles & do Nascimento Rebelatto, Daisy Aparecida & Pyka, Andreas, 2017. "An Efficiency Analysis Of Economic Complexity And Human Development In 2013," EconStor Conference Papers 171455, ZBW - Leibniz Information Centre for Economics.
    13. Emilie Le Caous & Fenghueih Huarng, 2020. "Economic Complexity and the Mediating Effects of Income Inequality: Reaching Sustainable Development in Developing Countries," Sustainability, MDPI, vol. 12(5), pages 1-26, March.
    14. Ferraz, Diogo & Moralles, Hérick Fernando & Suarez Campoli, Jéssica & Ribeiro de Oliveira, Fabíola Cristina & do Nascimento Rebelatto, Daisy Aparecida, 2018. "Economic Complexity and Human Development: DEA performance measurement in Asia and Latin America," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 25(4), pages 839-853.
    15. 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).
    16. Jangho Yang, 2023. "Information‐theoretic model of induced technical change: Theory and empirics," Metroeconomica, Wiley Blackwell, vol. 74(1), pages 2-39, February.
    17. Zhen, Wang & Xin-gang, Zhao & Ying, Zhou, 2021. "Biased technological progress and total factor productivity growth: From the perspective of China's renewable energy industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).

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    More about this item

    Keywords

    Factor shares; Directed technological change; Total factor productivity; Emergent system property;
    All these keywords.

    JEL classification:

    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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