IDEAS home Printed from https://ideas.repec.org/a/nea/journl/y2020i48p12-40.html
   My bibliography  Save this article

Schumpeterian evolution of firms' capital-labor ratio distribution

Author

Listed:
  • Leonidov, A.

    (P.N. Lebedev Physical Institute, Moscow, Russia
    (NRU) Moscow Institute of Physics and Technology, Dolgoprudny, Russia)

  • Vasilyeva, E.

    (P.N. Lebedev Physical Institute, Moscow, Russia
    (NRU) Moscow Institute of Physics and Technology, Dolgoprudny, Russia)

Abstract

According to J. Schumpeter innovation and imitation are two key drivers of economic growth. A quantitative realization of this idea using the formalism of kinetic equations was described in a number of papers. In most of these studies only one firm efficiency factor, the total factor productivity, was considered. In general, a description of economic evolution should include more efficiency factors such as, e.g., total factor productivity (TFP) and capital-labor ratio. The present study makes a preliminary step in the direction of two factor model development by considering central planner's problem of endogenous growth driven by the capital-labor ratio. The model describes an evolution of a distribution of firms on an odel developmentefficient path by considering a difference-differential analogue of the Burgers' type equation operating at a set of discrete capital-labor ratio levels. It is shown that if investment efficiency does not depend on the investment size, and production is characterised by decreasing returns to scale then firms concentrate at a certain level of capital-labor ratio. In the case of decreasing efficiency of investment with respect to its size, one observes widening of the distribution of firms in the capital-labor ratio. In addition, it is shown that the latter result holds in the case of increasing returns to scale.

Suggested Citation

  • Leonidov, A. & Vasilyeva, E., 2020. "Schumpeterian evolution of firms' capital-labor ratio distribution," Journal of the New Economic Association, New Economic Association, vol. 48(4), pages 12-40.
    • Handle: RePEc:nea:journl:y:2020:i:48:p:12-40
    DOI: 10.31737/2221-2264-2020-48-4-1
    as

    Download full text from publisher

    File URL: http://www.econorus.org/repec/journl/2020-48-12-40r.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.31737/2221-2264-2020-48-4-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. , & Lorenz, Jan & ,, 2016. "Innovation vs. imitation and the evolution of productivity distributions," Theoretical Economics, Econometric Society, vol. 11(3), September.
    2. Henkin, Gennadi M. & Polterovich, Victor M., 1991. "Schumpeterian dynamics as a non-linear wave theory," Journal of Mathematical Economics, Elsevier, vol. 20(6), pages 551-590.
    3. Robert E. Lucas Jr. & Benjamin Moll, 2014. "Knowledge Growth and the Allocation of Time," Journal of Political Economy, University of Chicago Press, vol. 122(1), pages 1-51.
    4. Acemoglu, Daron & Cao, Dan, 2015. "Innovation by entrants and incumbents," Journal of Economic Theory, Elsevier, vol. 157(C), pages 255-294.
    5. Anton Korinek & Joseph E. Stiglitz, 2018. "Artificial Intelligence and Its Implications for Income Distribution and Unemployment," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 349-390, National Bureau of Economic Research, Inc.
    6. Philippe Aghion & Benjamin F. Jones & Charles I. Jones, 2018. "Artificial Intelligence and Economic Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 237-282, National Bureau of Economic Research, Inc.
    7. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    8. Timothy C. Sargent & Edgard R. Rodriguez, 2000. "Labour or Total Factor Productivity: Do We Need to Choose?," International Productivity Monitor, Centre for the Study of Living Standards, vol. 1, pages 41-44, Fall.
    9. Marco Leonardi, 2007. "Firm heterogeneity in capital-labour ratios and wage inequality," Economic Journal, Royal Economic Society, vol. 117(518), pages 375-398, March.
    10. Olivier Jean Blanchard & Stanley Fischer, 1989. "Lectures on Macroeconomics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262022834, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Polterovich, V., 2017. "The Theory of Endogenous Economic Growth and Equations of Mathematical Physics," Journal of the New Economic Association, New Economic Association, vol. 34(2), pages 193-201.
    2. Kishi, Keiichi, 2019. "Technology diffusion, innovation size, and patent policy," European Economic Review, Elsevier, vol. 118(C), pages 382-410.
    3. Polterovich, Victor, 2017. "Теория Эндогенного Экономического Роста И Уравнения Математической Физики [The theory of endogenous economic growth and equations of mathematical physics]," MPRA Paper 78622, University Library of Munich, Germany.
    4. Jess Benhabib & Jesse Perla & Christopher Tonetti, 2021. "Reconciling Models of Diffusion and Innovation: A Theory of the Productivity Distribution and Technology Frontier," Econometrica, Econometric Society, vol. 89(5), pages 2261-2301, September.
    5. Staley, Mark, 2015. "Firm Growth and Selection in a Finite Economy," MPRA Paper 67291, University Library of Munich, Germany.
    6. Etro, Federico, 2017. "Research in economics and macroeconomics," Research in Economics, Elsevier, vol. 71(3), pages 373-383.
    7. , & Lorenz, Jan & ,, 2016. "Innovation vs. imitation and the evolution of productivity distributions," Theoretical Economics, Econometric Society, vol. 11(3), September.
    8. Olivier Gallay & Fariba Hashemi & Max-Olivier Hongler, 2019. "Imitation, Proximity, And Growth — A Collective Swarm Dynamics Approach," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-43, August.
    9. Keiichi Kishi, 2016. "Technology Diffusion, Pareto Distribution, and Patent Policy," Discussion Papers in Economics and Business 16-31, Osaka University, Graduate School of Economics.
    10. Kishi, Keiichi & Okada, Keisuke, 2021. "The impact of trade liberalization on productivity distribution under the presence of technology diffusion and innovation," Journal of International Economics, Elsevier, vol. 128(C).
    11. Michael König & Kjetil Storesletten & Zheng Song & Fabrizio Zilibotti, 2022. "From Imitation to Innovation: Where Is All That Chinese R&D Going?," Econometrica, Econometric Society, vol. 90(4), pages 1615-1654, July.
    12. Julia M. Puaschunder, 2019. "Artificial Intelligence Market Disruption," Proceedings of the 13th International RAIS Conference, June 10-11, 2019 01 JP, Research Association for Interdisciplinary Studies.
    13. Aghion, Philippe & Akcigit, Ufuk & Howitt, Peter, 2014. "What Do We Learn From Schumpeterian Growth Theory?," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 2, chapter 0, pages 515-563, Elsevier.
    14. van de Klundert, Theo & Smulders, Sjak, 1995. "Strategies for Growth in a Macroeconomic Setting," The Manchester School of Economic & Social Studies, University of Manchester, vol. 63(4), pages 388-411, December.
    15. Anton Korinek & Joseph E. Stiglitz, 2018. "Artificial Intelligence and Its Implications for Income Distribution and Unemployment," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 349-390, National Bureau of Economic Research, Inc.
    16. Boyan Jovanovic & Sai Ma, 2023. "Growth through learning," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 50, pages 211-234, October.
    17. David de la Croix & Matthias Doepke & Joel Mokyr, 2018. "Clans, Guilds, and Markets: Apprenticeship Institutions and Growth in the Preindustrial Economy," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 1-70.
    18. Zhu, Jun & Zhang, Jingting & Feng, Yiqing, 2022. "Hard budget constraints and artificial intelligence technology," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    19. Rodrigo Suescún M., 2000. "Optimical Commodity Price Stabilizador Over the Business Cycle," Borradores de Economia 154, Banco de la Republica de Colombia.
    20. Greiner, Alfred & Semmler, Willi, 2001. "Externalities of investment and endogenous growth: theory and time series evidence," Structural Change and Economic Dynamics, Elsevier, vol. 12(3), pages 295-310, September.

    More about this item

    Keywords

    Schumpeterian evolution; economic growth; capital-labor ratio; firms' distribution; Burgers' equation;
    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
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nea:journl:y:2020:i:48:p:12-40. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Alexey Tcharykov (email available below). General contact details of provider: https://edirc.repec.org/data/nearuea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.