IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2204.07888.html
   My bibliography  Save this paper

AI, Ageing and Brain-Work Productivity: Technological Change in Professional Japanese Chess

Author

Listed:
  • Eiji Yamamura
  • Ryohei Hayashi

Abstract

Using Japanese professional chess (Shogi) players records in the novel setting, this paper examines how and the extent to which the emergence of technological changes influences the ageing and innate ability of players winning probability. We gathered games of professional Shogi players from 1968 to 2019. The major findings are: (1) diffusion of artificial intelligence (AI) reduces innate ability, which reduces the performance gap among same-age players; (2) players winning rates declined consistently from 20 years and as they get older; (3) AI accelerated the ageing declination of the probability of winning, which increased the performance gap among different aged players; (4) the effects of AI on the ageing declination and the probability of winning are observed for high innate skill players but not for low innate skill ones. This implies that the diffusion of AI hastens players retirement from active play, especially for those with high innate abilities. Thus, AI is a substitute for innate ability in brain-work productivity.

Suggested Citation

  • Eiji Yamamura & Ryohei Hayashi, 2022. "AI, Ageing and Brain-Work Productivity: Technological Change in Professional Japanese Chess," Papers 2204.07888, arXiv.org.
  • Handle: RePEc:arx:papers:2204.07888
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2204.07888
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    2. Daron Acemoglu & Pascual Restrepo, 2020. "The wrong kind of AI? Artificial intelligence and the future of labour demand," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 25-35.
    3. Alison Booth & Eiji Yamamura, 2018. "Performance in Mixed-Sex and Single-Sex Competitions: What We Can Learn from Speedboat Races in Japan," The Review of Economics and Statistics, MIT Press, vol. 100(4), pages 581-593, October.
    4. Daron Acemoglu & Claire Lelarge & Pascual Restrepo, 2020. "Competing with Robots: Firm-Level Evidence from France," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 383-388, May.
    5. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
    6. Daron Acemoglu & David Autor & Jonathon Hazell & Pascual Restrepo, 2020. "AI and Jobs: Evidence from Online Vacancies," NBER Working Papers 28257, National Bureau of Economic Research, Inc.
    7. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    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. Laura Abrardi & Carlo Cambini & Laura Rondi, 2022. "Artificial intelligence, firms and consumer behavior: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 969-991, September.
    2. Stefan Jestl, 2022. "Industrial Robots, and Information and Communication Technology: The Employment Effects in EU Labour Markets," wiiw Working Papers 215, The Vienna Institute for International Economic Studies, wiiw.
    3. Davide Dottori, 2021. "Robots and employment: evidence from Italy," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 38(2), pages 739-795, July.
    4. Alekseeva, Liudmila & Azar, José & Giné, Mireia & Samila, Sampsa & Taska, Bledi, 2021. "The demand for AI skills in the labor market," Labour Economics, Elsevier, vol. 71(C).
    5. Borsato, Andrea & Lorentz, André, 2023. "The Kaldor–Verdoorn law at the age of robots and AI," Research Policy, Elsevier, vol. 52(10).
    6. Albanesi, Stefania & Da Silva, António Dias & Jimeno, Juan F. & Lamo, Ana & Wabitsch, Alena, 2023. "New technologies and jobs in Europe," Working Paper Series 2831, European Central Bank.
    7. Fossen, Frank M. & Sorgner, Alina, 2022. "New digital technologies and heterogeneous wage and employment dynamics in the United States: Evidence from individual-level data," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    8. Chen, Yang & Cheng, Liang & Lee, Chien-Chiang, 2022. "How does the use of industrial robots affect the ecological footprint? International evidence," Ecological Economics, Elsevier, vol. 198(C).
    9. Jean-Philippe Deranty & Thomas Corbin, 2022. "Artificial Intelligence and work: a critical review of recent research from the social sciences," Papers 2204.00419, arXiv.org.
    10. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    11. David Kunst, 2019. "Deskilling among Manufacturing Production Workers," Tinbergen Institute Discussion Papers 19-050/VI, Tinbergen Institute, revised 30 Dec 2020.
    12. Lu, Jing & Xiao, Qinglan & Wang, Taoxuan, 2023. "Does the digital economy generate a gender dividend for female employment? Evidence from China," Telecommunications Policy, Elsevier, vol. 47(6).
    13. Goos, Maarten & Rademakers, Emilie & Röttger, Ronja, 2021. "Routine-Biased technical change: Individual-Level evidence from a plant closure," Research Policy, Elsevier, vol. 50(7).
    14. Montobbio, Fabio & Staccioli, Jacopo & Virgillito, Maria Enrica & Vivarelli, Marco, 2022. "Robots and the origin of their labour-saving impact," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    15. Gries, Thomas & Naudé, Wim, 2022. "Modelling artificial intelligence in economics," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 56, pages 1-12.
    16. Yuki, Kazuhiro, 2012. "Mechanization, task assignment, and inequality," MPRA Paper 37754, University Library of Munich, Germany.
    17. José-Ignacio Antón & David Klenert & Enrique Fernández-Macías & Maria Cesira Urzì Brancati & Georgios Alaveras, 2022. "The labour market impact of robotisation in Europe," European Journal of Industrial Relations, , vol. 28(3), pages 317-339, September.
    18. Songul Tolan & Annarosa Pesole & Fernando Martinez-Plumed & Enrique Fernandez-Macias & José Hernandez-Orallo & Emilia Gomez, 2020. "Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks," JRC Working Papers on Labour, Education and Technology 2020-02, Joint Research Centre.
    19. Daron Acemoglu & Pascual Restrepo, 2020. "The wrong kind of AI? Artificial intelligence and the future of labour demand," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 25-35.
    20. Marcel Steffen Eckardt, 2022. "Minimum wages in an automating economy," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 24(1), pages 58-91, February.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2204.07888. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.