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The Age-Productivity Gradient: Evidence from a Sample of F1 Drivers

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Abstract

Aging is a global phenomenon. If older individuals are less productive, an aging working population can lower aggregate productivity, economic growth and fiscal sustainability. Therefore, understanding the age-productivity gradient is key in a aging society. However, estimating the effect of aging on productivity is a daunting task. First, it requires clean measures of productivity. Wages are not such measures to the extent that they reward other workers attributes than their productivity. Second, unobserved heterogeneity at workers, firms and workers/firms level challenges the identification of the age-productivity gradient in cross-sectional data. Longitudinal data attenuate some identification issues, but give rise to the problem of partialling out the effect of aging from the pure effect of time. Third, the study of the age-productivity link requires investigating the role of experience and of seniority. We tackle these issues by focussing on a sample of Gran Prix Formula One drivers and show that the age-productivity link has an inverted U-shape profile, with a peak at around the age of 30-32.

Suggested Citation

  • Fabrizio Castellucci & Giovanni Pica, 2009. "The Age-Productivity Gradient: Evidence from a Sample of F1 Drivers," CSEF Working Papers 226, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    • Handle: RePEc:sef:csefwp:226
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    1. Eiji YAMAMURA & Ryohei HAYASHI, 2024. "AI, ageing and brain-work productivity: Technological change in professional Japanese chess," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-25, May.
    2. Lis, Maciej & Magda, Iga, . "Dynamika płac w cyklu życia a indywidualny stan zdrowia," Gospodarka Narodowa-The Polish Journal of Economics, Szkoła Główna Handlowa w Warszawie / SGH Warsaw School of Economics, vol. 2014(4).
    3. Law, Imogen, 2025. "Where Did They Come From, Where Did They Go: Analysis of Formula 1 Driver Career Paths," OSF Preprints e4pwh, Center for Open Science.
    4. Börsch-Supan, Axel & Weiss, Matthias, 2016. "Productivity and age: Evidence from work teams at the assembly line," The Journal of the Economics of Ageing, Elsevier, vol. 7(C), pages 30-42.
    5. Rachel Scarfe & Carl Singleton & Adesola Sunmoni & Paul Telemo, 2024. "The age‐wage‐productivity puzzle: Evidence from the careers of top earners," Economic Inquiry, Western Economic Association International, vol. 62(2), pages 584-606, April.
    6. Lucia Rizzica, 2020. "The Italian public sector workforce: recent evolution in the light of the rules on turnover," Questioni di Economia e Finanza (Occasional Papers) 560, Bank of Italy, Economic Research and International Relations Area.
    7. Konstantins Benkovskis & Olegs Tkacevs, 2019. "Getting Old Is No Picnic? Sector-Specific Relationship Between Workers Age and Firm Productivity," Discussion Papers 2019/03, Latvijas Banka.
    8. Bertoni, Marco & Brunello, Giorgio & Rocco, Lorenzo, 2015. "Selection and the age – productivity profile. Evidence from chess players," Journal of Economic Behavior & Organization, Elsevier, vol. 110(C), pages 45-58.
    9. Michael P. Cameron, 2023. "The measurement of structural ageing – an axiomatic approach," Journal of Population Research, Springer, vol. 40(1), pages 1-22, March.
    10. N. Cordemans, 2018. "Low productivity growth," Economic Review, National Bank of Belgium, issue iv, pages 67-80, december.
    11. Ester Gutiérrez & Sebastián Lozano, 2014. "A DEA Approach to Performance-Based Budgeting of Formula One Constructors," Journal of Sports Economics, , vol. 15(2), pages 180-200, April.
    12. Maciej Lis & Iga Magda, 2014. "Dynamika płac w cyklu życia a indywidualny stan zdrowia," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 4, pages 121-142.
    13. Francesco Principe & Jan van Ours, 2025. "Labor Market Dynamics in a Highly Competitive Industry," Tinbergen Institute Discussion Papers 25-020/V, Tinbergen Institute.
    14. Onur Burak Celik, 2020. "Survival of Formula One Drivers," Social Science Quarterly, Southwestern Social Science Association, vol. 101(4), pages 1271-1281, July.
    15. Maciej Lis, 2017. "Productivity based selection to retirement: Evidence from EU-SILC," IBS Working Papers 02/2017, Instytut Badan Strukturalnych.
    16. Ester Gutiérrez & Sebastián Lozano, 2020. "Benchmarking Formula One auto racing circuits: a two stage DEA approach," Operational Research, Springer, vol. 20(4), pages 2059-2083, December.
    17. repec:osf:osfxxx:e4pwh_v1 is not listed on IDEAS
    18. Antonio Filippin & Jan C. Ours, 2015. "Positive Assortative Matching: Evidence from Sports Data," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 54(3), pages 401-421, July.
    19. Michael A. Lapré & Candace Cravey, 2022. "When Success Is Rare and Competitive: Learning from Others’ Success and My Failure at the Speed of Formula One," Management Science, INFORMS, vol. 68(12), pages 8741-8756, December.

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    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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