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The age-productivity gradient: evidence from a sample of F1 drivers

Author

Listed:
  • Fabrizio Castellucci

    (Bocconi University)

  • Mario Padula

    (Department of Economics, University Of Venice C� Foscari)

  • Giovanni Pica

    (Department of Economics, University of Salerno)

Abstract

Estimating the effect of aging on productivity is a daunting task. First, it requires clean measures of productivity. Second, unobserved heterogeneity at workers, firms and workers/firms level challenges the identification of the age-productivity gradient in cross-sectional data. Finally, the study of the age-productivity link requires to partial out the role of experience and to account for the selection bias that arises if less able people drop out faster than more able ones. We tackle these issues by focussing on a panel 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 & Mario Padula & Giovanni Pica, 2009. "The age-productivity gradient: evidence from a sample of F1 drivers," Working Papers 2009_16, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2009_16
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Michael P. Cameron, 2023. "The measurement of structural ageing – an axiomatic approach," Journal of Population Research, Springer, vol. 40(1), pages 1-22, March.
    7. N. Cordemans, 2018. "Low productivity growth," Economic Review, National Bank of Belgium, issue iv, pages 67-80, december.
    8. 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.
    9. 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.
    10. Onur Burak Celik, 2020. "Survival of Formula One Drivers," Social Science Quarterly, Southwestern Social Science Association, vol. 101(4), pages 1271-1281, July.
    11. Maciej Lis, 2017. "Productivity based selection to retirement: Evidence from EU-SILC," IBS Working Papers 02/2017, Instytut Badan Strukturalnych.
    12. 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.
    13. 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.
    14. 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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    More about this item

    Keywords

    Aging; individual effects; firm effects; match effects; Formula One;
    All these keywords.

    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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