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

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  • Castellucci, Fabrizio
  • Padula, Mario
  • Pica, Giovanni

Abstract

Estimating the effect of aging on productivity requires clean measures of productivity. Additionally, one needs to control for unobserved heterogeneity at the worker, firm and worker/firm level, to account for the role of experience and to correct for selection bias. We tackle these issues exploiting a panel of Gran Prix Formula One drivers, which provides a unique setting to single out the data requirements needed to credibly estimate the effect of age on productivity. Results robust to the inclusion of worker, firm and match effects show that the age-productivity link has an inverted U-shape profile with a peak at the age of 30-32. The use of repeated cross-sections of individuals also produces consistent results provided that cohort effects are properly accounted for. Relying on team-average measures of productivity makes instead inference harder.

Suggested Citation

  • Castellucci, Fabrizio & Padula, Mario & Pica, Giovanni, 2011. "The age-productivity gradient: Evidence from a sample of F1 drivers," Labour Economics, Elsevier, vol. 18(4), pages 464-473, August.
    • Handle: RePEc:eee:labeco:v:18:y:2011:i:4:p:464-473
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    Cited by:

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    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 Employer-employee matched data;

    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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