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Bias in Returns to Tenure When Firm Wages and Employment Comove: A Quantitative Assessment and Solution

Author

Listed:
  • Pedro Martins
  • Andy Snell
  • Heiko Stueber
  • Jonathan Thomas

Abstract

It is well known that, unless worker-firm match quality is controlled for, returns to firm tenure (RTT) estimated directly via reduced form wage (Mincer) equations will be biased. In this paper we argue that even if match quality is properly controlled for there is a further pervasive source of bias, namely the co-movement of firm employment and firm wages. In a simple mechanical model where human capital is absent and separation is exogenous we show that positively covarying shocks (either aggregate or firm level) to firms employment and wages cause downward bias in OLS regression estimates of RTT. We show that the long established procedures for dealing with traditional RTT bias do not circumvent the additional problem we have identified. We argue that if a reduced form estimation of RTT is undertaken, firm-year fixed effects must be added in order to eliminate this bias. Estimates from two large panel datasets from Portugal and Germany show that the bias is empirically important. Adding firm-year fixed effects to the regression increases estimates of RTT in the two respective countries by between 3.5% and 4.5% of wages at 20 years of tenure -? over 80% (50%) of the estimated RTT level itself. The results extend to tenure correlates used in macroeconomics such as the minimum unemployment rate since joining the firm. Adding firm-year fixed effects changes estimates of these estimates also.

Suggested Citation

  • Pedro Martins & Andy Snell & Heiko Stueber & Jonathan Thomas, 2016. "Bias in Returns to Tenure When Firm Wages and Employment Comove: A Quantitative Assessment and Solution," Working Papers 64, Queen Mary, University of London, School of Business and Management, Centre for Globalisation Research.
  • Handle: RePEc:cgs:wpaper:64
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    File URL: http://cgr.sbm.qmul.ac.uk/CGRWP64.pdf
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    References listed on IDEAS

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    Cited by:

    1. Sarah Louise Jewell & Giovanni Razzu & Carl Singleton, 2020. "Who Works for Whom and the UK Gender Pay Gap," British Journal of Industrial Relations, London School of Economics, vol. 58(1), pages 50-81, March.
    2. Pedro Portugal & John T. Addison, 2022. "Retrieving the Returns to Experience, Tenure, and Job Mobility from Work Histories," Working Papers w202221, Banco de Portugal, Economics and Research Department.
    3. Martins, Pedro S. & Piracha, Matloob & Varejão, José, 2018. "Do immigrants displace native workers? Evidence from matched panel data," Economic Modelling, Elsevier, vol. 72(C), pages 216-222.
    4. Martins, Pedro S., 2021. "Should the maximum duration of fixed-term contracts increase in recessions? Evidence from a law reform," International Review of Law and Economics, Elsevier, vol. 68(C).
    5. Sónia Cabral & Pedro S. Martins & João Pereira dos Santos & Mariana Tavares, 2021. "Collateral Damage? Labour Market Effects of Competing with China—at Home and Abroad," Economica, London School of Economics and Political Science, vol. 88(350), pages 570-600, April.
    6. Galindo da Fonseca, João Alfredo & Gallipoli, Giovanni & Yedid-Levi, Yaniv, 2020. "Match quality and contractual sorting," Labour Economics, Elsevier, vol. 66(C).
    7. Osuna-Gomez, Daniel, 2023. "The impact of the COVID-19 pandemic on post-great recession entrants: Evidence from Mexico," Labour Economics, Elsevier, vol. 81(C).
    8. Osuna Gómez Daniel, 2022. "The Impact of the COVID-19 Pandemic on Post-Great Recession Formal Entrants: Evidence from Mexico," Working Papers 2022-19, Banco de México.

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    More about this item

    Keywords

    Matched data; Tenure effects; Germany; Portugal;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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