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Job satisfaction, age and tenure: A generalized dynamic random effects model

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  • Chaudhuri, Kausik
  • Reilly, Kevin T.
  • Spencer, David A.

Abstract

This paper examines the effects of age and tenure on job satisfaction. We estimate a generalized dynamic random effects ordered probit model using the British Household Panel Survey. Contrary to previous literature, we find that age has no significant impact on job satisfaction for females, and a limited impact for males who report higher levels of job satisfaction. We also find that tenure shares a non-monotonic relationship with job satisfaction for females. For males, the same result is found only at higher levels of job satisfaction. Our results suggest that age and tenure effects on job satisfaction vary with gender.

Suggested Citation

  • Chaudhuri, Kausik & Reilly, Kevin T. & Spencer, David A., 2015. "Job satisfaction, age and tenure: A generalized dynamic random effects model," Economics Letters, Elsevier, vol. 130(C), pages 13-16.
    • Handle: RePEc:eee:ecolet:v:130:y:2015:i:c:p:13-16
    DOI: 10.1016/j.econlet.2015.02.017
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    References listed on IDEAS

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    1. Aekapol Chongvilaivan & Nattavudh Powdthavee, 2014. "Do Different Work Characteristics Have Different Distributional Impacts on Job Satisfaction? A Study of Slope Heterogeneity in Workers’ Well-Being," British Journal of Industrial Relations, London School of Economics, vol. 52(3), pages 426-444, September.
    2. Gabriella Conti & Stephen Pudney, 2011. "Survey Design and the Analysis of Satisfaction," The Review of Economics and Statistics, MIT Press, vol. 93(3), pages 1087-1093, August.
    3. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54, January.
    4. Stefan Boes & Rainer Winkelmann, 2006. "Ordered Response Models," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 12, pages 167-181, Springer.
    5. Jesus M. Carro & Alejandra Traferri, 2014. "State Dependence And Heterogeneity In Health Using A Bias‐Corrected Fixed‐Effects Estimator," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(2), pages 181-207, March.
    6. Barmby, Tim & Bryson, Alex & Eberth, Barbara, 2012. "Human capital, matching and job satisfaction," Economics Letters, Elsevier, vol. 117(3), pages 548-551.
    7. Christian Pfarr & Andreas Schmid & Udo Schneider, 2011. "Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 54(1), pages 7-23.
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    Cited by:

    1. Eskil Heinesen & Christophe Kolodziejczyk & Jacob Ladenburg & Ingelise Andersen & Karsten Thielen, 2017. "Return to work after cancer and pre-cancer job dissatisfaction," Applied Economics, Taylor & Francis Journals, vol. 49(49), pages 4982-4998, October.
    2. Vieira, José António Cabral, 2019. "Climbing the Ladders of Job Satisfaction and Employees' Organizational Commitment: A Semi-Nonparametric Approach," IZA Discussion Papers 12787, IZA Network @ LISER.

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    More about this item

    Keywords

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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • J28 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Safety; Job Satisfaction; Related Public Policy

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