IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/30191.html
   My bibliography  Save this paper

On the Role of Learning, Human Capital, and Performance Incentives for Wages

Author

Listed:
  • Braz Camargo
  • Fabian Lange
  • Elena Pastorino

Abstract

Performance pay in general amounts to only a small fraction of total pay. In this paper, we show that performance pay is nevertheless important for the level and dynamics of wages over the life cycle because of the incentives it indirectly provides for human capital acquisition and because of its impact on the variability of total pay. We articulate this argument in the context of a model that combines three key mechanisms for wage growth and dispersion: employer learning about workers’ ability, human capital acquisition, and performance incentives. We use this model to account for the experience profile of wages, their dispersion, and their composition in terms of fixed and variable (performance) pay. The model admits an analytical decomposition of performance pay into four terms that capture (i) the trade-off between risk and incentives characteristic of settings of moral hazard; (ii) the insurance that firms provide against the wage risk due to the uncertainty about ability; (iii) incentives for effort arising from this uncertainty (career concerns); and (iv) incentives for effort generated by the prospect of human capital acquisition. We prove the model is identified under standard assumptions. Despite its parsimony, the model fits the data very well, including the empirical finding that performance pay as a share of total pay first increases and then decreases with experience. This feature of performance pay, which we are the first to document, runs contrary to the prediction of standard models of performance incentives that the ratio of performance pay to total pay increases with experience, especially at the end of the life cycle. Our estimates imply that effort to produce output augments human capital. Also, human capital acquisition and insurance against uncertainty about ability are quantitatively the main determinants of performance pay. Career-concerns incentives, on which the theoretical literature has focused, and the strength of the contemporaneous trade-off between risk and incentives—the primary determinant of variable pay in static moral-hazard models—are instead much less relevant. Importantly, we find that through the cumulative impact of effort on the job on human capital acquisition and the contribution of variable pay to the variance of total pay, performance incentives are a crucial source of wage growth and dispersion over the life cycle.

Suggested Citation

  • Braz Camargo & Fabian Lange & Elena Pastorino, 2022. "On the Role of Learning, Human Capital, and Performance Incentives for Wages," NBER Working Papers 30191, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:30191
    Note: EFG LS
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w30191.pdf
    Download Restriction: no
    ---><---

    More about this item

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • D86 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Economics of Contract Law
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J33 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Compensation Packages; Payment Methods
    • J41 - Labor and Demographic Economics - - Particular Labor Markets - - - Labor Contracts
    • J44 - Labor and Demographic Economics - - Particular Labor Markets - - - Professional Labor Markets and Occupations

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:30191. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.