IDEAS home Printed from
   My bibliography  Save this paper

The Economics of Presenteeism: A discrete choice & count model framework




There are three levels in this paper: A search for economic theories about presenteeism, a search for appropriate econometric approaches, and finally empirical results based on a unique Danish cross sectional data set. There are two economic approaches to presenteeism: 1. Productivity losses and 2. labor supply. The first is part of the indirect cost component in cost-of-illness studies and economic evaluation. There are two core questions in the productivity loss literature: Measurement of productivity losses (‘how much’) which has dominated the research agenda and valuation of incurred productivity losses (monetary value). Few economists have addressed the valuation issue and point out that the wage rate sometimes is inadequate. The starting point in the labor supply literature is sickness absence coupled with labor demand. The few economic models about presenteeism are explored and found lacking in the sense that they do not capture the essence of presenteeism. However, discrete choice models (random utility models) seem to be adequate in that the choice about going sick to work basically is a discrete choice situation that can be extended to include discrete counts, i.e. episodes of presenteeism within a given time period. The econometrics of presenteeism must have count models as the starting point due to the many zeroes, i.e. many persons do not experience presenteeism and, if they do, usually relatively few days (‘events’) in a given period and the discrete choice nature of presenteeism. Drawing on the econometric literature on utilization of medical services, the following models are discussed briefly: Poisson models, negative binominal, zero-inflated negative binomial, two part models (hurdle models) and latent class models (finite mixture models). This is in contrast to almost all previous literature where logistic regression has been the dominant statistical strategy. The Poissson model is discarded because an important feature (mean – variance) does not hold. The other models are all used in the empirical part of the paper, and an attempt at model selection is made. The empirical analyses are based on a cross-section survey of Danes in the labor force, N=4,060. The survey was designed with presenteeism in mind – one of the few available data sets at present. Ideally, theory/models should guide empirical work, but can do so only if fully specified theories are available and this is not the case for the random utility models that do no provide much guidance on relevant explanatory variables. The explanatory variables therefore are selected from the existing empirical works along with a number of new variables used in the survey, e.g. attitudinal variables about presenteeism and sickness absence and questions about work environment. A consistent result across all analyses is – not surprisingly - the importance of self reported health status: The worse health situation, the more presenteeism. . Another consistent result is that sickness absence and presenteeism are positively correlated. Persons with managerial positions also consistently have more presenteeism Age and genders are also (almost) consistently statistically significant. Fear of unemployment is also consistently and significantly related to presenteeism.

Suggested Citation

  • Pedersen, Kjeld Møller & Skagen, Kristian, 2014. "The Economics of Presenteeism: A discrete choice & count model framework," COHERE Working Paper 2014:2, University of Southern Denmark, COHERE - Centre of Health Economics Research.
  • Handle: RePEc:hhs:sduhec:2014_002

    Download full text from publisher

    File URL:
    File Function: Full text
    Download Restriction: no


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Boris HirschBy & Daniel S. J. Lechmann & Claus Schnabel, 2017. "Coming to work while sick: an economic theory of presenteeism with an application to German data," Oxford Economic Papers, Oxford University Press, vol. 69(4), pages 1010-1031.

    More about this item


    Presenteeism; sickness absence; labor supply; cost-of-illness; economic evaluation; count models;

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:hhs:sduhec:2014_002. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Terkel Christiansen). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.