Abstract

In health economic evaluation studies, to value productivity loss due to absenteeism, existing methods use wages as a proxy value for marginal productivity. This study is the first to test the equality between wage and marginal productivity losses due to absenteeism separately for team workers and non-team workers. Our estimates are based on linked employer-employee data from Canada. Results indicate that team workers are more productive and earn higher wages than non-team workers. However, the productivity gap between these two groups is considerably larger than the wage gap. In small firms, employee absenteeism results in lower productivity and wages, and the marginal productivity loss due to team worker absenteeism is significantly higher than the wage loss. No similar wage-productivity gap exists for large firms. Our findings suggest that productivity loss or gain is most likely to be underestimated when valued according to wages for team workers. The findings help to value the burden of illness-related absenteeism. This is important for economic evaluations that seek to measure the productivity gain or loss of a health care technology or intervention, which in turn can impact policy makers’ funding decisions.

Highlights

  • It is still under debate whether we should take account of productivity gains or losses from a health care intervention in economic evaluation studies [1, 2]

  • This study is the first to test the equality of the estimated absenteeism impacts on marginal productivity and wages using linked employer-employee data

  • Our findings support the theoretical predictions of Pauly et al [9, 11] and provide compelling evidence that the productivity loss due to worker absence exceeds the wage for team workers, especially in small firms

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Summary

Introduction

It is still under debate whether we should take account of productivity gains or losses from a health care intervention in economic evaluation studies [1, 2]. Hellerstein et al have developed a framework to simultaneously estimate firm-level wage equations and production functions on population-based datasets that link employees’ input to their employers’ output [27, 28]. Their approach yields estimated marginal productivity differentials and wage differentials for workers with different characteristics, and a framework to test their equality. Hellerstein et al use US population data to estimate wage and marginal productivity differentials for worker groups with different age, sex, and race characteristics [28]. Haegeland and Klette analyze wage and productivity gaps among Norwegian workers grouped by sex, education and work experience [29]; van Ours and Stoeldraijer identify 13 studies on age, wage and productivity using linked employeremployee data [30]

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