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Technology, Labour Characteristics and Wage‐productivity Gaps

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  • Pekka Ilmakunnas
  • Mika Maliranta

Abstract

We use plant‐level employer–employee data in production functions and wage equations to examine whether wages are based on productivity. We use a stepwise procedure to find out how the results are influenced by the kind of data that is available. The models include shares of employee groups based on age, level and field of education, and sex. The gap between the age‐related wage and productivity effects increases with age. Education increases productivity, but wage under‐compensates productivity especially for those with the highest level of non‐technical education. For women the results depend greatly on the specification and method used.

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  • Pekka Ilmakunnas & Mika Maliranta, 2005. "Technology, Labour Characteristics and Wage‐productivity Gaps," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(5), pages 623-645, October.
    • Handle: RePEc:bla:obuest:v:67:y:2005:i:5:p:623-645
    DOI: 10.1111/j.1468-0084.2005.00134.x
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    1. Judith K. Hellerstein & David Neumark, 2007. "Production Function and Wage Equation Estimation with Heterogeneous Labor: Evidence from a New Matched Employer-Employee Data Set," NBER Chapters, in: Hard-to-Measure Goods and Services: Essays in Honor of Zvi Griliches, pages 31-71, National Bureau of Economic Research, Inc.
    2. Haltiwanger, John C. & Lane, Julia I. & Spletzer, James R., 2007. "Wages, productivity, and the dynamic interaction of businesses and workers," Labour Economics, Elsevier, vol. 14(3), pages 575-602, June.
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