Technology, Labour Characteristics and Wage-productivity Gaps

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. Copyright 2005 Blackwell Publishing Ltd.

Age, Wage and Productivity in Dutch Manufacturing

Age, Wage, Productivity, J23, J31,

Age, Wage and Productivity

Previous empirical studies on the effect of age on productivity and wages find contradicting results. Some studies find that if workers grow older there is an increasing gap between productivity and wages, i.e. wages increase with age while productivity does not or does not increase at the same pace. However, other studies find no evidence of such an age related pay-productivity gap. We perform an analysis of the relationship between age, wage and productivity using a matched worker-firm panel dataset from Dutch manufacturing covering the period 2000-2005. We find little evidence of an age related pay-productivity gap.

The impact of training on productivity and wages : evidence from British panel data

It is standard in the literature on training to use wages as a sufficient statistic for productivity. But there are many reasons why wages and productivity may diverge. This paper is part of a smaller literature on the effects of work-related training on direct measures of productivity. We construct a panel of British industries between 1983 and 1996 containing training, productivity and wages. Using a variety of econometric estimation techniques (including system GMM) we find that training is associated with significantly higher productivity. Raising the proportion of workers trained in an industry by one percentage point (say from the average of 10% to 11%) is associated with an increase in value added per worker of about 0.6% and an increase in wages of about 0.3%. Furthermore, we find that the magnitude of the impact of training on wages is only half as large as the impact of training on productivity, implying that the existing literature has underestimated the importance of training. We also show evidence using complementary datasets (e.g. from individuals) that is suggestive of externalities of training and imperfect competition