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On the wage–productivity causal relationship

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Abstract

In a perfectly competitive labor market, wage rates are determined by labor productivity, so that wage dispersion reflects the marginal contribution to product of the different workers. Accordingly, wage inequality cannot be treated as an independent variable in a model of productivity, and thus economists have paid little attention to this relation. This paper studies the effects of wage inequality on labor productivity. We claim that wage inequality can lead to lower effort among workers who receive lower wages and hence lead in turn to lower aggregate labor productivity because of the lower aggregate effort level. To guide the empirical analysis, we look at aggregate panel data to investigate whether there is a relationship between wage inequality and average labor productivity. We use data for 34 OECD countries in the period 1995–2007, and by allowing country fixed effects, we exploit the longitudinal dimension of the data. We find that large wage inequality is associated with lower labor productivity. So the results suggest that higher levels of the Gini index of wage inequality are associated with lower labor productivity. Moreover, we study the question of whether wage inequality that causes productivity level or viceversa by assessing non-causal homogeneity in a panel Granger framework.

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Notes

  1. By relative deprivation, we refer to the payment of a wage that is less than expected.

  2. At the margin, an increase in low-end wages would leave profit unchanged, while raising productivity, output, and of course welfare for the low end of the wage distribution.

  3. In fact, Oulton (1995) and Eltis and Higham (1995), analyzing UK productivity since 1980, find that harsh recessions in the early 1980s (with of course, high levels of unemployment) may have caused inefficient business to exit (rising average level of productivity) and, at firm level, the least productive workers may have been made redundant first, once again rising average productivity.

  4. To avoid such problem, lagged GDP per capita is used as instrument for its actual level. Remember that the estimators of a dynamic panel data use internal instruments, which are defined as instruments based on previous realizations of the explanatory variables; this is in order to better consider the potential joint endogeneity of the regressors.

  5. Downloaded from the Quality of Government Institute’s Dataset, University of Gothenburg, 2017.

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Acknowledgements

We are grateful to anonymous referees for many helpful suggestions and constructive comments. We benefitted from discussions and comments from Costas Azariadis and seminar participants at IV CICSE CONFERENCE Structural Change, Dynamics, and Economic Growth (organized by Centro Interuniversitario Crescita and Sviluppo Economico (CICSE), University of Pisa).

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Authors and Affiliations

  1. Italian Ministry of Economy and Finance, Via Pietrapiana 53, Florence, 50121, Italy

    Laura Policardo

  2. Department of Economics and Statistics, University of Siena, Piazza S. Francesco 7, Siena, 53100, Italy

    Lionello F. Punzo

  3. INCT/PPED, UFRJ, Rio de Janeiro, Brazil

    Lionello F. Punzo

  4. Department of Economics, Society, and Politics, The University of Urbino Carlo Bo, Palazzo Battiferri, Via Saffi 42, Urbino, 61029, PU, Italy

    Edgar J. Sanchez Carrera

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  1. Laura Policardo
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  2. Lionello F. Punzo
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  3. Edgar J. Sanchez Carrera
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Correspondence to Edgar J. Sanchez Carrera.

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The opinions expressed in this publication are those of the authors and do not necessarily reflect the official opinion of the Italian Ministry of Economy and Finance.

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Policardo, L., Punzo, L.F. & Carrera, E.J.S. On the wage–productivity causal relationship. Empir Econ 57, 329–343 (2019). https://doi.org/10.1007/s00181-018-1428-5

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