The Measurement of Wage Discrimination with Imperfect Information: A Finite Mixture Approach

Juan Prieto-Rodriguez (), Juan Gabriel Rodríguez and Rafael Salas
Authors registered in the RePEc Author Service: Juan Gabriel Rodríguez

A chapter in Inequality, Redistribution and Mobility, 2020, vol. 28, pp 187-204 from Emerald Group Publishing Limited

Abstract: Studies on wage discrimination assume that independent observers are able to distinguish a priori which workers are suffering from discrimination. However, this may not be a good assumption when anti-discrimination laws mean that severe penalties can be imposed on discriminatory employers or when unobserved heterogeneity is significant. We develop a wage discrimination model in which workers are not classified a priori. It can be thought of as a generalization of the standard empirical framework, whereas the Oaxaca–Blinder model can be thought of as an extreme case. We propose a finite mixture model to explicitly model unobserved heterogeneity in individual characteristics and estimate the probabilities of being a discriminated or a non-discriminated worker. We illustrate this proposal by estimating wage discrimination in Germany and the UK.

Keywords: Wage discrimination; distribution of wage gaps; imperfect information; finite mixture models; D63; D83; J71 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eme:reinzz:s1049-258520200000028008

DOI: 10.1108/S1049-258520200000028008

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