An Investigation of the Relationship between Job Characteristics and the Gender Wage Gap

Abstract

This paper re-examines gender wage differences, taking into account not only worker characteristics but also job characteristics. Consideration of a wide set of “job quality” indicators can explain a fraction of the wage gap that would otherwise be attributed to pure wage discrimination. In any case, the fraction of the wage gap that remains associated to differential rewards for identical factors across sexes is still substantial. Our results suggest that in order to avoid overestimation of the fraction of the wage gap attributable to discrimination, it is necessary to control for job characteristics.

We specify a model accounting for the fact that the wage equation can have sample selection problems due to participation and that individuals sort themselves into different occupations. Wages are determined by several job characteristics and individual variables such as age and education. From the model estimates, we shall implement the wage decomposition procedure proposed by Neumark (1988). Given the presence of two selection processes in our model, we shall pay special attention to how decompositions of the wage gap need to be carried out in the presence of non random assignment to different groups in the labour market. In particular we shall follow the procedures proposed by Neuman and Oaxaca (1998) in carrying out the decompositions.

Our results suggest that job characteristics are important factors in explaining wages even when controlling for individual characteristics. Moreover, when we account for job characteristics, the fraction of the gender wage gap attributable to differential rewards for men and women is reduced, reflecting the fact that men tend to be assigned to the “best” jobs. However, there remains a substantial and significant “discriminatory” component, in that the reward for job and individual characteristics is higher for men.

In section 2 we present the econometric model. Section 3 comprises discussion of the data set. The empirical results are presented in section 4, while, finally, section 5 concludes.

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