Abstract
This article studies the trend in wage discrimination in Spain from 1995 to 2002, when the third plan for equal opportunities for men and women was in action. To account for the criticism of Heckman et al. (J Hum Cap 2:1–31, 2008), we first introduce a novel approach to the analysis of wage discrimination with methods that are robust to model (mis-) specification. Following their idea, we apply semiparametric methods for the Oaxaca-Blinder decomposition of wage differentials between men and women. We extend the methodology to semiparametric quantile estimation. The study is completed by some descriptive analysis, also based on nonparametric techniques. We find that, while the wage gap has diminished from 1995 to 2002 this is mainly due the smaller gap in returns of endowments for wages above the median, and due to the endowments of women for lower and particularly high wages. Respective the quantiles, in contrast to other EU member states, the Spanish wage gap is widest for low wages but almost U-shaped in 2002 whereas this was not that evident in 1995.
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Notes
The alternative semiparametric extension of Juhn et al. (1991) is discussed in a note by Moral-Arce and Sperlich (2008). That method would for example be especially appropriate for country specific differences between the income distributions to later compare the gender pay gaps of these countries, see for example Blau and Kahn (1992, 2003).
Note that for all estimation steps, including for the descriptive statistics, one has to account for the stratification by including the sampling weights.
More specifically, they argue that “employers may use statistical discrimination in wage-setting in order to pay a lower proportion of the training cost for women than for men”.
We are certainly aware of the quite abundant literature on over qualification which may justify this flattening of return to education without charging it to gender discrimination. However, most of the literature we know on this concerning Spain investigates several years before 1995. For a more recent study, see Budria and Moro-Egido (2006).
The estimates of g( ) were obtained with window sizes equal to 1.5 times Silverman’s rule of thumb.
There are very few old women with high experience (recall that this means tenure) which give a quite different shape to the curve at the upper right. Our general statements refer certainly to the mass of observations.
This is clear because in average women study much longer, see their educational level 2 in Table 3, time that rests from tenure.
See our discussion from above on the negative correlation between educational level and experience.
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Acknowledgements
We appreciated helpful discussion with R. Alaéz, M. Ullibarri, A. Alonso, and A. Madariaga (Instituto de la Mujer - Emakunde), C. Hundertmark and R. Ohinata, and the comments of an anonymous referee which helped us a lot to improve this article. The authors acknowledge financial support from FUNCAS (Fundacion de las Cajas de Ahorros) which published an earlier version as working paper (Nº 382/2008).
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Moral-Arce, I., Sperlich, S., Fernández-Saínz, A.I. et al. Trends in the Gender Pay Gap in Spain: A Semiparametric Analysis. J Labor Res 33, 173–195 (2012). https://doi.org/10.1007/s12122-011-9124-7
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DOI: https://doi.org/10.1007/s12122-011-9124-7
Keywords
- Wage differentials
- Semiparametric regression
- Counterfactual quartile regression
- Gender pay gap
- Oaxaca-Blinder decomposition