In this paper, we use quantile regression decomposition methods to analyze
the gender gap between men and women who work full time in the Netherlands.
Because the fraction of women working full time in the Netherlands is
quite low, sample selection is a serious issue. In addition to shedding light on
the sources of the gender gap in the Netherlands, we make two methodological
contributions. First, we prove that the Machado-Mata quantile regression
decomposition procedure yields consistent and asymptotically normal
estimates of the quantiles of the counterfactual distribution that it is designed
to simulate. Second, we show how the technique can be extended to
account for selection.
We find that there is a positive selection of women into full-time work
in the Netherlands, i.e., women who get the greatest return to working full
time do work full time. We find that about two-thirds of this selection is
due to observables such as education and experience with the remainder due
to unobservables. Our decompositions show that the majority of the gender
log wage gap is due to dierences between men and women in returns to
labor market characteristics rather than to differences in the characteristics.
This is true across the wage distribution, particularly in the top half of the