CEU Electronic Theses and Dissertations, 2018
|The Gender Wage Gap In Hungary: An Unconditional Quantile Regression-Based Decomposition Approach
|This thesis examines the gender wage gap in Hungary in 1998-2011 along the wage distribution by using decompositions with reentered influence function regression approach by Firpo et al.(2007). Using 1998, 2005, 2011 wage data from the National Employment Office, the regression-compatible decompositions at the mean show that the total wage gap in my sample increases over time, while the explained gap is negative in all the years, particularly due to firm characteristics, occupation and residence indicators. Along the distribution, the gender wage gap is upward sloping, indicating the glass ceiling effect for women. Before the recession, the total explained gap is positive starting from 95-99 quantile, while after the recession the total explained gap is negative at all the quantiles, especially due to education reversal. Thus, although before the recession women were less qualified for high paying jobs, in 2011 the women should earn higher wages at any quantile of the wage distribution in the absence of unexplained gap. To address the robustness of my findings I use the matching approach proposed by Ñopo (2008), which addresses the issue of differences in the supports of the distributions of characteristics. I find that once the industrial dummies and firm characteristics are added, the portion of matched observations decreases, which hints at the high industrial and firm segregation by gender.
Visit the CEU Library.
© 2007-2021, Central European University