Browsing by Subject "Unconditional quantile regression"

Now showing 1 - 6 of 6

Detailed RIF decomposition with selection : the gender pay gap in Italy
(2017) Töpfer, Marina
In this paper, we estimate the gender pay gap along the wage distribution using a detailed decomposition approach based on unconditional quantile regressions. Non-randomness of the sample leads to biased and inconsistent estimates of the wage equation as well as of the components of the wage gap. Therefore, the method is extended to account for sample selection problems. The decomposition is conducted by using Italian microdata. Accounting for labor market selection may be particularly relevant for Italy given a comparably low female labor market participation rate. The results suggest not only differences in the income gap along the wage distribution (in particular glass ceiling), but also differences in the contribution of selection effects to the pay gap at different quantiles.
Does downward nominal wage rigidity dampen wage increases?
(2010) Beißinger, Thomas; Stüber, Heiko
Focusing on the compression of wage cuts, many empirical studies find a high degree of downward nominal wage rigidity (DNWR). However, the resulting macroeconomic effects seem to be surprisingly weak. This contradiction can be explained within an intertemporal framework in which DNWR not only prevents nominal wage cuts but also induces firms to compress wage increases. We analyze whether a compression of wage increases occurs when DNWR is binding by applying Unconditional Quantile Regression and Seemingly Unrelated Regression to a data set comprising more than 169 million wage changes. We find evidence for a compression of wage increases and only very small effects of DNWR on average real wage growth. The results indicate that DNWR does not provide a strong argument against low inflation targets.
Migration and wage inequality : a detailed analysis for German regions over time
(2022) Schmid, Ramona
This study presents new evidence on immigrant-native wage differentials estimated in consideration of regional differences regarding the presence of Non-German population in metropolitan and non-metropolitan areas between 2000 and 2019 in Germany. Using linked employer-employee-data, unconditional quantile regression models are estimated in order to assess the degree of labor market integration of foreign workers. Applying an extended version of the Oaxaca-Blinder decomposition method, the results provide evidence on driving factors behind wage gaps along the entire wage distribution. There are not only changes in the relative importance of explanatory factors over time, but also possible sources of wage differentials shift between different points of the wage distribution. Differentiating between various areas in Germany, on average, larger wage gaps are revealed in metropolitan areas with at the same time a higher presence of the foreign population. Regarding the size of overall estimated wage gaps, after 2012 a reversal in trend and particular increasing tendencies around median wages are identified.
Overconfidence and gender differences in wage expectations
(2020) Satlukal, Sascha; Reuter, Mirjam; Pfeifer, Gregor; Osikominu, Aderonke; Briel, Stephanie
We analyze the impact of (over-)confidence on gender differences in expected start-ing salaries using elicited beliefs of prospective university students in Germany. According to our results, female students have lower wage expectations and are less overconfident than their male counterparts. Oaxaca-Blinder decompositions of the mean show that 7.7% of the gender gap in wage expectations is attributable to a higher overconfidence of males. Decompositions of the unconditional quantiles of expected salaries suggest that the contribution of gender differences in confidence to the gender gap is particularly strong at the bottom and top of the wage expectation distribution.
Recent developments in gender differences in pay
(2017) Töpfer, Marina; Beißinger, Thomas
Gender differences in pay continue to persist, despite decades of equal-pay legislation and the promotion of equal opportunities. This thesis examines differences in pay between men and women in Italy during the period 2005-2014 and puts special emphasis on the effects of sample selection. It decomposes the gender pay gap in different subsamples and identifies drivers of the gap that remained unobserved so far. In particular, it shows the empirical disappearance of the gender pay gap in Italy for public-contest recruited employees. It further reveals that the wage gap between men and women for overeducated workers is mainly explained by generally unobservable characteristics. From the methodological perspective, this work provides two novelties. First, it adds to the literature on quantile-regression approaches by adjusting the wage model based on unconditional quantile regression for sample selection. Second, an alternative estimation approach that builds on the omitted variable bias formula is proposed, in order to directly estimate the change of the gender pay gap and its components over time. The empirical part of this thesis is based on a large Italian data set (ISFOL PLUS 2005-2014). The case of Italy is particularly interesting for the study of gender differences in pay and gender-specific selection into wage work given low levels of the aggregate gender pay gap (approximately 6.0%) on the one hand, and high employment gaps between men and women (more than 20.0%) on the other hand.
The effect of women directors on innovation activity and performance of corporate firms - evidence from China
(2018) Töpfer, Marina
This paper elaborates whether women bringing their diversity, cross-cultural awareness and transformational leadership skills to corporate boards offer strategic advantages for firms. In the analysis the effect of women in the board room on innovation activity and corporate firm performance as well as the joint consequences of female directors and innovation activity on the firms success are examined. The latter may be particularly important in the context of gender diversity as more gender-diverse boards allow for higher levels of creativity and hence innovation. In order to account for endogeneity issues, different model specifications are employed (two-way fixed effects models and linear dynamic panel data models). Unconditional quantile regressions are used in order to go beyond the mean. The analysis is conducted using Chinese firm-level data from 2006-2015. The results suggest positive ef- fects of gender diversity in corporate boards and patenting activities on firm performance. Women directors are found to have statistically significant effects on both input-(positive) and output-oriented (negative) innovation activity.