Work in Progress

Gender equality and globalization: New international evidence (with Klaus Gründler, Niklas Potrafke and Jan-Egbert Sturm)

Automation threat, labour unions and wages (with Thomas Beißinger and Martyna Marczak)

Labour market transitions and wage dynamics in Germany (with Angelo Martelli)

Working Papers

Schmid, R. (2022). Mind the gap: effects of the national minimum wage on the gender wage gap in Germany (R&R in The Journal of Economic Inequality) (preliminary version available on request)

With its introduction in 2015, the statutory minimum wage in Germany intends to benefit primarily low-wage workers. Thus, this paper aims at estimating the effectiveness of the implemented wage floor on gender wage gaps in the lower half of the wage distribution. Using administrative data, distinct regional differences regarding magnitudes of wage differentials and responses to the minimum wage are identified. Overall, wage gaps between men and women at the 10th percentile decrease by 2.46 and 6.34 percentage points respectively in the West and East of Germany after 2015. Applying difference-in-differences analyses that consider counterfactual wage distributions, the study provides new evidence that around 60% and even 95% of the decline result from the introduction of the minimum wage in each region. Further, group-specific analyses identify concrete responses on the basis of age, educational level and occupational activity. Having yearly data, the study additionally reveals new results on the impact of the successive minimum wage raises in 2017 and 2019. Counterfactual aggregate decompositions of gender wage gaps finally indicate a decrease in discriminatory remuneration structures in the West of Germany due to the introduced wage floor.

Publications

Schmid, R. (2023). Migration and wage inequality: a detailed analysis for German metropolitan and non-metropolitan regions. Review of Regional Research, 1-55. Link 

This study presents new evidence on immigrant-native wage gaps considering regional-specific differences between 2000 and 2019 in Germany. Using linked employer-employee-data, unconditional quantile regression models are estimated in order to assess the degree of labour market integration of foreign workers. The applied extended version of the Oaxaca-Blinder decomposition method provides evidence on driving factors behind wage gaps along the entire wage distribution. Estimated results are presented not only for the whole ofWest Germany but also differentiated between metropolitan and nonmetropolitan areas. On average, larger wage differentials are identified in metropolitan areas with at the same time a higher presence of foreign population. Detailed decompositions show that 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. Decisive explanatory variables in this context are the practised profession, the economic sector affiliation and the extent of labour market experience. Distinguishing between metropolitan and non-metropolitan areas, provides evidence that especially differences in educational attainment impact wage gaps in urban areas. Regarding the size of overall estimated wage gaps, after 2012 a reversal in trend and particular increasing tendencies around median wages are revealed.

 Brall, F., & Schmid, R. (2023). Automation, robots and wage inequality in Germany: A decomposition analysis. LABOUR, 37(1), 33-95. Link

We conduct a decomposition analysis based on recentered influence function (RIF) regressions to disentangle the relative importance of automation and robotization for wage inequality in the manufacturing sector in Germany between 1996 and 2017. Our measure of automation threat combines occupation-specific scores of automation risk with sector-specific robot densities. We find that besides changes in the composition of individual characteristics, structural shifts among different automation threat groups are a non-negligible factor associated with wage inequality between 1996 and 2017. Moreover, the increase in wage dispersion among the different automation threat groups has contributed significantly to higher wage inequality in the 1990s and 2000s.