Unions, Internationalization, Tasks, Firms, and Worker Characteristics: A Detailed Decomposition Analysis of Rising Wage Inequality in Germany

  • Martin BiewenEmail author
  • Matthias Seckler


This paper provides a comprehensive quantitative assessment of the importance of the factors associated with the rise in male wage inequality in Europe’s largest economy over the period 1995-2010. We simultaneously consider an extensive set of explanatory factors including personal characteristics, measures of internationalization, task composition, union coverage, industry, region, and firm characteristics. Our study uses a different data source than most of the other prominent studies on wage inequality in Germany. We carefully assess differences implied by the different data and show that previous studies have most likely underestimated the dominating role of de-unionization for the rise in German wage inequality. As the second most important factor, we identify compositional effects of personal characteristics such as age and education. We find only moderate effects linked to internationalization, firm heterogeneity, task changes and regional convergence.


De-unionization Skill-biased technical change RIF regression 


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We would like to thank Philippe Van Kerm (the editor), two anonymous referees, as well as Bernhard Boockmann, Bernd Fitzenberger, Michael Burda, Albrecht Glitz, Phillip Heiler, Philip Harms, Jakob de Lazzer, Winfried Königer, Moritz Kuhn, Michael Neugart, Regina Riphahn, Thorsten Schank and seminar participants at the 6th network-workshop of the DFG priority programm 1764 and ESPE/EALE/EEA/ESEM 2017 for many helpful comments and discussions. Financial support through DFG Priority Program 1764 and DFG project BI 767/3-1 is gratefully acknowledged. This paper uses the German Structure of Earnings Surveys (GSES) 1995, 2001, 2006 and 2010 provided by the German Federal Statistical Office. We thank Kristin Nowak, Stefan Seitz, Isabella Brucker (FDZ Stuttgart) and Alexander Richter (FDZ Wiesbaden) for their generous support. The paper also uses supplementary data from the Linked-Employer-Employee Dataset (IAB) of the Institute for Employment Research (IAB), Nürnberg, and the BIBB Labor Force Survey of the Federal Institute for Vocational Training (BIBB).

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Business and EconomicsUniversity of TübingenTübingenGermany

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