Skip to main navigation Skip to search Skip to main content

Works Councils, Labor Productivity and Plant Heterogeneity: First Evidence from Quantile Regressions

    Research output: Journal contributionsJournal articlesResearchpeer-review

    19 Citations (Scopus)

    Abstract

    Using OLS and quantile regression methods and rich cross-section data sets for western and eastern Germany, this paper demonstrates that the impact of works council presence on labor productivity varies between manufacturing and services, between plants that are or are not covered by collective bargaining, and along the conditional distribution of labor productivity. No productivity effects of works councils are found for the service sector and in manufacturing plants not covered by collective bargaining. Besides demonstrating that it is important to look at evidence based on more than one data set, our empirical findings point to the efficacy of supplementing OLS with quantile regression estimates when investigating the behavior of heterogeneous plants.

    Original languageEnglish
    JournalJahrbücher für Nationalökonomie und Statistik
    Volume226
    Issue number5
    Pages (from-to)505-518
    Number of pages14
    ISSN0021-4027
    DOIs
    Publication statusPublished - 09.2006

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 8 - Decent Work and Economic Growth
      SDG 8 Decent Work and Economic Growth

    Research areas and keywords

    • Economics
    • labor productivity
    • works councils
    • quantile regression
    • heterogeneous firms
    • Heterogeneous firms
    • Labor productivity
    • Quantile regressions
    • Works councils

    ASJC Scopus Subject Areas

    • Social Sciences (miscellaneous)
    • Economics and Econometrics
    • Business, Management and Accounting(all)

    Fingerprint

    Dive into the research topics of 'Works Councils, Labor Productivity and Plant Heterogeneity: First Evidence from Quantile Regressions'. Together they form a unique fingerprint.

    Cite this