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Gender Wage Gaps Reconsidered: A Structural Approach Using Matched Employer-Employee Data

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  • Cristian Bartolucci

Abstract

In this paper I propose and estimate an equilibrium search model using matched employer-employee data to study the extent to which wage differentials between men and women can be explained by differences in productivity, disparities in friction patterns, segregation or wage discrimination. The availability of matched employer-employee data is essential to empirically disentangle differences in workers productivity across groups from differences in wage policies toward those groups. The model features rent splitting, on-the-job search and two-sided heterogeneity in productivity. It is estimated using German microdata. I find that female workers are less productive and more mobile than males. Female workers have on average slightly lower bargaining power than their male counterparts. The total gender wage gap is 42 percent. It turns out that most of the gap, 65 percent, is accounted for by differences in productivity, 17 percent of this gap is driven by segregation while differences in destruction rates explain 9 percent of the total wage-gap. Netting out differences in offer-arrival rates would increase the gap by 13 percent. Due to differences in wage setting, female workers receive wages 9 percent lower than male ones.

Suggested Citation

  • Cristian Bartolucci, 2009. "Gender Wage Gaps Reconsidered: A Structural Approach Using Matched Employer-Employee Data," Carlo Alberto Notebooks 116, Collegio Carlo Alberto, revised 2010.
  • Handle: RePEc:cca:wpaper:116
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    1. Antonczyk, Dirk & Fitzenberger, Bernd & Sommerfeld, Katrin, 2010. "Rising wage inequality, the decline of collective bargaining, and the gender wage gap," Labour Economics, Elsevier, vol. 17(5), pages 835-847, October.
    2. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    3. Cristian Bartolucci & Francesco Devicienti, 2012. "Better Workers Move to Better Firms: A Simple Test to Identify Sorting," Carlo Alberto Notebooks 259, Collegio Carlo Alberto.
    4. Becker, Gary S, 1973. "A Theory of Marriage: Part I," Journal of Political Economy, University of Chicago Press, vol. 81(4), pages 813-846, July-Aug..
    5. Alp E. Atakan, 2006. "Assortative Matching with Explicit Search Costs," Econometrica, Econometric Society, vol. 74(3), pages 667-680, May.
    6. Alda, Holger & Bender, Stefan & Gartner, Hermann, 2005. "The linked employer-employee dataset of the IAB (LIAB)," IAB Discussion Paper 200506, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
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    Cited by:

    1. Joanna Tyrowicz & Lucas van der Velde, 2017. "When the opportunity knocks: large structural shocks and gender wage gaps," GRAPE Working Papers 2, GRAPE Group for Research in Applied Economics.
    2. Gavrilova, Evelina, 2019. "A partner in crime: Assortative matching and bias in the crime market," Journal of Economic Behavior & Organization, Elsevier, vol. 159(C), pages 598-612.
    3. Vassil, Kristjan & Eamets, Raul & Mõtsmees, Pille, 2014. "Socio-demographic Model of Gender Gap in Expected and Actual Wages in Estonia," IZA Discussion Papers 8604, Institute of Labor Economics (IZA).
    4. Cristian Bartolucci & Francesco Devicienti & Ignacio Monzón, 2018. "Identifying Sorting in Practice," American Economic Journal: Applied Economics, American Economic Association, vol. 10(4), pages 408-438, October.
    5. Borowczyk-Martins, Daniel & Bradley, Jake & Tarasonis, Linas, 2018. "Racial discrimination in the U.S. labor market: Employment and wage differentials by skill," Labour Economics, Elsevier, vol. 50(C), pages 45-66.
    6. Cristian Bartolucci & Francesco Devicienti, 2012. "Better Workers Move to Better Firms: A Simple Test to Identify Sorting," Carlo Alberto Notebooks 259, Collegio Carlo Alberto.
    7. Rickne, Johanna, 2010. "Gender, Wages and Social Security in China’s Industrial Sector," Working Paper Series 2010:8, Uppsala University, Department of Economics.
    8. Nanos, Panagiotis & Schluter, Christian, 2014. "The composition of wage differentials between migrants and natives," European Economic Review, Elsevier, vol. 65(C), pages 23-44.
    9. David Neumark, 2018. "Experimental Research on Labor Market Discrimination," Journal of Economic Literature, American Economic Association, vol. 56(3), pages 799-866, September.
    10. Wei-Bin ZHANG, 2014. "Gender Discrimination, Education and Economic Growth in a Generalized Uzawa-Lucas Two-Sector Model," Timisoara Journal of Economics and Business, West University of Timisoara, Romania, Faculty of Economics and Business Administration, vol. 7(1), pages 1-34.
    11. Christopher Flinn & Ahu Gemici & Steven Laufer, 2017. "Search, Matching, and Training," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 25, pages 260-297, April.
    12. François Rycx & Yves Saks & Ilan Tojerow, 2015. "Does Education Raise Productivity and Wages Equally? The Moderating Roles of Age, Gender and Industry," Working Paper Research 281, National Bank of Belgium.
    13. Giovanni Sulis, 2012. "Gender wage differentials in Italy: a structural estimation approach," Journal of Population Economics, Springer;European Society for Population Economics, vol. 25(1), pages 53-87, January.
    14. Bartolucci, Cristian, 2012. "Credible threats in a wage bargaining model with on-the-job search," Economics Letters, Elsevier, vol. 117(3), pages 657-659.
    15. Antonczyk, Dirk & Fitzenberger, Bernd & Sommerfeld, Katrin, 2010. "Rising wage inequality, the decline of collective bargaining, and the gender wage gap," Labour Economics, Elsevier, vol. 17(5), pages 835-847, October.
    16. Cristian Bartolucci & Ignacio Monzon, 2014. "Frictions Lead to Sorting: a Partnership Model with On-the-Match Search," Carlo Alberto Notebooks 385, Collegio Carlo Alberto.
    17. Katarzyna Bech & Joanna Tyrowicz, 2017. "Estimating gender wage gap in the presence of efficiency wages -- evidence from European data," GRAPE Working Papers 20, GRAPE Group for Research in Applied Economics.
    18. Bustelo, Monserrat & Flabbi, Luca & Piras, Claudia & Tejada, Mauricio, 2019. "Female Labor Force Participation, Labor Market Dynamic and Growth in LAC," IDB Publications (Working Papers) 9420, Inter-American Development Bank.
    19. Stephan Kampelmann & François Rycx & Yves Saks & Ilan Tojerow, 2018. "Does education raise productivity and wages equally? The moderating role of age and gender," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 7(1), pages 1-37, December.
    20. Flinn, C. & Todd, P. & Zhang, W., 2020. "Personality Traits, Job Search and the Gender Wage Gap," Cambridge Working Papers in Economics 2053, Faculty of Economics, University of Cambridge.
    21. Christopher Flinn & James Mabli & Joseph Mullins, 2017. "Firms' Choices of Wage-Setting Protocols in the Presence of Minimum Wages," Working Papers 2017-070, Human Capital and Economic Opportunity Working Group.
    22. Matthias Collischon, 2017. "Is there a Glass Ceiling over Germany?," Working Papers 175, Bavarian Graduate Program in Economics (BGPE).

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    More about this item

    Keywords

    labor market discrimination; search frictions; structural estimation; matched employer-employee data;
    All these keywords.

    JEL classification:

    • J70 - Labor and Demographic Economics - - Labor Discrimination - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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