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Theory and evidence on the glass ceiling effect using matched worker-firm data


  • Mohamed Jellal

    () (Université Mohammed V, Rabat, Conseils-Eco, Toulouse)

  • Christophe Nordman

    () (DIAL, IRD, Paris)

  • François-Charles Wolff

    () (LEN, Université de Nantes, CNAV, INED)


(english) In this paper, we investigate the glass ceiling hypothesis according to which there exists larger gender wage gaps at the upper tail of the wage distribution. We demonstrate that in some circumstances, more qualified women may be offered lower wages than men at the equilibrium. This occurs for instance in a competitive model of wage determination where employers face gender-specific probabilities concerning the stability of their employees in their firms. Then, we focus on the relevance and the magnitude of the glass ceiling effect in France using a representative matched worker-firm data set in 1992 of about 130,000 employees and 14,000 employers. We estimate quantile regressions and use a principal component analysis to summarize information specific to the firms. Our different results show that accounting for firm-related characteristics, in particular firm-specific wage policies, reduces the gender earnings gap at the top of the distribution, but the latter still remains much higher at the top than at the bottom. _________________________________ (français) Nous analysons l’existence du phénomène de “plafond de verre” selon lequel il existerait des écarts salariaux selon le genre plus importants dans le haut de la distribution des revenus. Nous montrons dans un modèle compétitif de détermination des salaires que, sous certaines hypothèses et à l’équilibre, les femmes les plus qualifiées reçoivent des salaires plus faibles que ceux des hommes de même niveau de qualification. Cela se produit si les employeurs apprécient différemment selon le sexe des employés la stabilité des travailleurs dans leur entreprise. Nous examinons ensuite la pertinence de cette hypothèse et l’étendue de l’effet de plafond de verre à l’aide de données représentatives de l’industrie privée française en 1992 liant quelque 130.000 employés à plus de 14.000 établissements. Nous estimons des régressions de quantiles et utilisons une analyse factorielle pour résumer les informations spécifiques à chaque établissement. Nos différents résultats montrent que prendre en compte les caractéristiques des établissements, en particulier leur politique salariale spécifique, réduit l’estimation de l’écart de revenus entre sexes en haut de la distribution, mais celui-ci n’en demeure pas moins beaucoup plus important en haut qu’en bas de cette distribution des revenus.

Suggested Citation

  • Mohamed Jellal & Christophe Nordman & François-Charles Wolff, 2006. "Theory and evidence on the glass ceiling effect using matched worker-firm data," Working Papers DT/2006/03, DIAL (Développement, Institutions et Mondialisation).
  • Handle: RePEc:dia:wpaper:dt200603

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    References listed on IDEAS

    1. Ernst Fehr & Simon Gächter, 2000. "Fairness and Retaliation: The Economics of Reciprocity," Journal of Economic Perspectives, American Economic Association, vol. 14(3), pages 159-181, Summer.
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    4. Corneo, Giacomo & Gruner, Hans Peter, 2002. "Individual preferences for political redistribution," Journal of Public Economics, Elsevier, vol. 83(1), pages 83-107, January.
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    Cited by:

    1. Pendakur, Krishna & Pendakur, Ravi & Woodcock, Simon, 2006. "Glass Ceilings and Sticky Floors: A Representation Index," MPRA Paper 133, University Library of Munich, Germany.
    2. Mohamed Jellal & Christophe Nordman & Francois-Charles Wolff, 2008. "Evidence on the glass ceiling effect in France using matched worker-firm data," Applied Economics, Taylor & Francis Journals, vol. 40(24), pages 3233-3250.
    3. repec:dau:papers:123456789/4377 is not listed on IDEAS

    More about this item


    Gender wage gap; Glass ceiling; Quantile regressions; Matched worker-firm data; Écart salarial selon le genre; Plafond de verre; Régressions de quantiles; Données appariées employeurs-employés.;

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

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