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The intra-firm gender wage gap: a new view on wage differentials based on linked employer–employee data


We provide a new view on the nature of the gender wage gap (GWG) by analyzing the wage differentials within establishments. Based on linked employer–employee data for Germany, we show that the GWGs vary tremendously across establishments, even if we assume that male and female employees have the same human capital characteristics within each establishment. This heterogeneity is linked to firm and institutional characteristics: For instance, firms with works council and those covered by collective wage agreements have smaller GWGs. Furthermore, we find some evidence that firms operating under strong product market competition behave in a more egalitarian way.

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Fig. 1


  1. The relative firm size is measured by the number of employees within the firm relative to the number of employees within the whole industry sector in western Germany. Thanks to the IAB-establishment panel, we can distinguish between 41 industries. Note, however, that the industry employment relies only on establishments covered by the panel. Since the industry structure of the panel sample is supposed to be representative, this figure should serve as a good proxy of the concentration in the market.

  2. Alternatively, Black and Brainerd (2004) use the import quota and the four-firm concentration ratio to measure the competitive pressure within an industry.

  3. Since we have no information on the female share among the works council members, we cannot directly test the implications derived from the insider-outsider theory.

  4. Alternatively, one may use female wages and characteristics to determine the remuneration of human capital. Given that the regression of male wages are unlikely to be biased due to selection problems and that men are less concerned with discrimination, we argue that male wage coefficients better represent the market value of selected qualification characteristics.

  5. Note that the inclusion of firm effects or industry-level variables is not required in this specification because we run firm-specific wage regressions and hence identification would not be feasible.

  6. In order to correct for this selection, we would have to estimate employment probabilities (Datta Gupta 1993). Due to the lack of information on the household context and the individual background, it is difficult to implement this procedure, which requires convincing exclusion restrictions.


  8. Detailed information on the IAB-establishment panel is given by Kölling (2000).

  9. Information on the Employment Statistics Register is given by Bender et al. (2000).

  10. These are people who, as employees, have paid contributions to the pension system or who have been covered by the pension system through contributions to the unemployment insurance scheme or by being a parent (depending on the birth year of the child, a set number of years is counted as child caring time during which the non-working parent is entitled to receive pension benefits).

  11. To deal with the problem of overlapping spells, we apply a hierarchical order of activities where employment trumps all other activities.

  12. Fitzenberger and Wunderlich (2002) show that this particularly affects the wage rate of high-skilled employees. According to their results, about 50% of high-skilled men earn wages above the upper earnings limit. Among high-skilled full-time females, this share amounts to at least 20%.

  13. Eastern German firms are not considered in the analysis because both the wage level as well as the wage setting process is still very different. Given the small number of union members in eastern Germany and the limited application of co-determination, the importance of the institutional framework is supposed to be less relevant. A common investigation of both regions would therefore not be very meaningful. Furthermore, the GWG is much smaller in eastern Germany. A separate analysis for each region would not be comparable either because the wage setting process and the resulting GWG in eastern German establishments is likely to be driven by internal processes, which cannot be captured by our data, such as the devaluation of female labor as well as the crowding out of women in the labor market and particularly women in occupations which were dominated by females in eastern Germany before unification.

  14. In the case of a firm-specific wage agreement, the firm negotiates directly with the corresponding union. The female share of the union members is hence merged in the same way as in the case of industry-wide wage agreements.

  15. For instance, five separate unions covering the service sector merged to form the large union “ver.di” in 2001 and other small unions joined more powerful unions like “IG Metall”.

  16. These 493 observations refer to 198 different establishments. Most switches across clusters were due to changes in the union status, that is, establishments quit the employer association and did not negotiate with unions anymore (189 establishments). Only nine establishments actually changed their industry sector.

  17. The average wage level is not correlated with the GWG per definition because the first figure relies on the establishment panel and corresponds to all employees, while the GWG only refers to the selected employees as described in Section 4.

  18. By calculating this number of employees, we assume that the total number of employees in the industry sector is constant, and for simplification, we use the average of the total number of employees in the industry sector.

  19. Alternatively, we calculated the relative firm size in terms of turnover. However, the results do not provide empirical evidence for the hypotheses derived in Section 2.

  20. The p values are 0.6052 for the raw wage gap and 0.9209 for the adjusted wage gap.

  21. This result partly contrasts with the conclusion of Amuedo-Dorantes and De la Rica (2006) for Spain, who find evidence for different effects of firm-level agreements and more centralized collective bargaining agreements on the GWG in 2002, but not in 1995. Blau and Kahn (2003) also reason that the decentralization of bargaining processes raises the overall wage gap.

  22. The reasons for the lower job mobility of women are manifold. First, the availability of family-friendly jobs is still limited. In this setting, wages become a less important job criterion compared with flexible work schedules, commuting, or career perspectives for part-time employees. Second, since husbands earn higher wages in general, local mobility is mostly driven by men.

  23. To test a model of monopsonistic discrimination according to Burdett and Mortensen (1998), one would need gender-specific labor turnover rates, strictly speaking the resignation rate of men and women and the potential to recruit new male and female employees for each firm. These indicators may be constructed by imposing relatively strong assumptions, though.


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We would like to thank Miriam Beblo, Michael C. Burda, Bernd Fitzenberger, Alfred Garloff, Susanne Steffes, the workshop participants of the DFG priority program “Potentials for Flexibility in Heterogeneous Labor Markets” (6–7 October 2005 in Bonn) and the conference “Gender in the Workplace” (17–18 November 2005 in Copenhagen), the auditory of the ESPE annual conference 2005 in Paris, as well as two anonymous referees for useful comments and discussions. We are particularly thankful to the staff of the Research Data Centre (FDZ) at the Institute for Employment Research (IAB) in Nuremberg for data processing. The financial support by the German Research Foundation (DFG) within the priority program “Potentials for Flexibility in Heterogeneous Labour Markets” (Grant-No. PF 331/3-1) is gratefully acknowledged.

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Correspondence to Elke Wolf.

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Responsible editor: Deborah Cobb-Clark



Table 6 Distribution of firm size and industry sector in our sample and the original LIAB sample (2001)
Table 7 Description of the sample and the gender wage gap (differentiated by the number of male employees) in establishments with at least 100 male employees
Table 8 Summary statistic of individual characteristics for the firm-specific wage regressions (pooled over 1997–2001)
Table 9 Summary statistic of individual characteristics for the pooled wage regression (pooled over 1997–2001)
Table 10 Summary statistic of firm characteristics (pooled over 1997–2001)
Table 11 Percentiles of the coefficients of the wage estimations (firms ≥ 100 male employees)

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Heinze, A., Wolf, E. The intra-firm gender wage gap: a new view on wage differentials based on linked employer–employee data. J Popul Econ 23, 851–879 (2010).

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  • Gender wage gap
  • Linked employer–employee data
  • Labor relations

JEL Classification

  • J16
  • J31