Occupational Segregation and the Female–Male Wage Differentials: Evidence for Spain

Abstract

Using matched employer–employee data from the Spanish labour market in 2010, we analyse the effects of industrial, establishment and occupational segregation on the gender wage differential, disaggregating the latter contribution by different groups of workers belonging to different occupational areas and responsibility levels. These workers are employed in 61 occupations within 26,492 establishments in 51 different industries. Since the matched employer–employee data exhibit a particular type of grouped structure, which contrasts the statistical properties of such data with the random sample case, we estimate the effects of each type of gender segregation on the wage gap using a robust specification. We find that the major part of the contribution of gender segregation is not explained by differences in the observable characteristics. Furthermore, the estimations show that the educational female advantage has helped to narrow the gender wage gap caused by occupational segregation within each establishment only for those groups of workers with the lowest educational requirements.

This is a preview of subscription content, access via your institution.

Notes

  1. 1.

    In this study, we consider that individuals with the same responsibility level within the same occupational area in the same establishment hold the same job. For this reason, occupations are classified in different occupational areas and responsibility levels as explained later.

  2. 2.

    Korkeamäki and Kyyrä [17] use the same wage equation without an industry latent effect. In Spain, there is some specific legislation which affects workers in all the establishments of the same industry. For example, sectorial bargaining agreements are automatically extended to cover all firms within an industry. Employers and employees can also negotiate firm-specific contracts. However, while the alternative to a firm-specific contract in Spain is the prevailing sectorial contract, the alternative is the largely non union “outside” labor market in the USA and the UK [8]. Thus, bargaining agreements at the industry level can set a floor when employers and employees try to negotiate the wage structure at a firm level in Spain.

  3. 3.

    If individual preferences for jobs are common to all workers belonging to the same job, the estimation of the fixed effect model could also help to control for this endogeneity problem.

  4. 4.

    However, if there were a known variance–covariance structure of the errors within each industry, the generalized least squares estimations (GLS) would be a more efficient methodology. In cases of large datasets, efficiency is not a significant issue and we are only interested in the regressor coefficients. Hence, we choose estimations with clustered standard errors instead of GLS.

  5. 5.

    Theoretical work experience is equal to the age of each worker minus his/her years of completed schooling minus 6.

  6. 6.

    Generally, any occupations which do not fit into a specific area are assigned to the category of “unclassifiable” occupations.

  7. 7.

    If the mean difference between men and women for a variable or its estimated coefficient is not significantly different from zero at a 10 % level, we assign zero to its relative impact.

  8. 8.

    These estimations have been repeated, removing workers in the “unclassifiable” occupations from the sample. The qualitative results are similar to those discussed previously with only small quantitative changes in all the variables.

  9. 9.

    These mean differences are weighted by the number of workers from the sample who hold jobs in each occupational area or responsibility level, and are expressed as a percentage. Thus, the value of each cell indicates the mean gender difference of the latent job effect for each area or responsibility level. This value will be high if the wage gap is caused by a very different distribution of each gender between jobs, or if the proportion of workers from the sample in this occupational area or responsibility level is high.

  10. 10.

    It is possible that the correlation of errors among workers in different establishments within the same industry could be very small. In this case, standard errors clustered at the industry level would be very close to the standard ones. For this reason, we also estimate the random effect model adding industry dummies and random effects for establishments and jobs, and then, we cluster at the establishment level. In this case, we also obtain that the most of the contributions of establishment segregation and of occupational segregation within each establishment cannot be explained by differences in the observable characteristics of establishments and jobs where men and women work. These results are available from the authors upon request.

  11. 11.

    These results are available from the authors on request.

References

  1. 1.

    Amuedo-Dorantes, C., & De La Rica, S. (2006). The role of segregation and pay structure on the gender wage gap: Evidence from matched employer-employee data for Spain. Contributions to Economic Analysis & Policy, 5(1), 1–32.

    Google Scholar 

  2. 2.

    Azmat, G., & Petrongolo, B. (2014). Gender and the labor market: What have we learned from field and lab experiments? Labour Economics, 30, 32–40.

    Article  Google Scholar 

  3. 3.

    Bayard, K., Hellerstein, J., Neumark, D., & Troske, K. (2003). New evidence on sex segregation and sex differences in wages from matched employee-employer data. Journal of Labor Economics, 21(4), 887–922.

    Article  Google Scholar 

  4. 4.

    Bettio, F., & Verashchagina, A. (2009). Gender segregation in the labour market: Root causes, implications and policy responses in the EU. Luxembourg: European Commission’s Expert Group on Gender and Employment (EGGE).

    Google Scholar 

  5. 5.

    Bielby, W., & Baron, J. (1984). A woman’s place is with other women: Sex segregation within organizations. In B. F. Reskin (Ed.), Sex segregation in the workplace: Trends, explanations, remedies (pp. 27–55). Washington, DC: National Academy Press.

    Google Scholar 

  6. 6.

    Blau, F. D. (1977). Equal pay in the office. Lexington, MA: Health.

    Google Scholar 

  7. 7.

    Blau, F. D., & Kahn, L. M. (2001). Understanding international differences in the gender pay gap. Journal of Labor Economics, 21(1), 106–144.

    Article  Google Scholar 

  8. 8.

    Card, D., & De la Rica, S. (2004). The effect of firm-level contracts on the structure of wages: Evidence from matched employer-employee data. IZA discussion paper series, no. 1421. Bonn: Institute for the Study of Labor.

  9. 9.

    Carrington, W. J., & Troske, K. R. (1998). Sex segregation in the U.S. manufacturing. Industrial and Labor Relations Review, 51, 445–464.

    Article  Google Scholar 

  10. 10.

    Datta, N., & Rothstein, D. S. (2001). The impact of worker and establishment level characteristics on male-female wage differentials: Evidence from Danish matched employee-employer data. Centre for Labour Market and Social Research Working Paper 01-09-2001, University of Aarhus.

  11. 11.

    De La Rica, S., Dolado, J. J., & Llorens, V. (2008). Ceilings or floors? Gender wage gaps by education in Spain. Journal of Population Economics, 21, 751–776.

    Article  Google Scholar 

  12. 12.

    Dolado, J. J., Felgueroso, F., & Jimeno, J. F. (2002). Recent trends in occupational segregation by gender: A look across the Atlantic. In A. Argandona & J. Gual (Ed.), The social dimensions of employment: Institutional reforms in labour markets. Cheltenham, UK and Northampton MA: Elgar; Williston, VT: American International Distribution Corporation.

  13. 13.

    Dolado, J. J., Felgueroso, F., & Jimeno, J. F. (2004). Where do women work? An analysis patterns of occupational segregation by gender. Annales d’Economíe et de Statistique, Special Issue July–December 2003, 0, 293–315.

  14. 14.

    Fields, J., & Wolff, E. N. (1995). Interindustry wage differentials and the gender wage gap. Industrial and Labor Relations Review, 49, 105–120.

    Article  Google Scholar 

  15. 15.

    Fuller, W. A., & Battese, G. E. (1973). Transformations for estimation of linear models with nested-error structure. Journal of the American Statistical Association, 68(343), 626–632.

    Article  Google Scholar 

  16. 16.

    Groshen, E. L. (1991). The structure of the female/male wage differentials: Is it who you are, what you do, or where you work? Journal of Human Resources, 26(3), 457–472.

    Article  Google Scholar 

  17. 17.

    Korkeamäki, O., & Kyyrä, T. (2006). A gender wage gap decomposition for matched employer–employee data. Labour Economics, 13, 611–638.

    Article  Google Scholar 

  18. 18.

    Luukkonen, A. (2003). The gender wage gap in private service occupations (in Finnish). VATT discussion paper 321, 1–45. Government Institute for Economic Research, Helsinki.

  19. 19.

    Macpherson, D. A., & Hirsh, B. T. (1995). Wages and gender composition: Why do women’s jobs pay less? Journal of Labor Economics, 13(3), 426–471.

    Article  Google Scholar 

  20. 20.

    Meulders D., Plasman, R., Rigo, A., & O’Dorchai, S. (2010). Horizontal and vertical segregation. Meta-analysis of gender and science research—Topic report. 7th RTD Framework Programme of the European Union (RTD-PP-L4-2007-1).

  21. 21.

    Meyersson, E. M., Petersen, T., & Snartland, V. (2001). Equal pay for equal work? Evidence from Sweden and a comparison with Norway and the US. Scandinavian Journal of Economics, 103(4), 559–583.

    Article  Google Scholar 

  22. 22.

    Moulton, B. R. (1986). Random group effects and the precision of regression estimates. Journal of Econometrics, 32, 385–397.

    Article  Google Scholar 

  23. 23.

    OECD. (2008). Employment outlook, the price of prejudice: Labour market discrimination on the grounds of gender and ethnicity. París: OECD.

    Google Scholar 

  24. 24.

    OECD. (2011). Labour force statistics, Stats Estracts. París: OECD.

    Google Scholar 

  25. 25.

    Petersen, T., & Morgan, L. A. (1995). Separate and unequal: Occupation-establishment sex segregation and the gender wage gap. American Journal of Sociology, 101(2), 329–365.

    Article  Google Scholar 

  26. 26.

    Petersen, T., Snartland, V., Becken, L. E., & Olsen, K. M. (1997). Within-job wage discrimination and the gender wage gap: The case of Norway. European Sociological Review, 13(2), 199–213.

    Article  Google Scholar 

  27. 27.

    Petrongolo, B. (2004). Gender segregation in employment contracts. Journal of the European Economic Association, 2(2–3), 331–345.

    Article  Google Scholar 

  28. 28.

    Pissarides, C., Garibaldi, P., Olivetti, C., Petrongolo, B., & Wasmer, E. (2003). Women in the labour force: How well is Europe doing? Report. Annual European conference of the Fondazione Rudolfo De Benedetti, Milan.

  29. 29.

    Polavieja, J. G. (2012). Socially embedded investments: Explaining gender differences in job-specific skills. American Journal of Sociology, 118(3), 592–634.

    Article  Google Scholar 

  30. 30.

    Simón, H. (2012). The gender gap in earnings: An international comparison with European matched employer–employee data. Applied Economics, 44, 1985–1999.

    Article  Google Scholar 

  31. 31.

    Spanish Women Institute. (2005). Estudio sobre la Conciliación de la Vida Familiar y la Vida laboral en España: Situación Actual, Necesidades y Demanda. MTAS, March 2005.

Download references

Acknowledgments

We would like to thank Tomi Kyyrä for his useful suggestions. Any remaining errors are responsibility of the authors.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Juan Antonio Campos-Soria.

Appendix

Appendix

See Table 11.

Table 11 National occupational classification-1994 (NOC-94)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Campos-Soria, J.A., Ropero-García, M.A. Occupational Segregation and the Female–Male Wage Differentials: Evidence for Spain. Gend. Issues 33, 183–217 (2016). https://doi.org/10.1007/s12147-015-9148-z

Download citation

Keywords

  • Gender wage difference
  • Industrial segregation
  • Establishment segregation
  • Occupational segregation
  • Gender discrimination
  • Spain

JEL Classification

  • J16
  • J31
  • J71