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.
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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.
Korkeamäki and Kyyrä  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 . 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.
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.
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.
Theoretical work experience is equal to the age of each worker minus his/her years of completed schooling minus 6.
Generally, any occupations which do not fit into a specific area are assigned to the category of “unclassifiable” occupations.
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.
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.
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.
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.
These results are available from the authors on request.
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We would like to thank Tomi Kyyrä for his useful suggestions. Any remaining errors are responsibility of the authors.
See Table 11.
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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
- Gender wage difference
- Industrial segregation
- Establishment segregation
- Occupational segregation
- Gender discrimination