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Ceilings or floors? Gender wage gaps by education in Spain

An Erratum to this article was published on 24 October 2007


This paper analyzes the gender gap throughout the wage distribution in Spain using data from the European Community Household Panel. Quantile regression and panel data techniques are used to estimate wage regressions. In contrast with the steep increasing pattern found in other countries, the flatter evolution of the Spanish gender gap hides an intriguing composition effect. For highly educated workers, in line with the conventional glass ceiling hypothesis, the gap increases as we move up the distribution. However, for less-educated workers the gap decreases. We label this novel fact as a floor pattern and argue that it can be explained by statistical discrimination exerted by employers in countries where less-educated women have low participation rates.

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  1. Well-known examples of this type of research are Chamberlain (1994) and Buchinsky (1994, 1998a,b) who use quantile regressions to analyze the overall wage structure in the US. An application of this method to Spain can be found in Abadie (1997). Applications to gender wage discrimination are discussed in Fitzberger et al. (2001), Newell and Reilly (2001), and Albrecht et al. (2003). As regards the latter topic in Spain, there are two related studies to ours. On the one hand, García et al. (2001), using the 1991 Encuesta de Conciencia, Biografía y Estructura de Clase, controlled for the endogeneity of education and the selection of women into the labor market and concluded that the discrimination component is higher at the top than at the bottom of the wage distribution. On the other, Gardeazábal and Ugidos (2005), making use of the 1995 Encuesta de Salarios, also found that the raw gender gap increases along the distribution but, by contrast, estimated that the discrimination component, in relative terms, is larger at the bottom of the distribution.

  2. The Spanish female activity rate (percentage of population aged 15–64) in 2001 was 50.7%, whereas it reached 75.7% in Sweden and 60.2% in the EU. By educational levels, the corresponding rates in Spain were 80.4 and 48.0% for the women with tertiary education and less than tertiary education (84.6 and 68.3% in Sweden), respectively (see OECD 2002). Indeed, the group of Spanish working women is formed by very heterogeneous cohorts. Since the 1980s, female participation has surged (from 33.3% in 1980 until 50.7% in 2001) mainly due to an increase in access to higher education and a reduction in fertility rates (see, e.g., Arellano and Bover 1995).

  3. Educational attainments are treated as predetermined categories throughout the paper since our goal is not to estimate its returns.

  4. Indeed, a more appropriate name would be “glass ceilings at the ground floor” since it refers to gender pay gap at the bottom quantiles of the wage distribution. Since a “glass floor” could be wrongly interpreted as preventing women’s wages from falling too low, the term floor patterns will be used in the sequel for the sake of brevity. Notice that it should not be confused with the “sticky floors” concept related to the lower wages that women receive at the top of the distribution due to the lack of alternative job offers relative to men (see Booth et al. 2003). In a noncompetitive context where rents are shared between workers and firms, a higher male reservation wage could raise their wages above those of equally productive women.

  5. Similar patterns hold for the other ECHP waves.

  6. The compared percentiles correspond to the wage distributions of men and women separately. If we were to consider the position of women in the men’s distribution, it is found that 31% (5.4%) of women are in the bottom (top) decile.

  7. The reported gender gaps for Denmark, UK, Greece, and Italy also correspond to the 1999 wave of the ECHP. The Swedish gender gap is reproduced from Fig. 1 of Albrecht et al. (2003), which corresponds to 1998 with the data coming from Statistics Sweden. Activity rates by education in those countries can be found in Table 11 in the Appendix.

  8. Another possible explanation not mentioned below could arise from some form of unobserved heterogeneity affecting L-type women in relation to their male counterparts (see more about this in Section 5). Furthermore, the OECD (2002) warns about the possibility of having measurement errors in the survey stemming from the fact that the interviewed persons provide direct information about their own wages, rather than their employers, as is the case with matched employer–employee data. If those earning more, mainly men, have a larger propensity to understate their wages, the gap for the higher quantiles would be underestimated. Although this argument could imply a downward bias of the gap at the top of the distribution for both groups of workers, it cannot explain the pattern found at the bottom of the distribution for the L group.

  9. In either case, selectivity bias affects the mean gender gap in Southern Mediterranean countries; cf. Olivetti and Petrongolo 2005. The important issue is, however, how it affects the slope of the gap throughout the wage distribution.

  10. For example, Polacheck (1981) predicts that women choose occupations where the cost of career interruptions is low. The existence of occupational segregation by gender would support this argument; cf. Dolado et al. (2004). Another explanation relies upon the fact that women have a lower probability to be promoted to jobs with higher responsibilities even in the case where they have the same ability distribution than men. The model by Lazear and Rosen (1990) confers a higher productivity in the household to women, an assumption that makes employers reluctant to invest in their training on an equal basis with men. Hence, only the more productive women would be promoted. Finally, as mentioned earlier, the interpretation of the “sticky floors” model by Booth et al. (2003) relies upon men receiving a larger number of alternative offers.

  11. Details of the H group sample and empirical analysis on the glass ceiling phenomenon using quantile regressions methods can be found in an earlier working paper version of this work; cf. de la Rica et al. (2005).

  12. Given the cross-sectional nature of the data in the first approach, the interpretation of results in terms of our proposed explanation requires the underlying assumption—for which we provide some supporting evidence in Section 3—that age/experience and tenure increase as we move up the distribution, so that lower quantiles are likely to reflect wages at the initial stages of workers’ careers while higher quantiles correspond to wages at later stages. However, this assumption is not needed in the second approach where we can follow individuals in their jobs over time.

  13. This is just the average of the worker’s productivity and the outside wage, which is assumed to be zero. The weight 1/2 in the average is due to the choice of the uniform distribution in the illustration. Alternative distributions will give rise to a weighted average with unequal weights.

  14. Notice that for H-type workers ɛ m=ɛ f and, hence, GW1 = GW2 = 0.

  15. Thus, the glass ceiling pattern for the H-type workers has to be explained by some other theories, like those discussed in footnote 9, that we do not deal with in this paper.

  16. This explanation somewhat mimics the standard one available in the literature about statistical discrimination, concerning the employers’ private learning process about workers’ ability. As the employer learns more about the worker through a longer tenure in the job, the return on education (the signal) decreases while the return on experience/tenure increases (see, e.g., Farber and Gibbons 1996).

  17. In the estimation reported in Section 3.2, the nonparametric kernel is a truncated normal and two terms are used in the power series expansion.

  18. Descriptive statistics for nonworking men in the L group are not reported since their participation rate is high (see Table 11 in the Appendix). Moreover, attempts to correct for self-selection proved inconsequential: the selectivity term was highly insignificant and the remaining coefficients on observable characteristics hardly changed relative to the uncorrected regressions.

  19. We are grateful to a referee for raising the issue of whether the relationship between low tenure and holding a temporary contract could affect the validity of our statistical discrimination explanation. We think that this is not the case for at least four reasons. First, because we control for type of contract in the wage regressions. Secondly, because an interaction term between tenure and type of contract never proved to be significant. Thirdly because, since 1997, temporary contracts with a maximum fixed duration of 3 years were abolished; cf. Dolado et al. (2002). And, finally, as will be discussed below, because the counterfactual decompositions performed in Section 4 indicates that temporary contracts play a much smaller role than tenure in explaining the gender gap.

  20. Unfortunately, the ECHP does not provide information on parents’ education or occupation, which could provide appropriate instruments to correct for endogeneity.

  21. As explained in Buchinsky (1998b, p. 7), the constant and the coefficient on one of the continuous variables (e.g., age) are not identified in a single-index model. Hence, they are normalized by setting them equal to their values in a probit model so that the results are comparable.

  22. Differences in observed characteristics are typically evaluated in the decomposition at the men’s returns, under the assumption that they are not distorted by discriminatory behavior.

  23. Notice that by implementing this decomposition, in contrast to Albrecht et al. (2003), we are evaluating the difference in characteristics at the market returns of men. By interchanging the role of men and women in the MM procedure, which is what these authors do, we can obtain the alternative evaluation at women’s rewards, \( ^{{\text{f}}}_{\theta } \), so that the Returns component is evaluated at the male dataset for each quantile. The results of this alternative decomposition are not presented here but the qualitative findings about the unexplained gap remain similar.

  24. Small changes in the age boundaries of each of the groups do not make a significant difference in the results.

  25. Age replaces potential experience as a control because the age at which the individual started the first job does not change over time.


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We would like to thank three anonymous referees, an Editor, M. Arellano, S. Bentolila, F. Felgueroso, J. Gardeazábal, M. Jansen, B. Petrongolo, seminar participants at Amsterdam, Marseille, País Vasco (Bilbao), Toulouse, CEMFI, CES, ECARES, ESEM 2004 (Madrid) and SAE 2004 (Pamplona). Special thanks go to C. García-Peñalosa for her insightful comments and suggestions. The first two authors gratefully acknowledge financial support from the Spanish Ministry of Education (SEC2003-04826; SEC2004-04101) and the EC (MRTN-CT-2003-50496).

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Correspondence to Juan J. Dolado.

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Responsible editor: Deborah Cobb-Clark

An erratum to this article can be found at



Definition of variables

The variables are drawn from the 1999 (sixth wave) of the ECHP. Our group of interest is composed by wage earners working full-time (at least 1,560 h in a year or 30 h/week on average). In this section we provide a more detailed description of the variables used in the analysis.

Gross hourly wage The ECHP collects data on average monthly labor income (gross and net) from salaried workers. Labor income includes salary bonus (divided by working months) and overtime. When a worker has more than one job, only the main job income is considered. Weekly hours in the main job are available, including overtime hours. We have set an upper bound of 60 h to this variable to minimize the self-declared bias. This correction affects 2% of men and 0.9% of women from our total sample. Then, gross hourly wage is the monthly gross salary divided by 52/12 and multiplied by the weekly hours worked in the main job.

Experience This is defined as current age minus age at which the individual started working.

Exp×Children Interaction between experience and a binary variable that takes a value of 1 when an individual has dependent children (from 0 to 16 years). In the participation equation in Table 3, we considered separately the cases in which children are between 0 and 11 years (Exp×Children 0–11) or between 12 and 16 years (Exp×Children 12–16), but only the first one showed up significant.

Secondary education Dummy for having completed upper secondary education.

Individual characteristics Dummies for marital status, immigrant condition, district of residence, and district size.

Type of contract Temporary or permanent

Sector Private or public

Supervisory role Directive or managing position, supervisor of at least another employee and without responsibility for the rest of employees.

Tenure Obtained as the difference between the year of the survey, 1999, and the year of the start of the current job.

Firm size From 1 to 4 employees, from 5 to 19 employees, from 20 to 49 employees, from 50 to 99 employees, from 100 to 499 employees, and above 500 employees.

Occupation Fifteen occupational groups have been considered, corresponding to an intermediate level of aggregation of the ISCO-88 (COM) classification. The list is: legislators, senior officials and managers (OC1); physical, mathematical, engineering, life science and health professionals (OC2); teaching professionals (OC3); other professionals (OC4); physical, mathematical, engineering, life science and health associate professionals (OC5); teaching and other associate professionals (OC6); clerks (OC7), models, salespersons and demonstrators (OC8); personal and protective services workers (OC9); skilled agricultural and fishery workers (OC10); extraction and building trades workers, other craft and related trades workers (OC11); metal, machinery, precision, handicraft printing and related trades workers (OC12); plant and machinery operators and assemblers (OC13); sales and services elementary occupations (OC14); and agricultural, fishery and related laborers, laborers in mining, construction, manufacturing and transport (OC15).

Table 11 Labor activity rates by educational attainment (2002)

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de la Rica, S., Dolado, J.J. & Llorens, V. Ceilings or floors? Gender wage gaps by education in Spain. J Popul Econ 21, 751–776 (2008).

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  • Gender gap
  • Floor pattern
  • Quantile regressions

JEL Classification

  • J16
  • J71