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Gender gaps in skills and labor market outcomes: evidence from the PIAAC


Our paper makes the first attempt to address the empirical relationRicship between cognitive skills and gender gaps in labor market performance. We do so in a cross-country setting. To that end we use the PIAAC dataset, which contains information on OECD and non-OECD economies. Firstly, we document the existence of gender gaps in cognitive skills for numeracy, which are found to be around 2.5–4.6% and increase with age. These gaps remain even when comparing men and women within the same level and field of study. Next, we document sizable gender gaps in labor market outcomes, such as Labor Force Participation and hourly wages—around 18%, increase with age and rise remarkably for parents. Math skills are positively and strongly associated with these two labor market outcomes and its contribution to explain gender gaps, although significant, is limited—between 10 15% at most—in particular for parents.

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  1. Blau and Kahn (2017) estimate this unexplained component is around 85% of the raw gender gap. Boll et al. (2016) estimate this is around 60%.

  2. For instance, previous studies have concluded that for women, being married and having young children reduce labor force participation and the probability of paid employment, whereas for men being married increases labor force participation and the probability of paid work and having young children has no significant impact.

  3. Round-1 countries: Belgium, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Ireland, Italy, Japan, Korea, Netherlands, Norway, Poland, Slovak Republic, Spain, United Kingdom. Round-2 countries: Chile, Greece, Israel, Lithuania, New Zealand, Slovenia.

  4. At least 5000 adults participated in the PIAAC assessment in each country.

  5. The focus of the PIAAC is on certain aspects of literacy, in particular the understanding and use of texts. Writing skills and the ability to produce or format documents are not assessed. This is not because these skills are not considered as important aspects of literacy in broad terms but largely because of the practical difficulties associated with assessing adults’ writing in large-scale international surveys.

  6. “Problem solving in technology-rich environments” defined as the ability to use digital technology, communication tools, and networks to acquire and evaluate information, communicate with others, and perform practical tasks (ICT skills - that is, skills in using information and communications technology).

  7. Problem solving skills are not available for Spain, Italy, France and Chypre.

  8. The objective of the assessments is to describe the level and distribution of the skills of the adult population, not to test the proficiency of individuals. The total number of items used in the assessments is greater than the number answered by any single respondent, each of whom undertakes a subset of the tasks administered.

  9. We restrict our sample to native workers since results could be distorted by using the full sample for two reasons: Firstly, immigrants might face more problems in answering correctly concerning cognitive skills; secondly, measurement of educational attainment levels can be very different between samples of natives and immigrants.

  10. Gender differences for the oldest group may be due to many unobserved components, such as different trayectories, that our data cannot capture.

  11. To interpret these statistics appropriately, note that in PIAAC each area of cognitive skill is a latent variable that is estimated using item-response-theory models (see OECD 2013 for details). The database PIAAC provides 10 plausible values rather than only one individual score for each respondent and each skill domain. Using the average of the 10 plausible values provides an unbiased estimate of individual skills in each domain. The sample statistics shown in Table 1 use this average, which uses the weights provided by the PIACC to control for sampling variance that reflects uncertainty due to obtaining a specific sample from the population.

  12. In many OCDE countries, there are more graduating females from four-year colleges than males (Goldin et al. 2006). Additionally, the high school dropout rate tends to be lower for females compared to males.

  13. Using cross country variation, we obtain that the correlation between the degree of field feminization of fields of studies and math scores is −39%

  14. Using cross country variability, we obtain that the empirical correlation between the degree of occupational feminization and the gender gap in numeracy skills within each occupational group is around −12%

  15. Results available from authors upon request.

  16. Hanushek et al. (2015) use individual information on numeracy cognitive skills from the PIAAC to account more precisely for the size of the returns on skills for wages and conclude that a one-standard-deviation increase in numeracy skills is associated with an 18% increase in wages among prime age workers. Note, however, that their baseline model does not include years of schooling. Hampf et al. (2017) also use the PIAAC to explore several approaches that seek to address potential threats to causal identification of returns on skills in terms of both higher wages and better employment chances.

  17. For wages, we use pre-tax earnings which has the advantage of capturing how the market rewards certain characteristics before the effect of the tax system is felt. However, it might potentially bias the cross-country comparison of wage dispersion to the extent that different countries differ in the progressivity of their tax systems. In addition, our definition of wages also considers discretionary bonus payments since the unexplained part of the gender wage gap is typically higher and this is typically related to more qualified jobs or jobs where skills might play a major role.

  18. The reason for this result is that wages for women with primary and secondary studies are very similar. Nevertheless, standard deviation in wages for secondary studies is much higher than for primary studies.

  19. Retired individuals and full-time students are excluded from the sample. The main sample statistics for all covariates are presented in Table 17 in the Appendix.

  20. In the estimation, math scores have been divided by ten, henceforth, to compute this effect we have to multiply the estimated marginal effect by 4.5 points instead of 45 points. This must be taken into account along the rest of the paper.

  21. This result is extracted from the comparison between the estimated gender gap in columns 1 and 3 of Table 10. The contribution of the field of study is even smaller when comparing adjusted gender gaps when math skills are considered (columns 2 and 4)

  22. Remember that to obtain these marginal effects we multiply the value of the one standard deviation in math scores (4.5) with the estimated marginal effect for each corresponding age cohort (1.08, 1.03 and 0.72)

  23. As explained in Section 4.1, this marginal effect is obtained by taking the difference between the marginal effects of the covariates for Men*No Children and Women*No Children. We also tested whether or not this marginal effect was statistically significant, and we concluded that it was not statistically significant at 95% of confidence.

  24. These are adjusted gender gap computed using coefficient estimates—marginal effects—from Table 8 (full sample) and 9 (Non-Parent versus Parents). For parents, the adjusted gender gap is the coefficient estimate for women with children since the reference group is men with children. For non-parents, the adjusted gender gap is the difference between the corresponding coefficient estimates for women without children minus coefficient estimates for males without children. For the model without math scores, these adjusted gender gap are −0.060 (se = 0.009), −0.027 (se = 0.015), −0.0854 (se = 0.015), −0.0735 (se = 0.020) for the full sample, age cohort 24–29, age cohort 30–39 and age cohort 40–49, respectively. For the model with math scores, these estimated gender gaps are −0.054 (sd = 0.009), −0.019 (sd = 0.015), −0.0800 (sd = 0.014), −0.070 (sd = 0.020). We have tested whether these gender gaps are statistically different. The results from these adjusted Wald tests lead us to reject the null hypothesis (99% confidence) of equality of gender gaps between these two models—without and with math scores—, for all samples except for age group 40–49 where the null is rejected at 90% confidence.

  25. The main sample statistics for the additional covariates used in the analysis are presented in the Table 18 in the Appendix.

  26. For sake of brevity, detailed results are not included in the paper but are provided upon request.

  27. We consider this association to be important since it might implied that the estimated contribution of gender gaps in math skills into gender gaps in wages presented in this paper is a lower bound of the true contribution.

  28. Basically, women who would have low wages may be unlikely to choose to work, and thus the sample of observed wages is biased upward.

  29. For the sake of brevity, we do not include separate wage equations for parents and non-parents taking into account self selection, as the patterns remain very similar, both for non-parents and parents. The only difference, as seen in Table 13, is that gender wage gaps are higher, both for parents than for non-parents.

  30. The covariate years of working experience correlates with performance in the PIAAC mostly among less educated individuals (see Jimeno et al. 2016).


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Financial support from MINECO of Spain through project ECO2013-48884-C3-3-P, and from Junta de Andalucía through project SEJ-1512 is gratefully acknowledged.

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Correspondence to Yolanda F. Rebollo-Sanz.

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Table 17 Main sample characteristics used in lmp equation (24–49)
Table 18 Main sample characteristics used in the wage equation
Table 19 Labor market participation equation: gender gaps in labor market participation (detailed results)
Table 20 Wage equation (log hourly wages): gender gaps in wages—detailed results
Table 21 Heckman selection model: wage equation (log hourly wages): gender gaps in wages—detailed results

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Rebollo-Sanz, Y.F., De la Rica, S. Gender gaps in skills and labor market outcomes: evidence from the PIAAC. Rev Econ Household 20, 333–371 (2022).

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JEL classification

  • J16
  • J24
  • J31


  • Gender wage gap
  • Gender gap in labor force
  • Cognitive skills