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Occupational skills and the gender wage gap in Seoul, Korea: a multilevel approach

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Abstract

The purpose of this study was to examine the effects of occupational skills on the gender wage gap in the Seoul metropolitan region, Korea, by employing a multilevel model. To account for the diverse dimensions of skills, we measure workers’ skills using disaggregated measures of occupational skills requirements from the Korea Network for Occupations and Workers data. Using factor analysis, we identify three broad occupational skills categories: cognitive, technical, and physical. The multilevel analysis yields several important empirical results. First, the average occupational wage tends to be higher in occupations that are associated with higher cognitive skills and lower levels of technical and physical skills. Second, the gender effect on wages is larger in occupations requiring higher levels of physical skills and lower levels of technical skills, when controlling for individual- and occupation-level variables. However, the wage penalty associated with technical skills is more severe for women than it is for men, suggesting a significant wage disadvantage for women in occupations associated with technical skills. Finally, in addition to the main effect of the three dimensions of occupational skills, the effect of a greater proportion of female workers within an occupation is significant and negative for all workers, but this effect is less pronounced for men.

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Notes

  1. This modeling approach is known as a model with intercepts and slopes-as-outcomes.

  2. The inclusion of occupational dummy variables in the occupation-level equations may reduce the unexplained component in the estimated regressions. Although the skills of men and women working within the occupations are more homogeneous as disaggregation by occupation occurs more, the direct inclusion of dummy variables for occupations is inappropriate. Because the measures of occupational skills are more informative than simple occupation groupings, we can attribute wage variation to differences in specific skills attributes of occupations.

  3. It is assumed that \(u_{0j}\) and \(u_{1j}\) are independent, that is, \(\hbox {Cov}(u_{0j}, u_{1j})=0\).

  4. Abilities refer to innate or enduring attributes of the individual that influence performance, such as cognitive abilities, psychomotor abilities, physical abilities, and sensory abilities. By contrast, skills refer to developed capacities that facilitate learning or the more rapid acquisition of knowledge, such as reading comprehension, active listening, writing, speaking, critical thinking, active learning, and monitoring. Because it is difficult to differentiate these two categories in practice, the KNOW integrates the two categories into one. The 44 detailed descriptors in the abilities and skills category can be found in Table 1.

  5. While the same skill can be important for a variety of occupations, the level of the skill needed in those occupations can vary dramatically. Although Scott (2009) and Scott and Mantegna (2009) focus only on the importance scale, we consider both the importance and level scales, following Florida et al. (2012).

  6. In this study, occupational skills factors with loadings of at least \(\pm 0.5\) are used to interpret each skill factor. Although factor loadings of \(\pm 0.30\) to \(\pm 0.40\) are minimally acceptable, values greater than \(\pm 0.50\) generally are considered necessary for practical significance (Stevens 2009; Hair et al. 2010).

  7. In this study, we initially apply the latent root criterion of retaining factors with eigenvalues greater than 1.0. Although the eigenvalue for the fourth factor, 1.047, was slightly higher than 1.0, only two variables are loaded exclusively on the fourth factor. In general, the interpretability of a factor is improved when it is measured by at least three variables (Hutcheson and Sofroniou 1999; Hair et al. 2010; O’Rourke and Hatcher 2013). Unfortunately, the initial solution is unsatisfactory because the fourth factor is composed of less than three variables. On the basis of interpretability, a three-factor solution is finally computed on the current data.

  8. The one-way random effects ANOVA model predicts the outcome within each level-1 unit with just one level-2 parameter, the intercept \(\beta _{0j}\), yielding

    $$\begin{aligned} y_{ij}= & {} \beta _{0j}+e_{ij}\\ {\beta }_{0j}= & {} \gamma _{00}+u_{0j}. \end{aligned}$$

    In this model, \(\beta _{0j}\) is the mean outcome for the j-th unit. The combined model becomes

    $$\begin{aligned} y_{ij}=\gamma _{00}+u_{0j}+e_{ij} \end{aligned}$$

    which is the one-way ANOVA model with grand mean \(\gamma _{00}\), a group (level-2) effect \(u_{0j}\), and an individual (level-1) effect \(e_{ij}\) (Raudenbush and Bryk 2002).

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Authors and Affiliations

  1. Department of Urban Planning and Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-749, Korea

    Up Lim & Ye Seul Choi

  2. Korea Environment Institute, 370 Sicheong-daero, Sejong, 339-007, Korea

    Heonyoung Lee

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  1. Up Lim
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  2. Ye Seul Choi
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  3. Heonyoung Lee
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Correspondence to Up Lim.

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Lim, U., Choi, Y.S. & Lee, H. Occupational skills and the gender wage gap in Seoul, Korea: a multilevel approach. Ann Reg Sci 55, 335–356 (2015). https://doi.org/10.1007/s00168-015-0702-0

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