Abstract
This paper examines gender (in)equality in the European Union and the EU countries across six core life domains, ‘Work & Money’, ‘Knowledge’, ‘Power’, ‘Health’, ‘Safety, Security & Trust’, and ‘Life Satisfaction’, for the period 2010–2018. The paper advocates a non-parametric (Benefit-of-the-Doubt) frontier estimation approach for estimating gender inequality across these six domains. The results reveal considerable differences across the domains with larger gender inequalities towards women being observed in the domains ‘Power’ and ‘Work & Money’. However, overall, gender gaps seem to have decreased over the years. By 2018, results indicate a situation of (near) gender equality in four of the six domains in the European Union. At the level of the Member States, results show a geographical pattern with northern countries being more gender-equal and attaining higher achievement scores across the domains, followed by western and then southern countries.
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Data availability
The data analysed during the current study are publicly available from Eurostat. We refer to the source – indicator codes in the Online Appendix.
Notes
The choice for the GFC formula as in (5) and not the seemingly more straightforward formula in which the GFC is computed as a relative change (GFC = (GFI2018 – GFI2010)/ GFI2010) is because of the GFC-values as computed by the latter formula not allowing to distinguish between a decrease or increase in gender gap. To illustrate this issue, consider the combination of the following three illustrative examples: 1) a GFI value equal to 1.05 in 2018 and 0.97 in 2010, 2) Reversing the values as in the previous example, GFI2018 = 0.97 and GFI2010 = 1.05, and 3) a GFI2018 = 0.91 and GFI2010 = 0.95. In example 1 there was an increase in the gender gap between 2010 and 2018, with a gender gap in favour of females in 2010 changing to a larger gender gap in favour of males in 2018. In example 2, the gender gap decreased between the period 2010 and 2018, with a gender gap in favour of males in 2010 changing to a smaller gender gap in favour of females in 2018. In example 3, there was in both 2018 and 2010 a gender gap in favour of females, with the gender gap being larger in 2018 as compared to in 2010 (so an increase in the gender gap). The GFC-values computed by relative change formula in examples 1 and 3, two examples where the gender gap increased, were respectively positive (GFC = 0.0825) and negative (GFC = −0.0762). Likewise, the GFC-values computed by the relative change formula in examples 2 and 3 were both negative (GFC = −0.0762 and GFC = -0.0421), whereas in example 2 the gender decreased and in example 3 the gender gap increased. The use of the formula GFC = |1-GFI2018|/|1-GFI2010| does allow to distinguish between decreases and increases in the gender gap as it takes the ratio of the gender gap in 2018 (in favour of males or in favour of females) and the gender gap in 2010 (in favour of males or in favour of females).
Note that, different to the global (EU-level) GFI-scores and GFC-scores as in (4) and (5), the country-level GFIc-scores and GFCc-scores, though given an idea on whether the country became more or less gender equal over time, are not well-suited for multilateral country comparisons. One key reason or why such comparisons are not possible is that the BoD-estimates of the achievement score exploit the BoD-perspective (data driven weighting) to the fullest extent: each country is equipped with its proper optimal BoD-estimated importance weights. One possibility to come up with country-level GFIc-scores and GFCc-scores that enable transitive country comparisons in (changes in) gender gaps is to compute multilateral versions of these country-level GFIc-scores and GFCc-scores. One way of doing so could be to give a central role to one (“average” or “median”) country achievement (pseudo-) observation that defines the BoD-based importance weights and applying these BoD-weights in the computation of the country-level GFIc-scores and GFCc-scores for all countries in the dataset. An alternative idea would be to determine uniform BoD-estimated importance weights for all countries following the notion of cross-efficiency, as developed by Sexton, Silkman and Hogan (1986) and Doyle and Green (1994) (see also Cook and Zhu, 2015).
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Dehaspe, P., Rogge, N. The gender-equal Union? Measuring female (dis)advantage and achievement in European Union Member States using a benefit-of-the-doubt framework. J Prod Anal 60, 129–145 (2023). https://doi.org/10.1007/s11123-023-00686-z
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DOI: https://doi.org/10.1007/s11123-023-00686-z