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Meta-data | Gender wage gaps around EU and across methods


Meta-data: Language and (the estimates of) the gender wage gap (published on Economics Letters)

In this paper we link the estimates of the gender wage gap with the gender sensitivity of the language spoken in a given country. We find that nations with more gender neutral languages tend to be characterized by lower estimates of GWG. The results are robust to a number of sensitivity checks.

Our source of estimates for the adjusted gender wage gap is an updated version of the dataset developed by Doris Wichselbaumer and Rudolf Winter-Ebmer for a paper published in Journal of Economic Surveys in 2005 and a paper published in Kyklos in 2008. The original data covers articles published until 2005, whereas we include studies published between 2005 and 2014. In total we added 1197 estimates of the adjusted GWG from 117 new studies for 56 countries.

To ensure continuity, we adopted the same conventions with respect to the language of publication (English) and the search engine (EconLit). We also used the same keywords: “(wage* or salar* or earning*) and (discrimination or differen*) and (sex or gender)”. To test if this search was not excessively narrow, we erased one keyword at a time from the first parenthesis, subsequently erasing the logical connectors (“or” “and”). We included published final or the most recent available versions of articles (chapters and books excluded). The complete list is available upon request.} Similarly to WWE, we excluded incomparable estimates of the adjusted GWG (e.g. non-parametric estimates along the wage distribution).

All the data needed to replicate our analysis is available here. The zip file contains:

    • A csv file with the information on the articles added.
    • Data on language gender intensity come from World Atlas of Language Structures
    • Complete data set (combining meta-data and country characteristics)
    • Do files

Gender wage gaps around EU and across methods

Gender wage gaps are typically measured by the means of decomposition. Proliferation of methods makes the choice of the correct estimator for a given data a conceptual challenge, especially if data availability necessitates simplifications. The challenge lies in accounting for observable differences adequately, which in itself is not only a data issue, but also a conceptual issue. Ideally, one would want to compare men and women actually “alike” in terms of all relevant characteristics, including hours effectively worked, commitment, talent. However, many of these characteristics are not observable (or are imperfectly measured, e.g. human capital).

Decompositions are prone to multiple risks. For example, the urge to compare only the comparable implies that a decision needs to be made about the use of observations which clearly are not comparable. Nopo (2008) proposes to use these observations to infer about the possible selectivity in this process, but alternative approaches consist of reweighing or neglecting this issue. Similar choices concern the treatment of distributional issues. Finally, for the parametric methods, the dependence on the functional form may influence the results as well. Consequently, depending on the features of a given labor market, an estimate of gender wage gap obtained with a given method is likely to overstate or understate the extent of true unjustified inequality in wages.

We make available a dataset which provides a full selection of gender wage gap estimates for the EU countries, using data from EU-SILC. Across countries and years, everybody can see for themselves, what is the source of the gender wage inequality in a given European country. We provide:

    • A dta file with the full set of estimates for the gender wage gaps and a generating dofile [download]
    • Data documentation[download]