|Wage inequality and structural change||
In this project, we created a large set of wage inequality indicators. We used a large collection of individual level data. We acquired over 1600 individual level data for 44 countries over three decades. We provide several measures of wage inequality (Gini Index, mean log deviation, log of 90/10 percentiles, log of 90/50 percentiles, log of 50/10 percentiles, log of 75/25 percentiles) for each country and year.
We propose to use covariate balancing propensity score by Imai and Ratković (2014) to obtain matching weights for balancing the online samples of Wage Indicator Project to nationally representative samples. We balance the Wage Indicator data on individual characteristics such as age, gender and education. We provide balancing weights for 17 countries, sometimes for alternative representative samples.
|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.
|Language and (the estimates of) the gender wage gap||
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.
Aplikacja szacująca skutki reformy emerytalnej z 1999 oraz późniejszych zmian w systemie emerytalnym (2011 i 2013). Możesz samodzielnie dowolnie modyfikować założenia demograficzne i makroekonomiczne.