|OLG model with multiple sources of shocks.||
Our model accounts for multiple sources of shocks. Households face risks related to discount factors, labor income, and returns on capital.
Model codes and detailed descriptions are available on our GitHub page.
|Ubóstwo w Polsce||
Dane dotyczące zasięgu ubóstwa w Polsce w latach 1993-2019. Miary policzono dla emerytów, pracujących i rolników oraz wg typów gospodarstw domowch (dwoje pracujących rodziców, samodzielny rodzic, rodzina wielopokoleniowa z samodzielnym rodzicem, rodzina wielopokoleniowa z dwojgiem rodziców, samodzielnie gospodarujący emeryt i dwoje emerytów). Zastosowano definicje ubóstwa absolutnego i relatywnego, tak wg konsumpcji jak i wg dochodu. Obliczenia na podstawie danych z Badanie Budżetów Gospodarstw Domowych.
|Survival of state-owned enterprises||
This page contains our unique dataset about the historical fates of plants functioning in Poland in 1988. We include the full dataset and description of the data.
|Estimates of the gender employment gap||
This data contains the estimates of gender employment gaps on nearly 1600 micro databases from over 40 countries, spanning from Kazakhstan to Spain and covering 30 years of history. The estimates of gender employment gap are adjusted for individual characteristics. We use this data to ask if the existing instruments are sufficient to further reduce the gender inequality in employment.
|Aging and inequality||
This data summarizes the evolution of consumption and wealth inequality over the forthcoming decades of longevity. In a defined contribution system, with extending life span on retirement, pension benefits are bound to decline (at least, if the retirement age is not raised). These declining pension benefits will encourage agents to increase voluntary savings in other to smooth consumption over lifetime. This is likely to affect wealth and consumption inequality, despite unchanged institutional arrangement and stable productivity heterogeneity within cohorts.
|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.