Working Papers

The WNE Working Papers series has been published by the Faculty of Economic Sciences at the University of Warsaw since 2008.

The WNE Working Papers series provides a fast, open channel for disseminating research conducted at the Faculty of Economic Sciences, University of Warsaw. The papers hosted here are working versions (pre-prints) and may evolve as authors refine their analyses, incorporate feedback, or progress through formal peer-review. What you read is the current version released by the authors, timestamped and assigned DOI/ISSN identifiers to ensure precise citation and version tracking. Copyright remains with the authors, who may, at any time, upload a revised file or add a note directing readers to a later, peer-reviewed publication.

The Working Papers series accepts articles by research employees of the Faculty and publications from conferences organised at the Faculty of Economic Sciences at the University of Warsaw. Articles should be original research papers which have not been previously published, on the subject of economics.

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Number of Results 3

WP(18/2019)303. Reevaluating distributional consequences of the transition to market economy in Poland: new results from combined household survey and tax return data

Authors: Brzeziński Michał, Myck Michał, Najsztub Mateusz
We use Pareto imputation, survey reweighting, and microsimulation methods applied to combined household survey and tax return data to reevaluate distributional consequences of the post-socialist transition in Poland. Our approach results in the first…

WP303. Reevaluating distributional consequences of the transition to market economy in Poland: new results from combined household survey and tax return data

We use Pareto imputation, survey reweighting, and microsimulation methods applied to combined household survey and tax return data to reevaluate distributional consequences of the post-socialist transition in Poland. Our approach results in the first estimates of top-corrected inequality trends for real equivalized disposable incomes over the years 1994-2015. We find that the top-corrected Gini coefficient grew by 14-26% more compared to the unadjusted survey-based estimates. This implies that over the last three decades Poland has become one of the most unequal European countries among those for which top-corrected inequality estimates exist. The highest-income earners benefited the most during the post-socialist transformation: the annual rate of in-come growth for the top 5% of the population exceeded 3.5%, while the median income grew by about 2.5%.


Brzeziński Michał, Myck Michał, Najsztub Mateusz
income inequality Gini index top income shares tax record survey data Pareto distribution Poland

WP(9/2019)294. Wealth inequality in Central and Eastern Europe: evidence from joined household survey and rich lists’ data

Authors: Brzeziński Michał, Sałach Katarzyna, Wroński Marcin
We study how the problem of the 'missing rich', the underrepresentation of the wealthiest in household surveys, affects wealth inequality estimates for the post-socialist countries of Central and Eastern Europe (CEE). The survey data from the…

WP294. Wealth inequality in Central and Eastern Europe: evidence from joined household survey and rich lists’ data

We study how the problem of the 'missing rich', the underrepresentation of the wealthiest in household surveys, affects wealth inequality estimates for the post-socialist countries of Central and Eastern Europe (CEE). The survey data from the second wave of the Household Finance and Consumption Survey (HFCS) are joined with the data from the national rich lists for Esto-nia, Hungary, Latvia, Poland and Slovakia. Pareto distribution is fitted to the joined survey and rich lists' data to impute the missing observations for the largest wealth values. We provide the first estimates of the top-corrected wealth inequality for the CEE region in 2013/2014. Despite a short period of wealth accumulation during the post-1989 market economy period, our adjust-ment procedure reveals that wealth inequality in the Baltic countries is comparable to that of Germany (one of the most wealth unequal countries in Europe), while in Poland and Hungary it has reached levels observed in France or Spain. We discuss possible explanations of these find-ings with reference to the speed and range of privatization processes, extent of income inequali-ty, and the role of inheritances and wealth taxes in the region.


Brzeziński Michał, Sałach Katarzyna, Wroński Marcin
wealth inequality missing rich Pareto distribution rich lists Forbes Household Finance and Consumption Survey transition countries Central and Eastern Europe

WP(32/2013)117. Robust estimation of the Pareto index: A Monte Carlo Analysis

Authors: Brzeziński Michał
The Pareto distribution is often used in many areas of economics to model the right tail of heavy-tailed distributions. However, the standard method of estimating the shape parameter (the Pareto index) of this distribution the maximum likelihood esti…

WP117. Robust estimation of the Pareto index: A Monte Carlo Analysis

The Pareto distribution is often used in many areas of economics to model the right tail of heavy-tailed distributions. However, the standard method of estimating the shape parameter (the Pareto index) of this distribution the maximum likelihood estimator (MLE) is non-robust, in the sense that it is very sensitive to extreme observations, data contamination or model deviation. In recent years, a number of robust estimators for the Pareto index have been proposed, which correct the deficiency of the MLE. However, little is known about the performance of these estimators in small-sample setting, which often occurs in practice. This paper investigates the small-sample properties of the most popular robust estimators for the Pareto index, including the optimal B-robust estimator (OBRE) (Victoria-Feser and Ronchetti, 1994, The Canadian Journal of Statistics 22: 247-258), the weighted maximum likelihood estimator (WMLE) (Dupuis and Victoria-Feser, 2006, Canadian Journal of Statistics 34: 639-658), the generalized median estimator (GME) (Brazauskas and Serfling, 2001a, Extremes 3, 231-249), the partial density component estimator (PDCE) (Vandewalle et al., 2007, Computational Statistics & Data Analysis 51: 6252-6268), and the probability integral transform statistic estimator (PITSE) (Finkelstein et al., 2006, North American Actuarial Journal 10, 1-10). Monte Carlo simulations show that the PITSE offers the desired compromise between ease of use and power to protect against outliers in the small-sample setting. 


Brzeziński Michał
Pareto distribution Pareto index power-law distribution robust estimation Monte Carlo simulation small-sample performance
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