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Polish inequality statistics reconsidered: are the poor really that poor?

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Abstract In the present study income inequality in Poland is evaluated using corrected income data to provide more reliable estimates. According to most empirical studies based on household surveys and considering the European standards, the recent income inequality in Poland is moderate and decreased significantly after reaching its peaks during the first decade of the 21st century. These findings were challenged by Brzeziński et al. (2022), who placed Polish income inequality among the highest in Europe. Such a conclusion was possible when combining the household survey data with information on personal income tax. In the present study the above-mentioned findings are further explored using 2014 and 2015 data and employing additional corrections to the household survey incomes. Incomes of the poorest people are replaced by their predictions made on a large set of well-being correlates, using the hierarchical correlation reconstruction. Applying this method together with the corrections based on Brzeziński’s et al. results reduces the 2014 and 2015 revised Gini indices, still keeping them above the values obtained with the use of the survey data only. It seems that the hierarchical correlation reconstruction offers more accurate proxies to the actual low incomes, while matching tax data provides better proxies to the top incomes.
Title: Polish inequality statistics reconsidered: are the poor really that poor?
Description:
Abstract In the present study income inequality in Poland is evaluated using corrected income data to provide more reliable estimates.
According to most empirical studies based on household surveys and considering the European standards, the recent income inequality in Poland is moderate and decreased significantly after reaching its peaks during the first decade of the 21st century.
These findings were challenged by Brzeziński et al.
(2022), who placed Polish income inequality among the highest in Europe.
Such a conclusion was possible when combining the household survey data with information on personal income tax.
In the present study the above-mentioned findings are further explored using 2014 and 2015 data and employing additional corrections to the household survey incomes.
Incomes of the poorest people are replaced by their predictions made on a large set of well-being correlates, using the hierarchical correlation reconstruction.
Applying this method together with the corrections based on Brzeziński’s et al.
results reduces the 2014 and 2015 revised Gini indices, still keeping them above the values obtained with the use of the survey data only.
It seems that the hierarchical correlation reconstruction offers more accurate proxies to the actual low incomes, while matching tax data provides better proxies to the top incomes.

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