Regional differentiation of income distributions in Poland

Alina Jędrzejczak; Małgorzata Misztal; Dorota Pekasiewicz

doi:https://doi.org/10.59139/stattrans-2025-031

Regional differentiation of income distributions in Poland

Alina Jędrzejczak Department of Statistical Methods, University of Lodz; Lodz, Poland & Statistical Office in Lodz, Lodz, Poland ORCID:https://orcid.org/0000-0002-5478-928 , Małgorzata Misztal Department of Statistical Methods, University of Lodz, Lodz, Poland ORCID:https://orcid.org/0000-0002-8719-2097 , Dorota Pekasiewicz Department of Statistical Methods, University of Lodz, Lodz, Poland ORCID:https://orcid.org/0000-0001-8275-3345 Statistics in Transition new series, vol. 26, 2025, 3, pages: 135-154 Published online: 5 September 2025 https://doi.org/10.59139/stattrans-2025-031 Citation: Jędrzejczak A., Misztal M., Pekasiewicz D., 2025. Regional differentiation of income distributions in Poland. Statistics in Transition new series, 26(3), pp. 135-154 https://doi.org/10.59139/stattrans-2025-031

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ABSTRACT

Income inequality is observed to have recently increased both at the country and regional level. Consequently, inequality, poverty and social stratification have become important issues of debate among economists, sociologists and social policy-makers. Thus, an in-depth statistical analysis of the income situation of households is particularly important when counteracting the social effects of the discussed phenomenon. In the literature, the situation of regions in Poland is compared primarily in terms of the differences in GDP or average wages observed for households. The aim of the article is to analyze the regional differences in the entire income distribution in Poland, taking into account not only average income levels, but also income inequality and poverty parameters. The study, based on individual data from the Household Budget Survey, used parametric and non-parametric methods for estimating inequality and poverty measures, as well as cluster analysis methods. In the parametric approach, the empirical income distributions in Poland were approximated using the theoretical Dagum distribution. This enabled the segmentation of voivodships in terms of the estimated characteristics of the equivalent household income distribution. The results of the calculations confirmed the assumption that income distributions in Poland differ significantly across regions, and the obtained clusters allowed the detection of groups of regions that may require a separate social policy aimed at improving the material situation of households.

KEYWORDS

income distribution; inequality; poverty; Dagum model; cluster analysis.

REFERENCES

Barca, F., (2009). An agenda for a reformed cohesion policy: A place-based approach to meeting European Union challenges and expectations, Independent Report prepared at the request of Danuta Hübner. Commissioner for Regional Policy, EU Commission, Brussels.

Brzezińska, J., (2018). Statistical Analysis of Economic Poverty in Poland Using R. Econometrics. Advances in Applied Data Analysis, 22(2), pp. 45–53.

Bonesmo Fredriksen, K., (2012). Income Inequality in the European Union. OECD Economics Department Working Papers, n. 952, OECD Publishing, Paris.

Dagum, C., (1977). A New Model of Personal Income Distribution: Specification and Estimation. Economie Appliquee, 30, pp. 413-437.

Dańska-Borsiak, B., (2024). Ocena adekwatności PKB per capita jako miary poziomu życia w powiatach. Wiadomości Statystyczne. The Polish Statistician, 69(7), pp. 1–21.

Deininger, K., Squire, L., (1998). New Ways of Looking at Old Issues: Inequality and Growth. Journal of Development Economics, 57(2).

Dudek, A., (2020). Silhouette Index as Clustering Evaluation Tool, (in) Jajuga K. et al. (eds.), Classification and Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization. Springer, pp. 19–33.

Eurostat, (2014). Statistics in Focus 12/2014. https://ec.europa.eu/eurostat/statisticsexplained/.

Fei J., Ranis G., Kuo S., (1978). Growth and the Family Distribution of Income by Factor Components. Quarterly Journal of Economics, 92, pp. 17–53.

Gastwirth, J. L., (1975). Statistical Measures of Earning Differentials. American Statistician, 29, pp. 32–35.

Gini, C., (2012). Variabilita e mutabilita: contributo allo studio delle distribuzioni e delle relazioni statistiche. Studi Economico-Giuridici, Facolta di Giurisprudenza della Regia Universit`a di Cagliari, anno III, parte II, Cuppini, Bologna.

Gini, C., (1914). Sulla Misura Della Concentrazione e Della Variabilita` dei Caratteri, [w:] Atti del Reale Istituto Veneto di Scienze, Lettere ed Arti. Anno Accademico 1913–1914, Tomo LXXIII – Parte Seconda.

GUS, (2024|). Budżety gospodarstw domowych w 2023 roku. Zakład Wydawnictw Statystycznych, Warszawa. https://stat.gov.pl/obszary-tematyczne/warunki-zycia/dochody-wydatki-i-warunki-zycia-ludnosci/budzety-gospodarstw-domowych-w-2023-roku,9,22.html.

Li, H., Squire, L. and Zou H., (1998). Explaining International and Intertemporal Variations in Income Inequality. Economic Journal, 108 (446), pp. 26–43.

OECD, (2008). Growinq Unequal: Income Distribution and Poverty in OECD Countries. OECD Publishing, Paris. https://www.oecd-ilibrary.org/social-issuesmigration-health/growing-unequal_9789264044197-en.

OECD, (2011). Divided We Stand. Why Inequality Keeps Rising. OECD Publishing, Paris. https://www.oecd-ilibrary.org/social-issues-migration-health/the-causes-ofgrowing-inequalities-in-oecd-countries_9789264119536-en.

OECD, (2015). In It Together: Why Less Inequality Benefits All. OECD Publishing, Paris. https://www.oecd-ilibrary.org/employment/in-it-together-why-less-inequalitybenefits-all_9789264235120-en.

OECD, (2024). Income and wealth inequalities, [in:] Society at a Glance 2024: OECD Social Indicators.

Jędrzejczak, A., (2011). Metody analizy rozkładów dochodów i ich koncentracji. Wydawnictwo Uniwersytetu Łódzkiego, Łódź.

Jędrzejczak, A., (2015). Regional Income Inequalities in Poland and Italy. Comparative Economic Research, 18(4), pp. 27–45.

Jędrzejczak, A., Kubacki, J., (2017). Analiza rozkładów dochodu rozporządzalnego według województw z uwzględnieniem czasu, [in:] Klasyfikacja i analiza danych. Teoria i zastosowanie. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu. Taksonomia, 29(469), pp. 69–81.

Jędrzejczak, A., (2023). Klasyczne i nieklasyczne metody analizy nierówności dochodowych. Wydawnictwo Uniwersytetu Łódzkiego, Łódź.

Jędrzejczak, A., Pekasiewicz, D., (2020). Teoretyczne rozkłady dochodów gospodarstw domowych i ich estymacja. Wydawnictwo Uniwersytetu Łódzkiego, Łódź.

Jędrzejczak, A., Pekasiewicz, D., (2022). Nierównomierność ekwiwalentnych dochodów gospodarstw domowych w województwie łódzkim. Wiadomości Statystyczne, 67(6), pp. 29–51.

Kaufman, L., Rousseeuw, P. J., (1990). Finding groups in data: an introduction to cluster analysis. John Wiley, New York.

Kleiber, C., Kotz, S., (2003). Statistical Size Distributions in Economics and Actuarial Sciences. Wiley, Hoboken.

Malina, A., (2020). Analiza przestrzennego zróżnicowania poziomu rozwoju społeczno- gospodarczego województw Polski w latach 2005–2017. Nierówności Społeczne a Wzrost Gospodarczy, No. 61(1), pp. 138–155.

Panek, T., (2011). Ubóstwo, wykluczenie społeczne i nierówności. Teoria i praktyka pomiaru. Oficyna Wydawnicza SGH, Warszawa.

Sen, A., (1976). Poverty: An Ordinal Approach to Measurement. Econometrica, 44(2), pp. 219–231.

Walesiak, M., Dudek, A., (2020). The Choice of Variable Normalization Method in Cluster Analysis, In Soliman KS (ed.), Education Excellence and Innovation Management: A 2025 Vision to Sustain Economic Development During Global Challenges, pp. 325–340, ISBN 978-0-9998551-4-1.

Walesiak, M., (2016). Uogólniona miara odległości GDM w statystycznej analizie wielowymiarowej z wykorzystaniem programu R. Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu, Wrocław.

Zenga, M. M., Pasquazzi, L. and Zenga, M., (2010). Rapporto n. 188: First applications of a new three-parameter distribution for non-negative variables. Mediolan: Dipartimento di Metodi Quantitativi per le Scienze Economiche ed Aziendali, Universita degli Studi di Milano Bicocca.