Arcagni, A., Porro, F. (2013). On the parameters of Zenga distribution. Statistical Methods & Applications, 22(3), 285–303. DOI 10.1007/s10260-012-0219-y.
Brzeziński, M. (2013). Parametric modelling of income distribution in Central and Eastern Europe. Central European Journal of Economic Modelling and Econometrics, 35, 207–230.
Christofides, L.N., Cirello, J., Hoy, M. (2001). Family Income and Post-Secondary Education in Canada. Canadian Journal of Higher Education, 31(1), 177–208.
Clementi, F., Gallegati, M. (2005). Pareto’s Law of Income Distribution: Evidence for Germany, the United Kingdom, and the United States. In: A. Chatterjee, S. Yarlagadda, B.K. Chakrabarti (eds.), Econophysics of Wealth Distributions. New Economic Windows. Springer, Milano. DOI: 10.1007/88-470-0389-X_1.
Corak, M., Lipps, G., Zhao, J. (2021). Family income and participation in post-secondary education. In Family Income and Participation in Post-Secondary Education: Corak, Miles| uLipps, Garth| uZhao, John. [Sl]: SSRN, DOI: 10.2139/ssrn.491484.
Ćwiek, M., Trzcińska, K. (2022). Sytuacja ekonomiczna gospodarstw domowych w Polsce i Czechach. Analiza porównawcza. Nierówności Społeczne a Wzrost Gospodarczy, 72, 26–43. DOI: 10.15584/nsawg.2022.4.2.
Drăgulescu, A.A., Yakovenko, V.M. (2001). Exponential and Power-Law Probability Distributions of Wealth and Income in the United Kingdom and the United States. Physica A, 299, 213–221. DOI: 10.1016/S0378-4371(01)00298-9.
Furceri, D., Loungani, P., Ostry, J.D., Pizzuto, P. (2022). Will COVID-19 have long-lasting effects on inequality? Evidence from past pandemics. The Journal of Economic Inequality, 20(4), 811–839. DOI: 10.1007/s10888-022-09540-y.
Gini, C. (1914). Sulla Misura della Concentrazione e della Variabilità dei Caratteri. Atti del R. Istituto Veneto di Scienze, Lettere ed Arti, 62, 1203–1248.
Grawe, Nathan (forthcoming). Intergenerational Mobility for Whom? The Experience of High and Low Earnings Sons in International Perspective. In: M. Corak (ed.), Generational Income Mobility in North America and Europe. Cambridge: Cambridge University Press.
Greselin, F., Pasquazzi, L. (2009). Asymptotic confidence intervals for a new inequality measure. Communications in Statistics-Simulation and Computation, 38(8), 1742–1756. DOI: 10.1080/03610910903121974
Greselin, F., Pasquazzi, L., Zitikis, R. (2013). Contrasting the Gini and Zenga indices of economic inequality. Journal of Applied Statistics, 40(2), 282–297. DOI: 10.1080/02664763.2012.740627.
Jamison, D.T., Moock P.R. (1984). Farmer education and farm efficiency in Nepal: The role of schooling, extension services, and cognitive skills. World Development, 12(1), 67–86. DOI: 10.1016/0305-750X(84)90036-6.
Jolliffe, D. (2002). Whose education matters in the determination of household income? Evidence from a developing country. Economic Development and Cultural Change, 50(2), 287–312. DOI: 10.1086/322880.
Langel, M., Tillé, Y. (2012). Inference by linearization for Zenga’s new inequality index: a comparison with the Gini index. Metrika, 75, 1093–1110. DOI: 10.1007/s00184-011-0369-1.
Leven, B. (2016). Poverty in Poland and the United States: A Comparison of Key Characteristics, Composition, and Prospects. Modern Economy, 7, 526–535. DOI: 10.4236/me.2016.75057.
Lorenz, M.O. (1905). Methods of Measuring the Concentration of Wealth. Publications of the American Statistical Association, 9, 209–219. DOI: 10.2307/2276207.
Majumder, A., Chakravarty, S.R. (1990). Distribution of personal income: Development of a new model and its application to US income data. Journal of applied econometrics, 5(2), 189–196. DOI:10.1002/jae.3950050206.
Porro, F. (2015). Zenga distribution and inequality ordering. Communications in Statistics- Theory and Methods, 44(18), 3967–3977. DOI: 10.1080/03610926.2013.819921.
Radaelli, P. (2010). On the decomposition by subgroups of the Gini index and Zenga’s uniformity and inequality indexes. International Statistical Review, 78(1), 81–101, DOI: 10.1111/j.1751-5823.2010.00100.x.
Rocki, M. (2017). Ocena dopasowania oferty dydaktycznej kierunków ekonomicznych do potrzeb rynku pracy na podstawie czasu poszukiwania pracy przez absolwentów. Handel Wewnętrzny, 4(369), 156–168.
Rocki, M. (2022). Zatrudnialność absolwentów szkół wyższych. Biuletyn Polskiego Towarzystwa Ekonomicznego, 96(1), 1–9. Retrieved from https://cor.sgh.waw.pl/bitstream/handle/20.500.12182/1051/Zatrudnialność.pdf?sequence=2&isAllowed=y.
Sulewski, P., Szymkowiak, M. (2023). Modelling income distributions based on theoretical distributions derived from normal distributions. Wiadomości Statystyczne. The Polish Statistician, 68(6), 1–23. DOI: 10.59139/ws.2023.06.1.
Trzcińska, K. (2020). Analysis of household income in Poland based on the Zenga distribution and selected income inequality measure. Folia Oeconomica Stetinensia, 20(1), 421–436. DOI: 10.2478/foli-2020-0025.
Trzcińska, K. (2021). Analysis of Household Income in Poland by Regions Based on Selected Income Distribution. Acta Universitatis Lodziensis. Folia Oeconomica, 1(352), 111–126. DOI: 10.18778/0208-6018.352.06.
Wałęga, A., Wałęga, G. (2021). Self-employment and over-indebtedness in Poland: Modelling income and debt repayments distribution. Entrepreneurial Business and Economics Review, 9(4), 51–65. DOI: 10.15678/EBER.2021.090404.
Wolszczak-Derlacz, J. (2013). Efektywność naukowa, dydaktyczna i wdrożeniowa public-znych szkół wyższych w Polsce – analiza nieparametryczna. Wydawnictwo Politechniki Gdańskiej.
Zenga, M. (2007). Inequality curve and inequality index based on the ratios between lower and upper arithmetic means. Statistica & Applicazioni, 5(1), 3–27.