IDEAS home Printed from https://ideas.repec.org/a/vrs/foeste/v23y2023i2p387-401n14.html
   My bibliography  Save this article

A Comparative Analysis of Household Incomes of People with Different Levels of Education in Poland and the USA

Author

Listed:
  • Trzcińska Kamila

    (University of Lodz, Department of Statistical Methods, Poland)

  • Zalewska Elżbieta

    (University of Lodz, Department of Statistical Methods, Poland)

Abstract

Research background Household income is one of the most important economic categories depending on various factors, in particular on the level of education of the head of the household. Salaries are treated as the market valuation of graduates made by employers. Hence, the question arises of how the education system affects the distribution of household income, in particular in the group of people with higher education. Purpose The aim of the paper is to apply the Singh-Maddala model to describe the income distribution of people with primary, secondary and higher education in Poland and the USA. On the basis of numerical characteristics and measures of income inequality, the situation of education, in particular higher education, and its impact on household income in the analyzed countries were assessed. Research methodology The study used data from the Luxembourg Income Study Database (LIS) from 2020. All analyzes were based on the Singh-Maddala model. The maximum likelihood method was used to estimate the model parameters. Results The analysis showed that the higher the level of education, the better the income situation of the household. In Poland, salaries in the group of people with higher education are the most unequal, while in the USA the greatest income inequalities occur in the group of people with primary education. Novelty The paper describes the issue of household income distribution and income inequality, which is an important and current socio-economic problem. To describe and analyze the economic situation of the household incomes of people with different levels of education in Poland and the USA statistical data from the Luxembourg Income Study Database (LIS) was used. Empirical studies conducted for different samples add significantly to existing knowledge on the topic.

Suggested Citation

  • Trzcińska Kamila & Zalewska Elżbieta, 2023. "A Comparative Analysis of Household Incomes of People with Different Levels of Education in Poland and the USA," Folia Oeconomica Stetinensia, Sciendo, vol. 23(2), pages 387-401, December.
    • Handle: RePEc:vrs:foeste:v:23:y:2023:i:2:p:387-401:n:14
    DOI: 10.2478/foli-2023-0037
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/foli-2023-0037
    Download Restriction: no
    File URL: https://libkey.io/10.2478/foli-2023-0037?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Majumder, Amita & Chakravarty, Satya Ranjan, 1990. "Distribution of Personal Income: Development of a New Model and Its Application to U.S. Income Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(2), pages 189-196, April-Jun.
    2. Davide Furceri & Prakash Loungani & Jonathan D. Ostry & Pietro Pizzuto, 2022. "Will COVID-19 Have Long-Lasting Effects on Inequality? Evidence from Past Pandemics," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(4), pages 811-839, December.
    3. Michał Brzeziński, 2013. "Parametric Modelling of Income Distribution in Central and Eastern Europe," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(3), pages 207-230, September.
    4. Paolo Radaelli, 2010. "On the Decomposition by Subgroups of the Gini Index and Zenga's Uniformity and Inequality Indexes," International Statistical Review, International Statistical Institute, vol. 78(1), pages 81-101, April.
    5. Craig C. Wu, 1977. "Education in Farm Production: The Case of Taiwan," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 59(4), pages 699-709.
    6. Francesco Porro, 2015. "Zenga Distribution and Inequality Ordering," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(18), pages 3967-3977, September.
    7. Fane, George, 1975. "Education and the Managerial Efficiency of Farmers," The Review of Economics and Statistics, MIT Press, vol. 57(4), pages 452-461, November.
    8. Alberto Arcagni & Francesco Porro, 2013. "On the parameters of Zenga distribution," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(3), pages 285-303, August.
    9. Jamison, Dean T. & Moock, Peter R., 1984. "Farmer education and farm efficiency in Nepal: The role of schooling, extension services, and cognitive skills," World Development, Elsevier, vol. 12(1), pages 67-86, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kamila Trzci?ska & El?bieta Zalewska, 2024. "A Comparative Analysis of Household Incomes of People with Different Levels of Education in Poland and the USA," LIS Working papers 877, LIS Cross-National Data Center in Luxembourg.
    2. David Bishai, 1996. "Quality time: How parents' schooling affects child health through its interaction with childcare time in Bangladesh," Health Economics, John Wiley & Sons, Ltd., vol. 5(5), pages 383-407, September.
    3. Vladimir Hlasny, 2021. "Parametric representation of the top of income distributions: Options, historical evidence, and model selection," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1217-1256, September.
    4. Phanhpakit ONPHANHDALA, 2009. "Farmer Education and Agricultural Efficiency: Evidence from Lao PDR," GSICS Working Paper Series 20, Graduate School of International Cooperation Studies, Kobe University.
    5. Sharada Weir, 1999. "The effects of education on farmer productivity in rural Ethiopia," CSAE Working Paper Series 1999-07, Centre for the Study of African Economies, University of Oxford.
    6. Trzcińska Kamila, 2020. "Analysis of Household Income in Poland Based on the Zenga Distribution and Selected Income Inequality Measure," Folia Oeconomica Stetinensia, Sciendo, vol. 20(1), pages 421-436, June.
    7. Jolliffe, Dean, 1997. "Whose education matters in the determination of household income," FCND discussion papers 39, International Food Policy Research Institute (IFPRI).
    8. Małgorzata Ćwiek & Kamila Trzcińska, 2023. "Assessment of goodness of fit of income distribution in France and Germany based on the Zenga distribution," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4013-4027, October.
    9. Jolliffe, Dean, 2004. "The impact of education in rural Ghana: examining household labor allocation and returns on and off the farm," Journal of Development Economics, Elsevier, vol. 73(1), pages 287-314, February.
    10. Jayasri Dutta & J. A. Sefton & M. R. WEALE, 2001. "Income distribution and income dynamics in the United Kingdom," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 599-617.
    11. Fabio Clementi & Mauro Gallegati & Giorgio Kaniadakis, 2010. "A model of personal income distribution with application to Italian data," Empirical Economics, Springer, vol. 39(2), pages 559-591, October.
    12. van den Berg, Gerard J., 2007. "On the uniqueness of optimal prices set by monopolistic sellers," Journal of Econometrics, Elsevier, vol. 141(2), pages 482-491, December.
    13. Jamison, Eliot A. & Jamison, Dean T. & Hanushek, Eric A., 2007. "The effects of education quality on income growth and mortality decline," Economics of Education Review, Elsevier, vol. 26(6), pages 771-788, December.
    14. Baidu-Forson, J., 1999. "Factors influencing adoption of land-enhancing technology in the Sahel: lessons from a case study in Niger," Agricultural Economics, Blackwell, vol. 20(3), pages 231-239, May.
    15. Munir Ahmad & Ghulam Mustafa Chaudhry & Mohammad Iqbal, 2002. "Wheat Productivity, Efficiency, and Sustainability: A Stochastic Production Frontier Analysis," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 41(4), pages 643-663.
    16. João Tovar Jalles & Bryn Battersby & Rachel Lee, 2024. "Effectiveness of Fiscal Announcements: Early Evidence from COVID-19," Open Economies Review, Springer, vol. 35(3), pages 623-658, July.
    17. Chapman, Bruce, 2006. "Income Contingent Loans for Higher Education: International Reforms," Handbook of the Economics of Education, in: Erik Hanushek & F. Welch (ed.), Handbook of the Economics of Education, edition 1, volume 2, chapter 25, pages 1435-1503, Elsevier.
    18. Vladimir Hlasny & Paolo Verme, 2018. "Top Incomes and Inequality Measurement: A Comparative Analysis of Correction Methods Using the EU SILC Data," Econometrics, MDPI, vol. 6(2), pages 1-21, June.
    19. Weaver, Robert D. & Curtiss, Jarmila & Brümmer, Bernhard, 2005. "Technical Efficiency Effects of Technological Change: Another Perspective on GM Crops," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24528, European Association of Agricultural Economists.

    More about this item

    Keywords

    income distribution; Singh-Maddala model; Gini index; Poland; USA; quality of education;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:foeste:v:23:y:2023:i:2:p:387-401:n:14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.