Inability to face unexpected expenses and monetary poverty in Poland: Are these two faces on the same coin?

Abstract

Research background: The economic literature often states that monetary poverty does not coincide with other types of poverty. The paper examines monetary poverty and financial distress, which refer to distinct aspects of poverty. It addresses the issue by explaining how the same household characteristics affect these different types of poverty.

Purpose of the article: The paper aims to identify socioeconomic variables influencing financial distress and monetary poverty in Poland. In addition, the relative contribution of household-level variables in explaining McFadden’s R2 for the financial dimensions under consideration is assessed.

Methods: The study relies on data from the EU Statistics on Income and Living Conditions (EU-SILC) survey in 2022. Logistic regression analysis empirically tests the impact of socioeconomic variables on financial distress and income poverty. Moreover, the relative importance of regressors is determined using the Shapley-Owen decomposition analysis.

Findings & value added: The results have revealed that the smallest group consisted of only monetary poor households, followed by both monetary poor and financially distressed. The largest group was made up of households that experienced only financial distress. Such an incomplete overlap in experiencing the examined types of poverty implies the importance of studying financial distress alongside traditional income indicator. The study indicated a statistically significant role for characteristics such as disability, unemployment, education, the burden of the repayment of debts, household type, and tenure status in experiencing all the types of poverty considered. Furthermore, it was observed that the explanatory power of the models varied depending on the types of poverty under consideration. The results also revealed a substantial relative contribution of education to McFadden’s R2 in all models, indicating that education level substantially explains vulnerability to financial fragility. The contribution of other regressors varied among the models describing the types of poverty analyzed. These findings should stimulate policymakers, as effective policies are needed to alleviate different types of poverty.

Keywords

financial distress, monetary poverty, household, Shapley-Owen decomposition, logit model

Author Biography

Hanna Dudek

Hanna Dudek is an Associate Professor at the Department of Econometrics and Statistics, Institute of Economics and Finance, Warsaw University of Life Sciences. Her main research fields include applied econometrics, measurement of material deprivation, and multidimensional poverty analysis. She has published more than 100 papers published in scientific journals and monographs.

Joanna Landmesser

Joanna Landmesser-Rusek is an Associate Professor at the Department of Econometrics and Statistics, Institute of Economics and Finance, Warsaw University of Life Sciences. Her main research interests focus on microeconometric modelling: counterfactual scenarios analysis, decomposition of income inequalities, hazard models, and multidimensional poverty. She has published more than 80 research papers in scientific journals and three monographs.

References

1. Alkire, S., Kanagaratnam, U., Nogales, R., & Suppa, N. (2022). Revising the Global Multidimensional Poverty Index: Empirical insights and robustness. Review of Income and Wealth, 68(S2), 347–384. DOI: https://doi.org/10.1111/roiw.12573
   View in Google Scholar
2. Atkinson, A. B. (2019). Measuring poverty around the world. New York: Princeton University Press. DOI: https://doi.org/10.1515/9780691191898
   View in Google Scholar
3. Ayala, L., Jurado, A., & Perez-Mayo, J. (2011). Income poverty and multidimensional deprivation: Lessons from cross-regional analysis. Review of Income and Wealth, 57(1), 40–60. DOI: https://doi.org/10.1111/j.1475-4991.2010.00393.x
   View in Google Scholar
4. Ayllón, S., & Gábos, A. (2017). The interrelationships between the Europe 2020 Poverty and Social Exclusion Indicators. Social Indicators Research, 130, 1025–1049. DOI: https://doi.org/10.1007/s11205-015-1212-2
   View in Google Scholar
5. Aysenur, A., Bulent, A., & Seyfettin, G. (2017). Mismatch between material deprivation and income poverty: The case of Turkey. Journal of Economic Issues, 51(3), 828–842.
   View in Google Scholar
6. Cameron, A. C., & Trivedi, P. K. (2022a). Microeconometrics using Stata, Second Edition, Volume I: Cross-sectional and panel regression models. College Station: Stata Press.
   View in Google Scholar
7. Cameron, A. C., & Trivedi, P. K. (2022b). Microeconometrics using Stata, Second Edition, Volume II: Nonlinear models and causal inference methods. College Station: Stata Press.
   View in Google Scholar
8. Chavez Juarez, F. (2012). SHAPLEY2: Stata module to compute additive decomposition of estimation statistics by regressors or groups of regressors. Statistical Software Components S457543, Revised 17 Jun 2015. Boston College Department of Economics.
   View in Google Scholar
9. Decerf, B. (2023). Absolute and relative income poverty measurement: A survey. In U. R. Wagle (Ed.). Research handbook on poverty and inequality (pp. 36–51). Cheltenham, UK Northampton, USA: Edward Elgar Publishing. DOI: https://doi.org/10.4337/9781800882300.00009
   View in Google Scholar
10. Dudek, H., & Szczesny, W. (2021). Multidimensional material deprivation in Poland focuses on changes in 2015–2017. Quality & Quantity, 55, 741–763. DOI: https://doi.org/10.1007/s11135-020-01024-3
    View in Google Scholar
11. Dudek, H., & Landmesser-Rusek, J. (2023). What explains the differences in material deprivation between rural and urban areas in Poland before and during the COVID-19 pandemic? Statistics in Transition, 24(4), 37–52. DOI: https://doi.org/10.59170/stattrans-2023-050
    View in Google Scholar
12. Eurostat (2021). Statistics explained. Glossary: Monetary poverty. Retrieved from https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Mon etary_poverty (15.01.2024).
    View in Google Scholar
13. Evans, M., Nogales, R., & Robson, M. (2024). Monetary and multidimensional poverty: correlation, mismatches, and a combined approach. Journal of Development Studies, 60(1), 147–170. DOI: https://doi.org/10.1080/00220388.2023.2252140
    View in Google Scholar
14. Fabrizzi, E., Mussida, C., & Parisi, M. L. (2023). Comparing material and social deprivation indicators: Identification of deprived populations. Social Indicators Research, 165, 999–1020. DOI: https://doi.org/10.1007/s11205-022-03058-6
    View in Google Scholar
15. Fusco, A., Guio, A. C., & Marlier, E. (2010). Characterising the income poor and the materially deprived in European countries. In A. B. Atkinson & E. Marlier (Ed.). Income and living conditions in Europe (pp. 133–153). Luxembourg: Publications Office of the European Union.
    View in Google Scholar
16. Glaeser, E. L., Laibson, D., & Sacerdote, B. (2002). An economic approach to social capital. Economic Journal, 112(483), F437–F458. DOI: https://doi.org/10.1111/1468-0297.00078
    View in Google Scholar
17. Greene, W. (2012). Econometric analysis. Boston: Pearson Education Limited.
    View in Google Scholar
18. Hazelkorn, E., & Mihut, G. (Ed.) (2021). Research handbook on university rankings: Theory, methodology, influence and impact. Cheltenham: Edward Elgar Publishing. DOI: https://doi.org/10.4337/9781788974981
    View in Google Scholar
19. Hicks, R. (2016). Material poverty and multiple deprivations in Britain: The distinctiveness of multidimensional assessment. Journal of Public Policy, 36(2), 277–308. DOI: https://doi.org/10.1017/S0143814X14000348
    View in Google Scholar
20. Hilbe, J. (2009). Logistic regression models. Chapman and Hall/CRC: Boca Raton, FL. DOI: https://doi.org/10.1201/9781420075779
    View in Google Scholar
21. Israel, S. (2016). More than cash: Societal influences on the risk of material deprivation. Social Indicators Research, 129, 619–637. DOI: https://doi.org/10.1007/s11205-015-1138-8
    View in Google Scholar
22. Jackson, S., & Yu, D. (2023). Re-examining the Multidimensional Poverty Index of South Africa. Social Indicators Research, 166, 1–25. DOI: https://doi.org/10.1007/s11205-023-03062-4
    View in Google Scholar
23. Jung, W. (2022). The discrepancy between two approaches to global poverty: What Does it reveal?. Social Indicators Research, 162, 1313–1344. DOI: https://doi.org/10.1007/s11205-021-02866-6
    View in Google Scholar
24. Kośny, M. (2019). Upper tail of the income distribution in tax records and survey data. Evidence from Poland. Argumenta Oeconomica, 1(42), 55–80. DOI: https://doi.org/10.15611/aoe.2019.1.03
    View in Google Scholar
25. Long, J. S., & Freese, J. (2006). Regression models for categorical dependent variables using Stata. College Station, TX: Stata Press.
    View in Google Scholar
26. McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.). Frontiers of econometrics (pp. 105–142). New York: Academic Press.
    View in Google Scholar
27. Mussida, C., & Parisi, M. L. (2021). Social exclusion and financial distress: Evidence from Italy and Spain. Economia Politica, 38(3), 995–1024. DOI: https://doi.org/10.1007/s40888-021-00228-6
    View in Google Scholar
28. Owen, G. (1977). Values of games with a priori unions. In R. Henn, O. Moeschlin (Ed.). Mathematical economics and game theory: Lecture notes in economics and mathematical systems, 141 (pp. 76–88). Berlin, Heidelberg: Springer. DOI: https://doi.org/10.1007/978-3-642-45494-3_7
    View in Google Scholar
29. Panek, T. (2010). A multidimensional approach to poverty measurement: Fuzzy measures of the incidence and the depth of poverty. Statistics in Transition, 11(2), 361–379. DOI: https://doi.org/10.59170/stattrans-2010-022
    View in Google Scholar
30. Pratiwi, I. E. (2023). Financial inclusion in Indonesia: Does education matter?. Economics and Sociology, 16(1), 265–281. DOI: https://doi.org/10.14254/2071-789X.2023/16-2/16
    View in Google Scholar
31. Ravallion, M., (2016). The economics of poverty: History, measurement, and policy. Oxford: Oxford University Press. DOI: https://doi.org/10.1093/acprof:oso/9780190212766.001.0001
    View in Google Scholar
32. Saunders, P., & Naidoo, Y. (2020). The overlap between income poverty and material deprivation: Sensitivity evidence for Australia. Journal of Poverty and Social Justice, 28(2), 187–206. DOI: https://doi.org/10.1332/175982720X15791323755614
    View in Google Scholar
33. Saunders, P., Naidoo, Y., & Wong, M. (2022). Comparing the monetary and living standards approaches to poverty using the Australian experience. Social Indicators Research, 162, 1365–1385. DOI: https://doi.org/10.1007/s11205-022-02888-8
    View in Google Scholar
34. Saltkjel, T., & Malmberg-Heimonen, I. (2017). Welfare generosity in Europe: A multi-level study of material deprivation and income poverty among disadvantaged groups. Social Policy & Administration, 51, 1287–1310. DOI: https://doi.org/10.1111/spol.12217
    View in Google Scholar
35. Sedefoğlu, G., & Dudek, H., (2024). Material and social deprivation in the European Union: Country-level analysis. Economics and Sociology, 17(1), 23–35. DOI: https://doi.org/10.14254/2071-789X.2024/17-1/2
    View in Google Scholar
36. Sen, A. K. (1992). Inequality re-examined. Oxford: Oxford University Press.
    View in Google Scholar
37. SDGs United Nations (2015). The Sustainable Development Goals: End poverty in all its forms everywhere. Retrieved from https://www.un.org/sustainabledevelo pment/poverty/ (13.03.2024).
    View in Google Scholar
38. Shapley, L. S. (1953). A value for n-person games. In W. Kuhn, A. W. Tucker (Ed.). Contributions to the theory of games, annals of mathematical studies, 28 (pp. 307–317). Princeton: Princeton University Press.
    View in Google Scholar
39. Szulc, A. (2008). Checking the consistency of poverty in Poland: 1997–2003 evidence. Post-Communist Economies, 20(1), 33–55. DOI: https://doi.org/10.1080/14631370701865714
    View in Google Scholar
40. Verbunt, P., & Guio, A.-C. (2019). Explaining differences within and between countries in the risk of income poverty and severe material deprivation: Comparing single and multilevel analyses. Social Indicators Research. 144, 827–868. DOI: https://doi.org/10.1007/s11205-018-2021-1
    View in Google Scholar
41. Wann, C. R., & Burke-Smalley, L. A. (2023). Attributes of households that engage in higher levels of family financial planning. Journal of Family and Economic Issues, 44, 98–113. https://doi.org/0.1007/s10834-021-09805-0. DOI: https://doi.org/10.1007/s10834-021-09805-0
    View in Google Scholar
42. Wirth, H., & Pforr, K. (2022). The European Union statistics on income and living conditions after 15 years. European Sociological Review, 38(5), 832–848. DOI: https://doi.org/10.1093/esr/jcac024
    View in Google Scholar
43. Wołoszyn, A., & Wysocki, F. (2020). Income inequality of Polish rural and urban households in 2010-2017. Annals of Polish Association of Agricultural Economists and Agribusiness, 22(1), 360–368. DOI: https://doi.org/10.5604/01.3001.0013.7776
    View in Google Scholar
44. Young, H. P. (1985). Monotonic solutions of cooperative games. International Journal of Game Theory, 14(2), 65–72. DOI: https://doi.org/10.1007/BF01769885
    View in Google Scholar