Is the Subjective Financial Well-being of Polish Households Changing with Time? An Empirical Study Based on Constrained Latent Markov Models

Authors

DOI:

https://doi.org/10.15678/krem.18792

Keywords:

constrained latent Markov model, financial well-being, homogeneity, transition matrix

Abstract

Objective: According to the most recent available Eurostat data from 2018, Poles’ satisfaction with their financial situation stood at 6.3 points, which was below the EU average. We focus on how households’ behaviours have evolved over the past 15 years and their impact on perceived financial well-being.

Research Design & Methods: To comprehensively assess the financial situation of Polish households, this study leverages the Social Diagnosis panel research, a longitudinal dataset spanning eight waves from 2000 to 2015. We employ latent Markov models with varying numbers of latent structures, incorporating survey weights and different sets of transition matrix constraints to analyse the dynamics of financial behaviours and identify potential shifts over time. We consider a heterogeneous transition matrix (not constrained) where the transitions are allowed to vary freely across time points, and partial-homogeneity, allowing some transition probabilities to vary while others remain stable across time.

Findings: Through these models, we find three latent states of Poles with similar level of income perception and describe the process of changing opinion in the analysed period of time. We demonstrate the self-reported financial satisfaction in each round of the national longitudinal Polish survey with a particular interest in financial behaviours before and after the financial crisis. Our analysis reveals a distinct pattern following the peak of the financial crisis: Individuals exhibited a higher probability of remaining in the unsatisfied group, a lower probability of maintaining full satisfaction, and reduced likelihood of transitioning from dissatisfaction to general satisfaction, particularly during the two waves immediately following the crisis.

Implications / Recommendations: Our findings underscore the critical role of financial well-being in shaping individual behaviour and societal outcomes. These insights can inform policymakers as they allocate public funds, emphasising the need to prioritise policies that support financial stability and resilience. Moreover, the proposed approach could be extended to examine respondent behaviour before and after other significant global events, such as the EU accession (2004), the COVID-19 pandemic, or the Russia-Ukraine war.

Contribution: A key contribution of this approach is our conceptualisation of self-reported income satisfaction as a latent, discrete variable, which we analyse using a particular latent variable modelling techniques. This research addresses a critical gap in understanding the evolving dynamics of households’ behaviour and the impact of economic downturns on financial perceptions in developing countries like Poland. Moreover, compared to the most recent studies on financial well-being that also cover other national surveys, we demonstrate changes in attitudes across society by analysing all waves of the national longitudinal survey, accounting for the heterogeneous structure of the data and incorporating survey weights.

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References

Akaike, H. (1973). Information Theory as an Extension of the Maximum Likelihood Principle. In: B. N. Petrov, F. Csaki (Eds), Proceedings of the 2nd International Symposium on Information Theory (pp. 267–281). Akademiai Kiado.

Bacci, S., Pandolfi, S., & Pennoni, F. (2014). A Comparison of Some Criteria for States Selection in the Latent Markov Model for Longitudinal Data. Advances in Data Analysis and Classification, 8, 125–145. https://doi.org/10.1007/s11634-013-0154-2

Bartolucci, F. (2007). A Class of Multidimensional IRT Models for Testing Unidimensionality and Clustering Items. Psychometrika, 72, 141–157. https://doi.org/10.1007/s11336-005-1376-9

Bartolucci, F., Farcomeni, A., & Pennoni, F. (2013). Latent Markov Models for Longitudinal Data. Chapman and Hall/CRC.

Bartolucci, F., Lupparelli, M., & Montanari, G. E. (2009). Latent Markov Model for Longitudinal Binary Data: An Application to the Performance Evaluation of Nursing Homes. Annals of Applied Statistics, 3(2), 611–636. https://doi.org/10.1214/08-AOAS230

Bartolucci, F., Pandolfi, S., & Pennoni, F. (2017). LMest: An R Package for Latent Markov Models for Longitudinal Categorical Data. Journal of Statistical Software, 81(4), 971–985. https://doi.org/10.18637/jss.v081.i04

Bartolucci, F., Pandolfi, S., & Pennoni, F. (2022). Discrete Latent Variable Models. Annual Review of Statistics, 9, 425–452. https://doi.org/10.1146/annurev-statistics-040220-091910

Baum, L. E., Petrie, T., Soules, G., & Weiss, N. (1970). A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains. Annals of Mathematical Statistics, 41(1), 164–171. https://doi.org/10.1214/aoms/1177697196

Betti, G., Dourmashkin, N., Rossi, M., Verma, V., & Yin, Y. P. (2001). Study of the Problem of Consumer Indebtedness: Statistical Aspects Final Report. Report to the Commission of the European Communities. Directorate-General for Health and Consumer Protection, Commission of the European Communities. ORC Macro.

Bialowolski, P., & Weziak-Bialowolska, D. (2014). The Index of Household Financial Condition, Combining Subjective and Objective Indicators: An Appraisal of Italian Households. Social Indicators Research, 118(1), 365–385. https://doi.org/10.1007/s11205-013-0401-0

Białowolski, P. (2018). Hard Times! How Do Households Cope with Financial Difficulties? Evidence from the Swiss Household Panel. Social Indicators Research, 139(1), 147–161. https://doi.org/10.1007/s11205-017-1711-4

Binder, M. (2014). Subjective Well-being Capabilities: Bridging the Gap Between the Capability Approach and Subjective Well-being Research. Journal of Happiness, 15, 1197–1217. https://doi.org/10.1007/s10902-013-9471-6

Brusa, L., Bartolucci, F., & Pennoni, F. (2023). Tempered Expectation-maximization Algorithm for the Estimation of Discrete Latent Variable Models. Computational Statistics, 38, 1391–1424. https://doi.org/10.1007/s00180-022-01276-7

Brzozowski, M., & Spotton Visano, B. (2019). “Havin’ Money’s Not Everything, Not Havin’ It Is”: The Importance of Financial Satisfaction for Life Satisfaction in Financially Stressed Households. Journal of Happiness Studies, 21, 573–591. https://doi.org/10.1007/s10902-019-00091-0

Chzhen, Y. (2016). Perceptions of the Economic Crisis in Europe: Do Adults in Households with Children Feel a Greater Impact? Social Indicators Research, 127, 341–360. https://doi.org/10.1007/s11205-015-0956-z

Clark, A., Etilé, F., Postel-Vinay, F., Senik., C., & Van der Straeten, K. (2005). Heterogeneity in Reported Well-being: Evidence from Twelve European Countries. The Economic Journal, 115(502), 118–132. https://doi.org/10.1111/j.0013-0133.2005.00983.x

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm (with Discussion). Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1–38. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x

Diego-Rosell, P., Tortora, R., & Bird, J. (2018). International Determinants of Subjective Well-being: Living in a Subjectively Material World. Journal of Happiness, 19, 123–143. https://doi.org/10.1007/s10902-016-9812-3

Ernst, L. (1989). Weighting Issues for Longitudinal Household and Family Estimates. In: D. Kasprzyk, G. Duncan, G. Kalton, M. P. Singh (Eds), Panel Surveys (pp. 139–159). Wiley.

Eurostat. (2019). Subjective Well-being – Statistics. Retrieved from: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Subjective_well-being_-_statistics (accessed: 1.08.2025).

Eurostat. (2024). Quality of Life Indicators – Overall Experience of Life. Retrieved from: https://ec.europa.eu/eurostat/statistics-explained/index.php/Quality_of_life_indicators_-_overall_experience_of_life (accessed: 1.08.2025).

Gasińska, M. (2016). Dochody gospodarstw domowych w Polsce – wybrane obiektywne i subiektywne ujęcia i dane. Zeszyty Naukowe Uczelni Vistula, 50(5), 100–142.

Gasiorowska, A. (2015). The Impact of Money Attitudes on the Relationship between Income and Financial Satisfaction. Polish Psychological Bulletin, 46(2), 197–208. https://doi.org/10.1515/ppb-2015-0026

Genge, E. (2019). Graphical Tools of Discrete Longitudinal Data Presentation in R. Econometrics. Advances in Applied Data Analysis, 23(3), 26–39. https://doi.org/10.15611/eada.2019.3.03

Genge, E. (2021). LC and LC-IRT Models in the Identification of Polish Households with Similar Perception of Financial Position. Sustainability, 13(8), 4130. https://doi.org/10.3390/su13084130

Genge, E. (2023). An Evaluation of Self-reported Material Well-being Using Latent Markov Models with Covariates. Longitudinal and Life Course Studies, 14(4), 514–541. https://doi.org/10.1332/175795921X16719290621875

Gomułka, S. (2016). Poland’s Economic and Social Transformation 1989–2014 and Contemporary Challenges. Central Bank Review, 16(1), 19–23. https://doi.org/10.1016/j.cbrev.2016.03.005

Gradzewicz, M., Growiec, J., Kolasa, M., Postek, Ł., & Strzelecki, P. (2014). Poland’s Exceptional Performance during the World Economic Crisis: New Growth Accounting Evidence (NBP Working Paper No. 186). Narodowy Bank Polski.

Hanusik, K., & Łangowska-Szczęśniak, U. (2013). Uwarunkowania samooceny sytuacji materialnej gospodarstw domowych w Polsce w 2011 r. Konsumpcja i Rozwój, 1(4), 83–98.

Helliwell, J. F., Huang, H., & Wang, S. (2014). Social Capital and Well-being in Times of Crisis. Journal of Happiness Studies, 15, 145–162. https://doi.org/10.1007/s10902-013-9441-z

Kalinowski, S., & Kozera-Kowalska, M. (2017). Samoocena sytuacji dochodowej ludności wiejskiej o niepewnych dochodach. Handel Wewnętrzny, 4(369), 110–121.

Kalton, G. (1989). Modeling Considerations: Discussion from a Survey Sampling Perspective. In: D. Kasprzyk, G. Duncan, G. Kalton, M. P. Singh (Eds), Panel Surveys (pp. 575–585). Wiley.

Mahdzan, N. S., Zainudin, R., Sukor, M. E. A., Zanir, F., & Ahmad, W. M. W. (2019). Determinants of Subjective Financial Well-being Across Three Different Household Income Groups in Malaysia. Social Indicators Research, 146, 699–726. https://doi.org/10.1007/s11205-019-02138-4

Panek, T., & Czapiński, J. (2013). Warunki życia gospodarstw domowych. Dochody i sposób gospodarowania dochodami. Diagnoza Społeczna 2013. Warunki i jakość życia Polaków – Raport (Special issue). Contemporary Economics, 7, 40–53. https://doi.org/10.5709/ce.1897-9254.97

Panek, T., & Czapiński, J. (2015a). Household Living Conditions. Income and Income Management. In: J. Czapiński, T. Panek (Eds), Social Diagnosis 2015. The Objective and Subjective Quality of Life in Poland (pp. 34–47). The Councing for Social Monitoring.

Panek, T., & Czapiński, J. (2015b). Warunki życia gospodarstw domowych. Dochody i sposób gospodarowania dochodami. In: J. Czapiński, T. Panek (Eds), Diagnoza społeczna 2015. Warunki i jakość życia Polaków (pp. 36–50). Rada Monitoringu Społecznego.

Pennoni, F., & Genge, E. (2018). Predicting Trends of Institutional Confidence through a Hidden Markov Model with Survey Weights and Missing Responses. ERCIM WG on Computational and Methodological Statistics 11th International Conference of the ERCIM WG on Computational and Methodological Statistics, 12th International Conference on Computational and Financial Econometrics, University of Pisa, 14–16 December.

Pennoni, F., & Genge, E. (2020). Analysing the Course of Public Trust via hidden Markov Models: A Focus on the Polish Society. Statistical Methods and Applications, 29, 399–425. https://doi.org/10.1007/s10260-019-00483-9

Popova, D., & Pishniak, A. (2017). Measuring Individual Material Well-being Using Multidimensional Indices: An Application Using the Gender and Generation Survey for Russia. Social Indicators Research, 130, 883–910. https://doi.org/10.1007/s11205-016-1231-7

The R Core Team. (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.

Schwarz, G. (1978). Estimating the Dimension of a Model. The Annals of Statistic, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136

Sirovátka, T., & Mareš, P. (2009). Poverty, Deprivation and Social Exclusion: The Unemployed and the Working Poor. In: M. C. Fournier, C. S. Mercier (Eds), Economics of Employment and Unemployment (pp. 1–32). Nova Science Publishers.

Social Diagnosis. (2015). Objective and Subjective Quality of Live in Poland. J. Czapinski, T. Panek (Eds). Council for Social Monitoring. http://www.diagnoza.com/index-en.html (accessed: 15.10.2016).

Stapleton, L. M. (2002). The Incorporation of Sample Weights into Multilevel Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal, 9(4), 475–502. https://doi.org/10.1207/S15328007SEM0904_2

Thomas, S. L., & Heck, R. H. (2001). Analysis of Large-scale Secondary Data in Higher Education Research: Potential Perils Associated with Complex Sampling Designs. Research in Higher Education, 42, 517–540. https://doi.org/10.1023/A:1011098109834

Tullio, F., & Bartolucci, F. (2022). Causal Inference for Time-varying Treatments in Latent Markov Models: An Application to the Effects of Remittances on Poverty Dynamics. Annals of Applied Statistics, 16(3), 1962–1985. https://doi.org/10.1214/21-AOAS1578

Wałęga, A. (2015). Sytuacja materialna a konsumpcja gospodarstw domowych ludzi młodych w Polsce. Konsumpcja i Rozwój, 2(11), 61–73.

Wiggins, L. M. (1973), Panel Analysis: Latent Probability Models for Attitude and Behavior Processes. Elsevier.

Verbič, M., & Stanovnik, T. (2006). Analysis of Subjective Economic Well-being in Slovenia. Eastern European Economics, 44(2), 60–70.

Verma, V., Betti, G., & Ghellini, G. (2007) Cross-sectional and Longitudinal Weighting in a Rotational Household Panel: Applications to EU-SILC. Statistics in Transition, 8(1), 5–50.

Zalega, T. (2012). Diagnoza sytuacji materialnej polskich gospodarstw domowych w okresie kryzysu finansowo-ekonomicznego. Management and Business Administration. Central Europe, 20(5), 50–82. https://www.doi.org/10.7206/mba.ce.2084-3356.29

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Published

22-09-2026

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How to Cite

Genge, E. (2026). Is the Subjective Financial Well-being of Polish Households Changing with Time? An Empirical Study Based on Constrained Latent Markov Models. Krakow Review of Economics and Management Zeszyty Naukowe Uniwersytetu Ekonomicznego W Krakowie, 3(1013), 155-176. https://doi.org/10.15678/krem.18792