Unveiling financial well-being: Insights from retired people in Third Age group in Poland, Spain and Denmark

Authors

DOI:

https://doi.org/10.18559/ebr.2024.3.981

Keywords:

ability to make ends meet, financial well-being, LightGBM, SHAP values, silver economy

Abstract

The study investigates the financial well-being of older people in Poland, Spain and Denmark, with a particular focus on their ability to make ends meet. Using data from the SHARE survey to analyse retired individuals aged 65 to 79 years, it aims to identify the socio-economic factors that influence financial well-being among older people in these countries. In terms of methodology, it uses Light Gradient Boosting Machine algorithm and SHAP value calculations to predict the ability to make ends meet and determine the importance of 167 various features. The study concludes that household income and financial resources are the primary determinants of older people’s ability to make ends meet. The findings underscore the need for policymakers and practitioners the fields of ageing and economics to address specific challenges, such as housing costs in Denmark and food expenditure in Poland and Spain, to improve the financial well-being of older individuals.

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References

1. Albuquerque, B., & Green, G. (2022). MAR financial concerns and the marginal propensity to consume in COVID times: Evidence from UK survey data. IMF Working Papers, 22/47. https://doi.org/10.5089/9798400203466.001
View in Google Scholar

2. Artazcoz, L., Cortès-Franch, I., Escribà-Agüir, V., & Benavides, F. G. (2021). Financial strain and health status among European workers: Gender and welfare state inequalities. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.616191
View in Google Scholar

3. Badowski, K. (2022). A strategy& survey: More modest lifestyles and less spending— the lives of Polish consumers. https://www.pwc.pl/pl/pdf-nf/2022/Strategyand_report_More_modest_lifestyles_and_less_spending-the_lives_of_Polish_consumers.pdf
View in Google Scholar

4. Badri, M., Aldhaheri, H., Alkhaili, M., Yang, G., Albahar, M., Alrashdi, A., & Alsawai, A. (2022). Wellbeing determinants of household’s ability to make ends meet—a hierarchical regression model for Abu Dhabi. International Journal of Social Sciences and Economic Review, 4(3), 26–36. https://doi.org/10.36923/ijsser.v4i3.175
View in Google Scholar

5. Barković Bojanić, I., Erceg, A., & Damoska Sekuloska, J. (2024). Silver entrepreneurship: A golden opportunity for ageing society. Economics and Business Review, 10(1), 153–178. https://doi.org/10.18559/ebr.2024.1
View in Google Scholar

6. Bergmann, M., & Börsch-Supan, A. (Eds.). (2021). SHARE Wave 8 methodology: Collecting cross-national survey data in times of COVID-19. MEA, Max Planck Institute for Social Law and Social Policy.
View in Google Scholar

7. BIG InfoMonitor. (2021). InfoDług – Ogólnopolski raport o zaległym zadłużeniu i niesolidnych dłużnikach. https://media.big.pl/publikacje/650730/infodlug-ogolnopolski-raport-o-zaleglym-zadluzeniu-i-niesolidnych-dluznikach-marzec-2021-41-edycja
View in Google Scholar

8. Börsch-Supan, A. (2022). Survey of health, ageing and retirement in Europe (SHARE) wave 8. Release version: 8.0.0. SHARE-ERIC.
View in Google Scholar

9. Brünner, R. N., & Andersen, S. S. (2018). Making meaning of financial scarcity in old age. Journal of Aging Studies, 47, 114–122. https://doi.org/10.1016/j.jaging.2018.04.001
View in Google Scholar

10. CFPB (Consumer Financial Protection Bureau). (2015). Measuring financial well-being: A guide to using the CFPB Financial Well-Being Scale. https://www.consumerfinance.gov/data-research/research-reports/financial-well-being-scale/
View in Google Scholar

11. CFPB (Consumer Financial Protection Bureau). (2017). CFPB Financial Well-Being Scale: Scale development technical report. https://www.consumerfinance.gov/data-research/research-reports/financial-well-being-technical-report/
View in Google Scholar

12. CFPB (Consumer Financial Protection Bureau). (2020). Insights from the making ends meet survey. https://www.consumerfinance.gov/data-research/research-reports/insights-making-ends-meet-survey
View in Google Scholar

13. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13–17 August 2016. https://doi.org/10.1145/2939672.2939785
View in Google Scholar

14. Danziger, S., & Wang, H. C. (2005). Does it pay to move from welfare to work? Reply to Robert Moffitt and Katie Winder. Journal of Policy Analysis and Management, 24(2), 411–417. https://doi.org/10.1002/pam.20096
View in Google Scholar

15. Dudek, H., & Wojewódzka-Wiewiórska, A. (2023). Household inability to make ends meet: What changed in the first year of the COVID-19 pandemic in Poland? Communications of International Proceedings, (2). https://doi.org/10.5171/2023.4119423
View in Google Scholar

16. European Commission. (2021). Methodological guidelines and description of EU-SILC target variables. https://ec.europa.eu/eurostat/documents/203647/16195750/2021_Doc65_EUSILC_User_Guide.pdf
View in Google Scholar

17. European Commission. (2024). Ageing Europe—statistics on working and moving into retirement. https://ec.europa.eu/eurostat/statistics-explained/index.php?ol-did=581874#Employment_patterns_among_older_people
View in Google Scholar

18. Eurostat. (2021). Ageing Europe—2021 interactive edition. https://ec.europa.eu/eurostat/cache/digpub/ageing/
View in Google Scholar

19. Eurostat. (2022a). Ability to make ends meet becoming harder. https://ec.europa.eu/eurostat/web/products-eurostat-news/w/DDN-20221128-2
View in Google Scholar

20. Eurostat. (2022b). Quality of life indicators—material living conditions. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Quality_of_life_indi-cators_-_material_living_conditions
View in Google Scholar

21. Eurostat. (2024). Population structure indicators at national level. https://ec.europa.eu/eurostat/databrowser/view/demo_pjanind/default/table?lang=en
View in Google Scholar

22. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232.
View in Google Scholar

24. Gray, A. (2009). The social capital of older people. Ageing and Society, 29(1), 5–31.
View in Google Scholar

25. Gumà-Lao, J. (2022). The influence of economic factors on the relationship between partnership status and health: A gender approach to the Spanish case. International Journal of Environmental Research and Public Health, 19(5), 2975. https://doi.org/10.3390/ijerph19052975
View in Google Scholar

26. Hébert, S., & Gyarmati, D. (2014). Financial capability and essential skills: An exploratory analysis. https://www.canada.ca/content/dam/canada/financial-consumer-agency/migration/eng/resources/researchsurveys/documents/fincapesss-kill-capfincompess-eng.pdf
View in Google Scholar

27. Heflin, C. (2016). Family instability and material hardship: Results from the 2008 survey of income and program participation. Journal of Family and Economic Issues, 37(3), 359–372.
View in Google Scholar

28. Horowitz, J., Brown, A., & Minkin, R. (2021). A year into the pandemic, long-term financial impact weighs heavily on many Americans. https://www.pewresearch.org/social-trends/2021/03/05/a-year-into-the-pandemic-long-term-financial-impact-weighs-heavily-on-many-americans/
View in Google Scholar

29. Johar, G., Meng, R., & Wilcox, K. (2015). Thinking about financial deprivation: Rumination and decision making among the poor. Association for Consumer Research, 43, 208–211.
View in Google Scholar

30. Kahneman, D., & Deaton, A. (2010). High income improves evaluation of life but not emotional well-being. Proceedings of the National Academy of Sciences of the United States of America, 107(38), 16489–16493. https://doi.org/10.1073/ pnas.1011492107
View in Google Scholar

31. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. https://github.com/Microsoft/LightGBM
View in Google Scholar

33. LightGBM Documentation. (2024). https://lightgbm.readthedocs.io/en/stable/Lundberg,
View in Google Scholar

S., & Lee, S. I. (2017). A unified approach to interpreting model predictions. https://arxiv.org/abs/1705.07874
View in Google Scholar

35. Madakkatel, I., Chiera, B., & McDonnell, M. D. (2019). Predicting financial well-being using observable features and gradient boosting. Lecture Notes in Computer Science, 11919, 228–239. https://doi.org/10.1007/978-3-030-35288-2_19
View in Google Scholar

36. Marjanovic, Z., Greenglass, E. R., Fiksenbaum, L., De Witte, H., Garcia-Santos, F., Buchwald, P., Peiró, J. M., & Mañas, M. A. (2015). Evaluation of the financial threat scale (FTS) in four European, non-student samples. Journal of Behavioral and Experimental Economics, 55, 72–80. https://doi.org/10.1016/j.socec.2014.12.001
View in Google Scholar

Meng, A., Sundstrup, E., & Andersen, L. L. (2020). Factors contributing to retirement decisions in Denmark: Comparing employees who expect to retire before, at, and after the state pension age. International Journal of Environmental Research and Public Health, 17(9), 3338. https://doi.org/10.3390/ijerph17093338
View in Google Scholar

38. Mercer. (2023). Mercer CFA institute global pension index 2023. https://www.mercer.com/insights/investments/market-outlook-and-trends/mercer-cfa-global-pension-index/
View in Google Scholar

39. Netemeyer, R. G., Warmath, D., Fernandes, D., & Lynch, J. G. (2018). How am I doing? Perceived financial well-being, its potential antecedents, and its relation to overall well-being. Journal of Consumer Research, 45(1), 68–89. https://doi.org/10.1093/jcr/ucx109
View in Google Scholar

40. Niemczyk, A., Szalonka, K., Gardocka-Jałowiec, A., Nowak, W., Seweryn, R., & Gródek-Szostak, Z. (2023). The silver economy. Routledge. https://doi.org/10.4324/9781003377313
View in Google Scholar

41. Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008). Rethinking rumination. Perspectives on Psychological Science, 3(5), 400–424. https://doi.org/10.1111/j.1745-6924.2008.00088.x
View in Google Scholar

42. OECD. (2021). COVID-19 and well-being: Life in the pandemic. OECD Publishing. https://doi.org/10.1787/1e1ecb53-en
View in Google Scholar

43. Olson, R. S., La Cava, W., Mustahsan, Z., Varik, A., & Moore, J. H. (2017). Data-driven advice for applying machine learning to bioinformatics problems. https://arxiv.org/abs/1708.05070
View in Google Scholar

44. Parker, K., Minkin, R., & Bennett, J. (2020). Economic fallout from COVID-19 continues to hit lower-income Americans the hardest. https://www.pewresearch.org/social-trends/2020/09/24/economic-fallout-from-covid-19-continues-to-hit-low-er-income-americans-the-hardest/
View in Google Scholar

45. Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x
View in Google Scholar

46. Sconti, A. (2022). Having trouble making ends meet? Financial literacy makes the difference. Italian Economic Journal, 10, 377–408. https://doi.org/10.1007/s40797-022-00212-4
View in Google Scholar

47. Serrano, J. P., Latorre, J. M., & Gatz, M. (2014). Spain: Promoting the welfare of older adults in the context of population aging. Gerontologist, 54(5), 733–740. https://doi.org/10.1093/geront/gnu010
View in Google Scholar

48. Seto, H., Oyama, A., Kitora, S., Toki, H., Yamamoto, R., Kotoku, J., Haga, A., Shinzawa, M., Yamakawa, M., Fukui, S., & Moriyama, T. (2022). Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data. Scientific Reports, 12(1), 15889. https://doi.org/10.1038/s41598-022-20149-z
View in Google Scholar

49. Silberman-Beltramella, M., Ayala, A., Rodríguez-Blázquez, C., & Forjaz, M. J. (2022). Social relations and health in older people in Spain using SHARE survey data. BMC Geriatrics, 22(1), 29–75. https://doi.org/10.1186/s12877-022-02975-y
View in Google Scholar

50. Tilly, L. (2012). Having friends—they help you when you are stuck from money, friends and making ends meet research group. Learning Disabilities, 40(2), 128–133.
View in Google Scholar

51. Tur-Sinai, A., Paz, A., & Doron, I. (2022). Self-rated health and socioeconomic status in old age: The role of gender and the moderating effect of time and welfare regime in Europe. Sustainability, 14(7), 74240. https://doi.org/10.3390/su14074240
View in Google Scholar

Watanabe, M., Eguchi, A., Sakurai, K., Yamamoto, M., Mori, C., Kamijima, M., Yamazakii, S., Ohya, Y., Kishi, R., Yaegashi, N., Hashimoto, K., . Mori, C., Ito, S., Yamagata, Z., Inadera, H., Nakayama, T., Sobue, T., Shima, M., Kageyama, S., … Katoh, T. (2023). Prediction of gestational diabetes mellitus using machine learning from birth cohort data of the Japan environment and children’s study. Scientific Reports, 13(1), 17419. https://doi.org/10.1038/s41598-023-44313-1
View in Google Scholar

53. Wilkinson, L. R. (2016). Financial strain and mental health among older adults during the Great Recession. The Journals of Gerontology: Series B, 71(4), 745–754. https://doi.org/10.1093/geronb/gbw001
View in Google Scholar

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Published

2024-09-26 — Updated on 2024-09-26

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How to Cite

Jajko-Siwek, A. (2024). Unveiling financial well-being: Insights from retired people in Third Age group in Poland, Spain and Denmark. Economics and Business Review, 10(3), 7-33. https://doi.org/10.18559/ebr.2024.3.981

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