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

  
06 oct. 2024
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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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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

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 Search in Google Scholar

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/ Search in Google Scholar

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/ Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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

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

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

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

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

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

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

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 Search in Google Scholar

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/fincapessskill-capfincompess-eng.pdf Search in Google Scholar

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. Search in Google Scholar

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-impactweighs-heavily-on-many-americans/ Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

LightGBM Documentation. (2024). https://lightgbm.readthedocs.io/en/stable/ Search in Google Scholar

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

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 Search in Google Scholar

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 Search 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 Search in Google Scholar

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

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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

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 Search in Google Scholar

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-lower-income-americans-the-hardest/ Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search 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 Search in Google Scholar

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 Search in Google Scholar