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Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach

Year 2024, Volume: 74 Issue: 1, 281 - 314, 04.09.2024

Can Verberi , Muhittin Kaplan

https://doi.org/10.26650/ISTJECON2023-1360545

Abstract

This study aims to investigate the effects of personality traits, in addition to basic financial literacy, private pension literacy and behavioural factors on Private Pension System (PPS) participation using machine learning algorithms. The PPS participation model was trained using both random forest and LightGBM algorithms, and the contributions of model inputs in the prediction of pension participation were interpreted using the Tree SHAP algorithms with swarmplots. The data employed in the empirical analysis is survey data collected from the Şırnak province of Türkiye with a sample size of 449. The findings of the study shows that: (i) PPS participation is more likely for females and middle-aged people; (ii) High basic financial literacy has a negative impact on PPS participation; (iii) Extraversion is the key personality trait affecting PPS participation; (iv) Advanced pension literacy has more impact on participation than simple pension literacy: (v) Present-fatalistic tendency is key behavioural factor and it negatively affects PPS; (vi) Present-hedonistic, conscientiousness, future-time orientation, and locus of control tendencies increase PPS participation. Furthermore, the distribution of colours in LightGBM has a greater degree of uniformity in both directions compared with the random forest algorithm. Finally, to increase PPS participation, the results of the study suggest the implementation of the following policy measures: Tailored pension literacy programmes can help to increase pension participation rates. Incentives should be created to prevent narrow-minded behaviour and establish a sense of protection and control around PPS, targeting middle-aged individuals and women.

JEL Classification : C60 , G41 , J32

Keywords

Private pension system Behavioural factors Personality traits Machine learning algorithms Tree SHAP

References

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Year 2024, Volume: 74 Issue: 1, 281 - 314, 04.09.2024

Can Verberi , Muhittin Kaplan

https://doi.org/10.26650/ISTJECON2023-1360545

Abstract

References

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T google scholar
  • Ajzen, I., & Schmidt, P. (2020). Changing behavior using the theory of planned behavior. In M. Hagger, L. Cameron, K. Hamilton, N. Hankonen, & T. Lintunen (Eds.), The handbook of behavior change (Cambridge handbooks in psychology, pp. 17-31). Cambridge University Press. https:// doi.org/10.1017/9781108677318.002 google scholar
  • Asebedo, S. D., & Browning, C. M. (2020). The psychology of portfolio withdrawal rates. Psychology and Aging, 35(1), 78. https://doi.org/10.1037/pag0000424 google scholar
  • Balasuriya, J., & Yang Y. (2019). The role of personality traits in pension decisions: findings and policy recommendations, Applied Economics, 51(27), 2901-2920. https://doi.org/10.1080/00036846. 2018.1563670 google scholar
  • Barr, N., & Diamond, P. (2009). Reforming pensions: Principles, analytical errors and policy directions. International social security review, 62(2), 5-29. https://doi.org/10.1111/j.1468-246X.2009.01327.x google scholar
  • Bayar, Y., Gündüz, M., Öztürk, Ö. F., & Şaşmaz, M. Ü. (2020). Finansal okuryazarlığın Bireysel Emeklilik Sistemine katılım üzerindeki etkisi: Uşak Üniversitesi personeline yönelik bir uygulama, Business & Management Studies: An International Journal, 8(2), 1972-1989. https://doi.org/10.15295/ bmij.v8i2.1514 google scholar
  • Bengtson, V.L., Biblarz T. J., & Roberts R.E. (2002). How families still matter: A longitudinal study of youth in two generations, Cambridge University Press. google scholar
  • Beverly, S. (1997). How can the poor save? Theory and evidence on saving in low-income households. (CSD Working Paper No. 97-3), Washington University, Center for Social Development. https://openscholarship.wustl.edu/cgi/viewcontent. cgi?article=1242&context=csd_research google scholar
  • Bottazzi, R., Jappelli, T., & Padula, M. (2006). Retirement expectations, pension reforms, and their impact on private wealth accumulation, Journal of Public Economics, 90(12), 2187-2212. https:// doi.org/10.1016/j.jpubeco.2006.03.005 google scholar
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  • Brody, N., & Ehrlichman, H. (1998). Personality psychology: The science of individuality. Prentice Hall. google scholar
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  • Canöz, İ., & Baş, H. (2020). Bireysel Emeklilik Sistemi’ne giriş kararlarını belirleyen etmenler: Devlet ve vakıf üniversitelerinde çalışan akademisyenlerin karşılaştırılması, Sosyal Siyaset Konferansları Dergisi, (78), 361-390. https://doi.org/10.26650/jspc.2019.78.0040 google scholar
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  • Diaz D., Ruiz J.L., Tapia P. (2021). The role of pension knowledge in voluntary pension and banking savings in Chile, Academia Revista Latinoamericana de Administracion, 34(4), 545-560. https:// doi.org/10.1108/ARLA-12-2020-0264 google scholar
  • Doğan, M. (2016). Davranışsal finans eğilimleri ile bireysel emeklilik fon tercihleri arasındaki ilişkinin test edilmesi: Türkiye’deki banka çalışanları üzerine bir uygulama, Uluslararası Yönetim İktisat ve İşletme Dergisi, 12(12), 339-357. Retrieved from https://dergipark.org.tr/en/pub/ijmeb/ issue/54621/745043 google scholar
  • Donnelly, G., Iyer, R., & Howell, R. T. (2012). The Big Five personality traits, material values, and financial well-being of self-described money managers. Journal of Economic Psychology, 33(6), 1129-1142. https://doi.org/10.1016/j.joep.2012.08.001 google scholar
  • Dragos, S. L., Dragos, C. M., Muresan, G. M. (2020). From intention to decision in purchasing life insurance and private pensions: Different effects of knowledge and behavioural factors, Journal of Behavioral and Experimental Economics, 87, 101555. https://doi.org/10.1016/j. socec.2020.101555 google scholar
  • Duesenberry, J. S. (1967). Income, saving, and the theory of consumer behavior. New York, NY: Oxford University Press (Original work published 1949). google scholar
  • Ertuğrul, H. M., Gebeşoğlu, P. F., & Atasoy, B. S. (2018). Mind the gap: Turkish case study of policy change in private pension schemes. Borsa Istanbul Review, 18(2), 140-149. https://doi. org/10.1016/j.bir.2017.11.003 google scholar
  • Fang J., Hao W., & Reyers M. (2022). Financial advice, financial literacy and social interaction: What matters to retirement saving decisions?, Applied Economics, 54(50), 5827-5850. https://doi.org/ 10.1080/00036846.2022.2053654 google scholar
  • Fornero E., & Monticone C. (2011). Financial literacy and pension plan participation in Italy, Journal of Pension Economics & Finance, 10(4), 547-564. https://doi.org/10.1017/S1474747211000473 google scholar
  • Friedman, M. (1957). A theory of the consumption function. Princeton, NJ: Princeton University Press. google scholar
  • Gosling, S. D., Rentfrow, P. J., & Swann Jr, W. B. (2003). A very brief measure of the Big-Five personality domains. Journal of Research in Personality, 37(6), 504-528. https://doi.org/10.1016/S0092-6566(03)00046-1 google scholar
  • Han, S. (2022). Identifying the roots of inequality of opportunity in South Korea by application of algorithmic approaches. Humanities and Social Sciences Communications, 9(1), 1-10. https://doi. org/10.1057/s41599-021-01026-y google scholar
  • Heckhausen, J., & Schulz, R. (1995). A life-span theory of control. Psychological Review, 102(2), 284304. https://doi.org/10.1037/0033-295X.102.2.284 google scholar
  • Holzmann, R., & Hinz, R. (2005). Old age income support in the 21st century: An international perspective on pension systems and reform. Washington, DC: The World Bank google scholar
  • Joseph, V. R. (2022). Optimal ratio for data splitting. Statistical Analysis and Data Mining: The ASA Data Science Journal, 15(4), 531-538. https://doi.org/10.1002/sam.11583 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. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 30 (pp. 3146-3154). Curran Associates, Inc. google scholar
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  • Kocabıyık, T., & Küçükçakal Z., (2018) Türkiye’de Bireysel Emeklilik Sistemi ve çalışanların otomatik katılımdan ayrılma nedenleri: Isparta İlinde Bir Uygulama, Journal of Life Economics, 5(4), 233254. https://doi.org/10.15637/jlecon.272 google scholar
  • Landerretche, O.M., & Martfrıez C. (2013). Voluntary savings, financial behavior and pension finance literacy: Evidence from Chile, Journal of Pension Economics & Finance, 12(3), 251-297. https://doi. org/10.1017/S1474747212000340 google scholar
  • Littauer, F. (1995). Personality plus: How to understand others by understanding yourself, Monarch Books (Original work published 1983). google scholar
  • Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 30). Curran Associates, Inc. https:// proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf google scholar
  • Lusardi, A., & Mitchell, O.S. (2011). Financial literacy and planning: Implications for retirement wellbeing (No. w17078), National Bureau of Economic Research. https://www.nber.org/system/ files/working_papers/w17078/w17078.pdf, google scholar
  • Modigliani, F., & Brumberg, R. (1954). Utility analysis and the consumption function: An interpretation of cross-section data. In K. K. Kurihara (Ed.), Post-Keynesian economics (pp. 388436). New Brunswick, NJ: Rutgers University Press. google scholar
  • Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106. google scholar
  • Niu, G., Zhou Y., Gan H. (2020). Financial literacy and retirement preparation in China, Pacific-Basin Finance Journal, 59, 101262. https://doi.org/10.1016/j.pacfin.2020.101262 google scholar
  • O’Dea, C., & Sturrock, D., (2019). Survival pessimism and the demand for annuities. (IFS working papers No. W19/02). https://direct.mit.edu/rest/article-pdf/105/2/442/2073390/rest_a_01048. pdf google scholar
  • O’Guinn, T.C., & Faber, R. J., (1989). Compulsive buying: A phenomenological exploration. Journal of Consumer Research, 16(2), 147-157. https://doi.org/10.1086/209204 google scholar
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There are 58 citations in total.

Details

Primary Language English
Subjects Finance and Investment (Other)
Journal Section Research Article
Authors

Can Verberi 0000-0003-4876-8564

Muhittin Kaplan 0000-0002-0685-7641

Publication Date September 4, 2024
Submission Date September 20, 2023
Published in Issue Year 2024 Volume: 74 Issue: 1

Cite

APA Verberi, C., & Kaplan, M. (2024). Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach. İstanbul İktisat Dergisi, 74(1), 281-314. https://doi.org/10.26650/ISTJECON2023-1360545
AMA Verberi C, Kaplan M. Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach. İstanbul İktisat Dergisi. September 2024;74(1):281-314. doi:10.26650/ISTJECON2023-1360545
Chicago Verberi, Can, and Muhittin Kaplan. “Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach”. İstanbul İktisat Dergisi 74, no. 1 (September 2024): 281-314. https://doi.org/10.26650/ISTJECON2023-1360545.
EndNote Verberi C, Kaplan M (September 1, 2024) Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach. İstanbul İktisat Dergisi 74 1 281–314.
IEEE C. Verberi and M. Kaplan, “Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach”, İstanbul İktisat Dergisi, vol. 74, no. 1, pp. 281–314, 2024, doi: 10.26650/ISTJECON2023-1360545.
ISNAD Verberi, Can - Kaplan, Muhittin. “Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach”. İstanbul İktisat Dergisi 74/1 (September 2024), 281-314. https://doi.org/10.26650/ISTJECON2023-1360545.
JAMA Verberi C, Kaplan M. Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach. İstanbul İktisat Dergisi. 2024;74:281–314.
MLA Verberi, Can and Muhittin Kaplan. “Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach”. İstanbul İktisat Dergisi, vol. 74, no. 1, 2024, pp. 281-14, doi:10.26650/ISTJECON2023-1360545.
Vancouver Verberi C, Kaplan M. Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach. İstanbul İktisat Dergisi. 2024;74(1):281-314.