Biases and Willingness to Take a Student Loan: A Behavioral Economics Approach

Document Type : Research Paper

Authors

1 Associate Professor of Economics, Department of Economics, Faculty of Literature and Humanities, Ilam University, Ilam, Iran

2 Master of Economics, Department of Economics, Faculty of Literature and Humanities, Ilam University, Ilam, Iran

Abstract

This study's primary objective is to analyze the impact of behavioral economics biases on students' inclination to apply for a loan. A 2x2x2 factorial model was utilized, incorporating two levels of framing (positive or negative), two levels of need for a loan (present bias or no bias), and two levels of probability of time limit (high or low probability). The independent variables include positive framing, an urgent need for a loan, and an increased likelihood of time limit. In contrast, the dependent variable is the willingness to take out a student loan. A random sample of 400 students from Ilam and Allameh Tabatabai University was included in the study. The results indicate that the present bias significantly increases the willingness to apply for a loan, suggesting that the perceived urgency of the loan influences individuals' decisions. However, the positive frame variable does not independently impact the overall sample's willingness to apply for a loan.
Further analysis demonstrated that positive framing, with a low probability of time limit and absence of bias, positively affects the inclination to take out a loan. This implies that in situations where students are not biased, positive framing significantly influences their decision-making process. The findings of this research provide valuable insights for policymakers in higher education, emphasizing the need to leverage behavioral economics strategies to motivate students to avail themselves of available financial resources.

Keywords

References

  1.  

    1. Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. M. (2023). When should you adjust standard errors for clustering?. The Quarterly Journal of Economics, 138(1), 1-35.
    2. Abbaspour, A., Keramati Tavlayi, M. M., Rahimian, H., & Jahangard, E. (2020). Development of a Model for Financing Iran’s Higher Education System Based on Grounded Theory. Strategy of Culture , 13(49), 189-221 (in Persian).
    3. Abraham, K. G., Filiz-Ozbay, E., Ozbay, E. Y., & Turner, L. J. (2020). Framing effects, earnings expectations, and the design of student loan repayment schemes. Journal of Public Economics, 183, 104067.
    4. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
    5. Akhishjan, M., Tavousi, M., & Danaeifa, M. (2019). The Policies of Financial Support from Ph.D Students and The Promotion of Human Resources Productivity and Research Institutes. Science and Technology Policy Letters, 09(1), 93-104 (in Persian).
    6. Asgari, H., & Pouralimardan, M. (2023). Factor Analysis of Health Biases: A Behavioral Economics Approach. Quarterly Journal of Applied Theories of Economics, 10(2), 119-144 (in Persian).
    7. Barbosa, R. D. (2022). The Influence of Framing Effects on the Borrower's Risk Perception During a Mortgage Decision. Master thesis, The University of Porto, Portugal.
    8. Boatman, A., Callender, C., & Evans, B. (2022). Comparing anigh school students' attitudes towards borrowing for higher education in England and the United States: Who are the most loan averse?. European Journal of Education, 57(2), 199-217.
    9. Boatman, A., Evans, B. J., & Soliz, A. (2017). Understanding loan aversion in education: Evidence from high school seniors, community college students, and adults. Aera Open, 3(1), 2332858416683649.
    10. Cunningham, A. F., & Santiago, D. A. (2008). Student Aversion to Borrowing: Who Borrows and Who Doesn't. Institute for Higher Education Policy.
    11. Dadgar, Y., Dargahi, H., & Gholizadeh, S. (2023). The Role of Investor Sentiment and Government Behaviour in Volatility of Tehran Stock Exchange Market: A Behavioural Economics Approach. Quarterly Journal of Applied Theories of Economics, 10(1), 191-214 (in Persian).
    12. Evans, B. J., Boatman, A., & Soliz, A. (2019). Framing and labeling effects in preferences for borrowing for college: An experimental analysis. Research in Higher Education, 60, 438-457.
    13. Fehr, E. (2002).”The economics of impatience”. Nature 415(6869): 269-272.
    14. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. MA: Addison-Wesley.
    15. Haghi Karam Allah, M., & Foladgar, M. (2014). Strategic Planning To Promote The Welfare Of Students Of Higher Education In Iran. Social Development & Welfare Planning, 5(18), 109-139 (in Persian).
    16. Heidhues, P., & Kőszegi, B. (2010). “Exploiting naivete about self-control in the credit market”. American Economic Review 100(5), 2279-2303.
    17. Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American economic review, 93(5), 1449-1475.
    18. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-292.
    19. Keller, T., & Szakál, P. (2023). The Framing of Information Nudge Affects Students' Anticipated Effort: A Large-Scale, Randomized Survey Experiment. Journal of Behavioral and Experimental Economics, 104, 102012.
    20. Kuchler, T., & Pagel, M. (2021). Sticking to your plan: The role of present bias for credit card paydown. Journal of Financial Economics, 139(2), 359-388.
    21. Laibson, D. (1997). “Golden eggs and hyperbolic discounting”. The Quarterly Journal of Economics 112 (2), 443-478.
    22. Lin, C. J., & Jia, H. (2023). Time pressure affects the risk preference and outcome evaluation. International journal of environmental research and public health, 20(4), 3205.
    23. Luan, J. (2021). Research on influence of attribute frame effect on loan decision of undergraduate and risk assessment model of undergraduate loan behavior. Mobile Information Systems, 2021, 1-8.
    24. Meier, S., & Sprenger, C. (2010). Present-biased preferences and credit card borrowing. American Economic Journal: Applied Economics, 2(1), 193-210.
    25. Menges, K. K., & Leonhard, C. (2016). Factors that affect willingness to borrow student loans among community college students. Journal of Student Financial Aid, 46(2), 5.
    26. Mountain, T. P., Kim, N., Gutter, M. S., Kiss, E., Cho, S. H., & Johnson, C. L. (2020). An exploration of gender bias, framing, and student loan decisions through an experimental design. Journal of Family and Economic Issues, 41, 350-363.
    27. Naderi, A, Kharazi, k, Entezari, Y & Mahjob Eshrat Abadi, H, (2013). Mechanisms of resources and characteristics in higher education. Journal of Human Resource Studies, 3(10), 91-120 (in Persian).
    28. Navehebrahim, A., & Azizi Shomami, M. (2012). A Study of the Relationships between Students Socio-economic Status and Their Perception of Investment in Graduate Education. Quarterly Journal of Research and Planning in Higher Education, 18(2), 109-130 (in Persian).
    29. Nguyen, Q. (2016). Linking loss aversion and present bias with overspending behavior of tourists: Insights from a lab-in-the-field experiment. Tourism Management, 54, 152-159.
    30. Parijs, B. (2023). The Influence of Present Bias in Students’ Attitudes Towards Borrowing & Financial Literacy: An Online Survey Experiment. Master thesis, Radboud University, Netherlands.
    31. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press. Translated by Mehri Modabadi. Tehran. Hormazd Publications
    32. Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty. science, 185(4157), 1124-1131.

Files

Share

How to cite

Statistics