Summary:
Over-borrowing is a common irrational decision. In recent years, fierce competition in the online loan lending market has lowered the threshold for applying for loans, leading people with low financial literacy to start participating in online borrowing, further exacerbating the phenomenon of over-borrowing. This article examines a behavioral bias strongly associated with financial literacy—the framing effect. The framing effect can cause the market to have an incentive to manipulate the way information is presented, which implicitly manipulates consumers' decision-making. Therefore, understanding the framing effect in loan decision-making can help policy makers make more specific regulations on the displaying form of loan content and protect the rights and interests of financial consumers more effectively. This paper uses a laboratory experiment which provides subjects with economically equivalent loan products, and at the same time exogenously change the form of loan costs to study whether the displaying form of loan costs affects loan take-up decisions. Existing literature generally designs loan decisions as intertemporal risk decisions in the laboratory (Giné et al., 2010; Wu Zuguang et al., 2012; Baland et al., 2017). On this basis, we added conditions such as loan cost, consumption value, future income, and overdue fine to simulate the characteristics of consumption loans. This experiment adopts a between-subject experimental design, and the subjects randomly enter into one of Group A (monthly interest amount), Group B (monthly interest rate), and Group C (annual compound interest rate). The information content of the three experimental groups is economically equivalent, and the only difference lies in the displaying form of the "loan cost". Taking the monthly interest rate of 0.5% as an example, Group A sees "the monthly interest rate is 0.5%", Group B sees "the monthly interest amount is 2.5 experimental coins (assuming the loan amount is 500 experimental coins)", Group C sees "the annual compound interest rate is 6.17%". Holding other variables constant, the loan take-up rate in Group B (monthly interest amount) is 21.30 percentage points higher than that of Group A (monthly interest rate) (which is 21.30/43.42=49.06% higher than that of Group A), and it is statistically significant. Holding other variables constant, the loan take-up rate in group C (annualized compound interest) is 7.91 percentage points lower than Group A (monthly interest rate) on average (which is 7.91/43.42=18.22% lower than that of Group A), and it is also statistically significant. This paper further explores the channels of the two framing effects and finds that they result from different channels of financial literacy. In a broad sense, financial literacy includes not only people's mastery of financial knowledge, but also people's ability to process economic information and make wise financial decisions (Lusardi and Mitchell, 2014; Liu, 2018). After the loan take-up decisions, we test the financial literacy of the subjects. More than 90% of the subjects correctly answer the questions about the conversion of interest rates and interest amount. We also test the subjects' calculating ability to proxy the heuristics in financial decision-making. Subjects with weaker calculating ability are more prone to the framing effect of interest amount and interest rate. The financial literacy quiz also finds that 71.92% of the subjects have the bias of underestimating exponential growth. The stronger the exponential growth bias, the more prone to the framing effect of single-period interest rate and multi-period compound interest. Therefore, the framing effect of interest amount and interest rates can be explained by the heuristics of financial literacy, while the framing effect of single-period interest rate and multi-period compound interest can be explained by lacking financial knowledge. This paper proposes a new explanation for over-borrowing in the context of online consumption loan-the framing effect of loan costs, and provides clean and reliable evidence for this using the method of experimental economics. Secondly, this paper divides financial literacy into knowledge and heuristics, and distinguishes the different causes of the two framing effects. This paper suggests that we need the combination of financial education and financial policy to manage the over-borrowing problem in the Internet age. To alleviate the framing effect of single-period interest rate and multi-period compound interest, government can provide more financial education on the concept of compound interest to the public. To alleviate the framing effect of interest amount in absolute values and interest rate in percentage points, government can directly make clear regulations on the displaying form of loan information, such as adjusting the displaying form of loan costs from single-period interest amount to single-period interest rate, or further adjusting to multi-period compound interest rate.
Agarwal, Sumit, and Bhashkar Mazumder. 2013. “Cognitive Abilities and Household Financial Decision Making.” American Economic Journal: Applied Economics 5 (1): 193～207.
[18]
Andreoni, James, and Charles Sprenger. 2012. “Estimating Time Preferences from Convex Budgets.” The American Economic Review 102 (7): 3333～56.
[19]
Anufriev, Mikhail, Te Bao, Angela Sutan, and Jan Tuinstra. 2019. “Fee Structure and Mutual Fund Choice: An Experiment.” Journal of Economic Behavior & Organization 158 (2): 449～74.
[20]
Baland, Jean-Marie, Lata Gangadharan, Pushkar Maitra, and Rohini Somanathan. 2017. “Repayment and Exclusion in a Microfinance Experiment.” Journal of Economic Behavior & Organization 137: 176～90.
[21]
Benartzi, Shlomo, and Richard H. Thaler. 1999. “Risk Aversion or Myopia？ Choices in Repeated Gambles and Retirement Investments.” Management Science 45 (3): 364～81.
[22]
Bertrand, Marianne, and Adair Morse. 2011. “Information Disclosure, Cognitive Biases, and Payday Borrowing.” The Journal of Finance 66 (6): 1865～93.
[23]
Carrell, Scott, and Jonathan Zinman. 2014. “In Harm's Way？ Payday Loan Access and Military Personnel Performance.” The Review of Financial Studies 27 (9): 2805～40.
[24]
Carvalho, Leandro, Arna Olafsson, and Dan Silverman. 2019. “Misfortune and Mistake: The Financial Conditions and Decision-Making Ability of High-Cost Loan Borrowers.” Working Paper 26328. National Bureau of Economic Research.
[25]
Chen, Shih-Fen S, Kent B Monroe, and Yung-Chien Lou. 1998. “The Effects of Framing Price Promotion Messages on Consumers' Perceptions and Purchase Intentions.” Journal of Retailing 74 (3): 353～72.
[26]
Christelis, Dimitris, Tullio Jappelli, and Mario Padula. 2010. “Cognitive Abilities and Portfolio Choice.” European Economic Review 54 (1): 18～38.
[27]
DelVecchio, Devon, H. Shanker Krishnan, and Daniel C. Smith. 2007. “Cents or Percent？ The Effects of Promotion Framing on Price Expectations and Choice.” Journal of Marketing 71 (3): 158～70.
[28]
Fehr, Ernst, and Lorenz Goette. 2007. “Do Workers Work More if Wages are high？ Evidence from a Randomized Field Experiment.” American Economic Review 97 (1): 298-317.
[29]
Giné, Xavier, Pamela Jakiela, Dean Karlan, and Jonathan Morduch. 2010. “Microfinance Games.” American Economic Journal: Applied Economics 2 (3): 60～95.
[30]
Glaser, Markus, Zwetelina Iliewa, and Martin Weber. 2019. “Thinking about Prices Versus Thinking about Returns in Financial Markets.” The Journal of Finance 74 (6): 2997～3039.
[31]
Glaser, Markus, Thomas Langer, Jens Reynders, and Martin Weber. 2007. “Framing Effects in Stock Market Forecasts: The Difference Between Asking for Prices and Asking for Returns.” Review of Finance 11 (2): 325～57.
[32]
Gneezy, Uri, and Jan Potters. 1997. “An Experiment on Risk Taking and Evaluation Periods.” The Quarterly Journal of Economics 112 (2): 631～45.
[33]
Heijden, Eline van der, Tobias J. Klein, Wieland Müller, and Jan Potters. 2012. “Framing Effects and Impatience: Evidence from a Large Scale Experiment.” Journal of Economic Behavior & Organization 84 (2): 701～11.
[34]
Holt, Charles A., and Susan K. Laury. 2002. “Risk Aversion and Incentive Effects.” The American Economic Review 92 (5): 1644～55.
[35]
Huber, Christoph, and Jürgen Huber. 2019. “Scale Matters: Risk Perception, Return Expectations, and Investment Propensity under Different Scalings.” Experimental Economics 22 (1): 76～100.
[36]
Kumar, Alok, and Sonya Seongyeon Lim. 2008. “How Do Decision Frames Influence the Stock Investment Choices of Individual Investors？” Management Science 54 (6): 1052～64.
[37]
Laibson, David, Andrea Repetto, and Jeremy Tobacman. 2003. A Debt Puzzle. Pp. 228～66 in Knowledge, Information, and Expectations in Modern Macroeconomics: In Honor of Edmund S. Phelps, edited by Philippe Aghion, Roman Frydman, Joseph Stiglitz, and Michael Woodford. Princeton, N.J.: Princeton University Press.
[38]
Leary, Jesse, and Jialan Wang. 2016. “Liquidity Constraints and Budgeting Mistakes: Evidence from Social Security Recipients.” Working paper.
[39]
Li, King King, Dan Liu, Xin Da Mai, Qiu Ju Zhang, Xiao Yu Ren, Gao Qian Xu, and Kai Zhang. 2020. “What Drive Excessive Borrowing and Under-borrowing？ A Field Experiment.” SSRN Scholarly Paper ID 3795838. Rochester, NY: Social Science Research Network.
[40]
Lusardi, Annamaria, and Olivia S. Mitchell. 2007. “Baby Boomer Retirement Security: The Roles of Planning, Financial Literacy, and Housing Wealth.” Journal of Monetary Economics 54 (1): 205～24.
[41]
Lusardi, Annamaria, and Olivia S. Mitchell. 2014. “The Economic Importance of Financial Literacy: Theory and Evidence.” Journal of Economic Literature 52 (1): 5～44.
[42]
Lusardi, Annamaria, and Carlo de Bassa Scheresberg. 2013. “Financial Literacy and High-cost Borrowing in the United States.” Working Paper 18969. National Bureau of Economic Research.
[43]
Mani, Anandi, Sendhil Mullainathan, Eldar Shafir, and Jiaying Zhao. 2013. “Poverty Impedes Cognitive Function.” Science 341 (6149): 976-980.
[44]
Martinez, Seung-keun, Stephan Meier, and Charles Sprenger. 2017. “Procrastination in the Field: Evidence from Tax Filing.” Working paper.
[45]
Meier, Stephan, and Charles Sprenger. 2010. “Present-biased Preferences and Credit Card Borrowing.” American Economic Journal: Applied Economics 2 (1): 193～210.
[46]
Melzer, Brian T. 2011. “The Real Costs of Credit Access: Evidence from the Payday Lending Market.” The Quarterly Journal of Economics 126 (1): 517-555.
[47]
Melzer, Brian T. 2018. “Spillovers from Costly Credit.” The Review of Financial Studies 31 (9): 3568～94.
[48]
Morgan, Donald P., Michael R. Strain, and Ihab Seblani. 2012. “How Payday Credit Access Affects Overdrafts and other outcomes.” Journal of Money, Credit and Banking 44 (2-3): 519-531.
[49]
Skiba, Paige Marta, and Jeremy Tobacman. 2008. “Payday Loans, Uncertainty and Discounting: Explaining Patterns of Borrowing, Repayment, and Default.” Vanderbilt Law and Economics Research Paper, no. 08～33.
[50]
Shah, Anuj K., Sendhil Mullainathan, and Eldar Shafir. 2012. “Some Consequences of Having too Little.” Science 338 (6107): 682-685.
[51]
Snowberg, Erik, and Leeat Yariv. 2021. “Testing the Waters: Behavior Across Participant Pools.” American Economic Review 111 (2): 687～719.
[52]
Song, Changcheng. 2020. “Financial Illiteracy and Pension Contributions: A Field Experiment on Compound Interest in China.” The Review of Financial Studies 33 (2), 916～949.
[53]
Stango, Victor, and Jonathan Zinman. 2009. “Exponential Growth Bias and Household Finance.” The Journal of Finance 64 (6): 2807～49.
[54]
Tversky, Amos, and Daniel Kahneman. 1981. “The Framing of Decisions and the Psychology of Choice.” Science, New Series 211 (4481): 453～58.
