Do Risky Scenarios Affect Forecasts of Savings and Expenses?
Abstract
:1. Introduction
2. Literature
3. Materials and Methods
3.1. Pilot Study
3.2. Participants for the Main Study
3.3. Procedure
Imagine the following scenario: you come home after a busy day feeling very tired and you are looking forward to a relaxing evening. However, upon arrival, you open your door and the hallway is full of water. A water pipe has broken and water has leaked everywhere. You hurry to shut off the water supply and search the phone number of a local plumber as fast as you can. You call the plumber. After an hour’s wait, he comes by and assesses the damage. The quote he gives amounts to 80% of your monthly income. How does this affect your savings and expenses expense and savings forecasts for the next three months?
Imagine that you arrive at work on Monday morning. You notice the atmosphere is a bit tense. When you go to check your mailbox, you notice that a company-wide meeting invite has been sent for a meeting later that day. Rumours are flying around that the company is in trouble. You and others are starting to feel quite nervous. When the meeting starts, the rumours are confirmed: the firm is losing money and will need to take action. Unfortunately, this means that some people will have to be let go. The manager informs the audience that, 4 out of 5 people (80%) in your department will hear the bad news by the end of the week.
3.4. Variables and Measures
3.4.1. Predicted Expenses, Target Savings, Predicted Savings and Categories of Savings
3.4.2. Likelihood and Impact of Scenario
3.4.3. Adjusted Predictions
3.4.4. Financial Wellbeing
3.4.5. Financial Literacy
4. Results
4.1. Exploratory Analysis
4.1.1. Experimental Conditions Perception Check
4.1.2. Role of Background Variables
4.2. Experimental Analyses
4.2.1. Scenarios
4.2.2. Risk
4.2.3. Categories of Savings
Additional Analyses
5. Discussion
5.1. Discussion of Results
5.1.1. Scenarios
5.1.2. Risk
5.1.3. Categories of Savings
5.1.4. Limitations and Directions for Future Research
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix B
Financial Wellbeing | Financial Literacy (Self-Assessment) | Financial Literacy (Performance Score) | |
---|---|---|---|
PE | 0.150 ** (0.007) | 0.153 ** (0.006) | 0.226 ** (0.000) |
PS | 0.320 ** (0.000) | 0.196 ** (0.000) | 0.065 (0.242) |
TS | 0.235 ** (0.000) | 0.179 ** (0.001) | 0.064 (0.249) |
EFS | 0.152 ** (0.006) | 0.186 ** (0.001) | −0.012 (0.826) |
RS | 0.130 * (0.019) | 0.105 (0.058) | −0.041 (0.456) |
PerS | 0.258 ** (0.000) | 0.140 * (0.012) | 0.028 (0.619) |
PE %change | −0.059 (0.290) | 0.057 (0.308) | 0.031 (0.577) |
PS %change | 0.073 (0.187) | −0.008 (0.891) | −0.020 (0.719) |
TS %change | 0.092 (0.097) | 0.025 (0.650) | 0.010 (0.853) |
EFS %change | 0.007 (0.897) | 0.017 (0.762) | −0.030 (0.586) |
RS %change | 0.053 (0.343) | 0.011 (0.849) | −0.028 (0.620) |
PerS %change | −0.051 (0.362) | 0.100 (0.070) | −0.060 (0.279) |
Scenario Context | Risk Level | Mean | SD | n | |
---|---|---|---|---|---|
Predicted Expenses (%change) | Expense | No risk | 2.61 | 14.50 | 54 |
Low risk | 14.54 | 34.18 | 56 | ||
High risk | 10.02 | 34.47 | 57 | ||
Income Loss | No risk | −4.49 | 97.55 | 54 | |
Low risk | −21.03 | 27.56 | 52 | ||
High risk | −15.40 | 21.91 | 52 | ||
Target Savings (%change) | Expense | No risk | 1.90 | 14.00 | 54 |
Low risk | −8.50 | 22.62 | 56 | ||
High risk | −15.87 | 28.94 | 57 | ||
Income Loss | No risk | −5.59 | 22.36 | 54 | |
Low risk | 0.58 | 31.91 | 52 | ||
High risk | −4.27 | 32.50 | 52 | ||
Predicted Savings (%change) | Expense | No risk | 6.59 | 55.23 | 54 |
Low risk | −5.01 | 30.46 | 56 | ||
High risk | −30.51 | 35.27 | 57 | ||
Income Loss | No risk | 15.29 | 157.18 | 54 | |
Low risk | 5.68 | 59.11 | 52 | ||
High risk | −1.76 | 53.66 | 52 | ||
Predicted Emergency Funds Savings (%change) | Expense | No risk | 3.82 | 30.42 | 54 |
Low risk | 5.88 | 32.43 | 56 | ||
High risk | 7.70 | 114.65 | 57 | ||
Income Loss | No risk | 2.87 | 32.92 | 54 | |
Low risk | 14.14 | 87.79 | 52 | ||
High risk | 11.28 | 41.04 | 52 | ||
Predicted Retirement Savings (%change) | Expense | No risk | −2.04 | 10.62 | 54 |
Low risk | 0.51 | 7.07 | 56 | ||
High risk | −3.46 | 14.16 | 57 | ||
Income Loss | No risk | −2.93 | 18.55 | 54 | |
Low risk | −1.61 | 18.97 | 52 | ||
High risk | −2.53 | 22.83 | 52 | ||
Predicted Personal Savings (%change) | Expense | No risk | −2.12 | 16.73 | 54 |
Low risk | −9.06 | 20.37 | 56 | ||
High risk | −18.21 | 36.62 | 57 | ||
Income Loss | No risk | −3.28 | 42.18 | 54 | |
Low risk | 3.55 | 95.99 | 52 | ||
High risk | −10.58 | 35.75 | 52 |
Risk Level | Scenario | Output Variable | Proportion No Change (n (% Total of Scenario)) |
---|---|---|---|
Zero risk | Income loss | Target Savings | 37 (68.50%) |
Predicted Savings | 28 (51.90%) | ||
Predicted Expenses | 20 (37.00%) | ||
Expenses | Target Savings | 43 (79.60%) | |
Predicted Savings | 29 (53.70%) | ||
Predicted Expenses | 25 (46.30%) | ||
Low risk | Income loss | Target Savings | 21 (40.40%) |
Predicted Savings | 20 (38.50%) | ||
Predicted Expenses | 17 (32.70%) | ||
Expenses | Target Savings | 38 (67.90%) | |
Predicted Savings | 28 (50.00%) | ||
Predicted Expenses | 17 (30.40%) | ||
High risk | Income loss | Target Savings | 31 (59.60%) |
Predicted Savings | 21 (40.40%) | ||
Predicted Expenses | 14 (26.90%) | ||
Expenses | Target Savings | 31 (54.4%) | |
Predicted Savings | 19 (33.33%) | ||
Predicted Expenses | 11 (19.30%) |
References
- Grinstein-Weiss, M.; Russell, B.D.; Gale, W.G.; Key, C.; Ariely, D. Behavioral Interventions to Increase Tax-Time Saving: Evidence from a National Randomized Trial. J. Consum. Aff. 2017, 51, 3–26. [Google Scholar] [CrossRef]
- Hogarth, J.M.; Anguelov, C.E.; Lee, J. Can the poor save? J. Financ. Couns. Plan. 2003, 14, 1–18. [Google Scholar]
- Lusardi, A.; Schneider, D.J.; Tufano, P. Financially Fragile Households: Evidence and Implications. Available online: https://www.nber.org/papers/w17072 (accessed on 11 July 2021).
- Weller, C.E.; Logan, A.M. Measuring Middle Class Economic Security. J. Econ. Issues 2009, 43, 327–336. [Google Scholar] [CrossRef]
- Frederick, S.; Loewenstein, G.; O’Donoghue, T. Time Discounting and Time Preference: A Critical Review. J. Econ. Lit. 2002, 40, 351–401. [Google Scholar] [CrossRef]
- Tam, L.; Dholakia, U.M. Delay and duration effects of time frames on personal savings estimates and behavior. Organ. Behav. Hum. Decis. Processes 2011, 114, 142–152. [Google Scholar] [CrossRef]
- Ainslie, G. Picoeconomics; Cambridge University Press: Cambridge, UK, 1992. [Google Scholar]
- Angeletos, G.-M.; Laibson, D.; Repetto, A.; Tobacman, J.; Weinberg, S. The Hyperbolic Consumption Model: Calibration, Simulation, and Empirical Evaluation. J. Econ. Perspect. 2001, 15, 47–68. [Google Scholar] [CrossRef] [Green Version]
- O’Donoghue, T.; Rabin, M. Doing it now or later. Am. Econ. Rev. 1999, 89, 103–124. [Google Scholar] [CrossRef] [Green Version]
- Howard, C.; Hardisty, D.; Sussman, A.; Knoll, M. Understanding the expense prediction bias. In Advances in Consumer Research; Moreau, P., Puntoni, S., Eds.; Association for Consumer Research: Duluth, MN, USA, 2016; Volume 44, pp. 190–194. [Google Scholar]
- Weinstein, N.D.; Klein, W.M. Unrealistic Optimism: Present and Future. J. Soc. Clin. Psychol. 1996, 15, 1–8. [Google Scholar] [CrossRef]
- Newby-Clark, I.R.; Ross, M. Conceiving the Past and Future. Pers. And Soc. Psy. Bull. 2003, 29, 807–818. [Google Scholar] [CrossRef]
- Thaler, R.; Sunstein, C.R. Behavioral economics, public policy and paternalism: Libertarian paternalism. Am. Econ. Rev. 2003, 93, 175–179. [Google Scholar] [CrossRef] [Green Version]
- Ratner, R.K.; Soman, D.; Zauberman, G.; Ariely, D.; Carmon, Z.; Keller, P.A.; Kim, B.K.; Lin, F.; Malkoc, S.; Small, D.A.; et al. How behavioral decision research can enhance consumer welfare: From freedom of choice to paternalistic intervention. Mark. Lett. 2008, 19, 383–397. [Google Scholar] [CrossRef]
- Johnson, E.J.; Shu, S.B.; Dellaert, B.G.C.; Fox, C.; Goldstein, D.G.; Häubl, G.; Larrick, R.P.; Payne, J.W.; Peters, E.; Schkade, D.; et al. Beyond nudges: Tools of a choice architecture. Mark. Lett. 2012, 23, 487–504. [Google Scholar] [CrossRef]
- OECD. OECD Economic Outlook No. 94; OECD: Paris, France, 2013. [Google Scholar]
- Dugas, C. Retirement Crisis Looms as Many Come Up Short. USA Today. 19 July 2002. Available online: http://globalag.igc.org/pension/us/private/retirement.htm (accessed on 13 December 2021).
- Munnell, A.; Webb, A.; Delorme, L. Retirements at Risk: A New National Retirement Index; Center for Retirement Research at Boston College: Newton, MA, USA, June 2006. [Google Scholar]
- Collinson, P. One in three UK retirees will have to rely solely on state pension. The Guardian. 21 October 2017. Available online: https://www.theguardian.com/money/2017/oct/21/uk-retirees-state-pension-financial-future (accessed on 13 December 2021).
- Benartzi, S.; Thaler, R. Behavioral economics and the retirement savings crisis. Science 2013, 339, 1152–1153. [Google Scholar] [CrossRef] [PubMed]
- Nova, A. Americans need to double their retirement savings. CNBC 2018. Available online: https://www.cnbc.com/2018/11/13/most-americans-arent-saving-nearly-enough-for-retirement.html (accessed on 13 December 2021).
- Chartrand, T.L.; Huber, J.; Shiv, B.; Tanner, R.J. Nonconscious Goals and Consumer Choice. J. Consum. Res. 2008, 35, 189–201. [Google Scholar] [CrossRef]
- Sussman, A.B.; Alter, A.L. The Exception Is the Rule: Underestimating and Overspending on Exceptional Expenses. J. Consum. Res. 2012, 39, 800–814. [Google Scholar] [CrossRef]
- Loewenstein, G.; Prelec, D. Anomalies in intertemporal choice: Evidence and an interpretation. Q. J. Econ. 1992, 107, 573–597. [Google Scholar] [CrossRef]
- Read, D.; Scholten, M. Future-oriented decisions: Intertemporal choice. In Economic Psychology; Ranyard, R., Ed.; John Wiley & Sons: Hoboken, NJ, USA, 2018. [Google Scholar]
- Thaler, R. Some Empirical Evidence on Dynamic Inconsistency. Econ. Lett. 1981, 8, 201–207. [Google Scholar] [CrossRef]
- Zauberman, G.; Lynch, J.J.G. Resource slack and propensity to discount delayed investments of time versus money. J. Exp. Psychol. Gen. 2005, 134, 23–37. [Google Scholar] [CrossRef]
- Newby-Clark, I.R.; Ross, M.; Buehler, R.; Koehler, D.J.; Griffin, D. People focus on optimistic scenarios and disregard pessimistic scenarios while predicting task completion times. J. Exp. Psychol. Appl. 2000, 6, 171–182. [Google Scholar] [CrossRef]
- Liberman, N.; Trope, Y. The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory. J. Personal. Soc. Psychol. 1998, 75, 5–18. [Google Scholar] [CrossRef]
- Thaler, R.H.; Benartzi, S. Save More Tomorrow (TM): Using behavioral economics to increase employee saving. J. Political Econ. 2004, 112, S164–S187. [Google Scholar] [CrossRef]
- Thaler, R.; Sunstein, C.R. Nudge: Improving Decisions about Health, Wealth and Happiness; Yale University Press: New Haven, CT, USA, 2008. [Google Scholar]
- Lofgren, A.; Nordblom, K. A theoretical framework of decision making explaining the mechanisms of nudging. J. Econ. Behav. Organ. 2020, 174, 1–12. [Google Scholar] [CrossRef]
- Gigerenzer, G.; Gaissmaier, W. Heuristic Decision Making. Annu. Rev. Psychol. 2011, 62, 451–482. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tversky, A.; Kahneman, D. Judgment under uncertainty: Heuristics and biases. Science 1974, 185, 1124–1131. [Google Scholar] [CrossRef]
- Hummel, D.; Maedche, A. How effective is nudging? A quantitative review on the effect sizes and limits of empirical nudging studies. J. Behav. Exp. Econ. 2019, 80, 47–58. [Google Scholar] [CrossRef]
- Cadario, R.; Chandon, P. Which Healthy Eating Nudges Work Best? A Meta-Analysis of Field Experiments. Mark. Sci. 2020, 39, 465–486. [Google Scholar] [CrossRef]
- Chetty, R.; Friedman, J.N.; Leth-Petersen, S.; Nielsen, T.H.; Olsen, T. Subsidies vs. nudges: Which policies increase saving the most? Issue Brief 2013, 13, 7. [Google Scholar]
- García, J.M.; Vila, J. Financial literacy is not enough: The role of nudging toward adequate long-term saving behavior. J. Bus. Res. 2020, 112, 472–477. [Google Scholar] [CrossRef]
- Ebrahimi, O.V.; Hoffart, A.; Johnson, S.U. Viral mitigation and the COVID-19 pandemic: Factors associated with adherence to social distancing protocols and hygienic behaviour. Psychol. Health 2021, 1–24. [Google Scholar] [CrossRef]
- Renosa, M.D.C.; Landicho, J.; Wachinger, J.; Dalglish, S.L.; Barnighausen, K.; Barnighausen, T.; McMahon, S.A. Nudging toward vaccination: A systematic review. BMJ Glob. Health 2021, 6, e006237. [Google Scholar] [CrossRef] [PubMed]
- Weijers, R.J.; de Koning, B.B. Nudging to Increase Hand Hygiene during the COVID-19 Pandemic: A Field Experiment. Can. J. Behav. Sci. 2021, 53, 353–357. [Google Scholar] [CrossRef]
- Cardella, E.; Kalenkoski, C.M.; Parent, M. Less is not more: 401(k) plan information and retirement planning choices. J. Pension Econ. Financ. 2021, 1–21. [Google Scholar] [CrossRef]
- Medina, P.C. Side Effects of Nudging: Evidence from a Randomized Intervention in the Credit Card Market. Rev. Financ. Stud. 2021, 34, 2580–2607. [Google Scholar] [CrossRef]
- Hendy, P.; Slonim, R.; Atalay, K. Unsticking credit card repayments from the minimum: Advice, anchors and financial incentives. J. Behav. Exp. Financ. 2021, 30, 100505. [Google Scholar] [CrossRef]
- Sunstein, C.R. Nudging: A very short guide. Bus. Econ. 2019, 54, 127–129. [Google Scholar] [CrossRef]
- Onkal, D.; Sayim, K.Z.; Gonul, M.S. Scenarios as channels of forecast advice. Technol. Forecast. Soc. 2013, 80, 772–788. [Google Scholar] [CrossRef] [Green Version]
- Goodwin, P.; Gonul, M.S.; Onkal, D. When providing optimistic and pessimistic scenarios can be detrimental to judgmental demand forecasts and production decisions. Eur. J. Oper. Res. 2019, 273, 992–1004. [Google Scholar] [CrossRef]
- Fildes, R.; Goodwin, P.; Lawrence, M. The design features of forecasting support systems and their effectiveness. Decis. Support Syst. 2006, 42, 351–361. [Google Scholar] [CrossRef] [Green Version]
- Wright, G.; Goodwin, P. Decision making and planning under low levels of predictability: Enhancing the scenario method. Int. J. Forecast. 2009, 25, 813–825. [Google Scholar] [CrossRef] [Green Version]
- Satterfield, T.; Slovic, P.; Gregory, R. Narrative valuation in a policy judgment context. Ecol. Econ. 2000, 34, 315–331. [Google Scholar] [CrossRef]
- Goodwin, P.; Gönül, S.; Önkal, D.; Kocabıyıkoğlu, A.; Göğüş, C.I. Contrast effects in judgmental forecasting when assessing the implications of worst and best case scenarios. J. Behav. Decis. Mak. 2019, 32, 536–549. [Google Scholar] [CrossRef]
- Schoemaker, P.J.H. When and How to Use Scenario Planning—A Heuristic Approach with Illustration. J. Forecast. 1991, 10, 549–564. [Google Scholar] [CrossRef]
- Leika, M.; Marchettini, D. A Generalized Framework for the Assessment of Household Financial Vulnerability; International Monetary Fund: Washington, DC, USA, 2017. [Google Scholar]
- Christelis, D.; Jappelli, T.; Paccagnella, O.; Weber, G. Income, wealth and financial fragility in Europe. J. Eur. Soc. Policy 2009, 19, 359–376. [Google Scholar] [CrossRef]
- Brounen, D.; Koedijk, K.G.; Pownall, R.A.J. Household financial planning and savings behavior. J. Int. Money Financ. 2016, 69, 95–107. [Google Scholar] [CrossRef]
- Önkal, D.; Gönül, S.; Goodwin, P. Judgmental adjustments and scenario use: Individual versus group forecasts. In Proceedings of the ISF 2020: 40th International Symposium on Forecasting, Virtual, Rio de Janeiro, Brazil, 25 October 2020. [Google Scholar]
- Yaniv, I.; Schul, Y. Acceptance and elimination procedures in choice: Noncomplementarity and the role of implied status quo. Organ. Behav. Hum. Decis. Processes 2000, 82, 293–313. [Google Scholar] [CrossRef]
- Yaniv, I. The benefit of additional opinions. Curr. Dir. Psychol. Sci. 2004, 13, 75–78. [Google Scholar] [CrossRef] [Green Version]
- Keynes, J.M. The General Theory of Employment, Interest and Money; Macmillan: New York, NY, USA, 1936. [Google Scholar]
- Thaler, R.H. Mental accounting Matters. J. Behav. Decis. Mak. 1999, 12, 183–206. [Google Scholar] [CrossRef]
- Antonides, G.; de Groot, I.M.; van Raaij, W.F. Mental budgeting and the management of household finance. J. Econ. Psychol. 2011, 32, 546–555. [Google Scholar] [CrossRef]
- Soman, D.; Cheema, A. Earmarking and Partitioning: Increasing Saving by Low-Income Households. J. Mark. Res. 2011, 48, S14–S22. [Google Scholar] [CrossRef]
- Sussman, A.B.; O’Brien, R.L. Knowing When to Spend: Unintended Financial Consequences of Earmarking to Encourage Savings. J. Mark. Res. 2016, 53, 790–803. [Google Scholar] [CrossRef]
- Zhang, C.Y.; Sussman, A.B. Perspectives on mental accounting: An exploration of budgeting and investing. Financ. Plan. Rev. 2018, 1, e1011. [Google Scholar] [CrossRef] [Green Version]
- Canova, L.; Rattazzi, A.M.M.; Webley, P. The hierarchical structure of saving motives. J. Econ. Psychol. 2005, 26, 21–34. [Google Scholar] [CrossRef]
- Krosnick, J.A. Improving question design to maximize reliability and validity. In The Palgrave Handbook of Survey Research; Vannette, L., Krosnick, J.A., Eds.; Springer Nature: Berlin, Germany, 2018; pp. 95–101. [Google Scholar]
- Russo, J.E.; Schoemaker, P.J.; Russo, E.J. Decision Traps: Ten Barriers to Brilliant Decision Making and How to Overcome Them; Doubleday: New York, NY, USA, 1989. [Google Scholar]
- Crawford, M.M. A comprehensive scenario intervention typology. Technol. Forecast. Soc. 2019, 149, 119748. [Google Scholar] [CrossRef]
- McLeod, S.A. What is central limit theorem in statistics? Simply Psychol. 2019. Available online: https://www.simplypsychology.org/central-limit-theorem.html (accessed on 13 December 2021).
- Netemeyer, R.G.; Warmath, D.; Fernandes, D.; Lynch, J.J.G. How Am I Doing? Perceived Financial Well-Being, Its Potential Antecedents, and Its Relation to Overall Well-Being. J. Consum. Res. 2018, 45, 68–89. [Google Scholar] [CrossRef]
- Porter, N.M.; Garman, E.T. Testing a conceptual model of financial well-being. Financ. Couns. Plan. 1993, 4, 135–164. [Google Scholar]
- OECD. OECD/INFE Toolkit for Measuring Financial Literacy and Financial Inclusion; OECD: Paris, France, 2018. [Google Scholar]
- Lusardi, A.; Mitchelli, O.S. Financial literacy and retirement preparedness: Evidence and implications for financial education. Bus. Econ. 2007, 42, 35–44. [Google Scholar] [CrossRef] [Green Version]
- Thaler, R. Mental Accounting and Consumer Choice. Mark. Sci. 1985, 4, 199–214. [Google Scholar] [CrossRef]
- Kahneman, D. Maps of bounded rationality: Psychology for behavioral economics. Am. Econ. Rev. 2003, 93, 1449–1475. [Google Scholar] [CrossRef] [Green Version]
- Locke, E.A.; Latham, G.P. A Theory of Goal Setting and Task Performance; Prentice-Hall: Englewood Cliffs, NJ, USA, 1990. [Google Scholar]
- Berinsky, A.J.; Huber, G.A.; Lenz, G.S. Evaluating Online Labor Markets for Experimental Research: Amazon.com’s Mechanical Turk. Political Anal. 2012, 20, 351–368. [Google Scholar] [CrossRef] [Green Version]
- Krupnikov, Y.; Levine, A.S. Cross-Sample Comparisons and External Validity. J. Exp. Political Sci. 2014, 1, 59–80. [Google Scholar] [CrossRef]
- Mullinix, K.J.; Leeper, T.J.; Druckman, J.N.; Freese, J. The Generalizability of Survey Experiments. J. Exp. Political Sci. 2015, 2, 109–138. [Google Scholar] [CrossRef] [Green Version]
- Paolacci, G.; Chandler, J.; Ipeirotis, P.G. Running Experiments on Amazon Mechanical Turk. Judgm. Decis. Mak. 2010, 5, 411–419. [Google Scholar]
- Thomas, K.A.; Clifford, S. Validity and Mechanical Turk: An assessment of exclusion methods and interactive experiments. Comput. Hum. Behav. 2017, 77, 184–197. [Google Scholar] [CrossRef]
- Zhang, J.W.; Howell, R.T.; Bowerman, T. Validating a brief measure of the Zimbardo Time Perspective Inventory. Time Soc. 2013, 22, 391–409. [Google Scholar] [CrossRef]
- Lynch, J.G.; Netemeyer, R.G.; Spiller, S.A.; Zammit, A. A Generalizable Scale of Propensity to Plan: The Long and the Short of Planning for Time and for Money. J. Consum. Res. 2010, 37, 108–128. [Google Scholar] [CrossRef]
- Kirby, K.N.; Petry, N.M.; Bickel, W.K. Heroin addicts have higher discount rates for delayed rewards than non-drug-using control. J. Exp. Psychol. Gen. 1999, 128, 78–87. [Google Scholar] [CrossRef]
- Sulphey, M.M. A study on the effect of long-term orientation and risk propensity on resilience. Int. J. Sociol. Soc Pol 2020, 40, 1585–1610. [Google Scholar] [CrossRef]
- Szustak, G.; Gradon, W.; Szewczyk, L. Household Financial Situation during the COVID-19 Pandemic with Particular Emphasis on Savings-An Evidence from Poland Compared to Other CEE States. Risks 2021, 9, 166. [Google Scholar] [CrossRef]
- Levine, R.; Lin, C.; Tai, M.Z.; Xie, W.S. How Did Depositors Respond to COVID-19? Rev. Financ. Stud. 2021, 34, 5438–5473. [Google Scholar] [CrossRef]
- Heo, W.; Grable, J.E.; Rabbani, A.G. A test of the association between the initial surge in COVID-19 cases and subsequent changes in financial risk tolerance. Rev. Behav. Financ. 2021, 13, 3–19. [Google Scholar] [CrossRef]
- Chhatwani, M.; Mishra, S.K. Does financial literacy reduce financial fragility during COVID-19? The moderation effect of psychological, economic and social factors. Int. J. Bank Mark. 2021, 39, 1114–1133. [Google Scholar] [CrossRef]
% Mentions | Examples | Likelihood | Impact | |
---|---|---|---|---|
Expenses | 46.4% | “My car breaking down”; “attending a wedding (gift cost)” | 60.15% (SD = 14.89) | 3.58 (SD = 0.62) |
Income loss | 53.6% | “losing my job”; “going to part-time employment” | 42.00% (SD = 16.13) | 3.77 (SD = 0.72) |
Scenario | Risk Level | Likelihood: Mean (SD) | Impact: Mean (SD) |
---|---|---|---|
Expenses | No risk | 2.28 (1.27) | 2.69 (1.33) |
Low risk | 2.38 (1.17) | 3.50 (1.25) | |
High risk | 2.54 (1.45) | 4.26 (1.04) | |
Income loss | No risk | 2.46 (1.38) | 3.35 (1.33) |
Low risk | 2.46 (1.08) | 3.87 (1.03) | |
High risk | 2.29 (1.32) | 4.00 (1.16) |
Savings Category | Estimation Point | Mean (SD) |
---|---|---|
Emergency Fund Savings | Initial estimate | 257.59 (874.47) |
Adjustment size | 19.64 (62.80) | |
Adjustment direction | 7.54 (65.37) | |
Retirement Savings | Initial estimate | 161.29 (692.77) |
Adjustment size | 4.30 (15.61) | |
Adjustment direction | −2.01 (16.07) | |
Personal Savings | Initial estimate | 290.19 (721.54) |
Adjustment size | 19.16 (44.91) | |
Adjustment direction | −6.78 (48.37) |
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De Baets, S.; Önkal, D.; Ahmed, W. Do Risky Scenarios Affect Forecasts of Savings and Expenses? Forecasting 2022, 4, 307-334. https://doi.org/10.3390/forecast4010017
De Baets S, Önkal D, Ahmed W. Do Risky Scenarios Affect Forecasts of Savings and Expenses? Forecasting. 2022; 4(1):307-334. https://doi.org/10.3390/forecast4010017
Chicago/Turabian StyleDe Baets, Shari, Dilek Önkal, and Wasim Ahmed. 2022. "Do Risky Scenarios Affect Forecasts of Savings and Expenses?" Forecasting 4, no. 1: 307-334. https://doi.org/10.3390/forecast4010017