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Psychological factor, barrier 2 of 8

Loss aversion and status quo bias

Prospect theory (Kahneman and Tversky, 1979) proposed that losses weigh more heavily than equal gains, making people and institutions reluctant to change.

Evidence

  • Average size. A 2024 meta-analysis of 607 estimates from 150 articles found losses weigh 1.96 times as much as gains on average (Brown et al., 2024).
  • Setting matters. A separate meta-analysis of risky choices found a smaller average of 1.31, so the size of loss aversion depends on the setting (Walasek et al., 2024).
  • The 2.25 figure. The often-quoted figure of 2.25 comes from a study of 25 graduate students (Tversky and Kahneman, 1992), not from the 1979 paper.

What can be done

Actions rated on the strength-rating scale (A strong and replicated, B solid but limited, C weak or debated, D contested or failed), applied to the specific claim made.

  • Make the beneficial option the default, such as automatic enrollment in retirement savings (A for participation, B for long-run savings). Participation was about 86% under automatic enrollment versus 37% before, with the largest gains among low-paid, young, Black, and Hispanic workers (Madrian and Shea, 2001). In Denmark, about 85% of people are passive savers, and each $1 of tax subsidy raised total saving by only 1 cent, while automatic contributions raised it substantially (Chetty et al., 2014). Automatic enrollment of U.S. Army civilian employees added 4.1% of salary in contributions after four years with little sign of financial distress (Beshears et al., 2022). Across 58 studies, defaults had an average effect of d = 0.68 (Jachimowicz et al., 2019). Many people stay at the default rate, so defaults should be set carefully and paired with automatic increases.
  • Opt-out organ donation laws (C). Defaults change stated willingness to donate (Johnson and Goldstein, 2003), and one panel study found higher donation under presumed consent (Abadie and Gay, 2006). But a 35-country comparison found no significant difference in deceased donors and fewer living donors (4.8 versus 15.7 per million) in opt-out countries (Arshad, Anderson, and Sharif, 2019), deceased donors in Wales did not rise after its law (101 versus 104) (Noyes et al., 2019), and a review found donation infrastructure at least as important as the default (Steffel, Williams, and Tannenbaum, 2019).
  • Not effective: loss-framed health messages (A for no advantage). Across 93 studies with 21,656 people, loss-framed prevention messages were no more persuasive than gain-framed ones, and gain framing had a slight edge driven by dental hygiene (O'Keefe and Jensen, 2007).
  • Unproven: loss-framed incentives paid up front and clawed back (C). In schools they raised math achievement in the first wave of a trial but not in the second (Fryer et al., 2022).
  • Context: On average losses weigh about 1.96 times as much as equal gains (A) (Brown et al., 2024), but some researchers argue the effect is far less general (C) (Gal and Rucker, 2018), so actions should not depend on loss aversion being universal.

Proposed and experimental methods

Methods that are proposed, under trial, approved in some places, or tried and then failed. Each shows a stage label and an evidence rating. A stage label shows how far a method has progressed, not whether it works. The stage labels are explained on the psychological factor page.

  • Regulatory sandboxes (Approved but not scaled, B). A regulator lets firms test new products with real customers for a limited time under supervision and tailored rules. After entering the UK sandbox, fintech firms raised about 15% more capital (about $700,000) over the next two years, and their quarterly probability of raising capital rose by about 3.1 percentage points from an average of 6.1%, about 50% (Cornelli et al., 2024). Entry was not random, the sample was small (56 sandbox firms in the main analysis), and no evidence was found on consumer outcomes or regulatory quality.
  • Trial periods before permanent decisions (Early trial, C). A contested change is introduced for a limited time so people experience it before deciding, since expected losses tend to loom larger before a change than after it. In Stockholm, support for congestion charges was 36% just before the 2006 trial and 52% during it, 53% of voters (excluding blank votes) chose to keep the charges in the September 2006 referendum, and support reached 66% in December 2007 and over 70% in May 2011, while traffic across the cordon fell 21% in March to June 2006 and stayed 18% to 20% lower in 2008 to 2011 (Börjesson et al., 2012). In London, a Tube strike forced many commuters to try new routes, and lasting changes afterwards showed that a significant share had not been using their best route (Larcom et al., 2017). Evidence comes from few cases, and trials can be timed or designed to favor a result.
  • Sludge audits (Theoretical, C). Regular reviews of paperwork, waiting times and other friction that keep people in the status quo or out of programs they qualify for, followed by removal of friction that is not justified. The main source is a conceptual argument that such friction often costs more than it delivers and hurts vulnerable people most (Sunstein, 2022). No evaluation of audits themselves was found.
  • Sunset clauses and experimental legislation (Approved but not scaled, contested, C). Laws, rules or agencies expire unless actively renewed, which reverses the default, and experimental laws test a rule on a small scale first (Ranchordás, 2014). The Texas Sunset Advisory Commission reports 42 agencies and programs abolished, 54 abolished and transferred or consolidated, and a return of $17 for every $1 appropriated to it since 1985 (self-reported) (Texas Sunset Advisory Commission, n.d.). About a dozen U.S. states dropped sunset laws by the end of the 1980s, partly because "almost nothing was terminated" (Romboldi and Quandt, 2026), and a 2025 analysis found that with synthetic control methods only Tennessee showed a robust gain (about $3,000 in income per person) (Jones and Quandt, 2025).

Sources cited on this page

  1. Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47(2), 263-292. JSTOR A Strong
  2. Brown, A. L., Imai, T., Vieider, F. M., & Camerer, C. F. (2024). Meta-analysis of empirical estimates of loss aversion. Journal of Economic Literature, 62(2), 485-516. DOI B Moderate: A that loss aversion exists on average
  3. Walasek, L., Mullett, T. L., & Stewart, N. (2024). A meta-analysis of loss aversion in risky contexts. Journal of Economic Psychology, 103, 102740. DOI B Moderate
  4. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297-323. C Limited: for the 2.25 figure
  5. Abadie, A., & Gay, S. (2006). The impact of presumed consent legislation on cadaveric organ donation: A cross-country study. Journal of Health Economics, 25(4), 599-620. link C Limited
  6. Anderson, M. L. (2008). Multiple inference and gender differences in the effects of early intervention: A reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects. Journal of the American Statistical Association, 103(484), 1481-1495. link B Moderate
  7. Arshad, A., Anderson, B., & Sharif, A. (2019). Comparison of organ donation and transplantation rates between opt-out and opt-in systems. Kidney International, 95(6), 1453-1460. link C Limited
  8. Beshears, J., Choi, J. J., Laibson, D., Madrian, B. C., & Skimmyhorn, W. L. (2022). Borrowing to save? The impact of automatic enrollment on debt. The Journal of Finance, 77(1), 403-447. link B Moderate
  9. Chetty, R., Friedman, J. N., Leth-Petersen, S., Nielsen, T. H., & Olsen, T. (2014). Active vs. passive decisions and crowd-out in retirement savings accounts: Evidence from Denmark. The Quarterly Journal of Economics, 129(3), 1141-1219. link A Strong
  10. Fryer, R. G., Jr., Levitt, S. D., List, J., & Sadoff, S. (2022). Enhancing the efficacy of teacher incentives through framing: A field experiment. American Economic Journal: Economic Policy, 14(4), 269-299. link C Limited
  11. Gal, D., & Rucker, D. D. (2018). The loss of loss aversion: Will it loom larger than its gain? Journal of Consumer Psychology, 28(3), 497-516. link C Limited
  12. Jachimowicz, J. M., Duncan, S., Weber, E. U., & Johnson, E. J. (2019). When and why defaults influence decisions: A meta-analysis of default effects. Behavioural Public Policy, 3(2), 159-186. link A Strong: for retirement defaults, B for defaults in general
  13. Johnson, E. J., & Goldstein, D. (2003). Do defaults save lives? Science, 302(5649), 1338-1339. link A Strong: for the default effect on stated consent, C for effects on donation
  14. Madrian, B. C., & Shea, D. F. (2001). The power of suggestion: Inertia in 401(k) participation and savings behavior. The Quarterly Journal of Economics, 116(4), 1149-1187. link A Strong: for participation, B for long-run total savings
  15. Noyes, J., McLaughlin, L., Morgan, K., Walton, P., Curtis, R., Madden, S., et al. (2019). Short-term impact of introducing a soft opt-out organ donation system in Wales: Before and after study. BMJ Open, 9(4), e025159. link B Moderate
  16. O'Keefe, D. J., & Jensen, J. D. (2007). The relative persuasiveness of gain-framed and loss-framed messages for encouraging disease prevention behaviors: A meta-analytic review. Journal of Health Communication, 12(7), 623-644. link A Strong
  17. Steffel, M., Williams, E. F., & Tannenbaum, D. (2019). Does changing defaults save lives? Effects of presumed consent organ donation policies. Behavioral Science & Policy, 5(1), 69-88. link B Moderate
  18. Börjesson, M., Eliasson, J., Hugosson, M. B., & Brundell-Freij, K. (2012). The Stockholm congestion charges: 5 years on. Effects, acceptability and lessons learnt. Transport Policy, 20, 1-12. link B Moderate
  19. Cornelli, G., Doerr, S., Gambacorta, L., & Merrouche, O. (2024). Regulatory sandboxes and fintech funding: Evidence from the UK. Review of Finance, 28(1), 203-233. link B Moderate
  20. Jones, T., & Quandt, R. (2025). An iridescent sunset: An empirical analysis of sunset legislation. Journal of Regulatory Economics, 68(2), 85-123. link C Limited
  21. Larcom, S., Rauch, F., & Willems, T. (2017). The benefits of forced experimentation: Striking evidence from the London Underground network. The Quarterly Journal of Economics, 132(4), 2019-2055. link B Moderate
  22. Ranchordás, S. (2014). Constitutional sunsets and experimental legislation: A comparative perspective. Edward Elgar. link B Moderate
  23. Romboldi, I., & Quandt, R. (2026). What happens after sunset? Regulation, Fall 2026. Cato Institute. link C Limited
  24. Sunstein, C. R. (2022). Sludge audits. Behavioural Public Policy, 6(4), 654-673. link C Limited

Every source for this factor is listed on the psychological factor page.