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Cited by 483Decision-Making and Behavioral EconomicsExperimental Behavioral Economics StudiesBehavioral Health and Interventions

The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index; none of the 27 sources its claims cite is checked for support yet.

When and why defaults influence decisions: a meta-analysis of default effects

Open access
JACHIMOWICZ, JON M., DUNCAN, SHANNON, WEBER, ELKE U., JOHNSON, ERIC J.·2019·Behavioural Public Policy
DOI 10.1017/bpp.2018.43
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Abstract

When people make decisions with a pre-selected choice option – a ‘default’ – they are more likely to select that option. Because defaults are easy to implement, they constitute one of the most widely employed tools in the choice architecture toolbox. However, to decide when defaults should be used instead of other choice architecture tools, policy-makers must know how effective defaults are and when and why their effectiveness varies. To answer these questions, we conduct a literature search and meta-analysis of the 58 default studies (pooled n = 73,675) that fit our criteria. While our analysis reveals a considerable influence of defaults ( d = 0.68, 95% confidence interval = 0.53–0.83), we also discover substantial variation: the majority of default studies find positive effects, but several do not find a significant effect, and two even demonstrate negative effects. To explain this variability, we draw on existing theoretical frameworks to examine the drivers of disparity in effectiveness. Our analysis reveals two factors that partially account for the variability in defaults’ effectiveness. First, we find that defaults in consumer domains are more effective and in environmental domains are less effective. Second, we find that defaults are more effective when they operate through endorsement (defaults that are seen as conveying what the choice architect thinks the decision-maker should do) or endowment (defaults that are seen as reflecting the status quo). We end with a discussion of possible directions for a future research program on defaults, including potential additional moderators, and implications for policy-makers interested in the implementation and evaluation of defaults.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This meta-analysis finds that opt-out defaults substantially increase uptake of the pre-selected option, with a medium-sized average effect across 58 observations. It also shows substantial variability, partly explained by domain and by whether defaults activate endorsement or endowment channels.

Key findings

  • Across 58 default studies (pooled n = 73,675), opt-out defaults increased choice of the pre-selected option versus opt-in defaults: d = 0.68, 95% CI = 0.53–0.83, p < 0.001; the three-level model gave d+ = 0.63, 95% CI = 0.47–0.80, p < 0.001.
  • For binary outcomes (38 of 58 observations), opt-out defaults increased uptake by an absolute average of 27.24% (Cramér’s V+ = 0.29, 95% CI = 0.21–0.37, p < 0.001).
  • Consumer domains showed larger default effects (b = 0.73, SE = 0.23, p = 0.003), while environmental domains were marginally weaker (b = −0.47, SE = 0.27, p = 0.08); in the channels model, consumer remained positive (b = 0.89, p = 0.0003) and environmental became negative (b = −0.60, p = 0.028).
  • Endorsement (b = 0.32, SE = 0.15, p = 0.038) and endowment (b = 0.31, SE = 0.15, p = 0.044) significantly moderated default effects, but ease did not (b = −0.05, SE = 0.15, p = 0.75). Heterogeneity was very high (I² = 98.21%; three-level I² = 98.01%).

Methods

  • Literature search and meta-analysis of studies explicitly comparing opt-in and opt-out default conditions; included binary and continuous choice measures, with multiple relevant measures or groups included as separate observations when appropriate.
  • Final dataset: 58 observations from 55 studies in 35 articles, pooled n = 73,675.
  • Random-effects models using restricted maximum likelihood in R with metafor, inverse-variance weights, and a three-level model (observation, study, article); study characteristics and three default channels (endorsement, ease, endowment) were coded as moderators, with robust variance estimation as a check.

Limitations

  • The paper states that channel coding is a noisy approximation, that underlying mechanisms were not measured in the vast majority of prior default studies, and that ease was not systematically manipulated in the included studies.
  • The paper notes high heterogeneity and cautions that Egger’s regression is prone to Type I errors when heterogeneity is high; publication-bias results should be interpreted cautiously.
  • The text makes evident that domain explanations are speculative, that the analysis relies on researchers’ design choices rather than systematic variation, and that the meta-analysis cannot directly compare defaults with alternative choice architecture tools outside one focal study.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1017/bpp.2018.43

Cites

94 of 94 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

0 of 27 cited sources checked · 0 supported · 0 flagged

Show all 51 claims

  • It may state two separate claims as one. 21
  • The quote only partly states this claim. 1
  • Probably not a substantial claim. 10
29 of 29

  • The paper relies on this without support.

    The coders' ratings of the extent to which each default study's design likely triggered endorsement, ease, and endowment validly index the psychological channels actually activated in decision-makers.

    “In the absence of such information, we trained two codersa graduate student and a senior research assistant, who are not part of the author team to rate each default study on the extent to which its design likely triggered each of the three channels”
    “Ideally, we would have access to study respondents' ratings of the choice architect to evaluate endorsement (as collected by Tannenbaum et al., 2017, andBang et al., 2018), measures of reaction time to evaluate ease (as collected by Dinner et al., 2011) and measures of thoughts to evaluate endowment (as collected by Dinner et al., 2011).However, these data are not available in the vast majority of”

  • The authors state this as an assumption.

    A default has a stronger effect if more of the three channels are involved.

    “Presumably, a default has a stronger effect if more channels are involved.”

  • The authors state this as an assumption.

    The coders can accurately identify which channel is activated in each study.

    “In addition, this analysis relies on our coders' ability to identify which channel is activated, which may be called into question.”
3 of 3

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Statistics recomputed

Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.

  • Consistentt(56) = -0.39reported p = .69 · recomputed p = .698

    “t(56) = -0.39,p = .69”

1 of 1

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Abstract counts observations as studies

    Minor

    The abstract reports 58 default studies, but the methods identify 58 observations from 55 studies. The 58 figure is therefore an observation count, not a study count.

1 of 1

Citations

Cites

Flagged first, then by citations

Show all 92 references

Research works printed but not linked: 21

  1. Chandon, 2018, Marketing Science
  2. Shu, 2018, Behavioural Public Policy, 1
  3. Shafir, 2018, Behavioural Public Policy, 1
  4. 1946, Scandinavian Acturial Journal, 85
  5. 1988, Statistical power analysis for the behavioral sciences
  6. Bockenholt, 2012, Journal of Consumer Psychology, 25, 333
  7. Baruch, 2014, The Israel Journal of Psychiatry and Related Sciences, 51, 199
  8. McCullough, 2014, Frontiers in Psychology
  9. Rabin, 1999, Behavioral dimensions of retirement economics, 125
  10. 2010, Journal of Statistical Software, 36, 1
  11. Sunstein, 2016, Ecology LQ, 43
  12. 2011, Thinking, fast and slow
  13. Hom, 2000, Journal of Management, 26, 463
  14. Sunstein, 2008, Penguin Group
  15. Ioannidis, 2015, PLOS Biology, 15
  16. 2015, SSRN
  17. Sutton, 2011, BMJ, 343
  18. Chaiken, 1995, Attitude strength: Antecedents and consequences
  19. Johnson, 2011, Journal of Experimental Psychology: Applied, 17, 332
  20. Heller, 2002, Five-factor model of personality and job satisfaction: A meta-analysis, 87, 530
  21. 2017, Harvard Business Review

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Metadata

Authors
JACHIMOWICZ, J. M., DUNCAN, S., WEBER, E. U., & JOHNSON, E. J.
Affiliations
  • Columbia University (US)
  • Princeton University (US)
Corresponding author
JACHIMOWICZ, JON M.
ORCID
Venue
Behavioural Public Policy
Publisher
Cambridge University Press (CUP)
Published
2019-01-24
Volume
3
Issue
02
Pages
159-186
ISSN
  • 2398-063X (ISSN-L)
  • 2398-0648
DOI
10.1017/bpp.2018.43
OpenAlex
W2742773638
Cited by
481
References
92
Citations by year
2026: 62 · 2025: 76 · 2024: 62 · 2023: 61 · 2022: 61 · 2021: 54 · 2020: 72 · 2019: 24
FWCI
6.45
Type
journal article
Language
en
Open access
Licence
Locations
  • Behavioural Public Policy (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • SSRN Electronic Journal
Topics
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Experimental Behavioral Economics Studies
  • Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
Keywords
default effects, choice architecture, status quo bias, consumer choice, environmental choice, nudging

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