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

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.

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

92 of 92 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

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

  • Findingown data

    No study characteristic other than consumer and environmental domain explained further systematic variance in default effects.

    “No other study characteristic explained any further systematic variance in the variability of default effects present in prior studies.”
    Quote in the paperSubstantial claim
  • Findingown data

    Reviewing funnel plots suggests potential publication bias in the published defaults literature.

    “Reviewing the funnel plots highlights that several observations appear outside of the funnel on both sides, suggesting potential publication bias (Duval & Tweedie, 2000).”
    Quote in the paperNot a substantial claim
  • Findingown data

    Including study characteristics as moderators reduces heterogeneity by 4.67% to I2 = 93.54%.

    “Including study characteristics as moderators reduces the heterogeneity by 4.67% to I 2 = 93.54%.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Defaults in environmental domains may be less effective (b = -0.47, SE = 0.27, p = 0.08).

    “while the regression coefficient for environmental domains is marginally significant and negative (b = -0.47,SE = 0.27, p = 0.08).”
    Quote in the paperSubstantial claim
  • Findingown data

    The meta-analysis reveals a considerable influence of defaults (d = 0.68, 95% confidence interval = 0.53-0.83).

    “While our analysis reveals a considerable influence of defaults (d = 0.68, 95% confidence interval = 0.53-0.83)”
    Quote in the paperSubstantial claim
  • May combine two claims. 21
  • The quote only partly states this claim. 1
  • Classified as probably not a substantial claim. 10

  • May combine two claims.

    Findingown data

    Egger et al.'s test for asymmetry of the funnel plot is not significant (t(56) = -0.39, p = 0.69), so the likely absence of studies does not lead to inadequate estimation of the default effect.

    “However, because Egger et al.'s (1997) test for asymmetry of the funnel plot is not significant (t(56) = -0.39,p = 0.69), the likely absence of studies does not lead to inadequate estimation of the default effect.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    There is substantial variation in default effects: the majority of default studies find positive effects, but several do not find a significant effect, and two demonstrate negative effects.

    “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.”
    Quote in the paperSubstantial claim

  • May combine two claims. The quote only partly states this claim.

    Findingown data

    Trim-and-fill analysis estimates eight studies are missing from the funnel plot; including them increases the overall effect to d+ = 0.80, 95% CI = 0.65-0.96, p < 0.001.

    “Including these studies increases the overall effect to d + = 0.80, 95% CI = 0.65-0.96,p < 0.001; this estimate remains directionally the same as our prior analysis and is significantly different from zero.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The base model has I2 = 98.21%, and the three-level model has I2 = 98.01%, indicating very high heterogeneity.

    “In our base model, we find an I 2 of 98.21%.We employ methods that apply the use of I 2 to multilevel meta-analytic models (Nakagawa & Santos, 2012) and find an I 2 of 98.01% for our three-level model (observation level, study level, and article level), which is considered to be very high heterogeneity (Higgins et al., 2003).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The default effect size varies widely: 46 observations find a statistically significant positive effect, ten do not find a statistically significant effect, and two find a statistically significant negative effect.

    “46 observations find a statistically significant and positive effect (i.e., the observations are to the right of 0 and the confidence interval excludes 0), ten observations do not find a statistically significant effect (i.e., the confidence interval includes 0) and two observations find a statistically significant and negative effect (i.e., the observations are to the left of 0 and the confidence”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Defaults in consumer domains are more effective and in environmental domains are less effective.

    “First, we find that defaults in consumer domains are more effective and in environmental domains are less effective.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Heterogeneity is distributed as 30.21% at the article level, 63.58% at the studies-within-articles level, 4.21% at the observations-within-studies level, and 2.00% due to sampling variance.

    “Parceling out these distinct sources, we find that 30.21% of the heterogeneity is at the article level, 63.58% is at the studies within-articles level, 4.21% is at the observations within-studies level, and the remaining 2.00% is due to sampling variance.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Opt-out defaults lead to greater uptake of the pre-selected decision than opt-in defaults, with a medium-sized effect (d = 0.68, 95% CI = 0.53-0.83, p < 0.001).

    “Our analysis reveals that opt-out defaults lead to greater uptake of the preselected decision than opt-in defaults (d = 0.68, 95% confidence interval [CI] = 0.53-0.83],p < 0.001), producing a medium-sized effect given conventional criteria (Cohen, 1988).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    Ease does not seem to be systematically manipulated in studies, but is often varied in real-world applications.

    “In particular, ease does not seem to be systematically manipulated in studies, but is often varied in realworld applications.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In the base model (without moderators), I2 is 92.85%; when adding study characteristics, I2 is 90.47%; and when additionally entering the default channel coding, I2 is 89.12%.

    “In the base model (without moderators), we find an I 2 of 92.85%; when adding study characteristics, we find an I 2 of 90.47%; and when additionally entering the default channel coding, we find an I 2 of 89.12%.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisereasoning

    Choice architects currently do not know how effective they can expect an implementation of a default to be, nor which factors in the design decisions may systematically alter how influential the application of a default may be.

    “In fact, choice architects currently do not know how effective they can expect an implementation of a default to be, nor which factors in the design decisions may systematically alter how influential the application of a default may be.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Organ donation defaults can be very simple, even consisting of the one-word difference between opt-in and opt-out.

    “Organ donation defaults can be very simple, even consisting of the one-word difference between”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Johnson & Goldstein, 2003

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Defaults are more effective in consumer domains and the default effect may be weaker in environmental domains.

    “Our analysis suggests that defaults are more effective in consumer domains and that the default effect may be weaker in environmental domains.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Choice architects commonly do not have access to information on how effective a tool is and are often inaccurate in their estimations of the default effect.

    “because choice architects commonly do not have access to this information and are often inaccurate in their estimations of the default effect (Zlatev et al., 2017)”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Zlatev et al., 2017

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The results did not meaningfully change when using robust variance estimation.

    “However, the results of the analyses did not meaningfully change when using this estimation.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    Under robust variance estimation, the endowment channel and environmental domain drop to marginal significance; all other results hold.

    “we find that in this analysis the endowment channel (b = 0.31, SE = 0.16, p = 0.081) and the environmental domain (b = -0.60,SE = 0.28, p = 0.056) drop to marginal significance; all other results hold in this specification.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Both the context in which a default is used and whether the default's design triggers its underlying channels partially explain the variability in the default effect.

    “That is, both the context in which a default is used and whether the default's design triggers its underlying channels partially explain the variability in the default effect.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: The coders can accurately identify which channel is activated in each study.

  • May combine two claims.

    Interpretationown data

    A choice architect who deploys a default may have difficulty estimating the effect size he or she can expect from an implementation of defaults; this effect size may be substantially lower or higher than the meta-analytic average.

    “In addition, our analysis also reveals that there is substantial variation in the effectiveness of defaults, which indicates that a choice architect who deploys a default may have difficulty estimating the effect size he or she can expect from an implementation of defaults; this effect size may be substantially lower or higher than the meta-analytic average.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    Decision-makers are more likely to choose the pre-selected option because they evaluate other options in reference to the pre-selected option with which they are already endowed.

    “(3) they will evaluate other options in reference to the pre-selected option with which they are already endowed (Dinner et al., 2011).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Dinner et al., 2011

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    Decision-makers are more likely to choose the pre-selected option because they can exert less effort when staying with it.

    “(2) they can exert less effort when staying with the pre-selected option”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Dinner et al., 2011

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductown data

    Two coders not part of the author team were trained to rate each default study on the extent to which its design likely triggered endorsement, ease, and endowment.

    “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 (endorsement, ease and endowment; see Cadario &Chandon, 2018, andJachimowicz, Wihler et al., 2018, for similar approaches).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: 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.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Design choices that may have come about inadvertently and may seem inconsequential can have substantial consequences for the size of the default effect.

    “For policy-makers, our findings contain an important lesson: design choices that may have come about inadvertently and may seem inconsequential can have substantial consequences for the size of the default effect.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: The coders can accurately identify which channel is activated in each study.

  • Probably not a substantial claim: may not pass S1 (asserted).

    Premisecitation

    Decision-makers are more likely to choose the pre-selected option because they believe the choice architect's intentions are beneficial to them.

    “(1) they believe that the intentions of the choice architect, as suggested through the choice design, are beneficial to them”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Dinner et al., 2011

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Three channels for defaults' effectiveness have been identified in the prior literature, and the effect of any given default in a study may happen through three, two, one or none of these channels.

    “Three channels for defaults' effectiveness have been identified in the prior literature, and the effect of any given default in a study may happen through three, two, one or none of these channels.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Dinner et al., 2011

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data

    Interrater reliability was acceptable for endorsement (κ = 0.59), ease (κ = 0.58), and endowment (κ = 0.80).

    “Interrater reliability, calculated via Cohen's κ, was acceptable for endorsement (κ = 0.59), ease (κ = 0.58) and endowment (κ = 0.80; Landis & Koch, 1977).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    Defaults vary in their effectiveness, and setting a default may not always be the most suitable intervention.

    “However, defaults vary in their effectiveness, and setting a default may not always be the most suitable intervention.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Defaults influence decisions through three psychological channels-endorsement, ease, and endowment-that can play a role individually and in parallel.

    “we draw on the framework developed by Dinner et al. (2011), who propose that defaults influence decisions through three psychological channelsendorsement, ease, and endowmentthat can play a role both individually and in parallel”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Dinner et al., 2011

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    There is no evidence for multicollinearity among the independent variables; correlations were small to moderate and most were nonsignificant.

    “we test for multicollinearity by examining the correlation matrix of the independent variables and do not find evidence for multicollinearity, as all correlations were small to moderate, and most were nonsignificant.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    The default effect found in this meta-analysis is considerably larger than the effects found in meta-analyses of healthy eating nudges (d=0.23), Opower's descriptive social norm intervention on energy savings (d=0.32), and framing effects on risky choice (d=0.31).

    “we note that the effect for defaults we find in the current meta-analysis is considerably larger than a recent meta-analysis evaluation of healthy eating nudges (including plate size changes or nutrition labels, d = 0.23; Cadario & Chandon, 2018), a recent meta-analysis of the effect of Opower's descriptive social norm intervention on energy savings (d = 0.32; Jachimowicz, Hauser et al., 2018), as”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Cadario & Chandon, 2018

      Not checked in the free run.

    • cites Jachimowicz, Hauser et al., 2018

      Not checked in the free run.

    • cites Kühberger, 1998

      Not checked in the free run.

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

Cites

Flagged first, then by citations

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

Cited by

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Related

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 · 2018: 6 · 2017: 3
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 › Safety Research › 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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