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 accessAbstract
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.
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In the paper
“No other study characteristic explained any further systematic variance in the variability of default effects present in prior studies.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Results: study characteristics
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In the paper
“Reviewing the funnel plots highlights that several observations appear outside of the funnel on both sides, suggesting potential publication bias (Duval & Tweedie, 2000).”
§ When and why defaults influence decisions: a meta-analysis of default effects > Publication bias
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In the paper
“Including study characteristics as moderators reduces the heterogeneity by 4.67% to I 2 = 93.54%.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Results: study characteristics
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In the paper
“while the regression coefficient for environmental domains is marginally significant and negative (b = -0.47,SE = 0.27, p = 0.08).”
§ When and why defaults influence decisions: a meta-analysis of default effects > Results: study characteristics
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In the paper
“While our analysis reveals a considerable influence of defaults (d = 0.68, 95% confidence interval = 0.53-0.83)”
§ When and why defaults influence decisions: a meta-analysis of default effects > Abstract
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In the paper
“When people make decisions with a pre-selected choice optiona 'default'they are more likely to select that option.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Abstract
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In the paper
“only the regression coefficient for consumer domains (b = 0.73, SE = 0.23, p = 0.003; see Table 1) is statistically significant and positive”
§ When and why defaults influence decisions: a meta-analysis of default effects > Results: study characteristics
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In the paper
“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).”
§ When and why defaults influence decisions: a meta-analysis of default effects > Abstract
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In the paper
“We again find that opt-out defaults lead to significantly greater uptake of the pre-selected decision than opt-in defaults (V + = 0.29, 95% CI = 0.21-0.37],p < 0.001) by an absolute average of 27.24%.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Binary studies
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In the paper
“Contrary to our prediction, ease is not a significant moderator (b = -0.05,SE = 0.15, p = 0.75).”
§ When and why defaults influence decisions: a meta-analysis of default effects > Results: default channels
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In the paper
“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.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Publication bias
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In the paper
“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.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Abstract
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In the paper
“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.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Publication bias
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In the paper
“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).”
§ When and why defaults influence decisions: a meta-analysis of default effects > Why do default effects vary?
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“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”
§ When and why defaults influence decisions: a meta-analysis of default effects > Why do default effects vary?
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In the paper
“First, we find that defaults in consumer domains are more effective and in environmental domains are less effective.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Abstract
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“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.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Why do default effects vary?
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In the paper
“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).”
§ When and why defaults influence decisions: a meta-analysis of default effects > Results: effect size
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“In particular, ease does not seem to be systematically manipulated in studies, but is often varied in realworld applications.”
§ When and why defaults influence decisions: a meta-analysis of default effects > General discussion
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In the paper
“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%.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Ease
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“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.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Introduction
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“Organ donation defaults can be very simple, even consisting of the one-word difference between”
§ When and why defaults influence decisions: a meta-analysis of default effects > Introduction
What the sources say
- Not checked yetcites Johnson & Goldstein, 2003
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our analysis suggests that defaults are more effective in consumer domains and that the default effect may be weaker in environmental domains.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Results: study characteristics
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In the paper
“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)”
§ When and why defaults influence decisions: a meta-analysis of default effects > Introduction
What the sources say
- Not checked yetcites Zlatev et al., 2017
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In the paper
“However, the results of the analyses did not meaningfully change when using this estimation.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Results: study characteristics
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In the paper
“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.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Results: default channels
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Conclusion
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- The authors state this as an assumption: The coders can accurately identify which channel is activated in each study.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ When and why defaults influence decisions: a meta-analysis of default effects > General discussion
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In the paper
“(3) they will evaluate other options in reference to the pre-selected option with which they are already endowed (Dinner et al., 2011).”
§ When and why defaults influence decisions: a meta-analysis of default effects > Do different channels explain variation in default effect size?
What the sources say
- Not checked yetcites Dinner et al., 2011
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In the paper
“(2) they can exert less effort when staying with the pre-selected option”
§ When and why defaults influence decisions: a meta-analysis of default effects > Do different channels explain variation in default effect size?
What the sources say
- Not checked yetcites Dinner et al., 2011
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In the paper
“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).”
§ When and why defaults influence decisions: a meta-analysis of default effects > Coding the activation of the three default channels
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- 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.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ When and why defaults influence decisions: a meta-analysis of default effects > General discussion
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- The authors state this as an assumption: The coders can accurately identify which channel is activated in each study.
In the paper
“(1) they believe that the intentions of the choice architect, as suggested through the choice design, are beneficial to them”
§ When and why defaults influence decisions: a meta-analysis of default effects > Do different channels explain variation in default effect size?
What the sources say
- Not checked yetcites Dinner et al., 2011
Tests✗ Asserted: fails✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. The authors may not put it forward as true.
In the paper
“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.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Additional information
What the sources say
- Not checked yetcites Dinner et al., 2011
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ When and why defaults influence decisions: a meta-analysis of default effects > Coding the activation of the three default channels
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, defaults vary in their effectiveness, and setting a default may not always be the most suitable intervention.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Limitations and future directions
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In the paper
“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”
§ When and why defaults influence decisions: a meta-analysis of default effects > Do different channels explain variation in default effect size?
What the sources say
- Not checked yetcites Dinner et al., 2011
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ When and why defaults influence decisions: a meta-analysis of default effects > Results: default channels
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None of the sources cited for this claim have been checked yet.
In the paper
“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”
§ When and why defaults influence decisions: a meta-analysis of default effects > General discussion
What the sources say
- Not checked yetcites Cadario & Chandon, 2018
- Not checked yetcites Jachimowicz, Hauser et al., 2018
- Not checked yetcites Kühberger, 1998
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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.”
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?
In the citing paper
“When occupying the role of an editor, the presentation of a default body of text for editing restricts autonomy because people are unconsciously influenced by default effects15,46.”
From Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence (2024) · cites this paper as [46]
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”
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
MinorThe 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.
“the 58 default studies (pooled n = 73,675)”
“58 observations from 55 studies in 35 articles”
Citations
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Measuring inconsistency in meta-analyses
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003) · BMJ · doi:10.1136/bmj.327.7414.557 · Cited by 63,139
Bias in meta-analysis detected by a simple, graphical test
Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997) · BMJ · doi:10.1136/bmj.315.7109.629 · Cited by 56,722
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
Quantifying heterogeneity in a meta‐analysis
Higgins, J. P. T., & Thompson, S. G. (2002) · Statistics in Medicine · doi:10.1002/sim.1186 · Cited by 36,763
Conducting Meta-Analyses in R with the metafor Package
Viechtbauer, W. (2010) · Journal of Statistical Software · doi:10.18637/jss.v036.i03 · Cited by 20,464
Duval, S., & Tweedie, R. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.00455.x · Cited by 14,099
Nudge: improving decisions about health, wealth, and happiness
(2008) · Choice Reviews Online · doi:10.5860/choice.46-0977 · Cited by 11,624
(2000) · Cited by 9,198
The Power of Suggestion: Inertia in 401(k) Participation and Savings Behavior
Madrian, B. C., & Shea, D. F. (2001) · The Quarterly Journal of Economics · doi:10.1162/003355301753265543 · Cited by 3,119
Flagged first, then by citations
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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.
- 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
- 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
- hybrid
- doi.org
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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