Cited by 362Cognitive Abilities and TestingIntergenerational and Educational Inequality StudiesGenetic Associations and Epidemiology

The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index; 13 of the 26 sources its claims cite were checked for support.

Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence

Open access
Tucker-Drob, Elliot M., Bates, Timothy C.·2016·Psychological Science

Abstract

A core hypothesis in developmental theory predicts that genetic influences on intelligence and academic achievement are suppressed under conditions of socioeconomic privation and more fully realized under conditions of socioeconomic advantage: a Gene × Childhood Socioeconomic Status (SES) interaction. Tests of this hypothesis have produced apparently inconsistent results. We performed a meta-analysis of tests of Gene × SES interaction on intelligence and academic-achievement test scores, allowing for stratification by nation (United States vs. non–United States), and we conducted rigorous tests for publication bias and between-studies heterogeneity. In U.S. studies, we found clear support for moderately sized Gene × SES effects. In studies from Western Europe and Australia, where social policies ensure more uniform access to high-quality education and health care, Gene × SES effects were zero or reversed.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This meta-analysis finds a positive gene × socioeconomic status (G × SES) interaction on intelligence in US studies but no such effect—or a reversed effect—in Western Europe and Australia. The cross-national difference is statistically significant and not explained by publication bias.

Key findings

  • In US studies, the G × SES interaction was significant and positive (a′ = .074, SE = .020, p < .0005); in non-US studies from Western Europe and Australia, it was trivial and non-significant (a′ = −.027, SE = .022, p = .223). The difference between US and non-US effects was significant (b = .101, SE = .032, p = .001).
  • The unconditional meta-analytic mean G × SES effect was non-significant (a′ = .029, SE = .019, p = .136), with significant heterogeneity (random-effect SD = .048, SE = .019, p = .011). Excluding Netherlands studies made non-US effects essentially zero (a′ = −.001, SE = .033, p = .979).
  • In the US model, genetic variance in intelligence increased from .24 at 2 SD below mean SES to .61 at 2 SD above mean SES, corresponding to heritability estimates of 26% and 61%, respectively. Shared and non-shared environmental variance decreased with SES, but these interactions were not statistically significant.
  • Publication-bias checks found no evidence of bias: funnel asymmetry tests were non-significant, and p-curve analysis indicated evidential value for US studies (z = −3.427, p < .001) but not for non-US studies (z = −1.264, p = .103). A power simulation estimated that 3,300 twin pairs are needed for 80% power based on the meta-analytic US estimate, versus 500 based on Turkheimer et al. (2003).

Methods

  • The authors meta-analyzed published and unpublished G × SES studies on objective intelligence/achievement measures, requiring sibling/twin genetic relatedness, known zygosity, childhood family SES as moderator, and no selection on diagnoses or test scores. The final dataset included k = 43 effect sizes from K = 14 independent studies and N = 24,926 twin/sibling pairs (approximately 50,000 individuals); US samples contributed k = 18, K = 8, N = 10,831, and non-US samples k = 25, K = 6, N = 14,095.
  • To ensure comparable estimates, the authors reanalyzed raw data, obtained reanalyses from original authors, or reanalyzed published subgroup covariance matrices. They fitted Purcell’s (2002) biometric model of latent Gene × Measured Shared Environment interaction, with Z-transformed phenotypes and SES, extracting a, c, e, s, a′, c′, and e′ parameters.
  • Effect sizes were meta-analyzed using random-effects meta-regression in Mplus, allowing multiple effect sizes per sample, weighting by the reciprocal number of effect sizes and inverse sampling variance, and using a sandwich estimator to correct standard errors for non-independence. Moderator analyses and publication-bias/robustness checks were also conducted.

Limitations

  • The authors state they could not fully test whether the US G × SES effects were driven by greater racial and ethnic diversity in US samples, though they cite individual studies suggesting this is not the case. They also note that candidate mechanisms for national differences—such as educational quality, medical/educational access, social mobility, and income support—were not directly tested.
  • The non-US evidence is limited to Western Europe and Australia, so the “non-US” estimate may not generalize to other nations. The authors also excluded several notable studies for methodological reasons (e.g., Scarr-Salapatek, 1971; NLSY kinship data; Nagoshi & Johnson, 2005; Friend et al.; Prescott et al.).
  • Evident from the text: the meta-analysis cannot establish causal environmental mechanisms, and residual non-US heterogeneity was driven by Netherlands studies, meaning the non-US summary may mask country-level variation. Publication-bias tests reduce but cannot entirely rule out bias.

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

Integrity signals

Registry

Crossref · 10.1177/0956797615612727

Cites

50 of 50 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

51 Own result · 8 Own interpretation · 5 Partly supported · 4 Sources not found · 4 No full text of the sources · 4 Own method · 3 Supported · 3 No basis given · accuracy not yet measured

Show all 58 claims

  • Probably not a substantial claim. 6
  • It may state two separate claims as one. 20
  • The quote only partly states this claim. 2
24 of 24
  • The authors state this as an assumption.

    A continuous normal distribution underlies ordered categorical SES distributions.

    “The assumption that a continuous normal distribution underlies ordered categorical SES distributions was considered reasonable, because the studies that did employ continuous measures of SES typically transformed their SES measure to normality if it was not already approximately normally distributed.”
1 of 1

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.

  • Not confirmedz = 1.253reported p = .895 · recomputed p = .210

    “(z = 1.253, p = .895”

  • Not confirmedz = 3.427reported p = 1 · recomputed p < .001

    “(z = 3.427, p = 1.000”

  • Not confirmedz = -1.264reported p = .103 · recomputed p = .206

    “(z = −1.264, p = .103”

  • Not confirmedz = 0.122reported p = .548 · recomputed p = .903

    “(z = .122, p = .548”

  • Not confirmedz = 1.264reported p = .897 · recomputed p = .206

    “(z = 1.264, p = .897”

  • Consistentz = -3.427reported p < .001 · recomputed p < .001

    “(z = −3.427, p < .001”

6 of 6

Self-check

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

Self-check results appear here once an independent check has confirmed them.

Citations

Cites

Show all 48 references

Research works printed but not linked: 4

  1. Jacobson K. C., 2012, Behavior Genetics, 42, 941
  2. Muthén L. K., 2012, Mplus user’s guide
  3. Prescott C. A., 2014, Poster presented at the annual meeting of the Behavior Genetics Association
  4. Soden-Hensler B. (2012). An examination of Gene × Socioeconomic Status interactions for reading achievement (Doctoral dissertation, Florida State University). Retrieved from http://diginole.lib.fsu.edu/cgi/viewcontent.cgi?article=6923&context=etd

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Metadata

Authors
Tucker-Drob, E. M., & Bates, T. C.
Affiliations
  • Department of Psychology, University of Texas at Austin
  • University of Edinburgh (GB)
Venue
Psychological Science
Published
2015-12-15
Volume
27
Issue
2
Pages
138-149
ISSN
  • 0956-7976 (ISSN-L)
  • 1467-9280
OpenAlex
W2197899923
Cited by
359
References
48
Citations by year
2026: 26 · 2025: 28 · 2024: 46 · 2023: 28 · 2022: 45 · 2021: 40 · 2020: 29 · 2019: 39 · 2018: 31 · 2017: 30 · 2016: 14 · 2015: 1
FWCI
38.23
Type
journal article
Language
en
Open access
Locations
  • Psychological Science (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Russell Sage Foundation
  • National Institutes of Health (5p2chd042849-22)
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (# P2CHD042849, R21 HD081437, R24 HD042849, P01HD031921)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
  • Social Sciences › Sociology and Political Science › Intergenerational and Educational Inequality Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
intelligence, academic performance, genetic influences on intelligence, cross-national differences, publication bias, study heterogeneity, educational inequality, Western Europe, Australia
MeSH terms
Educational Measurement, Humans, Intelligence, Models, Genetic, Models, Psychological, Social Class, United States, Gene-Environment Interaction

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