Submission status
Accepted
Submission Editor
Noah Carl
Title
Honesty, Intelligence, and Race
Abstract
Research shows that honesty correlates positively with intelligence. Similarly, there are racial differences in honesty, with Europeans being more honest than various other ethnic groups. It is currently unknown to what degree race differences in intelligence can explain the differences in honesty. We investigated this question using data from the National Longitudinal Study of Youth 1997 (NLSY97), a large American longitudinal dataset. We replicate prior findings that honesty correlated with measures of intelligence (r = .38, 95% CI [.34, .41]) and that Blacks (d = -0.67, 95% CI [-.76, -.59]) and Hispanics (d = -0.4, 95% CI [-.50, -.31]) are less honest than Whites, and this holds whether honesty is measured by self-reports, interviewer-reports or by parent-reports. In addition, race differences in honesty remained between Blacks and Whites but not between Whites and Hispanics after controlling for intelligence.
Differences between Blacks and Whites but not Whites and Hispanics were noticeably lower in self-reports (Blacks: d = -0.18 [-0.24, -0.11], Hispanics: d = -0.24 [-0.31, -0.17]) than parent-reports (Blacks: d = -0.43 [-0.52, -0.35], Hispanics: -0.24 [-0.33, -0.15]) and interviewer-reports (Blacks: d = -0.7 [-0.64, -0.75], Hispanics: -0.3 [-0.25, -0.36]).
Cross-national comparisons were made using national IQ data and Hofstede’s cultural dimensions. Bayesian model averaging suggests that Hofstede’s individualism dimension (β = .64, PIP = 100%), national IQs (β = .25, PIP = 73.6%), and masculinity (β = -.35, PIP = 100%) predict differences in honesty between countries. Parking violations per diplomat were only predicted by national IQs (r = -0.28, p < .001), given that no other variable reached a posterior inclusion probability above 0% besides national IQs. Implications and theories concerning these findings are discussed.
Keywords
intelligence,
IQ,
race,
Ethnicity,
black,
honesty,
white,
hispanic
Supplemental materials link
https://osf.io/vw4sa/
Reviewer 1: Accept
Reviewer 2: Accept
Public Note
Changes: -Interviewer-reported honesty included as well -Graphs improved -SAM models used instead of SEM models -Bayesian model averaging used instead of regression to determine best predictors of honesty and parking violations between countries