Socioeconomic Status Modifies Heritability of IQ in Young Children
Scores on the Wechsler Intelligence Scale for Children were analyzed in a sample of 7-year-old twins from the National Collaborative Perinatal Project. A substantial proportion of the twins were raised in families living near or below the poverty level. Biometric analyses were conducted using models allowing for components attributable to the additive effects of genotype, shared environment, and nonshared environment to interact with socioeconomic status (SES) measured as a continuous variable. Results demonstrate that the proportions of IQ variance attributable to genes and environment vary nonlinearly with SES. The models suggest that in impoverished families, 60% of the variance in IQ is accounted for by the shared environment, and the contribution of genes is close to zero; in affluent families, the result is almost exactly the reverse.
VOL. 14, NO. 6, NOVEMBER 2003 Copyright © 2003 American Psychological Society
SOCIOECONOMIC STATUS MODIFIES HERITABILITY OF IQ
IN YOUNG CHILDREN
Eric Turkheimer, Andreana Haley, Mary Waldron, Brian D’Onofrio,
and Irving I. Gottesman
University of Virginia
Scores on the Wechsler Intelligence Scale for Children
were analyzed in a sample of 7-year-old twins from the National Col-
laborative Perinatal Project. A substantial proportion of the twins were
raised in families living near or below the poverty level. Biometric
analyses were conducted using models allowing for components attrib-
utable to the additive effects of genotype, shared environment, and non-
shared environment to interact with socioeconomic status (SES) measured
as a continuous variable. Results demonstrate that the proportions of IQ
variance attributable to genes and environment vary nonlinearly with
SES. The models suggest that in impoverished families, 60% of the vari-
ance in IQ is accounted for by the shared environment, and the contri-
bution of genes is close to zero; in afﬂuent families, the result is almost
exactly the reverse.
Although the heritability of cognitive ability in childhood is well
established (McGue, Bouchard, Iacono, & Lykken, 1993; Plomin, 1999),
the magnitude, mechanisms, and implications of the heritability of IQ
remain unresolved. Historically, the most controversial question sur-
rounding the heritability of intelligence is whether genetic effects on IQ
place serious constraints on the effectiveness of efforts to raise IQ, either
by improving impoverished socioeconomic conditions or by exposing
children to remedial educational programs such as Headstart (Herrn-
stein & Murray, 1994; Jensen, 1981). Adding to the controversy is an
apparent contradiction between studies using different methodologies
to study the development of cognitive abilities: Studies of correlations
among twins or adoptees and their biological and adoptive parents typ-
ically yield large genetic effects and relatively smaller effects of family
environment, whereas studies that compare the mean IQs of children
rescued from poverty with the IQs of their parents or impoverished sib-
lings often ﬁnd large differences that are attributed to the environment
One possible resolution of this paradox is that the effect of family
environment on cognitive ability could be nonlinear (Jensen, 1981; Scarr,
1981). If differences between impoverished environments and adequate
ones have large consequences for cognitive ability, but differences between
adequate and enriched environments do not, one would expect amelio-
ration of impoverished status to show a substantial effect, whereas corre-
lational ﬁndings based on middle-class family members in typical twin
and adoption studies would not. Unfortunately, genetically informed
empirical tests of this hypothesis have been rare, for two reasons. First,
few twin studies include children from highly impoverished backgrounds,
and because impoverished parents are generally unable to adopt, impover-
ished environments are systematically censored in adoption studies
(Stoolmiller, 1999). Second, until very recently, there have been severe re-
strictions on researchers’ ability to model interactions between a contin-
uous variable like socioeconomic status (SES) and latent genetic and
environmental inﬂuences on cognitive ability.
Nevertheless, several previous studies have addressed differential
heritability as a function of race, social class, or parental education. Scarr-
Salapatek (1971) obtained a sample of twins from the Philadelphia school
system and used standardized test scores as a measure of ability. SES was
estimated from census-tract information. A major limitation of the study
was that it did not include zygosity information about the twin pairs;
instead, analyses were based on comparisons of same-sex pairs (that
combined monozygotic, or MZ, twins and dizygotic, or DZ, twins) and
opposite-sex pairs (all DZ twins), a method with considerably less sta-
tistical power than the classical twin design (Eaves & Jinks, 1972). For
both Black and White children, estimated heritabilities were lower in
children from families in lower-SES census tracts than in those from
middle- or higher-status tracts.
Scarr subsequently (1981, chap. II.4) obtained an independent sample
of 160 Black and 212 White twin pairs from the Philadelphia school sys-
tem. Unlike in the earlier study, SES was measured for each family, and
zygosity was determined by blood typing. The twins were administered a
battery of intelligence tests, including the Raven Standard Progressive
Matrices, Peabody Picture Vocabulary Test, Columbia Mental Maturity
Scale, and Benton Revised Visual Retention Test. MZ and DZ twin cor-
relations were computed separately for Black and White twin pairs and
for low- and high-SES groupings. Differences between MZ and DZ twin
correlations were consistently higher for the White pairs; no consistent
differences emerged when the twins were grouped by SES within race.
Fischbein (1980) studied a Swedish sample of 87 MZ and 126 DZ
pairs of 12-year-old schoolchildren. SES was estimated from parental
education and occupation and used to divide the twin sample into three
groups. Ability scores were obtained from a self-administered test that
included measures of verbal ability and inductive reasoning. For both
measures, heritabilities were highest in the high-SES group and lowest
in the low-SES group.
Rowe and his colleagues have undertaken several studies of differ-
ential heritability in large national data sets. Van den Oord and Rowe
(1997) used hierarchical linear models to study differential genetic
and environmental contributions to scores on the Peabody Individual
Achievement Test in a sample of siblings, half-siblings, and cousins drawn
from the National Longitudinal Survey of Youth. They reported only the
scantiest evidence of covariation between the magnitude of genetic and en-
vironmental variance components and measures of the home environment
in which the children were raised.
More recently, Rowe, Jacobson, and van den Oord (1999) analyzed
a large sample of twins (176 MZ pairs; 347 DZ pairs), siblings (795
pairs), half-siblings (269 pairs), cousins (118 pairs), and unrelated sib-
lings reared together (204 pairs) from the National Longitudinal Study
of Adolescent Health. Parental education was employed as an indica-
tor of SES, and ability was measured using an abridged version of the
Peabody Picture Vocabulary Test–Revised. The authors used DF analy-
sis (DeFries & Fulker, 1985; LaBuda & DeFries, 1990) to estimate the
Address correspondence to Eric Turkheimer, University of Virginia, P.O.
Box 400400, Charlottesville, VA 22904-4400; e-mail: email@example.com.
Socioeconomic Status and IQ in Twin Children
VOL. 14, NO. 6, NOVEMBER 2003
biometric parameters and their interaction with continuously mea-
sured education. Results showed that both heritability and shared envi-
ronment interacted signiﬁcantly with education: Heritability increased
and effects of shared environment decreased as parental education
increased. Similar ﬁndings using DF analysis have been reported by
Thompson, Tiu, and Detterman (1999).
In the current study, we used data from the National Collaborative
Perinatal Project, which included a large national sample of American
mothers, who were enrolled into the study during pregnancy (
48,197), and their children (
59,397), who were followed from
birth until age 7 (Nichols & Chen, 1981). Participants were recruited
from 12 urban hospitals around the country and included a high pro-
portion of racial minorities and impoverished families. Extensive medical,
psychological, and socioeconomic data were obtained for the mothers
during pregnancy, and for the children at birth and at ages 8 months,
1 year, 4 years, and 7 years. The Wechsler Intelligence Scale for Children
(WISC) was administered at age 7. Verbal IQ (VIQ), Performance IQ
(PIQ), and Full-Scale IQ (FSIQ) scores were estimated from a slightly
reduced set of WISC subtests. Socioeconomic scores were obtained at
mother’s registration in the study and at the 7-year evaluation. These
scores were based on the 100-point system of Myrianthopoulos and French
(1968) and computed from a linear combination of parental education, oc-
cupational status, and income.
The sample included 623 twin births. Of these, 320 pairs with com-
plete data regarding IQ, SES, and zygosity remained at the 7-year fol-
low-up. Twins remaining in the sample at 7 years of age did not differ
from twins lost to the sample in terms of birth order, mother’s marital
status at birth, family SES at birth, race, or family income at birth. One
additional DZ pair was eliminated as an outlier, because of an 81-point
difference between the IQ scores of the twins; the twin with the lower
IQ was identiﬁed as brain damaged at birth. Of the remaining 319 pairs,
114 were monozygotic and 205 were dizygotic. Of the DZ pairs, 81
were same-sex pairs and 124 were opposite-sex pairs. There were no
signiﬁcant mean differences for any analysis variables between same-
and opposite-sex DZ pairs, and no differences in the twin correlations,
so the opposite-sex pairs were combined with the same-sex pairs in all
The twins were classiﬁed as 43% White, 54% Black, and 3% “other.”
The sample included a high proportion of impoverished families. The
median number of years of education of the head of household was be-
tween 10 and 11 years, and 25% of household heads were not edu-
cated past the ninth grade. The median occupation was “service worker”;
25% of the household heads received occupational ratings of “laborer” or
lower, including 14% with no occupation. The median family income
was between $6,000 and $7,000 annually, equivalent to $22,100 in 1997
dollars, the most recent year for which an equivalent scale is available.
Twenty-ﬁve percent of the families had incomes below the 1973 poverty
level for a family of four (U.S. Census Bureau, 2002).
In traditional biometric analyses of cognitive ability (Plomin, De-
Fries, McClearn, & McGufﬁn, 2000), variation in IQ is partitioned into
three independent components attributable to additive variation in ge-
notype (A), shared environment (C), and nonshared environment (E).
Our goal in the current analyses was to ﬁt a model in which the magni-
tude of the variance components themselves varied as a continuous
function of SES. That is, rather than modeling children’s IQ as a simple
Fig. 1. Path diagram of the biometric model including interaction of biometric parameters
with observed socioeconomic status (SES). A, C, and E are variances of additive genetic,
shared environmental, and nonshared environmental components, respectively, and have
been ﬁxed at 1.0; a, c, and e are the main effects of A, C, and E on IQ; and a, c, and e are
the interaction terms. The main effect of SES on IQ is denoted by s. The diamond containing
SES and the empty circle are the Mx convention for denoting a latent variable interaction.
E. Turkheimer et al.
VOL. 14, NO. 6, NOVEMBER 2003
linear function of A, C, and E, we allowed each of the three compo-
nents to interact linearly with observed SES, leading to the structural
are the main effects of A, C, and E, respectively,
represent the interaction of A, C, and E with SES.
coefﬁcient represents the main effect of SES on IQ. The model
is illustrated in Figure 1. The model was ﬁt using the Mx maximum
likelihood modeling program (Neale, Boker, Xie, & Maes, 1999). A
recent addition to Mx allows the user to place the value of an observed
variable on a path, as indicated by the diamond denoting SES in Fig-
ure 1. Application of standard path-tracing rules to the path model in
Figure 1 results in the structural equation speciﬁed by Equation 1. Mx
uses maximum likelihood estimation to ﬁt the parameters to the raw-
data matrix (i.e., the observed IQ and SES scores of the twins, as op-
posed to their covariance matrix). Fitting to the raw-data matrix is a re-
quirement for the interaction models we report, and results in degrees of
freedom equal to the total number of observations (the number of partic-
ipants times the number of variables per participant) minus the number
of estimated parameters (Neale et al., 1999). Fit indices are therefore on
a different scale from those based on traditional ﬁtting to covariance
Results of the model ﬁtting are given in Table 1. The parameters of
the main-effects model and the interaction model are provided for the
three tests (A, C, E, and main effect of SES for the main-effects model,
and these parameters plus A
, and E
interaction parameters for the
The role of sampling error in the results can be assessed in three
ways. With assumptions of normally distributed errors, the difference be-
tween the ﬁts of the two models can be interpreted as a chi-square with
three degrees of freedom arising from the addition of the three interac-
tion parameters (see Table 2). The probabilities associated with these chi-
square values assess the simultaneous contribution of the three interac-
tions to the model. They suggest that the interactions jointly contribute a
signiﬁcant improvement in ﬁt for FSIQ and PIQ, but not for VIQ.
A second method of assessing sampling error in our estimates is
with conﬁdence intervals on the individual main-effect and interaction
parameters in the interaction model. These conﬁdence intervals are
also reported in Table 1. Mx computes them empirically by reﬁtting the
model, moving estimates from their optimized values until the chi-square
ﬁt function is increased by the appropriate amount (e.g., a chi-square
change of 3.84 for a 95% conﬁdence interval on a single parameter). This
method has signiﬁcant advantages over computing standard errors based
on asymptotic distributional assumptions (Neale & Miller, 1997). The
conﬁdence intervals around most of the individual A
, and E
action parameters for FSIQ and PIQ exclude zero. The tests of individ-
ual parameters are not as robustly signiﬁcant as the simultaneous test
of all three of them, however, suggesting that models in which one of
, and E
interaction parameters equaled zero would ﬁt nearly
as well as a model with all three interactions.
Finally, sampling error can be assessed by the 95% conﬁdence in-
tervals around the results when the interaction is interpreted by com-
puting the A, C, and E variance components for different levels of SES.
We present these results next.
IQ sSES a a′SES+()Acc′SES+()Cee′SES+()E,+++=
The parameters in the main-effects model are interpreted as in any
other twin-based model: The sum of their squares is equal to the phe-
notypic variance of the IQ score. The main-effect parameters in the in-
teraction model are the estimated values of A, C, and E when SES
equals zero, that is, in the most impoverished families. The interaction
Results of model ﬁtting, showing path coefﬁcients
(rows labeled A, C, E, A
A 10.41 2.33
C 11.66 16.83 11.43 21.87
E 9.14 13.32 10.92 15.68
SES 0.36 0.36 0.28 0.44
0.16 0.05 0.29
A 7.16 2.69
C 8.56 12.03 7.48 16.32
E 6.97 7.42 5.66 9.32
SES 0.26 0.26 0.2 0.31
C 9.41 11.97 7.69 15.89
E 9.48 12.11 10.21 14.04
SES 0.22 0.22 0.16 0.28
0.16 0.01 0.27
Additive variation in genotype, shared environment, and
nonshared environment is denoted by A, C, and E, respectively. Their
interactions with socioeconomic status (SES) are denoted by A
Full-Scale IQ; VIQ
Verbal IQ; PIQ
Chi-square ﬁts of the main-effects and interaction
models and tests of the difference between the models
Full-Scale IQ 8,236.4 8,220.6 15.8 .001
Verbal IQ 7,841.2 7,837.5 3.7 .3
Performance IQ 8,014.5 8,005.8 8.7 .034
Socioeconomic Status and IQ in Twin Children
VOL. 14, NO. 6, NOVEMBER 2003
parameters are difﬁcult to interpret numerically because of the com-
plex scaling issues introduced by the interactions with observed SES,
measured on a scale from 0 to 100. The interactions are easiest to un-
derstand graphically, by plotting the variance in IQ accounted for by
A, C, and E for different observed values of SES. The variance of each
component (A, C, and E) of the phenotypic IQ variance is equal to the
square of that component in Equation 1. For example, the IQ variance
accounted for by additive genetic effects is given by
presents the results of this analysis for FSIQ, VIQ, and PIQ.
The ﬁgure shows 95% conﬁdence intervals computed on the three vari-
ance components at intervals of SES ranging from 0 to 100. Note that
the conﬁdence intervals exclude a solution in which the individual vari-
ance components are constant across SES for A and E on FSIQ and for
E on PIQ.
The percentage of the total IQ variance accounted for by each com-
ponent (A, C, and E) is the ratio of the variance accounted for by that
component to the total variance. For example, the percentage of IQ
variability accounted for by genetic effects (the broad heritability) is
shows the FSIQ variance accounted for by the three com-
ponents, with 95% conﬁdence intervals. In the most impoverished fam-
ilies, the modeled heritability of FSIQ is essentially 0, and C accounts
for almost 60% of the variability; in the most afﬂuent families, virtually
all of the modeled variability in IQ is attributable to A.
It is also informative to analyze the data more traditionally. We
dichotomized the pairs into those above the median SES and those
below the median SES and used random-effects analysis of variance
(Guo & Wang, 2002) to estimate the intraclass correlations for MZ
and DZ twins in the two SES groups. In the low-SES group, the intra-
class correlation was .63 for DZ twins and .68 for MZ twins, consis-
of .10 and
of .58; for the high-SES group, the DZ twin
Fig. 2. Magnitude of the variance components A, C, and E for Full-Scale IQ (FSIQ), Verbal IQ (VIQ), and Performance IQ (PIQ) plotted as a
function of observed socioeconomic status (SES). Shading indicates 95% conﬁdence intervals.
E. Turkheimer et al.
VOL. 14, NO. 6, NOVEMBER 2003
correlation was .51 and the MZ twin correlation was .87, consistent
of .72 and
These ﬁndings suggest that a model in which variability in intelli-
gence among children is partitioned into independent components
attributable to genes and environments is too simple for the dynamic
interaction of genes and real-world environments during development.
The relative importance of environmental differences in causing dif-
ferences in observed intelligence appears to vary with the SES of the
homes in which children were raised. SES is a complex variable, how-
ever, and the substantive interpretation to be placed on our results de-
pends on an interpretation of what SES actually measures.
The most obvious interpretation of SES in this study is that it mea-
sured the quality of the environment in which the children were born
and raised. Indeed, this is the function for which SES was intended. Un-
der this interpretation, the observed interaction between SES and the bi-
ometric components of IQ could be indicative of precisely the kind of
nonlinear relationship between rearing environment and intelligence
that has been suggested by Scarr (1981) and Jensen (1981), with differ-
ences among poor environments contributing more to differences in
phenotypic outcome than differences among middle-class or better envi-
It would be naive, however, to interpret SES strictly as an environ-
mental variable. Most variables traditionally thought of as markers
of environmental quality also reﬂect genetic variability (Plomin &
Bergeman, 1991). Children reared in low-SES households, therefore,
may differ from more afﬂuent children both environmentally and ge-
netically (Gottesman, 1968), and the models we employed in this
study do not allow us to determine which aspect of SES is responsible
for the interactions we observed. Indeed, it will be difﬁcult to separate
the genetic and environmental aspects of SES or other measures of the
family environment in research designs of this kind, because children
raised in the same home necessarily have the same SES.
Genetic variability in SES might also introduce a complication to
the models themselves. Phenotypic SES and IQ are correlated, and that
correlation is potentially mediated both genetically and environmen-
tally. Therefore, the models are attempting to detect an interaction be-
tween genotype and environment in the presence of a correlation between
genotype and environment, raising the concern that the presence of the
correlation might introduce bias into the estimation of the interaction.
However, Purcell (2003) has conducted an exhaustive series of simula-
tions that suggest no bias is introduced, as long as the main effect of
the moderating variable is included in the model, as we have done
here. The presence in the model of the main effect of SES means that
the biometric model ﬁtting is actually being conducted on the portion
of IQ that is independent of both the genetic and environmental com-
ponents of SES. (We note, however, that omitting the main effect from
the model did not change the results to a signiﬁcant degree.)
The developmental mechanisms underlying the effect remain un-
clear. Although the models indicate that the A
, and E
jointly contributed signiﬁcant variance to differences in FSIQ and
PIQ, the models were less able to distinguish which of the individual
interactions with A, C, and E was most important in the effect. The in-
teraction could be mediated primarily along genetic pathways, mean-
ing that genetic differences among individuals are accentuated in favorable
environments, as has been theorized by Bronfenbrenner (Bronfenbrenner
& Ceci, 1994). It could also be that the slope of the IQ
function is steeper at low levels of environment, as suggested by Scarr
(1981) and Jensen (1981). Or maybe outcome simply becomes less pre-
dictable in poor environments, leading to an increase in E variability,
as we have suggested (Turkheimer & Waldron, 2000) based on other
evidence. To resolve this issue, it will be most important to study large
Fig. 3. Proportion of total Full-Scale IQ variance accounted for by A, C, and E plotted as a function of observed socioeconomic status (SES).
Shading indicates 95% conﬁdence intervals.
Socioeconomic Status and IQ in Twin Children
VOL. 14, NO. 6, NOVEMBER 2003
samples, which will provide greater power to discriminate differences
among the genetic and environmental interactions.
It should also be noted that we found the interaction for FSIQ and
PIQ only; the interaction was in the predicted direction for VIQ but
did not approach statistical signiﬁcance. Specifying more precisely the
kinds of abilities for which the interaction is likely to occur is the cur-
rent focus of investigation in our laboratory. In the National Collabo-
rative Perinatal Project, scores are available for the individual subtests
of the WISC, and for a variety of other ability and achievement mea-
sures. We are developing models of interactions between SES and ge-
netic and environmental variance in multivariate analyses of factor
scores based on correlations among individual tests.
In the fractious history of scientiﬁc investigations of the heritability
of intelligence, the effects of poverty, and the relations between them,
there has been only one contention with which everyone could agree: Ad-
ditive models of linear and independent contributions of genes and envi-
ronment to variation in intelligence cannot do justice to the complexity of
the development of intelligence in children. Only recently have statis-
tical models and computational capacity advanced to the point that
less simplistic models can actually be ﬁt. Although there is much that
remains to be understood, our study and the ones that have preceded it
have begun to converge on the hypothesis that the developmental forces
at work in poor environments are qualitatively different from those at
work in adequate ones. Clariﬁcation of the nature of these differences
promises to be a fascinating, and hopefully unifying, subject for future
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