{"id":70669,"date":"2015-07-07T11:55:30","date_gmt":"2015-07-07T19:55:30","guid":{"rendered":"http:\/\/www.lukeford.net\/blog\/?p=70669"},"modified":"2015-07-08T12:00:00","modified_gmt":"2015-07-08T20:00:00","slug":"the-atlantic-most-states-elect-no-black-prosecutors","status":"publish","type":"post","link":"https:\/\/lukeford.net\/blog\/?p=70669","title":{"rendered":"The Atlantic: Most States Elect No Black Prosecutors"},"content":{"rendered":"

Report<\/a>:<\/p>\n

\nThe overwhelming dominance of white men among district attorneys could have huge effects on charging, enforcement, and plea bargains.<\/p>\n

It\u2019s no mistake that the most enduring fictional prosecutors are white guys\u2014whether they\u2019re dignified older men like Jack McCoy or hotheaded, handsome younger ones like Dan Kaffee. Art doesn\u2019t always imitate life, but here it does. According to a new survey, an overwhelming portion of the elected officials ultimately responsible for charging criminals, deciding what sentences to seek, and determining whether to allow them to strike plea bargains are white men.<\/p>\n

How overwhelming? Here are a few of the numbers, according to a report on elected prosecutors commissioned by the Women Donors Network and conducted by the Center for Technology and Civic Life, a nonpartisan group that grew out of the progressive National Organizing Institute:<\/p>\n

95 percent of elected prosecutors are white;
\n79 percent are white men;
\nthree in five states have no black elected prosecutors;
\n14 states have no elected prosecutors of color at all*;
\njust 1 percent of elected prosecutors are minority women.
\nAs The New York Times, which first published the findings, noted, the media has focused intently on the racial composition of police forces over the last year or so, since Michael Brown was shot and killed by an officer in Ferguson, Missouri. That focus makes sense: Each incident of a black man being killed by police under questionable (at best) circumstances seems to be succeeded by another. While there are few good statistics on the number of fatal shootings by American police each year, the absence of black police officers can be seen and measured. <\/p><\/blockquote>\n

The article doesn\u2019t mention how many times Black candidates have run for elected prosecutor positions.<\/p>\n

In California, the attorney general, who is biracial (black and east Indian) identifies as black and in Los Angeles our district attorney, Jackie Lacey, is black.<\/p>\n

I wonder if there might be any criminal and cognitive differences between the races that might account for the lack of black prosecutors? I assume that being a convicted felon hampers your ability to be hired as a police officer and prosecutor. <\/p>\n

THIRTY YEARS OF RESEARCH ON RACE DIFFERENCES IN COGNITIVE ABILITY by J. Philippe Rushton and Arthur R. Jensen<\/a>:<\/p>\n

Throughout the history of psychology, no question has been so persistent or
\nso resistant to resolution as that of the relative roles of nature and nurture in
\ncausing individual and group differences in cognitive ability (Degler, 1991;
\nLoehlin, Lindzey, & Spuhler, 1975). The scientific debate goes back to the
\nmid-19th century (e.g., Galton, 1869; Nott & Glidden, 1854). Starting with the
\nwidespread use of standardized mental tests in World War I, average ethnic and
\nracial group differences were found. Especially vexing has been the cause(s) of
\nthe 15-point Black\u2013White IQ difference in the United States.
\nIn 1969, the Harvard Educational Review published Arthur Jensen\u2019s lengthy
\narticle, \u201cHow Much Can We Boost IQ and School Achievement?\u201d Jensen concluded
\nthat (a) IQ tests measure socially relevant general ability; (b) individual
\ndifferences in IQ have a high heritability, at least for the White populations of the
\nUnited States and Europe; (c) compensatory educational programs have proved
\ngenerally ineffective in raising the IQs or school achievement of individuals or
\ngroups; (d) because social mobility is linked to ability, social class differences in
\nIQ probably have an appreciable genetic component; and tentatively, but most
\ncontroversially, (e) the mean Black\u2013White group difference in IQ probably has
\nsome genetic component.
\nJensen\u2019s (1969) article was covered in Time, Newsweek, Life, U.S. News &
\nWorld Report, and New York Times Magazine. His conclusions, the theoretical
\nissues they raised, and the public policy recommendations that many saw as
\nstemming directly from them were dubbed \u201cJensenism,\u201d a term which entered the
\ndictionary. Since 1969, Jensen has continued to publish prolifically on all of these
\nissues, and increasing numbers of psychometricians and behavioral geneticists
\nhave come to agree with one or more of the tenets of Jensenism (Snyderman &
\nRothman, 1987, 1988).
\nThe Bell Curve (Herrnstein & Murray, 1994) presented general readers an
\nupdate of the evidence for the hereditarian position along with several policy
\nrecommendations and an original analysis of 11,878 youths (including 3,022
\nBlacks) from the 12-year National Longitudinal Survey of Youth. It found that
\nmost 17-year-olds with high scores on the Armed Forces Qualification Test,
\nregardless of ethnic background, went on to occupational success by their late 20s
\nand early 30s, whereas those with low scores were more inclined to welfare
\ndependency. The study also found that the average IQ for African Americans was
\nlower than those for Latino, White, Asian, and Jewish Americans (85, 89, 103,
\n106, and 113, respectively; Herrnstein & Murray, 1994, pp. 273\u2013278).
\nCurrently, the 1.1 standard deviation difference in average IQ between Blacks
\nand Whites in the United States is not in itself a matter of empirical dispute. A
\nmeta-analytic review by Roth, Bevier, Bobko, Switzer, and Tyler (2001) showed
\nit also holds for college and university application tests such as the Scholastic
\nAptitude Test (SAT; N \u0001 2.4 million) and the Graduate Record Examination
\n(GRE; N \u0001 2.3 million), as well as for tests for job applicants in corporate settings
\n(N \u0001 0.5 million) and in the military (N \u0001 0.4 million). Because test scores are
\nthe best predictor of economic success in Western society (Schmidt & Hunter,
\n1998), these group differences have important societal outcomes (R. A. Gordon,
\n1997; Gottfredson, 1997).
\nThe question that still remains is whether the cause of group differences in
\naverage IQ is purely social, economic, and cultural or whether genetic factors are
\nalso involved. Following publication of The Bell Curve, the American Psychological
\nAssociation (APA) established an 11-person Task Force (Neisser et al.,
\n1996) to evaluate the book\u2019s conclusions. Based on their review of twin and other
\nkinship studies, the Task Force for the most part agreed with Jensen\u2019s (1969)
\nHarvard Educational Review article and The Bell Curve, that within the White
\npopulation the heritability of IQ is \u201caround .75\u201d (p. 85). As to the cause of the
\nmean Black\u2013White group difference, however, the Task Force concluded: \u201cThere
\nis certainly no support for a genetic interpretation\u201d (p. 97).
\nAmong the factors contributing to the longstanding lack of resolution of this
\nimportant and controversial issue are the difficulty of the subject matter, the
\npolitical issues associated with it and the emotions they arouse, and the different
\nmeta-theoretical perspectives of the experimental and correlational methodologies.
\nCronbach (1957) referred to these conflicting approaches as the two \u201chalves\u201d
\nof psychology because researchers are predisposed to draw different conclusions
\ndepending on whether they adopt a \u201cmanipulations-lead-to-change\u201d or a \u201ccorrelations-find-stability\u201d
\nparadigm.
\nHere we review in detail the research that has accumulated since Jensen\u2019s
\n(1969) article and compare our findings with earlier reviews and evaluations such
\nas those by Loehlin et al. (1975), P. E. Vernon (1979), Herrnstein and Murray
\n(1994), the APA Task Force (Neisser et al., 1996), and Nisbett (1998). Facts in
\nthemselves typically do not answer scientific questions. For a question so complex
\nas the cause of the average Black\u2013White group difference in IQ, no one fact, one
\nstudy, nor indeed any single line of evidence, can hope to be determinative.
\nRather, resolving the issue requires examining several independent lines of
\nevidence to determine if, when taken together, they confirm or refute rival
\nhypotheses and research programs.
\nThe philosophy of science methodology used here is guided by the view that,
\njust as in individual studies the principal of aggregation holds that a set of
\nmeasurements provides a more reliable indicator than any single measure taken
\nfrom the set (Rushton, Brainerd, & Pressley, 1983), so in reviewing multiple lines
\nof evidence, making strong inferences from a number of contending hypotheses
\nis more efficacious than considering only one hypothesis at a time (Platt, 1964).
\nAlthough strong inference is the method of science, it has, more often than not,
\nbeen eschewed in this controversial debate.
\nThe final section of this article addresses the question of what these conclusions
\nimply for policy, specifically for the issues of educational and psychological
\ntesting, health, race relations, and conflicting worldviews about the essence of
\nhuman nature. It suggests that the distributional model that takes genetic factors
\ninto account must temper the discrimination model that explains Black\u2013White
\ndifferences in socially valued outcomes.
\nSection 2: The Two Conflicting Research Programs
\nHere, we review the research on Black\u2013White difference in average IQ
\npublished since Jensen\u2019s (1969) now 36-year-old article. We then apply the
\nphilosophy of science methodologies of Platt (1964), Lakatos (1970, 1978), and
\nUrbach (1974a, 1974b) to determine if the preponderance of this new evidence
\nstrengthens or weakens Jensen\u2019s (1969) tentative assertion that it is more likely
\nthan not that some part of the cause of the mean Black\u2013White difference is
\ngenetic. The data reviewed have been collated from articles in specialist journals
\nand a number of scholarly monographs on the nature of intelligence, behavioral
\ngenetics, and social policy issues, as well as recent book-length reviews (Devlin,
\nFeinberg, Resnick, & Roeder, 1997; Herrnstein & Murray, 1994; Jencks &
\nPhillips, 1998; Jensen, 1998b; Lynn & Vanhanen, 2002; Rushton, 2000; Sternberg,
\n2000). While we focus on the mean Black\u2013White difference in IQ because
\nit is the topic on which most of the research to date has been conducted, studies
\nof other traits (e.g., reaction times) and other groups (e.g., East Asians) are
\nincluded when those data are sufficient and informative.
\nSome have argued that the cause of Black\u2013White differences in IQ is a pseudo
\nquestion because \u201crace\u201d and \u201cIQ\u201d are arbitrary social constructions (Tate &
\nAudette, 2001). However, we believe these constructs are meaningful because the
\nempirical findings documented in this article have been confirmed across cultures
\nand methodologies for decades. The fuzziness of racial definitions does not negate
\ntheir utility. To define terms, based on genetic analysis, roughly speaking, Blacks
\n(Africans, Negroids) are those who have most of their ancestors from sub-Saharan
\nAfrica; Whites (Europeans, Caucasoids) have most of their ancestors from Europe;
\nand East Asians (Orientals, Mongoloids) have most of their ancestors from
\nPacific Rim countries (Cavalli-Sforza, 2000; Cavalli-Sforza, Menozzi, & Piazza,
\n1994; Nei & Roychoudhury, 1993; Risch, Burchard, Ziv, & Tang, 2002). Although
\nhe eschewed the term race, Cavalli-Sforza\u2019s (2000, p. 70) maximum
\nlikelihood tree made on the basis of molecular genetic markers substantially
\nsupports the traditional racial groups classification. Of course, in referring to
\npopulation or racial group differences we are discussing averages. Individuals are
\nindividuals, and the three groups overlap substantially on almost all traits and
\nmeasures.
\nThe hereditarian position originated in the work of Charles Darwin (1859,
\n1871) and then was elaborated by his cousin Sir Francis Galton (1869, 1883).
\nBased on research models used in behavioral genetics, this view contends that a
\nsubstantial part (say 50%) of both individual and group differences in human
\nbehavioral traits is genetic. It therefore follows that even if all individuals in both
\ngroups were treated identically, average group differences would not disappear,
\nthough they might diminish.
\nThe opposing culture-only position finds no need to posit any genetic causation,
\nstating that if the environments for all individuals could be equalized, the
\nobserved group differences in average IQ would effectively disappear, though this
\nmight be difficult to achieve. This position has been predominant in the social
\nsciences since the 1930s.
\nIt is essential to keep in mind precisely what the two rival positions do and do
\nnot say\u2014about a 50% genetic\u201350% environmental etiology for the hereditarian
\nview versus an effectively 0% genetic\u2013100% environmental etiology for the
\nculture-only theory. The defining difference is whether any significant part of the
\nmean Black\u2013White IQ difference is genetic rather than purely cultural or environmental
\nin origin. Hereditarians use the methods of quantitative genetics, and
\nthey can and do seek to identify the environmental components of observed group
\ndifferences. Culture-only theorists are skeptical that genetic factors play any
\nindependently effective role in explaining group differences.
\nMost of those who have taken a strong position in the scientific debate about
\nrace and IQ have done so as either hereditarians or culture-only theorists.
\nIntermediate positions (e.g., gene\u2013 environment interaction) can be operationally
\nassigned to one or the other of the two positions depending on whether they
\npredict any significant heritable component to the average group difference in IQ.
\nFor example, if gene\u2013 environment interactions make it impossible to disentangle
\ncausality and apportion variance, for pragmatic purposes that view is indistinguishable
\nfrom the 100% culture-only program because it denies any potency to
\nthe genetic component proposed by hereditarians.
\nIt is also important to define and interpret heritability correctly. Heritability
\nrefers to the genetic contribution to the individual differences (variance) in a
\nparticular group, not to the phenotype of a single individual. Heritability is not a
\nconstant that holds for all groups or in all environments. A heritability of 1.00
\nmeans all the observed differences in that group are due to genetic differences and
\nnot at all to their differences in the environment. A heritability of zero (0.00)
\nmeans the converse. A heritability of 0.50 means the observed variation is equally
\nthe result of genetic and of environmental differences. The heritability of height
\nin modern industrial populations, for example, is about 90%, which means that
\nmost of the differences in height among the individuals are due to their genetic
\ndifferences.
\nBehavioral Genetics by Plomin, DeFries, McClearn, and McGuffin (2001)
\nprovides a detailed explanation of heritability (see also Jensen, 1973; Miele, 2002,
\nfor general readers). Heritability estimates are true only for particular populations
\nat particular times. They can vary in different populations or at different times.
\nEqualizing environments, for example, produces the counterintuitive result of
\nincreasing heritability because any individual differences that remain must be due
\nto genetic differences.
\nThe cause of individual differences within groups has no necessary implication
\nfor the cause of the average difference between groups. A high heritability
\nwithin one group does not mean that the average difference between it and another
\ngroup is due to genetic differences, even if the heritability is high in both groups.
\nHowever, within-groups evidence does imply the plausibility of the betweengroups
\ndifferences being due to the same factors, genetic or environmental. If
\nvariations in level of education or nutrition or genes reliably predict individual
\nvariation within Black and within White groups, then it would be reasonable to
\nconsider these variables to explain the differences between Blacks and Whites. Of
\ncourse, independent evidence would then be needed to establish any relationship.
\nHeritability describes what is the genetic contribution to individual differences
\nin a particular population at a particular time, not what could be. If either
\nthe genetic or the environmental influences change (e.g., due to migration, greater
\neducational opportunity, better nutrition), then the relative impact of genes and
\nenvironment will change. Heritability has nothing to say about what should be. If
\na trait has a high heritability it does not mean that it cannot be changed.
\nEnvironmental change is possible. For example, phenylketunuria (PKU) is a
\nsingle-gene disorder that causes mental retardation but that can be prevented by
\nbeginning a diet low in phenylaline early in life. (Note that the only effective
\ntreatment for PKU is aimed directly at the specific chemical factor that causes it.)
\nThe fact that the heritability of IQ is between 0.50 and 0.80 does not mean that
\nindividual differences are fixed and permanent. It does tell us that some individuals
\nare genetically predisposed to be more teachable, more trainable, and more
\ncapable of changing than others, under current conditions (Jensen, 1973; Miele,
\n2002).
\nHaving defined the terms of the debate, we now discuss approaches for
\nresolving it. Lakatos\u2019s (1970, 1978) analytical methodology classifies research
\nprograms as being either progressive or degenerating. A progressive program not
\nonly explains existing phenomena and theoretical anomalies but also offers novel
\npredictions, some of which can be tested and then either confirmed or rejected. A
\ndegenerating program merely accommodates existing anomalies by a series of
\nnew, unrelated, ad hoc hypotheses, ignores them, or denies their existence.
\nThe philosopher Peter Urbach (1974a, 1974b) applied this methodology and
\nconcluded that the hereditarian\/culture-only IQ debate is really a conflict of
\nresearch programs that goes back to their classic proponents\u2014Francis Galton
\n(1869) for the hereditarians and J. B. Watson (1924) for the environmentalists.
\nEach has an underlying set of assumptions, termed its hard core, and a heuristic
\nmachinery that generates hypotheses. The hard core of the hereditarian program
\nis that (a) all individuals possess some level of general mental capacity called
\ngeneral intelligence that, to some degree, influences all cognitive activity, and (b)
\nthe differences between individuals and between groups in general intelligence
\nare largely the result of genetic differences. The hard core of the culture-only
\nprogram is that (a) there are a number of different learned mental skills or
\nintelligences, and (b) any observed differences in cognitive performance are the
\nresult of environmental factors.
\nHereditarian heuristics include constructing better tests, developing better
\ntechniques for measuring mental abilities, and discovering biological correlates
\n(e.g., heritability, inbreeding depression and heterosis, brain size, brain metabolic
\nrate, brain evoked potentials, brain imaging) of these tests. The process then
\ninvolves examining the similarities of the scores among people whose varying
\ndegrees of genetic resemblance can be predicted from Mendelian theory (Fisher,
\n1918). Culture-only heuristics include searching for the environmental factors that
\ncause differences in intellectual performance and discovering the bias in existing
\ntests. If two groups differ in mean IQ, culture-only theorists conjecture either that
\nthe lower scoring group has been exposed to one or more deleterious experience
\nor been deprived of some beneficial environmental stimuli or that the tests are not
\nvalid measures of their true ability. Compensatory training might be initiated and
\nthe hypothesis confirmed if the groups then obtain more nearly equal scores, or if
\nless biased tests are developed on which the group differences are reduced but still
\npredict outside criteria. Of course, these two programs overlap to some degree,
\nand a given experiment might well combine elements of the heuristics of each.
\nReviewed here are new data sets for 10 categories of evidence that have
\nbecome available since Jensen\u2019s (1969) article. They include the international
\npattern of IQ test scores, more and less g-loaded components of tests, heritability,
\nbrain-size and cognitive-ability relations, transracial adoption, racial admixture,
\nregression to the mean, the race\u2013 behavior matrix, human origins research, and
\nhypothesized environmental variables. These findings are then used to evaluate
\nthe culture-only and hereditarian models in terms of the methodology proposed by
\nLakatos (1970, 1978).
\nSection 3: Mean Race\u2013IQ Differences: A Global Perspective
\nThe IQ debate became worldwide in scope when it was shown that East
\nAsians scored higher on IQ tests than did Whites, both within the United States
\nand in Asia, even though IQ tests were developed for use in the Euro American
\nculture (Lynn, 1977, 1978, 1982; P. E. Vernon, 1979, 1982). Around the world,
\nthe average IQ for East Asians centers around 106; that for Whites, about 100; and
\nthat for Blacks, about 85 in the United States and 70 in sub-Saharan Africa. Most
\nof the early research was conducted in the United States, but some was also
\nperformed in Canada and the Caribbean (Eysenck, 1971, 1984; Jensen, 1969,
\n1973; Osborne & McGurk, 1982; Shuey, 1958, 1966; cf. Flynn, 1980; Kamin,
\n1974; Lewontin, Rose, & Kamin, 1984). In the United States, 15% to 20% of the
\nBlack IQ distribution exceeds the White median IQ, so many Blacks obtain scores
\nabove the White average. This same order of mean group differences is also found
\non \u201cculture-fair\u201d tests and on reaction time tasks. Hundreds of studies on millions
\nof people have confirmed the three-way racial pattern (Jensen, 1998b; Lynn &
\nVanhanen, 2002; Rushton, 2000).
\nRacial-group differences in IQ appear early. For example, the Black and the
\nWhite 3-year-old children in the standardization sample of the Stanford\u2013Binet IV
\nshow a 1 standard deviation mean difference after being matched on gender, birth
\norder, and maternal education (Peoples, Fagan, & Drotar, 1995). Similarly, the
\nBlack and the White 21\u20442- to 6-year-old children in the U.S. standardization sample
\nof the Differential Aptitude Scale have a 1 standard deviation mean difference. No
\ndata are available for East Asian children at the youngest ages. On the Differential
\nAptitude Battery, by age 6, however, the average IQ of East Asian children is 107,
\ncompared with 103 for White children and 89 for Black children (Lynn, 1996).
\nThe size of the average Black\u2013White difference does not change significantly over
\nthe developmental period from 3 years of age and beyond (see Jensen, 1974,
\n1998b).
\nSerious questions have been raised about the validity of using tests for racial
\ncomparisons. However, because the tests show similar patterns of internal item
\nconsistency and predictive validity for all groups, and because the same differences
\nare found on relatively culture-free tests, many psychometricians have
\nconcluded that the tests are valid measures of racial differences, at least among
\npeople sharing the culture of the authors of the test (Jensen, 1980; Wigdor &
\nGarner, 1982). This conclusion was endorsed by the APA Task Force\u2019s statement:
\n\u201cConsidered as predictors of future performance, the tests do not seem to be
\nbiased against African Americans\u201d (Neisser et al., 1996, p. 93).
\nMost disputed is the validity of the low mean IQ scores reported for subSaharan
\nAfricans. Lynn\u2019s (1991) review of 11 studies found a mean IQ of 70. A
\nsubsequent review of over two dozen studies by Lynn and Vanhanen (2002) found
\nan average IQ of 70 for West, Central, East, and Southern Africa. For example,
\nin Nigeria, Fahrmeier (1975) collected data on 375 children ages 6 to 13 years in
\na study of the effects of schooling on cognitive development. The children\u2019s mean
\nscore on the Colored Progressive Matrices was 12 out of 36, which is at the 4th
\npercentile for 91\u20442-year-olds on U.S. norms, or an IQ equivalent of about 75
\n(Raven et al., 1990, pp. 97\u201398). In Ghana, Glewwe and Jacoby (1992) reported a
\nWorld Bank study that tested a representative sample of 1,736 individuals ranging
\nin age from 11 to 20 years old from the entire country. All had completed primary
\nschool; half were attending middle school. Their mean score on the Colored
\nProgressive Matrices was 19 out of 36, which is below the 1st percentile for
\n151\u20442-year-olds on U.S. norms, an IQ equivalent of less than 70. In Kenya,
\nSternberg et al. (2001) administered the Colored Progressive Matrices to 85
\nchildren ages 12 to 15 years old who scored 23.5 out of 36, which is about the 2nd
\npercentile for 131\u20442-year-olds, an IQ equivalent of 70. In Zimbabwe, Zindi (1994)
\nreported mean IQs for 204 African 12- to 14-year-olds of 67 on the Wechsler
\nIntelligence Scale for Children\u2014Revised (WISC\u2013R) and of 72 on the Standard
\nProgressive Matrices. In South Africa, Owen (1992) found that 1,093 African 12-
\nto 14-year-old high school students solved 28 out of 60 problems on the Standard
\nProgressive Matrices, which is around the 10th percentile, or an IQ equivalent of
\nabout 80 (Raven, Raven, & Court, 1998, p. 77). Again in South Africa, Skuy,
\nSchutte, Fridjhon, and O\u2019Carroll (2001) found mean scores 1 to 2 standard
\ndeviations below U.S. norms on a wide variety of individually administered tests
\ngiven to 154 African high school students under optimized conditions.
\nBlack university students in South Africa also show relatively low mean test
\nscores. Sixty-three undergraduates at the all-Black universities of Fort Hare,
\nZululand, the North, and the Medical University of South Africa had a full-scale
\nIQ of 77 on the Wechsler Adult Intelligence Scale\u2014Revised (Avenant, 1988,
\ncited in Nell, 2000, pp. 26 \u201328). In a study at the University of Venda in South
\nAfrica\u2019s Northern Province by Grieve and Viljoen (2000), 30 students in 4th-year
\nlaw and commerce averaged a score of 37 out of 60 on the Standard Progressive
\nMatrices, equivalent to an IQ of 78 on U.S. norms. A study at South Africa\u2019s
\nUniversity of the North by Zaaiman, van der Flier, and Thijs (2001) found the
\nhighest scoring African sample to that date\u2014147 first-year mathematics and
\nscience students who scored 52 out of 60 on the Standard Progressive Matrices,
\nwhich is equivalent to an IQ of 100. This higher score may reflect the fact that
\nthey were mathematics and science students, specially selected for admission to
\nthe university from a pool of 700 applicants on the basis of a math-science
\nselection test.
\nAt the University of the Witwatersrand in Johannesburg, South Africa,
\nRushton, Skuy, and colleagues gave the Raven\u2019s Progressive Matrices in four
\nseparate studies under optimal testing conditions. Rushton and Skuy (2000) found
\n173 African 1st-year psychology students averaged an IQ equivalent of 84. Skuy
\net al. (2002) tested another 70 psychology students who averaged an IQ equivalent
\nof 83. After receiving training on how to solve Matrices-type items, their mean
\nscore rose to an IQ equivalent of 96. Rushton, Skuy, and Fridjhon (2002, 2003)
\ngave nearly 200 African 1st-year engineering students both the Standard and the
\nAdvanced version of the Raven\u2019s test and found they averaged an IQ of 97 on the
\nStandard and 103 on the Advanced, making them the highest scoring African
\nsample on record. (The White university students in these four studies had IQs
\nfrom 105 to 117; East Indian students had intermediate IQs, from 102 to 106.)
\nMany critics claim that Western-developed IQ tests are not valid for groups
\nas culturally different as sub-Saharan Africans (e.g., Nell, 2000). The main
\nevidence to support a claim of external bias would be if the test failed to predict
\nperformance for Africans. Even if tests only underpredicted performance for
\nAfricans compared with non-Africans, it would suggest that their test scores
\nunderestimated their \u201ctrue\u201d IQ scores. However, a review by Kendall, Verster,
\nand von Mollendorf (1988) showed that test scores for Africans have about equal
\npredictive validity as those for non-Africans (e.g., 0.20 to 0.50 for students\u2019
\nschool grades and for employees\u2019 job performance). The review also showed that
\nmany of the factors that influence scores in Africans are the same as those for
\nWhites (e.g., coming from an urban vs. a rural environment; being a science rather
\nthan an arts student; having had practice on the tests; and the well-documented
\ncurvilinear relationship with age). Similarly, Sternberg et al.\u2019s (2001) study of
\nKenyan 12- to 15-year-olds found that IQ scores predicted school grades, with a
\nmean r \u0001 .40 (p .001; after controlling for age and socioeconomic status [SES],
\nr \u0001 .28, p .01). In Rushton et al.\u2019s (2003) study of African and non-African
\nengineering students at the University of the Witwatersrand, scores on the
\nAdvanced Progressive Matrices correlated with scores on the Standard Progressive
\nMatrices measured 3 months earlier (.60 for Africans; .70 for non-Africans)
\nand with end-of-year exam marks measured 3 months later (.34 for Africans; .28
\nfor non-Africans). Figure 1 shows the regression of exam marks on test scores for
\nthese university students.
\nAlthough predictive validity is the ultimate pragmatic criterion for absence of
\nbias, critics also suggest that the items have different meanings for Africans than
\nthey do for Whites or East Indians (Nell, 2000). This hypothesis of internal bias
\nhas been tested. The psychometric studies by Owen (1992) on thousands of high
\nschool students, and by Rushton and Skuy (2000; Rushton et al., 2002, 2003) on
\nhundreds of university students, found almost identical item structures in Africans,
\nWhites, and East Indians on the Progressive Matrices. Items found difficult
\nby one group were difficult for the others; items found easy by one group were
\neasy for the others (mean rs \u0001 .90, p .001). The item\u2013total score correlations
\nfor Africans, Whites, and East Indians were also similar, indicating that the items
\nmeasured similar psychometric constructs in all three groups. (Section 4 reviews
\nevidence of the similarity of the g factor in Africans and non-Africans.) The only
\nreliable example of bias so far discovered in this extensive literature is the rather
\nobvious internal bias on the Vocabulary components of tests such as the Wechsler
\nfor groups that do not have English as their first language (e.g., Skuy et al., 2001).
\nEven here, the language factor only accounts for about 0.5 of a standard deviation,
\nout of the overall 2.0 standard deviation difference, between Africans and Whites.
\nCould it make a difference that Africans have less experience in solving
\nproblems such as those on the Raven\u2019s, are less testwise, and have less access to
\ncoaching than non-Africans? Raven (2000) showed that students who were
\nencouraged to engage in complex cognitive tasks increased in self-direction,
\nunderstanding, and competence. In South Africa, Skuy and Shmukler (1987)
\napplied Feuerstein\u2019s (1980) Mediated Learning Experience and raised the Raven
\nscores of Black high school students. Skuy, Hoffenberg, Visser, and Fridjhon
\n(1990) found generalized improvements for Africans with what they termed a
\nfacilitative temperament. In an intervention study with 1st-year psychology students
\nat the University of the Witwatersrand, Skuy et al. (2002) increased Raven\u2019s
\ntest scores in both Africans and non-Africans after intervention training. Both
\nexperimental groups improved over the baseline compared with their respective
\ncontrol groups, with significantly greater improvement for the African group (IQ
\nscore gains of 83 to 97 in Africans; 103 to 107 in non-Africans). The question
\nremains, however, whether such intervention procedures only increase performance
\nthrough mastery of subject-specific knowledge or whether they increase
\ng-like problem-solving ability that generalizes to other tests as well (te Nijenhuis,
\nVoskuijl, & Schijve, 2001).
\nSome argue that African students are less interested, more anxious, work less
\nefficiently, or give up sooner on items they find difficult, perhaps because the
\nproblems have less meaning for them (e.g., Nell, 2000). Four findings argue
\nagainst these hypotheses. First, Rushton and Skuy (2000) closely observed the
\ntest-taking behavior of Africans and noted that they worked very diligently,
\ntypically staying longer than Whites to recheck their answers. Second and third,
\nthere are the similar predictive validities and internal consistencies previously
\ndiscussed. Finally, there is supporting evidence from reaction-time research.
\nReaction time is one of the simplest culture-free cognitive measures. Most
\nreaction time tasks are so easy that 9- to 12-year-old children can perform them
\nin less than 1 s. But even on these very simple tests, children with higher IQ scores
\nperform faster than do children with lower scores, perhaps because reaction time
\nmeasures the neurophysiological efficiency of the brain\u2019s capacity to process
\ninformation accurately\u2014the same ability measured by intelligence tests (Deary,
\n2000; Jensen, 1998b). Children are not trained to perform well on reaction time
\ntasks (as they are on certain paper-and-pencil tests), so the advantage of those with
\nhigher IQ scores on these tasks cannot arise from practice, familiarity, education,
\nor training.
\nFor three reaction time tasks (the simple, choice, and odd-man-out tasks),
\nindividuals with higher IQ scores average faster and less variable reaction times.
\nFor any one task, the correlation between reaction time and IQ normally lies
\nbetween .20 and .40. A review of several studies concluded that the six measures
\ncombined (i.e., the average time and the variability for the three reaction time
\ntasks) produce a multiple correlation of .67 (Deary, 2000). This is about the same
\nmagnitude as the correlation between two conventional intelligence tests of, say,
\nreasoning ability and vocabulary.
\nLynn and his colleagues carried out a series of reaction time studies on over
\n1,000 nine-year-old East Asian children in Japan and Hong Kong, White children
\nin Britain and Ireland, and Black children in South Africa (summarized by Lynn
\n& Vanhannen, 2002, pp. 66 \u2013 67). The Progressive Matrices were given as a
\nnonverbal test of intelligence, along with the simple, choice, and odd-man-out
\ntasks. Reaction times and variabilities were measured by computer and hence
\nwere not subject to any human error in recording. For details, see Shigehisa and
\nLynn (1991) for Japan; Chan and Lynn (1989) for Hong Kong and Britain; Lynn
\n(1991) for Ireland; and Lynn and Holmshaw (1990) for South Africa.
\nThe correlations between IQ and reaction times for the five countries are
\nsummarized in Table 1. The East Asian children in Hong Kong and Japan
\nobtained the highest IQs, followed in descending order by the White children in
\nBritain and Ireland, and then the Black children in South Africa. The medians for
\nsimple reaction time, choice reaction time, and odd-man-out reaction time follow
\nthe same descending order as the IQs. Because all the tasks take less than 1 s, all
\nchildren found them easy. The variabilities in the three reaction time measures for
\nthe three groups follow the same general descending trend.
\nThe same pattern of average scores on these and other reaction time tasks (i.e.,
\nEast Asians faster than Whites faster than Blacks) is found within the United
\nStates. Jensen (1993) and Jensen and Whang (1994) examined the time taken by
\nover 400 schoolchildren ages 9 to 12 years old in California to retrieve overlearned
\naddition, subtraction, or multiplication of single digit numbers (from 1 to
\n9) from long-term memory. All of the children had perfect scores on paper-andpencil
\ntests of this knowledge, which was then reassessed using the Math
\nVerification Test. The response times significantly correlated (negatively) with
\nRaven Matrices scores, whereas movement times have a near-zero correlation.
\nThe average reaction times for the three racial groups differ significantly (see
\nFigure 2). They cannot be explained by the groups\u2019 differences in motivation
\nbecause the East Asian children averaged a shorter response time but a longer
\nmovement time than did the Black children.
\nSection 4: The g Factor and Mean Race\u2013IQ Differences
\nJensen (1998b) showed that a test\u2019s g loading (g being the general factor of
\nintelligence) is the best predictor, not just of that test\u2019s correlation with scholastic
\nand workplace performance, but of heritability coefficients determined from twin
\nstudies, inbreeding depression scores calculated in children of cousin-marriages,
\nbrain evoked potentials, brain pH levels, brain glucose metabolism, as well as
\nnerve conduction velocity, reaction time, and other physiological factors. These
\ncorrelations argue strongly for the heritable and biological, as opposed to the mere
\nstatistical reality of g. Because the mean Black\u2013White group difference in IQ is
\nmore pronounced on high-g-loaded tests than it is on low-g-loaded tests, it suggests
\nthat it is not the result of any idiosyncratic cultural peculiarities of this or that test.
\nSpearman (1927, p. 379) first proposed the hypothesis that the mean Black\u2013
\nWhite group difference in IQ would be \u201cmost marked in just those [tests] which
\nare known to be saturated with g.\u201d Jensen (1980, p. 535) designated it as
\n\u201cSpearman\u2019s hypothesis\u201d and developed the method of correlated vectors to test
\nit. This method correlates the standardized Black\u2013White mean differences on a set
\nof cognitive tests with their respective g loadings, a significant positive correlation
\nsupporting the hypothesis. The rationale is straightforward. If g is the main source
\nof between- and within-group differences, then there should be a positive relationship
\nbetween a given test\u2019s g loading and the mean Black\u2013White group
\ndifference on that test: The more g-loaded the test, the greater the Black\u2013White
\ngroup difference on that test. A corollary is the prediction that when race (scored
\nas Blacks \u0001 1, Whites \u0001 2) is factor analyzed along with scores from a number
\nof diverse cognitive tests, its highest loading on the resulting correlation matrix
\nwill be with the g factor.
\nJensen (1998b, pp. 369 \u2013379) summarized 17 independent data sets of nearly
\n45,000 Blacks and 245,000 Whites derived from 149 psychometric tests and
\nfound that the g loadings consistently predicted the magnitude of the mean
\nBlack\u2013White group difference (r \u0001 .62, p .05). This was borne out even among
\n3-year-olds administered eight subtests of the Stanford\u2013Binet in which the rank
\ncorrelation between g loadings and the mean Black\u2013White group differences was
\n.71 (p .05; Peoples et al., 1995). Subsequently, Nyborg and Jensen (2000)
\nanalyzed a unique battery of 19 highly diverse cognitive tests from an archival
\ndata set of 4,462 males who had served in the U.S. Armed Forces. The g factor
\nwas extracted using different methods. Spearman\u2019s hypothesis was confirmed,
\nwith an average correlation of .81 between the race difference on a test and its g
\nloading. Nyborg and Jensen concluded that Spearman\u2019s original conjecture about
\nthe mean Black\u2013White difference on the g factor \u201cshould no longer be regarded
\nas just an hypothesis but as an empirically established fact\u201d (p. 599). Only one
\nstudy to date has examined East Asian\u2013White difference on psychometric tests as
\na function of their g loadings; it confirmed the hypothesis for 15 cognitive tests
\nadministered to two generations of Americans of Japanese, Chinese, and European
\nancestry. In this case, the more g-loaded the test, the greater the mean East
\nAsian\u2013White group difference favoring East Asians (Nagoshi, Johnson, DeFries,
\nWilson, & Vandenberg, 1984).
\nStudies in Southern Africa have also found the mean Black\u2013White IQ
\ndifference is mainly on g. Lynn and Owen (1994) were the first to test explicitly
\nSpearman\u2019s hypothesis in sub-Saharan Africa, administering the Junior Aptitude
\nTest to 1,056 White, 1,063 Indian, and 1,093 Black 16-year-old high school
\nstudents in South Africa. They found a 2 standard deviation difference between
\nthe Africans and Whites (yielding an average African IQ of about 70) and a 1
\nstandard deviation difference between the Whites and Indians (yielding an average
\nIndian IQ of 85). They then tested Spearman\u2019s hypothesis and found the
\nAfrican\u2013White differences correlated .62 (p .05) with the g factor extracted
\nfrom the African sample, but only .23 with g extracted from the White sample.
\nThey did not find any White\u2013Indian differences on g.
\nJensen (1998b, p. 388) noted some problems with Lynn and Owen\u2019s (1994)
\nSouth African study, but their results on Black\u2013White differences have been well
\ncorroborated since then and extended to include East Indians and \u201cColoreds\u201d (the
\nterm used to refer to the mixed-race population of South Africa). Thus, Rushton
\n(2001) reanalyzed data on 10 subtests of the WISC\u2013R published on 154 high
\nschool students in South Africa by Skuy et al. (2001) and found African\u2013White
\ndifferences were mainly on g. Rushton and Jensen (2003) compared data on the
\nWISC\u2013R from 204 African 12- to 14-year-olds from Zimbabwe published by
\nZindi (1994) with the U.S. normative sample for Whites and found 77% of the
\nbetween-groups race variance was attributable to a single source, namely g.
\nSpearman\u2019s hypothesis has been confirmed in South Africa using test item
\nanalyses as well. Rushton and Skuy (2000) studied 309 university students at the
\nUniversity of the Witwatersrand and found that the more an individual item from
\nthe Raven\u2019s Standard Progressive Matrices measured g (estimated by its item\u2013
\ntotal correlation), the more it correlated with the standardized African\u2013White
\ndifference on that item. Rushton (2002) analyzed the item data from 4,000 high
\nschool students in South Africa on Raven\u2019s Standard Progressive Matrices published
\nby Owen (1992) and found the four-way African\u2013Colored\u2013East Indian\u2013
\nWhite differences were all on g. In two studies of engineering students, Rushton
\net al. (2002, 2003) found that the more the items from both the Standard and the
\nAdvanced Progressive Matrices loaded on g, the better they predicted the magnitude
\nof African\u2013East Indian\u2013White differences. The g loadings showed crosscultural
\ngenerality; those calculated on the East Indian students predicted the
\nmagnitude of the African\u2013White differences.
\nSpearman\u2019s hypothesis was also confirmed when the g factor was extracted
\nfrom 12 reaction time variables given to the 820 nine- to twelve-year-olds. While
\nall of the children could do the tasks in less than 1 s, the correlations between the
\ng loadings and the mean Black\u2013White differences on the reaction time tasks range
\nfrom .70 to .81 (Jensen, 1993). These results bear out Spearman\u2019s hypothesis even
\nmore strongly than do those from conventional psychometric tests. The hypothesis
\nthat the mean Black\u2013White group difference on these tests reflects a difference in
\nmotivation is again disconfirmed by the fact that although Whites averaged faster
\nreaction times than Blacks, Blacks averaged faster movement times than Whites.
\nAnd again, East Asians typically averaged higher than Whites on the g factor
\nextracted from their (faster) reaction time measures (Jensen & Whang, 1994).
\nSpearman\u2019s hypothesis, as demonstrated by the method of correlated vectors,
\ncannot be a chimera or a methodological artifact, as a few critics have claimed
\n(e.g., Gould, 1996, p. 350; Scho\u00a8nemann, 1992). In the method of correlated
\nvectors, the means and standard deviations of the variables cannot have any
\nmathematical relationship with the factor structure of the correlation matrix
\nbecause the means and the variances of all the tests in the factor-analyzed
\ncorrelation matrix are totally removed by the Pearson correlations, which convert
\nall variables to z scores. Therefore, any systematic relationship between factor
\nloadings and standardized group means (or group mean differences) must be an
\nempirical fact, not an artifact (Jensen, 1992).
\nOther claims of artifact are contradicted by Dolan\u2019s (1997) technical commentaries
\non the method of correlated vectors (Dolan, 1997, 2000). Dolan argued
\nthat the method of correlated vectors is not incorrect but that it lacks specificity;
\nthat is, it does not incorporate tests of alternative models of the factor structure of
\ngroup differences or incorporate statistical tests to compare them for goodnessof-fit.
\nIn its place, he advocated use of the multigroup confirmatory factor model
\nfor testing Spearman\u2019s hypothesis. Statistical tests of significance are a built-in
\nfeature of this procedure. Dolan and Hamaker (2001) have applied it to two large
\npublished data sets (Jensen & Reynolds, 1982; Naglieri & Jensen, 1987). The
\nresults statistically confirmed the conclusion derived from the method of correlated
\nvectors regarding a \u201cweak form\u201d of Spearman\u2019s hypothesis: Black\u2013White
\ngroup differences were predominantly on the g factor, although the groups also
\nshowed differences on some lower order factors (e.g., short-term memory and
\nspatial ability) independent of g.
\nSection 5: Gene\u2013Environment Architecture and Mean Black\u2013White
\nIQ Differences
\nDozens of twin, adoption, and family studies have confirmed the high heritability
\nof intellectual and behavioral traits, and even reaction time tasks, within
\na race (Bouchard, 1996; Bouchard & Loehlin, 2001; Deary, 2000; Plomin et al.,
\n2001). Most of these estimates have been calculated on White samples. One study
\nof 543 pairs of identical and 134 pairs of nonidentical 12-year-old Japanese twins
\nin Japan reported a substantial heritability of 0.58 for IQ (Lynn & Hattori, 1990).
\nThe hereditarian model views race differences simply as aggregated individual
\ndifferences of this sort.
\nThe culture-only model, however, predicts that special factors such as poverty,
\nthe history of slavery, and White racism have operated on the Black
\npopulation and suppressed natural levels of intelligence and so made heritabilities
\nin Blacks substantially lower than they are in Whites. In arguing against Galton\u2019s
\n(1869) hereditarian position, Charles H. Cooley (1897), a founder and first
\npresident of the American Sociological Association, was the first to introduce the
\npowerful analogy that corn seeds given a normal environment grow plants of full
\nheight whereas seeds given a deprived environment grow plants of stunted height.
\nAccording to this view, cultural deprivation, not heredity, is the cause of any
\nBlack\u2013White IQ differences.
\nIt is an empirical question whether heritabilities are the same for Blacks as for
\nWhites. Loehlin et al. (1975, pp. 114 \u2013116) reviewed the literature to that date and
\nfound that while there was some evidence suggesting a lower heritability of
\nintelligence for Blacks than for Whites (e.g., Scarr-Salapatek, 1971), a larger body
\nof evidence suggested equal heritabilities in the two groups. Subsequently, Osborne\u2019s
\n(1980) Georgia Twin Study compared 123 Black and 304 White pairs of
\n12- to 18-year-old twins drawn from schools in Georgia, Kentucky, and Indiana,
\ngiven the Basic Test Battery, along with smaller subsets of twins given the
\nPrimary Mental Abilities test and the Cattell Culture Fair Intelligence test.
\nOsborne found heritabilities of about 50% for both Blacks and Whites, all
\nsignificantly different from zero but not from each other. (The heritabilities of the
\nBasic, Primary, and Cattell tests were, respectively, for Whites, 0.61, 0.37, and
\n0.71, and for Blacks, 0.75, 0.42, and 0.19; Osborne, 1980, pp. 68 \u2013 69, 89, 98.)
\nAnother way of answering the question is to compare their psychometric
\nfactor structures of kinship patterns, background variables, and subtest correlations.
\nIf there are minority-specific developmental processes arising from cultural
\nbackground differences between the races at work, they should be reflected in the
\ncorrelations between the background variables and the outcome measures. Rowe
\n(1994; Rowe, Vazsonyi, & Flannery, 1994, 1995) examined this hypothesis in a
\nseries of studies using structural equation models. One study of six data sources
\ncompared cross-sectional correlational matrices (about 10 10) for a total of
\n8,528 Whites, 3,392 Blacks, 1,766 Hispanics, and 906 Asians (Rowe et al., 1994).
\nThese matrices contained both independent variables (e.g., home environment,
\npeer characteristics) and developmental outcomes (e.g., achievement, delinquency).
\nA LISREL goodness-of-fit test found each ethnic group\u2019s covariance
\nmatrix equal to the matrix of the other groups. Not only were the Black and White
\nmatrices nearly identical, but they were as alike as the covariance matrices
\ncomputed from random halves within either group. There were no distortions in
\nthe correlations between the background variables and the outcome measures that
\nsuggested any minority-specific developmental factor.
\nAnother study examined longitudinal data on academic achievement (Rowe et
\nal., 1995). Again, any minority-specific cultural processes affecting achievement
\nshould have produced different covariance structures among ethnic and racial
\ngroups. Correlations were computed between academic achievement and family
\nenvironment measures in 565 full-sibling pairs from the National Longitudinal
\nSurvey of Youth, each tested at ages 6.6 and 9.0 years (White N \u0001 296 pairs;
\nBlack N \u0001 149 pairs; Hispanic N \u0001 120 pairs). Each racial group was treated
\nseparately, yielding three 8 8 correlation matrices, which included age as a
\nvariable. Because LISREL analysis showed the matrices were equal across the
\nthree groups, there was no evidence of any special minority-specific developmental
\nprocess affecting either base rates in academic achievement or any changes
\ntherein over time.
\nA nearly identical statistical structure on intellectual variables across ethnic
\nand racial groups has been reported in large-scale studies of military samples. Ree
\nand Carretta (1995) examined a nationally representative sample of young Black,
\nWhite, and Hispanic men and women who took the Armed Services Vocational
\nAptitude Battery (ASVAB; N \u0001 9,173). The ASVAB, which is used to select
\napplicants for all military enlistments and assign them to first jobs, consists of 10
\nseparately scored subtests (General Science, Arithmetic Reasoning, Word Knowledge,
\nParagraph Comprehension, Numerical Operations, Coding Speed, Auto and
\nShop Information, Mathematics Knowledge, Mechanical Comprehension, and
\nElectronics Information). Despite the especially wide variety of subtests, Ree and
\nCarretta found the hierarchical factor structure of ASVAB subtest scores was
\nvirtually identical across the three groups. Similarly, Carretta and Ree (1995)
\nexamined the more specialized and diverse Air Force Officer Qualifying Test, a
\nmultiple-aptitude battery that had been given to 269,968 applicants (212,238
\nWhites, 32,798 Blacks, 12,647 Hispanics, 9,460 Asian Americans, and 2,551
\nNative Americans). The g factor accounted for the greatest amount of variance in
\nall groups, and its loadings differed little by ethnicity. Thus, the factor structure
\nof cognitive ability is nearly identical for Blacks and for Whites, as was found in
\nthe studies by Owen (1992) and Rushton and Skuy (2000; Rushton et al., 2002,
\n2003) comparing Africans, East Indians, and Whites on the item structures of tests
\ndescribed in Section 3. There was no \u201cFactor X\u201d specific to race.
\nWithin-race heritabilities have also been calculated using structural equation
\nmodeling. Rowe and Cleveland (1996) estimated the genetic architecture for
\nBlack and White full- and half-siblings from the National Longitudinal Survey of
\nYouth (106 pairs of Black half-sibs, 53 pairs of White half-sibs; 161 pairs of
\nBlack full-sibs, 314 pairs of White full-sibs). Three Peabody Individual Achievement
\nTests were used (Mathematics, Reading Comprehension, and Reading
\nRecognition). The best-fitting model was one in which the sources of the differences
\nbetween individuals within race and of the differences between racial means
\nwas the same\u2014about 50% genetic and 50% environmental. Similarly, Jensen
\n(1998b, p. 465) used structural equation modeling to reanalyze a subset of the
\nGeorgia Twin Study (comprising 123 Black and 304 White pairs of 12- to
\n18-year-old twins). He broke down the phenotypic mean differences into their
\ngenetic and environmental contributions and tested four alternative models: only
\ngenetic factors, only environmental factors, neither genes nor environment, and
\ngenes plus environment. The model of both genetic and environmental factors
\nbest explained the observed Black\u2013White group differences in IQ, whereas both
\nthe genetic-only and the environmental-only explanations were inadequate.
\nHeritability data are especially informative when the hereditarian and the
\nculture-only models make opposite predictions. For example, the hereditarian
\nmodel predicts race differences will be greater on those subtests that are more
\nheritable within races, whereas culture-only theory predicts they will be greater on
\nsubtests that are more culturally malleable (i.e., those with lower heritabilities) on
\nwhich races should grow apart as a result of dissimilar experiences. Analyses of
\nseveral independent data sets support the genetic hypothesis.
\nNichols (1972, cited in Jensen, 1973, pp. 116 \u2013117) was the first to apply
\ndifferential heritabilities in the study of racial-group differences. He estimated the
\nheritability of 13 tests from 543 pairs of 7-year-old siblings, including an equal
\nnumber of Blacks and Whites, and found a .67 correlation between the heritability
\nof a test and the magnitude of the Black\u2013White group difference on that test.
\nSubsequently, Jensen (1973, pp. 103\u2013119) calculated the environmentality of a
\ntest (defined as the degree to which sibling correlations departed from the pure
\ngenetic expectation of 0.50) in Black and in White children and found it was
\ninversely related to the magnitude of the Black\u2013White group difference (r \u0001
\n\u2013.70); that is, the more environmentally influenced a test, the less pronounced its
\nBlack\u2013White group difference.
\nPrompted by Jensen\u2019s approach, Rushton (1989) estimated genetic influence
\nfrom the amount of inbreeding depression found on the 11 tests of the WISC.
\nInbreeding depression occurs in offspring who receive the same harmful recessive
\ngenes from each of their closely related parents. Rushton found a positive
\ncorrelation between inbreeding depression scores calculated from 1,854 cousinmarriages
\nin Japan and the magnitude of the mean Black\u2013White group difference
\nin the United States on the same 11 Wechsler tests (.48). This contradicts
\nculture-only theory, which predicts that mean differences between Blacks and
\nWhites should be greater on those subtests most affected by the environment (i.e.,
\nthose showing the lowest amount of inbreeding depression). We know of no
\nnongenetic explanation for the relation between inbreeding depression scores
\nfrom Japan and mean Black\u2013White group differences in the United States.
\nOther aspects of the gene\u2013 environment architectural matrix also pertain to the
\nquestion of mean Black\u2013White group differences. First, it is possible to distinguish
\nbetween two different types of environmental effects. Shared (also called
\ncommon or between-family) environmental effects are due to variables all children
\nreared in the same family (whether genetically related or adopted) have in
\ncommon but that differ between families (e.g., father\u2019s occupation, family cultural
\npractice, parents\u2019 child-rearing style). Nonshared (also called unique or withinfamily)
\neffects are specific to each child in the same family and therefore differ
\nwithin families (e.g., an accident, illness, or chance friendship that happens to one
\nsibling and not to the other). Twin and adoption studies can be used to measure
\nthe two types of environmental effect (Plomin, DeFries, & Loehlin, 1977; see also
\nPlomin & Daniels, 1987; Plomin et al., 2001).
\nBased on within-race data, Figure 3 summarizes the changes with age in the
\nproportions of the total IQ variance attributable to genetic factors and to the
\neffects of the shared and the nonshared environment. It is based on an analysis of
\n6,370 monozygotic and 7,212 dizygotic twin pairs reared together (McGue,
\nBouchard, Iacona, & Lykken, 1993). As can be seen, the estimated proportion of
\nIQ variance associated with shared environmental factors is relatively constant at
\napproximately 30% for ages up to 20 years but then drops to 0% in adulthood. The
\nestimated proportion of IQ variance associated with genetic factors increases
\nthroughout development, but especially after 20 years of age.
\nThese results are corroborated by studies of monozygotic twins reared apart
\nand of other kinships groups (Plomin et al., 2001). Because the variables usually
\nproposed to explain mean racial-group differences are part of the shared family
\nenvironment (such as social class, religious beliefs, cultural practices, father
\nabsence, and parenting styles), and these account for little variance within a race,
\nthey are unlikely to account for the differences between races. Rather, mean
\ndifferences between races are primarily due to nonshared family effects, which
\ninclude not only genetics but also a range of idiosyncratic environmental events
\nthat, within-families, affect one sibling and not the other (Jensen, 1997).
\nHereditarians have also examined the question of whether group differences
\noccur in shared and in nonshared environmental effects as well as in genetic
\neffects. For example, Rushton and Osborne (1995) reanalyzed 125 Black and 111
\nWhite pairs of 12- to 18-year-old twins from the Georgia Twin Study and
\nestimated their cranial capacities from head size measures. They found a lower
\nrange of heritabilities for Blacks than for Whites (12% to 31% against 47% to
\n56%) and a higher range of common environmental (i.e., shared family) effects
\nfor Blacks than for Whites (42% to 46% against 28% to 32%). However, these
\npercentage differences between Blacks and Whites were not significant, although
\nall heritabilities within each race were significantly above zero.
\nAlso relevant to the question of the mean Black\u2013White group differences are
\nthe changes in heritability that occur with increases in age (see Plomin et al.,
\n2001). The average correlation of IQ between full siblings reared together reaches
\n.49 in adulthood. The correlation in IQ for siblings reared apart as children is .24,
\nwhich increases to .49 in adulthood. This shows that siblings grow more similar
\nto each other as they age. In genetically unrelated people reared together, such as
\nadopted children, the correlation for IQ is .25 in childhood but decreases to .01 in
\nadulthood (McGue et al., 1993). This shows, conversely, that unrelated people
\nreared together grow less similar over time. Between childhood and adulthood the
\ninfluence of the shared home environment on IQ decreases, whereas the effect of
\ngenetic similarity increases.
\nThe diminishing or even vanishing effect of differences due to the shared
\nhome environment can best be understood in terms of three components of
\ngene\u2013 environment correlation and the change in their relative importance during
\ndevelopment (Plomin et al., 1977; Plomin et al., 2001). The passive component of
\nthe gene\u2013 environment correlation reflects all those things that happen to the
\nphenotype, independent of its own characteristics. For example, children of
\nacademically oriented parents may inherit genes for academic ability and also be
\nexposed (through no effort of their own) to stimulating intellectual environments.
\nThe reactive component of gene\u2013 environment correlation results from the reaction
\nof others to the expression of genetically based abilities, as when children
\nwith an unusual curiosity about science are given chemistry sets. The active
\ncomponent of the gene\u2013 environment correlation results from children actively
\nseeking experiences compatible with their genotypes, for example, going to
\nscience fairs rather than sports events or music concerts. From early childhood to
\nlate adolescence the predominant component of the gene\u2013 environment covariance
\ngradually shifts from passive to reactive to active. The child\u2019s enlarging world is
\nlike a cafeteria in which choices become increasingly biased by genetic factors
\n(Scarr, 1996; Scarr & McCartney, 1983). As individuals mature they seek out and
\neven create their own experiential environment.
\nSection 6: Race, Brain Size, and Cognitive Ability
\nStudies on over 700 participants show that individuals with larger brain
\nvolumes have higher IQ scores. About two dozen studies using magnetic resonance
\nimaging (MRI) to measure the volume of the human brain have found an
\noverall correlation with IQ of greater than .40 (Rushton & Ankney, 1996; P. A.
\nVernon, Wickett, Bazana, & Stelmack, 2000). The correlation of .40 using MRI
\nis much higher than the .20 correlation found in earlier research using simple head
\nsize measures, although the .20 correlation is also reliable and significant. Rushton
\nand Ankney (1996) reviewed 32 studies correlating measures of external head size
\nwith IQ scores or with measures of educational and occupational achievement,
\nand they found a mean r \u0001 .20 for people of all ages, both sexes, and various
\nethnic backgrounds, including African Americans.
\nThe most likely reason why larger brains are, on average, more intelligent
\nthan smaller brains is that they contain more neurons and synapses, which make
\nthem more efficient. Haier et al. (1995) tested the brain efficiency hypothesis by
\nusing MRI to measure brain volume and glucose metabolic rate to measure
\nglucose uptake (an indicator of energy use). They found a correlation of \u2013.58
\nbetween glucose metabolic rate and IQ, suggesting that more intelligent individuals
\nhave more efficient brains because they use less energy in performing a given
\ncognitive task. Several other studies supporting the brain-size\/efficiency model
\nwere reviewed in Gignac, Vernon, and Wickett (2003). In any individual, however,
\nenergy use increases with the increasing complexity of the cognitive task.
\nEstimates from twin studies indicate that genes contribute from 50% to 90%
\nof the variance to both cranial capacities based on external head size measures and
\nto brain volume measured by MRI (Bartley, Jones, & Weinberger, 1997; Pennington
\net al., 2000; Posthuma et al., 2002; Rushton & Osborne, 1995; Thompson
\net al., 2001). Common genetic effects mediate from 50% to 100% of the brainsize\/IQ
\ncorrelation (Pennington et al., 2000; Posthuma et al., 2002). Studies have
\nalso shown that correlations between brain size and IQ also hold true within
\nfamilies as well as between families (Gignac et al., 2003; Jensen, 1994; Jensen &
\nJohnson, 1994), which also implies shared genetic effects. However, one study
\nthat examined only sisters failed to find the within-family relation (Schoenemann,
\nBudinger, Sarich, & Wang, 2000). Families with larger brains overall tend to have
\nhigher IQs and, within a family, the siblings with the larger brains tend to have
\nhigher IQ scores. The within-family finding is of special interest because it
\ncontrols for most of the sources of variance that distinguish families, such as
\nsocial class, styles of child rearing, and general nutrition, that differ between
\nfamilies.
\nRace differences in average brain size are observable at birth. A study by
\nRushton (1997) analyzed recorded head circumference measurements and IQ
\nscores from 50,000 children in the Collaborative Perinatal Project followed from
\nbirth to age 7 (Broman, Nichols, Shaugnessy, & Kennedy, 1987). Using the head
\ncircumference measures to calculate cranial capacity at birth, 4 months, 1 year,
\nand 7 years, at each of these ages, the Asian American children averaged larger
\ncranial volumes than did the White children, who averaged larger cranial volumes
\nthan did the Black children. Within each race, cranial capacity correlated with IQ
\nscores. By age 7, the Asian American children averaged an IQ of 110; the White
\nchildren, 102; and the Black children 90. Because the Asian American children
\nwere the shortest in stature and the lightest in weight while the Black children
\nwere the tallest in stature and the heaviest in weight, these average race differences
\nin brain-size\/IQ relations were not due to body size.
\nExternal head size measurements (length, width, height) also have been used
\nto estimate cranial capacities in adults. Rushton carried out five studies of large
\narchival data sets. The first (Rushton, 1991) examined head size measures in 24
\ninternational military samples collated by the U.S. National Aeronautics and
\nSpace Administration. After adjusting for the effects of body height, weight, and
\nsurface area, the mean cranial capacity for East Asians was 1,460 cm3 and for
\nEuropeans 1,446 cm3
\n. The second (Rushton, 1992) demonstrated that even after
\nadjusting for the effects of body size, sex, and military rank in a stratified random
\nsample of over 6,000 U.S. Army personnel, the average cranial capacity of East
\nAsians, Whites, and Blacks were 1,416, 1,380, and 1,359 cm3
\n, respectively. The
\nthird study (Rushton, 1993) reanalyzed a set of anthropometric data originally
\npublished by Melville Herskovits (who concluded there were not race differences
\nin cranial capacity) and found Whites averaged a cranial capacity of 1,421 and
\nBlacks, 1,295 cm3
\n. The fourth study (Rushton, 1994) analyzed data obtained on
\ntens of thousands of people from around the world collated by the International
\nLabor Office in Geneva, Switzerland. It found that after adjusting for the effects
\nof body size and sex, samples from the Pacific Rim, Europe, and Africa had
\naverage cranial capacities, of 1,308, 1,297, and 1,241 cm3 respectively. Finally,
\nRushton and Osborne (1995) analyzed the Georgia Twin Study of adolescents and
\nfound that after correcting for body size and sex, Whites had an average cranial
\ncapacity of 1,269 cm3
\n, Blacks 1,251 cm3
\n.
\nRushton\u2019s results, based on calculating average cranial capacity from external
\nhead size measures, join those from dozens of other studies from the 1840s to the
\npresent on different samples using three different methods (endocranial volume
\nfrom empty skulls, wet brain weight at autopsy, and high-tech MRI). All show the
\nsame strong pattern of East Asians averaging larger and heavier brains than
\nWhites who average larger and heavier brains than Blacks. For example, using
\nMRI technology, Harvey, Persaud, Ron, Baker, and Murray (1994) found that 41
\nBlacks in Britain averaged a smaller brain volume than did 67 British Whites.
\nThe American anthropologist Samuel George Morton (1849) filled over 1,000
\nskulls with packing material to measure endocranial volume and found that
\nBlacks averaged about 5 cubic inches less cranial capacity than Whites. His
\nresults were confirmed by Todd (1923), H. L. Gordon (1934), and Simmons
\n(1942). The most extensive study of race differences in endocranial volume to
\ndate measured 20,000 skulls from around the world and reported East Asians,
\nEuropeans, and Africans had average cranial volumes of 1,415, 1,362, and 1,268
\ncm3
\n, respectively (Beals, Smith, & Dodd, 1984).
\nUsing the method of weighing brains at autopsy, Paul Broca (1873) reported
\nthat Whites averaged heavier brains than did Blacks, with larger frontal lobes and
\nmore complex convolutions. (Broca also used endocranial volume and found East
\nAsians averaged larger cranial capacities than Europeans, who averaged larger
\nthan Blacks.) Other early autopsy studies found a mean Black\u2013White group
\ndifference in brain weight of about 100 g (Bean, 1906; Mall, 1909; Pearl, 1934;
\nVint, 1934). A more recent autopsy study of 1,261 American adults found that the
\nbrains of 811 White Americans in their sample averaged 1,323 g and the brains
\nof 450 Black Americans averaged 1,223 g\u2014a difference of 100 g (Ho, Roessmann,
\nStraumfjord, & Monroe, 1980). Because the Blacks and Whites in the study
\nwere similar in body size, this was not responsible for the differences in brain
\nweight.
\nRushton (2000; Rushton & Ankney, 1996) summarized the world database
\nusing the three methods on which there are a sufficient number of studies
\n(autopsies, endocranial volume, and head measurements), as well as head measurements
\ncorrected for body size (see Rushton, 2000, pp. 126 \u2013132, Table 6.6).
\nThe results in cm3 or equivalents were as follows: East Asians \u0001 1,351, 1,415,
\n1,335, and 1,356 (M \u0001 1,364); Whites \u0001 1,356, 1,362, 1,341, and 1,329 (M \u0001
\n1,347); and Blacks \u0001 1,223, 1,268, 1,284, and 1,294 (M \u0001 1,267). The overall
\nmean for East Asians is 17 cm3 more than that for Whites and 97 cm3 more than
\nthat for Blacks. Within-race differences due to differences in method of estimation
\naveraged 31 cm3
\n. Because 1 cubic inch of brain matter contains millions of brain
\ncells and hundreds of millions of synapses or neural connections, these group
\ndifferences in average brain size may explain group differences in average IQ.
\nJensen and Johnson (1994) showed that for both Blacks and Whites, the head
\nsize by IQ correlation is true within families as well as between families,
\nindicating the intrinsic or functional relationship mentioned earlier. Further,
\nwithin each sex, Blacks and Whites fit the same regression line of head size on IQ.
\nWhen Blacks and Whites are perfectly matched for true-score IQ (i.e., IQ
\ncorrected for measurement error) at either the Black mean or the White mean, the
\noverall average Black\u2013White group difference in head circumference is virtually
\nnil. (Matching Blacks and Whites for IQ eliminates the average difference in head
\nsize, but matching the groups on head size does not equalize their IQs. This is
\nwhat one would expect if brain size is only one of a number of brain factors
\ninvolved in IQ.)
\nIn another analysis of the Georgia Twin Study, Jensen (1994) showed that the
\nmean Black\u2013White group difference in head\/brain size is also related to the
\nmagnitude of the mean Black\u2013White group difference in g. The correlation
\ncoefficient of each test with the head measurements was correlated with the
\nmagnitude of the Black\u2013White group difference on that test, thus forming two
\nvectors. The column vector of IQ test and head size correlations indicated a
\ncorrelation of .51 (p .05) with the vector of standardized Black\u2013White group
\ndifferences on each of the tests.
\nSection 7: Mean Race\u2013IQ Differences and Transracial Adoption Studies
\n\u201cTransracial adoption is the human analog of the cross-fostering design,
\ncommonly used in animal behavior genetics research. . . .There is no question that
\nadoption constitutes a massive intervention\u201d (Scarr & Weinberg, 1976, p. 726).
\nStudies of Korean and Vietnamese children adopted into White homes show that
\nalthough as babies many had been hospitalized for malnutrition, they nonetheless
\ngrew to have IQs 10 or more points higher than their adoptive national norms. By
\ncontrast, Black and mixed-race (Black\u2013White) children adopted into White middle-class
\nfamilies typically have lower average scores than the White siblings with
\nwhom they had been reared or than White children adopted into similar homes.
\nThe Minnesota Transracial Adoption Study, the largest and best-known
\ntransracial study, was designed specifically by Sandra Scarr and Richard Weinberg
\nto separate genetic factors from rearing conditions as causal influences on the
\ncognitive performance of Black children (Scarr & Weinberg, 1976; Weinberg,
\nScarr, & Waldman, 1992). It is also the only transracial adoption study that
\nincludes a longitudinal follow-up, with testing at ages 7 and 17 years. Scarr and
\nWeinberg compared the IQ and academic achievement scores of Black, White,
\nand mixed-race Black\/White children adopted into upper-middle-class White
\nfamilies in Minnesota by adopting parents whose mean IQ was more than 1
\nstandard deviation above the population mean of 100 (see Table 2). The biological
\nchildren of these parents were also tested.
\nThe first testing of 265 children was carried out in 1975 when they were 7
\nyears old and the second in 1986 when the 196 remaining in the study were 17
\nyears old. The 7-year-old White biological (i.e., nonadopted) children had an
\naverage IQ of 117 (see Table 2, 2nd column), similar to that found for children of
\nWhite upper-middle-class parents. The adopted children with two White biological
\nparents had a mean IQ of 112. The adopted children with one Black and one
\nWhite biological parent averaged 109. The adopted children with two Black
\nbiological parents had an average IQ of 97. (A mixed group of 21 Asian, North
\nAmerican Indian, and Latin American Indian adopted children averaged an IQ of
\n100 but were not included in the main statistical analyses.)
\nScarr and Weinberg (1976) interpreted the results of the testing at age 7 as
\nsupport for the culture-only position. They drew special attention to the fact that
\nthe mean IQ of 105 for all \u201csocially classified\u201d Black children (i.e., those with
\neither one or two Black parents) was significantly above the U.S. White mean.
\nThe poorer performance of children with two Black biological parents was
\nattributed to their more difficult and later placement. Scarr and Weinberg also
\npointed out that this latter group had both natural and adoptive parents with
\nsomewhat lower educational levels and abilities (2 points lower in adoptive
\nparents\u2019 IQ). They found no evidence for the expectancy effects hypothesis that
\nadoptive parents\u2019 beliefs about the child\u2019s racial background influence the child\u2019s
\nintellectual development. The mean score for 12 children wrongly believed by
\ntheir adoptive parents to have two Black biological parents was virtually the same
\nas that of the 56 children correctly classified by their adoptive parents as having
\none Black and one White biological parent.
\nTable 2 also presents the results for the 196 children retested at age 17
\n(Weinberg et al., 1992). There were four independent assessments of the children\u2019s
\ncognitive performance at this later age: (a) an individually administered IQ
\ntest, (b) an overall grade point average, (c) a class rank based on school performance,
\nand (d) four special aptitude tests in school subjects administered by the
\neducational authority, which we averaged. The results are concordant with the
\nearlier testing. The nonadopted White children had a mean IQ of 109, a grade
\npoint average of 3.0, a class rank at the 64th percentile, and an aptitude score at
\nthe 69th percentile. The adopted children with two White biological parents had
\na mean IQ of 106, a grade point average of 2.8, a class rank at the 54th percentile,
\nand an aptitude score at the 59th percentile. The adopted children with one Black
\nand one White biological parent had a mean IQ of 99, a grade point average of 2.2,
\na class rank at the 40th percentile, and an aptitude score at the 53rd percentile. The
\nadopted children with two Black biological parents had a mean IQ of 89, a grade
\npoint average of 2.1, a class rank at the 36th percentile, and an aptitude score at
\nthe 42nd percentile. (The 12 remaining mixed group of Amerindian\/Asian children
\nhad an IQ of 96 with no data provided on school achievement.)
\nBecause different tests based on different standardization groups were used in
\nthe first testing than in the follow-up, the overall average difference of about 8 IQ
\npoints (evident for all groups, including the nonadopted group) between the two
\ntest periods does not bear on the hypothesis of interest. The relevant comparisons
\nare those between the adopted groups of different races within each age level. The
\nmean of 89 for adopted children with two Black parents was slightly above the
\nnational Black mean of 85 but not above the Black mean for Minnesota.
\nWeinberg et al. (1992) interpreted their follow-up results as further support
\nfor the culture-only theory. Emphasizing the beneficial effects of the rearing
\nenvironment, they pointed out that at both age 7 and 17 all groups of adopted
\nchildren averaged above their expected population means. Their analyses frequently
\ncombined the two \u201csocially classified Black\u201d groups with \u201cother\u201d mixedrace
\nchildren who had one parent of unknown, Asian, Amerindian, or other racial
\nbackground. In their age 17 breakdowns, Weinberg et al. (1992, p. 132) stated that
\n\u201c[b]iological mothers\u2019 race remained the best single predictor of adopted child\u2019s
\nIQ when other variables were controlled,\u201d which they then attributed to \u201cunmeasured
\nsocial characteristics.\u201d Their overall conclusion was that \u201cthe social environment
\nmaintains a dominant role in determining the average IQ level of Black
\nand interracial children and that both social and genetic variables contribute to
\nindividual variations among them\u201d (p. 133).
\nLevin (1994) and Lynn (1994) disputed Weinberg et al.\u2019s (1992) culture-only
\ninterpretation. They each proposed a straightforward, hereditarian alternative: The
\nmean IQ and school achievement scores of Black children reflected their degree
\nof African ancestry. At both age 7 and 17, the adopted children with two Black
\nbiological parents had lower average IQs and school achievement scores than did
\nthose with one Black and one White biological parent, and these children, in turn,
\naveraged lower scores than did those with two White biological parents. Waldman,
\nWeinberg, and Scarr (1994) responded to Levin (1994) and Lynn (1994)
\nwith further regression analyses that indicated the children\u2019s preadoptive experience
\nwas confounded with racial ancestry, and so an unambiguous interpretation
\nof the results was not possible.
\nSubsequently, Jensen (1998b) discussed these studies at length and reviewed
\nthe evidence showing that age of adoption does not influence children\u2019s IQ scores
\nafter age 7 (e.g., Fisch, Bilek, Deinard, & Chang, 1976). Studies of severely
\nmalnourished, late-adopted, East Asian children (see below) provide substantial
\nevidence that age of adoption does not adversely influence IQ in transracial
\nadoptions. More generally, as reviewed in Section 5, dozens of adoption, twin,
\nand family studies of Whites show that although the shared-family environmental
\ncomponent of true-score IQ variance can be quite large at age 7, by late adolescence
\nit is the smallest component. After that age, genetic and within-family
\n(nonshared) environmental effects account for the largest components of variance
\n(see Figure 3).
\nSmall sample studies of very young children reared in enriched environments
\nsometimes find an absence of the usual race differences in IQ. In two studies of
\n2- to 5-year-olds raised in English residential nurseries, Tizard (1974) compared
\nBlack (African and West Indian), White, and mixed-parentage children and found
\nno significant differences among the three groups on several language comprehension
\ntests and on the Wechsler Preschool and Primary Scale of Intelligence
\n(WPPSI); the single significant difference was in favor of the non-White children.
\nMoore (1986) found that at age 7, 23 Black children adopted by middle-class
\nWhite families had a mean IQ of 117, whereas a similar group of children adopted
\nby middle-class Black families had a mean IQ of 104, both significantly above the
\nnational Black mean of 85. To be more informative, future studies need to be
\nsupplemented by follow-up testing, as in the Minnesota Study. Behavior genetic
\nstudies consistently show that, as people age, their genes exert ever more influence,
\nwhereas family socialization effects decrease (see Figure 3). Trait differences
\nnot apparent early in life begin to appear at puberty and are completely
\napparent by age 17.
\nThree studies of East Asian children adopted by White families support the
\nhereditarian hypothesis. In the first, 25 four-year-olds from Vietnam, Korea,
\nCambodia, and Thailand, all adopted into White American homes prior to 3 years
\nof age, excelled in academic ability with a mean IQ score of 120, compared with
\nthe U.S. norm of 100 (Clark & Hanisee, 1982). Prior to placement, half of the
\nbabies had required hospitalization for malnutrition.
\nIn the second study, Winick, Meyer, and Harris (1975) found 141 Korean
\nchildren adopted as infants by American families exceeded the national average
\nin both IQ and achievement scores when they reached 10 years of age. The
\nprincipal interest of the investigators was on the possible effects of severe
\nmalnutrition on later intelligence, and many of these Korean children had been
\nmalnourished in infancy. When tested, those who had been severely malnourished
\nas infants obtained a mean IQ of 102; a moderately well-nourished group obtained
\na mean IQ of 106; and an adequately nourished group obtained a mean IQ of 112.
\nA study by Frydman and Lynn (1989) examined 19 Korean infants adopted
\nby families in Belgium. At about 10 years of age, their mean IQ was 119, the
\nverbal IQ was 111, and the performance IQ was 124. Even correcting the Belgian
\nnorms upward to 109 to account for the increase in IQ scores over time (about 3
\nIQ points a decade; see Section 13), the Korean children still had a statistically
\nsignificant 10-point advantage in mean IQ over indigenous Belgian children.
\nNeither the social class of the adopting parents nor the number of years the child
\nspent in the adopted family had any effect on the child\u2019s IQ.
\nSection 8: Mean Race\u2013IQ Differences and Racial Admixture
\nIn the Minnesota Transracial Adoption Study, the IQs of the mixed-race
\n(Black\/White) adoptees averaged between those of the \u201cnonmixed\u201d White and the
\n\u201cnonmixed\u201d Black adoptees, as expected under a genetic hypothesis (see Table 2).
\nResults from some other types of studies are also consistent with that hypothesis.
\nIn her review, Shuey (1966) found that in 16 of 18 studies in which skin color
\ncould be used as a proxy for amount of admixture, Blacks with lighter skin color
\naveraged higher scores than those with darker skin, although the magnitude of the
\nassociation was quite low (r \u0001 .10). The Black American average IQ of 85 (15
\npoints higher than the sub-Saharan African average of 70; see Section 3) is also
\nconsistent with the genetic hypothesis, given the approximately 20% White
\nadmixture of Black Americans (Chakraborty, Kamboh, Nwankwo, & Ferrell,
\n1992; Parra et al., 1998). The mixed-race \u201cColored\u201d population of South Africa
\nalso has an average IQ of 85, intermediate to the respective African and White
\nmeans of 70 and 100 (Owen, 1992). Early studies of brain weight data also fit with
\nthe genetic hypothesis. Bean (1906) found, as did Pearl (1934), that the greater the
\namount of White admixture (judged independently from skin color), the higher the
\nmean brain weight at autopsy in Black groups. More recent data of this nature are
\nnot available.
\nThe average IQ scores of around 70 for Black Americans in certain areas of
\nthe Deep South of the United States where the degree of White admixture is
\nsignificantly below the general average (Chakraborty et al., 1992; Parra et al.,
\n1998) are also consistent with the hereditarian interpretation of the effects of
\nhybridization. An average IQ of 71 was found for all of the Black children in an
\nentire school district from a rural county in Georgia; the average White IQ in the
\nsame county was 101 (Jensen, 1977). Similarly, Stanley and Porter (1967) found
\nthe scores on the SAT of all-Black college students in Georgia were too low to be
\npredictive of college grades, thereby raising the question of whether test scores on
\nBlack Americans are as valid as those for White Americans. However, when Hills
\nand Stanley (1970) gave the School and College Ability Test (a much easier test
\nto pass) to similar students, they found that their scores were normally distributed
\nand did predict college grades, though the average for the Black college students
\nwas at about the 50th percentile on eighth-grade national norms.
\nMost recently, Lynn (2002) and Rowe (2002) analyzed data from large,
\npublicly available, archival data sets, which show that groups of mixed-race
\nindividuals have mean scores intermediate to unmixed groups of Blacks and of
\nWhites. Lynn examined the 1982 National Opinion Research Center\u2019s survey of
\na representative sample of the adult population, excluding non-English speakers.
\nThe 442 Blacks in the sample were asked whether they would describe themselves
\nas \u201cvery dark,\u201d \u201cdark brown,\u201d \u201cmedium brown,\u201d \u201clight brown,\u201d or \u201cvery light.\u201d
\nThe correlation between these self-ratings and a 10-word vocabulary test score
\nwas .17 (p .01). Rowe examined the 1994 National Longitudinal Study of
\nAdolescent Health\u2019s survey of a representative sample of youths, with intentional
\noversampling of Black children of highly educated parents. The mean age for the
\nentire sample (9,830 Whites, 4,017 Blacks, and 119 mixed-race individuals) was
\n16 years. The Black adolescents averaged a lower birth weight, a lower verbal IQ,
\nand a higher number of sexual partners than did the White adolescents. For each
\ncharacteristic, the mixed-race mean fell between the means of the other two
\ngroups. Rowe found the social class explanation of the group differences \u201cunconvincing\u201d
\nbecause, of the three variables, only verbal IQ showed a moderate
\ncorrelation with social class and statistically adjusting for it left the main findings
\nunchanged. He also rejected the \u201cdiscrimination based on skin tone\u201d hypothesis
\nbecause it was eliminated by deliberately selecting only those mixed-race adolescents
\nwho were judged by their interviewers to be Black, based on their
\nphysical appearance.
\nThree studies of racially mixed individuals at first appear to support the
\nculture-only hypothesis against the genetic hypothesis. Eyferth (1961; Eyferth,
\nBrandt, & Hawel, 1960) reported IQ data for out-of-wedlock children fathered by
\nsoldiers stationed in Germany after World War II and then reared by White
\nGerman mothers. The mean IQs for 83 White children and for 98 racially mixed
\nchildren were both about 97 (97.2 for the Whites, 96.5 for the racially mixed). As
\nLoehlin et al. (1975, pp. 126 \u2013128) noted, however, these results are ambiguous
\nfor three reasons. First, the children were still very young when tested. One third
\nof the children were between 5 and 10 years of age, and two thirds were between
\n10 and 13 years. As discussed in Section 5 (see Figure 3), behavior genetic studies
\nshow that while family socialization effects on IQ are often strong before puberty,
\nafter puberty they dwindle, sometimes to zero. Second, 20% to 25% of the
\n\u201cBlack\u201d fathers were not African Americans but French North Africans (i.e.,
\nlargely Caucasian or \u201cWhites\u201d as we have defined the terms here). Third, there
\nwas rigorous selection based on IQ score in the U.S. Army at the time, with a
\nrejection rate for Blacks on the preinduction Army General Classification Test of
\nabout 30%, compared with 3% for Whites (see Davenport, 1946, Tables I and III).
\nThe second study reports a 9-point IQ advantage for the 4-year-old offspring
\nof couples with a White mother and a Black father (mean IQ \u0001 102, N \u0001 101)
\ncompared with those from the offspring of a Black mother and a White father
\n(mean IQ \u0001 93, N \u0001 28). Willerman, Naylor, and Myrianthopoulos (1974),
\nassuming White mothers provide better pre- or postnatal environments for their
\nchildren than do Black mothers, interpreted their data as more consistent with a
\ncultural than a genetic hypothesis (see also Nisbett, 1998). However, Loehlin et
\nal. (1975, p. 126) noted that the mixed-race pairs with White mothers averaged
\nalmost a year more schooling than did the pairs with Black mothers. Thus the
\nWhite mothers may have had a higher average IQ than the Black ones. The
\nmid-parent IQs have to be the same for the results to be interpretable. Also, the
\ntwo sets of mixed-race children averaged an IQ of 98, intermediate to the White
\nand Black children in the sample from whom the mixed-race children had been
\ndrawn (IQs \u0001 105 and 91, respectively; Broman, Nichols, & Kennedy, 1975,
\np. 43).
\nThe third study seeming to support the culture-only hypothesis is a subsidiary
\nanalysis by Moore (1986; see Section 8) of a small number of 7-year-old children
\nadopted by middle-class White parents. Moore found no difference in IQ between
\nthose children with only one and those with two Black biological parents. The
\nmean IQ for the group of 9 adopted children with two Black biological parents
\nwas 109, and the mean IQ for the group of 14 children with one Black and one
\nWhite biological parent was 107. Given the young age of these children, a
\nfollow-up to adolescence would be informative.
\nStudies of blood groups provide no support for the hereditarian perspective.
\nBoth Loehlin, Vandenberg, and Osborne (1973) and Scarr, Pakstis, Katz, and
\nBarker (1977) found that blood groups distinguishing African from European
\nancestry did not predict IQ scores in Black samples. However, these studies failed
\nto choose genetic markers with large allele frequency differences between Africans
\nand Europeans (Jensen, 1998b, pp. 480, 524 n.64).
\nMolecular genetic technology was unsophisticated in the 1970s. In the future,
\nthe issue may be resolved by calculating individual admixture through the use of
\nDNA markers as already occurs in medicine (Risch et al., 2002). On the basis of
\nexisting surveys, an individual\u2019s racial group can be determined by testing his or
\nher DNA at 100 random sites along the genome, or at 30 specifically chosen ones.
\nEven different ethnic groups within a race can be distinguished using some 50
\nspecifically chosen sites. A genetic hypothesis predicts that for those Black
\nindividuals who possess more White genes, their physical, behavioral, and other
\ncharacteristics will approach those of Whites.
\nAlthough the studies of racial hybrids are generally consistent with the genetic
\nhypothesis, to date they are not conclusive. It may be true, for example, that
\nlighter skinned Cape Coloreds and African Americans have better nutrition, have
\ngreater opportunities for learning, or are treated better by their societies. On the
\nother hand, the Minnesota Transracial Adoption Study (Table 2) held many such
\nfactors constant and removed the most frequently proposed causal agents such as
\npoverty, malnutrition, poor schools, and dysfunctional neighborhoods. Yet, here
\ntoo, the mixed-race children had a higher mean IQ than did the children of two
\nBlack parents, and the means for each group were very similar to those for their
\nrespective counterparts elsewhere in the United States. The discussion in this
\nsection is particularly supportive of Loehlin\u2019s (2000) conclusion that \u201cResearch
\nusing larger samples and better techniques for estimating ancestry is called for and
\nquite feasible\u201d (p. 188).
\nSection 9: Mean Race\u2013IQ Differences and Regression to the Mean
\nRegression toward the mean provides still another method of testing if the
\ngroup differences are genetic. Regression toward the mean is seen, on average,
\nwhen individuals with high IQ scores mate and their children show lower scores
\nthan their parents. This is because the parents pass on some, but not all, of their
\ngenes to their offspring. The converse happens for low IQ parents; they have
\nchildren with somewhat higher IQs. Although parents pass on a random half of
\ntheir genes to their offspring, they cannot pass on the particular combinations of
\ngenes that cause their own exceptionality. This is analogous to rolling a pair of
\ndice and having them come up two 6\u0004s or two 1\u0004s. The odds are that on the next
\nroll, you will get some value that is not quite as high (or as low). Physical and
\npsychological traits involving dominant and recessive genes show some regression
\neffect. Genetic theory predicts the magnitude of the regression effect to be
\nsmaller the closer the degree of kinship between the individuals being compared
\n(e.g., identical twin \u0005 full-sibling or parent\u2013 child \u0005 half-sibling). Culture-only
\ntheory makes no systematic or quantitative predictions.
\nFor any trait, scores should move toward the average for that population. So
\nin the United States, genetic theory predicts that the children of Black parents of
\nIQ 115 will regress toward the Black IQ average of 85, whereas children of White
\nparents of IQ 115 will regress toward the White IQ average of 100. Similarly,
\nchildren of Black parents of IQ 70 should move up toward the Black IQ average
\nof 85, whereas children of White parents of IQ 70 should move up toward the
\nWhite IQ average of 100. This hypothesis has been tested and the predictions
\nconfirmed. Regression would explain why Black children born to high IQ,
\nwealthy Black parents have test scores 2 to 4 points lower than do White children
\nborn to low IQ, poor White parents (Jensen, 1998b, p. 358). High IQ Black
\nparents do not pass on the full measure of their genetic advantage to their children,
\neven though they gave them a good upbringing and good schools, often better than
\ntheir own. (The same, of course, applies to high IQ White parents.) Culture-only
\ntheory cannot predict these results but must argue that cultural factors somehow
\nimitate the effect theoretically predicted by genetic theory, which have also been
\ndemonstrated in studies of physical traits and in animals.
\nJensen (1973, pp. 107\u2013119) tested the regression predictions with data from
\nsiblings (900 White sibling pairs and 500 Black sibling pairs). These provide an
\neven better test than parent\u2013 offspring comparisons because siblings share very
\nsimilar environments. Black and White children matched for IQ had siblings who
\nhad regressed approximately halfway to their respective population means rather
\nthan to the mean of the combined population. For example, when Black children
\nand White children were matched with IQs of 120, the siblings of Black children
\naveraged close to 100, whereas the siblings of White children averaged close to
\n110. A reverse effect was found with children matched at the lower end of the IQ
\nscale. When Black children and White children are matched for IQs of 70, the
\nsiblings of the Black children averaged about 78, whereas the siblings of the
\nWhite children averaged about 85. The regression line showed no significant
\ndeparture from linearity throughout the range of IQ from 50 to 150, as predicted
\nby genetic theory but not by culture-only theory.<\/p>\n

Section 10: The Race\u2013Behavior Matrix
\nAround the world, the rate of dizygotic (i.e., two-egg) twinning is less than 4
\nper 1,000 births among East Asians, 8 among Whites, and 16 or greater among
\nBlacks (Bulmer, 1970). Multiple birthing rates have been shown to be heritable,
\nbased on the race of the mother, regardless of the race of the father, as found in
\nEast Asian\u2013White crosses in Hawaii and White\u2013Black crosses in Brazil (Bulmer,
\n1970).
\nOn average, Black babies are born a week earlier than White babies, yet they
\nare more mature as measured by pulmonary function, amniotic fluid, and bone
\ndevelopment. In the United States, 51% of Black children have been born by week
\n39 of pregnancy compared with 33% of White children. Black African babies,
\neven those born to mothers in the professional classes, are also born earlier than
\nWhite babies (Papiernik, Cohen, Richard, de Oca, & Feingold, 1986). They are
\nnot born premature but sooner, and they are biologically more mature.
\nAfter birth, Black babies continue to mature faster, on average, than White
\nbabies, whereas East Asian babies average an even slower rate. X-rays show a
\nfaster rate of average bone growth in Black children than in White children, and
\na faster rate in White children than in East Asian children (Eveleth & Tanner,
\n1990, pp. 154 \u2013155). Black babies at a given age also average greater muscular
\nstrength and a more accurate reach for objects. Black children average a younger
\nage of sitting, crawling, walking, and putting on their own clothes than Whites or
\nEast Asians. The average age of walking is 13 months in East Asian children, 12
\nmonths in White children, and 11 months in Black children (Bayley, 1965;
\nBrazelton & Freedman, 1971).
\nBlacks average a faster rate of dental development than do Whites, who have
\na faster rate than do East Asians. On average, Black children begin the first stage
\nof permanent tooth growth at about 5.8 years, whereas Whites and East Asians do
\nnot begin until 6.1 years (Eveleth & Tanner, 1990, pp. 158 \u2013161). Blacks also have
\nan earlier age of sexual maturity than do Whites, who in turn have an earlier
\naverage age than do East Asians, whether measured by age of first menstruation,
\nfirst sexual experience, or first pregnancy (Rushton, 2000, pp. 147\u2013150).
\nMyopia (nearsightedness) is positively correlated with IQ and may be caused
\nby extra myelinization in the eye and so possibly linked to brain size (Miller,
\n1994). The relationship appears to be pleiotropic (Cohn, Cohn, & Jensen, 1988);
\nthat is, a gene affecting one trait also has some effect on one or more others. There
\nare significant racial and ethnic differences in the frequency of myopia, with the
\nhighest rates found in East Asians, the lowest rates among Blacks, with Whites
\nintermediate (Post, 1982).
\nNot just in the United States but around the world, East Asians and Blacks fall
\nat the two ends of a continuum with Whites intermediate, not only on mean
\ncognitive test scores and brain size measures but also on 60 life-history variables
\nthat provide measures of maturation, personality, reproduction, and social organization.
\nIt seems unlikely that social factors alone could produce this consistent
\npattern on so diverse a set of behaviors (see Table 3; Rushton, 2000, p. 5, Table
\n1.1 for complete list). This evidence raises the theoretical question of whether
\nsingle traits such as intelligence are part of a broader \u201clife-history\u201d perspective.<\/p>\n

Section 11: Mean Race\u2013IQ Differences and Human Origins
\nThe currently most commonly accepted view of human origins, the \u201cOut-ofAfrica\u201d
\ntheory, posits that Homo sapiens arose in Africa about 150,000 years ago,
\nexpanded northward beyond Africa about 100,000 years ago, with a European\u2013
\nEast Asian split about 41,000 years ago (Cavalli-Sforza et al., 1994; Stringer &
\nMcKie, 1996). In Cavalli-Sforza\u2019s (2000) maximum likelihood tree devised on
\nthe basis of molecular genetic markers, the most distant group was the Africans,
\nwith Europeans and Asians being closer. Cavalli-Sforza observed, \u201cAll world
\ntrees place the earliest split between Africans and non-Africans, which is expected
\ngiven that all humans originated in Africa\u201d (p. 72). This is also the conclusion of
\nother reviewers (e.g., Risch et al., 2002).
\nEvolutionary selection pressures were different in the hot savanna where
\nAfricans lived than in the cold northern regions Europeans experienced, or the
\neven colder Arctic regions of East Asians. These ecological differences affected
\nnot only morphology but also behavior. It has been proposed that the farther north
\nthe populations migrated out of Africa, the more they encountered the cognitively
\ndemanding problems of gathering and storing food, gaining shelter, making
\nclothes, and raising children successfully during prolonged winters (Rushton,
\n2000). As these populations evolved into present-day Europeans and East Asians,
\nthe ecological pressures selected for larger brains, slower rates of maturation, and
\nlower levels of testosterone\u2014with concomitant reductions in sexual potency,
\naggressiveness, and impulsivity; increases in family stability, advanced planning,
\nself-control, rule following, and longevity; and the other characteristics listed in
\nTable 3. The fact that the three-way pattern in IQ, brain size, and other traits is not
\nunique to the United States but occurs internationally is consistent with a single,
\ngeneral (genetic\u2013 evolutionary) theory, whereas culture-only theory must invoke a
\nnumber of highly localized, specific explanations.
\nAs Homo sapiens migrated further away from Africa, the random genetic
\nmutations that occur at a constant rate in all living species accumulated, along
\nwith the adaptive changes. The resulting differences in allele frequencies are
\nsufficient for numerous and extensive genetic investigations to yield essentially
\nthe same picture and identify the same major racial groupings as did the morphological
\nmarkers of classical anthropology. The greatest genetic divergence
\nwithin the human species is between Africans (who have had the most time for
\nrandom mutations to accumulate) and non-Africans (Cavalli-Sforza 2000; Cavalli-Sforza
\net al., 1994; Nei & Roychoudhury, 1993). Jensen (1998b, pp. 517\u2013520)
\ncarried out a principal-components analysis of data on genetic markers from Nei
\nand Roychoudhury (1993) and found the familiar clustering of races: (a) East
\nAsians, (b) Europeans and East Indians, (c) South Asians and Pacific Islanders, (d)
\nAfricans, (e) North and South Amerindians and Eskimos, and (f) Aboriginal
\nAustralians and Papuan New Guineans. Howells\u2019s (1993) analysis of betweengroups
\nvariation in craniometric data also revealed a similar population tree. The
\ngenetic hypothesis is consistent with the latest findings on human origins and
\ngenetic variation, whereas culture-only theory is indifferent to them (Crow, 2002).
\nSection 12: How Well Have Culture-Only Theories of Mean Race\u2013IQ
\nDifferences Held Up?
\nCulture-only hypotheses have not explained the mean Black\u2013White group
\ndifferences in IQ. (They have especially not explained the findings on East
\nAsians.) One early view was that the mean Black\u2013White group difference in IQ
\nwas due to the then obvious differences in (segregated) school facilities (Myrdal,
\n1944). However, despite the U.S. Supreme Court Brown v. Board of Education
\n(1954) decision striking down segregated schooling, and the consequent nationwide
\nprogram of school busing, the mean Black\u2013White group difference has not
\ndecreased. Moreover, the Coleman Report (Coleman et al., 1966) found that the
\nracial composition of schools per se was not related to achievement in either
\nBlacks or Whites. Most of the variation in IQ scores occurred within schools and
\nless than 20% occurred between schools. Negligible, and in some cases, negative
\ncorrelations were found between IQ and variables such as pupil expenditure,
\nteachers\u2019 salaries, teachers\u2019 qualifications, student\/teacher ratios, and the availability
\nof other school professionals (see also Coleman, 1990 \u20131991).
\nThe most frequently stated culture-only hypothesis is that the mean IQ
\ndifferences are due to SES. In fact, controlling for SES only reduces the mean
\nBlack\u2013White group difference in IQ by about a third, around 5 IQ points. The
\ngenetic perspective does not regard this control for SES as being entirely environmental.
\nIt holds that the parents\u2019 socioeconomic level in part reflects their
\ngenetic differences in intelligence. Moreover, according to the culture-only theory,
\nas Black groups advance up the socioeconomic ladder, their children should
\nbe less exposed to environmental deficits and therefore should do better and, by
\nextension, close the distance separating the Black mean with the White. In fact,
\nthe magnitude of the mean Black\u2013White group difference in IQ for higher SES
\nlevels, when measured in standard deviations, is larger (Herrnstein & Murray,
\n1994, pp. 286 \u2013289).
\nOther nongenetic hypotheses are that standard IQ tests are culturally biased
\nbecause the test items are not equally familiar and motivating to all groups or that
\nthey only measure familiarity with middle-class language or culture. However,
\ndespite attempts to equate items for familiarity and culture-fairness, no \u201cculturefair\u201d
\ntest has eliminated the mean group difference. American Blacks actually
\nhave higher average scores on culturally loaded tests than on culturally reduced
\ntests, which is the opposite to what is found for some other groups such as
\nMexican Indians and East Asians. (The mean Black\u2013White group differences are
\ngreatest on the g factor, regardless of the type of test from which g is extracted;
\nsee Section 4.) Moreover, the three-way pattern of mean Black\u2013White\u2013East Asian
\ngroup differences occurs worldwide on culture-fair reaction time measures, which
\nall children can do in less than 1 s (see Section 3).
\nSubsequent culture-only hypotheses have pointed to specific aspects of deprivation
\nas possible determinants of IQ. These include the following: (a) lack of
\nreading material in the home, (b) poor cultural amenities in the home, (c) weak
\nstructural integrity of the home, (d) foreign language in the home, (e) low
\npreschool attendance, (f) no encyclopedia in the home, (g) low level of parental
\neducation, (h) little time spent on homework, (i) low parental educational desires
\nfor child, (j) low parental interest in school work, (k) negative child self-concept
\n(self-esteem), and (l) low child interest in school and reading. However, both
\nwithin-race kinship studies and across-race adoption studies show that these
\nenvironmental variables have increasingly smaller effects on the adoptees\u2019 IQ as
\nthey reach adolescence (see Sections 5 and 7). Moreover, other studies found that
\nAmerican Indians and East Asians averaged higher in IQ than Blacks, even
\nthough they averaged lower on these proposed causal factors (Coleman et al.,
\n1966, p. 20). Another example comes from the Inuit, who live above the Arctic
\nCircle and have higher average IQs than do either American or Jamaican Blacks
\n(Berry, 1966; MacArthur, 1968) even though their socioeconomic conditions are
\nextremely poor and unemployment is high (P. E. Vernon, 1965, 1979).
\nIn the 1960s, culture-only theory formed the basis for implementing \u201cHead
\nStart\u201d-type intervention programs as a way to eliminate the group differences in
\nIQ and scholastic achievement. Although federal matching grants were given to
\nimprove the learning skills, social skills, and health status of low-income preschool
\nchildren so that they could begin schooling on an equal footing with their
\nmore advantaged peers, the mean Black\u2013White group difference in IQ was not
\neliminated or permanently reduced. Currie and Thomas (1995) reviewed the
\nliterature and carried out a longitudinal study using a national sample of over
\n4,000 children in which they compared siblings to control for selection bias. They
\nfound that although Head Start led to large and significant immediate gains in test
\nscores for both White and Black groups, these gains were quickly lost for Black
\ngroups, although some remained for White groups. Even more intensive and
\nprolonged educational interventions than Head Start have not produced lasting
\neffects on IQ or scholastic performance (Jensen, 1998b, pp. 333\u2013344) or that
\ngeneralize to other measures or criteria.
\nSome culture-only theorists propose that SES should not be assessed in terms
\nof crude material measures but must be seen as a complex of attitudes, aspirations,
\nself-images, and societal stereotypes (Loury, 2002; Ogbu, 2002; Sowell, 1994).
\nSome of these types of cultural factors have been tested as well. Matching Black
\nand White children for the geographical areas of their homes, the schools they
\nattend, and other finer grade socioeconomic indicators again reduces the mean
\ngroup IQ difference but does not eliminate it. Black children from the best areas
\nand schools (those producing the highest average scores) still average slightly
\nlower than do White children with the lowest socioeconomic indicators (Herrnstein
\n& Murray, 1994, pp. 286 \u2013289; Jensen, 1998b, pp. 357\u2013360). This is an
\nanomaly for the culture-only theory but is explained by genetic theory through
\nregression to the mean (see Section 10).
\nOther culture-only hypotheses have invoked Black role models, test anxiety,
\nself-esteem, and racial stress as causal agents, but none of these have ever been
\nconsistently confirmed (Jensen, 1980, 1998b). Other ideas, such as stereotype
\nthreat (Steele, 1997), involuntary-minorities-are-castes (Ogbu, 2002), and race
\nstigma (Loury, 2002), do not explain the low IQ of Africans south of the Sahara,
\nwhere Blacks are in the majority. Nor is there any evidence from analyses of large
\narchival data sets that unique minority-specific factors such as the history of
\nslavery, White racism, lowered expectations, or heightened stress make cultural
\ninfluences stronger for one group than for another (see Section 5). Neither can
\nracial stigmatization (Loury, 2002) explain why East Asians average higher in IQ
\nand brain size than Whites. A progressive theory of racial group differences must
\naddress all the known facts.
\nCulture-only theory must offer some explanation why its main variables\u2014
\npoverty, social class, religious beliefs, cultural practices, father absence, and
\nparenting styles\u2014account for so little variance within groups. Given these repeated
\nfindings, it is unlikely such variables can account for differences between
\ngroups (see Section 5). Adoption and twin studies show that the environmental
\nvariables influencing IQ and social behavior are primarily those that occur within
\nfamilies rather than between families (see Figure 3). Although the causes of
\nwithin-group differences are logically separate from the causes of between-groups
\ndifferences (Section 2), even when the combined set of within- and betweenfamilies
\nvariables is examined together, there are still no identifiable race-specific
\nvariables (Section 5).
\nIt is always possible that new data with sharper hypotheses and better controls
\ncould require a revision of the finding of no shared family or minority-specific
\ncultural effects on race\u2013IQ differences. There were hints (but no more than that)
\nof a lower heritability and a greater shared environment component in Black
\nadolescents than in White adolescents in Rushton and Osborne\u2019s (1995) twin
\nstudy of cranial capacity (Section 5). Similarly, an epidemiological study of
\nlow-birth-weight and normal children, followed from 6 to 11 years of age,
\nreported an IQ decline in mainly Black inner-city children with no similar IQ
\ndecline in mainly White suburban children. The authors interpreted their results as
\na between-community effect and the racial makeup of the schools the children
\nattended, more than to individual and family factors (Breslau et al., 2001).
\nBehavioral genetic designs using traditional modeling procedures (Section 5),
\nalong with new individual admixture measures on mixed-race participants (Section
\n8), could provide counterevidence to our conclusions. Unfortunately, behavioral
\ngeneticists (who have the most knowledge of the best techniques) have for
\nthe most part avoided the racial question.
\nOne culture-only hypothesis currently enjoying much support is based on the
\nsecular increase in test scores, known as the Flynn effect because of the repeated
\ndemonstration by James Flynn (1984, 1987, 1999) that the average IQ in several
\ncountries has increased by about 3 points a decade over the last 50 years. Some
\nhave suggested that the Flynn effect implies that the 1 standard deviation difference
\nin the mean Black\u2013White IQ difference in the United States will gradually
\ndisappear over time (Flynn, 1999). However, one statistical analysis shows that
\nthe Flynn effect is not on the g factor, the principal source of the mean Black\u2013
\nWhite group difference.
\nTable 4 (based on Rushton, 1999) shows the results of a principal-components
\nanalysis of the secular gains in IQ from the United States, Germany, Austria, and
\nScotland, along with Black\u2013White IQ difference scores from the United States,
\ninbreeding depression scores from cousin-marriages in Japan, and g loadings from
\nthe standardization samples of the WISC\u2013R and WISC\u2013III. The relevant findings
\nare as follows: (a) The IQ gains on the WISC\u2013R and WISC\u2013III form a cluster,
\nshowing that the secular trend in overall test scores is a reliable phenomenon; but
\n(b) this cluster is independent of a second cluster formed by Black\u2013White
\ndifferences, inbreeding depression scores (a purely genetic effect), and g factor
\nloadings (a largely genetic effect).
\nThis analysis shows that the secular increase in IQ behaves differently from
\nthe mean Black\u2013White group difference in IQ. Flynn\u2019s (1999) hypothesis that the
\nIQ gains over time imply a purely environmental origin of mean racial-group
\ndifferences is not supported. Although the Flynn effect does suggest that improving
\nthe environment, especially at the low end of the IQ distribution, can improve
\ntest scores, the cluster analysis shows that the g factor is independent of the Flynn
\neffect. Instead, g is associated with inbreeding depression, for which there is no
\nnongenetic explanation, which implies strongly that g is less amenable to environmental
\nmanipulation. These findings are consistent with an analysis of adoption
\ndata, which shows the IQ gains that result from being adopted into high SES
\nhomes do not produce a gain in g but only in non-g factors and in specificity of
\nthe various subtests. The adopted children\u2019s g factor scores reflected the SES level
\nof their biological parents (Jensen, 1998a).
\nDickens and Flynn (2001) replied to Rushton\u2019s (1999) cluster analysis with a
\nmore general statement of having resolved the paradox of how high heritabilities
\ncould go along with large secular increases in IQ. Their solution rests on the role
\nof genotype\u2013 environment correlation. Recall from Section 5 that this occurs
\nlargely through the individual\u2019s genetic tendency to encounter, select, or create
\ncertain aspects of the environment in preference to alternatives. Genotype\u2013
\nenvironment correlation is part of the mechanism by which genetic proclivities
\nbecome realized. Dickens and Flynn hypothesized that the positive feedback
\neffects from even small initial environmental advantages stimulate mental development
\nand lead to an even more favorable environment, stimulating yet more IQ
\ndevelopment.
\nDickens and Flynn\u2019s (2001) model, however, appears inconsistent with
\nsome empirical evidence. Gene\u2013 environment correlation cannot explain the
\nmean Black\u2013White group difference in IQ because it implies that Black
\ngroups, in comparison with White groups, become increasingly disadvantaged
\nduring the developmental period from early childhood to maturity. With
\nincreasing age there would be cumulative unfavorable effects on IQ for Black
\ngroups with respect to White groups. Yet national data (reviewed in Section
\n3) show that the size of the mean Black\u2013White group difference remains
\napproximately constant at 1 standard deviation and shows no significant
\nchange throughout the developmental period after about 3 years of age. The
\nfollow-up results of the Minnesota Transracial Adoption Study (Table 2), and
\nthe fact that the heritability for IQ generally increases from about 0.40 in early
\nchildhood to about 0.80 in later maturity (Figure 3), both contradict the
\nDickens\u2013Flynn thesis. So too does the fact that both g estimates calculated
\nfrom East Indians in South Africa and genetic estimates calculated from the
\nJapanese in Japan are able to predict the magnitude of Black\u2013White differences
\nin South Africa and in the United States (see Sections 4 and 5). Such
\nrobust generalization implies that the mean Black\u2013White group difference in
\nIQ is sufficiently persistent across cultures as to be unaffected by major
\nchanges in gene\u2013 environment correlations.
\nDickens and Flynn (2001) provided no empirical evidence that gene\u2013 environment
\ncorrelation constitutes either a large component of the phenotypic variance
\nor that it increases with age (both of which are required by their model).
\nThey also did not provide any other direct empirical evidence. In addition, their
\nmodels have been criticized for not taking the stability of IQ scores over time into
\naccount and for having too many free parameters (Loehlin, 2002; Rowe &
\nRodgers, 2002), to which Dickens and Flynn (2002) have replied. Because to date
\nDickens and Flynn have not given the high heritability of IQ any independent
\ncausal effect in explaining the mean Black\u2013White group difference, their thesis is
\nbest placed in the culture-only camp.
\nSection 13: Evaluating the Culture-Only and the Hereditarian
\nResearch Programs
\nTable 5 summarizes the 30-plus-years of research on Black\u2013White IQ differences
\ncarried out since Jensen\u2019s (1969) Harvard Educational Review article. It
\ncompares and contrasts the predictions of the hereditarian and the culture-only
\ntheories against the existing data reviewed in Sections 3 through 12, to which we
\nthen assigned \u201cscores.\u201d We assigned the highest score (\u0007\u0007) when, in our opinion,
\nthe results confirmed a novel prediction first derived from theory that was then
\ntested and confirmed. We awarded the next highest score (\u0007) when the results
\nwere consistent with theory but not predicted from it. We gave a neutral score (0)
\nwhen the results could not be predicted from theory so that it could be either
\nconfirmed or disconfirmed. We assigned a negative score (\u2013) when the predicted
\nresults were disconfirmed. Because some diacritical tests have two components,
\nthe maximum possible support for either research program would be a score of
\n12 2 \u0001 24; maximum disconfirmation would be a score of \u201324. Naturally these
\nscores reflect our particular evaluation of how well the two competing theories
\npredict and explain the evidence. We acknowledge that others might see things
\ndifferently, and we invite them to assign their scores. Our purpose is to advance
\nthe debate.
\nOur evaluation of the evidence supports a cumulative score of 17 for the
\nhereditarian model and \u20137 for the culture-only model. We therefore suggest that
\nthe hypothesis of some genetic component in the mean Black\u2013White group
\ndifference in IQ should be considered \u201cprovisionally true.\u201d Naturally, we do not
\nexpect everyone to agree with this assessment. Our own perspective is obviously
\nhereditarian (Jensen, 1998b; Rushton, 2000). Those working from a different
\nperspective may arrive at alternative tallies or add new dimensions to be tallied
\nthat we have overlooked. Before discussing our conclusion, we consider in more
\ndetail the data on each of the categories in Table 5.<\/p>\n

Mean Race\u2013IQ Differences Are Found Worldwide (Section 3)
\nThe mean Black\u2013White IQ difference in the United States of 85 versus 100
\ncan be, and has been, explained both by the hereditarian model (in terms of some
\ngenetic difference) and by the culture-only model (in terms of nutrition, poverty,
\nSES, family structure, schooling, racism, and the legacy of slavery). Hence,
\ninitially we were inclined to give both the hereditarian model and the culture-only
\nmodel a score of (\u0007). The hereditarian model, however, also predicted that the
\nsame pattern would be found worldwide, with lower scores for sub-Saharan
\nAfrica than for Black Americans, and that the differences would also be found on
\nculture-fair tests and on reaction time tasks that measure the speed and efficiency
\nwith which the brain processes information (and which all children can perform
\nin less than 1 s). These predictions were confirmed. The culture-only hypothesis
\nis disconfirmed by the differences on culture-fair and reaction time tests. Nor can
\nthe culture-only model easily explain why the East Asian average IQ of 106 is
\nhigher than the average White IQ, including on these same speed-of-processing
\ntasks. Within the United States, the mean Black\u2013White group difference in IQ has
\nnot changed significantly over the past 100 years despite significant improvements
\nin the conditions of Black Americans. The same magnitude of difference is
\nobserved as early as age 21\u20442 years. Our score for Section 3: hereditarian model
\n(\u0007); culture-only model (\u2013).
\nRace\u2013IQ Differences Are Most Pronounced on the More g-Loaded
\nComponents of Tests and Least So on the Most Culturally Loaded Items
\n(Section 4)
\nThe hereditarian model made the novel prediction that the mean Black\u2013White
\ngroup difference in IQ is not the result of idiosyncratic cultural peculiarities in this
\nor that test but would be more pronounced on highly g-loaded tests. Because the
\nprediction was confirmed, it counts as evidence for the hereditarian position while
\nalso contradicting the prediction from the culture-only model that the differences
\nare due to culturally loaded tests. In South Africa, g loadings calculated on East
\nIndians predicted mean Black\u2013White group differences, showing substantial
\ncross-cultural generalizability. A test\u2019s g loading is the best predictor, not just of
\nits correlation with scholastic and workplace performance, but also of its correlation
\nwith reaction time measures, heritability coefficients determined from twin
\nstudies, inbreeding depression scores calculated in children of cousin-marriages,
\nand neurological variables such as brain size, brain evoked potentials, brain pH
\nlevels, brain glucose metabolism, and nerve conduction velocity. Thus, we conclude
\nthe evidence reviewed in Section 4 strongly supports the hereditarian model
\n(\u0007\u0007) and argues against the culture-only model (\u2013).
\nRace\u2013IQ Differences Are Most Pronounced on the More Heritable
\nComponents of Tests With Little or No Evidence of Race-Specific
\nDevelopmental Processes (Section 5)
\nResearch based on this novel prediction from the hereditarian model established
\nthat (a) the heritability of IQ among Black groups is around 0.50, not
\nsignificantly different from that found in White groups; (b) there is no evidence
\nof the effect of any special minority-specific developmental process resulting
\nfrom the legacy of slavery or of White racism in large sets of archival correlation
\nmatrices between background variables and outcome measures, or on relations
\namong subtests; (c) IQ subtests with higher heritabilities predict mean Black\u2013
\nWhite group differences better than do subtests with lower heritabilities; and (d)
\nthe shared environment type of variables usually proposed to explain group
\ndifferences (e.g., differences in income, schools) decrease in effect size with age.
\nCross-cultural generality was demonstrated by the fact that the magnitude of
\ninbreeding depression scores on various subtests calculated on the Japanese in
\nJapan predicted the magnitude of Black\u2013White differences in the United States.
\nBecause the empirical results confirmed a novel prediction from the hereditarian
\nmodel (\u0007\u0007) but disconfirmed several predictions from culture-only theory (\u2013),
\nwe scored Section 5: hereditarian model (\u0007\u0007); culture-only model (\u2013).
\nMean Race\u2013IQ Differences Are Associated With Mean Brain Size
\nDifferences (Section 6)
\nOverall, MRI studies show that brain size is related to IQ differences within
\nrace. Moreover, the three-way pattern of group differences in average brain size
\nis detectable at birth. By adulthood, East Asians average 1 cubic inch more cranial
\ncapacity than Whites, and Whites average 5 cubic inches more cranial capacity
\nthan Blacks. These findings on group differences in average brain size have been
\nreplicated using MRI, endocranial volume from empty skulls, wet brain weight at
\nautopsy, and external head size measures. They were acknowledged by Ulric
\nNeisser, Chair of the APA\u2019s Task Force on intelligence, who noted that, with
\nrespect to \u201cracial differences in the mean measured sizes of skulls and brains (with
\nEast Asians having the largest, followed by Whites and then Blacks) . . . there is
\nindeed a small overall trend\u201d (Neisser, 1997, p. 80). The hereditarian model
\nexplains these in terms of genetic differences. The culture-only position can
\nexplain them in terms of nutrition, SES, or early cognitive stimulation. Adding the
\nEast Asian data, however, literally \u201cchanges the shape of the table.\u201d The hereditarian
\nmodel posits that if East Asians average higher IQs than do Whites, then
\nthey must also average larger brains than Whites, and that perhaps both the higher
\nIQ and the larger brain are most parsimoniously explained in terms of the natural
\nselection experienced in colder climates during human evolution (\u0007\u0007). The
\nculture-only position has yet to explain both the higher IQ and the larger brain
\nsize of East Asians, given that these groups have also been subjected to prejudice
\nin White societies or severe malnutrition in their homelands. We scored Section
\n6: hereditarian model (\u0007\u0007); culture-only model (\u2013).
\nMean Race Differences in IQ Remain Following Transracial
\nAdoption (Section 7)
\nTransracial adoption studies provide one of the best methods for resolving the
\nquestion of group differences in mean IQ. The above-average IQ scores of Black
\nadoptees at age 7 confirmed the culture-only predictions. The results of the
\nfollow-up testing at age 17 were more ambiguous. The hereditarian model
\npredicted that when the longitudinal study was carried out, the Black\u2013White
\ndifference would emerge (based on the increasing size of the genetic effect on IQ
\nwith age), and this is one interpretation of the data, though a culture-only
\ninterpretation is also plausible. However, support for the hereditarian model again
\ncomes from adding the East Asian data to the mix. Korean and Vietnamese
\nchildren adopted into White homes, even though as babies many had been
\nhospitalized for malnutrition, nonetheless grew to have IQs 10 or more points
\nhigher than their adoptive national norms. The culture-only model cannot explain
\nthat finding. Further, it argues against the culture-only hypothesis that the high
\nperformance of East Asian children is due to \u201ctrying harder\u201d or other cultural
\nvalues emphasized by East Asian families. Our score for Section 7: hereditarian
\nmodel (\u0007\u0007); culture-only model (\u2013).
\nStudies of Racial Admixture Reflect Mean Black\u2013White IQ
\nDifferences (Section 8)
\nBoth the hereditarian and the culture-only model can explain why groups of
\nlighter skinned African Americans and the (also lighter skinned) mixed-race
\n\u201cColoreds\u201d of South Africa have average IQs between those of (for the most part)
\nunmixed groups of Blacks and Whites. Both models can also explain the fact that
\nthe degree of White admixture is correlated with brain weight at autopsy. The
\nculture-only position does so in terms of societal discrimination based on skin
\ncolor as well as its possible cascading effects on nutrition and health (\u0007); the
\nhereditarian model does so in terms of the hypothesized genetic difference in
\naverage IQ and its correlations with race and skin color (\u0007). Some evidence
\nagainst the culture-only position comes from studying the misclassified adoptees
\nin the Minnesota Transracial Adoption Study (\u2013). The expectancy effects hypothesis,
\nthat adoptive parents\u2019 beliefs about their child\u2019s racial background influence
\nthe child\u2019s intellectual development, is not supported by the finding that the mean
\nIQ score for 12 children wrongly believed by their adoptive parents to have had
\ntwo Black biological parents was about the same as that of the 56 children
\ncorrectly believed by their adoptive parents to have had one Black and one White
\nbiological parent. While the number of children is small, this conclusion is
\nsupported by Rowe\u2019s study in which 119 mixed-race children were selected as
\n\u201clooking African American\u201d but their IQ scores also turned out to be intermediate.
\nOur score for Section 8: hereditarian model (\u0007); culture-only model (0).
\nIQs Show Regression Toward Predicted Racial Means (Section 9)
\nThe phenomenon of regression to the mean is predicted from basic genetic
\ntheory and has been documented for a number of physical traits in humans and in
\nother species. The hereditarian model applied this reasoning to IQ studies to make
\na novel prediction about the amount of regression across the whole IQ distribution
\nand various degrees of kinship. The results showed that the children of Black
\nparents of IQ 115 regressed toward the Black average IQ of 85, whereas children
\nof White parents of IQ 115 regressed toward the White average IQ of 100. The
\nconverse occurred at the low end of the scale. Even stronger support for the
\nhereditarian position came from sibling data. The regression lines for both Blacks
\nand for Whites showed no significant departure from linearity throughout the
\nrange of IQ from 50 to 150. A failure of this prediction would have argued against
\nthe hereditarian model but would have been neutral for the culture-only model.
\nThe predictions from the hereditarian model were tested and confirmed. The
\nculture-only theory must argue that environmental effects or chance variation
\nmimics the predicted genetic effects. We scored Section 9: hereditarian model
\n(\u0007\u0007); culture-only model (0).
\nMean Race\u2013IQ Differences Are Paralleled by a Matrix of Other Traits
\nand Behaviors (Section 10)
\nA suite of over 60 life-history variables, including rate of two-egg twinning,
\nspeed of maturation and longevity, personality and temperament, family stability
\nand crime, sexual behavior and fertility, as well as intelligence and brain size,
\nhave been identified on which East Asian and African groups consistently average
\nat the two ends of a continuum, with European groups intermediate, regardless of
\nwhere they presently live. This race\u2013 behavior matrix constitutes a series of novel
\npredictions derived from an evolutionary theory of the origin of races that were
\ntested and confirmed. The culture-only model has only partially addressed this
\nrace\u2013 behavior matrix, with (sometimes contradictory) supplementary hypotheses.
\nOur score for Section 10: hereditarian model (\u0007\u0007); culture-only model (\u2013).
\nMean Race\u2013IQ Differences and Human Evolution (Section 11)
\nOne theory of human evolution argues that the farther north the ancestral
\nhuman populations migrated out of Africa, about 100,000 years ago, the more
\nthey encountered the cognitively demanding problems of gathering and storing
\nfood, gaining shelter, making clothes, and raising children successfully during
\nprolonged winters. (This is not the only theory of human evolution, nor do all who
\nendorse it concur with our interpretation.) Ecological pressures selected for larger
\nbrains, slower rates of maturation, lower levels of sex hormone, and all the other
\nlife-history characteristics. From this perspective, the data from both human
\ngenetics and human evolution mesh with the race\u2013 behavior matrix (\u0007\u0007). Genetic\u2013
\nevolutionary theory acknowledges factors such as East Asian family
\nstrength or African poverty, but as effects rather than causes. The consistency of
\nthe pattern of traits in Table 3 also supports the argument, as do genetic analyses,
\nagainst the view that race is only a social construction based on a few salient traits
\nsuch as skin color (Crow, 2002; Risch et al., 2002). Our score for Section 11:
\nhereditarian model (\u0007\u0007); culture-only model (\u2013).
\nCulture-Only Hypotheses Fail to Account for Mean Race\u2013IQ
\nDifferences (Section 12)
\nThis section reviewed a number of well-known culture-only hypotheses for
\nthe mean Black\u2013White group difference in IQ. The most widely accepted is that
\nthey are due to differences in SES. Adjusting for SES, however, only reduces the
\nmean Black\u2013White IQ difference by about one third. Other culture-only hypotheses,
\nsuch as the effects of segregation, bias in tests, or the consequences of being
\na minority in a White society are not supported by our review of the evidence. The
\nHead Start program in the United States has produced some modest gains in
\nincreasing school retention and graduation rates among White groups, though not
\namong Black groups (Currie & Thomas, 1995). Neither the narrowing of the
\nBlack\u2013White social conditions nor the Flynn effect (i.e., the secular rise in IQ) has
\nnarrowed the Black\u2013White IQ gap. However, the Flynn effect, based on increases
\nin nutrition, health care, and intellectual stimulation, does appear to support the
\nculture-only model (\u0007), but it is neutral (hence 0) for the hereditarian model
\nbecause large environmental effects (up to 50% of the variance) are compatible
\nwith large genetic effects (also up to 50% of the variance). The finding in Table
\n4 that the secular increase did not cluster with g and its biological correlates,
\nhowever, may support the hereditarian model. Because the real area of conflict is
\nthe cause of the mean racial group difference in cognitive ability, the hereditarian
\nhypothesis is not disproven by the Flynn effect, whatever its cause(s). Overall, on
\nSection 12, we gave the hereditarian model a score of (0) and the culture-only
\nmodel (0).
\nSection 14: Progressive Research Leads to Provisional Truth
\nOur conclusion, that the Black\u2013White IQ difference is partly heritable, accords
\nwith previous analytic reviews of this literature. Loehlin et al. (1975)
\nconcluded that Black\u2013White IQ differences \u201cprobably\u201d reflected \u201cgenetic differences
\namong the groups\u201d (p. 238). P. E. Vernon (1979) tabulated 30 main topics,
\neach scored on a 4-point scale, and concluded that \u201calthough the total number of
\nitems favoring genetic influences (G and G?) is roughly balanced by the number
\nof environmental points (E and E?), more of the highly convincing items are G
\nrather than E\u201d (p. 319). The survey of over 1,000 experts in behavioral genetics
\nand psychometrics by Snyderman and Rothman (1987) also found that a plurality
\nbelieved the Black\u2013White IQ difference \u201cto be a product of both genetic and
\nenvironmental variation\u201d (p. 141). However, there are also notable statements to
\nthe contrary. The APA Task Force on intelligence, for example, concluded
\n\u201c[t]here is certainly no support for a genetic interpretation\u201d (Neisser et al., 1996,
\np. 97). Likewise, Nisbett (1998) reached the conclusion that \u201cthe most relevant
\nstudies provide no evidence for the genetic superiority of either race\u201d (p. 101).
\nIn our opinion, the present review, similar to those of Loehlin et al. (1975) and
\nVernon (1979) earlier, should be given greater weight because they surveyed a
\ngreater range of evidence. Examining all the documentation allows a greater
\nchance of finding accurate explanations than does selecting a few items from the
\nwhole. The 10 categories of predictions reviewed in Table 5 were derived from
\nthe \u201chard core\u201d assumptions of the two competing research programs, each of
\nwhich tries to explain the Black\u2013White IQ difference (see Section 2). Based on
\nLakatos\u2019s (1970, 1978) criteria for evaluating research programs, and a philosophy
\nof science methodology that evaluates rival theories by generating multiple
\nstrong inferences and assessing the preponderance and the consilience of many
\nlines of evidence, we believe the hereditarian theory has satisfied the criteria for
\na \u201cprogressive\u201d research program, whereas the culture-only program has not. Both
\nhave drawn implications to make numerous, testable, novel predictions, but we
\nfound the hereditarian predictions were mostly confirmed, whereas those from
\nculture-only theory mostly were not.
\nSome attempts to salvage the culture-only theory have been problematic.
\nSuggestions that \u201cWhite flight\u201d undermined desegregation and busing programs
\nso that \u201ctrue\u201d integration has not yet been properly tried, that Head Start programs
\nstill have not been fully funded, that culture-fair tests were not really fair, that the
\nsensory deprivation and race-of-examiner hypotheses were of only minor significance,
\nor that being Black in America is really a matter of caste, not class or race,
\nfall short of being strong inference. Urbach (1974b, p. 237) dismissed such
\nclaims, and that the mean Black\u2013White IQ difference is a system problem, and
\nthat caste and class differences are complex, subtle, and attitudinal rather than
\ngrossly econometric, as \u201cpseudo-scientific maneuvers,\u201d noting that \u201cit is intellectually
\nimproper to obscure facts by continually retreating behind the trivial truth
\nthat the world is complex [italics added]\u201d.
\nWe believe the burden of proof must shift to those who argue for a 100%
\nculture-only position. For example, they need to address why, if important
\nminority-specific developmental processes such as stereotype threat (Steele, 1997)
\nand racial stigma (Loury, 2002) exert such a powerful influence on school
\nachievement, the correlation matrices representing developmental processes can
\nbe so similar across ethnic and racial groups (Section 5). They need to explain
\nwhy, if gene\u2013 environment interactions are as widespread and difficult to disentangle
\nas often claimed (e.g., Block, 1995), identical twins reared apart grow to be
\nso similar (Bouchard, 1996; Bouchard & Loehlin, 2001). Some culture-only
\nhypotheses are too ambiguous to be tested.
\nA conundrum for theorists of all persuasions, however, is that there is too little
\nevidence of any environmental effects. The hereditarian model of Black\u2013White IQ
\ndifferences proposed in Section 2 (50% genetic and 50% environmental), far from
\nprecluding environmental factors, requires they be found. Although evidence in
\nSections 3 to 11 provided strong support for the genetic component of the model,
\nevidence from Section 12 was unable to identify the environmental component.
\nOn the basis of the present evidence, perhaps the genetic component must be
\ngiven greater weight and the environmental component correspondingly reduced.
\nIn fact, Jensen\u2019s (1998b, p. 443) latest statement of the hereditarian model,
\ntermed the default hypothesis, is that genetic and cultural factors carry the exact
\nsame weight in causing the mean Black\u2013White difference in IQ as they do in
\ncausing individual differences in IQ, about 80% genetic\u201320% environmental by
\nadulthood.
\nOne current challenge, therefore, is to identify significant sources of nongenetic
\nvariance. It is hoped that the recent models of gene\u2013 culture correlation put
\nforward by Dickens and Flynn (2001, 2002) to explain the paradox of large
\nheritability estimates and large environmental effects mark a new trend in the
\nrace\u2013IQ debate. Their models accept the empirical reality of both genetic and
\nenvironmental influences. Within Whites, most of the nongenetic variance appears
\nto result from a small number of random adverse effects such as prenatal
\nproblems, complications in the birth process, maternal health, and childhood
\ndisease and trauma (Jensen, 1997).
\nAnother challenge is to explain the pattern of covariant traits shown in Table
\n3 and described in Section 10. Several life-history theorists working from an
\nevolutionary perspective have postulated that there is some latent dimension on
\nwhich \u201cunpredictable environments\u201d and \u201cscarcity of resources\u201d can move individuals
\nup or down because traits need to be coherent and harmonized, rather than
\nhaving some go to one pole while others go to the other (see Rushton, 2000, pp.
\n252\u2013255, 271\u2013273, for discussion). Most life-history explanations that have
\nfocused on behavioral rather than biological traits have typically avoided the race
\ndifferences and have hypothesized postnatal events such as father absence, parental
\ndivorce, and sexual and physical abuse as causes rather than effects (e.g.,
\nBelsky, Steinberg, & Draper, 1991; Chisholm, 1999). However, the fact that brain
\nsize differences show up at birth, as do those in gestation time and speed of
\nphysical maturation, implies some prenatal and biological mediation.
\nWriting from a life-history perspective, Mealey (1990) suggested that the
\nwider pattern of racial-group differences described in Section 10 was \u201cinteresting
\nand worth pursuing, but . . . may be environmentally contingent rather than
\ngenetic\u201d (p. 387). Nyborg (1994, pp. 146 \u2013149) hypothesized that an estradialtestosterone
\nhormonal trade-off existed, with Blacks averaging the most testosterone,
\nEast Asians the least, and Whites in between. Eysenck (1991) proposed a
\nnutritional deficiency hypothesis. Lynn (1990; Lynn & Vanhanen, 2002) also
\nproposed a crucial role for nutrition, finding that the heavier twin at birth typically
\nhas a larger head and brain size, as well as a higher IQ in adolescence, and that
\ninfants fed breast milk typically average higher IQs (8.3 points) at age 8 than those
\nfed formulas. Masters (1997, p. 142) put forward a \u201cneurotoxity hypothesis,\u201d in
\nwhich race differences in pre- and postnatal exposure to metal pollutants interact
\nwith stress, poor diet, alcohol, and drugs. He pointed out that breast-feeding
\nreduces the infants\u2019 exposure to metal pollutants while providing infants the long
\nchains of proteins necessary for brain development. Mothers from low socioeconomic
\ngroups typically do not breast-feed their infants, and Black mothers are
\nonly one third as likely to breast-feed their infants as White mothers (see also
\nJensen, 1998b, pp. 506 \u2013508).
\nSection 15: Implications for Public Policy
\nIt is a widely accepted fact of behavioral science that there is great variability
\nwithin each racial group and there is an ethical consensus that we treat people as
\nindividuals. Although no specific policies necessarily follow from knowing about
\nthe causes of group differences, they may serve as guides to action on some
\nissues. The conclusion reached in Sections 13 and 14\u2014that about 50% of the
\nvariance in mean Black\u2013White group differences in IQ is due to heredity\u2014is
\ncompatible with a wide range of recommendations, from programs for the
\ndisadvantaged and laissez-faire approaches to selection and opportunity grouping
\nin certain educational and vocational situations.
\nIn The Bell Curve, Herrnstein and Murray (1994) offered some specific policy
\nrecommendations based on their conclusions about genetic variation and IQ,
\nwhich are generally concordant with political conservatism, such as scaling back
\naffirmative action, reducing the intrusiveness of government, and returning to
\nindividualism. Most political conservatives, however, support these recommendations,
\nno matter how the nature\u2013nurture question is \u201cresolved,\u201d an argument
\nwith which Murray agreed (Miele, 1995). Arthur Jensen, also writing from the
\nhereditarian perspective, recently opined that giving primacy to individual rights
\nmaximizes fairness, which he pragmatically defined as the ability of each individual
\nto reach his or her full potential (Miele, 2002). He therefore argued for a
\nrestructuring of the educational system by tailoring methods to fit the individual,
\nand letting the group outcomes become what they may, rather than allowing
\nclaims of differential performance to justify group rights over individual rights.
\nA multifaceted approach to the policy implications of the hereditarian conclusion
\nabout mean Black\u2013White differences is required. Many policy preferences
\nare not at all affected by our scientific conclusion. Granting equal rights under the
\nlaw and whether to provide social welfare, for example, are based more on moral
\nand political philosophy than on research findings. Still other policy issues (e.g.,
\naffirmative action, the value of diversity) might merit reconsideration based on the
\ndegree to which heredity as opposed to culture turns out to be the causal agent.
\nFinally, certain policies do follow from our conclusions, such as the need for
\ngreater equality and neutrality in the treatment of the culture-only and genetic
\nhypothesis within both the scientific and policy arenas, and in researching and
\nameliorating the biological basis of group differences in IQ, health, and so on.
\nTwo fundamentally different, policy-organizing models typically used to
\nexplain why racial groups differ in average rate of socially valued outcomes
\nshould be examined, specifically, with reference to the issues of race relations,
\neducational and psychological testing, health, and conflicting worldviews about
\nthe nature of human nature.
\nDiscrimination or Distribution?
\nHerrnstein (1990) termed the two fundamentally different models put forth to
\nexplain why racial groups differ in their average rate of socially valued outcomes
\nthe distributional model and the discrimination model. Each may be partially
\ncorrect. The discrimination model focuses on social and institutional practices that
\ndiscriminate against members of one group (or favor members of another), thus
\ntilting the playing field. It assumes that in the absence of discrimination, outcomes
\nshould be about equal for all populations; thus evidence of differential performance
\nin itself constitutes evidence of discrimination. Factors hypothesized under
\nthis model that cause mean race differences include relative poverty, anti-Black
\nbias, a lack of access to legitimate channels of upward mobility, and dysfunctional
\nfamily organization growing out of the legacy of slavery (see Sections 12 and 13).
\nThe discrimination model has also been used to explain the overrepresentation
\nof some groups in valued outcomes. Blacks are said to excel in sports such
\nas boxing, basketball, track and field, and football because other channels of
\nupward mobility are closed to them (Hoberman, 1997). As early as the 1920s,
\nsociologists explained the underrepresentation of East Asians in U.S. crime
\nstatistics as being due to the East Asian \u201cghetto.\u201d This self-imposed segregation
\nwas seen as a response to external prejudice, which protected its members from
\nthe disruptive tendencies of the outside society.
\nThe distributional model, on the other hand, explains the overlapping of the
\nracial groups and their differing averages in terms of their mean group characteristics\u2014for
\nexample, the mean differences in heritable IQ and possibly other
\ntraits too. However, it could also fit Sowell\u2019s (1994) theory of socialization
\nthrough subtle cultural traditions, or Loury\u2019s (2002) theory of racial stigma, which
\npostulates a unique type of gene\u2013 culture correlation in which people react to
\nothers on the basis of physical appearance. Other factors hypothesized to underlie
\na distributional model include deep-rooted cultural values and family structures
\nendemic to certain populations, as well as biological variables such as body type,
\nhormonal levels, and personality and temperament. Thus according to the distributional
\nmodel, population differences are expected to occur and to do so
\nglobally.
\nThe research supporting the role of heredity in human behavior implies that
\nthe distributional model is more correct than the discrimination model. It explains
\nsome of the mean Black\u2013White group difference in IQ-related outcomes in terms
\nof the differential distribution of the genes for general mental ability. For example,
\nIQ is a significant predictor of such socially disadvantageous outcomes as
\ndropping out of high school, being unemployed, being divorced within 5 years of
\nmarriage, having an illegitimate child, living in poverty, being on welfare, and
\nincarceration. In today\u2019s technological society, everyday life itself is a type of IQ
\ntest (see R. A. Gordon, 1997; Gottfredson, 1997; Herrnstein & Murray, 1994). On
\nall of the above measures, the group means favor Whites over Blacks. Of course,
\nthis does not deny that many other attributes are also important for success in life.
\nRace Relations
\nSome have suggested that we cannot expect members of ethnic groups to
\nsimply accept the genetic component in the mean-group differences in IQ and
\nother traits. Yet, with regard to individuals within families, we do acknowledge
\nthat some siblings are more intelligent, more athletic, more physically attractive,
\nor more socially charming than others. We also accept that some families are
\ngenetically more gifted in certain areas than other families. We should, therefore,
\nby extension, be able to generalize to all the members of the human family. If
\nviewed against the backdrop that group differences are simply aggregated individual
\ndifferences, the former may be easier to accept than has hitherto been
\nthought.
\nAlthough the hereditarian model may not specify particular policies, it does
\nargue against the feasibility of some programs based on the discrimination model.
\nOne very socially significant example is that a demonstration of differential racial
\nperformance (good or bad) could not, by itself, be offered as proof of racial
\ndiscrimination because, as the evidence reviewed in this article demonstrated,
\ngenetic factors play a role in producing these differences. Rather, the burden
\nwould be on the plaintiffs to prove that the defendants had discriminated on the
\nbasis of race and not educational or vocational performance associated with race.
\nHowever, the view that one segment of the population is largely to blame for
\nthe problems of another segment can be even more harmful to racial harmony, by
\nfirst producing demands for compensation and thereby inviting a backlash. Equating
\ngroup disparities in success with racism on the part of the more successful
\ngroup guarantees mutual resentment. As overt discrimination fades, still large
\nracial disparities in success lead Blacks to conclude that White racism is not only
\npervasive but also insidious because it is so unobservable and \u201cunconscious.\u201d
\nWhites resent that nonfalsifiable accusation and the demands to compensate
\nBlacks for harm they do not believe they caused. Misplaced blame can also
\nendanger institutions. Objective standards of merit that yield racially uneven
\noutcomes when evenly applied, whether in college admissions, hiring, or day-today
\noperations of the legal system, have increasingly come under attack\u2014for
\nexample, by critical legal theorists as inherently pro-White and hence illegitimate.
\nAlthough the distributional model does not rule out affirmative action or compensation-type
\ninitiatives, it does reduce the impact of arguments in their favor
\nbased on an exclusive adherence to the discrimination model (Gottfredson, 2000;
\nLevin, 1997).
\nEducational, Vocational, and Psychological Testing
\nBecause the means for Blacks and Hispanics are lower on tests of academic
\nand vocational achievement, such as the SAT, the General Aptitude Test Battery,
\nand the ASVAB, than those for Whites and East Asians, some have claimed the
\ntests are racially biased. Yet the evidence reviewed and the distributional model
\npredict that such differences will occur worldwide (see Section 3). This is
\nsupported by the fact that these tests have about equal predictive validity for all
\ngroups who speak the same language and have been schooled in the culture of the
\ntest.
\nEthnic disparities in cognitive performance are not just \u201can American dilemma\u201d
\n(Myrdal, 1944) but are found around the world. In India, members of the
\nhigher castes obtain higher mean scores and examination marks than do those of
\nthe lower castes. In Malaysia, members of the Chinese and East Indian racial
\nminorities have higher mean scores than does the majority Malay population. In
\nSouth Africa, members of the White, East Indian, and Colored population groups
\nobtain higher mean scores than does the indigenous Black African majority
\n(Klitgaard, 1986; Lynn & Vanhanen, 2002; Nell, 2000). These facts present a
\nchallenge for policymakers attempting to design educational, vocational, and
\nmilitary systems.
\nThe apparent failure of equal opportunity programs to enable all groups in
\nsociety to perform equally scholastically or even to narrow the gap in the test
\nscores used in selection for higher education, jobs, and the Armed Forces has
\ncaused some to disparage psychological testing. Jensen (2000) proposed emphasizing
\nface validity in test construction by making item content more obviously
\nrelevant to the purpose of selection and thereby improve the public\u2019s perception
\nof the utility of tests. An example would be having all the items in a test used to
\nselect police personnel involve a typical crime scenario rather than abstract
\nanalogies (though a correct solution would still require logical reasoning).
\nMore generally, there is a need to educate the public about the true nature of
\nindividual and group differences, genetics, and evolutionary biology. Ultimately,
\nthe public must accept the pragmatic reality that some groups will be overrepresented
\nand other groups underrepresented in various socially valued outcomes.
\nOrganizations such as the APA could play a critical role in changing the zeitgeist.
\nTo do so will not be easy, for it requires overcoming deeply ingrained biases that
\noperate at several levels of the APA (Redding, 2001). The standard models of
\nsocial science from the 1930s to the present have assumed a tabula rasa perspective
\nthat precludes any analysis of hereditary group differences.
\nThe expectation of average group differences includes the statistical certainty
\nthat it does not apply to all individuals, a point that can easily be overlooked.
\nThus, any part of a general program of education must include distributional
\nstatistics so that people also learn not to stereotype or overgeneralize. This may
\nnot be as difficult a task as might be supposed. Even kindergarten children are
\ncapable of learning that although boys are typically taller than girls, many girls are
\ntaller than the average boy.
\nHealth, Medical Genetics, and Pharmaco-Anthropology
\nThe distributional model also relates to questions raised about bias in the
\nhealth system based on ethnic differences in rates of certain diseases (Risch et al.,
\n2002). From an epidemiological perspective, failure to take ethnicity into account
\ncan confound a study, particularly if the disease in question is more common in
\none ethnic group than another. The incidence of hypertension, prostate cancer, and
\nother diseases is higher in Blacks than Whites. To cite a specific example, the
\ninfant mortality rate of American Blacks is twice that of American Whites, and
\nthis difference remains significant after controlling for SES in each race.
\nDespite overlap, drug and food effects often differ sufficiently to test for them
\nindependently. One well-known example of the harm done by ignoring group
\ndifferences involves lactose intolerance. The ability of adults to digest milk easily
\nis largely limited to Caucasoids, and a lack of this knowledge may have increased
\nmortality among the needy in Third World countries that were inadvertently
\nprovided with milk products to alleviate hunger.
\nSome physicians are becomingly increasingly concerned that assuming
\nBlacks are identical to Whites neglects the problems faced by Blacks. For
\nexample, 30% of the people who have kidney failure and undergo dialysis are
\nBlack, but estimates are that fewer than 10% of organ donors are Black. There is
\nalso evidence that Blacks fare better when given organs donated by other Blacks.
\nAnother example is that it now appears that genetics may underlie the increased
\nrates and levels of hypertension in Blacks. Black males tend to have higher blood
\npressure and increased rates of cardiovascular disease, including strokes, than
\nWhite males, but the cause is widely debated. Some evidence suggests that Black
\nmales experience a faster heart rate when performing moderate exercise, although
\nthe pulse rates of the Black and White males when resting show no significant
\ndifference. Blacks have a higher incidence of prostate cancer than Whites, who in
\nturn have a higher rate than East Asians, for which the underlying cause may be
\nthe mean group differences in testosterone level (Polednak, 1989; Risch et al.,
\n2002).
\nConflicting Worldviews
\nA prevailing worldview throughout history has been that economic, cultural,
\nand other environmental forces are the preeminent causes of group and individual
\nbehavior. Modern social science has typically taken this perspective and promoted
\nthe idea that all babies are born more or less equally endowed in intelligence and
\nlearning ability. It followed therefore that inequalities were the result of social,
\neconomic, and political forces. This worldview generated many strategies for
\nintervention in the home, the workplace, the mass media, the criminal justice
\nsystem, and even the entire social\u2013 economic system. Some have been effective
\nand are almost universally accepted, whereas others have failed and produced
\nonly shattered expectations, resentment, and interethnic hostility.
\nThe major policy implication of the research reviewed here is that adopting an
\nevolutionary\u2013 genetic outlook does not undermine our dedication to democratic
\nideals. As E. O. Wilson (1978) aptly noted: \u201cWe are not compelled to believe in
\nbiological uniformity in order to affirm freedom and dignity\u201d (p. 52). He went on
\nto quote the sociologist Bressler (1968): \u201cAn ideology that tacitly appeals to
\nbiological equality as a condition for human emancipation corrupts the idea of
\nfreedom. Moreover, it encourages decent men to tremble at the prospect of
\n\u2018inconvenient\u2019 findings that may emerge in future scientific research\u201d (E. O.
\nWilson, 1978, p. 52). Denial of any genetic component in human variation,
\nincluding between groups, is not only poor science, it is likely to be injurious both
\nto unique individuals and to the complex structure of societies.<\/p>\n","protected":false},"excerpt":{"rendered":"

Report: The overwhelming dominance of white men among district attorneys could have huge effects on charging, enforcement, and plea bargains. It\u2019s no mistake that the most enduring fictional prosecutors are white guys\u2014whether they\u2019re dignified older men like Jack McCoy or … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[34,237,29582,36],"tags":[],"class_list":["post-70669","post","type-post","status-publish","format-standard","hentry","category-blacks","category-crime","category-iq","category-race"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=\/wp\/v2\/posts\/70669","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=70669"}],"version-history":[{"count":13,"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=\/wp\/v2\/posts\/70669\/revisions"}],"predecessor-version":[{"id":70808,"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=\/wp\/v2\/posts\/70669\/revisions\/70808"}],"wp:attachment":[{"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=70669"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=70669"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=70669"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}