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Journal Issue: School Readiness: Closing Racial and Ethnic Gaps Volume 15 Number 1 Spring 2005

Genetic Differences and School Readiness
William T. Dickens


1. The review necessarily highlights only the most important studies; a complete review of all the arguments on both sides of this debate would require hundreds of pages and be beyond the scope of this article.

2. Heritability is estimated by examining the similarity of people with different degrees of genetic similarity raised in similar sorts of environments, and there is some reason to believe that most estimates are somewhat overstated by existing methods. Robert Plomin and others, Behavioral Genetics, 4th ed. (New York: Worth Publishers, 2001), in chapter 5 and the appendix, provide a thorough discussion of the methods used to estimate heritability. Mike Stoolmiller, “Implications of the Restricted Range of Family Environments for Estimates of Heritability and Nonshared Environment in Behavior-Genetic Adoption Studies,” Psychological Bulletin 125 (1999): 392–409, shows that adoption studies probably overstate the degree of heritability and speculates on reasons why some other methods may as well.

3. Robert Plomin and others, Behavioral Genetics (see note 2), examine learning disorders on pp. 145–49, ADHD on pp. 227–29, and personality in chapter 12. For the effects of genes on cognitive ability, see Marcie L. Chambers and others, “Variation in Academic Achievement and IQ in Twin Pairs,” Intelligence (forthcoming); Lee Anne Thompson and others, “Associations between Cognitive Abilities and Scholastic Achievement: Genetic Overlap but Environmental Differences,” Psychological Science 2 (1991): 158–65; and Sally J. Wadsworth, “School Achievement,” in Nature and Nurture during Middle Childhood, edited by John C. DeFries, Robert Plomin, and David W. Fulker (Oxford: Blackwell, 1994), pp. 86–101.

4. James R. Flynn, Race, IQ, and Jensen (London: Routledge, 1980).

5. Richard Nisbett, “Race, Genetics, and IQ,” in The Black-White Test Score Gap, edited by Christopher Jencks and Meredith Phillips (Brookings, 1998), pp. 86–102.

6. Arthur Jensen, The g Factor (Westport, Conn.: Praeger, 1998), pp 350–531.

7. Jane R. Mercer, “What Is a Racially and Culturally Nondiscriminatory Test? A Sociological and Pluralistic Perspective,” in Perspectives on “Bias in Mental Testing,” edited by Cecil R. Reynolds and Robert T. Brown (New York: Plenum Press, 1984); Jonathan Crane, “Race and Children's Cognitive Test Scores: Empirical Evidence That Environment Explains the Entire Gap,” mimeo, University of Illinois at Chicago, 1994; and Jeanne Brooks-Gunn and others, “Ethnic Differences in Children's Intelligence Test Scores: Role of Economic Deprivation, Home Environment, and Maternal Characteristics,” Child Development 67, no. 2 (1996): 396–408.

8. This is based on the account by James R. Flynn (Race, IQ, and Jensen, pp. 84–87; see note 4) of Klaus Eyferth, “Leistungen verschiedener Gruppen von Besatzungskindern in Hamburg-Wechsler Intelligenztest fur Kinder (HAWIK),” Archiv fur die gesamte Psychologie 113 (1961): 222–41.

9. Flynn, Race, IQ, and Jensen, pp. 84–102 (see note 4).

10. Barbara Tizard, “IQ and Race,” Nature 247, no. 5439 (February 1, 1974).

11. Flynn, Race, IQ, and Jensen, pp. 108–11 (see note 4).

12. Elsie G. J. Moore, “Family Socialization and the IQ Test Performance of Traditionally and Transracially Adopted Black Children,” Developmental Psychology 22 (1986): 317–26; and Lee Willerman and others, “Intellectual Development of Children from Interracial Matings: Performance in Infancy and at 4 Years,” Behavioral Genetics 4 (1974): 84–88.

13. Sandra Scarr and Richard A. Weinberg, “IQ Test Performance of Black Children Adopted by White Families,” American Psychologist 31 (1976): 726–39; and Sandra Scarr and Richard A. Weinberg, “The Minnesota Adoption Studies: Genetic Differences and Malleability,” Child Development 54 (1983): 260–67.

14. These are IQ scores, which have a mean of 100 and a standard deviation of 15 in the U.S. population.

15. Sandra Scarr and others, “The Minnesota Transracial Adoption Study: A Follow-Up of IQ Test Performance at Adolescence,” Intelligence 16 (1992): 117–35.

16. But see Arthur Jensen, The g Factor, pp. 477–78 (see note 6), on whether late adoption can explain the difference.

17. One body of evidence is difficult to judge. See J. Philippe Rushton, Race, Evolution, and Behavior: A Life History Perspective, 3rd ed. (Port Huron, Mich.: Charles Darwin Research Institute, 2000). Rushton has proposed a theoretical framework that would explain a genetic gap in cognitive ability between blacks and whites and has marshaled evidence for it. But because much of the evidence was known before the theory was proposed, some view the theory as nothing more than post hoc rationalization for hereditarian views on the black-white gap. At most it suggests that some of the black-white gap may be genetic, but it does not suggest how much.

18. Arthur Jensen, Educability and Group Differences (New York: Harper and Row, 1973), pp. 135–39, 161–73, 186–90; Arthur Jensen, Educational Differences (London: Methuen, 1973), pp. 408–12; Jensen, The g Factor, pp. 445–58 (see note 6); and Richard Herrnstein and Charles Murray, The Bell Curve: Intelligence and Class Structure in American Life (New York: Simon and Schuster, 1994), pp. 298–99.

19. Plomin and others, Behavioral Genetics, p. 177 (see note 2); and Ulric Neisser and others, “Intelligence: Knowns and Unknowns,” American Psychologist 51, no. 2(1996): 85.

20. Author's calculations from the 1979 National Longitudinal Survey of Youth.

21. John B. Carol, Human Cognitive Abilities: A Survey of Factor-Analytic Studies (Cambridge University Press, 1993), is the most comprehensive survey of what is known about the correlation of scores on different types of mental tests.

22. See J. Philippe Rushton and Arthur Jensen, “Thirty Years of Research on Race Differences in Cognitive Ability,” Psychology, Public Policy, and Law (forthcoming), for a review of this evidence and citations to the original studies.

23. David Rowe, Alexander Vazsonyi, and Daniel Flannery, “Ethnic and Racial Similarity in Developmental Process: A Study of Academic Achievement,” Psychological Review 101, no. 3 (1994): 396–413; Jensen, The g Factor, pp. 464–67 (see note 6).

24. James R. Flynn, “Massive Gains in 14 Nations: What IQ Tests Really Measure,” Psychological Bulletin 101 (1987): 171–91; James R. Flynn, “IQ Gains over Time,” in Encyclopedia of Human Intelligence, edited by Robert J. Sternberg (New York: Macmillan, 1994), pp. 617–23; James R. Flynn, “IQ Gains over Time: Toward Finding the Causes,” in The Rising Curve: Long-Term Gains in IQ and Related Measures, edited by Ulric Neisser (Washington: American Psychological Association, 1998), pp. 551–53.

25. Existing evidence suggests that IQ gains across subtests are probably positively correlated with g loading. See Roberto Colom, Manuel Juan-Espinosa, and Luís F. García, “The Secular Increase in Test Scores Is a ‘Jensen effect,'” Personality and Individual Differences 30 (2001): 553–58; and Manuel Juan-Espinosa and others, “Individual Differences in Large-Spaces Orientation: g and Beyond?” Personality and Individual Differences 29 (2000): 85–98, for much stronger correlations between g loadings and IQ gains. Jensen, The g Factor, pp. 320–21 (see note 6), reviews a number of studies of the relation between subtests gains and g loadings, all of which show weak positive correlations. J. Philippe Rushton, “Secular Gains in IQ Not Related to the g Factor and Inbreeding Depression—unlike Black-White Differences: A Reply to Flynn,” Personality and Individual Differences 26 (1999): 381–89, finds that a measure of g developed on the Wechsler Intelligence Scale for Children has loadings that are negatively correlated with subtest gains in several countries. But see James R. Flynn, “The History of the American Mind in the 20th Century: A Scenario to Explain IQ Gains over Time and a Case for the Irrelevance of g,” in Extending Intelligence: Enhancement and New Constructs, edited by P. C. Kyllonon, R. D. Roberts, and L. Stankov (Hillsdale, N.J.: Erlbaum, forthcoming.) for an argument that IQ gains are greatest on tests of fluid g rather than crystallized g. He finds a positive (though statistically insignificant) correlation between a measure of fluid g he develops and IQ gains in the data used by Rushton. Olev Must, Aasa Must, and Vilve Raudik, “The Flynn Effect for Gains in Literacy Found in Estonia Is Not a Jensen Effect,” Personality and Individual Differences 33 (2001); and Olev Must, Aasa Must, and Vilve Raudik, “The Secular Rise in IQs: In Estonia the Flynn Effect Is Not a Jensen Effect,” Intelligence 31 (2003): 461–71, find no correlation between g loadings and gains on two tests in Estonia, but these are achievement tests with a strong crystallized bias.

26. Plomin and others, Behavioral Genetics, pp. 173–77 (see note 2).

27. Irving Lazar and Richard Darlington, “Lasting Effects of Early Education: A Report from the Consortium for Longitudinal Studies,” Monographs of the Society for Research in Child Development 47, nos. 2–3 (1982).

28. William T. Dickens and James Flynn, “Heritability Estimates versus Large Environmental Effects,” Psychological Review 108, no. 2 ( 2001).

29. This is not to say that there are no permanent or long-lasting environmental effects on cognitive ability. The effects of brain damage can be severe and permanent. However, such permanent environmental effects evidently explain only a small fraction of normal variation in cognitive ability. Shared family environment plays a large role in explaining variance in cognitive ability when children are spending most of their time in the home, with their activities strongly influenced by their parents. But that effect fades as they spend more of their time away from home and in self-directed activities.

30. Eric A. Hanushek and others, “Does Peer Ability Affect Student Achievement?” Working Paper 8502 (Cambridge, Mass.: National Bureau of Economic Research, 2001); Caroline Hoxby, “Peer Effects in the Classroom: Learning from Gender and Race Variation,” Working Paper 7867 (Cambridge, Mass.: National Bureau of Economic Research, 2001); Dan M. Levy, “Family Income and Peer Effects as Determinants of Educational Outcomes,” Ph.D. diss., Northwestern University, 2000; Donald Robertson and James Symons, “Do Peer Groups Matter? Peer Group versus Schooling Effects on Academic Achievement,” Economica 70 (2003): 31–53; Bruce Sacerdote, “Peer Effects with Random Assignment: Results from Dartmouth Roommates,” Quarterly Journal of Economics (May 2001): 681–704; David J. Zimmerman, “Peer Effects in Academic Outcomes: Evidence from a Natural Experiment,” Review of Economics and Statistics 85 (2003): 9–23.

31. Michael A. Boozer and Stephen E. Cacciola, “Inside the ‘Black Box' of Project STAR: Estimation of Peer Effects Using Experimental Data,” Discussion Paper 832 (Economic Growth Center, Yale University, 2001).

32. In statistics this is referred to as the law of large numbers—that the variance of a mean falls as the number of items being averaged goes up. See Eugene Lukacs, Probability and Mathematics Statistics: An Introduction (New York: Academic Press, 1972). It applies whether or not the weights being put on the elements are equal. Because the variance and standard deviation of the mean fall, while the average difference stays the same, the difference in standard deviations grows. The example assumes that the effects are all uncorrelated with each other and that each has a normal distribution in the white and the black populations. If the effects were assumed to be correlated or the weights unequal, the results would be less dramatic, but with observed values for correlations of environmental factors, increasing the number of items to be averaged could produce the same results.

33. Dickens and Flynn, “Heritability Estimates vs. Large Environmental Effects” (see note 28).

34. Plomin and others, Behavioral Genetics, p. 201 (see note 2).

35. Eric Turkheimer and others, “Socioeconomic Status Modifies Heritability of IQ in Young Children,” Psychological Science 14, no. 6 (2003). Their own study finds that shared family environment explains 60 percent of the variance of an IQ test score in low-socioeconomic-status seven-year-olds, which is a much larger share than other studies have found. For example, see Kathryn Asbury and others, “Environmental Moderators of Genetic Influence on Verbal and Nonverbal Abilities in Early Childhood” (Institute of Psychiatry, De Crespigny Park, London, 2004).