The Bell Curve: Intelligence and Class Structure in American Life

by Richard J. Herrnstein

  Chapter 22

  1 The phrasing draws from Rawls 1971, pp. 14-15.

  2 For discussion of this transformation, see, for example, Brown 1988.

  3 Thomas Hobbes postulated an axiom—Hobbes saw it as literally an axiom, in the mathematical sense—for governing people with equal rights to liberty: “That a man be willing, when others are so too … to lay down this right to all things; and be contented with so much liberty against other men, as he would allow other men against himself.” Hobbes 1651, Chap. 14.

  4 Hobbes expressed the gloomy prospect of perfect anarchy in the one sentence for which he is best remembered: “And the life of man [would be] solitary, poore, nasty, brutish and short.” Hobbes 1651, Chap. 13.

  5 Locke 1689, Second Treatise, sec. 4.

  6 Locke 1689 Bk. IV, Chap. XX.

  7 See, for example, Wills 1978; Beer 1993.

  8 Mayo 1942, pp. 77-78.

  9 Costopoulos 1990, p. 50.

  10 Costopoulos 1990, p. 47.

  11 Mayo 1942, p. 78.

  12 Costopoulos 1990, p. 47.

  13 Quoted in Diamond 1976, p. 16.

  14 Costopoulos 1990, p. 48.

  15 That fact, combined with the “irresistible corruption” that Adams saw as infecting all political systems, caused him to be deeply pessimistic about the survival of the experiment in human government that he had been so instrumental in founding. He sometimes wondered gloomily whether a hereditary aristocracy on the British model might be necessary to offset the unrestrained avarice and factiousness of Jefferson’s natural aristocracy.

  16 Aristotle 1905 ed., p. 207.

  17 Hamilton et al. 1787, No. 10.

  18 White 1958, p. 122.

  19 Huber 1988; Olson 1991.

  20 Bureau of Labor Statistics 1982, Table C-23, 1989, Table 42.

  21 In 1990 dollars in all cases: the annual income of male year-round, full-time nonfarm, non-mine laborers was $16,843 in 1958. (SAUS 1970, Table 347). The comparable earnings for “handlers, equipment cleaners, helpers, and laborers” in 1991 was $16,777. U.S. Bureau of the Census, 1992, Table 32. The full-time weekly earnings of “lower-skilled labor” in 1920 was $169 in 1990 dollars, or $8,459 for a fifty-week year (U.S. Bureau of the Census 1975, Series D 765-778).

  22 For a full presentation of the following argument, see Murray 1988b, Chap. 12.

  23 Wilson 1993.

  24 It is doubtless harder even for bright people to lead law-abiding lives when the laws become more complex, but the marginal effects will be smaller on them than on the less bright.

  25 Ellwood 1988.

  26 For an accessible discussion of the pros and cons of the EITC, see Kosters 1993. A more ambitious approach that we think deserves consideration would replace the entire structure of federal transfers to individuals—income supplements, welfare, in-kind benefits, farm subsidies, and even social security—with a negative income tax of the kind proposed by Milton Friedman in Friedman 1962. Like Friedman, we are attracted to this strategy only if it replaces everything else, a possibility so unlikely that it is hard to talk about seriously. This does not diminish its potential merit.

  Afterword

  Short citations refer to works that are already cited in full in the bibliography.

  1 M. W. Brown, What is intelligence, and who has it? New York Times Book Review, October 16, 1994, pp. 3-6.

  2 Murray 1984, 1988b.

  3 Herrnstein 1973.

  4 M. Novak, Sins of the cognitive elite, National Review, December 5, 1994, pp. 58-61. T. Sowell, Can we find a way to discuss intelligence intelligently? Washington Times, October 21, 1994.

  5 Ibid., p. 59.

  6 Gould 1981; Gardner 1983.

  7 Snyderman and Rothman 1988.

  8 S. J. Gould, Curveball, New Yorker, November 28, 1994, pp. 143-144.

  9 Steve Blinkhorn, quoted in B. D. Davis, Neo-Lysenkoism, IQ, and the press, Public Interest, no. 73 (1983), 44. The Davis article is an illuminating review of the contrasting receptions of The Mismeasure of Man accorded by the press and by the scientific community.

  10 J. B. Carroll, Human Cognitive Abilities: A Survey of Factor-Analytic Studies (Cambridge: Cambridge University Press, 1993).

  11 For examples, see Jensen 1987 or B. Bower, Images of intellect: Brain scans may colorize intelligence, Science News, October 8, 1994, pp. 236-237.

  12 Mainstream science on intelligence, Wall Street Journal, December 13, 1994.

  13 For a recent and comprehensive presentation of Rushton’s argument and evidence, see J. P. Rushton, Race, Evolution, and Behavior (New Brunswick, N.J.: Transaction, 1994).

  14 Ibid., chapter 6.

  15 J. Rosen and C. Lane, Neo-Nazis! New Republic, October 31, 1994, pp. 14-15; C. Lane, The tainted sources of “The Bell Curve,” New York Review of Books, December 1, 1994.

  16 L. J. Kamin, Behind the curve, Scientific American (February 1995 ) : 99-103.

  17 K. Owen, The suitability of Raven’s Standard Progressive Matrices for various groups in South Africa, Personality and Individual Differences 13 ( 1992): 149-159.

  18 F. Zindi, Differences in psychometric performance. Psychologist 7 (1994): 549-554.

  19 Kamin 1995, p. 103.

  20 J. J. Heckman, Cracked bell, Reason (March 1995): 53.

  21 A. Goldberger, Journal of Economic Literature 33(1995): 762-776.

  22 Kamin 1995, p. 102.

  23 As I write, I have learned of just one computational error out of the hundreds of statistical results presented in the book, in the table on p. 591 (Appendix 3) in the hardcover edition, caused by the miscoding of nine cases. The numbers have been corrected for this edition. The changes did not require any alteration in the wording of the discussion.

  24 R. Nisbett, Race, IQ, and scientism, in S. Fraser (ed. ), The Bell Curve Wars: Race, Intelligence, and the Future of America (New York: Basic Books, 1995), p. 45.

  25 Jeanne Brooks-Gunn et al., Early intervention in low-birth-weight pre-mature infants, JAMA 272 (1994): 1257-1262.

  26 Howard Gardner, Cracking open the IQ box, American Prospect (Winter 1994): 71-80. Lisbeth Schorr and Daniel Schorr, Within Our Reach: Breaking the Cycle of Disadvantage (New York: Doubleday, 1988).

  Appendix 1

  1 The figure depicts 250 18-year-old males drawn randomly from the NLSY sample.

  2 Based on the NLSY subjects, born from 1957 through 1964, as of 1982, when the youngest was 18 years old, the mean height of contemporary Americans is a little over 5 feet 7 inches, with a standard deviation of about 4 inches.

  3 Based on the 1983 ETS norm study (Braun and King 1987) and dropout rates in the 1980s, we estimate the mean for all 18-year-olds (including dropouts) at 325, with an standard deviation of 105. This would indicate that the 99th centile begins at a score of 569. The example in the text is phrased conservatively.

  4 The Pearson’s r is .501 in both cases. The number 3,068 refers to males with weight and height data in 1982.

  5 For simplicity’s sake, we are assuming that the variables can have only linear relationships with each other.

  Appendix 2

  1 The NLSY on CD-ROM disk is available for a nominal fee from the Center for Human Resource Research, Ohio State University.

  2 Inquiries should be directed to Prof. Richard J. Herrnstein, Department of Psychology, William James Hall, Harvard University, Cambridge, MA 02138, or to Dr. Charles Murray, American Enterprise Institute, 1150 17th St. NW, Washington, DC 20036.

  3 Data for 1991 had become available in time to be used for the analysis, but for budgetary reasons, the NLSY had to cut the supplementary sample of low-income whites as of 1991. We decided that the advantages of including low-income whites in the analysis outweighed the advantages of an additional year of data.

  4 We followed the armed forces’ convention of limiting subtest scores to a maximum of three standard deviations from the mean. We gratefully acknowledge the assistance of Dr. Malcolm J. Ree, who led the revision of the AFQT, in computing the revised
scores for the NLSY.

  5 This procedure is facilitated by the large sample sizes (at least 1,265 with valid AFQT scores in each birth year, which are as large as the samples commonly used for national norms in tests such as the WISC and WAIS), and the fact that the NLSY sample was balanced for ethnic group and gender within birth years.

  6 We also experimented with groupings based not on the calendar year, but the school year. The differences in centile produced by the two procedures were never as much as two, so we remained with calendar year as the basis.

  7 See Users Guide 1993, pp. 157-162.

  Appendix 3

  1 The subtests are General Science (GS), Arithmetic Reasoning (AR), Work Knowledge (WK), Paragraph Comprehension (PC), Numerical Operations (NO), Coding Speed (CS), Auto/Shop Information (AS), Mathematics Knowledge (MK), Mechanical Comprehension (MC), and Electronics Information (EL). Two subtests (Numerical Operations and Coding Speed) are highly speeded; the other eight are “power” rather than speed tests.

  2 Ree and Earles 1990a, 1990b, 1991c.

  3 We use the term factor in a generic sense. Within psychometries, terms like factor and component are used selectively, depending on the particular method of analysis used to extract the measures.

  4 E.g., Gould 1981.

  5 Jensen 1987a, 1987b; Ree and Earles 1991c; Welsh, Watson, and Ree 1990.

  6 To account for literally 100 percent of the variance takes ten factors (because there are ten subtests), with the final few of them making increasingly negligible contributions. In the case of ASVAB, the final five factors collectively account for only 10 percent of the total variance in scores.

  7 Sperl, Ree and Steuck 1990.

  8 Carroll 1988; Jensen 1987a.

  9 Ree and Earles, 1990a, 1990b, 1991c.

  10 Gordon 1984; Jensen and Figueroa 1975.

  11 Note that the General Science subtest and the Electronics Information subtest are as highly g-loaded as the subtests used in the AFQT. Why not use them as well? Because they draw on knowledge that is specific to certain courses that many youths might not have taken, whereas the mathematics and reading subtests require only material that is ordinarily covered in the courses taken by every student who goes to elementary and secondary school. But this is a good illustration of a phenomenon associated with IQ tests: People who acquire knowledge about electronics and science also tend to have high mathematics and verbal ability.

  12 Jensen 1980, Table 6.10.

  13 Within a single test, the test score might mean any of several percentile scores, depending on the age of the student; hence the reason for using percentiles. For the analyses in the text, scores were used only if both a test score and a percentile were recorded. Anomalous scores were discarded as follows: For the California Test of Mental Maturity, one test score of 700. For the Otis-Lennon Mental Ability Test, eight cases in which the test score was under 30 and the percentile was over 70; one case in which the test score was 176 and the percentile was only 84. For the Henmon-Nelson Test of Mental Maturity, one test score of 374. For the Differential Aptitude Test, sixteen test scores over 100. For the Lorge-Thorndike Intelligence Test and the Kuhlmann-Anderson Intelligence Test, which showed uninterpretable scatter plots of test scores against percentiles, cases were retained if the test score normed according to a mean of 100 and a standard deviation of 15 was within 10 centiles of the reported percentile score. The number of eligible scores on the Stanford-Binet and the Wechsler Intelligence Scale for Children (18 and 16, respectively) was too small to analyze.

  14 Jensen 1980, Table 8.5.

  15 This list is taken from Jensen 1980, p. 72. Jensen devotes a chapter (Chap. 4) to the distribution of mental ability, which we recommend as an excellent single source for readers who want to pursue this issue.

  16 For an exploration of the relationships as of the late 1960s, see Jencks et al. 1972, Appendix B. For separate studies, see Rutter 1985; Hale, Raymond, and Gajar 1982; Wolfe 1982; Schiff and Lewontin, 1986.

  17 Husén and Tuijnman, 1991. See also Ceci 1991, for a case that schooling has a greater influence on IQ than has generally been accepted, drawing heavily on data from earlier decades when the natural variation in schooling was large.

  Appendix 5

  1 Validity is measured by the correlation between predictor and outcome, which, multiplied by the ratio of the standard deviations of the outcome to the predictor, gives the regression coefficient of the outcome on the predictor. To keep this discussion simple, we assume an increasing monotonic relationship between the validity and the regression coefficient here. For a discussion that does not make this simplifying assumption, see Jensen 1980.

  2 In the following sources, one can find varying estimates of the magnitude of predictive validity of intelligence tests and varying opinions about whether the tests are a net benefit to society, but they unanimously accept the conclusion that no bias against blacks in educational or occupational prediction has been found: Breland, 1979; Grouse and Trusheim 1988; Hartigan and Wigdor 1989; Hunter and Schmidt 1990; Jensen 1980; Klitgaard 1985; Reynolds and Brown 1984; Schmidt 1988.

  3 For a discussion of the sources of error and their relevance to meta-analyses of occupational outcomes in particular, see Hunter and Schmidt 1990. For a more general discussion, including educational outcomes, see Jensen 1980.

  4 Jensen 1984b, p. 523.

  5 Occasionally, one may find a study that finds differential predictive validity for one ethnic group or another for a particular test—e.g., the K-ABC test for Latinos and non-Latino whites (Valencia and Rankin 1988). But even for Latinos, validity generalization has generally been confirmed (e.g., Reynolds and Gutkin 1980; Valdez and Valdez 1983).

  6 Jensen 1980, Table 10.4.

  7 Breland 1979, Table 3b.

  8 Ibid.

  9 Hartigan and Wigdor 1989, Table 9.5.

  10 Ibid., pp. 181-182.

  11 The example given here is a special case of a more general phenomenon: As long as the product of the regression coefficient (which is assumed not to differ for the groups) and the mean difference between groups in the predictor is smaller than the mean difference in the outcome, there will be overprediction for the lower-scoring group.

  12 For a review of the literature through the early 1980s, see Jensen 1985, also discussed in Chapter 13. For studies since then, see Braden 1989; Jensen 1992, 1993b. The single contrary study extant is Gustafsson 1992.

  13 McGurk 1951. Also in 1951, Kenneth Eells’s doctoral thesis at the University of Chicago showed that test item difficulty did not vary much across white ethnics of different types, thereby failing to support the intuition that cultural factors are dominant (Eells et al. 1951). See Jensen 1980, Chap. 11, for more on McGurk’s and Eells’s work and on other early studies of test item bias.

  14 For a review of the literature through the late 1970s, see Jensen 1980, Chap. 11. For studies since 1980, see Bart et al. 1986; Ross-Reynolds and Reschly 1983; Sandoval et al. 1983; Jensen and McGurk 1987; Cook 1987; Koh, Abbatiello, and Mcloughlin 1984; Reschly and Ross-Reynolds 1982; Mishra 1983. All found no item differences, or differences that explained only a fraction of the differences in group scores. Are there any exceptions? We identified one such study for blacks (Montie and Fagan 1988), based on 3-year-olds. There may very well be other studies of similar size (the sample in Montie and Fagan was 86) that are lurking in the literature, but we know of no studies using large-scale representative samples that establish item bias against blacks. Some studies of Latinos have found evidence of bias, mostly associated with Spanish and English language characteristics. See Valencia and Rankin 1988; Whitworth and Chrisman 1987, Munford and Munoz 1980. But the factor structure of the test results has generally been found to be the same for Latino and non-Latinos (e.g., see Mishra 1981).

  15 See Jensen 1980, Table 11.12. Also see Miele 1979.

  16 Scheuneman 1987.

  17 For a literature review, see Jensen 1980, Chap. 12.

  18 Dyer 1970.

  19 For
studies specifically dealing with differential racial effects of coaching and practice through the late 1970s, see Baughman and Dahlstrom 1968; Costello 1970; Dubin, Osburn, and Winick 1969; Jensen 1980. For studies bearing on the issue since 1980 (but not addressing it as directly as the earlier ones), see Powers 1987; Terrell and Terrell 1983; Johnson and Wallace 1989; Cole 1987.

  20 For literature reviews, see Sattler and Gwynne 1982; Jensen 1980.

  21 For a literature review, see Jensen 1980, Chap. 12.

  22 For a literature review, see Jensen 1980, Chap. 12.

  23 Jensen 1980, Chap. 12. See also note 14 regarding item bias for Latinos.

  24 Jensen 1980, Chap. 12.

  25 Quay 1971, 1972, 1974.

  26 Farrell 1983 and the attached responses.

  27 Johnson et al. 1984; Frederiksen 1986; Johnson 1988; Kerr et al. 1986; Madhere 1989; Scheuneman 1987; White et al. 1988

  28 Rock et al. 1985 details the changes between the two administrations, concluding that “the cautious position would be that neither administration had an advantage. A less cautious conclusion is that the 1980 subjects probably had some small advantage” (p. 18).

  29 Based on the white standard deviation for 1980, the first year that standard deviations by race were published.

  30 Congressional Budget Office, 1986, Fig. E-3.

  31 Contrary to popular belief, on the proposition whether brain size is correlated with IQ, the evidence strongly favors the pros over the cons, even after correcting for stature. A sampling of contemporary positions in this mini-controversy is Cain and Vanderwolf 1990; Gould 1978, 1981; Lynn 1989; Michael 1988; Passingham 1982; Rushton 1990d, in press; Valen 1974. Brain size is, however, not necessarily wholly determined by the genes; it could also be associated with nutrition or general health.

  32 The Rushton controversy has unfolded in a rapidly expanding scholarly literature. Some of the papers, pro and con, are Cain and Vanderwolf 1990; Lynn 1989b; Roberts and Gabor 1990; Rushton 1985, 1987, 1988, 1990a, 1990b, 1990c, 1990d, 1991a, 1991b; Rushton and Bogaert, 1978,1988; Silverman 1990; Weitzmann et al. 1990; Zuckerman and Brody 1988. For further substantiation of some of the race differences that Rushton invokes, see Ellis and Nyborg 1992; Lynn 1990c; Mangold and Powell-Griner 1991; Rowe, Rodgers, and Meseck-Bushey 1988; Valen 1974.