The Bell Curve: Intelligence and Class Structure in American Life

by Richard J. Herrnstein

  35 Once again, the changes are not caused by changes in the ethnic composition of the pool (for example, by an influx of test takers who do not speak English as their native language). The trendline for whites since 1980 parallels that for the entire test population.

  36 National Center for Education Statistics 1992, p. 57. We also examined the SAT achievement test results. They are harder to interpret than the SATs because the test is regularly rescaled as the population of students taking the test changes. For a description of the equating and rescaling procedures used for the achievement tests, see Donlon 1984, pp. 21-27. The effects of these rescalings, which are too complex to describe here, are substantial. For example the average student who took the Biology achievement test in 1976 had an SAT-Math score that was 71 points above the national mean; by 1992, that gap had increased to 126 points. The same phenomenon has occurred with most of the other achievement tests (Math II, the more advanced of the two math achievement tests, is an exception). Put roughly, the students who take them are increasingly unrepresentative of the college-bound seniors who take the SAT, let alone of the national population. We focused on the students scoring 700 or higher by again assuming that since the 1960s, a very high proportion of the nation’s students who could score higher than 700 on any given achievement test took the test. We examined trends on the English Composition, American History, Biology, and Math II tests from three perspectives: the students scoring above 700 as a proportion of (1) all students who took that achievement test; (2) all students who took the SAT; and (3) all 17-year-olds. Method 1 (as a proportion of students taking the achievement test) revealed flat trendlines—not surprisingly, given the nature of the rescaling. Methods 2 and 3 revealed similar patterns. With all the reservations appropriate to this way of examining what has happened, we find that the proportion scoring above 700 on English Composition and Math II mirrored the contrast we showed for Verbal and Math scores on the SAT: a sharp drop in the English Composition in the 1970s, with no recovery in the 1980s; an equally sharp and steep rise in the Math II scores beginning in the 1980s and continuing through the 1992 test. The results for American History and Biology were much flatter. Method 2 showed no consistent trend up or down, and only minor movement in either direction at any time. Method 3 showed similar shallow bowl-shaped curves: reductions during the 1970s, recovery during the 1980s that brought the American History results close to the first year of 1972, and brought Biology to a new high, although one that was only fractionally higher than the 1972 results. This is consistent with a broad theme that the sciences and math improved more in the 1980s than the humanities and social sciences did.

  37 Diane Ravitch’s account, one of the first, is still the best (Ravitch 1983), with Finn 1991; Sowell 1992; Ravitch 1985; Boyer 1983; and Porter 1990 providing perspectives on different pieces of the puzzle and guidance to the voluminous literature in magazines and journals regarding the educational changes in elementary and secondary schools. For basic texts by advocates of the reforms, see Goodman 1962; Kohl 1967; Silberman 1970; Kozol 1967; Featherstone 1971; Illich 1970; and the one that in some respects started it all, Neill 1960.

  38 Fiske 1984; Gionfriddo 1985.

  39 Sowell 1992, p. 7.

  40 Bishop 1993b.

  41 Bejar and Blew 1981; Breland 1976; Etzioni 1975; Walsh 1979.

  42 By the early 1980s, when the worst of the educational crisis had already passed, the High School and Beyond survey found that students averaged only three and a half hours per week on homework (Bishop 1993b).

  43 DES 1992b, Table 132.

  44 DES 1992b, Table 129. The picture is not unambiguous, however. Measured in “Carnegie units,” representing one credit for the completion of a one-hour, one-year course, high school graduates were still getting a smaller proportion of their education from academic units than from vocational or “personal” units (National Center for Education Statistics 1992, p. 69).

  45 We do not exempt colleges altogether, but there are far more exceptions to the corruption as we mean it at the university level than at the high school level, in large part because high schools are so much more shaped by a few standardized textbooks.

  46 Gionfriddo 1985.

  47 Irwin 1992, Table 1. The programs we designated as for the disadvantaged were the Title I basic and concentration grants, Even Start, the programs for migratory children, handicapped children, neglected and delinquent children, the rural technical assistance centers, the state block grants, inexpensive book distribution, the Ellender fellowships, emergency immigrant education, the Title V (drug and alcohol abuse) state grants, national programs, and emergency grants, Title VI (dropout), and bilingual program grants.

  48 DES 1992b, Table 347.

  49 Calvin Lockridge, quoted in “Old debate haunts Banneker’s future,” Washington Post, March 29, 1993, p. A10.

  50 Ibid.

  51 Bishop 1993b.

  52 For a coherent and attractive list of such reforms, see Bishop 1990b.

  53 Stevenson et al. 1990.

  54 E.g., 63 percent of respondents in a recent poll conducted by Mellman-Lazarus-Lake for the American Association of School Administrators thought that the nation’s schools needed “major reform,” compared to only 33 percent who thought their neighborhood schools needed major reform. Roper Organization 1993.

  55 E.g., Powell, Farrar, and Cohen 1985.

  56 Bishop has developed these arguments in several studies: Bishop 1988b, 1990a, 1990b, 1993a, 1993b.

  57 Bishop 1993b (p. 20) cites the example of Nationwide Insurance, which in the single year of 1982 sent out over 1,200 requests for high school transcripts and got 93 responses.

  58 Bishop 1988a, 1988b, 1990a, 1993a, 1993b.

  59 Bishop 1990b.

  60 Ibid.

  61 The Wonderlic Personnel Test fits this description. For a description, see E. F. Wonderlic & Associates 1983. The value of a high school transcript applies mainly to recent high school graduates who have never held a job, so that employers can get a sense of whether this person is likely to come to work every day, on time. But after the first job, it is the job reference that will count, not what the student did in high school.

  62 The purposes of such a program are primarily to put the federal government four-square on the side of academic excellence. It would not appreciably increase the number of high-scoring students going to college. Almost all of them already go. But one positive side effect would be to ease the financial burden on many middle-class and lower-middle-class parents who are too rich to qualify for most scholarships and too poor to send their children to private colleges.

  Chapter 19

  1 Quotas as such were ruled illegal by the Supreme Court in the famous Bakke case.

  2 Except as otherwise noted, our account is taken from Maguire, 1992.

  3 A. Pierce et al., “Degrees of success,” Washington Post, May 8, 1991, p. A31.

  4 Seven COFHE schools provided data on applicants and admitted students, but not on matriculated students. Those schools were Barnard, Bryn Mawr, Carleton, Mount Holyoke, Pomona, and Smith. The ethnic differences in scores of admitted students for these schools were in the same range as the differences for the schools shown in the figure on page 452. Yale did not supply any data by ethnicity. Data are taken from Consortium on Financing Higher Education 1992, Appendix D.

  5 “Best Colleges,” U.S. News & World Report, Oct. 4, 1993, pp. 107-27.

  6 Data for the University of Virginia and University of California at Berkeley are for 1988 and were obtained from Sarich 1990 and L. Feinberg, “Black freshman enrollment rises 46% at U-Va,” Washington Post, December 26, 1988, p. C1.

  7 The figures for standard deviations and percentiles are based on the COFHE schools, omitting Virginia and Berkeley. The COFHE Redbook provides the SAT scores for the mean, 25th percentile, and 75th percentile by school. We computed the estimated standard deviation for the combined SATs as follows:

  Estimated standard deviation for each test (Ver
bal and Math): given the scores for the mean and any percentile, the corresponding SD is given by (x−m)/z, where x is the score for the percentile, m is the mean, and z is the standardized score for that percentile in a normal distribution. Two separate estimates were computed for each school, based on the 25th and 75th percentiles. These two estimates were averaged to reach the estimated standard deviation for each test.

  The formula for estimating the standard deviation of combined tests is , where r is the correlation between the two tests and represents the standard deviation of the two tests. The correlation of the verbal and math SATs as administered to the entire SAT population is .67 (Donlon 1984, p. 55). The correlation for elite schools is much smaller. For purposes of this exercise, we err on the conservative side by continuing to use the correlation of .67. We further err on the safe side by using the standard deviation for the entire student population, which is inflated by the very affirmative action admissions that we are analyzing. If instead we were to use the more appropriate baseline measure, the standard deviation for the white students, the Harvard standard deviation (known from unpublished data provided by the Admissions Office) would be 105 instead of 122. For both reasons, the analysis of the gap between minority and white students in the COFHE data is understated. To give an idea of the magnitude, our procedure underestimated the known black-white gap at Harvard by 14 percent.

  8 The Berkeley figure for Latinos is an unweighted average of Chicanos and other Latino means.

  9 Scholars who have tried to do work in this area have had a tough time obtaining data, up to and including researchers from the Office for Civil Rights in the Department of Education (Chun and Zalokar 1992, note, p. 108).

  10 The Berkeley figure for Latinos is an unweighted average of Chicanos and other Latino means. For Davis, only a Chicano category is broken out. Virginia had no figure for Latino students.

  11 Chun and Zalokar 1992.

  12 Committee on Minority Affairs 1984, p. 2.

  13 Chan and Wang 1991; Hsia 1988; Li 1988; Takagi 1990; Bunzel and Au 1987.

  14 K. Gewertz, “Acceptance rate increases to 76% for class of 1996,” Harvard University Gazette, May 15, 1992, p. 1.

  15 F. Butterfield, “Colleges luring black students with incentives,” New York Times, Feb. 28, 1993, p. 1

  16 For Chicano and other Latino students at Berkeley, the comparative position with whites also got worse. SAT scores did not rise significantly for Latino students during the 1978-1988 period, and the net gap increased from 165 to 254 points for the Chicanos and from 117 points to 214 points for other Latinos.

  17 Powers 1977, as reported with supplementary analysis in Klitgaard 1985, Table A1.6, p. 205.

  18 The 12-15 range cuts off the upper 11.5 percent, 14.9 percent, and 7.5 percent of matriculants with known MCAT scores for the biological sciences, physical sciences, and verbal reasoning tests respectively. By way of comparison, the top 10 percent in the SAT-Math in 1993 was a little above 650; in the SAT-Verbal, in the high 500s.

  19 Shea and Fullilove 1985, Table 4, reporting 1979 and 1983 data, indicate that blacks with MCAT scores in the 5-7 range had approximately twice the chance of admission of white students. In another glimpse, a multivariate analysis of applicants to medical school from among the undergraduates at two University of California campuses (Berkeley and Davis) during the last half of the 1970s began with the average white male applicant, who had a 17.8 percent chance of being admitted. Holding other characteristics constant, being black raised the probability of admission to 94.6 percent. Being an American Indian or Chicano raised the probability to 95.0 percent (Olmstead and Sheffrin, 1980a). An Asian with identical age and academic credentials had a 25 percent chance of admission, higher than the white probability but not statistically significantly so. Williams, Cooper, and Lee 1979 present the odds from the opposite perspective: A study of ten medical schools by the Rand Corporation found that a minority student with a 50 percent chance of admission would have had about a 5 percent chance of admission if he were white with the same qualifications.

  20 Klitgaard 1985.

  21 Proponents of affirmative action commonly cite preference for children of the alumni and students from distant states as a justification for affirmative action. Given the size of the racial discrepancies we have reported, it would be useful to have an open comparison of the discrepancies associated with these other forms of preference. We have found data from only one school, Harvard, where the legacy of having a Harvard parent continues to be a plus in the admissions process but small in terms of test scores. For the decade starting in 1983, the average Verbal score of alumni children admitted to Harvard was 674 compared to 687 earned by the admitted children of nonalumni; for Math scores, the comparable scores were 695 versus 718, respectively. Office of Civil Rights 1990.

  22 Higham 1984. The arguments against admitting Jews were likely to mention that gentile families might not send their children to a college with “too many” Jews (institutional self-interest) or that anti-Semitism would make it hard for Jewish alumni to use their college education for society’s welfare (social utility).

  23 Berger 1987.

  24 Lloyd 1990; Peller 1991.

  25 The formal explication of this standard is Thorndike 1971. For a discussion of how slippery the notion of “acceptable” performance can be, see Brown 1980.

  26 The comparisons are based on NLSY subjects who went to the same four-year colleges and universities (again, excluding historically black schools). Excluding junior colleges eliminates problems of interpretation if different proportions of different ethnic groups attended junior colleges rather than four-year institutions. Since the framework for the analysis assumes a multiracial campus, it seemed appropriate to exclude the 103 NLSY subjects (all but 6 of whom were black) who attended historically black institutions. For the record, the mean AFQT score of black students who first attended historically black institutions and blacks who first attended other four-year institutions were within two IQ points of each other.

  27 We used the top and bottom half of socioeconomic status rather than a more restrictive definition (such as the top and bottom quartile) to give large enough sample sizes for us to have confidence in the results. When we used the more restrictive definitions, the results showed admissions decisions that were even farther out of line with the rationale, but with small samples numbering just 15 pairs for two of the cells. The procedure for the analysis was as follows: The NLSY includes the FICE (Federal Interagency Committee on Education) code for each institution the NLSY subjects attended. This analysis is based on the first such institution attended after high school. The matching procedure sometimes creates multiple lines for one member of the pair. For example, suppose that three whites and one black have attended the same school. One may either enter the black score three times or eliminate duplicates, entering the black score only once. We consider that the elimination of duplicates is likely to introduce more error, on the assumption that the differences among colleges can be large. Imagine a sample consisting of two schools: an unassuming state teachers’ college, with three whites and three blacks in the NLSY sample, and Yale, with three whites and one black. The Yale scores are much higher than the teachers college scores. Eliminating duplicates—entering just one (high) black score for Yale instead of the same score three times—would defeat the purpose of matching schools. The figures reported in the text are thus based on means that have counted some people more than once but control for institutional effects. The mean used to compute a cell entry is the intercept of a regression in which the dependent variable is IQ score and the independent variables are the institutions, coded as a vector of nominal variables. Note that we also reproduced this analysis eliminating duplicates. The results are so similar that the alternative numbers could be inserted in the text without requiring the change of any of the surrounding discussion.

  In addition to this form of the analysis, we examined other ways of cutting off low and high socioeconomic
status, ranging from the most general, which divided the deciles into the top and bottom five, to the most extreme, which considered only the top and bottom deciles. For the latter analyses, we used the entire sample of NLSY students who attended four-year institutions, to preserve large enough sample sizes to analyze. Those results were consistent with the ones presented in the text. A positive weight attached to being black until reaching the most extreme comparison, of a white student in the bottom socioeconomic status decile compared to a black student in the top decile, at which point the edge for the black student fell to close to zero (but never actually reached zero). We further examined the results when the sample consisted of NLSY subjects who had received a bachelor’s degree (not just attended a four-year college). The pattern was identical for both blacks and Latinos, and even the magnitudes of the differences were similar except that, as in other replications, the gap between the disadvantaged white and disadvantaged black grew substantially over the one reported in the text.

  28 The computation, using IQ scores, was (black mean − white mean)/(SD of all whites who attended a four-year institution as their first college). In understanding the way that affirmative action operates, we take it that the reference point is the white student population, which indeed squares with most qualitative discussions of the issue, pro and con.

  29 Perhaps “low SES” for blacks meant a much worse background than “low SES” for whites? Not by much; the means for both groups were close (31st percentile for whites, 25th for blacks), and controlling for the difference did not appreciably change the story. Nor did it do any good to try to define “high” and “low” SES more strictly, such as people in the top and bottom quartiles. In that case, the disadvantaged blacks were admitted with even lower lower scores than disadvantaged whites, in the region of 1.5 standard deviations (depending on the specific form of the analysis)—and so on through the cells in the table.