The Bell Curve: Intelligence and Class Structure in American Life (133 page)

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.

30
We use this indirect measure because other more direct measures (e.g., the number of blacks enrolling in college out of high school, or the number of persons ages 20 to 21 enrolled in school) do not go back to the 1960s and 1950s.

From 1950-1969, data are available only for “blacks and others.” Overlapping data indicate that the figure for “blacks only” in the early 1970s was stable at approximately 95 percent of the “blacks and other” figure. The data for 1950-69 represent the “blacks and other” numbers multiplied by .95. If one assumes that the proportion was somewhat higher in the 1950s and early 1960s, this produces a fractional overestimate of the upward black trendline, but so small as to be visually imperceptible in the graph on page 469.

31
Carter 1991; D’Souza 1991; Sowell 1989; Sowell 1992; Steele 1991.

32
See, for example, Sarich 1990; Lynch 1991.

33
For a review of this literature through the 1970s, see Breland 1979. Research since then has not changed the picture. See also Linn 1983; Donlon 1984, pp. 155-159.

34
As in so many matters involving affirmative action, this indirect reasoning would be unnecessary if colleges and universities were to open their data on grades to researchers.

35
Altbach and Lomotey 1991; Bunzel 1992; D’Souza 1991.

36
E.g., Carter 1991; Steele 1991.

37
National Center for Education Statistics 1992, Tables 170, 249. In the NLSY sample, among all students who first entered a four-year nonblack university, 27 percent of the whites failed to get a bachelor’s degree compared to 57 percent of the blacks and 55 percent of Latinos. “Dropout” in the NLSY is defined as having failed to have completed a bachelor’s degree by the 1990 interview, despite having once entered a four-year college. By that time, the youngest members of the NLSY were 25 years old.

38
The real discrepancy in dropout rates involved Latinos. Using the same analysis, the probability that a Latino student with an IQ of 110 would get a bachelor’s degree was only 49 percent. These results are produced when the analysis is run separately for each race.

39
A. Hu, “Hu’s on first,”
Asian Week,
May 12, 1989, p. 7; Consortium on Financing Higher Education 1992.

40
A. Hu, “Minorities need more support,”
The Tech,
Mar. 17, 1987, p. 1

41
Carter 1991; Sowell 1992; Steele 1991; D’Souza 1991; Murray 1984.

42
There should probably also be some contraints on the spread of the ability distributions in various groups, but such specificity would be out of place here.

Chapter 20

1
This statement assumes that the violation of the 80 percent rule is statistically significant. With sufficiently small numbers of hirees or promotions, these percentages will fluctuate widely by chance.

2
The Uniform Guidelines are just guidelines, not laws. In one notable 1982 case
(Connecticut
v.
Teal),
the Supreme Court ruled that even the practice of meeting the 80 percent rule by hiring larger numbers of test passers from the protected than from the unprotected groups still falls short if the test produces disparate impact. Disparate impact, in and of itself, said the Court in
Teal,
deprives protected applicants of equal opportunity, even if the disproportionate numbers are corrected at the bottom line. Under this ruling, an employer who hires a given number of blacks will be violating the law if the blacks have high ability test scores, but not violating the law if the same number of blacks are hired without recourse to the scores at all, and thus are bound to have lower scores on average. This eventuality was lauded by Kelman 1991, who argues (p. 1169) that hiring a larger proportion of test-passing blacks than test-failing blacks “stigmatizes” blacks because it implicitly validates a test on which blacks on average score below whites. Better, he suggests, not to test at all, tacitly assuming that the test has no predictive power worth considering. For another view of
Teal,
see Epstein 1992.

3
The Hartigan Report is discussed in Chapter 3.

4
E.g., Kelman 1991.

5
Heckman and Payner 1989, p. 138.

6
The categories are based on those defined by the federal government. The professional-technical category was chosen to represent high-status jobs. The clerical category was chosen both to represent lower-status skilled jobs and also because, among those categories (others are sales workers and the craft workers), clerical is the only category that shows a visibly steeper increase after 1959 than before it. Two technical points about the graph on page 485 are important. First, the job classification system used by the Census Bureau was altered in 1983. Figures for 1983-1990 conform to the classification system in use from 1959-1982. The professional-technical category for 1983-1990 consists of the sum of the headings of “professional specialty,” “technical, sales, and administrative support,” “accountants and auditors,” and “personnel, training, and labor relations specialists.” The clerical category consists of the sum of “administrative support, including clerical,” and “cashiers.” Second, the data in the graph are for blacks only, corrected for the “blacks and others” enumeration that was used until 1973. The correction is based on the known ratio of jobs held by the “others” in “blacks and others” for overlapping data as of 1973. This assumes that the
“others” (mostly Asian) held a constant proportion of clerical and professional jobs held by “blacks and others” from 1959-1973. If in fact the proportion went down (blacks acquired these jobs disproportionately), then the pre-1973 line in the graph slightly underestimates the slope of the black increase. If in fact the proportion went up (the “others” acquired these jobs disproportionately), then the pre-1973 line in the graph slightly overestimates the slope of the black increase. Note, however, that even as of 1973, blacks constituted 87.9 percent of the “black and other” population ages 18 and over, compared to 91.9 percent in 1960, so the degree of error is unlikely to be visually perceptible in the graph. The alternative was to show “blacks and others” consistently from 1959 into the 1990s, but from a technical perspective this becomes increasingly inaccurate as the percentage of “others” increases rapidly in the 1970s and 1980. Visually, graphs prepared under either method show the same story.