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.
7 The main complications are, first, that the affirmative action policies evolved over a period of time, so that the landmark events are not as decisive as they may appear to be (see Appendix 7). Second, laws and regulations often institutionalize changes that were already under way for other reasons. This seems to be clearly the case with the hiring of minorities, and it, too, tends to blunt the impact of the laws and regulations when they come along. Third, different regions of the country probably reacted to the laws and regulations differently, thereby diluting their impact in national statistics.
8 Donohue and Heckman 1991 ; Epstein 1990; Freeman 1984; Heckman and Payner 1989; Heckman and Verkerke 1990; Leonard 1986; Welch 1981.
9 Brown and Erie, 1981 concluded that about 55 percent of the increase in black managerial, professional, and technical employment from 1960 to 1976 occurred in the public sector.
10 The classic exchange on this topic is Epstein 1992, Chap. 12; Heckman and Payner 1989.
11 The normative 1 standard deviation difference is assumed for this exercise. The observed difference in the NLSY is larger, hence would only exacerbate the conclusion suggested by the graphic on page 485.
12 Obviously, there will be employees who fall outside the range. But insofar as the tails at both ends are small and roughly equivalent, the calculation is not much affected. These particular numbers are based on the observed distribution of NLSY whites in these job categories. For clerical jobs, 90 percent of all white employees had IQs between 85.7 and 122.7, with a standard deviation of 11.3. For professional and technical jobs, 90 percent of all white employees had IQs of 98.0 and above, with a standard deviation of 11.8.
13 The assumptions used for the figure are extremely conservative. Most obviously, the standard deviation of 15 is too high. People within an occupational category will always tend to have a smaller dispersion than the general population. If we change nothing except reduce the sta
ndard deviations to 12 for both blacks and whites, in line with the observed standard deviations in the NLSY, the black-white ratios rise from 1.7 (professional-technical) and 1.6 (clerical) to 2.5 and 1.9 respectively. In addition, however, the graph on page 490 is conservative in using an IQ range that encompasses 90 percent of the white workers in an occupational category. The lower the bottom end of the range is, the more it disproportionately inflates the eligible portion of the black population (changes in the top end of the range are at the tail of the distribution and add very little to the eligible pool). Visualize the bell curve: By lowering the bottom cutoff for professional-technical professions from 100 to 98 (for example), everyone in that very fat part of the curve is treated as being just as eligible for a professional-technical occupation as anyone else—even though, in reality, they are much less likely than persons with higher IQs to get such jobs. If, for example, we base the range on the IQs that embrace 80 percent of the white workers in an occupation—more realistic in many respects—the black-white ratio in 1990 grows to 2.3 for professional-technical occupations and 1.8 for clerical. But the conclusions still hold even if we broaden the range still further than in the graph, to embrace 95 percent of all people in those occupations. In that case—which assumes, implausibly, that all people with IQs higher than 89.8 are equally likely to be hired for technical-professional jobs and that all people with IQs between 82.0 and 130.3 are equally likely to be hired for clerical jobs—the black-white ratio as of 1990 is still greater than 1 in both instances: 1.2 for professional-technical, 1.5 for clerical. In short, the differences produced by altering the assumptions can make substantial differences in the size of the estimates of disproportionate hiring, but even assumptions that go well beyond common sense and the available data do not change the overall conclusions drawn in the text.
14 The observations using the CPS and the NLSY are not completely independent, insofar as we took our estimate of the IQ range for clerical and professional-technical occupations from the data on NLSY whites. But those parameters did not constrain the results for blacks.
15 The sample in these analyses excluded persons who were still in school in 1990.
16 Jaynes and Williams 1989, Tables 44, 6-1.
17 Hartigan and Wigdor 1989. See also Chapters 3 and 13.
18 As of 1987, states had such a certification process. See Rudner 1988.
19 Straus and Sawyer 1986.
20 Lerner 1991.
21 In Pennsylvania, with the highest pass rates, the state commissioner of higher education openly acknowledged that Pennsylvania sought to avoid lawsuits alleging racial bias in the test by establishing a low cutoff score that they would subsequently try to raise. See H. Collins, “Minority groups are still lagging on teacher’s exam,” Philadelphia Inquirer, Aug. 5, 1989, p. B1.
22 The answer to the question of how such large differences can show up in otherwise credentialed teachers is, in effect, the topic of the preceding chapter, on affirmative action in higher education.
23 If we make the empirically more likely assumption that IQ does have a positive correlation with the nonintellectual skills, then the people with low intellectual skills will, on average, also have depressed nonintellectual job skills.
24 For examples of affirmative action programs in public bureaucracies, see Lynch 1991, pp. 24-32; Taylor 1992, Chaps. 4, 5.
25 Carlson 1993.
26 Carlson 1993, p. 28.
27 Carlson 1993, p. 30.
28 Washington Post, October 24-28, 1993.
29 Delattre 1989; Sechrest and Burns 1992.
30 Among the other stories we have located linking poor worker performance to hiring under affirmative action requirements are one reporting an increase in collisions and other accidents on the New York public transportation system (K. Foran, “TA lax on Safety,” Newsday, Sept. 19, 1990, p. 5), another describing the rise in criminal behavior among Detroit’s police officers (E. Salholz, “Going After Detroit’s rogue cops,” Newsweek, Sept. 5, 1988, p. 37), and one discussing the much higher rate of firings among Boston’s black postal workers, compared to white workers (B. McAllister, “Researchers say Postal Service tried to block article on firings,” Washington Post, Oct. 17, 1992, p. A3).
31 Silberberg 1985. See also Ford et al. 1986; Kraiger and Ford 1985.
32 Silberberg has his own interesting hypotheses about these differences, which we do not elaborate here. Nothing in his account is at variance with our conclusion that affirmative action procedures are exacting a cost in worker performance.
33 Hacker 1992, p. 25.
34 In fact, that was precisely the excuse often given by the major leagues for not hiring blacks.
35 For a detailed statement of this perspective, see Kelman 1991.
36 Quoted in Bolick 1988, p. 49. See also Taylor 1992, p. 126.
37 There is a presumption that if we cannot explain a group difference, it is appropriate to assume that there is no good reason for it. This is bad logic. Not knowing a good reason for a difference is not the same as knowing that there is no good reason.
38 We understand the argument that, in the long term, and taking the broadest possible view, if all businesses were to behave in “socially responsible” ways, there would result a better society that would provide a healthy climate for the businesses themselves. Our argument is somewhat more direct: Can a university president, thinking realistically about the foreseeable future, see that his university will be better qua university by admitting some students who are academically less qualified than their competitors? Generally, yes. Can the owner of a business, thinking realistically about the foreseeable future, see that his business will be better qua business by hiring people who are less productive than their competitors? Generally, no.
39 D. Pitt, “Despite revisions, few blacks passed police sergeant test,” New York Times, January 13, 1989, p. 1.
40 See Taylor 1992, pp. 129-137, for an account of some of the more egregious examples.
41 The largest difference, 1.6 SDs, was for persons with advanced degrees. For Latinos, the gap with whites ranged from .6 to 1.0 SDs.
42 Other approaches for contending with affirmative action constraints have surfaced. For example, New York’s Sanitation Department used a test on which 23,078 applicants out of 24,000 got perfect scores, and its Fire Department used a test with multiple choice questions for which a point of credit was given if the first choice is correct, a half-point if the second choice is correct, or a quarter-point if the third choice is correct, thereby inflating the grades for people who get lots of items wrong (Taylor 1992).
43 Hartigan and Wigdor 1989; Hunter and Hunter 1984.
44 For an account, see Hartigan and Wigdor 1989.
45 E. F Wonderlic * Associates, 1983, Table 18, p. 25. The scores of Asians are lower than the national mean (in contrast to results of IQ studies) probably because the Wonderlic, a pencil-and-paper test, is language sensitive and is widely used for lower-level jobs. It seems likely that substantial proportions of Asians who take the Wonderlic are recent immigrants for whom English is a second and often newly acquired language.
46 Summarized in Lynch 1991. See also Detlefsen 1991.
Chapter 21
1 Kaus 1992. Kaus’s analysis runs parallel with our own in many respects—among other things, in his use of the Herrnstein syllogism (Herrnstein 1971, 1973) to think about the stratifying influence of intelligence.
2 The remark appeared in the manuscript of The End of Equality. It is used here with permission of the author.
3 Quoted in Novak 1992, p. 24.
4 Surveys by the Roper Organization (Roper Reports 92-5), as reported in American Enterprise (May-June 1993): 86.
5 U.S. Bureau of the Census 1992, Table B-6, 1975.
6 U.S. Bureau of the Census, 1991, Table B-3. All data are based on pretax income, so the tax reforms of the 1980s are not implicated.
7 Reich 1991.
8 Voting estimated from Jennings 1991, Tables 7, 10, 13.
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9 Overall, 19.2 percent of children born to NLSY women from the mid-1970s through 1990 were born to unmarried mothers with below-average IQs. The national illegitimacy ratio grew steadily throughout that period.
10 “White” includes births to Caucasian Latinos. The National Center for Health Statistics has provided Latino/non-Latino breakdowns only since 1986. During that period, the non-Latino white illegitimacy ratio increased from 13.2 percent to 18.0 percent in 1991, the latest figures as we write.
11 Data refer to poverty in the year prior to birth, and to non-Latino and Latino whites combined, to be consistent with the use of “white” in this discussion. The proportions for non-Latino white women above and below the poverty line were quite similar, however: 6 percent and 44 percent respectively.
12 Unpublished detailed tables for Bachu 1993, available from the Bureau of the Census.
13 These continue to be figures for Latino and non-Latino whites combined. The figures for non-Latino whites may be found in Chapter 8. They are not so different (because non-Latino whites so dominate the total). Seventy-two percent of illegitimate children of non-Latino white mothers in the NLSY had IQs below 100, and 39 percent had IQs below 90.
14 Wilson 1987. For a complementary view, see Massey and Denton 1993.
15 In the NLSY, blacks from the lowest quartile of socioeconomic background had a mean IQ equivalent of 82.
16 For an early statement of this argument, see Murray 1988a.
17 Jencks and Peterson 1991.
18 Chapter 16 discussed some of these efforts with regard to intelligence. For broader-ranging assessments, see Murray 1984; Stromsdorfer 1987; Rossi 1987; Glazer 1988.
The Bell Curve: Intelligence and Class Structure in American Life Page 91
