The Bell Curve: Intelligence and Class Structure in American Life

by Richard J. Herrnstein

  8 If a third dropped out between ninth grade and twelfth grade, their average IQ must have been 101, compared to 107 for the seniors and graduates; if half dropped out, it must have been 103. Assuming a population average of 100, this implies that those who dropped out prior to ninth grade had still lower scores than those who dropped out afterward.

  9 Iowa State Department of Public Instruction, 1965.

  10 Dillon 1949, quoted in Jensen 1980, p. 334.

  11 Based on a comparison of the academic aptitude scores of the ninth graders in the sample who had and had not graduated from high school five years later. The IQ equivalents are computed from a graduate-dropout gap of 1.14 standard deviations (SDs) for boys and 1.00 SDs for girls, or approximately 1.05 SDs overall (Wise et al. 1977, Table A-3). In the late 1960s, the Youth in Transition study found a difference of about .8 SDs on the vocabulary subtest of the GATB and the Gates Reading Tests between dropouts and nondropouts, consistent with a 1 SD difference on a full-scale battery of tests (reconstructed from Table 6-1, p. 100, and Tables C-3-7 and C-3-8 in Bachman et al. 1971).

  12 Looking at these numbers, some readers will be wondering how much these dropout figures represent cause and how much effect. After all, wouldn’t a person who stayed through high school and then took the IQ test have got ten a higher score by virtue of staying in high school? This question of cause and effect may be raised with all of the topics using the NLSY, but it is most obvious for school dropout. But while age has an effect on AFQT scores and is always taken into account (either through age-equated scores in the descriptive statistics or by entering age as an independent variable in the regression analyses), there is no reason to think that presence in school is decisive. The simplest way to document this is by replicating the analyses for a restricted sample of youths who were age 16 and under when they took the test, thereby excluding almost all of the members of the sample who might create these artifacts. Having done so for all of the results reported in this chapter, we may report that it makes no difference in terms of interpretations. We will not present all of these duplicate results, but an example will illustrate.

  Using the full sample of whites, the mean IQs, expressed in standard scores, of those who completed high school via the normal route, those who got a high school equivalency, and those who dropped out permanently were +.37, -.14, and -.94 respectively. For whites who took the AFQT before they were age 17, the comparable means were +.34, -.04, and -.95. The main effect of using the age-restricted sample is drastically to reduce sample sizes, which we judged to be an unnecessary sacrifice. The NLSY data are consistent with other investigations of this issue (e.g., Husén and Tuijnman 1991). Continued schooling makes a modest contribution to intellectual capital but not enough to make much difference in the basic relationships linking IQ to other outcomes. Chapter 17 specifically discusses the impact of schooling on IQ, and Appendix 3 elaborates on the relationship of schooling to IQ in the NLSY.

  13 Other data confirm this general picture. In the High School and Beyond national sample conducted by the Department of Education in 1980, it was found that those in the lowest quartile on the cognitive ability test dropped out at a rate of 26.5 percent, compared to 14.7 percent, 7.8 percent, and 3.2 percent in the next three quartiles, respectively (Barro and Kolstad 1987, Table 6.1, p. 46). Similar results have been found in other recent studies of dropouts and cognitive ability (e.g., Alexander et al. 1985; Hill 1979). Comparable rates of dropping out across the IQ categories and across categories defined by vocabulary test scores were also found in the earlier Youth in Transition study, based on approximately 2,000 men selected to be representative of the national population in the tenth grade in 1967 (Bachman et al. 1971). For an estimate of the loss in cognitive ability that may be attributed to dropout itself, see Alexander et al. 1985.

  14 The General Educational Development exam is administered by the American Council on Education.

  15 Cameron and Heckman 1992.

  16 DES 1991, Tables 95, 97. In the NLSY, 9.5 percent of those classified as having a high school education got their certification through the GED.

  17 As depicted in, for example, Coles 1967, in his work on certain impoverished populations. The relative roles of socioeconomic background and IQ found in the NLSY are roughly comparable to those found for the Youth in Transition study based on students in the late 1960s, though the method of presentation in that study does not lend itself to a precise comparison (Bachman et al. 1971, Chap. 4-6).

  18 In passing, it may be noted that these results hold true for blacks as well. Of the blacks in the NLSY who permanently dropped out of school, none was in the top quartile of IQ. Only nine-tenths of 1 percent of black permanent dropouts were in the top half of IQ and the bottom half of SES. See Chapter 14 as well.

  19 In a logistic regression, with all independent variables expressed as standard scores, the coefficients for IQ, SES, Age, and the SES x IQ interaction term were 1.91, .98, -.06, and .32, respectively. The intercept was 2.81. The interaction term was significant at the .005 level, and r2 =.38. The equation is predicting “true” for a binary variable denoting high school graduation (with permanent dropout as the “false” state).

  20 Press accounts of the GED population suggest that the typical youngster in it had trouble with the routine of ordinary school and comes from un-commonly deprived family circumstances (e.g., Marriot 1993).

  21 Matarazzo 1972, pp. 178-180.

  22 The percentages were 68 and 23, respectively.

  Chapter 7

  1 The figure on page 156 also echoes some of the large macroeconomic forces that we did discuss in preceding chapters. To some extent, the pool of “16—19-year-olds not in school” has changed as high schools have retained more students longer and colleges have recruited larger numbers of the brightest into college. As the pool has changed, so perhaps has the em-ployability of its members. The greater employment problems shown by the figure also fit in with the discussion about earnings in Chapter 4 and the way in which income has stagnated or fallen for those without college educations. For concise reviews of the empirical literature on labor supply and unemployment, see Heckman 1993; Topel 1993. Studies focused on young disadvantaged men include Wolpin 1992; Cogan 1982; Bluestone and Harrison 1988; Cohen 1973; Holzer 1986. There is, of course, a large literature devoted explicitly to blacks. See Chapters 14 and 20.

  2 We conducted parallel analyses with a sample based on the most recent year of observation (back to 1984), which enabled us to include data on some men who were being followed earlier but subsequently disappeared from the NLSY sample. The purpose was to compensate for a potential source of attrition bias, on the assumption that men who disappeared from the NLSY sample might be weighted to some degree toward those with the fewest connections to a fixed address and (by the same token) to the labor market. The results obtained by this method were substantively indistinguishable from the ones reported.

  3 We replicated all of the analyses using the actual number of weeks out of the labor force as the dependent variable instead of a binary yes-no measure of whether any time was spent out of the labor force. The relative roles of the independent variables were the same as in the reported analyses, with similar comparative magnitudes as well as the same signs and levels of statistical significance. The relationship, such as it is, does not seem to be concentrated among the children of the very wealthy.

  4 A more fine-grained examination of the data reveals that absence from the labor force and job disabilities is extraordinarily concentrated within a limited set of the lowest-status jobs. Using a well-known index of job prestige, the Duncan index, 46 percent of the reports of job limitations and 63 percent of those who reported being prevented from working (but who were still listing an occupation) came from jobs scored 1 to 19 on the Duncan scale, which ranges from 1 to 100. A total of 975 white men in the NLSY listed such a job as their occupation in 1990. The five most common jobs in this range, accounting for 35 percent of the total, were truck
driver, automobile mechanic, construction laborer, carpenter, and janitor. Another 299 white males working in blue-collar jobs scored 20 to 29 on the Duncan scale. The five most common jobs in this range, accounting for 37 percent of the total, were welder, heavy equipment mechanic, other mechanic and repairman, brick mason, and farmer. Another 158 white males were working in blue-collar jobs scored 30 to 39 on the scale. The five most common jobs in this range, accounting for 47 percent of the total, were delivery man, plumber and pipefitter, machinist, sheet metal worker, and fireman.

  Looking over these jobs, it is not readily apparent that the lowest-rated jobs in terms of prestige are also the physically most dangerous or demanding. Construction work fits that description in the lowest category, but so does fireman, sheet metal worker, and others in the higher categories. Meanwhile, some of jobs in the lowest category (e.g., truck driver, janitor) are not self-evidently more dangerous or physically demanding than some jobs in the higher categories. Or to put it another way: If a third party were given these fifteen job titles and told to rank them in terms of potential accidents and the importance of physical fitness, it is unlikely that the list would also be rank-ordered according to the job prestige index or even that the rank ordering would have much of a positive correlation with the job prestige index.

  Instead, the index was created based on the pay and training that the jobs entail—both of which would tend to give higher ratings to cognitively more demanding jobs. And so indeed it works out. Here are the mean IQ scores of white males in blue-collar jobs, subdivided by groups on the Duncan scale, alongside the number per 1,000 who reported some form of job-related health limitation in 1989:

  Duncan Scale Score (Limited to Blue Collar Occupations) Mean IQ Percentile No. per 1,000 with Job-Related Health Disability

  0-9 35th 52

  10-19 40th 55

  20-29 48th 32

  30-39 56th 26

  40-49 59th 16

  In short, the results of the regression analysis indicating that IQ has an important relationship to job disability even among blue-collar jobs, and even after taking age and years of education into account, are not explained away by the differences in the physical risks of these occupations. The same conclusion holds true when the analysis is conducted only for blue-collar workers and the variable “years of education” is added to the equation. The coefficient relating IQ to likelihood of disability is about four times the coefficient for years of education (with age as the other independent variable constant). Intriguingly, the opposite is true when the analysis is conducted just for white-collar workers: Years of education is important, wiping out any independent role for IQ. Interpreting this is difficult, both because health disability is such a rare phenomenon among white-collar workers and because IQ becomes so tightly linked to advanced education, which in turn is associated with jobs in which physical disability is virtually irrelevant (short of a stroke or other accident causing a mental impairment).

  5 Terman and Oden 1947.

  6 Hill 1980; Mayer and Treat 1977; O’Toole 1990; Smith and Kirkham 1982.

  7 Grossman 1976; Kitagawa and Hauser 1960.

  8 Restriction of range (see Chapter 3) might also reduce the independent role of IQ among college graduates.

  Chapter 8

  1 For a review of the literature about family decline, see Popenoe 1993.

  2 U.S. Bureau of the Census 1992, Table 51.

  3 Retherford 1986.

  4 Garrison 1968; James 1989.

  5 The cognitive elite did get married at somewhat older ages than others, and this difference will grow as the NLSY cohort gets older. Judging from other data, almost all of those in the bottom half of the IQ distribution who will ever marry have already married by 30, whereas many of that 29 percent unmarried in Class I will eventually marry, raising their mean age of marriage by some unknown amount. If all of them married at, say, age 40, the average age at marriage would approach 30, which may be taken as the highest mean that the NLSY could plausibly produce as it follows its sample into middle age.

  6 In his famous lifetime study of intellectually gifted children born around 1910, Lewis Terman found that, as of the 1930s and 1940, highly gifted men eventually got married at higher rates than the national norms—about 84 percent, compared to a national rate of 67 percent for men of similar age. Gifted women married later than the average woman, but by their mid-30s they too had higher marriage rates than the general population, though the difference was not as great as for men: 84 percent compared to 78 percent (Terman and Oden 1947, p. 227).

  7 Cherlin 1981, Figure 1-5. His estimation procedure suggests that the odds of eventual divorce in 1980 were 54 percent. Also see Raschke 1987.

  8 We are here calculating odds ratios—the likelihood of marital survival divided by the likelihood of divorce within the first five years—from the table on page 174. The ratio of odds ratios for marital survival versus divorce during the first five years of marriage was 2.7, comparing Class I to Class V.

  9 In addition to the standard variables (age, parental socioeconomic status, and IQ), we added “date of first marriage.” We wished to add age at first marriage as well, but it was so highly correlated with the date of first marriage in the entire white sample (r = +.81 ) that the two variables could not be used together. It was possible to use them together in some of the subsamples we analyzed. The pattern of results was unchanged.

  10 Different subsets of white youths, both the entire sample of those who had married and the subset of those who had reached the age of 30, and the subset below the age of 30 all yielded similar results.

  11 E.g. Raschke 1987; Sweet and Bumpass 1987.

  12 Higher socioeconomic status is also associated with a lower probability of divorce in the college sample, though the independent effect of parental SES is much smaller than the independent effect of IQ. Socioeconomic status had an insignificantly direct relationship with divorce for the high school sample. Thinking back to the analysis of marriage, note a curious contrast: IQ makes a lot of difference in whether high school graduates get married but not in whether they get divorced. IQ makes little difference in whether college graduates get married by the age of 30 but a lot of difference in whether they get divorced. Why? We have no idea. In any case, embedded in this complicated set of findings are intriguing possibilities, which warrant a full-scale analysis.

  13 Raschke 1987; Sweet and Bumpass 1987; Teachman et al. 1987.

  14 Even a genetic component has been invoked to explain the fact that divorce runs in families. Not only do children tend to follow their parents’ path toward divorce, but identical twins are more correlated in their likelihood of divorce than fraternal twins, a difference that often betrays some genetic influence. McGue and Lykken 1992.

  15 Those living with only the father did as well as those living with both biological parents.

  16 See references in Raschke 1987; South 1985.

  17 Bronislaw Malinowski, Sex, Culture, and Myth (1930), quoted in Moynihan 1986,p. 170.

  18 The production of illegitimate babies per unit population has also increased during this period, with the fastest growth occurring during the 1970s. In the jargon, the rate of illegitimate births has increased as well as the ratio. The distinction between rate and ratio raises a technical issue that has plagued the discussion of illegitimacy in recent years. Traditionally, illegitimacy rates have been computed by dividing the number of illegitimate births by the number of unmarried women. In a period when marital patterns are also shifting, this has the effect of confounding two different phenomena: the number of illegitimate births in the numerator of the ratio and the number of unmarried women in the denominator. To estimate the rate of change in the production of illegitimate children per unit population, it is essential to divide the number of illegitimate births by the entire population (or, if one prefers, by the number of women of childbearing age). This is almost never done, however, in nontechnical discussions (or in many of the technical ones, for that mat
ter). For a discussion of the difference this makes in interpreting trends in illegitimacy, see Murray 1993.

  19 Sweet and Bumpass 1987, p. 95. In 1960, there were 73,000 never-married mothers between the ages of 18 and 34; in 1980, there were 1,022,000.

  20 Bachu 1991, Table 1. The figures for ages 18 to 34 are interpolated from the published figures for ages 15 to 34.

  21 Not to mention that IQ has changed in the wrong direction to explain increasing illegitimacy (see the Flynn Effect, discussed in Chapters 13 and 15).

  22 As in the case of school dropout, one may ask whether having a baby out of wedlock as a teenager caused school dropout, therefore resulting in an artificially low IQ score. As before, the cleanest way to test the hypothesis is to select all the women who had their first baby after they took the test in 1980 and repeat the analyses reported here, introducing a control for age at first birth. When this is done, the relationships reported continue to apply as strongly as, and in some cases more strongly than, they do for the entire sample.

  A similar causal tangle is associated with the age at first birth. Age at first birth is a powerful explanatory variable in a statistical sense. It can drastically change the parameters, especially the importance of socioeconomic status and IQ, in a regression equation. But, in the 1990s, what causes a girl in her teens to have a baby? Probably the same things that might cause her to have an illegitimate baby: She grew up in a low-status household where having a baby young was an accepted thing to do; she is not very bright and gets pregnant inadvertently or because she has not thought through the consequences; or she is poor and has a baby because it offers better rewards than not having a baby, whether those rewards are tangible in the form of an income and apartment of her own through welfare, or in the form of having someone to love. And in fact all three variables—parental SES, IQ, and whether she was living in poverty prior to the birth—are powerful predictors of age at first birth, explaining 36 percent of the variance. Furthermore, age at first birth cannot be a cause of parental SES and poverty in the year prior to birth. Empirically, it can be demonstrated not to be a “cause” of the AFQT score, using the same logic applied to the case of illegitimacy.