The Bell Curve: Intelligence and Class Structure in American Life (121 page)

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
r
2 =.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.

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:

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.

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.