The Bell Curve: Intelligence and Class Structure in American Life (112 page)

Controversy unprecedented even for the contentious subject of racial differences has erupted around the work of J. Philippe Rushton, a developmental psychologist at the University of Western Ontario. Rushton argues that the differences in the average intelligence test scores among East Asians, blacks, and whites are not only primarily genetic but part of a complex of racial differences that includes such variables as brain size,
³¹
genital size, rate of sexual maturation, length of the menstrual cycle, frequency of sexual intercourse, gamete production, sexual hormone levels, the tendency to produce dizygotic twins, marital stability, infant mortality, altruism, law abidingness, and mental health. For each variable, Rushton has concluded, the three races—Mongoloids, Caucasoids, and Negroids—fall in a certain order, with the average Caucasoid in the middle and the other two races on one side or the other. The ordering of the races, he further argues, has an evolutionary basis; hence these ordered racial differences must involve genes.

To reach his conclusion, Rushton starts with the well-established observation in biology that species vary in their reproductive strategies. Some species produce many offspring (per parent) of which only a small fraction survive; others produce small numbers of offspring with relatively high survival rates. The involvement of parents in their offsprings’ health and development (which biologists call “parental investment”) tends to be high for species having few offspring and high survival rates and low for those employing the other strategy (many offspring and low survival rates). Many other species differences are concomitant with this fundamental one, according to standard biological doctrine.

Rushton’s thesis is that this standard biological principle may be applied within our own species. Rushton acknowledges that human beings are as a species far out along the continuum of low reproduction, high offspring survival, and high parental investment, but he argues that the ordering of the races on the many variables he has identified can be explained as the result of evolutionary differences in how far out the races are. According to Rushton, the average Mongoloid is toward one
end of the continuum of reproductive strategies—the few offspring, high survival, and high parental investment end—the average Negroid is shifted toward the other end, and the average Caucasoid is in the middle.

Rushton paints with a broad brush, focusing on the major racial categories rather than the dozens of more finely drawn reproductively isolated human populations that might test his theory more conclusively. But beyond that, his thesis raises numerous questions—moral, pragmatic, and scientific. Many critics attack the theory on scientific, not just moral, grounds. They question whether Rushton has really shown that the races are consistently ordered in the way he says they are, or whether a biological theory that was meant to explain species differences can be properly applied to groups within a single species, or whether the evidence for genetic influences on his variables stands up. Rushton has responded to his critics with increasingly detailed and convincing empirical reports of the race differences in some of the traits on his list, and he cites preeminent biological authority for his use of the concept of reproductive strategies. He has strengthened the case for consistently ordered race differences, at least for some of the variables he discusses, since his first formulation of the theory in 1985. Nevertheless, the theory remains a long way from confirmation.

We cannot at present say who is more nearly right as a matter of science, Rushton or his critics.
³²
However, Rushton’s work is not that of a crackpot or a bigot, as many of his critics are given to charging. Nor are we sympathetic with Rushton’s academic colleagues or the politicians in Ontario who have called for his peremptory dismissal from a tenured professorship. Setting aside whether his work is timely or worthwhile—a judgment we are loath to make under any circumstances—it is plainly science. He is not alone in seeking an evolutionary explanation of the observed differences among the races.
³³
As science, there is nothing wrong with Rushton’s work in principle; we expect that time will tell whether it is right or wrong in fact.

AFQT scores reported for Latinos in the NLSY were supposed to be limited to persons who were fluent in English. However, a lingering question remains: To what extent might language difficulties have skewed the Latino results?

To investigate this issue, we first examined the scores on the subtests of the AFQT, looking for evidence that the pattern of Latino scores across subtests was different from the patterns for whites and blacks. For the Latino sample as a whole, no such evidence was found. The correlations between the verbal subtests and the arithmetic subtests were as high for Latinos as for whites and blacks. The size of the Latino-white differences in scores across subtests followed the same pattern as the black-white differences. The correlation between the overall AFQT score and educational attainment was about the same for Latinos as for blacks and whites.

We next broke the Latino sample into those who had been born abroad (26 percent of all Latinos in the NLSY) and those who had been born in the United States, hypothesizing that those who had been born abroad had usually learned English as a second language, and we looked for evidence that this constituted a special language disability.

The results generally paralleled those for the Latino sample as a whole. The correlations between the verbal and arithmetic subtests were substantially
higher
for Latinos born abroad than for whites, blacks, or Latinos born in the United States, the opposite of what would be expected if English fluency were a problem for the foreign-borns. This was true even of the “numerical operations” subtest, which has no verbal content at all. The correlation between the AFQT and educational attainment was higher for Latinos born abroad than for any other group. Furthermore, the rank order of the subtest differences between whites and foreign-born Latinos was the same as the rank order for other groups.

The magnitude of the difference between whites and foreign-born Latinos on the verbal test was greater than the difference separating whites and U.S.-born Latinos. An argument could be made (equivocally) that the differences were disproportionately larger for the verbal subtests than for the arithmetic subtests. On the other hand, it is also true that the socioeconomic background of the foreign-born Latinos was substantially lower than for U.S.-born Latinos, and socioeconomic background itself is associated with lower IQ scores, independently of any language problems.

Our overall conclusion is that it is difficult to make a case that language difficulties contribute significantly to the Latino-white difference in the NLSY. The parsimonious explanation is that the administrators of the NLSY did a good job of screening out subjects with language difficulties.

This appendix presents the regression analyses underlying the presentation in Chapter 14.

The results in Chapter 14 and in this appendix are based on separate regressions for each of the three ethnic groups in question (black, Latino, and white). This procedure was chosen in preference to a single regression entering ethnicity as a nominal variable so that the relationships would not be constrained to a single slope. The regressions used the entire NLSY sample, with exclusions as noted for specific analyses, applying 1990 sample weights.

All the indicators in Chapter 14 except for those involving income are binary variables, and the mode of analysis is logistic regression. The interpretation of logistic regressions is discussed in Appendix 4.

The data tables use short labels for the indicators. The full description of each indicator and associated characteristics of the analysis are shown in Table 1.

Table 2 first summarizes the results, by ethnic group, for four sets of regressions: when age (zAge) is the only independent variable, when age and IQ (zAFQT) are independent variables, when age and parental SES (zSES) are independent variables, and when all three are entered as independent variables. Three basic questions are then examined:

1. How much do ethnic differences change when IQ is taken into account?
   
2. How much do ethnic differences change when parental SES is taken into account?
   
3. What are the comparative roles of IQ and parental SES?
   

Table 1 Description of Indicators Used for Logistic Regression Analyses in Chapter 14

Because zAge, zAFQT, and zSES are all expressed as standard scores with mean of zero and standard deviation of 1, the intercept for the equation (abbreviated
Int.
in the tables) represents the expected value when those variables are set at their respective means. The coefficients for zAFQT and zSES are given so that you may examine the slopes associated with them.

The summary columns (Table 3) show the computed probabilities of the dependent variable when the independent variables are set at their means.

Following the tables showing the logistic regressions, we present the detailed results of the ordinary least squares regressions used to estimate differences in income by ethnicity (Table 4). Because education is such an important causal factor in income, we show analyses in which years of education (as of the 1990 interview) replaces IQ as an independent variable.

The first set of models shows the parameters for wages of full-time, year-round workers by ethnic group. The sample for this analysis consisted of all persons in the NLSY who reported working for fifty-two weeks in 1989, had a reported wage greater than 0 (a handful of apparently self-employed persons who reported working fifty-two weeks reported no income), had an identified occupation, and had valid scores for IQ, parental SES, and educational level as of 1990. The second set of models shows the parameters for total family income from all sources. The sample for this analysis includes all persons with valid scores on the independent variables, excluding only those who reported being out of the labor force in 1989 or 1990 because of enrollment in school.

Table 5 shows the results when IQ, parental SES, and education are all entered as independent variables. Education is expressed as the highest degree attained as of 1990 (no high school diploma, high school diploma, associate degree, bachelor’s degree, professional degree).