RUSHTON ON RACE DIFFERENCES AND REPRODUCTIVE STRATEGIES
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.
LATINOS AND LANGUAGE BIAS IN THE AFQT
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.
Appendix 6
Regression Analyses from Chapter 14
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.
LOGISTIC REGRESSIONS
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 ques
tions are then examined:
How much do ethnic differences change when IQ is taken into account?
How much do ethnic differences change when parental SES is taken into account?
What are the comparative roles of IQ and parental SES?
Table 1 Description of Indicators Used for Logistic Regression Analyses in Chapter 14
Short label Description Comments
Probability that an NLSY subject:
High school dropout Dropped out of high school before obtaining a diploma Includes persons who dropped out and later obtained a GED
Bachelor’s degree Obtained a bachelor’s degree or higher Excludes persons enrolled as an undergraduate in 1990
High-IQ occupation Was employed in a high-IQ occupation as of 1990 Excludes persons enrolled in college or graduate school in 1990
In poverty Had family income below the poverty line in 1989 Excludes persons not working because of school 1989 or 1990
Unemployed 1 mo. (men) Was unemployed for 4 weeks or more in 1989 (men only) Excludes persons not working because of school 1989 or 1990
Married by 30 Was married before the age of 30 Excludes persons under 30 as of the 1990 interview; age is omitted as a control variable
Ever on welfare (all women) Had ever been on welfare (all women, including nonmothers)
Ever on welfare (poor mothers) Had ever been on welfare (mothers in poverty year before birth)
Ever in jail (men) Was ever interviewed in jail (men only)
“Yes” on MCV index Scored “Yes” on the Middle Class Values index Excludes single persons who met other conditions of the MCV
Probability that a child of a NLSY woman:
Born out of wedlock Was born out of wedlock
Low birth weight Was low birth weight (less than 5.5 lbs.) Excludes premature babies under 5.5 lbs whose weight was appropriate for gestational age
In poverty 1st 3 years. Lived in poverty during the first three years of life
Ever in nonparental care Ever lived in foster care or with nonparental relatives
Worst decile: Scored in the worst decile of the:
HOME index HOME index Test year and child’s age category entered as control variables
Friendliness index Friendliness index (12-13 mos.) Test year entered as an additional control variable
Difficulty index Difficulty index (12-13 mos.) Test year entered as an additional control variable
Motor & Social Dev. index Motor & Social Dev. index (0-4 yrs.) Test year and child’s age category entered as control variables
Behavioral Problems index Behavioral Problems index (4-12 yrs.) Test year and child’s age category entered as control variables
Any developmental index At least one of the development indexes
PPVT (IQ) Peabody Picture Vocabulary Test (6 yrs. & older) Test year and child’s test age entered as control variable
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.
Income Analyses
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).
Table 6 shows the analysis of wages by ethnicity and occupational grouping based on the subjects occupation in the 1990 interview (the variable labeled “Occ90”), using the 1970 U.S. Census Occupational Classification System. The software used for these analyses, JMP 3.0, treats nominal variables differently from the convention in many other regression packages. See the introduction to Appendix 4 for details and an example.
Table 5 Income Analyses in Chapter 14 (in 1990 dollars), by Degree Attained
Dependent Variable: Annual Wages for Full-Time, Year-Round Workers, 1989 Total Family Incomea
Independent Variables White Black Latino White Black Latino
a For persons not out of labor force because of school in 1989 or 1990.
Intercept 26,994 27,048 26,474 40,813 38,050 41,271
Age 2,338 787 2,207 2,583 946 3,091
IQ 3,082 3,802 2,507 3,025 4,247 4,136
Parental SES 914 840 1,248 3,648 5,191 2,042
Highest degree attained
Less than high school −4,992 −3,688 −1,588 −9,743 −4,181 −9,461
GED −2,622 −3,950 −3,039 −5,202 −4,159 −7,683
High school diploma −2,602 −3,944 −1,151 −2,789 −2,817 −1,269
Bachelor’s degree 3,329 734 2,938 4,286 4,362 10,506
Graduate degree 6,887 10,848 2,840 13,448 6,795 7,907
Minority income as a percentage of white income 100.2% 98.1% 93.2% 101.1%
Sample sizes for the different occupations analyzed in Table 6 below are as follow:
White Black Latino
Professional/technical 605 143 129
Managers/administrators 462 110 103
Clerical workers 473 260 172
Sales workers 163 34 30
Craft and kindred workers 370 113 106
Transport operatives 95 55 40
Other operatives 231 143 67
Service workers 289 218 95
Unskilled laborers 98 78 40
Farmworkers 22 4 12
Because of the small numbers of farmworkers, that category is omitted from the table. Note, however, that farmworkers were included in the actual regression equation; hence the coefficients for the nominal occupation categories will not sum to zero.
Appendix 7
The Evolution of Affirmative Action in the Workplace
Much of the current debate about affirmative action in employment takes place in ignorance of the original objectives of affirmative action and the ways in which antidiscrimination law has evolved. Because we believe that returning to the original intention of affirmative action is a key to progress in social policy on many fronts and because our recommendation seems so radical in the prevailing context, this appendix presents a full discussion of the nature of the original objectives and the evolution as it pertains specifically to employment tests.
Affirmative action in the workplace, as distinguished from the broader and older civil rights movement, starts with Title VII of the Civil Rights Act of 1964. Title VII laid down principles of fair employment practice as regards race, religion, national origin, and sex, and it created the Equal Employment Opportunities Commission (EEOC) to administer and promote them. Besides Title VII (as amended over the years), affirmative action in the workplace comprises subsequent acts o
f Congress (and state legislatures), presidential executive orders, rulings by the EEOC and other branches of government, and landmark court cases. The basic intent of all of this energetic policymaking has been to make workplaces fairer to people from oppressed or mistreated groups.
As desirable as that goal may seem to just about anyone, a clear notion either of what it means or how to accomplish it does not emerge from the documents of this enormous (and spreading) battleground of law, regulation, litigation, and commentary. The good news is that many issues of fair employment practice need not concern us here.1 But we cannot avoid looking at how Title VII (and its elaborations) dealt with the use of ability tests in the selection or promotion of employees. Although the tests are given to individuals, the groups of which they are a part may average high or low compared to the population as a whole; as it happens, some of the groups who average low are protected from “unfair employment practices” by Title VII Hence the government gets into the business of regulating employment testing.
The ramifications of even the narrow issue of employment test regulation have ranged so far and wide that employment testing has become a new specialty in the creation and practice of law and in government regulation undreamed of by the Founders. Thousands, perhaps millions, of legislative and bureaucratic man hours have been lavished on it. Thousands of cases have been argued in court.2 Doubtless many more cases have not been argued, as the specter of legal action has shaped innumerable decisions in corporate offices and boardrooms. The stance of the government and the courts has increasingly been to distrust tests that produce group differences, as if they presume that, in the absence of illegal discrimination, the groups should be equal.
THE EVOLUTION OF TITLE VII
The Bell Curve: Intelligence and Class Structure in American Life Page 76
