Gould’s popularity is such that his review in the New Yorker was circulated by some nonpsychologists as the canonical refutation of The Bell Curve. But I think he made a mistake in reraising the factor-analytic argument. By doing so, he accomplished something that The Bell Curve alone could not do: He made scholars who know what the evidence shows angry enough to go public. By and large, scholars of intelligence are reclusive. The experiences in the 1970s of people like Arthur Jensen, Hans Eysenck, and Richard Herrnstein himself taught them that the consequences of being visible can be extremely punishing. But Gould was saying things that, to professionals in the field, were palpably wrong about a topic of deep importance. The early results were a few outraged letters sent to the New Yorker (none was printed). Then came a statement of mainstream intelligence signed by fifty-two scholars and published in the Wall Street Journal in which all of the main scientific findings of The Bell Curve were endorsed (without any explicit mention of the book or its critics).12 I also hear second-hand that reporters have called scholars about “this pseudoscience g business” and received an answer that they did not expect.
These may be harbingers of a shift in the media’s treatment of intelligence. There is now a real chance that the press will begin to discover that it has been missing the story. The big news about the study of intelligence is not that science has moved beyond the concept of a general mental ability but the remarkable resilience and utility of this construct called g.
Race, IQ, and Genes
I come now to the second example of how the attacks on The Bell Curve are likely to have unintended consequences: the determination of the critics to focus on race and genes, even though The Bell Curve does not.
In Chapter 13, The Bell Curve draws three important conclusions about intelligence and race: (1) All races are represented across the range of intelligence, from lowest to highest. (2) American blacks and whites continue to have different mean scores on mental tests, varying from test to test but usually about one standard deviation in magnitude—about fifteen IQ points. “One standard deviation” means roughly that the average black scores at the sixteenth percentile of the white distribution. (3) Mental-test scores are generally as predictive of academic and job performance for blacks as for other ethnic groups. Insofar as the tests are biased at all, they tend to overpredict, not underpredict, black performance.
These facts are useful in the quest to understand why (for example) occupational and wage differences separate blacks and whites, or why aggressive affirmative action has produced academic apartheid in our universities. More generally, Herrnstein and I believe that a broad range of American social issues cannot be interpreted without understanding the ways in which intelligence plays a role that is often, and wrongly, conflated with the role of race. When it comes to government policy, and as we say emphatically at various points in Part IV, there is just one authentic policy implication: Return as quickly as possible to the cornerstone of the American ideal—that people are to be treated as individuals, not as members of groups.
The furor over The Bell Curve and race has barely touched on these core points. Instead, the critics have been obsessed—no hyperbole here—with genes, trying to stamp out any consideration of the possibility that race differences have a genetic component.
You may read everything we say about the relationship of genes to race differences in intelligence on pp. 295-315. Our position does not take long to summarize, however: A legitimate scientific debate on the topic is underway; it is scientifically prudent at this point to assume that both environment and genes are involved, in unknown proportions; and, most important, people are getting far too excited about the whole issue. Genetically caused differences are not as fearful, or environmentally caused differences as benign, as many think. What matters is not the source but the existence of group differences and their intractability (for whatever reasons).
As I have watched the frenzied attacks on this scientifically unexceptional part of the book, I have decided that Richard Herrnstein and I were what is known as “prematurely right.” Certainly we were right empirically when we observed that the public at large is fascinated by the possibility of genetic differences (pp. 296-297) and that the intellectual elites have been “almost hysterically in denial about that possibility” (p. 315). I think we were right in trying to dampen that fascination. But as I listen to some of my most loyal friends insisting that I must be disingenuous when I continue to say that the genetic question is not a big deal, it also appears that Herrnstein and I failed to make the case persuasively. This does not mean I can now improve our presentation. I have reread the concluding pages of Chapter 13 many times since the publication of the book, pondering how we could have stated our case more clearly. To this day, I have no good ideas. As far as I can tell, we said it right the first time.
My main point here is that the attacks on our discussion of genes and race are not doing any good for the cause of those who want to discredit the idea that genes could be involved. They have based their attacks on the premise that a full, fair look at the data will make the issue go away. No one appears to have recognized that Herrnstein and I did not make nearly as aggressive a case for genetic differences as the data permit.
The most abundant source of data that we downplayed is in the work of J. Philippe Rushton, a psychologist who since 1985 has been publishing increasingly detailed data to support his theory that the three races he labels Negroid, Caucasoid, and Mongoloid vary not just in intelligence but in a wide variety of other characteristics. We put our discussion of Rushton in Appendix 5. The critics of The Bell Curve are putting him on the front page, often outrageously caricaturing his work. The trouble with this strategy is that Rushton is a serious scholar who has assembled serious data.13 Consider just one example: brain size. One of the most memorable features of Gould’s The Mismeasure of Man was his ridicule of the attempts by nineteenth-century scientists to establish a relationship between cranial capacity and intelligence. But the empirical reality, verified by numerous modern studies, including several based on magnetic resonance imaging, is that a significant and substantial relationship does exist between brain size and measured intelligence after body size is taken into account and that the races do have different distributions of brain size.14 Rushton brings this large empirical documentation together. The attacks on The Bell Curve ensure that such data will get more attention.
Among those who have tried to quell any consideration that genes might play a role in racial differences, Charles Lane and Leon Kamin probably miscalculated most egregiously. I refer to their highly publicized attack on the “tainted sources” used in The Bell Curve. Lane introduced this theme with an initial article in the New Republic and then a much longer one in the New York Review of Books.15 In the latter piece, he proclaimed that “no fewer than seventeen researchers cited in the bibliography of The Bell Curve have contributed to Mankind Quarterly … a notorious journal of’racial history’ founded, and funded, by men who believe in the genetic superiority of the white race.” Lane also discovered that we cite thirteen scholars who have received funding from the Pioneer Fund, founded and run (he alleged) by men who were Nazi sympathizers, eugenicists, and advocates of white racial superiority. Leon Kamin, a vociferous critic of IQ in all its manifestations, took up the same argument at length in his review of The Bell Curve in Scientific American.16
Never mind that The Bell Curve draws its evidence from more than a thousand scholars. Never mind that among the scholars in Lane’s short list are some of the most respected psychologists of our time and that almost all of the sources referred to as tainted are articles published in leading refereed journals. Never mind that the relationship between the founder of the Pioneer Fund and today’s Pioneer Fund is roughly analogous to that between Henry Ford and today’s Ford Foundation. The charges have been made, they have wide currency, and some people will always believe that The Bell Curve rests on data concocted by neo-Nazi eugenicists.
But i
n the process of making their case, Lane and Kamin tried to go beyond guilt by association: They tried to demonstrate the specific ways in which these Mankind Quarterly and Pioneer Fund scholars we cited were racist. To do that, they focused on our citations of studies of African IQ.
The topic of African IQ is a tiny piece of The Bell Curve—three paragraphs on pp. 288-289 intended to address a hypothesis Herrnstein and I heard frequently: The test scores of American blacks have been depressed by the experience of slavery and African blacks will be found to do better. We briefly summarize the literature indicating that African blacks in fact have lower test scores than American blacks.
Lane and Kamin assault this conclusion ferociously. We are an easy target. We say so little about African IQ that it is easy for Lane and Kamin to point to the many technical difficulties of knowing exactly what is going on. But we also omit many more details that make a strong case that African blacks have very low scores on standardized mental tests. Lane and Kamin want our sources to be weak and racist. That they are not bears importantly, if inconclusively, on possible genetic racial differences.
Blinded to that possibility by their seeming prejudgment of the issue, Lane and Kamin apparently are not worried about what will happen when their critiques lead other scholars to explore the studies that we cited. They should be. Even when samples of Africans are selected in ways that will tend to bias the results upward—for example, by limiting the sample to people who have completed primary school (many of the least able have dropped out by that time), people who are employed, or people who live in urban areas—and even when the tests involved are ones such as the Ravens Standard Progressive Matrices (SPM), designed for cross-cultural comparisons, devoid of any requirements of literacy or numeracy, the scores of African samples everywhere have been in the region of two standard deviations below European or East Asian means. The studies vary in quality, but some are excellent, and it is not the case that the better the study is, the higher the African score is found to be. On the contrary, some of the lowest scores have been found in the largest, most careful, and most recent studies.
To illustrate how troubling the results have been, let me turn to two studies postdating Richard Lynn’s review that we cite on p. 289. One was a South African study led by Kenneth Owen published in the refereed British journal Personality and Individual Differences.17 Its sample consisted of enrolled seventh-grade students: 1,056 whites, 778 coloureds (mixed race), 1,063 Indians, and 1,093 blacks. The SPM was administered without time limits. Except for the Indians, subjects were tested by school psychologists of the same ethnic group. Owen presents the full psychometric profile for the test results (distributional characteristics, reliability, item difficulty, item discrimination, congruence coefficients, and discriminant analysis), demonstrating that the test was measuring the same thing for the various ethnic groups. The differences in test means, expressed in standard deviations, were as follows: Indian-white: −.52; coloured-white: −1.35; black-white: −2.78.
The second example of a recent, careful study was conducted by a black scholar, Fred Zindi, and published in the Psychologist.18 It matched 204 black Zimbabwean pupils and 202 white English students from London inner-city schools for age (12—14 years old), sex, and educational level, both samples being characterized as “working class.” Despite the fact that the white sample was well below average for the whites, with a mean IQ measured by the Wechsler Intelligence Scale for Children-Revised (WISC-R) of only 95, the black-white difference was 1.97 standard deviations on the SPM and 2.36 standard deviations on the WISC-R. Professor Zindi expressed the SPM results as IQ scores. The means for the Zimbabwean sample were 72 for the SPM and 67 for the WISC-R, consistent with Richard Lynn’s estimates. There is reason to think that the WlSC-R score was somewhat depressed by language considerations but not much: The (nonverbal) performance IQ score of the Zimbabwean sample was only 70.
What should one make of these results? Above all, one must proceed cautiously in drawing conclusions, for all the reasons that kept us from presenting these results in detail in The Bell Curve. The problem is not, as often alleged, that such studies are written by racists (in the two instances just cited, a charge belied by Owen’s scholarly reputation and by Zindi’s race) but that the African story is still so incomplete. Our view was that the current differences will narrow over time, probably dramatically, as nutrition and the quality of schools for black Africans improve. Changes in black African culture may provide an environment more conducive to cognitive development among young children. But the current differences as measured through these samples as of the 1990s are not figments of anyone’s imagination. Lane, Kamin, and others who have attempted to discredit The Bell Curve by focusing on our “tainted sources” have ensured that the African data will get more attention.
The Statistical Importance of The Bell Curve’s results.
The third line of attack on The Bell Curve that I predict will have an unintended outcome is the attempt to dismiss the statistical power of the book’s results. Perhaps the most important section of The Bell Curve is Part II, the series of chapters describing the relationship of IQ to poverty, school dropout, unemployment, divorce and illegitimacy, welfare, parenting, crime, and citizenship, using non-Latino whites from the National Longitudinal Study of Youth. The eight chapters in this part deal with questions like, “What role does IQ play in determining whether a woman has a baby out of wedlock?” Or: “What are the comparative roles of socioeconomic disadvantage and IQ in determining whether a youngster grows up to be poor as an adult?” These are fascinating questions. But you will have a hard time figuring out from the published commentary on The Bell Curve that such questions were even asked, let alone what the answers were.
Instead, the main line of attack has been that no one really needs to pay any attention to those chapters because Herrnstein and I are confusing correlation with causation, IQ really does not explain much of the variance anyway, and even if that were not true, our measure of socioeconomic background is deficient. On all three counts, the critics are setting up a reexamination of the existing technical literature on social problems that will be embarrassing to them in the end.
First, regarding correlation and causation, read pp. 122-124 of the Introduction to Part II. Reduced to its essentials: The nonexperimental social sciences cannot demonstrate unequivocal causality. In trying to explain such social problems as poverty, illegitimacy, and crime, we use statistics to show what independent role is left for IQ after taking a person’s age, socioeconomic background, and education into account. When there are other obvious explanations—family structure, for example—we take them into account as well. Apart from the statistics, we describe in commonsense terms what the nature of the causal link might be—why, for example, a poor young woman of low intelligence might be more likely to have a baby out of wedlock than a poor young woman of high intelligence. At the end of this exercise, repeated in similar form for each of the eight chapters in Part II, there will still be unanswered questions, and we point out many of those unanswered questions ourselves. But readers will know more than they knew before, and the way will be opened for further explorations by our colleagues.
The statistical method we used throughout is the basic technique for discussing causation in nonexperimental situations: regression analysis, usually with only three independent variables. We interpret the results according to accepted practice. To enable readers to check for themselves, the printout of all the results is shown in Appendix 4.
The assault on this modest but useful analysis has been led by Leon Kamin in his Scientific American review. He argues that we cannot disentangle the role of IQ from socioeconomic background and suggests that in our database, the children of laborers have such uniformly low IQ scores that we cannot possibly tell whether the low IQ or the disadvantaged background is to blame for the higher rates of crime, unemployment, and illegitimacy that afflict such youngsters. “The significant question,” Kamin writes, “is,
why don’t the children of laborers acquire the skills that are tapped by IQ tests?”19
The answer to his significant question is that they do acquire such skills often enough to permit a good look at the comparative roles of socioeconomic background and IQ. Of the non-Latino whites used in the analyses throughout Part II, 1,589 came from families in the bottom quartile on our SES index. Of these, 451 had above-average IQs and 147 were in the top quartile of IQ. As we report throughout Part II, the results are encouraging: In America, bright children of laborers tend to do well in life, despite their humble origins. Herrnstein and I suggest that such a pattern points to causation. This is indeed an inference—a sensible inference.
We approach the correlation-causation tangle in other sensible ways as well. Consider the vexing case of education. People with high IQs tend to spend many years in school; people with low IQs tend to leave. Does the IQ cause the years of education, or the years of education the IQ? As we also discuss in the Introduction to Part II (pp. 124-125), it is unwise, for various technical reasons, to enter years of education as an additional independent variable, so instead we define two subsamples, each with homogeneous education: one of adults who had completed exactly twelve years of school and obtained a high school diploma, no more and no less; the other of adults who had completed .exactly sixteen years of school and obtained a bachelor’s degree, no more and no less, enabling us to report the independent effect of IQ for people with identical education.
Our procedure irritated a number of academic critics, who grumble that the state of the art permits much more. Yes, it does, and in the book we mention periodically how much we look forward to watching our colleagues apply more sophisticated techniques to unanswered questions. But more sophisticated modeling techniques would also have opened a wide variety of technical questions that Herrnstein and I wanted to avoid. The procedure we chose gives an excellent way of bounding the independent effects of education, and that was our purpose.
The Bell Curve: Intelligence and Class Structure in American Life Page 65
