The Bell Curve: Intelligence and Class Structure in American Life

by Richard J. Herrnstein

  THE BELL CURVE AND POLITICS

  If The Bell Curve is in fact a book of mainstream science cautiously interpreted, why did it cause such a stir? The obvious answer is race, the omnipresent backdrop to discussion of social policy in the United States. From the founding of the nation, the issue of race has preyed on the consciences of white Americans, especially the intellectual elite. This is natural and good: White America has had much to feel guilty about. But since the 1960s, for reasons that I do not fully understand, this long-standing discomfort among white elites has taken on the character of a sensitivity so acute that it resembles a disorder. Ever since the first wave of attacks on the book, I have had an image of The Bell Curve as a literary Rorschach test. I do not know how else to explain the extraordinary discrepancy between what The Bell Curve actually says about race and what many commentators have said that the book says, except as the result of some sort of psychological projection onto our text.

  The problems that beset America’s intellectuals when they try to think about race is not the only reason for the hysteria, however. The Bell Curve also scraped a political nerve that was far more sensitive than either Richard Herrnstein or I had realized. When we began work on the book, both of us assumed that it would provide evidence that would be more welcome to the political left than to the political right, via this logic: If intelligence plays an important role in determining how well one does in life, and intelligence is conferred on a person through a combination of genetic and environmental factors over which that person has no control (as we argue in the book), the most obvious political implication is that we need a Rawlsian egalitarian state, compensating the less advantaged for the unfair allocation of intellectual gifts. Neither of us thought that the most obvious implication was the right one, for reasons we describe in Chapter 22. But we recognized the burden on us to make the case. And yet The Bell Curve has been widely attacked as a book written to advance a right-wing political agenda.

  The reason for the attack arises partly from our known political positions. As those who have read either Losing Ground or In Pursuit2 know, I am on the right (though more libertarian than conservative) in my politics and Richard Herrnstein, although he had few published policy positions that could easily be characterized as liberal or conservative, had been denounced as a conservative because of his earlier writings on the heritability of IQ and its social consequences.3 A thoroughgoing liberal in his youth, Herrnstein had become moderately conservative over the last two decades of his life. We joked between ourselves that he was the Tory and I the Whig.

  So we had political opinions. It goes with the territory. Social scientists who are absorbed in policy issues tend also to have opinions about those issues. A few, like us, are somewhere on the right. James Q. Wilson in political science (another Tory) and Richard Epstein in legal studies (another Whig) are prominent examples. More commonly, they are on the left. Christopher Jencks, William Julius Wilson, David Ellwood, Andrew Hacker, Robert Reich, and Robert Haveman, each of whom has published an important book on social policy in the past decade, are all social democrats of one sort or another, as are many of the most vocal critics of The Bell Curve (e.g., Stephen Jay Gould, Howard Gardner, and Leon Kamin). The appropriate question to ask of any of these authors vis-à-vis their books is not whether they have political opinions but whether their presentations of the data are distorted by their politics, and whether they enable readers to understand how their political views enter into their policy conclusions. On both counts, Richard Herrnstein and I intended The Bell Curve to be exemplary. Once again, you have in your hands the means to judge for yourself.

  But I now understand, as neither of us fully anticipated during the writing, that this book must create a political firestorm among intellectuals simply by accepting that human beings differ widely in ways that matter to daily life, and that many of these differences, including intelligence, are not readily manipulated by public policy. We knew how heretical this position had been in the 1960s and 1970s (pp. 8-9 of the Introduction), but we underestimated how important it remains in the 1990s. Watching the reaction to the book, theologian Michael Novak and economist Thomas Sowell have written in similar terms how the dominant intellectual stance toward public policy continues to invest everything in a few core beliefs about society as the cause of problems, government as the solution, and the manipulability of human beings in reaching the goal of equality.4 For persons who hold this view, Novak writes, The Bell Curve’s “message cannot be true, because much more is at stake than a particular set of arguments from psychological science. A this-worldly eschatological hope is at stake. The sin attributed to Herrnstein and Murray is theological: they destroy hope.”5

  I am sure Novak and Sowell are on the right track, though there is still more to be learned. The underlying reasons for the reaction to The Bell Curve will be significant in their own right when they are fully understood, revealing much about the intellectual temper of our era, but perspective on that reaction must wait for some years. Let me make a more limited prediction: When the Sturm und Drang has subsided, nothing important in The Bell Curve will have been overturned. I say this not because Herrnstein and I were so brilliant or farsighted but because our conclusions were so conservatively phrased and anchored so firmly in the middle of the scientific road. Therein also lies the best way for you to decide what to make of the various commentaries on the book. Take whatever critical review you find to be most persuasive, delete the rhetoric, and identify the bare bones of an assertion: “Here is what Herrnstein and Murray said, and here is why they are wrong.” Then go to the relevant section of the book and put the assertion side by side with what we actually said. You will find the exercise instructive.

  THE UNINTENDED OUTCOMES OF THE ATTACKS ON THE BELL CURVE

  In the meantime I want to present my own assessment of where the debate stands. The problem is how to do it within a reasonable space and how to avoid being overtaken by events. The first wave of reviews and commentaries in the major media appeared between October 1994 and January 1995. The second wave, consisting of reviews in the academic journals, is on the way. As I write, I have already seen manuscript copies of some of these reviews, often highly technical, that will be published over the next year. The volume of this material reaches many hundreds of pages. To comment in detail on them would require another book. I will use this Afterword instead to present a general proposition about The Bell Curve and to illustrate it with examples.

  Much of the attack on The Bell Curve has a purpose that occasionally has been stated explicitly, but more often tacitly: somehow, to put the genie back in the bottle, quelling discussion of topics that the book brought into the open. This is a much more ambitious objective than merely disagreeing with our presentation and accounts (I hypothesize) for the attacks on our motives and character. It is not enough that The Bell Curve be refuted. It must be discredited altogether.

  The trouble with this strategy is that it will backfire. My proposition is that the critics of The Bell Curve are going to produce the very effects that their attacks have been intended to avert. I foresee a three-stage process.

  In the first stage, a critic approaches The Bell Curve absolutely certain that it is wrong. He feels no need to be judicious or to explore our evidence in good faith. He seizes on the arguments that come to hand to make his point and publishes them, with the invective and dismissiveness that seem to be obligatory for a Bell Curve critic.

  In the second stage, the attack draws other scholars to look at the issue. Many of them share the critic’s initial assumption that The Bell Curve is wrong, but they nonetheless start to examine evidence they would not have looked at otherwise and discover that the data are interesting. Some of them back off nervously, but others are curious, and they look further. And it turns out not just that The Bell Curve’s initial arguments were right but that there is much more out there than Herrnstein and I try to claim.

  In stage three, these scholars start to write new m
aterial on the topics that had come under attack in the first place. I doubt that many will choose to defend The Bell Curve, but they will build on its foundations and ultimately do far more damage to the critics’ “eschatological hope” than The Bell Curve itself did.

  I will give four examples of these unintended outcomes, drawing from the attacks on the “pseudoscience” of a general intelligence factor, on the link between genes and race differences in IQ, on the power of the statistical evidence, and on our pessimistic assessment of society’s current attempts to raise IQ through outside interventions.

  The “Pseudoscience” of a General Intelligence Factor

  One main line of attack on The Bell Curve’s science has been mounted not against anything in the book itself but against the psychometric tradition on which it is based. Specifically, Herrnstein and I accept that there is such a thing as a general factor of cognitive ability on which human beings differ: the famous g.

  Ever since the late 1960s, when IQ became a pariah in the world of ideas, this has been a politically incorrect position to take. In the early 1980s, two books cemented the discrediting of g: Stephen Jay Gould’s The Mismeasure of Man and Howard Gardner’s Frames of Mind: The Theory of Multiple Intelligences.6 Gould called the concept of g a fraud, and Gardner identified seven distinct and independent kinds of intelligence. Both of these views were swallowed uncritically and enthusiastically by the elite media, as documented by Mark Snyderman and Stanley Rothman in The IQ Controversy: The Media and Public Policy.7 From the time we began working on The Bell Curve, no putative refutations of our project were brought up nearly as often or as confidently as these two books when we talked with friends and colleagues who were not psychologists.

  Among scholars who work in the field of intelligence, Gould and Gardner have different reputations. Many psychometricians enjoy Gardner’s work. He stretches the exploration of intelligence into new disciplines and keeps people from ignoring all the many ways in which humans exhibit special talents that fall outside the classical conception of intelligence. But to accept these virtues in Gardner’s work is not to say that he has demonstrated the existence of “multiple intelligences” of remotely equivalent value in today’s world. If you want to predict someone’s success in life, you had better focus on his scores for “linguistic intelligence” or “logical-mathematical intelligence”—roughly, the talents measured by IQ tests—rather than on any of Gardner’s other five intelligences. Furthermore, Gardner has fared no better than anyone else in showing that the elements of “intelligence” as commonly understood—such as the ability to manipulate complex information or solve new problems—are in fact statistically independent in the way that Gardner’s labeling of the seven intelligences would imply. Such mental abilities tend to go together. That brings us back to g and Stephen Jay Gould.

  In The Mismeasure of Man, Gould based his denial of a general mental factor on a series of claims about the statistical method for identifying g. He resurrected the same arguments in his New Yorker review of The Bell Curve, “g cannot have inherent reality,” Gould writes, “for it emerges in one form of mathematical representation for correlations among tests and disappears (or greatly attenuates) in other forms, which are entirely equivalent in amount of information explained.” He continues: “The fact that Herrnstein and Murray barely mention the factor-analytic argument forms a central indictment of The Bell Curve and is an illustration of its vacuousness.” Where, Gould asks, is the evidence that g “captures a real property in the head”?8

  The reason we “barely mention the factor-analytic argument” is that it has no scholarly standing. Gould’s statistical indictment of g was old news when it appeared. As Mismeasure’s reviewer in the British science journal Nature put it, Gould’s “discussion of the theory of intelligence stops at the stage it was in more than a quarter of a century ago.”9 Indeed, the appearance of Gould’s book coincided with a renaissance of work on g that proceeded wholly unaffected by Gould’s charges.

  To see what this particular fight is about, a little more background than we give in the text is essential. As we noted in the Introduction (pp. 2-3), one of the earliest findings about mental tests was that the results of different tests of apparently different mental skills are positively correlated. Charles Spearman, the British founding father of modern psychometrics, was the first to hypothesize that the tests were correlated because each was tapping into a common construct: the general mental ability he then labeled g. The statistical technique of factor analysis is the method used to extract this general factor that accounts for the intercorrelations among subtests. Factor analysis permits alternative methods of extracting factors, however. The hero of Gould’s story is another pioneering psychometrician, L. L. Thurstone, who in the 1930s became Spearman’s great antagonist by demonstrating how factor analysis need not yield a dominant general factor. Gould is correct in stating that any of the alternative methods will have the same over-all power to account for the correlations among the tests.

  Gould is wrong, however, when he implies that by using an alternative method, an analyst can get rid of g. As Richard Herrnstein liked to say, “You can make g hide, but you can’t make it go away.” For those who want to pursue the technical issues, I recommend John B. Carroll’s recent book, Human Cognitive Abilities: A Survey of Factor-Analytic Studies.10 Carroll, a student of Thurstone and former director of the L. L. Thurstone Psychometric Laboratory at the University of North Carolina, recounts the controversy between Spearman and Thurstone over the existence of a general factor, pointing out that Thurstone proposed reasonable criteria for choosing among possible solutions to the factorial problem. In his later years, Thurstone also came to accept the notion of a general factor arising out of the correlations among “lower-order” factors.

  In any case, it has been decades since Gould’s statistical argument has been a live issue among those who specialize in factor analysis. There are established technical grounds for permitting factor analysis to extract a general factor from a battery of mental tests. Doing so will yield a dominant factor that not only explains more of the variance than any other factor but typically explains three times as much variance as all of the other factors combined. Thus the frustration among psychometricians who tried to get rid of g. After applying the particular factor-analytic method that prevents g from emerging, there was nowhere to take the results. Anyone who tried to label the independent factors as being distinct mental skills and develop a research agenda based on them was crushed by critics who could demonstrate that the results were more elegantly explained by g. For more than half a century, the holy grail of psychometrics was a set of statistically independent primary mental abilities. Careers were consumed in the search. No one succeeded.

  But there is a more direct way of asking whether g is a valid construct: g is a construct in the same way that energy is a construct. Both have theoretical underpinnings, but neither is a reified “thing.” Evidence that they are useful constructs is found in the ways they relate to real-world phenomena. In the case of g, we have three possibilities. One is that g is an arbitrary creation of number crunching. If so, it should be nothing but noise in statistical analysis, showing no more relationship to phenomena in the real world than numbers generated by a random number table. A second possibility is that g is a surrogate for something else—a proxy measure of educational attainment, perhaps, or socio-economic background. If this is the case, the correlations of g to real-world phenomena are spurious, and it should be easy to demonstrate by showing that the “real” causes (such as educational attainment) can explain everything that g explains, more parsimoniously. The third possibility is that g is a (partly) biological phenomenon in its own right—a basic characteristic of the organism that exerts some influence on its ability to reason, think, and learn.

  On the first two possibilities, the empirical record is rich and large. Chapter 3 tells this story for job productivity, showing how g explains productivity in ways that education and
socioeconomic background cannot. The eight chapters of Part II tell the story for a wide variety of social indicators, again after taking the contributions of education or socioeconomic status into account. As for the third possibility, that g is a biological phenomenon, let us count the ways in which g seems to capture a “real property in the head.”

  First, a growing body of evidence links g, and IQ scores more generally, with neurophysiological functioning and a genetic ground: The higher the g loading of a subtest is, the higher is its heritability. The higher the g loading of a subtest is, the higher is the degree of inbreeding depression (an established genetic phenomenon). Reaction times on elementary cognitive tasks that require no conscious thought, such as responding to a lighted button, show a significant correlation with IQ test scores. This correlation depends mostly, perhaps entirely, on g. A significant relationship exists between g and evoked electrical potentials of the cerebral cortex. A significant inverse relationship exists between nonverbal (and highly g loaded) IQ test scores and the brain’s consumption of glucose in the areas of the brain tapped by the cognitive test. The higher the scores are on IQ tests, the faster is the speed of neural and synaptic transmission in the visual tract.11

  For each of these statements, there is a corollary: No alternative casting of the test items can compete with g in producing such results. For example, suppose you give a psychometrician the chance to extract g and leave you with all the remaining factors in a given mental test. You cannot manipulate any one or any combination of those factors so as to produce the relationships I just listed. Only g, that supposedly arbitrary creation of the psychometricians, can do so. To sum up: The reality and importance of g has long since, in many ways, been established independent of its statistical properties.