The Bell Curve: Intelligence and Class Structure in American Life (95 page)

As scholars are supposed to, Herrnstein and I checked out these and many other possibilities—the results we report were triangulated in numbing detail during the years we worked on the book—and we knew before publishing
The Bell Curve
what the critics who bother to retrace our tracks will discover: There is no way to construct a measure of socioeconomic background using the accepted constituent variables that makes much difference in the independent role of IQ.
²³
In the jargon, our measure of SES is robust and as valid as everyone else’s has been.

But there’s the rub: How valid
have
everyone else’s been? Until
The Bell Curve
came along, measures of SES similar to ours were used without a second thought. Now, suddenly, they are to be questioned. I doubt whether the questioning will be confined to just
The Bell Curve.
But there is not much room to improve such measures, for there is no way around it: SES is in fact dominated by occupation, education, and income. What we did in the book is, in effect, to throw down a challenge: Anyone who does not like the way IQ dominates this thing called “socioeconomic status” in producing important social outcomes should come up with another way of measuring the environment.

Such measures have been emerging over the past few decades. The HOME index we discuss in Chapter 10 is an example. But the social sciences have only scratched the surface. It is now broadly accepted, as it was not only a decade ago, that the presence of the biological father in the home has many important positive effects on children independent of SES. How much more might be understood if we could add to mere presence a good measure of competence. Suppose, for example, that one could create a good measure of the “competency of a father in the raising of a female child.” That might have a large independent effect on the
girl’s chances of giving birth to a baby out of wedlock, whatever her IQ. Suppose that one could create a good measure of the “degree to which a young male is raised in an environment where high moral standards are enforced consistently and firmly.” I can imagine this having a major effect on the likelihood of becoming a criminal, independent of IQ.

But the same measures that compete with the importance of IQ are going to make starkly clear something that
The Bell Curve
has already suggested: The kinds of economic and social disadvantages that have been treated as decisive in recent discussions of social policy are comparatively unimportant. It may sound like an issue that concerns only social scientists. Far from it. If I were to nominate the biggest sleeper effect to emerge from
The Bell Curve,
it would be the degree to which the book undermines SES as a way of interpreting social problems, and with it the rationale for many of the social policies that came into vogue in the 1960s.

Raising the question of policy brings us to the last of my four examples of the potential backfire effect of attacks on
The Bell Curve:
the malleability of IQ. These attacks focused on Chapter 17. The cries of protest have been almost as loud as those directed to our chapter on race, and for the reason that Michael Novak identified: By arguing that no easy ways of raising IQ exist, we “destroy hope,” or at least the kind of hope that drives many of the educational and preschool interventions for disadvantaged youth.

Actually, we do express hope. Because the environment plays a significant role in determining intelligence—a point
The Bell Curve
states clearly and often—we say that sooner or later researchers ought to be able to figure out where the levers are. We urge that steps be taken to hasten the day when such knowledge becomes available. But in examining the
current
state of knowledge, we also urge realism. Speaking of the most popular idea, intensive intervention for preschoolers, we indeed conclude that “we and everyone else are far from knowing whether, let alone how, any of these projects have increased intelligence” (p. 409). We also predict that “many ostensibly successful projects will be cited as plain and indisputable evidence that we are willfully refusing to see the light” (p. 410).

This prediction has been borne out. Psychologist Richard Nisbett, for
example, writing in
The Bell Curve Wars,
accuses us of being “strangely selective” in our reports about the effects of intervention and wonders if we were “unaware of the very large literature that exists on the topic of early intervention.”
²⁴
The “very large literature” of which we were unaware? The only study Nisbett mentions is a 1992
Pediatrics
article about a program to provide special services to low birth weight babies. He describes the results as showing a nine-point IQ advantage at age 3 for participants in the intervention. Nisbett neglects to acknowledge the unreliability and instability of IQ measures at age 3. He fails to mention that two IQ measures were used, with the second one showing a gain of just 6.4 points. Most important, Nisbett fails to mention that a follow-up of the same project was published in 1994, when the children were age 5, old enough that IQ scores are beginning to become interpretable. The results? The experimental group had an advantage of just 2.5 points on one measure of IQ and two-tenths of a point on another—both differences being substantively trivial and statistically insignificant.
²⁵

There is one slender lead in this study. The 1994 follow-up broke down the results according to whether the babies were “lighter LBW [low birth weight]” (less than 2,000 grams) or “heavier LBW” (2,001-2,500 grams). That comparison showed a gain of 6.0 IQ points for the heavier LBW group on one IQ test and a 3.7 point gain on the other IQ test—not large gains but nevertheless significant. But with each bit of good news goes bad news: The lighter LBW sample showed a gain of only 0.6 point on one IQ test and a drop of 1.5 points on the other. This is the familiar story from Chapter 17: a little hope, as much disappointment, no breakthroughs.

The larger problem with those who claim that Herrnstein and I were too pessimistic is that they conflate improvements in educational achievement with improvements in cognitive ability. The distinction is crucial: Do we know how to take a set of youngsters with a given tested IQ and reliably improve their educational achievement? Yes. Do we know how to take a set of youngsters with a given tested IQ that would not (for example) allow them to become engineers, and reliably raise their cognitive functioning so that they can become engineers? No.

I will venture two broad statements on this issue. First, in the critiques to date, no one has pointed to a credible study showing evidence of significant, long-term effects on IQ scores that we do not consider in
The Bell Curve.
Second, our account of the record to date is, if anything, generous. The two major intensive interventions for raising the IQ of
children at high risk of mental retardation, the Milwaukee Project and the Abecedarian Project, have come under intense methodological criticism in the technical literature. We allude to the controversy on pp. 407-409, but in neither case is the evidence so clear that it was appropriate for us to come down hard on the “no-effect” conclusion, so we do not. If we err, it is in the direction of giving more credit to the interventions than is warranted.

But just as we predicted, many others are nominating “programs that work” that we mysteriously failed to consider. I am sure that some of them do work—for goals
other
than raising IQ. We would be the last to suggest that education cannot be made better, for example, or that the socialization of children cannot be improved. But in
The Bell Curve
we talk about a particular goal: improving the cognitive functioning of human beings over the long term. On that score, the record remains as Herrnstein and I describe it: Yes, it can be done, but apparently only in modest amounts for most children, usually temporarily, and inconsistently.

In this instance, I have reason to hope that the unintended effect of the attacks on
The Bell Curve
will be to crystallize a debate that has long needed crystallizing. The cry that “Herrnstein and Murray are too pessimistic” is going to force a great many claims to be laid on the table for examination. Howard Gardner’s review in
American Prospect
took us to task for not citing Lisbeth and Daniel Schorr’s book,
Within Our Reach,
for example. I would be delighted to join in a rigorous look at the programs they describe and see whether we find among them hard evidence for long-term improvement in cognitive functioning.
²⁶
Let us bring up all the other nominees for inspection as well. In short, I hope that academicians and politicians alike use the furor over
The Bell Curve
finally to come to grips with how difficult it is, given the current state of knowledge, for outside interventions to make much difference in the environmental factors that nurture cognitive development.

A few weeks after
The Bell Curve
appeared, a reporter said to me that the real message of the book is, “Get serious.” I resisted his comment at first, but now I think he was right. We never quite say it in so many words, but the book’s subtext is that America’s discussion of social policy since the 1960s has been carried on in a never-never land where human beings are easily changed and society can eventually become a Lake Wobegon where all the children are above average.
The Bell Curve
does indeed imply that it is time to get serious about how best to accommodate the
huge and often intractable individual differences that shape human society.

This is a counsel not of despair but of realism, including realistic hope. An individual’s
g
may not be as elastic as one would prefer, but the inventiveness of the species seems to have few bounds. In
The Bell Curve,
we are matter-of-fact about the limits facing low-IQ individuals in a postindustrial economy, but we also celebrate the capacity of people everywhere in the normal range on the bell curve to live morally autonomous, satisfying lives, if only the system will let them. Accepting the message of
The Bell Curve
does not mean giving up on improving social policy but thinking anew about how progress is to be achieved—and, even more fundamental, thinking anew about how progress is to be defined.

The verdict on the influence of
The Bell Curve
on policy is many years away. For now, the book may have another useful role to play that we did not anticipate. The attacks on it have often read like an unintentional confirmation of our view of the cognitive elite as a new caste, complete with high priests, dogmas, heresies, and apostates. But the violent response, unpleasant as it has been in the short run, is essential if
The Bell Curve
is to play its constructive role in the long run. The social science that deals in public policy has in the latter part of the twentieth century become self-censored and riddled with taboos—in a word, corrupt. Only the most profound, anguished, and divisive reexamination is going to change that situation, and it has to be done within the profession. Perhaps starting that reexamination will be
The Bell Curve’
s most important achievement.

Charles Murray
Washington, D.C.
20 June 1995

The short explanations of standard deviation (page 44), correlation (page 67), and regression (page 122) should be satisfactory for people who are at home with math but never took a statistics course. The longer explanations in this appendix are for people who would like to understand what
distribution, standard deviation, correlation,
and
regression
mean, but who are not at home with math.

Every day, formally or informally, people make comparisons—among people, among apples and oranges, among dairy cows or egg-laying hens, among the screws being coughed out by a screw machine. The standard deviation is a measure of how spread out the things being compared are. “This egg is a lot bigger than average,” a chicken farmer might say. The standard deviation is a way of saying precisely what “a lot” means.

To get a clear idea of what a frequency distribution is, imagine yourself back in your high school gym, with all the boys in the senior class in the school gym assembled before you (including both sexes would complicate matters, and the point of this discussion is to keep things simple). Line up these boys from left to right in order of height.

Now you have a long line going from shortest to tallest. As you look along the line you will see that only few boys are conspicuously short and tall. Most are in the middle, and a lot of them seem identical in height. Is there any way to get a better idea of how this pattern looks?

Tape a series of cards to the floor in a straight line from left to right, with “60 inches and shorter” written on the one at the far left, “80 inches and taller” on the card at the far right, and cards in 1-inch increments in between. Tell everyone to stand behind the card that corresponds to his height.

Someone loops a rope over the rafters and pulls you up in the air so you can look straight down on the tops of the heads of your classmates standing in their single files behind the height labels. The figure below shows what you see: a frequency distribution.
¹
What good is it? Looking at your high school classmates standing around in a mob, you can tell very little about their height. Looking at those same classmates arranged into a frequency distribution, you can tell a lot, quickly and memorably.

The raw material of a frequency distribution