Poverty - Cognitive Classes and Social Behavior - The Bell Curve: Intelligence and Class Structure in American Life - Richard J. Herrnstein, Charles Murray

The Bell Curve: Intelligence and Class Structure in American Life - Richard J. Herrnstein, Charles Murray (1996)

Part II. Cognitive Classes and Social Behavior

Whereas Part I dealt with positive outcomes—attainment of high educational levels, prestigious occupations, high incomes—Part II presents our best estimate of how much intelligence has to do with America’s most pressing social problems. The short answer is “quite a lot,” and the reason is that different levels of cognitive ability are associated with different patterns of social behavior. High cognitive ability is generally associated with socially desirable behaviors, low cognitive ability with socially undesirable ones.

“Generally associated with” does not mean “coincident with.” For virtually all of the topics we will be discussing, cognitive ability accounts for only small to middling proportions of the variation among people. It almost always explains less than 20 percent of the variance, to use the statistician’s term, usually less than 10 percent and often less than 5 percent. What this means in English is that you cannot predict what a given person will do from his IQ score—a point that we have made in Part I and will make again, for it needs repeating. On the other hand, despite the low association at the individual level, large differences in social behavior separate groups of people when the groups differ intellectually on the average.

We will argue that intelligence itself, not just its correlation with socioeconomic status, is responsible for these group differences. Our thesis appears to be radical, judging from its neglect by other social scientists. Could low intelligence possibly be a cause of irresponsible childbearing and parenting behaviors, for example? Scholars of childbearing and parenting do not seem to think so. The 850 double-column pages of the authoritative Handbook of Marriage and the Family, for example, allude to intelligence about half a dozen times, always in passing.1 Could low intelligence possibly be a cause of unemployment or poverty? Only a scattering of economists have broached the possibility.2

This neglect points to a gaping hole in the state of knowledge about social behavior. It is not that cognitive ability has been considered and found inconsequential but that it has barely been considered at all The chapters in Part II add cognitive ability to the mix of variables that social scientists have traditionally used, clearing away some of the mystery that has surrounded the nation’s most serious social problems.

We will also argue that cognitive ability is an important factor in thinking about the nature of the present problems, whether or not cognitive ability is a cause. For example, if many of the single women who have babies also have low IQ, it makes no difference (in one sense) whether the low IQ caused them to have the babies or whether the path of causation takes a more winding route. The reality that less intelligent women have most of the out-of-wedlock babies affects and constrains public policy, whatever the path of causation. The simple correlation, unadjusted for other factors—what social scientists called the zero-order correlation—between cognitive ability and social behaviors is socially important.

The chapters of Part II cover a wide range of topics, each requiring extensive documentation. Many statistics, many tables and graphs, many citations to technical journals crowd the pages. But the chapters generally follow a similar pattern, and many of the complexities will be less daunting if you understand three basics: the NLSY, our use of cognitive classes, and our standard operating procedure for statistical analysis.


In Part I, we occasionally made use of the National Longitudinal Survey of Youth, the NLSY. In the chapters that follow, it will play the central role in the analysis, with other studies called in as available and appropriate.

Until a few years ago, there were no answers to many of the questions we will ask, or only very murky answers. No one knew what the relationship of cognitive ability to illegitimacy might be, or even the relationship of cognitive ability to poverty. Despite the millions of mental tests that have been given, very few of the systematic surveys, and sometimes none, gave the analyst a way to conclude with any confidence that this is how IQ interacts with behavior X for a representative sample of Americans.

Several modern sources of data have begun to answer such questions. The TALENT database, the huge national sample of high school students taken in 1961, is the most venerable of the sources, but its follow-up surveys have been limited in the range and continuity of their data. The Panel Study of Income Dynamics, begun in 1968 and the nation’s longest-running longitudinal database, administered a brief vocabulary test in 1972 to part of its sample, but the scores allow only rough discriminations among people in the lower portions of the distribution of intelligence. The National Longitudinal Survey begun by the Department of Education in 1972 (not to be confused with the NLSY) provides answers to many questions associated with educational outcomes. The department’s more ambitious study, High School and Beyond, conducted in the early 1980s, is also useful.

But the mother lode for scholars who wish to understand the relationship of cognitive ability to social and economic outcomes is the NLSY, whose official name is the National Longitudinal Survey of Labor Market Experience of Youth. When the study began in 1979, the participants in the study were aged 14 to 22.3 There were originally 12,686 of them, chosen to provide adequate sample sizes for analyzing crucial groups (for example, by oversampling blacks, Latinos, and low-income whites), and also incorporating a weighting system so that analysts could determine the correct estimates for nationally representative samples of their age group. Sample attrition has been kept low and the quality of the data, gathered by the National Opinion Research Council under the supervision of the Center for Human Resources Research at Ohio State University, has been excellent.

The NLSY is unique because it combines in one database all the elements that hitherto had to be studied piecemeal. Only the NLSY combined detailed information on the childhood environment and parental socioeconomic status and subsequent educational and occupational achievement and work history and family formation and—crucially for our interests—detailed psychometric measures of cognitive skills.

The NLSY acquired its cognitive measures by a lucky coincidence. In 1980, a year after the first wave of data collection, the Department of Defense decided to update the national norms for its battery of enlistment tests. At the time, it was still using test scores from World War II recruits as the reference population. Because the NLSY had just gone through the technically difficult and tedious task of selecting a nationally representative sample, the Department of Defense proposed to piggyback its study on the NLSY sample.4 And so the NLSY became the beneficiary of an expensive, well-designed set of cognitive and aptitude tests that were given under carefully controlled conditions to almost 94 percent of the 12,686 young men and women in the NLSY sample.5

The measure of cognitive ability extracted from this test battery was the Armed Forces Qualification Test, the AFQT. It is what the psychometricians call “highly g-loaded,” meaning that it is a good measure of general cognitive ability.6 The AFQT’s most significant shortcoming is that it is truncated at the high end; about one person in a thousand gets a perfect score, which means both that the test does not discriminate among the very highest levels of intelligence and that the variance in the population is somewhat understated. Otherwise the AFQT is an excellent test, with psychometric reliability and validity that compare well with those of the other major tests of intelligence. Because the raw scores on the AFQT mean nothing to the average reader, we express them in the IQ metric (with a mean of 100 and a standard deviation of 15) or in centiles. Also, we will subsequently refer to them as “IQ scores,” in keeping with our policy of using IQ as a generic term for intelligence test scores. When we use centiles, they are age equated. A centile score of 45, for example, means that the subject would rank in the 45th percentile of everyone born in the same year, if everyone took the AFQT.7 A final point about the presentation of NLSY results is that all results are based on weighted analyses, which means that all may be interpreted in terms of a nationally representative sample of Americans in the NLSY age group. We use data collected through the 1990 interview wave.


To this point, we have been referring to cognitive classes without being specific. In these chapters, we divide the world into cognitive classes—five of them, because that has been the most common number among sociologists who have broken down socioeconomic status into classes and because five allows the natural groupings of “very high,” “high,” “mid,” “low,” and “very low.” We have chosen to break the intervals at the 5th, 25th, 75th, and 95th percentiles of the distribution. The figure shows how this looks for a normally distributed population.

Break points are arbitrary, but we did have some reasons for these. Mainly, we wanted to focus on the extremes; hence, we avoided a simple breakdown into quintiles (i.e., into equal cuts of 20 percent). A great deal of interest goes on within the top 20 percent and bottom 20 percent of the population. Indeed, if the sample sizes were large enough, we would have defined the top cognitive class as consisting of the top 1 or 2 percent of the population. Important gradations in social behavior occasionally separate the top 2 percent from the next 2 percent. This is in line with another of the themes that we keep reiterating because they are so easily forgotten: You—meaning the self-selected person who has read this far into this book—live in a world that probably looks nothing like the figure. In all likelihood, almost all of your friends and professional associates belong in that top Class I slice. Your friends and associates whom you consider to be unusually slow are probably somewhere in Class II. Those whom you consider to be unusually bright are probably somewhere in the upper fraction of the 99th centile, a very thin slice of the overall distribution. In defining Class I, which we will use as an operational definition of the more amorphous group called the “cognitive elite,” as being the top 5 percent, we are being quite inclusive. It does, after all, embrace some 12 1/2 million people. Class III, the normals, comprises half of the population. Classes II and IV each comprises 20 percent, and Class V, like Class I, comprises 5 percent.

Defining the cognitive classes


The labels for the classes are the best we could do. It is impossible to devise neutral terms for people in the lowest classes or the highest ones. Our choice of “very dull” for Class V sounds to us less damning than the standard “retarded” (which is generally defined as below an IQ of 70, with “borderline retarded” referring to IQs between 70 and 80). “Very bright” seems more focused than “superior,” which is the standard term for people with IQs of 120 to 130 (those with IQs above 130 are called “very superior” in that nomenclature).8


The basic tool for multivariate analysis in the social sciences is known as regression analysis.9 The many forms of regression analysis have a common structure. There is a result to explain, the dependent variable. There are some things that might be the causes, the independent variables. Regression analysis tells how much each cause actually affects the result, taking the role of all the other hypothesized causes into account—an enormously useful thing for a statistical procedure to do, hence its widespread use.

In most of the chapters of Part II, we will be looking at a variety of social behaviors, ranging from crime to childbearing to unemployment to citizenship. In each instance, we will look first at the direct relationship of cognitive ability to that behavior. After observing a statistical connection, the next question to come to mind is, What else might be another source of the relationship?

In the case of IQ, the obvious answer is socioeconomic status. To what extent is this relationship really founded on the social background and economic resources that shaped the environment in which the person grew up—the parents’ socioeconomic status (SES)—rather than intelligence? Our measure of SES is an index combining indicators of parental education, income, and occupational prestige (details may be found in Appendix 2). Our basic procedure has been to run regression analyses in which the independent variables include IQ and parental SES.10 The result is a statement of the form: “Here is the relationship of IQ to social behavior X after the effects of socioeconomic background have been extracted,” or vice versa. Usually this takes the analysis most of the distance it can sensibly be pushed. If the independent relationship of IQ to social behavior X is small, there is no point in looking further. If the role of IQ remains large independent of SES, then it is worth thinking about, for it may cast social behavior and public policy in a new light.

What Is a Variable?

The word variable confuses some people who are new to statistics, because it sounds as if a variable is something that keeps changing. In fact, it is something that has different values among the members of a population. Consider weight as a variable. For any given observation, weight is a single number: the number of pounds that an object weighed at the time the observation was taken. But over all the members of the sample, weight has different values: It varies, hence it is a variable. A mnemonic for keeping “independent” and “dependent” straight is that the dependent variable is thought to “depend on” the values of the independent variables.

But What About Other Explanations?

We do not have the choice of leaving the issue of causation at that, however. Because intelligence has been such a taboo explanation for social behavior, we assume that our conclusions will often be resisted, if not condemned. We can already hear critics saying, “If only they had added this other variable to the analysis, they would have seen that intelligence has nothing to do with X.” A major part of our analysis accordingly has been to anticipate what other variables might be invoked and seeing if they do in fact attenuate the relationship of IQ to any given social behavior. This was not a scattershot effort. For each relationship, we asked ourselves if evidence, theory, or common sense suggests another major causal story. Sometimes it did. When looking at whether a new mother went on welfare, for example, it clearly was not enough to know the general socioeconomic background of the woman’s parents. It was also essential to examine her own economic situation at the time she had the baby: Whatever her IQ is, would she go on welfare if she had economic resources to draw on?

At this point, however, statistical analysis can become a bottomless pit. It is not uncommon in technical journals to read articles built around the estimated effects of a dozen or more independent variables. Sometimes the entire set of variables is loaded into a single regression equation. Sometimes sets of equations are used—modeling even more complex relationships, in which all the variables can exert mutual effects on one another.

Why should we not press forward? Why not also ask if religious background has an effect on the decision to go on welfare, for example? It is an interesting question, as are another fifty others that might come to mind. Our principle was to explore additional dynamics when there was another factor that was not only conceivably important but for clear logical reasons might be important because of dynamics having little or nothing to do with IQ. This last proviso is crucial, for one of the most common misuses of regression analysis is to introduce an additional variable that in reality is mostly another expression of variables that are already in the equation.

The Special Case of Education

Education posed a special and continuing problem. On the one hand, education can be important independent of cognitive ability. For example, education tends to delay marriage and childbirth because the time and commitment involved in being in school competes with the time and commitment it takes to be married or have a baby. Education shapes tastes and values in ways that are independent of the cognitive ability of the student. At the same time, however, the role of education versus IQ as calculated by a regression equation is tricky to interpret, for four reasons.

First, the number of years of education that a youth gets is caused to an important degree by both the parents’ SES and the youth’s own academic ability. In the NLSY, for example, the correlation of years of education with parental SES and youth’s IQ are +.50 and +.64, respectively. This means that when years of education is used as an independent variable, it is to some extent expressing the effects of SES and IQ in another form.

Second, any role that education plays independent of intelligence is likely to be discontinuous. For example, it may make a big difference to many outcomes that a person has a college degree. But how is one to interpret the substantive difference between one year of college and two? Between one year of graduate school and two? They are unlikely to be nearly as important as the difference between “a college degree” and “no college degree.”

Third, variables that are closely related can in some circumstances produce a technical problem known as multicollinearity, whereby the solutions produced by regression equations are unstable and often misleading.

Fourth and finally, to take education’s regression coefficient seriously tacitly assumes that intelligence and education could vary independently and produce similar results. No one can believe this to be true in general: indisputably, giving nineteen years of education to a person with an IQ of 75 is not going to have the same impact on life as it would for a person with an IQ of 125. The effects of education, whatever they may be, depend on the coexistence of suitable cognitive ability in ways that often require complex and extensive modeling of interaction effects—once again, problems that we hope others will take up but would push us far beyond the purposes of this book.

Our solution to this situation is to report the role of cognitive ability for two subpopulations of the NLSY that each have the same level of education: a high school diploma, no more and no less in one group; a bachelor’s degree, no more and no less, in the other. This is a simple, but we believe reasonable, way of bounding the degree to which cognitive ability makes a difference independent of education.

We walk through all three of these basics—the NLSY, the five cognitive classes, and the format for the statistical analysis—in a step-by-step fashion in the next chapter, where we use poverty to set the stage for the social behaviors to follow. Chapter 6 returns to education, this time not just talking about how far people got but the comparative roles of IQ and SES in determining how far someone gets in school. Then, seriatim, we take up unemployment and labor force dropout (Chapter 7), single-parent families and illegitimacy (Chapter 8), welfare dependency (Chapter 9), parenting (Chapter 10), crime (Chapter 11), and civic behavior (Chapter 12).

In these eight chapters, we limit the analysis to whites, and more specifically to non-Latino whites.11 This is, we think, the best way to make yet another central point: Cognitive ability affects social behavior without regard to race or ethnicity. The influence of race and ethnicity is deferred to Part III.

Chapter 5. Poverty

Who becomes poor? One familiar answer is that people who are unlucky enough to be born to poor parents become poor. There is some truth to this. Whites, the focus of our analyses in the chapters of Part II, who grew up in the worst 5 percent of socioeconomic circumstances are eight times more likely to fall below the poverty line than those growing up in the top 5 percent of socioeconomic circumstances. But low intelligence is a stronger precursor of poverty than low socioeconomic background. Whites with IQs in the bottom 5 percent of the distribution of cognitive ability are fifteen times more likely to be poor than those with IQs in the top 5 percent.

How does each of these causes of poverty look when the other is held constant? Or to put it another way: If you have to choose, is it better to be born smart or rich? The answer is unequivocally “smart.” A white youth reared in a home in which the parent or parents were chronically unemployed, worked at only the most menial of jobs, and had not gotten past ninth grade, but of just average intelligence—an IQ of 100—has nearly a 90 percent chance of being out of poverty by his or her early 30s. Conversely, a white youth born to a solid middle-class family but with an IQ equivalently below average faces a much higher risk of poverty, despite his more fortunate background.

When the picture is complicated by adding the effects of sex, marital status, and years of education, intelligence remains more important than any of them, with marital status running a close second. Among people who are both smart and well educated, the risk of poverty approaches zero. But it should also be noted that young white adults who marry are seldom in poverty, even if they are below average in intelligence or education. Even in these more complicated analyses, low IQ continues to be a much stronger precursor of poverty than the socioeconomic circumstances in which people grow up.

We begin with poverty because it has been so much at the center of concern about social problems. We will be asking, “What causes poverty?” focusing on the role that cognitive ability might play. Our point of departure is a quick look at the history of poverty in the next figure, which scholars from the Institute for Research on Poverty have now enabled us to take back to the 1930s.1

Dramatic progress against poverty from World War II through the 1960s, stagnation since then


Sources: SAUS, various editions; Ross and others, 1987.

In 1939, over half of the people of the United States lived in families with an income below the amount that constitutes the present poverty line—in constant dollars, of course. This figure declined steeply through World War II, and then through the Truman, Eisenhower, Kennedy, and Johnson administrations. Then came a sudden and lasting halt to progress. As of 1992, 14.5 percent of Americans were below the poverty line, within a few percentage points of the level in 1969. This history provokes three observations.

The first is that poverty cannot be a simple, direct cause of such problems as crime, illegitimacy, and drug abuse. Probably no single observation about poverty is at once so indisputable and so ignored. It is indisputable because poverty was endemic at a time when those problems were minor. We know that reducing poverty cannot, by itself, be expected to produce less criminality, illegitimacy, drug abuse, or the rest of the catalog of social problems, else the history of the twentieth century would have chronicled their steep decline.

The second point illustrated by the graph of poverty is that the pool of poor people must have changed over time. As late as the 1940s, so many people were poor in economic terms that to be poor did not necessarily mean to be distinguishable from the rest of the population in any other way. To rephrase the dialogue between F. Scott Fitzgerald and Ernest Hemingway, the poor were different from you and me: They had less money. But that was almost the only reliable difference. As affluence spread, people who escaped from poverty were not a random sample of the population. When a group shrinks from over 50 percent of the population to the less than 15 percent that has prevailed since the late 1960s, the people who are left behind are likely to be disproportionately those who suffer not only bad luck but also a lack of energy, thrift, farsightedness, determination—and brains.

The third point of the graph is that some perspective is in order about what happened to poverty during the 1960s and the famous War on Poverty. The trendline we show for 1936-1969 would have had about the same slope if we had chosen any of the decades in between to calculate it. The United States was not only getting richer but had been reducing the percentage of people below the modern poverty line for at least three decades before the 1960s came to a close. We will not reopen here the continuing debate about why progress came to an end when it did.

In this chapter, we explore some basic findings about the different roles that intelligence and social background play in keeping individuals out of poverty. The basics may be stated in a few paragraphs, as we did in the chapter’s introduction. But we also want to speak to readers who ask, “Yes, but what about the role of…,” thinking of the many other potential causes of white poverty. By the end of the chapter, we will have drawn a controversial conclusion. How did we get there? What makes us think that we have got our causal ordering right? We will walk through the analyses that lie behind our conclusions, taking a more leisurely approach than in the chapters to come.


We need to deal at once with an issue that applies to most of the topics in Part II. We want to consider poverty as an effect rather than as a cause—in social science terminology, as a dependent, not an independent, variable.2Intelligence will be evaluated as a factor that bears on becoming poor. But what, after all, does an intelligence test score mean for an adolescent who has grown up poor? Wouldn’t his test score have been higher if his luck in home environment had been better? Can IQ be causing poverty if poverty is causing IQ?

The Stability of IQ over the Life Span

The stability of IQ over time in the general population has been studied for decades, and the main findings are not in much dispute among psychometricians. Up to about 4 or 5 years of age, measures of IQ are not of much use in predicting later IQ. Indeed, you will get a better prediction of the child’s IQ at age 15 by knowing his parents’ IQ than by any test of the child given before age 5.3 Between ages 5 and 10, the tests rapidly become more predictive of adult IQ.4 After about the age of 10, the IQ score is essentially stable within the constraints of measurement error.5 On the comparatively rare occasions when large changes in IQ are observed, there is usually an obvious explanation. The child had been bedridden with a long illness before one of the tests, for example, or there was severe emotional disturbance at the time of one or both of the tests.

The IQ score of an individual might have been higher if he had been raised in more fortunate circumstances. Chapter 17 discusses this issue in more detail. But for purposes of Part II, the question is not what might have been but what is. In discussions of intelligence, people obsess about nature versus nurture, thinking that it matters fundamentally whether a person with a low IQ at, say, age 15 came by that IQ through a deficient environment or by bad luck in the genetic draw. But it does not matter for the kinds of issues we consider in Part II. The AFQT test scores for the NLSY sample were obtained when the subjects were 15 to 23 years of age, and their IQ scores were already as deeply rooted a fact about them as their height.6


For a century after poverty became a topic of systematic analysis in the mid-1800s, it was taken for granted that there were different kinds of poor people, with “deserving” and “undeserving” being one of the primary divisions.7Some people were poor because of circumstances beyond their control; others were poor as a result of their own behavior. Such distinctions among types of poverty were still intellectually respectable into the beginning of the Kennedy administration in 1961. By the end of the 1960s, they were not. Poverty was now seen as a product of broad systemic causes, not of individual characteristics. To say otherwise was to “blame the victim.”8 Accordingly, the technical literature about the causes of current poverty deals almost exclusively in economic and social explanations rather than with individual characteristics. Much of this literature focuses on poverty among blacks and its roots in racism and does not apply to the topic at hand: poverty among whites.

It seems easy to make the case that poverty among whites also arises from social and economic causes. Using the NLSY, we convert information about the education, occupations, and income of the parents of the NLSY youths into an index of socioeconomic status (SES) in which the highest scores indicate advanced education, affluence, and prestigious occupations. The lowest scores indicate poverty, meager education, and the most menial jobs. Suppose we then take the SES index and divide all the NLSY youngsters into five socioeconomic classes on exactly the same basis that we defined cognitive classes (split into categories of 5-20-50-20-5 percent of the population). We then ask, What percentage of people who came from those socioeconomic backgrounds were below the poverty line in their late 20s and early 30s (i.e., in 1989)? We exclude those who were still in school. The answer for non-Latino whites in the NLSY sample is shown in the following table. What could be plainer? Hardly any of the lucky 5 percent who had grown up in the most advantaged circumstances were in poverty (only 3 percent). Meanwhile, the white children of parents in the lowest socioeconomic class had a poverty rate of 24 percent. Rank hath its privileges, and in the United States one of those privileges is to confer economic benefits on your children. The way to avoid poverty in the United States is to be born into an advantaged home.

White Poverty by Parents’ Socioeconomic Class

Parents’ Socioeconomic Class

Percentage in Poverty

Very high








Very low


Overall average


Now we switch lenses. Instead of using socioeconomic class, we now ask, What percentage of the people who are in the different cognitive classes were below the poverty line in 1989? The answer is in the next table. There are similarities at the top of the ladder. Those in the top three classes—75 percent of the population—in either socioeconomic background or intelligence had similar poverty rates. But then the story diverges. As cognitive ability fell below average, poverty rose even more steeply among the cognitively disadvantaged than the socioeconomically disadvantaged. For the very dull, in the bottom 5 percent in IQ, 30 percent were below the poverty line, fifteen times the rate for the people in the top cognitive class.

White Poverty by Cognitive Class

Cognitive Class

Percentage in Poverty

I Very bright


II Bright


III Normal


IV Dull


V Very dull


Overall average


Taken one variable at a time, the data fit both hypotheses: Poverty is associated with socioeconomic disadvantage and even more strongly with cognitive disadvantage. Which is really explaining the relationship? And so we introduce a way of assessing the comparative roles of intelligence and socioeconomic background, which we will be using several times in the course of the subsequent chapters.

We want to disentangle the comparative roles of cognitive ability and socioeconomic background in explaining poverty. The dependent variable, poverty, has just two possible values: Yes, the family had an income below the poverty line in 1989, or no, its income was above the poverty line. The statistical method is a type of regression analysis specifically designed to estimate relationships for a yes-no kind of dependent variable.9 In our first look at this question, we see how much poverty depends on three independent variables: IQ, age, and parental socioeconomic status (hereafter called “parental SES”). The sample consists of all whites in the NLSY who were out of school in 1989.10 We are asking a straightforward question:

Given information about intelligence, socioeconomic status, and age, what is our best estimate of the probability that a family was below the poverty line in 1989?

for which a computer, using the suitable software, can provide an answer. Then we ask a second question:

Taking the other factors into account, how much remaining effect does any one of the independent variables have on the probability of being in poverty?

for which the computer can also provide an answer.

When we apply these questions to the NLSY data, the figure below shows what emerges. First, age in itself is not important in determining whether someone is in poverty once the other factors of intelligence and parental family background are taken into account.11 Statistically, its impact is negligible.

This leaves us with the two competing explanations that prompted the analysis in the first place: the socioeconomic background in which the NLSY youth grew up, and his own IQ score.

The black line lets you ask, “Imagine a person in the NLSY who comes from a family of exactly average socioeconomic background and exactly average age.12 What are this person’s chances of being in poverty if he is very smart? Very dumb?” To find out his chances if he is smart, look toward the far right-hand part of the graph. A person with an IQ 2 SDs above the mean has an IQ of 130, which is higher than 98 percent of the population. Reading across to the vertical axis on the left, that person has less than a 2 percent chance of being in poverty (always assuming that his socioeconomic background was average). Now think about someone who is far below average in cognitive ability, with an IQ 2 SDs below the mean (an IQ of 70, higher than just 2 percent of the population). Look at the far left-hand part of the graph. Now, our imaginary person with an average socioeconomic background has about a 26 percent chance of being in poverty. The gray line lets you ask, “Imagine a person in the NLSY who is exactly average in IQ and age. What are this person’s chances of being in poverty if he came from an extremely advantaged socioeconomic background? An extremely deprived socioeconomic background?” As the gray line indicates, the probability of being in poverty rises if he was raised by parents who were low in socioeconomic status, but only gradually.

The comparative roles of IQ and parental SES in determining whether young white adults are below the poverty line


Note: For computing the plot, age and either SES (for the black curve) or IQ (for the gray curve) were set at their mean values.


½ standard deviation below and above the mean cuts off the 31st and 69th percentiles. A ½ SD difference is substantial.

1 standard deviation below and above the mean cuts off the 16th and 84th percentiles. A 1 SD difference is big.

2 standard deviations below and above the mean cuts off the 2d and 98th percentiles. A 2 SD difference is very big.

A “standard score” means one that is expressed in terms of standard deviations.

In general, the visual appearance of the graph lets you see quickly the result that emerges from a close analysis: Cognitive ability is more important than parental SES in determining poverty.13

This does not mean that socioeconomic background is irrelevant. The magnitude of the effect shown in the graph and its statistical regularity makes socioeconomic status significant in a statistical sense. To put it into policy terms, the starting line remains unequal in American society, even among whites. On the other hand, the magnitude of the disadvantage is not as large as one might expect. For example, imagine a white person born in 1961 who came from an unusually deprived socioeconomic background: parents who worked at the most menial of jobs, often unemployed, neither of whom had a high school education (a description of what it means to have a socioeconomic status index score in the 2d centile on socioeconomic class). If that person has an IQ of 100—nothing special, just the national average—the chance of falling below a poverty-level income in 1989 was 11 percent. It is not zero, and it is not as small as the risk of poverty for someone from a less punishing environment, but in many ways this is an astonishing statement of progress. Conversely, suppose that the person comes from the 2d centile in IQ but his parents were average in socioeconomic status—which means that his parents worked at skilled jobs, had at least finished high school, and had an average income. Despite coming from that solid background, his odds of being in poverty are 26 percent, more than twice as great as the odds facing the person from a deprived home but with average intelligence.

In sum: Low intelligence means a comparatively high risk of poverty. If a white child of the next generation could be given a choice between being disadvantaged in socioeconomic status or disadvantaged in intelligence, there is no question about the right choice.


Now let us consider whether education really explains what is going on. One familiar hypothesis is that if you can only get people to stick with school long enough, they will be able to stay out of poverty even if they have modest test scores.

As in subsequent chapters, we will consider two educational groups: white people with a high school degree (no more, no less) and those with a bachelor’s degree (no more, no less). The figure above shows the results when the poverty rates for these two groups are considered separately.

In the white high school sample, high IQ makes a difference in avoiding poverty; in the college sample, hardly anyone was poor


Note: For computing the plot, age and either SES (for the black curve) or IQ (for the gray curve) were set at their mean values.

First, look at the pair of lines for the college graduates. We show them only for values greater than the mean, to avoid nonsensical implications (such as showing predicted poverty rate for a college graduate with an IQ two standard deviations below the mean). The basic lesson of the graph is that people who can complete a bachelor’s degree seldom end up poor, no matter what. This makes sense. Although income varies importantly for college graduates at different cognitive levels (as we discussed in Chapters 2 through 4), the floor income is likely to be well above the poverty line. College has economic value independent of cognitive ability, whether as a credential, for the skills that are acquired, or as an indicator of personal qualities besides IQ (diligence, persistence) that make for economic success in life. It is impossible with these data to disentangle what contributions these different explanations make.

The two lines showing the results for high school graduates are much more informative. These people are taking a homogeneous and modest set of educational skills to the workplace. Within this group, IQ has a strong effect independent of socioeconomic background. A young adult at the bottom 2 percent of IQ had about a 24 percent change of being in poverty compared to less than a 2 percent chance for one at the top 2 percent of IQ (given average age and socioeconomic background, and just a high school diploma). The parents’ background made much less difference. Cognitive ability still has a major effect on poverty even within groups with identical education.


How does the information we have just presented help in trying to understand the nature of poverty in America? To illustrate, consider one of the most painful topics in recent American social policy, the growing proportion of poor who consist of children. As of the 1991 figures, 22 percent of all children under the age of 15 were below the official poverty line, twice as high as the poverty rate among those age 15 and over.14 It is a scandalously high figure in a country as wealthy as the United States. Presumably every reader wishes for policies that would reduce poverty among children.

Why are so many children in poverty in a rich country? In political debate, the question is usually glossed over. An impression is conveyed that poverty among children is something that has grown everywhere in the United States, for all kinds of families, for reasons vaguely connected with economic troubles, ungenerous social policies during the 1980s, and discrimination against women and minority groups.

Specialists who have followed these figures know that this explanation is misleading.15 Poverty among children has always been much higher in families headed by a single woman, whether she is divorced or never married. For families headed by a single woman, the poverty rate in 1991 was 36 percent; for all other American families, 6 percent.16 Indeed, the national poverty rate for households headed by a single woman has been above 30 percent since official poverty figures began to be available in 1959.17 The equation is brutally simple: The higher the proportion of children who live in households headed by single women, then, ceteris paribus, the higher the proportion of children who will live in poverty. An important part of the increasing child poverty in the United States is owed to the increasing proportion of children who live in those families.18 The political left and right differ in their views of what policies to follow in response to this state of affairs, but recently they have broadly agreed on the joint roles of gender and changes in family structure in pushing up the figures for child poverty.

Poverty Among Children: The Role of the Mother’s IQ

What does IQ add to this picture? It allows us to focus sharply on who is poor and why, and to dispense with a number of mistaken ideas. To see how, let us consider women, and specifically women with children.19 Here is the graph that results when we ask how often mothers with differing IQs and differing family structures suffer from poverty. (In the figure, the effects of the mothers’ socioeconomic background are held constant, as are the number of children, which is factored into the calculation of the poverty line.)

The first, glaring point of the figure is that marriage is a powerful poverty preventive, and this is true for women even of modest cognitive ability. A married white woman with children who is markedly below average in cognitive ability—at the 16th centile, say, one standard deviation below the mean—from an average socioeconomic background had only a 10 percent probability of poverty.

The role of the mother’s IQ in determining which white children are poor


Notes: For computing the plot, age and SES were set at their mean values.

The second point of the graph is that to be without a husband in the house is to run a high risk of poverty, even if the woman was raised in an average socioeconomic background. Such a woman, with even an average IQ, ran a 33 percent chance of being in poverty. If she was unlucky enough to have an IQ of only 85, she had more than a 50 percent chance—five times as high as the risk faced by a married woman of identical IQ and socioeconomic background. Even a woman with a conspicuously high IQ of 130 (two standard deviations above the mean) was predicted to have a poverty rate of 10 percent if she was a single mother, which is quite high compared to white women in general. Perhaps surprisingly, it did not make much difference which of the three kinds of “nonmarriage”—separation, divorce, or no marriage at all—was involved. The results for all three groups of women were drastically different from the results for married women, and quite similar to each other (which is why they are grouped in the figure.)

The third obvious conclusion is that IQ is extremely important in determining poverty among women without a husband present. A poverty rate of 10 percent for women with IQs of 130 may be high compared to some standards, but it is tiny compared to the steeply rising probabilities of poverty that characterize women with below average cognitive ability.

Poverty Among Children: The Role of the Mother’s Socioeconomic Background

Now we pursue the same issue but in terms of socioeconomic background. Remember that the steep downward curve in the figure above for unmarried mothers is the effect of IQ after holding the effects of socioeconomic status constant. What is the role of socioeconomic background after we take IQ into account? Not much, as the next figure shows.

We used the same scale on the vertical axis in both of the preceding graphs to make the comparison with IQ easier. The conclusion is that no matter how rich and well educated the parents of the mother might have been, a separated, divorced, or never-married white woman with children and an average IQ was still looking at nearly a 30 percent chance of being below the poverty line, far above the usual level for whites and far above the level facing a woman of average socioeconomic background but superior IQ. We cannot even be sure that higher socioeconomic background reduces the poverty rate at all for unmarried women after the contribution of IQ has been extracted; the downward slope of the line plotted in the graph does not approach statistical significance.20

The role of the mother’s socioeconomic background in determining which white children are poor


Note: For computing the plot, age and IQ were set at their mean values.

There are few clearer arguments for bringing cognitive ability into the analysis of social problems. Consider the hundreds of articles written about poverty among children and about the effects of single-parent families on poverty. Of course, these are important factors: Children are more often poor than adults. Family breakup is responsible for a major portion of the increase in child poverty. But if analysts are trying to understand the high rates of poverty among children, it must be done against the background that whatever other factors increase the risk of poverty among unmarried mothers, they hit unmarried mothers at low levels of intelligence much harder than they do those at high levels of intelligence—even after socioeconomic background is held constant.


You have been following a common process in social science. An initially simple issue becomes successively more complicated. And we have barely gotten started—an analysis in a technical journal seldom has as few independent variables as the ones we have examined. For that matter, even this simplified analysis represents only the end result of a long process. In the attached note, we describe how big the rest of the iceberg is.21

Complex analysis has both merits and faults. The merit is that the complications are part of reality. Einstein’s injunction that solutions should be as simple as possible, but no simpler, still applies. At the same time, social science often seems more in need of the inverse injunction, to introduce as much complexity as necessary, but no more. Complications can make us forget what we were trying to understand in the first place. Here is where we believe the situation stands:

By complicating the picture, we raise additional questions: Education is important in affecting poverty; the appropriate next step is to explore how intelligence and socioeconomic status are related to years of education. Marriage is important in determining poverty; we should explore how intelligence and socioeconomic status are related to marriage. These things we shall do in subsequent chapters.

But the simple picture, with only IQ, parental SES, and age in the equation, restricted to our all-white sample, continues to tell a story of its own. A major theme in the public dialogue in the United States has been that socioeconomic disadvantage is the primary driving force behind poverty. The simple picture shows that it just isn’t so for whites.22 The high rates of poverty that afflict certain segments of the white population are determined more by intelligence than by socioeconomic background. The force and relevance of this statement does not seem to us diminished by the complications it does not embrace.

Indeed, now that we are returning to basics, let us remember something else that could be overlooked in the welter of regression analyses. The poverty rate for whites in Class V was 30 percent—a percentage usually associated with poverty in poor urban neighborhoods. Ethnically and culturally, these are supposed to be the advantaged Americans: whites of European descent. But they have one big thing working against them: they are not very smart.

Like many other disabilities, low intelligence is not the fault of the individual. Everything we know about the causes of cognitive ability, genetic and environmental, tells us that by the time people grow to an age at which they can be considered responsible moral agents, their IQ is fairly well set. Many readers will find that, before writing another word, we have already made the case for sweeping policy changes meant to rectify what can only be interpreted as a palpably unfair result.

And yet between this and the chapters that will explore those policy issues stretch a few hundred pages of intervening analysis. There is a reason for them. By adding poverty to the portrait of cognitive stratification described in Part I, we hope to have set the terms of a larger problem than income inequality. The issue is not simply how people who are poor through no fault of their own can be made not poor but how we—all of us, of all abilities and income levels—can live together in a society in which all of us can pursue happiness. Changing policy in ways that affect poverty rates may well be part of that solution. But as we observed at the outset of the chapter, poverty itself has been declining as various discontents have been rising during this century, and curing poverty is not necessarily going to do much to cure the other pains that afflict American society. This chapter’s analysis should establish that the traditional socioeconomic analysis of the origins of poverty is inadequate and that intelligence plays a crucial role. We are just at the beginning of understanding how intelligence interacts with the other problems in America’s crisis.