In 1995, Skeptic magazine published one of our most controversial issues in our 25 year history, one dedicated to analyzing the claims of a book by Charles Murray and Richard Herrnstein titled The Bell Curve: Intelligence and Class Structure in American Life. It merits revisiting because of recent events in which Charles Murray has been booed off stage at two academic institutions in March and April of 2017: one at Middlebury College and the other at Villanova University. One of the startling facts to emerge from these recent protests is that both students and professors who were protesting admitted that they had never read any of Murray’s books but that the very idea that there could be group differences in intelligence was anathema and not open for discussion. They didn’t even want to hear Murray’s arguments, much less try to refute them.
In that issue, one article was critical of The Bell Curve and the other written in defense of the book and its thesis.
The former, presented below, is the cognitive psychologist Diane Halpern’s critique titled “The Skewed Logic of the Bell-Shaped Curve”. Dr. Halpern is one of the world’s leading scientists in the study of cognitive differences, including both race and gender, and she herself has encountered controversy for just daring to study the subject.
On April 26, 2017, we will republish the late U.C. Berkeley anthropologist Vince Sarich’s article, “In Defense of The Bell Curve: The Reality of Race and the Importance of Human Differences.” Sarich was also a very controversial scientist for his research on race and group differences, from which he never backed down.
As I read The Bell Curve by Richard J. Herrnstein and Charles Murray I was reminded of a cartoon from the popular children’s television show Sesame Street. As regular viewers of Sesame Street already know, every episode is brought to you courtesy of a number and letter. On those days when the star of the show is the letter “I,” we are shown a group of hard-working cartoon characters whose job it is to polish a giant letter “I” until it glistens like an expensive jewel in the sunlight. In fact, this small army of letter polishers spend their entire day polishing the letter “I” because it is such an Important and Interesting letter. In a similar manner, Herrnstein and Murray also polish their “I”—Intelligence—and its related measure, IQ, which assume the spotlight as the best predictors of socioeconomic class and a diverse range of variables that cover the rest of the alphabet from Abusive relationships to Xenophobia and Zealotry.
Commenting on The Bell Curve is a lot like trying to catch a ball of jello. The arguments are slick and, like most skilled rhetoricians who are attempting to change how people think, the authors provide a veneer of fairness to cover the flaws and biases in their message. In this case, the veneer is thin—so thin that it allows their hypocrisy and social agenda to peek through. In making their points, the authors present, discredit, and then dismiss all opposing points of view. Contradictory evidence is criticized as statistically or methodologically flawed. Unfortunately, the stringent criteria that they apply to counterarguments are abandoned when they present the evidence in support of their favored conclusions. The authors shape their arguments like skilled word smiths. A factual statement like “some educational programs have not worked” is gradually morphed into a misleading statement like “educational programs have not worked,” and then, “educational programs cannot work,” a subtle change in wording that occurs as the authors stray from their data.
7 main points in the book are analyzed to show how the authors leap from data to unfounded conclusions. The Bell Curve is really a book about how we should be spending our tax dollars.
Can anyone seriously believe that Murray was shocked and dismayed when he found that he had upset many people with his pronouncements of racial inequality or the way he used IQ data to support an ultra-conservative political agenda? The authors have created the perfect medium for a growing media frenzy with a very long book in which much of the supporting evidence is relegated to a statistical appendix and extreme claims are succinctly summarized. The voracious appetite of the media is whetted by controversies, sound bites, and simple explanations of complex subjects. Even lengthy and thoughtful articles are condensed into a few words for newspaper headlines that are supposed to pique the reader’s interest. This is the stuff that sells newspapers, keeps people tuned to the chatty banter that passes as television news, and sustains conversations in countless barber shops, bus stops, and kitchen tables. Despite Murray’s protestations to the contrary, this is a book about race, and race is one topic in which we are all self-proclaimed experts. Each of us has an opinion about racial similarities and differences and a story to tell that shows how right our own opinions are. Cognitive psychologists who study stereotypes and prejudice have known for a long time that strongly held beliefs are difficult to change, and that people cling to their beliefs even when confronted with evidence that shows that these beliefs are wrong. We are more likely to change our interpretations of experience and our memory for events so that they fit our existing belief system than we are to abandon our beliefs. Perhaps books like this one should be sold with warning labels in which readers are urged to be alert for misleading statements, missing evidence, and biased interpretations—sort of a surgeon general’s warning. The messages in The Bell Curve are at least as dangerous as cigarettes and alcohol.
My response to Herrnstein and Murray’s thesis is organized around a brief summary of seven main points that they make in their controversial and massive tome, so that my comments and criticisms can be understood in their appropriate context even by readers of this article who have not read their book.
1. Intelligence is important.
According to Herrnstein and Murray: This is a basic underlying assumption of the authors’ argument. It is difficult to disagree with the statement that intelligence is important, although I would have to add, “Important for what purpose?” Most of us would agree that it is also important to be a kind and loving person and that empathy and other socially desirable traits are at least equally important for the betterment of society or individual happiness. Although this is not the place to engage in philosophical musings about whether it is more important to be a good person or a smart one, the authors do provide a definitive answer to a similar burning question. They ask if it is better to be born intelligent or rich, which, for most of us, would seem to be a rhetorical question or one in which the answer depends on individual values. According to the authors, however, the correct answer is intelligent, and lots of intelligence is even better than lots of money. But, what is intelligence, and how can we tell who has more or less of it?
Commenting on The Bell Curve is a lot like trying to catch a ball of jello. The arguments are slick and, like most skilled rhetoricians who are attempting to change how people think, the authors provide a veneer of fairness to cover the flaws and biases in their message.
Intelligence is one of the most controversial topics in psychology even though the concept has a long history and the term is commonly used in everyday language. If I asked you to list the characteristics of an intelligent person, you would probably include terms like, “reasons logically and well,” “keeps an open mind,” “reads with high comprehension,” and “can understand complexities.” In addition, most people believe that they are about average or above average in intelligence. It seems that Garrison Keillor’s mythical Lake Wobegon is not the only place where the laws of mathematics are suspended so that everyone can be in the top half of the distribution.
Today’s most commonly used intelligence tests, the Stanford-Binet and Wechsler Intelligence Tests, are normed so that the average score is 100 and measures of the how the scores are spread out (standard deviations) are derived by transforming scores so that they conform to a mathematical formula. IQ scores greater than 100 indicate greater than average intelligence, and scores less than 100 indicate less than average intelligence. Intelligence tests are based on the idea that the more questions you answer correctly, the more intelligent you are. Tests of intelligence are like other sorts of tests, and the scores depend on all of the factors that affect performance on any other test—variables like the nature of the test questions and the test takers’ motivation, knowledge of the material, health, and willingness to guess when unsure of an answer. The scores that are obtained on intelligence tests are known as “intelligence quotients” (because they used to involve forming a fraction or quotient), or, more informally, IQ scores. IQ is a number that is obtained on a test that supposedly measures intelligence—it is not a direct measure of intelligence.
Here are some examples that are similar to questions on common intelligence tests:
- Verbal Test Items
- At what temperature does water freeze?
- Who wrote The Republic?
- How many inches are in 3 1/2 feet?
- Explain the meaning of “strange.”
- Explain the meaning of “adumbrate.”
- Repeat a series of digits after the test administrator recites them. For example, repeat the following digits: “8175621.”
- Performance Test Items
- Use wooden cubes painted red and white to duplicate a design shown on cards.
- Arrange a series of cartoons into a logical sequence.
- Assemble a jig-saw puzzle.
Most psychologists believe that intelligence is a multidimensional construct, although there is much disagreement over how many different kinds of intelligence there are. One way of dividing intelligence is to consider it as made up of fluid intelligence, the kind of intelligence that you would use when you are dealing with a novel task, like writing your first computer program, and crystallized intelligence, the kind of intelligence you would use when dealing with information that you have already learned, like finding the area of a pyramid when you know the formula. There are many other ways to divide intelligence including verbal intelligence, which involves the use of words and language and spatial intelligence, which involves the use of spatial displays like maps.
A factual statement like ‘some educational programs have not worked’ is gradually morphed into a misleading statement like ‘educational programs have not worked,’ and then, ‘educational programs cannot work,’ a subtle change in wording that occurs as the authors stray from their data.
A major controversy among psychologists concerns the existence of a general intelligence factor called “g.” The question is whether it makes sense to think about people as being generally “smart” or “dumb,” or is it more accurate to think that people can be smart in some ways and not in others? If people can be smart in some areas and not others, then a single score on an intelligence test will not be able to measure how intelligent they are, but if people can be thought of as generally smart or generally dumb, then a single number could assess the extent to which they are intelligent. In order to answer this question, the data from intelligence tests are analyzed with mathematical procedures to determine whether a single factor, “g,” emerges or whether the data are described more accurately with multiple factors. Some of the disagreements over the existence of a general factor of intelligence concern the mathematical procedures, and other disagreements concern the way that intelligence is conceptualized. The measurement of intelligence is not separable from the way it is conceptualized because the mathematics that we use influences the way psychologists think about intelligence, and the way we think about intelligence influences the mathematical procedures that we use. Many of the controversies surrounding the measurement of intelligence involve the mathematical analyses that are used in understanding the data. This is one of the reasons why it is difficult to explain to the general public why the experts cannot agree about intelligence.
2. When administered properly, intelligence tests are fair and valid measures of intelligence.
Although the authors have felled many trees to make this point, I do not agree that their conclusion is fair or valid. IQ is a number on a test. The test questions reflect the sort of information that most people know and the intellectual activities that most people can perform. IQ scores seem to predict academic success equally well for all racial and ethnic groups, a point that the authors make in several different places in their book, but this does not mean that they measure intelligence equally well. In addition, IQ scores can only account for a relatively small proportion of the variance in academic or job success. Success depends on many other variables like motivation, persistence, expectations, and education. The influences of variables other than intelligence are quickly dismissed by the authors, a practice that suggests that they are not important when, in fact, they are.
All intelligence tests are culturally-dependent, but all people are not equally exposed to the “majority” culture. Suppose we called “intelligence tests” by some other name, such as tests of acculturation to middle-class American life. This could be a descriptive name for these tests because the questions on the tests reflect what most people in the standardization sample knew and did not know at some point in time. For example, we might expect an average American adult in 1995 to know what a disk drive is, but we would not have expected this sort of knowledge from average Americans in 1985.
How can anyone conclude that formal and informal education doesn’t have a massive effect on intelligence (for those who are at least near average and above in intelligence), when we measure intelligence with information that is learned in school?
It is a fact that approximately 50% of African- Americans and other groups of ethnic minority children grow up in poverty. On the average, people who grow up in poverty do not have the same experiences as people who do not grow up in poverty. It is likely that fewer individuals from low income families will know what a disk drive is than individuals from families with higher incomes. Even if the same test predicts academic success equally well for all test-takers, it does not measure intelligence equally well, unless we decide to define intelligence as synonymous with academic success. This sort of definition leads to a type of circular reasoning (intelligence = academic success and academic success = intelligence) that would not be indicative of intelligent thought.
3. Intelligence is mostly inherited.
Of course, the authors prudently claim “we are not so rash as to assert that the environment or culture is wholly irrelevant” (p. 301); however, they definitively conclude that “IQ is substantially heritable” (p. 105). This is an example of the sort of weasel language that I referred to earlier. I do not believe that the data support this sort of blanket conclusion. Intelligence is far too complex to decide that it is mostly any one variable. It is clear to me that intelligence is partly inherited, but it is not meaningful or possible to quantify the size of that part. Also, the role of the environment is not a linear one as we climb the IQ scale. Consider, for example, a profoundly retarded individual—someone who scores below the cut-point designated as “educably retarded.” Many such individuals cannot learn to feed themselves, to talk, or to use the bathroom; they need constant custodial care, often with direct feeding through their stomachs. In these rare instances, intelligence is unaffected by environmental variables. By definition, they will not benefit from education. But, as we ascend the intelligence curve, environmental variables become increasingly important. The most brilliant rocket scientist would not be functioning at a high intellectual level if she never attended school or had an opportunity to learn to read or study science. Many of the items on intelligence tests are the sorts of items that are learned in school. How can anyone conclude that formal and informal education doesn’t have a massive effect on intelligence (for those who are at least near average and above in intelligence), when we measure intelligence with information that is learned in school?
There are many other problems with the dichotomization of nature and nurture and the attempt to assign a proportional value to each side of the nature-nurture equation. Nature and nurture are not separable components because biological propensities influence the environment that we seek, and through our interactions with the environment our biology changes. We now know that changes in the environment cause changes in brain structures, and altered brain structures change how we interact with the environment. Heredity and environment are like conjoined twins who share a common heart—they cannot be separated. It is impossible to declare a winner in the age-old tug-of-war between nature and nurture.
4. Low intelligence causes a wide range of social problems such as poverty, injury, crime, “illegitimate” births, and idleness.
My response to this list of social ills is a less-than intelligent “Huh?” Let’s consider the evidence and reasoning that the authors marshal for this conclusion. Take some time to examine the bell curve that is shown in Figure 1.
It is apparent that its name is descriptive of its bell-like shape. The large “hump” in the middle shows that most people are around average in intelligence. The bell curve, which is more formally known as the normal curve, is ubiquitous in the sciences with variables like height, weight, IQ, petal size in flowers, crop yields, length of pickles, and more—all showing this distribution.
There is a cluster of variables that tend to occur together at the low (left) end of the intelligence curve. They include such “socially undesirable” behaviors and characteristics as child abuse and neglect, poverty, low levels of education, unemployment, “idleness,” increased injuries, “illegitimate” births, welfare, higher birth rates, and crime. The opposites of these variables cluster with high intelligence and are shown at the upper (right) portion of the curve. The variables that cluster at the low-intelligence end of the distribution are the usual indicators of low socioeconomic status. The authors then conclude that low intelligence is the cause of the other variables in this cluster. They pronounce that: “Socioeconomic status is…a result of cognitive ability” (p. 286). How can they know that being unintelligent caused poverty and not the reverse, or, at least, a more reciprocal relationship in which poverty and low intelligence operate jointly and influence each other? Poor people differ from rich people in many ways—they have poorer health, poorer nutrition, and poorer living conditions. Would it not make more sense to reverse the causal arrow and hypothesize that poverty and all of its associates (lack of prenatal care, inadequate heat, ingestion of lead paint, poor diet, etc.) cause low intelligence? The statistical procedures that the authors used to establish which of these related variables was causal cannot be used to establish that low intelligence is the cause of the other variables. The variables are at least interactive or possibly even unidirectional—in the other direction.
5. Current social programs like welfare, affirmative action, and head start cannot work.
Finally, I understand the reason for this book. Although the data that were used to support their conclusions are from a fairly recent data set, the arguments themselves have been made countless times before. There is nothing new in the Herrnstein and Murray treatise. The Bell Curve is a book about money and values and how we should be spending tax dollars so that they reflect politically conservative values. Social programs like welfare are very expensive, and many, maybe even most, have not worked well. Why? Are the disappointing results because we have made many mistakes in how we set up these programs? Were our expectations too high? Did we set up the wrong contingencies or perhaps use insensitive measures of success? If so, then we should be able to find better ways to provide aid to the poor—ways that help more of them obtain jobs and move out of poverty. But, if social welfare programs cannot work because the recipients are too dumb or too idle or too criminal to benefit, then why spend money on programs that are either doomed to failure or actually increase the number on welfare by paying for out-of-wedlock babies? (Herrnstein and Murray prefer the term “illegitimate,” an old-fashioned term that blames the baby for its mother’s marital status. Their deliberate use of emotionally-laden terms like “illegitimate” makes my skin crawl.)
Although the authors reach an opposite conclusion, it is clear that one kind of social program that has reaped considerable social benefits is education. Many studies have shown that education does improve thinking abilities, and it is these very abilities that are at the heart of any definition of intelligence. In their usual style, the authors present some of the data that show the beneficial effects of education and then dismiss these data as unreplicated, suspicious, lacking control groups, statistically flawed, etc. It is especially surprising that they arrive at this conclusion because the senior author, Herrnstein, was a contributor to a major program to improve intelligence in Venezuela. The Venezuela program has undergone careful scrutiny by international scientists, including random assignment of subjects to experimental conditions and “blind” scoring so that experimenter expectations cannot influence the outcomes, and it clearly has yielded improvements in thinking skills for those who were involved in the program.
In understanding what is at the heart of the authors’ argument, it is important to distinguish between data and the interpretation of data. This relationship is shown in Figure 2.
Yes, poverty, crime, low intelligence, and high birth rates occur together. These are the data, and they are not in question, although the authors often present the data in misleading ways. What is in question is the way these authors interpreted the data and the “cure” or public policy recommendations that arise from their interpretation. Their interpretation or explanation of the data is influenced by their belief system, and their explanations and beliefs intervene between the data and the public policy recommendations that are built on the data. There is good reason to believe that their interpretation of the data is “tainted” or not as pure or databased as their academic affiliations, thick statistical appendix, and scientific-sounding language make it seem. Consider this quote from The Bell Curve: “The median earning of…workers in 1992 [was] $41,005 for white male graduates with a bachelor’s degree and only $31,001 for black males with a bachelor’s degree” (p. 324). Most readers would interpret these data as evidence of persistent discrimination in the labor market. After all, how else could you explain the finding that even when African- Americans and Whites have the same education, and other variables like sex are held constant, African-Americans are paid much less? The authors conclude that this disparity in income shows how important the differences in intelligence really are. The bias in their interpretation of these data is too obvious too deserve additional comment.
Similarly, Herrnstein and Murray cite high drop-out rates for students who are admitted to college as a result of affirmative action programs as evidence that these students lack the intelligence to succeed in college, and therefore affirmative action programs cannot work. Affirmative action admissions are almost always first-generation students from low income households. Why don’t they consider other explanations for the high drop-out rate of students admitted under affirmative action programs, like the fact that these students are more likely to work while they are in college and when they work, they work more hours than their wealthier counterparts? Why don’t they even consider the possibility that affirmative action students start college with deficits that are attributable to an inferior secondary education and social pressures that are not compatible with attending college? Wouldn’t these facts be expected to increase drop-out rates? Like other interpretations of data in The Bell Curve, these conclusions do not ring true.
Follow the Money
This Watergate maxim is a good one to follow here. In deciding whom to believe, it is important to determine if the speaker or writer has an ulterior motive in convincing you that a certain conclusion is valid. For example, if the patent holder on a miracle cream that claims to “melt unsightly fat” told you that it was a wonder product, you would be less likely to believe this claim than if you had heard it from an unbiased scientific source with no potential for financial gain. The authors show a particular bias to cite studies that were funded by the infamous Pioneer Fund, which dispenses about $1 million annually to academics who support the idea that intelligence is genetically determined and that humans should be bred selectively for intelligence. I had a brief run-in with some of the academics whose work they have sponsored. In my book entitled Sex Differences in Cognitive Abilities (2nd ed.), I summarized a large body of research on brain size and concluded that although males have, on the average, larger and heavier brains, when these values are adjusted for body size, there is no sex difference. Following the publication of this book, I received an article from Richard Lynn, an Irish researcher, in which he says that his work shows that I am wrong. At first, I gave this rebuke very little thought because it is not unusual for researchers to come up with different findings and different conclusions, although his results were at odds with those reported by virtually all of the other researchers in this field.
Philippe Rushton posits that those males with the largest penises have the lowest intelligence, and furthermore, there are racial differences in both penis size and intelligence.
I then received a copy of the Lynn article with a letter from a psychologist whom I know, Philippe Rushton, who is notable for his theory that intelligence is inversely related to penis size. He posits that those males with the largest penises have the lowest intelligence, and furthermore, there are racial differences in both penis size and intelligence. According to Rushton, the racial line-up in descending order of intelligence is Asians, Caucasians, and Africans, with the reverse order for penis size. (No, I don’t know how he collected his data, nor do I know how other ethnic groups fare in this linear array.) This sort of theory is reminiscent of the penis-centered theories of Freud which posited a universal stage of development for boys and girls that he named the phallic stage. The word “phallic” means “penis,” and Freud saw no reason why this stage should have a different name when it referred to female development. Rushton’s penis-centric theory of intelligence suggests that some things never change since he proposes that we can learn about the intelligence of both females and males in an ethnic group by reference to the male anatomy. Much of the contemporary research funded by the Pioneer fund is both racist and sexist. In fact, the founding fathers of this fund were also anti-Semitic with strong ties to the Nazi movement and its goal to rid the world of Jews. There are 23 separate references to Lynn in the bibliography of The Bell Curve and 11 to Rushton. Both of these critics of my work received high praise by Herrnstein and Murray, and, like other frequently cited researchers in The Bell Curve, received large amounts of money from the Pioneer Fund.
The parallels between sexist and racist theories became more apparent to me when I received a copy of Rushton’s latest research, which was published after The Bell Curve went to press. Based on a study of helmet sizes used by the military, he concluded that African-Americans have smaller heads and therefore smaller brains than Caucasians—a result that mirrors the one by Lynn that compared male and female brains. There are many problems with these studies. Most importantly, brain size, weight, and neural structures depend upon life experiences. That is, our brains respond to our environment, so that we cannot know whether larger and heavier brains caused different life experiences or the experiences caused differences in brain size and weight. Many of the correlates of poverty such as inadequate nutrition, alcohol and other drug use, lack of prenatal and pediatric health care, ingestion of lead-based paint and other toxins, all have negative effects on brain development during the critical prenatal and infancy periods when the brain is most vulnerable. I do not know if the brain weight data are valid, but even if they are, lower brain weight is more likely a consequence of poverty than the reverse. In addition, there is absolutely no evidence that heavy brains are found in smarter people or that skull size is a good measure of brain size. The leaps from the actual data to the conclusions are irresponsible.
There is good reason to believe that their interpretation of the data is “tainted” or not as pure or databased as their academic affiliations, thick statistical appendix, and scientific-sounding language make it seem.
Soon after The Bell Curve was published, I received a FAX and phone call from Linda Gottfredson, a professor at the University of Delaware, who summarized what she believed was the dominant professional view on intelligence. She asked me to sign her summary statement to indicate my support. She explained that this was important so that the media and the public had a single summary statement on intelligence to guide their understanding of the points raised by Herrnstein and Murray. I found her summary troubling as it essentially agreed with Herrnstein and Murray’s conclusions. In fact, I agree with many of the statements made in The Bell Curve, but there are many others that I believe are wrong. I did not sign the statement that appeared in The Wall Street Journal, although 52 other psychologists did. I later learned that she is also supported by the Pioneer Fund. Although there is nothing morally wrong with being financed by people who share an author’s ideological point of view, it is troubling when all of the research that is funded in this manner happens to support the ideology of the funding agency. If you understand the social and political agenda that has financed this work, the next conclusion made by Herrnstein and Murray should not surprise you.
6. Recent immigrants are less intelligent than immigrants who came to the United States earlier this century.
The reasons in support of this conclusion are so flimsy that I cannot present them in a meaningful way. The authors argue that recent immigrants obtain special entry status because they are related to citizens; whereas immigrants at the turn of the 20th century fled persecution and were more motivated to succeed. Frankly, I cannot understand the logic in this argument. Why should we expect that recent immigrants from war torn and poverty stricken areas of the world would differ in motivation or intelligence from those who fled persecution earlier in the century? The political philosophy that the authors espouse is blatantly anti-immigration, which is as legitimate as any other political philosophy—except that this one is “dressed up” to look like a data-based conclusion, which it is not.
It is not the politically-conservative point of view that I am objecting to in this review—it is the misuse of data and the blatant biases in the way the data are interpreted in support of this point of view that I find objectionable.
Herrnstein and Murray go on to argue that the recent flood of immigrants coupled with high birth rates among the low intelligence portions of the population have lowered the average intelligence of Americans. When the average intelligence of a country is lowered, it is less able to compete in world markets, it is less able to produce and use advanced technologies, and other dire consequences result. While this may seem to be a reasonable argument, they also present data that show that the average IQ scores have risen every decade, an effect known as the Flynn Effect, named for the individual who first hypothesized this rise. I do not know how to interpret these inconsistencies, except to say that they seem to be able to argue that average IQ is both rising and falling, depending on what is more convenient at the time.
7. There is some good news.
Readers may be thinking that The Bell Curve forecasts a bleak future unless we stop welfare programs and curtail immigration so that the intelligent portion (or the “over the hill” portion on the right hand side) of the curve will have higher birth rates and the less intelligent portion stops reproducing and entering the country. Well, there is also good news. You and I are not at fault! We are all in the “over the hill” gang, a group repeatedly referred to as the “cognitive elite” because we are intelligent enough to read their massive tome and rich enough to spend $30 to buy it. We can look down on the poor unfortunates who live on the other side of the intelligence hill from us, and like responsible parents we can decide to do the right thing and eliminate social programs. The solutions that the authors offer have a very contemporary sound because they are now heard on Capitol Hill. It is not the politically-conservative point of view that I am objecting to in this review—it is the misuse of data and the blatant biases in the way the data are interpreted in support of this point of view that I find objectionable. Yes, we have difficult contemporary problems with welfare and immigration, among others. Responsible social science data are needed to guide public policy on these immensely complex issues, but the authors provide blatantly biased interpretations that are closer to propaganda than responsible research. Social programs may very well be doomed to failure for economic, social, or political reasons, but they are not doomed for the reasons Herrnstein and Murray present.
They also offer other solutions. We can return to simpler times (p. 541) when all people had a “valued place” in society (p. 535). The authors define a “valued place” as “other people would miss you when you were gone” (p. 535). What does this sentimental dribble really mean? Slave masters missed their slaves when they were gone; does this mean that slaves had a “valued place” in society? The call for simpler laws seems like an excellent idea. In fact, I found myself nodding frequently with many of their recommendations until I realized that “simpler” laws really meant fewer rights and safeguards for citizens. The nostalgia for the good old days when the neighborhood cop was your friend were not so good for everyone. African-American children never assumed that the local police officer was their friend, especially if they grew up in the segregated South. Have the authors really thought through their suggestion that fathers who are not married should not be required to pay child support— so-called “deadbeat fathers?” This solution is misogynist, anti-child, and fiscally foolish. How can this proposed policy discourage out-of-wedlock births or save taxpayer dollars? It certainly will not provide males with incentives to use contraception, if they have no financial responsibility for the children they father. How is this policy consistent with the creation of a “valued place” for everyone? What will we gain as a society by getting those deadbeat toddlers off welfare—a move that virtually ensures that many of America’s children will be denied access to even the most basic of human needs like adequate nutrition, health care, and heat. I don’t know whether to cry for a society that sacrifices its young or rage in anger against the intelligent people who forgot to care about the rest of society. Herrnstein and Murray’s proposed solutions drip with hypocrisy and offer simplistic cures for society’s most difficult ills. And for these solutions I don’t think that even Forrest Gump, the lovable role model for those in the low-intelligence portion of the curve, would offer Herrnstein and Murray a piece of his coveted chocolates.
About the author
Dr. Diane Halpern is an international authority on and the author of the highly acclaimed scientific work, Sex Differences in Cognitive Abilities, now in its fourth edition. Professor Halpern is also one of the leaders in the critical thinking movement in America, authoring books on Enhancing Thinking Skills in the Sciences and Mathematics, Thought and Knowledge: An Introduction to Critical Thinking, and most recently an ambitious book on Changing College Classrooms: New Teaching and Learning Strategies for an Increasingly Complex World. Dr. Halpern is currently at Moscow State University (Russia) where she is teaching classes on sex differences and similarities and critical thinking as part of a Fulbright Fellowship. She is also on the Editorial Advisory Board of the Skeptics Society.