Freakonomics: A Rogue Economist Explores the Hidden Side of Everything

by Steven D. Levitt

the states with the highest abortion rates in the 1970s experienced the greatest crime drops in the 1990s, while states with low abortion rates experienced smaller crime drops.

In states with high abortion rates, the entire decline in crime was among the post-Roe cohort as opposed to older criminals. Also, studies of Australia and Canada have since established a similar link between legalized abortion and crime.

What the link between abortion and crime does say is this: when the government gives a woman the opportunity to make her own decision about abortion, she generally does a good job of figuring out if she is in a position to raise the baby well. If she decides she can’t, she often chooses the abortion.

The typical parenting expert, like experts in other fields, is prone to sound exceedingly sure of himself. An expert doesn’t so much argue the various sides of an issue as plant his flag firmly on one side. That’s because an expert whose argument reeks of restraint or nuance often doesn’t get much attention. An expert must be bold if he hopes to alchemize his homespun theory into conventional wisdom. His best chance of doing so is to engage the public’s emotions, for emotion is the enemy of rational argument.

“The basic reality,” Sandman told The New York Times, “is that the risks that scare people and the risks that kill people are very different.”

“When hazard is high and outrage is low, people underreact,” he says. “And when hazard is low and outrage is high, they overreact.”

The most radical shift of late in the conventional wisdom on parenting has been provoked by one simple question: how much do parents really matter?

Clearly, bad parenting matters a great deal. As the link between abortion and crime makes clear, unwanted children—who are disproportionately subject to neglect and abuse—have worse outcomes than children who were eagerly welcomed by their parents. But how much can those eager parents actually accomplish for their children’s sake? This question represents a crescendo of decades’ worth of research. A long line of studies, including research into twins who were separated at birth, had already concluded that genes alone are responsible for perhaps 50 percent of a child’s personality and abilities.

another famous study, the Colorado Adoption Project, which followed the lives of 245 babies put up for adoption and found virtually no correlation between the child’s personality traits and those of his adopted parents?

These nature-nurture discrepancies were addressed in a 1998 book by a little-known textbook author named Judith Rich Harris. The Nurture Assumption was in effect an attack on obsessive parenting,

Harris argued that the top-down influence of parents is overwhelmed by the grassroots effect of peer pressure, the blunt force applied each day by friends and schoolmates.

Harris’s theory was duly endorsed by a slate of heavyweights. Among them was Steven Pinker, the cognitive psychologist and bestselling author, who in his own book Blank Slate called Harris’s views “mind-boggling” (in a good way).

Still, the question of how much parents matter is a good one. It is also terribly complicated. In determining a parent’s influence, which dimension of the child are we measuring: his personality? his school grades? his moral behavior? his creative abilities? his salary as an adult? And what weight should we assign each of the many inputs that affect a child’s outcome: genes, family environment, socioeconomic level, schooling, discrimination, luck, illness, and so on?

we are less persuaded by parenting theory than by what the data have to say. Certain facets of a child’s outcome—personality, for instance, or creativity—are not easily measured by data. But school performance is. And since most parents would agree that education lies at the core of a child’s formation, it would make sense to begin by examining a telling set of school data.

So in 1980 the U.S. Department of Justice and the Chicago Board of Education teamed up to try to better integrate the city’s schools. It was decreed that incoming freshmen could apply to virtually any high school in the district.

In the interest of fairness, the CPS resorted to a lottery. For a researcher, this is a remarkable boon. A behavioral scientist could hardly design a better experiment in his laboratory. Just as the scientist might randomly assign one mouse to a treatment group and another to a control group, the Chicago school board effectively did the same. Imagine two students, statistically identical, each of whom wants to attend a new, better school. Thanks to how the ball bounces in the hopper, one student goes to the new school and the other stays behind. Now imagine multiplying those students by the thousands. The result is a natural experiment on a grand scale.

So what do the data reveal? The answer will not be heartening to obsessive parents: in this case, school choice barely mattered at all. It is true that the Chicago students who entered the school-choice lottery were more likely to graduate than the students who didn’t—which

the students who won the lottery and went to a “better” school did no better than equivalent students who lost the lottery and were left behind. That is, a student who opted out of his neighborhood school was more likely to graduate whether or not he actually won the opportunity to go to a new school.

the students—and parents—who choose to opt out tend to be smarter and more academically motivated to begin with. But statistically, they gained no academic benefit by changing schools.

And is it true that the students left behind in neighborhood schools suffered? No: they continued to test at about the same levels as before the supposed brain drain. There was, however, one group of students in Chicago who did see a dramatic change: those who entered a technical school or career academy. These students performed substantially better than they did in their old academic settings and graduated at a much higher rate than their past performance would have predicted.

In examining the income gap between black and white adults—it is well established that blacks earn significantly less—scholars have found that the gap is virtually eradicated if the blacks’ lower eighth-grade test scores are taken into account. In other words, the black-white income gap is largely a product of a black-white education gap that could have been observed many years earlier. “Reducing the black-white test score gap,” wrote the authors of one study, “would do more to promote racial equality than any other strategy that commands broad political support.”

In a paper called “The Economics of ‘Acting White,’” the young black Harvard economist Roland G. Fryer Jr. argues that some black students “have tremendous disincentives to invest in particular behaviors (i.e., education, ballet, etc.) due to the fact that they may be deemed a person who is trying to act like a white person (a.k.a. ‘selling-out’). Such a label, in some neighborhoods, can carry penalties that range from being deemed a social outcast, to being beaten or killed.” Fryer cites the recollections of a young Kareem Abdul-Jabbar,

I found myself being punished for everything I’d ever been taught was right. I got all A’s and was hated for it; I spoke correctly and was called a punk. I had to learn a new language simply to be able to deal with the threats.

In the late 1990s, the U.S. Department of Education undertook a monumental project called the Early Childhood Longitudinal Study. The ECLS sought to measure the academic progress of more than twenty thousand children from kindergarten through the fifth grade. The subjects were chosen from across the country to represent an accurate cross section of American schoolchildren.

Correlation is nothing more than a statistical term that indicates whether two variables move together. It tends to be cold outside when it snows; those two factors are positively correlated. Sunshine and rain, meanwhile, are negatively correlated. Easy enough—as long as there are only a couple of variables. But with a couple of hundred variables, things get harder. Regression analysis is the tool that enables an economist to sort out these huge piles of data. It does so by artificially holding constant every variable except the two he wishes to focus on, and then showing how those two co-vary.

Let’s say that we want to ask the ECLS data a fundamental question about parenting and education: does having a lot of books in your home lead your child to do well in school? Regression analysis can’t quite answer that question, but it can answer a subtly different one: does a child with a lot of books in his home tend to do better than a child with no books? The difference between the first and second questions is the difference between causality (question 1) and correlation (question 2). A regression analysis can demonstrate correlation, but it doesn’t prove cause.

It should be said that regression analysis is more art than science. (In this regard, it has a great deal in common with parenting itself.) But a skilled practitioner can use it to tell how meaningful a correlation is—and maybe even tell whether that correlation does indicate a causal relationship.

(To control for a variable is essentially to eliminate its influence,

After controlling for just a few variables—including the income and education level of the child’s parents and the mother’s age at the birth of her first child—the gap between black and white children is virtually eliminated at the time the children enter school.

The data reveal that black children who perform poorly in school do so not because they are black but because a black child is more likely to come from a low-income, low-education household. A typical black child and white child from the same socioeconomic background, however, have the same abilities in math and reading upon entering kindergarten.

the black-white gap reappears within just two years of a child’s entering school. By the end of first grade, a black child is underperforming a statistically equivalent white child. And the gap steadily grows over the second and third grades. Why does this happen? That’s a hard, complicated question. But one answer may lie in the fact that the school attended by the typical black child is not the same school attended by the typical white child, and the typical black child goes to a school that is simply . . . bad.

In terms of class size, teachers’ education, and computer-to-student ratio, the schools attended by blacks and whites are similar. But the typical black student’s school has a far higher rate of troublesome indicators, such as gang problems, nonstudents loitering in front of the school, and lack of PTA funding. These schools offer an environment that is simply not conducive to learning.

White children in these schools also perform poorly. In fact, there is essentially no black-white test score gap within a bad school in the early years once you control for students’ backgrounds. But all students in a bad school, black and white, do lose ground to students in good schools.

Consider this fact: the ECLS data reveal that black students in good schools don’t lose ground to their white counterparts, and black students in good schools outperform whites in poor schools.

once all other factors are controlled for, it is clear that students from rural areas tend to do worse than average. Suburban children, meanwhile, are in the middle of the curve, while urban children tend to score higher than average. (It may be that cities attract a more educated workforce and, therefore, parents with smarter children.) On average, girls test higher than boys, and Asians test higher than whites—although

A high socioeconomic status is strongly correlated to higher test scores, which seems sensible. Socioeconomic status is a strong indicator of success in general—it suggests a higher IQ and more education—and successful parents are more likely to have successful children.

A woman who doesn’t have her first child until she is at least thirty is likely to see that child do well in school. This mother tends to be a woman who wanted to get some advanced education or develop traction in her career. She is also likely to want a child more than a teenage mother wants a child. This doesn’t mean that an older first-time mother is necessarily a better mother, but she has put herself—and her children—in a more advantageous position.

according to the ECLS data, Head Start does nothing for a child’s future test scores. Despite a deep reservoir of appreciation for Head Start (one of this book’s authors was a charter student), we must acknowledge that it has repeatedly been proven ineffectual in the long term. Here’s a likely reason: instead of spending the day with his own undereducated, overworked mother, the typical Head Start child spends the day with someone else’s undereducated, overworked mother.

There is a strong correlation—a negative one—between adoption and school test scores. Why? Studies have shown that a child’s academic abilities are far more influenced by the IQs of his biological parents than the IQs of his adoptive parents, and mothers who offer up their children for adoption tend to have significantly lower IQs than the people who are doing the adopting.

So what does all this have to say about the importance of parents in general? Consider again the eight ECLS factors that are correlated with school test scores: The child has highly educated parents. The child’s parents have high socioeconomic status. The child’s mother was thirty or older at the time of her first child’s birth. The child had low birthweight. The child’s parents speak English in the home. The child is adopted. The child’s parents are involved in the PTA. The child has many books in his home.

To overgeneralize a bit, the first list describes things that parents are; the second list describes things that parents do. Parents who are well educated, successful, and healthy tend to have children who test well in school; but it doesn’t seem to much matter whether a child is trotted off to museums or spanked or sent to Head Start or frequently read to or plopped in front of the television.

But this is not to say that parents don’t matter. Plainly they matter a great deal. Here is the conundrum: by the time most people pick up a parenting book, it is far too late. Most of the things that matter were decided long ago—who you are, whom you married, what kind of life you lead. If you are smart, hardworking, well educated, well paid, and married to someone equally fortunate, then your children are more likely to succeed. (Nor does it hurt, in all likelihood, to be honest, thoughtful, loving, and curious about the world.) But it isn’t so much a matter of what you do as a parent; it’s who you are.

But the adoptive parents’ advantages had little bearing on the child’s school performance. As also seen in the ECLS data, adopted children test relatively poorly in school; any influence the adoptive parents might exert is seemingly outweighed by the force of genetics. But, Sacerdote found, the parents were not powerless forever. By the time the adopted children became adults, they had veered sharply from the destiny that IQ alone might have predicted. Compared to similar children who were not put up for adoption, the adoptees were far more likely to attend college, to have a well-paid job, and to wait until they were out of their teens before getting married. It was the influence of the adoptive parents, Sacerdote concluded, that made the difference.

Every generation seems to produce a few marquee academics who advance the thinking on black culture. Roland G. Fryer Jr., the young black economist who analyzed the “acting white” phenomenon and the black-white test score gap, may be among the next.

Fryer’s mission is the study of black underachievement. “One could rattle off all the statistics about blacks not doing so well,” he says. “You can look at the black-white differential in out-of-wedlock births or infant mortality or life expectancy. Blacks are the worst-performing ethnic group on SATs. Blacks earn less than whites. They are still just not doing well, period. I basically want to figure out where blacks went wrong, and I want to devote my life to this.”

Fryer came to wonder: is distinctive black culture a cause of the economic disparity between blacks and whites or merely a reflection of it?

As with the ECLS study, Fryer went looking for the answer in a mountain of data: birth-certificate information for every child born in California since 1961. The data, covering more than sixteen million births, included standard items such as name, gender, race, birthweight, and the parents’ marital status, as well as more telling factors about the parents: their zip code (which indicates socioeconomic status and a neighborhood’s racial composition), their means of paying the hospital bill (again, an economic indicator), and their level of education.

More than 40 percent of the black girls born in California in a given year receive a name that not one of the roughly 100,000 baby white girls received that year. Even more remarkably, nearly 30 percent of the black girls are given a name that is unique among the names of every baby, white and black, born that year in California.

What kind of parent is most likely to give a child such a distinctively black name? The data offer a clear answer: an unmarried, low-income, undereducated teenage mother from a black neighborhood who has a distinctively black name herself.

If black kids who study calculus and ballet are thought to be “acting white,” Fryer says, then mothers who call their babies Shanice are simply “acting black.”