You don’t have to believe in obsessive parenting to think that the second boy doesn’t stand a chance and that the first boy has it made. What are the odds that the second boy, with the added handicap of racial discrimination, will turn out to lead a productive life? What are the odds that the first boy, so deftly primed for success, will somehow fail? And how much of his fate should each boy attribute to his parents?
One could theorize forever about what makes the perfect parent. For two reasons, the authors of this book will not do so. The first is that neither of us professes to be a parenting expert (although between us we do have six children under the age of five). The second is that 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.
These data concern school choice, an issue that most people feel strongly about in one direction or another. True believers of school choice argue that their tax dollars buy them the right to send their children to the best school possible. Critics worry that school choice will leave behind the worst students in the worst schools. Still, just about every parent seems to believe that her child will thrive if only he can attend the right school, the one with an appropriate blend of academics, extracurriculars, friendliness, and safety.
School choice came early to the Chicago Public School system. That’s because the CPS, like most urban school districts, had a disproportionate number of minority students. Despite the U.S. Supreme Court’s 1954 ruling in Brown v. Board of Education of Topeka, which dictated that schools be desegregated, many black CPS students continued to attend schools that were nearly all-black. 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.
Aside from its longevity, there are several reasons the CPS school-choice program is a good one to study. It offers a huge data set—Chicago has the third-largest school system in the country, after New York and Los Angeles—as well as an enormous amount of choice (more than sixty high schools) and flexibility. Its take-up rates are accordingly very high, with roughly half of the CPS students opting out of their neighborhood school. But the most serendipitous aspect of the CPS program—for the sake of a study, at least—is how the school-choice game was played.
As might be expected, throwing open the doors of any school to every freshman in Chicago threatened to create bedlam. The schools with good test scores and high graduation rates would be rabidly oversubscribed, making it impossible to satisfy every student’s request.
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. This was hardly the goal in the mind of the Chicago school officials who conceived the lottery. But when viewed in this way, the lottery offers a wonderful means of measuring just how much school choice—or, really, a better school—truly matters.
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 seems to suggest that school choice does make a difference. But that’s an illusion. The proof is in this comparison: 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. What appears to be an advantage gained by going to a new school isn’t connected to the new school at all. What this means is that 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. So the CPS school-choice program did help prepare a small segment of otherwise struggling students for solid careers by giving them practical skills. But it doesn’t appear that it made anyone much smarter.
Could it really be that school choice doesn’t much matter? No self-respecting parent, obsessive or otherwise, is ready to believe that. But wait: maybe it’s because the CPS study measures high-school students; maybe by then the die has already been cast. “There are too many students who arrive at high school not prepared to do high school work,” Richard P. Mills, the education commissioner of New York State, noted recently, “too many students who arrive at high school reading, writing, and doing math at the elementary level. We have to correct the problem in the earlier grades.”
Indeed, academic studies have substantiated Mills’s anxiety. 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.”
So where does that black-white test gap come from? Many theories have been put forth over the years: poverty, genetic makeup, the “summer setback” phenomenon (blacks are thought to lose more ground than whites when school is out of session), racial bias in testing or in teachers’ perceptions, and a black backlash against “acting white.”
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, known then as Lew Alcindor, who had just entered the fourth grade in a new school and discovered that he was a better reader than even the seventh graders: “When the kids found this out, I became a target….It was my first time away from home, my first experiencein an all-black situation, and 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. I had good manners and was a good little boy and paid for it with my hide.”
Fryer is also one of the authors of “Understanding the Black-White Test Score Gap in the First Two Years of School.” This paper takes advantage of a new trove of government data that helps re
liably address the black-white gap. Perhaps more interestingly, the data do a nice job of answering the question that every parent—black, white, and otherwise—wants to ask: what are the factors that do and do not affect a child’s performance in the early school years?
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.
The ECLS measured the students’ academic performance and gathered typical survey information about each child: his or her race, gender, family structure, socioeconomic status, the level of his or her parents’ education, and so on. But the study went well beyond these basics. It also included interviews with the students’ parents (and teachers and school administrators), posing a long list of questions more intimate than those in the typical government interview: whether the parents spanked their children, and how often; whether they took them to libraries or museums; how much television the children watched.
The result is an incredibly rich set of data—which, if the right questions are asked of it, tells some surprising stories.
How can this type of data be made to tell a reliable story? By subjecting it to the economist’s favorite trick: regression analysis. No, regression analysis is not some forgotten form of psychiatric treatment. It is a powerful—if limited—tool that uses statistical techniques to identify otherwise elusive correlations.
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.
In a perfect world, an economist could run a controlled experiment just as a physicist or a biologist does: setting up two samples, randomly manipulating one of them, and measuring the effect. But an economist rarely has the luxury of such pure experimentation. (That’s why the school-choice lottery in Chicago was such a happy accident.) What an economist typically has is a data set with a great many variables, none of them randomly generated, some related and others not. From this jumble, he must determine which factors are correlated and which are not.
In the case of the ECLS data, it might help to think of regression analysis as performing the following task: converting each of those twenty thousand schoolchildren into a sort of circuit board with an identical number of switches. Each switch represents a single category of the child’s data: his first-grade math score, his third-grade math score, his first-grade reading score, his third-grade reading score, his mother’s education level, his father’s income, the number of books in his home, the relative affluence of his neighborhood, and so on.
Now a researcher is able to tease some insights from this very complicated set of data. He can line up all the children who share many characteristics—all the circuit boards that have their switches flipped the same direction—and then pinpoint the single characteristic they don’t share. This is how he isolates the true impact of that single switch on the sprawling circuit board. This is how the effect of that switch—and, eventually, of every switch—becomes manifest.
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. After all, there are several ways in which two variables can be correlated. X can cause Y; Y can cause X; or it may be that some other factor is causing both X and Y. A regression alone can’t tell you whether it snows because it’s cold, whether it’s cold because it snows, or if the two just happen to go together.
The ECLS data do show, for instance, that a child with a lot of books in his home tends to test higher than a child with no books. So those factors are correlated, and that’s nice to know. But higher test scores are correlated with many other factors as well. If you simply measure children with a lot of books against children with no books, the answer may not be very meaningful. Perhaps the number of books in a child’s home merely indicates how much money his parents make. What we really want to do is measure two children who are alike in every way except one—in this case, the number of books in their homes—and see if that one factor makes a difference in their school performance.
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.
So what does an analysis of the ECLS data tell us about schoolchildren’s performance? A number of things. The first one concerns the black-white test score gap.
It has long been observed that black children, even before they set foot in a classroom, underperform their white counterparts. Moreover, black children didn’t measure up even when controlling for a wide array of variables. (To control for a variable is essentially to eliminate its influence, much as one golfer uses a handicap against another. In the case of an academic study such as the ECLS, a researcher might control for any number of disadvantages that one student might carry when measured against the average student.) But this new data set tells a different story. 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.
This is an encouraging finding on two fronts. It means that young black children have continued to make gains relative to their white counterparts. It also means that whatever gap remains can be linked to a handful of readily identifiable factors. 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.
Great news, right? Well, not so fast. First of all, because the average black child is more likely to come from a low-income, low-education household, the gap is very real: on average, black children still are scoring worse. Worse yet, even when the parents’ income and education are controlled for, 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. Even fifty years after Brown v. Board, many American schools are virtually segregated. The ECLS project surveyed roughly one thousand schools, taking samples of twenty children from each. In 35 percent of those schools, not a single black child was included in the sample. The typical white child in the ECLS study attends a school that is only 6 percent black; the typical black child, meanwhile, attends a school that is about 60
percent black.
Just how are the black schools bad? Not, interestingly, in the ways that schools are traditionally measured. 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.
Black students are hardly the only ones who suffer in bad schools. 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. Perhaps educators and researchers are wrong to be so hung up on the black-white test score gap; the bad-school/good-school gap may be the more salient issue. 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.
So according to these data, a child’s school does seem to have a clear impact on his academic progress, at least in the early years. Can the same be said for parenting? Did all those Baby Mozart tapes pay off? What about those marathon readings of Goodnight Moon? Was the move to the suburbs worthwhile? Do the kids with PTA parents do better than the kids whose parents have never heard of the PTA?
The wide-ranging ECLS data offer a number of compelling correlations between a child’s personal circumstances and his school performance. For instance, 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 blacks, as we have already established, test similarly to whites from comparable backgrounds and in comparable schools.
Freakonomics Revised and Expanded Edition Page 15