FREAKONOMICS A Rogue Economist Explores the Hidden Side of Everything Steven D. Levitt and Stephen J. Dubner CONTENTS AN EXPLANATORY NOTE In which the origins of this book are clarified. INTRODUCTION: The Hidden Side of Everything In which the book’s central idea is set forth: namely, if morality represents how people would like the world to work, then economics shows how it actually does work.
Why the conventional wisdom is so often wrong…How “experts”—from criminologists to real-estate agents to political scientists—bend the facts…Why knowing what to measure, and how to measure it, is the key to understanding modern life…What is “freakonomics,” anyway? 1. What Do Schoolteachers and Sumo Wrestlers Have in Common? In which we explore the beauty of incentives, as well as their dark side—cheating. Who cheats?
Just about everyone…How cheaters cheat, and how to catch them…Stories from an Israeli day-care center…The sudden disappearance of seven million American children…Cheating schoolteachers in Chicago…Why cheating to lose is worse than cheating to win…Could sumo wrestling, the national sport of Japan, be corrupt? …What the Bagel Man saw: mankind may be more honest than we think. 2. How Is the Ku Klux Klan Like a Group of Real-Estate Agents? In which it is argued that nothing is more powerful than information, especially when its power is abused.
Going undercover in the Ku Klux Klan…Why experts of every kind are in the perfect position to exploit you…The antidote to information abuse: the Internet…Why a new car is suddenly worth so much less the moment it leaves the lot…Breaking the real-estate agent code: what “well maintained” really means…Is Trent Lott more racist than the average Weakest Link contestant? …What do online daters lie about? 3. Why Do Drug Dealers Still Live with Their Moms? In which the conventional wisdom is often found to be a web of fabrication, self-interest, and convenience.
Why experts routinely make up statistics; the invention of chronic halitosis…How to ask a good question…Sudhir Venkatesh’s long, strange trip into the crack den…Life is a tournament…Why prostitutes earn more than architects…What a drug dealer, a highschool quarterback, and an editorial assistant have in common…How the invention of crack cocaine mirrored the invention of nylon stockings…Was crack the worst thing to hit black Americans since Jim Crow? 4. Where Have All the Criminals Gone? In which the facts of crime are sorted out from the fictions. What Nicolae Ceau’scu learned—the hard way—about abortion…Why the 1960s were a great time to be a criminal…Think the roaring 1990s economy put a crimp on crime? Think again…Why capital punishment doesn’t deter criminals…Do police actually lower crime rates? …Prisons, prisons everywhere…Seeing through the New York City police “miracle”…What is a gun, really? …Why early crack dealers were like Microsoft millionaires and later crack dealers were like Pets. com…The superpredator versus the senior citizen…Jane Roe, crime stopper: how the legalization of abortion changed everything. 5. What Makes a Perfect Parent?
In which we ask, from a variety of angles, a pressing question: do parents really matter? The conversion of parenting from an art to a science…Why parenting experts like to scare parents to death…Which is more dangerous: a gun or a swimming pool? …The economics of fear…Obsessive parents and the nature-nurture quagmire…Why a good school isn’t as good as you might think…The black-white test gap and “acting white”…Eight things that make a child do better in school and eight that don’t. 6. Perfect Parenting, Part II; or: Would a Roshanda by Any Other Name Smell as Sweet?
In which we weigh the importance of a parent’s first official act—naming the baby. A boy named Winner and his brother, Loser…The blackest names and the whitest names…The segregation of culture: why Seinfeld never made the top fifty among black viewers…If you have a really bad name, should you just change it? …High-end names and low-end names (and how one becomes the other)…Britney Spears: a symptom, not a cause…Is Aviva the next Madison? …What your parents were telling the world when they gave you your name. EPILOGUE: Two Paths to Harvard In which the dependability of data meets the randomness of life.
Notes Acknowledgments Index About the Author Credits Copyright About the Publisher AN EXPLANATORY NOTE The most brilliant young economist in America—the one so deemed, at least, by a jury of his elders—brakes to a stop at a traffic light on Chicago’s south side. It is a sunny day in mid-June. He drives an aging green Chevy Cavalier with a dusty dashboard and a window that doesn’t quite shut, producing a dull roar at highway speeds. But the car is quiet for now, as are the noontime streets: gas stations, boundless concrete, brick buildings with plywood windows. An elderly homeless man approaches.
It says he is homeless right on his sign, which also asks for money. He wears a torn jacket, too heavy for the warm day, and a grimy red baseball cap. The economist doesn’t lock his doors or inch the car forward. Nor does he go scrounging for spare change. He just watches, as if through one-way glass. After a while, the homeless man moves along. “He had nice headphones,” says the economist, still watching in the rearview mirror. “Well, nicer than the ones I have. Otherwise, it doesn’t look like he has many assets. ” Steven Levitt tends to see things differently than the average person.
Differently, too, than the average economist. This is either a wonderful trait or a troubling one, depending on how you feel about economists. —The New York Times Magazine, August 3, 2003 In the summer of 2003, The New York Times Magazine sent Stephen J. Dubner, an author and journalist, to write a profile of Steven D. Levitt, a heralded young economist at the University of Chicago. Dubner, who was researching a book about the psychology of money, had lately been interviewing many economists and found that they often spoke English as if it were a fourth or fifth language.
Levitt, who had just won the John Bates Clark Medal (awarded every two years to the best American economist under forty), had lately been interviewed by many journalists and found that their thinking wasn’t very…robust, as an economist might say. But Levitt decided that Dubner wasn’t a complete idiot. And Dubner found that Levitt wasn’t a human slide rule. The writer was dazzled by the inventiveness of the economist’s work and his knack for explaining it. Despite Levitt’s elite credentials (Harvard undergrad, a PhD from MIT, a stack of awards), he approached economics in a notably unorthodox way.
He seemed to look at things not so much as an academic but as a very smart and curious explorer—a documentary filmmaker, perhaps, or a forensic investigator or a bookie whose markets ranged from sports to crime to pop culture. He professed little interest in the sort of monetary issues that come to mind when most people think about economics; he practically blustered with self-effacement. “I just don’t know very much about the field of economics,” he told Dubner at one point, swiping the hair from his eyes. I’m not good at math, I don’t know a lot of econometrics, and I also don’t know how to do theory. If you ask me about whether the stock market’s going to go up or down, if you ask me whether the economy’s going to grow or shrink, if you ask me whether deflation’s good or bad, if you ask me about taxes—I mean, it would be total fakery if I said I knew anything about any of those things. ” What interested Levitt were the stuff and riddles of everyday life. His investigations were a feast for anyone wanting to know how the world really works.
His singular attitude was evoked in Dubner’s resulting article: As Levitt sees it, economics is a science with excellent tools for gaining answers but a serious shortage of interesting questions. His particular gift is the ability to ask such questions. For instance: If drug dealers make so much money, why do they still live with their mothers? Which is more dangerous, a gun or a swimming pool? What really caused crime rates to plunge during the past decade? Do real-estate agents have their clients’ best interests at heart? Why do black parents give their children names that may hurt their career prospects?
Do schoolteachers cheat to meet high-stakes testing standards? Is sumo wrestling corrupt? And how does a homeless man in tattered clothing afford $50 headphones? Many people—including a fair number of his peers—might not recognize Levitt’s work as economics at all. But he has merely distilled the so-called dismal science to its most primal aim: explaining how people get what they want. Unlike most academics, he is unafraid of using personal observations and curiosities; he is also unafraid of anecdote and storytelling (but he is afraid of calculus). He is an intuitionist.
He sifts through a pile of data to find a story that no one else has found. He figures a way to measure an effect that veteran economists had declared unmeasurable. His abiding interests—though he says he has never trafficked in them himself—are cheating, corruption, and crime. Levitt’s blazing curiosity also proved attractive to thousands of New York Times readers. He was beset by questions and queries, riddles and requests—from General Motors and the New York Yankees and U. S. senators but also from prisoners and parents and a man who for twenty years had kept precise data on his sales of bagels.
A former Tour de France champion called Levitt to ask his help in proving that the current Tour is rife with doping; the Central Intelligence Agency wanted to know how Levitt might use data to catch money launderers and terrorists. What they were all responding to was the force of Levitt’s underlying belief: that the modern world, despite a surfeit of obfuscation, complication, and downright deceit, is not impenetrable, is not unknowable, and—if the right questions are asked—is even more intriguing than we think. All it takes is a new way of looking. In New York City, the publishers were telling Levitt he should write a book. “Write a book? he said. “I don’t want to write a book. ” He already had a million more riddles to solve than time to solve them. Nor did he think himself much of a writer. So he said that no, he wasn’t interested—“unless,” he proposed, “maybe Dubner and I could do it together. ” Collaboration isn’t for everyone. But the two of them—henceforth known as the two of us—decided to talk things over to see if such a book might work. We decided it could. We hope you agree. Levitt had an interview for the Society of Fellows, the venerable intellectual clubhouse at Harvard that pays young scholars to do their own work, for three years, with no commitments.
Levitt felt he didn’t stand a chance. For starters, he didn’t consider himself an intellectual. He would be interviewed over dinner by the senior fellows, a collection of world-renowned philosophers, scientists, and historians. He worried he wouldn’t have enough conversation to last even the first course. Disquietingly, one of the senior fellows said to Levitt, “I’m having a hard time seeing the unifying theme of your work. Could you explain it? ” Levitt was stymied. He had no idea what his unifying theme was, or if he even had one. Amartya Sen, the future Nobel-winning economist, jumped in and neatly summarized what he saw as Levitt’s theme.
Yes, Levitt said eagerly, that’s my theme. Another fellow then offered another theme. You’re right, said Levitt, my theme. And so it went, like dogs tugging at a bone, until the philosopher Robert Nozick interrupted. “How old are you, Steve? ” he asked. “Twenty-six. ” Nozick turned to the other fellows: “He’s twenty-six years old. Why does he need to have a unifying theme? Maybe he’s going to be one of those people who’s so talented he doesn’t need one. He’ll take a question and he’ll just answer it, and it’ll be fine. ” —THE NEW YORK TIMES MAGAZINE, AUGUST 3, 2003 INTRODUCTION: The Hidden Side of Everything
Anyone living in the United States in the early 1990s and paying even a whisper of attention to the nightly news or a daily paper could be forgiven for having been scared out of his skin. The culprit was crime. It had been rising relentlessly—a graph plotting the crime rate in any American city over recent decades looked like a ski slope in profile—and it seemed now to herald the end of the world as we knew it. Death by gunfire, intentional and otherwise, had become commonplace. So too had carjacking and crack dealing, robbery and rape. Violent crime was a gruesome, constant companion. And things were about to get even worse.
Much worse. All the experts were saying so. The cause was the so-called superpredator. For a time, he was everywhere. Glowering from the cover of newsweeklies. Swaggering his way through foot-thick government reports. He was a scrawny, big-city teenager with a cheap gun in his hand and nothing in his heart but ruthlessness. There were thousands out there just like him, we were told, a generation of killers about to hurl the country into deepest chaos. In 1995 the criminologist James Alan Fox wrote a report for the U. S. attorney general that grimly detailed the coming spike in murders by teenagers.
Fox proposed optimistic and pessimistic scenarios. In the optimistic scenario, he believed, the rate of teen homicides would rise another 15 percent over the next decade; in the pessimistic scenario, it would more than double. “The next crime wave will get so bad,” he said, “that it will make 1995 look like the good old days. ” Other criminologists, political scientists, and similarly learned forecasters laid out the same horrible future, as did President Clinton. “We know we’ve got about six years to turn this juvenile crime thing around,” Clinton said, “or our country is going to be living with chaos.
And my successors will not be giving speeches about the wonderful opportunities of the global economy; they’ll be trying to keep body and soul together for people on the streets of these cities. ” The smart money was plainly on the criminals. And then, instead of going up and up and up, crime began to fall. And fall and fall and fall some more. The crime drop was startling in several respects. It was ubiquitous, with every category of crime falling in every part of the country. It was persistent, with incremental decreases year after year. And it was entirely unanticipated—especially by the very experts who had been predicting the opposite.
The magnitude of the reversal was astounding. The teenage murder rate, instead of rising 100 percent or even 15 percent as James Alan Fox had warned, fell more than 50 percent within five years. By 2000 the overall murder rate in the United States had dropped to its lowest level in thirty-five years. So had the rate of just about every other sort of crime, from assault to car theft. Even though the experts had failed to anticipate the crime drop—which was in fact well under way even as they made their horrifying predictions—they now hurried to explain it. Most of their theories sounded perfectly logical.
It was the roaring 1990s economy, they said, that helped turn back crime. It was the proliferation of gun control laws, they said. It was the sort of innovative policing strategies put into place in New York City, where murders would fall from 2,245 in 1990 to 596 in 2003. These theories were not only logical; they were also encouraging, for they attributed the crime drop to specific and recent human initiatives. If it was gun control and clever police strategies and better-paying jobs that quelled crime—well then, the power to stop criminals had been within our reach all along.
As it would be the next time, God forbid, that crime got so bad. These theories made their way, seemingly without question, from the experts’ mouths to journalists’ ears to the public’s mind. In short course, they became conventional wisdom. There was only one problem: they weren’t true. There was another factor, meanwhile, that had greatly contributed to the massive crime drop of the 1990s. It had taken shape more than twenty years earlier and concerned a young woman in Dallas named Norma McCorvey.
Like the proverbial butterfly that flaps its wings on one continent and eventually causes a hurricane on another, Norma McCorvey dramatically altered the course of events without intending to. All she had wanted was an abortion. She was a poor, uneducated, unskilled, alcoholic, drug-using twenty-one-year-old woman who had already given up two children for adoption and now, in 1970, found herself pregnant again. But in Texas, as in all but a few states at that time, abortion was illegal. McCorvey’s cause came to be adopted by people far more powerful than she.
They made her the lead plaintiff in a class-action lawsuit seeking to legalize abortion. The defendant was Henry Wade, the Dallas County district attorney. The case ultimately made it to the U. S. Supreme Court, by which time McCorvey’s name had been disguised as Jane Roe. On January 22, 1973, the court ruled in favor of Ms. Roe, allowing legalized abortion throughout the country. By this time, of course, it was far too late for Ms. McCorvey/Roe to have her abortion. She had given birth and put the child up for adoption. (Years later she would renounce her allegiance to legalized abortion and become a pro-life activist. So how did Roe v. Wade help trigger, a generation later, the greatest crime drop in recorded history? As far as crime is concerned, it turns out that not all children are born equal. Not even close. Decades of studies have shown that a child born into an adverse family environment is far more likely than other children to become a criminal. And the millions of women most likely to have an abortion in the wake of Roe v. Wade—poor, unmarried, and teenage mothers for whom illegal abortions had been too expensive or too hard to get—were often models of adversity.
They were the very women whose children, if born, would have been much more likely than average to become criminals. But because of Roe v. Wade, these children weren’t being born. This powerful cause would have a drastic, distant effect: years later, just as these unborn children would have entered their criminal primes, the rate of crime began to plummet. It wasn’t gun control or a strong economy or new police strategies that finally blunted the American crime wave. It was, among other factors, the reality that the pool of potential criminals had dramatically shrunk.
Now, as the crime-drop experts (the former crime doomsayers) spun their theories to the media, how many times did they cite legalized abortion as a cause? Zero. It is the quintessential blend of commerce and camaraderie: you hire a real-estate agent to sell your home. She sizes up its charms, snaps some pictures, sets the price, writes a seductive ad, shows the house aggressively, negotiates the offers, and sees the deal through to its end. Sure, it’s a lot of work, but she’s getting a nice cut. On the sale of a $300,000 house, a typical 6 percent agent fee yields $18,000.
Eighteen thousand dollars, you say to yourself: that’s a lot of money. But you also tell yourself that you never could have sold the house for $300,000 on your own. The agent knew how to—what’s that phrase she used? — “maximize the house’s value. ” She got you top dollar, right? Right? A real-estate agent is a different breed of expert than a criminolo-gist, but she is every bit the expert. That is, she knows her field far better than the layman on whose behalf she is acting. She is better informed about the house’s value, the state of the housing market, even the buyer’s frame of mind. You depend on her for this information.
That, in fact, is why you hired an expert. As the world has grown more specialized, countless such experts have made themselves similarly indispensable. Doctors, lawyers, contractors, stockbrokers, auto mechanics, mortgage brokers, financial planners: they all enjoy a gigantic informational advantage. And they use that advantage to help you, the person who hired them, get exactly what you want for the best price. Right? It would be lovely to think so. But experts are human, and humans respond to incentives. How any given expert treats you, therefore, will depend on how that expert’s incentives are set up.
Sometimes his incentives may work in your favor. For instance: a study of California auto mechanics found they often passed up a small repair bill by letting failing cars pass emissions inspections—the reason being that lenient mechanics are rewarded with repeat business. But in a different case, an expert’s incentives may work against you. In a medical study, it turned out that obstetricians in areas with declining birth rates are much more likely to perform cesarean-section deliveries than obstetricians in growing areas—suggesting that, when business is tough, doctors try to ring up more expensive procedures.
It is one thing to muse about experts’ abusing their position and another to prove it. The best way to do so would be to measure how an expert treats you versus how he performs the same service for himself. Unfortunately a surgeon doesn’t operate on himself. Nor is his medical file a matter of public record; neither is an auto mechanic’s repair log for his own car. Real-estate sales, however, are a matter of public record. And real-estate agents often do sell their own homes.
A recent set of data covering the sale of nearly 100,000 houses in suburban Chicago shows that more than 3,000 of those houses were owned by the agents themselves. Before plunging into the data, it helps to ask a question: what is the real-estate agent’s incentive when she is selling her own home’simple: to make the best deal possible. Presumably this is also your incentive when you are selling your home. And so your incentive and the real-estate agent’s incentive would seem to be nicely aligned. Her commission, after all, is based on the sale price. But as incentives go, commissions are tricky.
First of all, a 6 percent real-estate commission is typically split between the seller’s agent and the buyer’s. Each agent then kicks back half of her take to the agency. Which means that only 1. 5 percent of the purchase price goes directly into your agent’s pocket. So on the sale of your $300,000 house, her personal take of the $18,000 commission is $4,500. Still not bad, you say. But what if the house was actually worth more than $300,000? What if, with a little more effort and patience and a few more newspaper ads, she could have sold it for $310,000?
After the commission, that puts an additional $9,400 in your pocket. But the agent’s additional share—her personal 1. 5 percent of the extra $10,000—is a mere $150. If you earn $9,400 while she earns only $150, maybe your incentives aren’t aligned after all. (Especially when she’s the one paying for the ads and doing all the work. ) Is the agent willing to put out all that extra time, money, and energy for just $150? There’s one way to find out: measure the difference between the sales data for houses that belong to real-estate agents themselves and the houses they sold on behalf of clients.
Using the data from the sales of those 100,000 Chicago homes, and controlling for any number of variables—location, age and quality of the house, aesthetics, and so on—it turns out that a real-estate agent keeps her own home on the market an average of ten days longer and sells it for an extra 3-plus percent, or $10,000 on a $300,000 house. When she sells her own house, an agent holds out for the best offer; when she sells yours, she pushes you to take the first decent offer that comes along. Like a stockbroker churning commissions, she wants to make deals and make them fast. Why not?
Her share of a better offer—$150—is too puny an incentive to encourage her to do otherwise. Of all the truisms about politics, one is held to be truer than the rest: money buys elections. Arnold Schwarzenegger, Michael Bloomberg, Jon Corzine—these are but a few recent, dramatic examples of the truism at work. (Disregard for a moment the contrary examples of Howard Dean, Steve Forbes, Michael Huffington, and especially Thomas Golisano, who over the course of three gubernatorial elections in New York spent $93 million of his own money and won 4 percent, 8 percent, and 14 percent, respectively, of the vote. Most people would agree that money has an undue influence on elections and that far too much money is spent on political campaigns. Indeed, election data show it is true that the candidate who spends more money in a campaign usually wins. But is money the cause of the victory? It might seem logical to think so, much as it might have seemed logical that a booming 1990s economy helped reduce crime. But just because two things are correlated does not mean that one causes the other. A correlation simply means that a relationship exists between two factors—let’s call them X and Y—but it tells you nothing about the direction of that relationship.
It’s possible that X causes Y; it’s also possible that Y causes X; and it may be that X and Y are both being caused by some other factor, Z. Think about this correlation: cities with a lot of murders also tend to have a lot of police officers. Consider now the police/murder correlation in a pair of real cities. Denver and Washington, D. C. , have about the same population—but Washington has nearly three times as many police as Denver, and it also has eight times the number of murders. Unless you have more information, however, it’s hard to say what’s causing what.
Someone who didn’t know better might contemplate these figures and conclude that it is all those extra police in Washington who are causing the extra murders. Such wayward thinking, which has a long history, generally provokes a wayward response. Consider the folktale of the czar who learned that the most disease-ridden province in his empire was also the province with the most doctors. His solution? He promptly ordered all the doctors shot dead. Now, returning to the issue of campaign spending: in order to figure out the relationship between money and elections, it helps to consider the incentives at play in campaign finance.
Let’s say you are the kind of person who might contribute $1,000 to a candidate. Chances are you’ll give the money in one of two situations: a close race, in which you think the money will influence the outcome; or a campaign in which one candidate is a sure winner and you would like to bask in reflected glory or receive some future in-kind consideration. The one candidate you won’t contribute to is a sure loser. (Just ask any presidential hopeful who bombs in Iowa and New Hampshire. ) So front-runners and incumbents raise a lot more money than long shots. And what about spending that money?
Incumbents and front-runners obviously have more cash, but they only spend a lot of it when they stand a legitimate chance of losing; otherwise, why dip into a war chest that might be more useful later on, when a more formidable opponent appears? Now picture two candidates, one intrinsically appealing and the other not so. The appealing candidate raises much more money and wins easily. But was it the money that won him the votes, or was it his appeal that won the votes and the money? That’s a crucial question but a very hard one to answer. Voter appeal, after all, isn’t easy to quantify. How can it be measured?
It can’t, really—except in one special case. The key is to measure a candidate against…himself. That is, Candidate A today is likely to be similar to Candidate A two or four years hence. The same could be said for Candidate B. If only Candidate A ran against Candidate B in two consecutive elections but in each case spent different amounts of money. Then, with the candidates’ appeal more or less constant, we could measure the money’s impact. As it turns out, the same two candidates run against each other in consecutive elections all the time—indeed, in nearly a thousand U. S. congressional races since 1972.
What do the numbers have to say about such cases? Here’s the surprise: the amount of money spent by the candidates hardly matters at all. A winning candidate can cut his spending in half and lose only 1 percent of the vote. Meanwhile, a losing candidate who doubles his spending can expect to shift the vote in his favor by only that same 1 percent. What really matters for a political candidate is not how much you spend; what matters is who you are. (The same could be said—and will be said, in chapter 5—about parents. ) Some politicians are inherently attractive to voters and others simply aren’t, and no amount of money can do much about it. Messrs. Dean, Forbes, Huffington, and Golisano already know this, of course. ) And what about the other half of the election truism—that the amount of money spent on campaign finance is obscenely huge? In a typical election period that includes campaigns for the presidency, the Senate, and the House of Representatives, about $1 billion is spent per year—which sounds like a lot of money, unless you care to measure it against something seemingly less important than democratic elections. It is the same amount, for instance, that Americans spend every year on chewing gum.
This isn’t a book about the cost of chewing gum versus campaign spending per se, or about disingenuous real-estate agents, or the impact of legalized abortion on crime. It will certainly address these scenarios and dozens more, from the art of parenting to the mechanics of cheating, from the inner workings of the Ku Klux Klan to racial discrimination on The Weakest Link. What this book is about is stripping a layer or two from the surface of modern life and seeing what is happening underneath. We will ask a lot of questions, some frivolous and some about life-and-death issues.
The answers may often seem odd but, after the fact, also rather obvious. We will seek out these answers in the data—whether those data come in the form of schoolchildren’s test scores or New York City’s crime statistics or a crack dealer’s financial records. (Often we will take advantage of patterns in the data that were incidentally left behind, like an airplane’s sharp contrail in a high sky. ) It is well and good to opine or theorize about a subject, as humankind is wont to do, but when moral posturing is replaced by an honest assessment of the data, the result is often a new, surprising insight.
Morality, it could be argued, represents the way that people would like the world to work—whereas economics represents how it actually does work. Economics is above all a science of measurement. It comprises an extraordinarily powerful and flexible set of tools that can reliably assess a thicket of information to determine the effect of any one factor, or even the whole effect. That’s what “the economy” is, after all: a thicket of information about jobs and real estate and banking and investment. But the tools of economics can be just as easily applied to subjects that are more—well, more interesting.
This book, then, has been written from a very specific worldview, based on a few fundamental ideas: Incentives are the cornerstone of modern life. And understanding them—or, often, ferreting them out—is the key to solving just about any riddle, from violent crime to sports cheating to online dating. The conventional wisdom is often wrong. Crime didn’t keep soaring in the 1990s, money alone doesn’t win elections, and—surprise—drinking eight glasses of water a day has never actually been shown to do a thing for your health.
Conventional wisdom is often shoddily formed and devilishly difficult to see through, but it can be done. Dramatic effects often have distant, even subtle, causes. The answer to a given riddle is not always right in front of you. Norma McCorvey had a far greater impact on crime than did the combined forces of gun control, a strong economy, and innovative police strategies. So did, as we shall see, a man named Oscar Danilo Blandon, aka the Johnny Appleseed of Crack. “Experts”—from criminologists to real-estate agents—use their informational advantage to serve their own agenda.
However, they can be beat at their own game. And in the face of the Internet, their informational advantage is shrinking every day—as evidenced by, among other things, the falling price of coffins and life-insurance premiums. Knowing what to measure and how to measure it makes a complicated world much less so. If you learn how to look at data in the right way, you can explain riddles that otherwise might have seemed impossible. Because there is nothing like the sheer power of numbers to scrub away layers of confusion and contradiction.
So the aim of this book is to explore the hidden side of…everything. This may occasionally be a frustrating exercise. It may sometimes feel as if we are peering at the world through a straw or even staring into a funhouse mirror; but the idea is to look at many different scenarios and examine them in a way they have rarely been examined. In some regards, this is a strange concept for a book. Most books put forth a single theme, crisply expressed in a sentence or two, and then tell the entire story of that theme: the history of salt; the fragility of democracy; the use and misuse of punctuation.
This book boasts no such unifying theme. We did consider, for about six minutes, writing a book that would revolve around a single theme—the theory and practice of applied microeconomics, anyone? —but opted instead for a sort of treasure-hunt approach. Yes, this approach employs the best analytical tools that economics can offer, but it also allows us to follow whatever freakish curiosities may occur to us. Thus our invented field of study: Freakonomics. The sort of stories told in this book are not often covered in Econ. 101, but that may change.
Since the science of economics is primarily a set of tools, as opposed to a subject matter, then no subject, however offbeat, need be beyond its reach. It is worth remembering that Adam Smith, the founder of classical economics, was first and foremost a philosopher. He strove to be a moralist and, in doing so, became an economist. When he published The Theory of Moral Sentiments in 1759, modern capitalism was just getting under way. Smith was entranced by the sweeping changes wrought by this new force, but it wasn’t only the numbers that interested him.
It was the human effect, the fact that economic forces were vastly changing the way a person thought and behaved in a given situation. What might lead one person to cheat or steal while another didn’t? How would one person’s seemingly innocuous choice, good or bad, affect a great number of people down the line? In Smith’s era, cause and effect had begun to wildly accelerate; incentives were magnified tenfold. The gravity and shock of these changes were as overwhelming to the citizens of his time as the gravity and shock of modern life seem to us today.
Smith’s true subject was the friction between individual desire and societal norms. The economic historian Robert Heilbroner, writing in The Worldly Philosophers, wondered how Smith was able to separate the doings of man, a creature of self-interest, from the greater moral plane in which man operated. “Smith held that the answer lay in our ability to put ourselves in the position of a third person, an impartial observer,” Heilbroner wrote, “and in this way to form a notion of the objective…merits of a case. Consider yourself, then, in the company of a third person—or, if you will, a pair of third people—eager to explore the objective merits of interesting cases. These explorations generally begin with the asking of a simple unasked question. Such as: what do schoolteachers and sumo wrestlers have in common? “I’d like to put together a set of tools that let us catch terrorists,” Levitt said. “I don’t necessarily know yet how I’d go about it. But given the right data, I have little doubt that I could figure out the answer. ” It might seem absurd for an economist to dream of catching terrorists.
Just as it must have seemed absurd if you were a Chicago schoolteacher, called into an office and told that, ahem, the algorithms designed by that skinny man with thick glasses had determined that you are a cheater. And that you are being fired. Steven Levitt may not fully believe in himself, but he does believe in this: teachers and criminals and real-estate agents may lie, and politicians, and even CIA analysts. But numbers don’t. —THE N EW Y ORK T IMES M AGAZINE, AUGUST 3, 2003 1 What Do Schoolteachers and Sumo Wrestlers Have in Common? Imagine for a moment that you are the manager of a day-care center.
You have a clearly stated policy that children are supposed to be picked up by 4 p. m. But very often parents are late. The result: at day’s end, you have some anxious children and at least one teacher who must wait around for the parents to arrive. What to do? A pair of economists who heard of this dilemma—it turned out to be a rather common one—offered a solution: fine the tardy parents. Why, after all, should the day-care center take care of these kids for free? The economists decided to test their solution by conducting a study of ten day-care centers in Haifa, Israel.
The study lasted twenty weeks, but the fine was not introduced immediately. For the first four weeks, the economists simply kept track of the number of parents who came late; there were, on average, eight late pickups per week per day-care center. In the fifth week, the fine was enacted. It was announced that any parent arriving more than ten minutes late would pay $3 per child for each incident. The fee would be added to the parents’ monthly bill, which was roughly $380. After the fine was enacted, the number of late pickups promptly went…up. Before long there were twenty late pickups per week, more than double the original average.
The incentive had plainly backfired. Economics is, at root, the study of incentives: how people get what they want, or need, especially when other people want or need the same thing. Economists love incentives. They love to dream them up and enact them, study them and tinker with them. The typical economist believes the world has not yet invented a problem that he cannot fix if given a free hand to design the proper incentive scheme. His solution may not always be pretty—it may involve coercion or exorbitant penalties or the violation of civil liberties— but the original problem, rest assured, will be fixed.
An incentive is a bullet, a lever, a key: an often tiny object with astonishing power to change a situation. We all learn to respond to incentives, negative and positive, from the outset of life. If you toddle over to the hot stove and touch it, you burn a finger. But if you bring home straight A’s from school, you get a new bike. If you are spotted picking your nose in class, you get ridiculed. But if you make the basketball team, you move up the social ladder. If you break curfew, you get grounded. But if you ace your SATs, you get to go to a good college.
If you flunk out of law school, you have to go to work at your father’s insurance company. But if you perform so well that a rival company comes calling, you become a vice president and no longer have to work for your father. If you become so excited about your new vice president job that you drive home at eighty mph, you get pulled over by the police and fined $100. But if you hit your sales projections and collect a year-end bonus, you not only aren’t worried about the $100 ticket but can also afford to buy that Viking range you’ve always wanted—and on which your toddler can now burn her own finger.
An incentive is simply a means of urging people to do more of a good thing and less of a bad thing. But most incentives don’t come about organically. Someone—an economist or a politician or a parent—has to invent them. Your three-year-old eats all her vegetables for a week’she wins a trip to the toy store. A big steelmaker belches too much smoke into the air? The company is fined for each cubic foot of pollutants over the legal limit. Too many Americans aren’t paying their share of income tax? It was the economist Milton Friedman who helped come up with a solution to this one: automatic tax withholding from employees’ paychecks.
There are three basic flavors of incentive: economic, social, and moral. Very often a single incentive scheme will include all three varieties. Think about the anti-smoking campaign of recent years. The addition of a $3-per-pack “sin tax” is a strong economic incentive against buying cigarettes. The banning of cigarettes in restaurants and bars is a powerful social incentive. And when the U. S. government asserts that terrorists raise money by selling black-market cigarettes, that acts as a rather jarring moral incentive. Some of the most compelling incentives yet invented have been put in place to deter crime.
Considering this fact, it might be worthwhile to take a familiar question—why is there so much crime in modern society? —and stand it on its head: why isn’t there a lot more crime? After all, every one of us regularly passes up opportunities to maim, steal, and defraud. The chance of going to jail—thereby losing your job, your house, and your freedom, all of which are essentially economic penalties—is certainly a strong incentive. But when it comes to crime, people also respond to moral incentives (they don’t want to do something they consider wrong) and social incentives (they don’t want to be seen by others as doing something wrong).
For certain types of misbehavior, social incentives are terribly powerful. In an echo of Hester Prynne’s scarlet letter, many American cities now fight prostitution with a “shaming” offensive, posting pictures of convicted johns (and prostitutes) on websites or on local-access television. Which is a more horrifying deterrent: a $500 fine for soliciting a prostitute or the thought of your friends and family ogling you on www. HookersAndJohns. com. So through a complicated, haphazard, and constantly readjusted web of economic, social, and moral incentives, modern society does its best to militate against crime.
Some people would argue that we don’t do a very good job. But taking the long view, that is clearly not true. Consider the historical trend in homicide (not including wars), which is both the most reliably measured crime and the best barometer of a society’s overall crime rate. These statistics, compiled by the criminologist Manuel Eisner, track the historical homicide levels in five European regions. HOMICIDES (per 100,000 People) NETHERLANDS ENGLAND AND BELGIUM 13th 23. 0 and 14th c. 15th c. n. a. 16th c. 7. 0 17th c. 5. 0 18th c. 1. 5 19th c. 1. 7 1900– 0. 8 1949 47. GERMANY AND SCANDINAVIA SWITZERLAND n. a. 37. 0 ITALY 56. 0 45. 0 25. 0 7. 5 5. 5 1. 6 1. 5 46. 0 21. 0 18. 0 1. 9 1. 1 0. 7 16. 0 11. 0 7. 0 7. 5 2. 8 1. 7 73. 0 47. 0 32. 0 10. 5 12. 6 3. 2 1950– 0. 9 1994 0. 9 0. 9 1. 0 1. 5 The steep decline of these numbers over the centuries suggests that, for one of the gravest human concerns—getting murdered—the incentives that we collectively cook up are working better and better. So what was wrong with the incentive at the Israeli day-care centers? You have probably already guessed that the $3 fine was simply too small.
For that price, a parent with one child could afford to be late every day and only pay an extra $60 each month—just one-sixth of the base fee. As babysitting goes, that’s pretty cheap. What if the fine had been set at $100 instead of $3? That would have likely put an end to the late pickups, though it would have also engendered plenty of ill will. (Any incentive is inherently a trade-off; the trick is to balance the extremes. ) But there was another problem with the day-care center fine. It substituted an economic incentive (the $3 penalty) for a moral incentive (the guilt that parents were supposed to feel when they came late).
For just a few dollars each day, parents could buy off their guilt. Furthermore, the small size of the fine sent a signal to the parents that late pickups weren’t such a big problem. If the day-care center suffers only $3 worth of pain for each late pickup, why bother to cut short the tennis game? Indeed, when the economists eliminated the $3 fine in the seventeenth week of their study, the number of late-arriving parents didn’t change. Now they could arrive late, pay no fine, and feel no guilt. Such is the strange and powerful nature of incentives. A slight tweak can produce drastic and often unforeseen results.
Thomas Jefferson noted this while reflecting on the tiny incentive that led to the Boston Tea Party and, in turn, the American Revolution: “So inscrutable is the arrangement of causes and consequences in this world that a two-penny duty on tea, unjustly imposed in a sequestered part of it, changes the condition of all its inhabitants. ” In the 1970s, researchers conducted a study that, like the Israeli day-care study, pitted a moral incentive against an economic incentive. In this case, they wanted to learn about the motivation behind blood donations.
Their discovery: when people are given a small stipend for donating blood rather than simply being praised for their altruism, they tend to donate less blood. The stipend turned a noble act of charity into a painful way to make a few dollars, and it wasn’t worth it. What if the blood donors had been offered an incentive of $50, or $500, or $5,000’surely the number of donors would have changed dramatically. But something else would have changed dramatically as well, for every incentive has its dark side. If a pint of blood were suddenly worth $5,000, you can be sure that plenty of people would take note.
They might literally steal blood at knifepoint. They might pass off pig blood as their own. They might circumvent donation limits by using fake IDs. Whatever the incentive, whatever the situation, dishonest people will try to gain an advantage by whatever means necessary. Or, as W. C. Fields once said: a thing worth having is a thing worth cheating for. Who cheats? Well, just about anyone, if the stakes are right. You might say to yourself, I don’t cheat, regardless of the stakes. And then you might remember the time you cheated on, say, a board game. Last week.
Or the golf ball you nudged out of its bad lie. Or the time you really wanted a bagel in the office break room but couldn’t come up with the dollar you were supposed to drop in the coffee can. And then took the bagel anyway. And told yourself you’d pay double the next time. And didn’t. For every clever person who goes to the trouble of creating an incentive scheme, there is an army of people, clever and otherwise, who will inevitably spend even more time trying to beat it. Cheating may or may not be human nature, but it is certainly a prominent feature in just about every human endeavor.
Cheating is a primordial economic act: getting more for less. So it isn’t just the boldface names—inside-trading CEOs and pillpopping ballplayers and perk-abusing politicians—who cheat. It is the waitress who pockets her tips instead of pooling them. It is the Wal-Mart payroll manager who goes into the computer and shaves his employees’ hours to make his own performance look better. It is the third grader who, worried about not making it to the fourth grade, copies test answers from the kid sitting next to him. Some cheating leaves barely a shadow of evidence. In other cases, the evidence is massive.
Consider what happened one spring evening at midnight in 1987: seven million American children suddenly disappeared. The worst kidnapping wave in history? Hardly. It was the night of April 15, and the Internal Revenue Service had just changed a rule. Instead of merely listing each dependent child, tax filers were now required to provide a Social Security number for each child. Suddenly, seven million children—children who had existed only as phantom exemptions on the previous year’s 1040 forms—vanished, representing about one in ten of all dependent children in the United States.
The incentive for those cheating taxpayers was quite clear. The same for the waitress, the payroll manager, and the third grader. But what about that third grader’s teacher? Might she have an incentive to cheat? And if so, how would she do it? Imagine now that instead of running a day-care center in Haifa, you are running the Chicago Public Schools, a system that educates 400,000 students each year. The most volatile current debate among American school administrators, teachers, parents, and students concerns “high-stakes” testing.
The stakes are considered high because instead of simply testing students to measure their progress, schools are increasingly held accountable for the results. The federal government mandated high-stakes testing as part of the No Child Left Behind law, signed by President Bush in 2002. But even before that law, most states gave annual standardized tests to students in elementary and secondary school. Twenty states rewarded individual schools for good test scores or dramatic improvement; thirty-two states sanctioned the schools that didn’t do well. The Chicago Public School system embraced high-stakes testing in 1996.
Under the new policy, a school with low reading scores would be placed on probation and face the threat of being shut down, its staff to be dismissed or reassigned. The CPS also did away with what is known as social promotion. In the past, only a dramatically inept or difficult student was held back a grade. Now, in order to be promoted, every student in third, sixth, and eighth grade had to manage a minimum score on the standardized, multiplechoice exam known as the Iowa Test of Basic Skills. Advocates of high-stakes testing argue that it raises the standards of learning and gives students more incentive to study.
Also, if the test prevents poor students from advancing without merit, they won’t clog up the higher grades and slow down good students. Opponents, meanwhile, worry that certain students will be unfairly penalized if they don’t happen to test well, and that teachers may concentrate on the test topics at the exclusion of more important lessons. Schoolchildren, of course, have had incentive to cheat for as long as there have been tests. But high-stakes testing has so radically changed the incentives for teachers that they too now have added reason to cheat.
With high-stakes testing, a teacher whose students test poorly can be censured or passed over for a raise or promotion. If the entire school does poorly, federal funding can be withheld; if the school is put on probation, the teacher stands to be fired. High-stakes testing also presents teachers with some positive incentives. If her students do well enough, she might find herself praised, promoted, and even richer: the state of California at one point introduced bonuses of $25,000 for teachers who produced big test-score gains.
And if a teacher were to survey this newly incentivized landscape and consider somehow inflating her students’ scores, she just might be persuaded by one final incentive: teacher cheating is rarely looked for, hardly ever detected, and just about never punished. How might a teacher go about cheating? There are any number of possibilities, from the brazen to the sophisticated. A fifth-grade student in Oakland recently came home from school and gaily told her mother that her super-nice teacher had written the answers to the state exam right there on the chalkboard.
Such instances are certainly rare, for placing your fate in the hands of thirty prepubescent witnesses doesn’t seem like a risk that even the worst teacher would take. (The Oakland teacher was duly fired. ) There are more subtle ways to inflate students’ scores. A teacher can simply give students extra time to complete the test. If she obtains a copy of the exam early—that is, illegitimately—she can prepare them for specific questions. More broadly, she can “teach to the test,” basing her lesson plans on questions from past years’ exams, which isn’t considered cheating but certainly violates the spirit of the test.
Since these tests all have multiple-choice answers, with no penalty for wrong guesses, a teacher might instruct her students to randomly fill in every blank as the clock is winding down, perhaps inserting a long string of Bs or an alternating pattern of Bs and Cs. She might even fill in the blanks for them after they’ve left the room. But if a teacher really wanted to cheat—and make it worth her while—she might collect her students’ answer sheets and, in the hour or so before turning them in to be read by an electronic scanner, erase the wrong answers and fill in correct ones. And you always thought that no. 2 pencil was for the children to change their answers. ) If this kind of teacher cheating is truly going on, how might it be detected? To catch a cheater, it helps to think like one. If you were willing to erase your students’ wrong answers and fill in correct ones, you probably wouldn’t want to change too many wrong answers. That would clearly be a tip-off. You probably wouldn’t even want to change answers on every student’s test—another tip-off. Nor, in all likelihood, would you have enough time, because the answer sheets are turned in soon after the test is over.
So what you might do is select a string of eight or ten consecutive questions and fill in the correct answers for, say, one-half or two-thirds of your students. You could easily memorize a short pattern of correct answers, and it would be a lot faster to erase and change that pattern than to go through each student’s answer sheet individually. You might even think to focus your activity toward the end of the test, where the questions tend to be harder than the earlier questions. In that way, you’d be most likely to substitute correct answers for wrong ones.
If economics is a science primarily concerned with incentives, it is also—fortunately—a science with statistical tools to measure how people respond to those incentives. All you need are some data. In this case, the Chicago Public School system obliged. It made available a database of the test answers for every CPS student from third grade through seventh grade from 1993 to 2000. This amounts to roughly 30,000 students per grade per year, more than 700,000 sets of test answers, and nearly 100 million individual answers. The data, organized by classroom, included each student’s question-by-question answer strings for reading and math tests. The actual paper answer sheets were not included; they were habitually shredded soon after a test. ) The data also included some information about each teacher and demographic information for every student, as well as his or her past and future test scores—which would prove a key element in detecting the teacher cheating. Now it was time to construct an algorithm that could tease some conclusions from this mass of data. What might a cheating teacher’s classroom look like? The first thing to search for would be unusual answer patterns in a given classroom: blocks of identical answers, for instance, especially among the harder questions.
If ten very bright students (as indicated by past and future test scores) gave correct answers to the exam’s first five questions (typically the easiest ones), such an identical block shouldn’t be considered suspicious. But if ten poor students gave correct answers to the last five questions on the exam (the hardest ones), that’s worth looking into. Another red flag would be a strange pattern within any one student’s exam—such as getting the hard questions right while missing the easy ones—especially when measured against the thousands of students in other classrooms who scored similarly on the same test.
Furthermore, the algorithm would seek out a classroom full of students who performed far better than their past scores would have predicted and who then went on to score significantly lower the following year. A dramatic one-year spike in test scores might initially be attributed to a good teacher; but with a dramatic fall to follow, there’s a strong likelihood that the spike was brought about by artificial means. Consider now the answer strings from the students in two sixth-grade Chicago classrooms who took the identical math test.
Each horizontal row represents one student’s answers. The letter a, b, c, or d indicates a correct answer; a number indicates a wrong answer, with 1 corresponding to a, 2 corresponding to b, and so on. A zero represents an answer that was left blank. One of these classrooms almost certainly had a cheating teacher and the other did not. Try to tell the difference—although be forewarned that it’s not easy with the naked eye.
Classroom A 112a4a342cb214d0001acd24a3a12dadbcb4a0000000 d4a2341cacbddad3142a2344a2ac23421c00adb4b3cb 1b2a34d4ac42d23b141acd24a3a12dadbcb4a2134141 dbaab3dcacb1dadbc42ac2cc31012dadbcb4adb40000 d12443d43232d32323c213c22d2c23234c332db4b300 db2abad1acbdda212b1acd24a3a12dadbcb400000000 d4aab2124cbddadbcb1a42cca3412dadbcb423134bc1 1b33b4d4a2b1dadbc3ca22c000000000000000000000 d43a3a24acb1d32b412acd24a3a12dadbcb422143bc0 313a3ad1ac3d2a23431223c000012dadbcb400000000 db2a33dcacbd32d313c21142323cc300000000000000 d43ab4d1ac3dd43421240d24a3a12dadbcb400000000 b223a24acb11a3b24cacd12a241cdadbcb4adb4b300 db4abadcacb1dad3141ac212a3a1c3a144ba2db41b43 1142340c2cbddadb4b1acd24a3a12dadbcb43d133bc4 214ab4dc4cbdd31b1b2213c4ad412dadbcb4adb00000 423b4d4a23d24131413234123a243a2413a21441343 3b3ab4d14c3d2ad4cbcac1c003a12dadbcb4adb40000 dba2ba21ac3d2ad3c4c4cd40a3a12dadbcb400000000 d122ba2cacbd1a13211a2d02a2412d0dbcb4adb4b3c0 144a3adc4cbddadbcbc2c2cc43a12dadbcb4211ab343 d43aba3cacbddadbcbca42c2a3212dadbcb42344b3cb Classroom B db3a431422bd131b4413cd422a1acda332342d3ab4c4 d1aa1a11acb2d3dbc1ca22c23242c3a142b3adb243c1 d42a12d2a4b1d32b21ca2312a3411d00000000000000 3b2a34344c32d21b1123cdc000000000000000000000 34aabad12cbdd3d4c1ca112cad2ccd00000000000000 d33a3431a2b2d2d44b2acd2cad2c2223b40000000000 23aa32d2a1bd2431141342c13d212d233c34a3b3b000 d32234d4a1bdd23b242a22c2a1a1cda2b1baa33a0000 d3aab23c4cbddadb23c322c2a222223232b443b24bc 3d13a14313c31d42b14c421c42332cd2242b3433a3343 d13a3ad122b1da2b11242dc1a3a12100000000000000 d12a3ad1a13d23d3cb2a21ccada24d2131b440000000 314a133c4cbd142141ca424cad34c122413223ba4b40 d42a3adcacbddadbc42ac2c2ada2cda341baa3b24321 db1134dc2cb2dadb24c412c1ada2c3a341ba20000000 d1341431acbddad3c4c213412da22d3d1132a1344b1b 1ba41a21a1b2dadb24ca22c1ada2cd32413200000000 dbaa33d2a2bddadbcbca11c2a2accda1b2ba20000000 If you guessed that classroom A was the cheating classroom, congratulations. Here again are the answer strings from classroom A, now reordered by a computer that has been asked to apply the cheating algorithm and seek out suspicious patterns. Classroom A (With cheating algorithm applied) 1. 112a4a342cb214d0001acd24a3a12dadbcb4a0000000 2. b2a34d4ac42d23b141acd24a3a12dadbcb4a2134141 3. db2abad1acbdda212b1acd24a3a12dadbcb400000000 4. d43a3a24acb1d32b412acd24a3a12dadbcb422143bc0 5. d43ab4d1ac3dd43421240d24a3a12dadbcb400000000 6. 1142340c2cbddadb4b1acd24a3a12dadbcb43d133bc4 7. dba2ba21ac3d2ad3c4c4cd40a3a12dadbcb400000000 8. 144a3adc4cbddadbcbc2c2cc43a12dadbcb4211ab343 9. 3b3ab4d14c3d2ad4cbcac1c003a12dadbcb4adb40000 10. d43aba3cacbddadbcbca42c2a3212dadbcb42344b3cb 11. 214ab4dc4cbdd31b1b2213c4ad412dadbcb4adb00000 12. 313a3ad1ac3d2a23431223c000012dadbcb400000000 13. d4aab2124cbddadbcb1a42cca3412dadbcb423134bc1 14. dbaab3dcacb1dadbc42ac2cc31012dadbcb4adb40000 15. db223a24acb11a3b24cacd12a241cdadbcb4adb4b300 16. 122ba2cacbd1a13211a2d02a2412d0dbcb4adb4b3c0 17. 1423b4d4a23d24131413234123a243a2413a21441343 18. db4abadcacb1dad3141ac212a3a1c3a144ba2db41b43 19. db2a33dcacbd32d313c21142323cc300000000000000 20. 1b33b4d4a2b1dadbc3ca22c000000000000000000000 21. d12443d43232d32323c213c22d2c23234c332db4b300 22. d4a2341cacbddad3142a2344a2ac23421c00adb4b3cb Take a look at the answers in bold. Did fifteen out of twenty-two students somehow manage to reel off the same six consecutive correct answers (the d-a-d-b-c-b string) all by themselves? There are at least four reasons this is unlikely. One: those questions, coming near the end of the test, were harder than the earlier questions.
Two: these were mainly subpar students to begin with, few of whom got six consecutive right answers elsewhere on the test, making it all the more unlikely they would get right the same six hard questions. Three: up to this point in the test, the fifteen students’ answers were virtually uncorrelated. Four: three of the students (numbers 1, 9, and 12) left at least one answer blank before the suspicious string and then ended the test with another string of blanks. This suggests that a long, unbroken string of blank answers was broken not by the student but by the teacher. There is another oddity about the suspicious answer string. On nine of the fifteen tests, the six correct answers are preceded by another identical string, 3-a-1-2, which includes three of four incorrect answers. And on all fifteen tests, the six correct answers are ollowed by the same incorrect answer, a 4. Why on earth would a cheating teacher go to the trouble of erasing a student’s test sheet and then fill in the wrong answer? Perhaps she is merely being strategic. In case she is caught and hauled into the principal’s office, she could point to the wrong answers as proof that she didn’t cheat. Or perhaps— and this is a less charitable but just as likely answer—she doesn’t know the right answers herself. (With standardized tests, the teacher is typically not given an answer key. ) If this is the case, then we have a pretty good clue as to why her students are in need of inflated grades in the first place: they have a bad teacher.
Another indication of teacher cheating in classroom A is the class’s overall performance. As sixth graders who were taking the test in the eighth month of the academic year, these students needed to achieve an average score of 6. 8 to be considered up to national standards. (Fifth graders taking the test in the eighth month of the year needed to score 5. 8, seventh graders 7. 8, and so on. ) The students in classroom A averaged 5. 8 on their sixth-grade tests, which is a full grade level below where they should be. So plainly these are poor students. A year earlier, however, these students did even worse, averaging just 4. 1 on their fifth-grade tests.
Instead of improving by one full point between fifth and sixth grade, as would be expected, they improved by 1. 7 points, nearly two grades’ worth. But this miraculous improvement was short-lived. When these sixth-grade students reached seventh grade, they averaged 5. 5—more than two grade levels below standard and even worse than they did in sixth grade. Consider the erratic year-to-year scores of three particular students from classroom A: 5TH GRADE SCORE Student 3 Student 6 3. 0 3. 6 6TH GRADE SCORE 6. 5 6. 3 7. 1 7TH GRADE SCORE 5. 1 4. 9 5. 6 Student 14 3. 8 The three-year scores from classroom B, meanwhile, are also poor but at least indicate an honest effort: 4. 2, 5. 1, and 6. 0.
So an entire roomful of children in classroom A suddenly got very smart one year and very dim the next, or more likely, their sixth-grade teacher worked some magic with a no. 2 pencil. There are two noteworthy points to be made about the children in classroom A, tangential to the cheating itself. The first is that they are obviously in terrible academic shape, which makes them the very children whom high-stakes testing is promoted as helping the most. The second point is that these students would be in for a terrible shock once they reached the seventh grade. All they knew was that they had been successfully promoted due to their test scores. (No child left behind, indeed. They weren’t the
