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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