Misbehaving: The Making of Behavioral Economics (3 page)

This book is the story of how this happened, at least as I have seen it. Although I did not do all the research—as you know, I am too lazy for that—I was around at the beginning and have been part of the movement that created this field. Following Amos’s dictum, there will be many stories to come, but my main goals are tell the tale of how it all happened, and to explain some of the things we learned along the way. Not surprisingly, there have been numerous squabbles with traditionalists who defended the usual way of doing economics. Those squabbles were not always fun at the time, but like a bad travel experience, they make for good stories after the fact, and the necessity of fighting those battles has made the field stronger.

Like any story, this one does not follow a straight-line progression with one idea leading naturally to another. Many ideas were percolating at different times and at different speeds. As a result, the organizational structure of the book is both chronological and topical. Here is a brief preview. We start at the beginning, back when I was a graduate student and was collecting a list of examples of odd behaviors that did not seem to fit the models I was learning in class. The first section of the book is devoted to those early years in the wilderness, and describes some of the challenges that were thrown down by the many who questioned the value of this enterprise. We then turn to a series of topics that occupied most of my attention for the first fifteen years of my research career: mental accounting, self-control, fairness, and finance. My objective is to explain what my colleagues and I learned along the way, so that you can use those insights yourself to improve your understanding of your fellow Humans. But there may also be useful lessons about how to try to change the way people think about things, especially when they have a lot invested in maintaining the status quo. Later, we turn to more recent research endeavors, from the behavior of New York City taxi drivers, to the drafting of players into the National Football League, to the behavior of participants on high-stakes game shows. At the end we arrive in London, at Number 10 Downing Street, where a new set of exciting challenges and opportunities is emerging.

My only advice for reading the book is stop reading when it is no longer fun. To do otherwise, well, that would be just misbehaving.

________________

*
   One economist who did warn us about the alarming rate of increase in housing prices was my fellow behavioral economist Robert Shiller.

2

The Endowment Effect

I
began to have deviant thoughts about economic theory while I was a graduate student in the economics department at the University of Rochester, located in upstate New York. Although I had misgivings about some of the material presented in my classes, I was never quite sure whether the problem was in the theory or in my flawed understanding of the subject matter. I was hardly a star student. In that
New York Times Magazine
article by Roger Lowenstein that I mentioned in the preface, my thesis advisor, Sherwin Rosen, gave the following as an assessment of my career as a graduate student: “We did not expect much of him.”

My thesis was on a provocative-sounding topic, “The Value of a Life,” but the approach was completely standard. Conceptually, the proper way to think about this question was captured by economist Thomas Schelling in his wonderful essay “The Life You Save May Be Your Own.” Many times over the years my interests would intersect with Schelling’s, an early supporter and contributor to what we now call behavioral economics. Here is a famous passage from his essay:

Let a six-year-old girl with brown hair need thousands of dollars for an operation that will prolong her life until Christmas, and the post office will be swamped with nickels and dimes to save her. But let it be reported that without sales tax the hospital facilities of Massachusetts will deteriorate and cause a barely perceptible increase in preventable deaths—not many will drop a tear or reach for their checkbooks.

Schelling writes the way he speaks: with a wry smile and an impish twinkle in his eye. He wants to make you a bit uncomfortable.
*
Here, the story of the sick girl is a vivid way of capturing the major contribution of the article. The hospitals stand in for the concept Schelling calls a “statistical life,” as opposed to the girl, who represents an “identified life.” We occasionally run into examples of identified lives at risk in the real world, such as the thrilling rescue of trapped miners. As Schelling notes, we rarely allow any identified life to be extinguished solely for the lack of money. But of course thousands of “unidentified” people die every day for lack of simple things like mosquito nets, vaccines, or clean water.

Unlike the sick girl, the typical domestic public policy decision is abstract. It lacks emotional impact. Suppose we are building a new highway, and safety engineers tell us that making the median divider three feet wider will cost $42 million and prevent 1.4 fatal accidents per year for thirty years. Should we do it? Of course, we do not know the identity of those victims. They are “merely” statistical lives. But to decide how wide to make that median strip we need a value to assign to those lives prolonged, or, more vividly, “saved” by the expenditure. And in a world of Econs, society would not pay more to save one identified life than twenty statistical lives.

As Schelling noted, the right question asks how much the users of that highway (and perhaps their friends and family members) would be willing to pay to make each trip they take a tiny bit safer. Schelling had specified the correct question, but no one had yet come up with a way to answer it. To crack the problem you needed some situation in which people make choices that involve a trade-off between money and risk of death. From there you can infer their willingness to pay for safety. But where to observe such choices?

Economist Richard Zeckhauser, a student of Schelling’s, noted that Russian roulette offers a way to think about the problem. Here is an adaptation of his example. Suppose Aidan is required to play one game of machine-gun Russian roulette using a gun with many chambers, say 1,000, of which four have been picked at random to have bullets. Aidan has to pull the trigger once. (Mercifully, the gun is set on single shot.) How much would Aidan be willing to pay to remove one bullet?
†
Although Zeckhauser’s Russian roulette formulation poses the problem in an elegant way, it does not help us come up with any numbers. Running experiments in which subjects point loaded guns at their heads is not a practical method for obtaining data.

While pondering these issues I had an idea. Suppose I could get data on the death rates of various occupations, including dangerous ones like mining, logging, and skyscraper window-washing, and safer ones like farming, shopkeeping, and low-rise window-washing. In a world of Econs, the riskier jobs would have to pay more, otherwise no one would do them. In fact, the extra wages paid for a risky job would have to compensate the workers for taking on the risks involved (as well as any other attributes of the job). So if I could also get data on the wages for each occupation, I could estimate the number implied by Schelling’s analysis, without asking anyone to play Russian roulette. I searched but could not find any source of occupational mortality rates.

My father, Alan, came to the rescue. Alan was an actuary, one of those mathematical types who figure how to manage risks for insurance companies. I asked him if he might be able to lay his hands on data on occupational mortality. I soon received a thin, red, hardbound copy of a book published by the Society of Actuaries that listed the very data I needed. By matching occupational mortality rates to readily available data on wages by occupation, I could estimate how much people had to be paid to be willing to accept a higher risk of dying on the job.

Getting the idea and the data were a good start, but doing the statistical exercise correctly was key. I needed to find an advisor in the economics department whom I could interest in supervising my thesis. The obvious choice was the up-and-coming labor economist mentioned earlier, Sherwin Rosen. We had not worked together before, but my thesis topic was related to some theoretical work he was doing, so he agreed to become my advisor.

We went on to coauthor a paper based on my thesis entitled, naturally, “The Value of Saving a Life.” Updated versions of the number we estimated back then are still used in government cost-benefit analyses. The current estimate is roughly $7 million per life saved.

While at work on my thesis, I thought it might be interesting to ask people some hypothetical questions as another way to elicit their preferences regarding trade-offs between money and the risk of dying. To write these questions, I first had to decide which of two ways to ask the question: either in terms of “willingness to pay” or “willingness to accept.” The first asks how much you would pay to reduce your probability of dying next year by some amount, say by one chance in a thousand. The second asks how much cash you would demand to increase the risk of dying by the same amount. To put these numbers in some context, a fifty-year-old resident of the United States faces a roughly 4-in-1,000 risk of dying each year.

Here is a typical question I posed in a classroom setting. Students answered both versions of the question.

A. Suppose by attending this lecture you have exposed yourself to a rare fatal disease. If you contract the disease you will die a quick and painless death sometime next week. The chance you will get the disease is 1 in 1,000. We have a single dose of an antidote for this disease that we will sell to the highest bidder. If you take this antidote the risk of dying from the disease goes to zero. What is the most you would be willing to pay for this antidote? (If you are short on cash we will lend you the money to pay for the antidote at a zero rate of interest with thirty years to pay it back.)

B. Researchers at the university hospital are doing some research on that same rare disease. They need volunteers who would be willing to simply walk into a room for five minutes and expose themselves to the same 1 in 1,000 risk of getting the disease and dying a quick and painless death in the next week. No antidote will be available. What is the least amount of money you would demand to participate in this research study?

Economic theory has a strong prediction about how people should answer the two different versions of these questions. The answers should be nearly equal. For a fifty-year-old answering the questions, the trade-off between money and risk of death should not be very different when moving from a risk of 5 in 1,000 (.005) to .004 (as in the first version of the question) than in moving from a risk of .004 to .005 (as in the second version). Answers varied widely among respondents, but one clear pattern emerged: the answers to the two questions were not even close to being the same. Typical answers ran along these lines: I would not pay more than $2,000 in version A but would not accept less than $500,000 in version B. In fact, in version B many respondents claimed that they would not participate in the study at any price.

Economic theory is not alone in saying the answers should be identical. Logical consistency demands it. Again consider a fifty-year-old who, before he ran into me, was facing a .004 chance of dying in the next year. Suppose he gives the answers from the previous paragraph: $2,000 for scenario A and $500,000 for scenario B. The first answer implies that the increase from .004 to .005 only makes him worse off by at most $2,000, since he would be unwilling to pay more to avoid the extra risk. But, his second answer said that he would not accept the same increase in risk for less than $500,000. Clearly, the difference between a risk of .004 and .005 cannot be
at most
$2,000 and
at least
$500,000!

This truth is not apparent to everyone. In fact, even when explained, many people resist, as you may be doing right now. But the logic is inescapable.
‡
To an economist, these findings were somewhere between puzzling and preposterous. I showed them to Sherwin and he told me to stop wasting my time and get back to work on my thesis. But I was hooked. What was going on here? Sure, the putting-your-life-at-risk scenario is unusual, but once I began to look for examples, I found them everywhere.

One case came from Richard Rosett, the chairman of the economics department and a longtime wine collector. He told me that he had bottles in his cellar that he had purchased long ago for $10 that were now worth over $100. In fact, a local wine merchant named Woody was willing to buy some of Rosett’s older bottles at current prices. Rosett said he occasionally drank one of those bottles on a special occasion, but would never dream of paying $100 to acquire one. He also did not sell any of his bottles to Woody. This is illogical. If he is willing to drink a bottle that he could sell for $100, then drinking it has to be worth more than $100. But then, why wouldn’t he also be willing to buy such a bottle? In fact, why did he refuse to buy any bottle that cost anything close to $100? As an economist, Rosett knew such behavior was not rational, but he couldn’t help himself.
§

These examples all involve what economists call “opportunity costs.” The opportunity cost of some activity is what you give up by doing it. If I go for a hike today instead of staying home to watch football, then the opportunity cost of going on the hike is the forgone pleasure of watching the game. For the $100 bottle of wine, the opportunity cost of drinking the bottle is what Woody was willing to pay Rosett for it. Whether Rosett drank his own bottle or bought one, the opportunity cost of drinking it remains the same. But as Rosett’s behavior illustrated, even economists have trouble equating opportunity costs with out-of-pocket costs. Giving up the opportunity to sell something does not hurt as much as taking the money out of your wallet to pay for it. Opportunity costs are vague and abstract when compared to handing over actual cash.

My friend Tom Russell suggested another interesting case. At the time, credit cards were beginning to come into widespread use, and credit card issuers were in a legal battle with retailers over whether merchants could charge different prices to cash and credit card customers. Since credit cards charge the retailer for collecting the money, some merchants, particularly gas stations, wanted to charge credit card users a higher price. Of course, the credit card industry hated this practice; they wanted consumers to view the use of the card as free. As the case wound its way through the regulatory process, the credit card lobby hedged its bets and shifted focus to form over substance. They insisted that if a store
did
charge different prices to cash and credit card customers, the “regular price” would be the higher credit card price, with cash customers offered a “discount.” The alternative would have set the cash price as the regular price with credit card customers required to pay a “surcharge.”