Misbehaving: The Making of Behavioral Economics Read Online Free
Author: Richard H. Thaler
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
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
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