30 Behavioral Economics

30 Behavioral Economics - CHAPTER 3 0 BEHAVIORAL ECONOMICS...

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Unformatted text preview: CHAPTER 3 0 BEHAVIORAL ECONOMICS The economic model of consumer choice that we have studied is simple and elegant, and is a reasonable starting place for many sorts of analy— sis. However, it is most definitely not the whole story, and in many cases a deeper model of consumer behavior is necessary to accurately describe choice behavior. The field of behavioral economics is devoted to studying how con- sumers actually make choices. It uses some of the insights from psychology to develop predictions about choices people will make and many of these predictions are at odds with the conventional economic model of “rational” consumers. _ In this chapter we will look at some of the most important phenomena that have been identified by behavioral economists, and contrast the pre— dictions of these behavioral theories with those presented earlier in this book.1 1 In writing this chapter, I have found Colin F. Camerer, George Loewenstein, and Matthew Rabin’s book Advances in Behavioral Economics, Princeton University Press, 2003, to be very useful, particularly the introductory survey by Camerer and Loewenstein. Other works will be noted as the relevant topics are discussed. FRAMING EFFECTS IN CONSUMER CHOICE 549 30.1 Framing Effects in Consumer Choice In the basic model of consumer behavior, the choices were described in the abstract: red pencils or blue pencils, hamburgers and french fries, and so on. However, in real life, people are strongly affected by how choices are presented to them or framed. A faded pair of jeans in a thrift shop may be perceived very differently than the same jeans sold in an exclusive store. The decision to buy a stock may feel quite different than the decision to sell a stock, even if both transactions end up with the same portfolio. A store might sell dozens of copies of a book priced at $29.95, whereas the same book priced at $29.00 would have substantially fewer sales. These are all examples of framing effects, and they are clearly a pow— erful force in choice behavior. Indeed, much of marketing practice is based on understanding and utilizing such biases in consumer choice. The Disease Dilemma Framing effects are particularly common in choices involving uncertainty. For example, consider the following decision problem:2 A serious disease threatens 600 people. You are offered a choice between two treatments, A and B, which will yield the following outcomes. Treatment A. Saving 200 lives for sure. Treatment B. A 1/3 chance of saving 600 lives and a 2/3 chance of saving no one. Which would you choose? Now consider the choices between these treat ments. Treatment C. Having 400 people die for sure. Treatment D. A 2/3 chance of 600 people dying and a 1/3 chance of no one dying. Now which treatment would you choose? 2 A. Tversky and D. Kahneman, 1981, “The framing of decisions and the psychology of choice,” Science, 211, 453—458. 550 BEHAVIORAL ECONOMICS (Ch. 30) In the positive framing comparison—which describes how many people will livewmost individuals choose A over B, but in the negative framing comparison most people choose D over C even though the outcomes in A-C and BB are exactly the same. Apparently, framing the question positively (in terms of lives saved) makes a treatment much more attractive than framing the choice negatively (in terms of lives lost). Even expert decisions makers can fall into this trap. When psychologists tried this question on a group of physicians, 72 percent of them chose the safe treatment A over the risky treatment B. But when the question was framed negatively, only 22 percent chose the risky treatment C while 72 percent chose the safe treatment. Though few of us are faced with life-or—death decisions, there are similar examples for more mundane choices, such as buying or selling stocks. A rational choice of an investment portfolio would, ideally, depend on an assessment of the possible outcomes of the investments rather than how one acquired those investments. For example, suppose that you are given 100 shares of stock in Concrete— Blockscom (whose slogan is “We give away the blocks, you pay for packing and shipping”). You might be reluctant to sell shares you received as a gift despite the fact that you would never consider buying them yourself. People are often reluctant to sell losing stocks, thinking that they will “come back.” Maybe they will, maybe they won’t. But ultimately you shouldn’t let history determine your investment portfolio~ethe right ques- tion to ask is whether you have the portfolio choices today that you want. Anchoring Effects The hypothetical ConcreteBlocks.com example described above is related to the so—called anchoring effect. The idea here is that people’s choices can be influenced by completely spurious information. In a classic study the experimenter spun a wheel of fortune and pointed out the number that came up to a subject.3 The subject was then asked whether the number of African countries in the United Nations was greater or less than the number on the wheel of fortune. After they responded, the subjects were asked for their best guess about how many African countries were in the United Nations. Even though the number shown on the wheel of fortune was obviously random, it exerted a significant influence on the subjects’ reported guesses. In a similar experimental design, MBA students were given an expensive bottle of wine and then asked if they would pay an amount for that bottle equal to the last two digits of their Social Security number. For example, 3 D. Kahneman and A. Tversky, 1974, “Judgment under uncertainty: Heuristics and biases,” Science, 185: 1124e1131. FRAMING EFFECTS IN CONSUMER CHOICE 551 if the last two digits were 29, the question was “Would you pay $29 for this bottle of wine?” After answering that question, the students were asked What the maxi~ mum amount is that they were willing to pay for the wine. Their answers to this latter question were strongly influenced by the price determined by the last two digits of their Social Security number. For example, those with Social Security digits of 50 or under were willing to pay $11.62 on average, while those with digits in the upper half of the distribution were willing to pay $19.95 on average. Again, these choices seem like mere laboratory games. However, there are very serious economic decisions that can also be influenced by minor variations in the way the choice is framed. Consider, for example, choices of pension plans. Some economists looked at data from three employers that offered an— tomatic enrollment in 401(k) plans. Employees could opt out, but they had to make an explicit choice to do so. The economists found that the participation rate in these programs with automatic enrollment was spec— tacularly high, with over 85 percent of workers accepting the default choice of enrolling in the 401(k) plans. That’s the good news. The bad news is that almost all of these workers also chose the default investment, typically a money market fund with very low returns and a low monthly contribution. Presumably, the employers made the default investment highly conservative to eliminate downside risk and possible employee lawsuits. In subsequent work, these economists examined the experience at a com— pany where there was no default choice of pension plan: within a month of starting work, employees were required to choose either to enroll in the 401(k) plan or to postpone enrollment. By eliminating the standard default choices of non—enrollment, and of enrollment in a fund that had low rates of return, this “active decision” approach raised participation rates from 35 percent to 70 percent for newly hired employees. Moreover, employees who enrolled in the 401(k) plan overwhelmingly chose high savings rates. As this example illustrates, careful design of human resources benefits programs can make a striking difference in which programs are chosen, potentially having a large effect on consumer savings behavior. 4 Bracketing People often have trouble understanding their own behavior, finding it too difficult to predict what they will actually choose in different circumstances. 4 James Choi, David Laibson, Brigitte Madrian, and Andrew Metrick, “For Better or for Worse: Default Effects and 401(k) Savings Behavior,” NBER working paper, W8651, 2001. 552 BEHAVIORAL ECONOMICS (Ch. 30) For example, a marketing professor gave students a choice of six different snacks that they could consume in each of three successive weeks during class.5 (You should be so lucky!) In one treatment, the students had to choose the snacks in advance; in the other treatment, they chose the snacks on each day then immediately consumed them. When the students had to choose in advance, they chose a much more diverse set of snacks. In fact, 64 percent chose a different snack each week in this treatment compared to only 9 percent in the other group. When faced with making the choices all at once, people apparently preferred variety to exclusivity. But when it came down to actually choosing, they made the choice with which they were most comfortable. We are all creatures of habit, even in our choice of snacks. Too Much Choice Conventional theory argues that more choice is better. However, this claim ignores the costs of making choices. In affluent countries, consumers can easily become overwhelmed with choices, making it difficult for them to arrive at a decisiori. In one experiment, two marketing researchers set up sampling booths for jam in a supermarket.6 One booth offered 24 flavors and one offered only 6. More people stopped at the larger display, but substantially more people actually bought jam at the smaller display. More choice seemed to be attractive to shoppers, but the profusion of choices in the larger display appeared to make it more difficult for the shoppers to reach a decision. Two experts in behavioral finance wondered whether the same problem with “excessive choice” showed up in investor decisions. They found that people who designed their own retirement portfolios tended to be just as happy with the average portfolio chosen by their co—workers as they were with their own choice. Having the flexibility to construct their own retire- ment portfolios didn’t seem to make investors feel better off.7 Constructed Preferences How are we to interpret these examples? Psychologists and behavioral economists argue that preferences are not a guide to choice; rather, prefer- ences are “discovered” in part through the experiences of choice. 5 I. Simonson, 1990, “The effect of purchase quantity and timing on variety—seeking behavior,” Journal of Marketing Research, 17: 1507164. 6 Sheena S. Iyengar and Mark R. Lepper, “When Choice is demotivating: can one desire too much of a good thing?" Journal of Personality and Social Psychology, 2000. 7 Shlomo Benartzi and Richard Thaler, “How Much Is Investor Autonomy Worth?” UCLA working paper, 2001. UNCERTAINTY 553 Imagine watching someone in the supermarket picking up a tomato, putting it down, then picking it up again. Do they want it or not? Is the price—quality combination offered acceptable? When you watch such behavior, you are seeing someone who is “on the margin” in terms of mak- ing the choice. They are, in the psychologists’ interpretation, discovering their preferences. Conventional theory treats preferences as preexisting. In this view, pref- erences explain behavior. Psychologists instead think of preferences as being constructedgpeople develop or create preferences through the act of choosing and consuming. It seems likely that the psychological model is a better description of What actually happens. However, the two viewpoints are not entirely incompat— ible. As we have seen, once preferences have been discovered, albeit by some mysterious process, they tend to become built-in to choices. Choices, once made, tend to anchor decisions. If you tried to buy that tomato from that consumer once they have finally decided to choose it, you would likely have to pay more than it cost them. 30.2 Uncertainty Ordinary choice is complicated enough, but choice under uncertainty tends to be particularly tricky. We’ve already seen that people’s decisions may depend on how choice alternatives are phrased. But there are many other biases in behavior in this domain. Law of Small Numbers If you have taken a course in statistics, you might be familiar with the Law of Large Numbers. This is a mathematical principle that says (roughly) that the average of a large Sample from a population tends to be close to the mean of that population. The Law of Small Numbers is a psychological statement that says that people tend to be overly influenced by small samples, particularly if they experience them themselves.8 Consider the following question:9 8 The term originated with A. Tversky and D. Kahneman, 1971, “Belief in the law of small numbers,” Psychological Bulletin,76, 2: 105—110. Much of the following discus— sion is based on a working paper by Matthew Rabin of the University of California at Berkeley entitled “Inference by Believers in the Law of Small Numbers.” 9 A. Tversky and D. Kahneman, 1982, “Judgments of and by Representativeness,” in Judgment under Uncertainty: Heuristics and Biases, D. Kahneman, P. Slovic, and A. Tversky, Cambridge University Press, 84798. 554 BEHAVIORAL ECONOMICS (Ch. 30) “A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As you know, about 50 percent of all babies are boys. However, the exact percentage varies from day to day. Sometimes it may be higher than 50 percent, sometimes lower. For a period of 1 year, each hospital recorded the days on which more than 60 percent of the babies born were boys. Which hospital do you think recorded more such days?” In a survey of college students, 22 percent of the subjects said that they thought that it was more likely that the larger hospital recorded more such days, while 56 percent said that they thought the number of days would be about the same. Only 22 percent correctly said that the smaller hospital would report more days. If the correct account seems peculiar to you, suppose the smaller hospi- tal recorded 2 births per day and the larger hospital 100 births per day. Roughly 25 percent of the time the smaller hospital would have 100 percent male births, while this would be very rare for the large hospital. It appears that people expect samples to look like the distribution from which they are drawn. Or, saying this another way, people underestimate the actual magnitude of the fluctuations in a sample. A related issue is that people find it difficult to recognize randomness. In one experiment, subjects were asked to write down a series of 150 “random” coin tosses. About 15 percent of the sequences they produced had heads or tails three times in a row, but this pattern would occur randomly about 25 percent of the time. Only 3 percent of the subjects” sequences had 4 heads or 4 tails in a row, while probability theory says that this should occur about 12 percent of the time. This has important implications for game theory, for example. We saw that in many cases people should try to randomize their strategy choices so as to keep their opponents guessing. But, as the psychological literature shows, people aren’t very good at randomizing. On the other hand, people aren’t very good at detecting non-random behavior either, at least without some training in statistics. The point of mixed strategy equilibria is not that choices are mathematically unpredictable, but rather that they should be unpredictable by the players in the game. Some economic researchers studied final and semi-final tennis matches at Wimbledon.10 Ideally, tennis players should switch their serves from side to side so that their opponent can’t guess which side the serve is coming from. However, even very ascomplished players can’t do this quite as well as one might expect. According to the authors: “Our tests indicate that the tennis players are not quite playing ran— 10 M. Walker and J. Wooders, 1999, “Minimax Play at Wimbledon,” University of Ari- zona working paper. UNCERTAINTY 555 domly: they switch their serves from left to right and vice versa somewhat too often to be consistent with random play. This is consistent with ex- tensive experimental research in psychology and economics which indicates that people who are attempting to behave truly randomly tend to “switch too often.” Asset Integration and Loss Aversion In our study of expected utility we made an implicit assumption that what individuals cared about was the total amount of wealth that they ended up with in various outcomes. This is known as the asset integration hypothesis. Even though most people would accept this as a reasonable thing to do, it is hard to put into practice (even for economists). In general, people tend to avoid too many small risks and accept too many large risks. Suppose that you make $100,000 a year and that you are offered a coin flip. If heads comes up you get $14 and if tails comes up you lose $10. This bet has an expected value of $12 and has a minuscule effect on your total income in a given year. Unless you have moral scruples about gambling, this would be a very attractive bet and you should almost certainly take it. However, a surprisingly large number of people won’t take such a bet. This excess risk aversion shows up in insurance markets where people tend to over-insure themselves against various small events. For example, people buy insurance against loosing their cell phone, even though they can often replace it at quite a low cost. People also buy auto insurance with deductibles that are much too low to make economic sense. In general, when making insurance decisions you Should look at the “house odds.” If cell phone insurance costs you $3 a month, or $36 a year, and a new cell phone costs $180, then the house odds are 36/180, or 20 percent. The cell phone insurance would pay off in expected value only if you have more than a 20 percent chance of losing your phone or if it would be an extreme financial hardship to replace it. It appears that people aren’t really risk averse as much as they are loss averse. That is, people put seemingly excessive weight on the status quo—«where they startwas opposed to where they end up. In an experiment that has been replicated many times, two researchers gave half of the subjects in a group coffee mugs.11 They asked this group to report the lowest price at which they would sell the mugs. Then they asked the group that didn’t have mugs the highest price at which they would buy a mug. Since the groups were chosen randomly, the buying and selling prices should be about equal. However, in the experiment, the median 11 D. Kahneman, J. L. Kitsch, and R. Thaler, 1990, “Experimental tests of the endow- ment effect and the Coase theorem,” Journal of Political Economy, 98, 1325—1348. 556 BEHAVIORAL ECONOMICS (Ch. 30) selling price was $5.79 and the median buying price was $2.25, a substantial difference. Apparently, the subjects with coffee mugs were more reluctant to part with them than subjects without mugs. Their preferences seemed to be influenced by their endowment, contrary to standard consumer theory. A similar effect shows up in what is known as the sunk cost fallacy. Once you have bought something, the amount you paid is “sunk,” or no longer recoverable. So future behavior should not be influenced by sunk costs. But, alas, real people tend to care about how much they paid for some- thing. Researchers have found that the price at which owners listed con— dominiums in Boston was highly correlated with the buying price.12 As pointed out earlier, owners of stock are very reluctant to realize losses, even when it would be advantageous for tax reasons. The fact that ordinary people are subject to the sunk cost fal...
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