1. MICROECONOMICS by Robert S. Pindyck Daniel Rubinfeld Ninth Edition
Copyright © 2016, 2012, 2009 Pearson Education, Inc. All Rights Reserved

2. Chapter 19 Behavioral Economics (1 of 2)
CHAPTER OUTLINE 19.1 Reference Points and Consumer Preferences 19.2 Fairness 19.3 Rules of Thumb and Biases in Decision Making 19.4 Bubbles 19.5 Behavioral Economics and Public Policy
LIST OF EXAMPLES 19.1 Selling a House 19.2 New York City Taxicab Drivers 19.3 Credit Card Debt 19.4 Paying Not to Go to the Gym 19.5 The Housing Price Bubble (1) 19.6 The Housing Price Bubble (2)

3. Behavioral Economics (2 of 2)
What if consumers and firms do not always follow “rational” models of economic behavior? In this chapter we delve into the psychological aspects of how people make decisions. Preferences are not always clear or might vary depending on the context in which choices are made, and consumer choices are not always utility-maximizing. Some aspects of consumer behavior cannot be easily explained with the basic utility-maximizing assumptions. Adjustments to the standard model of consumer preferences and demand can be grouped into three categories: A tendency to value goods and services in part based on the setting one is in, a concern about the fairness of an economic transaction, and the use of simple rules of thumb as a way to cut through complex economic decisions.

4. 19.1 Reference Points and Consumer Preferences (1 of 4)
Psychologists and market research studies have found that perceived value depends in part on the setting in which the purchasing decision occurs. That setting creates a reference point. reference point The point from which an individual makes a consumption decision. Reference points can develop for many reasons: our past consumption of a good, our experience in a market, our expectation about how prices should behave, and even the context in which we consume a good. Reference points can strongly affect the way people approach economic decisions.

5. 19.1 Reference Points and Consumer Preferences (2 of 4)
ENDOWMENT EFFECT endowment effect Tendency of individuals to value an item more when they own it than when they do not. One way to think about this effect is to consider the gap between the price that a person is willing to pay for a good and the price at which she is willing to sell the same good to someone else. LOSS AVERSION loss aversion Tendency for individuals to prefer avoiding losses over acquiring gains. These effects tend to disappear as consumers gain relevant experience.
Our basic theory of consumer behavior says that the prices for buying and selling should be the same, but many experiments suggest that is not what happens in practice.

6. 19.1 Reference Points and Consumer Preferences (3 of 4)
FRAMING framing Tendency to rely on the context in which a choice is described when making a decision. SALIENCE salience The perceived importance of a good or service. Salience is closely related to framing—emphasis on a feature of a product or service is a means of framing a consumer’s choice. As with many of the central concepts of behavioral economics, accounting for salience enriches basic microeconomic theory in several ways. First, by placing emphasis on important features, it can improve the accuracy of individual beliefs about their available choices. Second, it can improve individuals’ knowledge about the costs and benefits of those choices.

7. 19.1 Reference Points and Consumer Preferences (4 of 4)
EXAMPLE 19.1 SELLING A HOUSE Often, the owners will set an asking price that is well above any realistic expectation of what the house can actually sell for. During that time the owners have to continue to maintain the house and pay for taxes, utilities, and insurance. This seems irrational. Why not set an asking price closer to what the market will bear? The endowment effect is at work here. The homeowners view their house as special. If housing prices have been falling, loss aversion could also be at work. During the burst of the housing bubble in 2008, some homeowners were affected by loss aversion when deciding on an asking price, especially if they bought their home at a time near the peak of the bubble. Averting the reality of the loss may serve to explain the reluctance of home owners to take that final step of selling their home.

8. 19.2 Fairness (1 of 2)
People sometimes do things because they think it is appropriate or fair to do so, even though there is no financial or other material benefit. Examples include charitable giving, volunteering time, or tipping in a restaurant.
FIGURE 19.1 DEMAND FOR SNOW SHOVELS Demand curve D1 applies during normal weather. Stores have been charging $20 and sell Q1 shovels per month. When a snowstorm hits, the demand curve shifts to the right. Had the price remained $20, the quantity demanded would have increased to Q2. But the new demand curve (D2) does not extend up as far as the old one. Consumers view an increase in price to, say, $25 as fair, but an increase much above that as unfair gouging. The new demand curve is very elastic at prices above $25, and no shovels can be sold at a price much above $30.

9. 19.2 Fairness (2 of 2)
Another example of fairness arises in the ultimatum game. Imagine that, under the following rules, you are offered a chance to divide 100 one-dollar bills with a stranger whom you will never meet again: You first propose a division of the money between you and the stranger. The stranger will respond by either accepting or rejecting your proposal. If he accepts, you each get the share that you proposed. If he rejects, you both get nothing. What should you do? You should propose that you get $99 while the other person gets only $1. However, because participants consider the fairness of this offer, the result is usually different. When this game is played experimentally, typical sharing proposals range between 67/33 and 50/50, and such offers are normally accepted.

10. 19.3 Rules of Thumb and Biases in Decision Making (1 of 4)
When economic decisions are complex, people often resort to rule of thumb or other mental shortcuts to help them make those decisions. The use of such rules of thumb, however, can introduce a bias into our economic decision making—something that our basic model does not allow. ANCHORING anchoring Tendency to rely heavily on one prior (suggested) piece of information when making a decision. The mental rules that we use in making decisions frequently depend on both the context in which the decisions are made and the information available. For example, marketers understand that consumers tend to overemphasize the first digit of prices, and also to think in terms of price categories like “under $20” or “over $20.” Thus to the consumer, who may not be thinking too carefully, $19.95 seems much cheaper than $20.01.

11. 19.3 Rules of Thumb and Biases in Decision Making (2 of 4)
RULES OF THUMB Whereas depending on rules of thumb can introduce biases in decision making, they can save time and effort and result in only small biases. Thus, they should not be dismissed outright. THE LAW OF SMALL NUMBERS People tend to overstate the probability that certain events will occur when faced with relatively little information from recent memory. The roulette player who bets on black after seeing red come up three times in a row: He has ignored the laws of probability. Investors in the stock market are often subject to a small-numbers bias, believing that high returns over the past few years are likely to be followed by more high returns over the next few years. Our estimation of subjective probabilities may be close to true probabilities, but often they are not.
If a tragedy such as a plane crash has occurred recently, many people will tend to overestimate the probability of it happening to them. Likewise, when a probability for a particular event is very, very small, many people simply ignore that possibility in their decision making.

12. EXAMPLE 19.2 NEW YORK CITY TAXICAB DRIVERS
In many cities, taxicab rates are fixed by regulation and do not change from day to day. On busy days, drives can earn a high income. Traditional economic theory would predict that drivers will work longer hours on busy days than on slow days. Surprisingly, researchers have found that most drivers drive more hours on slow days and fewer hours on busy days. Behavioral economics can explain this result. Suppose that most taxicab drivers have an income target for each day. That target effectively serves as a reference point. An income target provides a simple decision rule for drivers. A challenge to this “behavioral” explanation is that the decision to stop is based on the cumulative number of hours already worked that day and not on hitting a specific income target. In yet another study of this phenomenon, the authors of another study found that the traditional economic model explains most workday decisions of taxicab drivers, but that a behavioral model that accounts for reference points and targeted goals (for income and hours) can do even better

13. 19.3 Rules of Thumb and Biases in Decision Making (3 of 4)
OVERCONFIDENCE overconfidence Overestimating an individual’s prospects or abilities. over-optimism An unrealistic belief that things will work out well. over-precision An unrealistic belief that one can accurately predict outcomes.
An example of overconfidence is the belief that, if the stock market rose considerably during the last year or two, the stock prices will continue to increase during the coming year, and thereby underestimate the risk of investing.
Examples of over-optimism are a biased belief that one’s investments will perform better than average, a worker’s belief that she will get a job promotion much faster than her peers, or a consumer’s belief that he will pay off his credit card debt (and thereby avoid high interest payments) sooner than what is realistic.
An example of over-precision is a consumer’s choice of a cell phone data plan that turns out to be more costly than necessary because the choice was based on the incorrect belief that he can accurately predict how much data he will download each month.

14. 19.3 Rules of Thumb and Biases in Decision Making (4 of 4)
FIGURE 19.2 AN OVERCONFIDENT INVESTOR An investor is dividing her funds between two assets: Treasury bills, which are risk-free, and stocks. The budget line describes the trade-off between the expected return and its riskiness, as measured by the standard deviation of the return. The utility- maximizing portfolio is at the point where the investor’s indifference curve U1 is just tangent to the budget line. The investor, however, is overconfident; she perceives the riskiness of stocks to be smaller than it really is, making her perceived budget line steeper. She will choose a portfolio at the point of tangency of the indifference curve U2 with the perceived budget line, which makes the fraction of stocks in her portfolio larger than is optimal.
Recall that the slope of the budget line is (Rm – Rf )/σm, so if our overconfident investor perceives σm to be smaller than it really is, the perceived budget line will have a steeper slope.
This investor will think the standard deviation of the return on her portfolio is only σ’ but in fact it is σ**.

15. EXAMPLE 19.3 (1 of 2) CREDIT CARD DEBT
About half of credit card holders do not pay their entire balance each month and thus pay interest charges. What might seem strange is the fact that the interest rates that card holders must pay are extremely high—far higher than rates on home mortgages, car loans, and other sources of credit. Why would a consumer hold credit card debt that imposes an interest cost of as much as 17% or more? The answer is that many consumers believe— unrealistically—that they can control their card purchases so that they can avoid most finance charges. They suffer from overconfidence. In particular, they are often over-optimistic, thinking that their credit card balance at the end of the month will be lower than what it actually turns out to be. In addition, some cardholders have difficulty understanding what a 17% interest rate implies. Card issuers are aware of and take advantage of these behavioral shortcomings and know that they can profit by charging such high interest rates.

16. EXAMPLE 19.3 (2 of 2) CREDIT CARD DEBT
FIGURE 19.3 INTEREST RATES PAID ON CREDIT CARD BALANCES This graph shows the annual percentage rate (APR) that users of credit cards paid (in 2013), based on the Federal Reserve data. Some consumers managed to obtain credit cards with APRs of 10% or less, but the average APR was about 17%, and many consumers paid APRs that were far higher.

17. EXAMPLE 19.4 PAYING NOT TO GO TO THE GYM
Most consumers have difficulty predicting how often they will work out each week. Based on their perceptions of how much they will work out each week, do they choose the membership option that is fully rational and utility maximizing? The answer is no, according to a compelling study of consumer choices over health club memberships made by over 7,000 individuals in the Boston area. The health clubs offered three alternative membership contracts: (1) a monthly membership of $70 per month with unlimited usage; (2) an annual membership of $700, again with unlimited usage; and (3) a pay-per-visit option of either $12 per visit or $100 for a ten-visit pass. Analyzing actual choices, the authors found that consumers are overly optimistic with respect to how often they will work out. For example, consumers who chose the unlimited, $70 monthly fee ended up going to the gym only 4.3 times per month. They could have chosen to purchase a ten-visit pass or simply pay $12. Similar findings apply to those who purchased annual memberships. However, monthly and annual members did learn about their attendance patterns and adjusted accordingly—some changing their contracts and others canceling their memberships.

18. 19.4 Bubbles (1 of 2)
Many people thought that the Internet’s potential was virtually unbounded, and yes, the Internet has certainly changed the way most of us live. But does that mean that any company with a name that ends in “.com” is sure to make high profits in the future? Probably not. And yet many investors (perhaps “speculators” is a better word) bought the stocks of Internet companies at very high prices, prices that were increasingly difficult to justify based on fundamentals. Bubbles are often the result of irrational behavior. People stop thinking straight. They buy something because the price has been going up, and they believe (perhaps encouraged by their friends) that the price will keep going up. The United States experienced a prolonged housing price bubble that burst in 2008, causing financial losses to large banks that had sold mortgages to home buyers who could not afford to make their monthly payments (but thought housing prices would keep rising).
Recall from Section 4.3 that speculative demand is driven not by the direct benefits one obtains from owning or consuming a good but instead is driven by an expectation that the price of the good will increase.

19. EXAMPLE 19.5 (1 of 2) THE HOUSING PRICE BUBBLE (1)
In 1998, the S&P/Case-Shiller housing price index showed housing prices starting to rise rapidly, with the index increasing about 10 percent per year until it reached its peak of 190 in During that 8- year period, many people bought into the myth that housing was a sure-fire investment, and that prices could only keep going up. Many banks also bought into this myth and offered mortgages to people with incomes well below what it would take to make the monthly interest and principal payments over the long term. In 2007 prices started falling rapidly, and by 2008 it had become clear that the great housing boom was just a bubble, and the bubble had burst. From its peak in early 2006 through 2011, housing prices fell by over 33 percent in nominal terms. (In real terms they fell by nearly 40% percent.) In some states, the price drop was much worse. European countries suffered similar fates, contributing to a worldwide debt crisis. Other apparent bubbles have yet to deflate. Many Chinese cities, including Shanghai and Beijing, have seen rapidly rising housing and land prices, with some apartments reportedly doubling in value in mere months.

20. EXAMPLE 19.5 (2 of 2) THE HOUSING PRICE BUBBLE (1)
FIGURE 19.4 INTEREST RATES PAID ON CREDIT CARD BALANCES This graph shows the annual percentage rate (APR) that users of credit cards paid (in 2013), based on the Federal Reserve data. Some consumers managed to obtain credit cards with APRs of 10% or less, but the average APR was about 17%, and many consumers paid APRs that were far higher.

21. 19.4 Bubbles (2 of 2)
Informational Cascades If you are considering the purchase of stock in a company whose value you find difficult to evaluate, the actions of other investors could well be informative and lead you to rationally adjust your own valuation of the company. Your investment decisions are based not on fundamental information that you have obtained, but rather on the investment decisions of others. informational cascade assessment (e.g., of an investment opportunity) based in part on the actions of others, which in turn were based on the actions of others.

22. EXAMPLE 19.6 (1 of 2) THE HOUSING PRICE BUBBLE (2)
Informational cascades may help to explain the housing bubbles that occurred in the U.S. and other countries. For example, from 1999 to 2006, home prices in Miami nearly tripled. In the years prior to 2006, some analysts projected large increases in the demand for housing there. The projections were based on a growing number of aging retirees that want to move to someplace warm, and in part on an influx of immigrants with family or other roots in Miami. If investors acted on the belief that these analysts had done their homework, investing might have been rational. Rational or not, investors should have known that considerable risk was involved in buying real estate there. Looking back, we now know that many of these investors lost their shirts (not to mention their homes).

23. EXAMPLE 19.6 (2 of 2) THE HOUSING PRICE BUBBLE (2)
FIGURE 19.5 S&P/CASE-SHILLER HOUSING PRICE INDEX FOR FIVE CITIES The Index shows the average home price for each of five cities (in nominal terms). For some cities, the housing bubble was much worse than for others. Los Angeles, Miami, and Las Vegas experienced some of the sharpest increases in home prices, and then starting in 2007, prices plummeted. Cleveland, on the other hand, largely avoided the bubble, with home prices increasing, and then falling, only moderately.

24. 19.5 Behavioral Economic and Public Policy (1 of 3)
The typical response to excessive carbon dioxide (CO2) emissions as suggested by policy analysts is a carbon tax, which would raise the marginal private cost of burning fossil fuels and thereby reduce CO2 emissions. This assumes, however, that firms and consumers are fully informed about their own private costs of burning fossil fuels. But this assumption might be incorrect. It might well be that if they were properly informed, consumers and firms, on their own and without the incentives of a tax, would reduce their use of fossil fuels. Likewise, energy consumption can be reduced if consumers are better informed about the cost savings of better insulation, “smart” thermostats, and so on. This is where behavioral economics comes into play in the design of public policy. If the policy objective is to reduce energy use, we need to understand how people’s behavior affects the decisions they make regarding energy. One element of public policy would be to educate consumers about LED bulbs, perhaps through paid advertisements or even classes on “home economics and finance” in schools and colleges.

25. 19.5 Behavioral Economic and Public Policy (2 of 3)
FIGURE 19.6 EXTERNAL COST—A BEHAVIORAL ANALYSIS With a bit of education, consumers and firms would realize that they can save money by reducing their emissions of the pollutant. That would lower the marginal external cost curve (from MEC to MEC’ in the figure) and likewise lower the marginal social cost curve (to MSC’). Output might still be too large (in the figure, Q3 instead of Q2), but a much smaller tax (t* instead of t) would be needed to correct the problem.

26. 19.5 Behavioral Economic and Public Policy (3 of 3)
In the case of CO2 emissions and climate change, there are other ways that consumers and firms might be induced to reduce their energy consumption. One example would be moral persuasion. Moral obligations can certainly enter people’s utility functions, so that reducing energy consumption would indeed be utility-maximizing. Giving consumers a “nudge” in the direction of choices that they would likely make if fully informed can help them to maximize utility. Summing Up Should we dispense with the traditional consumer theory discussed in Chapters 3 and 4? Not at all. In fact, the basic theory that we learned up to now works quite well in many situations. The developing field of behavioral economics tries to explain and to elaborate on those situations that are not well explained by the basic consumer model.
If you continue to study economics, you will notice many cases in which economic models are not a perfect reflection of reality. Economists have to carefully
decide, on a case-by-case basis, what features of the real world to include and what simplifying assumptions to make so that models are neither too complicated
to study nor too simple to be useful.

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