Misbehaving: The Making of Behavioral Economics

by Richard H. Thaler

Investors should be wary of pouring money into markets that are showing signs of being overheated, but also should not expect to be able to get rich by successfully timing the market. It is much easier to detect that we may be in a bubble than it is to say when it will pop, and investors who attempt to make money by timing market turns are rarely successful.

when a closed-end fund selling at a discount decides to change its structure to an open-end fund, often under pressure from shareholders when it is selling at a large discount, its price converges to NAV.

Institutions shy away from the shares of small companies because these shares do not trade enough to provide the liquidity a big investor needs,

Economist Rex Thompson wrote his thesis on closed-end funds, and found that a strategy of buying the funds with the biggest discounts earned superior returns (a strategy also advocated by Benjamin Graham).

The smart-money trade in this situation is to buy undervalued 3Com shares and sell short an appropriate number of shares of Palm. Then, when the deal is completed, the investor sells the shares of Palm he receives, uses those shares to repay his loan, and is left with a profit equal to whatever price 3Com is selling for as a stand-alone company.

In this case, the smart trade is to buy whichever is the cheaper version of the stock and sell the expensive version short.

My conclusion: the price is often wrong, and sometimes very wrong. Furthermore, when prices diverge from fundamental value by such wide margins, the misallocation of resources can be quite big. For example, in the United States, where home prices were rising at a national level, some regions experienced especially rapid price increases and historically high price-to-rental ratios. Had both homeowners and lenders been Econs, they would have noticed these warning signals and realized that a fall in home prices was becoming increasingly likely. Instead, surveys by Shiller showed that these were the regions in which expectations about the future appreciation of home prices were the most optimistic. Instead of expecting mean reversion, people were acting as if what goes up must go up even more.

This lesson is one of the most important to take away from the research about market efficiency. If policy-makers simply take it as a matter of faith that prices are always right, they will never see any need to take preventive action. But once we grant that bubbles are possible, and the private sector appears to be feeding the frenzy, it can make sense for policy-makers to lean against the wind in some way.

The reason this result is important for the law is that judges often decide who owns a certain right, and the Coase theorem says that if transaction costs are low, then what the judge decides won’t actually determine what economic activities will take place; the judge will just decide who has to pay.

Coase theorem worked in theory, when trading for tokens redeemable for cash, but it did not work in practice, when trading for real-world objects like coffee mugs.

When people are given what they consider to be unfair offers, they can get angry enough to punish the other party, even at some cost to themselves.

The difference between an office of 190 square feet and one of 210 square feet is not a noticeable difference. Most people who visit the school don’t even realize that offices differ in size. But if the only thing you are staring at on a spreadsheet is a list of offices with their measurements, this factor is bound to be overweighted. If there is a number, people will use it.

Five findings from the psychology of decision-making supported our hypothesis that early picks will be too expensive:

People are overconfident.

People make forecasts that are too extreme.

The winner’s curse.

The false consensus effect.

Present bias.

This binding budget constraint means that the only way to build a winning team is to find players that provide more value than they cost.

In reality, across the entire draft, the chance that the earlier player will be better is only 52%. In the first round it is a bit higher, 56%.

the first pick for five early second-round picks, but we are finding that each of those second-round picks yields more surplus to the team than the first-round pick they are together traded for! In all my years of studying market efficiency, this is the most blatant violation I have ever seen.

So our research yielded two simple pieces of advice to teams. First, trade down. Trade away high first-round picks for additional picks later in the draft, especially second-round picks. Second, be a draft-pick banker. Lend picks this year for better picks next year.

The best one can hope to do is to buy a bad team, and at least for a while, improve their drafting strategy by trading down.

But my hunch is that as the importance of a decision grows, the tendency to rely on quantitative analyses done by others tends to shrink. When the championship or the future of the company is on the line, managers tend to rely on their gut instincts.

In my paper with Eric Johnson that had been motivated by my colleague’s poker-playing proclivities, we found two situations that induce people to be less risk averse than normal, in fact, actively risk-seeking. The first is when they are ahead in the game and “playing with the house money.” The other is when they are behind in the game and have a chance to break even.

The results were a surprise to me. I thought that choosing in front of the crowd would induce students to take more risks, but in fact the opposite happened. The students were more risk averse in front of the crowd.

Cooperation rates fall as the stakes rise only because when the stakes were unusually low by the standards of this show, cooperation rates were exceptionally high.

could we predict who would split and who would steal? We analyzed a host of demographic variables, but the only significant finding is that young men are distinctly less likely to split. Never trust a man under thirty.

If someone makes an explicit promise to split, she is 30 percentage points more likely to do so.

This reflects a general tendency. People are more willing to lie by omission than commission. If I am selling you a used car, I do not feel obligated to mention that the car is burning a lot of oil, but if you ask me explicitly: “Does this car burn a lot of oil?” you are likely to wangle an admission from me that yes, there has been a small problem along those lines. To get at the truth, it helps to ask specific questions.

The first obstacle is inertia. Surveys reveal that most people in retirement savings plans think they should be saving more, and plan to take action, uh, soon. But then they procrastinate, and never get around to changing their saving rate. In fact, most plan participants rarely make any changes to their saving options unless they change jobs and are confronted with a new set of forms they have to fill out. Overcoming inertia is the problem that automatic enrollment magically solves. The same concept should be included in a plan to increase saving rates. If we could somehow get people started on a plan to increase their saving rates and let that kick in automatically, inertia could work for us instead of against us.

The second obstacle is loss aversion. We know that people hate losing and, in particular, hate to see their paychecks go down. Based on the findings from our fairness study, we also know that in this domain, loss aversion is measured in nominal dollars, that is, without adjusting for inflation. So, if we could figure out a way that employees would not feel any cuts to their paychecks, there would be less resistance to saving more.

The third behavioral insight was related to self-control. A key finding from the research on this topic is that we have more self-control when it comes to the future than the present. Even the kids in Walter Mischel’s marshmallow experiments would have no trouble if today they were given the choice between one marshmallow at 2 p.m. tomorrow or three marshmallows at 2:15 p.m. tomorrow. Yet, we know that if we g...

Before automatic enrollment, only 49% of employees joined the plan during their first year of eligibility; after automatic enrollment, that number jumped to 86%! Only 14% opted out. That is a pretty impressive change in behavior produced by a supposedly irrelevant factor.

In allocating the source of the new saving that comes from these programs, the authors attribute only 1% of the increase to the tax breaks. The other 99% comes from the automatic features. They conclude: “In sum, the findings of our study call into question whether tax subsidies are the most effective policy to increase retirement savings. Automatic enrollment or default policies that nudge individuals to save more could have larger impacts on national saving at lower fiscal cost.”

We were all trying to dig into the question that had been the elephant in the room for decades: if people make systematic mistakes, how should that affect government policy, if at all?

The premise of the article, and later the book, is that in our increasingly complicated world people cannot be expected to have the expertise to make anything close to optimal decisions in all the domains in which they are forced to choose.

Our premise was simple. Because people are Humans, not Econs (terms we coined for Nudge), they make predictable errors. If we can anticipate those errors, we can devise policies that will reduce the error rate.

In countries where the default is to be a donor, almost no one opts out, but in countries with an opt-in policy, often less than half of the population opts in!

Instead we liked a variant that had recently been adopted by the state of Illinois and is also used in other U.S. states. When people renew their driver’s license, they are asked whether they wish to be an organ donor. Simply asking people and immediately recording their choices makes it easy to sign up.† In Alaska and Montana, this approach has achieved donation rates exceeding 80%.

if you want people to comply with some norm or rule, it is a good strategy to inform them (if true) that most other people comply.

In that study, overdue taxpayers were sent a variety of letters in an effort to get them to pay, with messages varying from telling them what their money would be spent on to threatening legal action, but the most effective message was simply telling people that more than 90% of Minnesota taxpayers paid their taxes on time.

If you want to encourage someone to do something, make it easy.

Much to everyone’s surprise, the behavioral approach to economics has had its greatest impact in finance.

The facts are that the capital asset pricing model has clearly been rejected as an adequate description of the movements of stock prices. Beta, the only factor that was once thought to matter, does not appear to explain very much.

We now know more about how and when prices can diverge from intrinsic value and what prevents the “smart money” from driving prices back into line. (In some cases, investors who are aspiring to be the “smart money” can make more money by betting on riding the bubble and hoping to get out faster than others, than by betting on a return to sanity.)

As we have seen throughout this book, treating all money as the same, and also as the primary driver of human motivation, is not a good description of reality.

one intriguing finding by Roland Fryer suggests that rewarding students for inputs (such as doing their homework) rather than outputs (such as their grades) is effective.

The pre-informing texts increased student performance on the math test by the equivalent of one additional month of schooling, and students in the bottom quartile benefited most. These children gained the equivalent of two additional months of schooling, relative to the control group. Afterward, both parents and students said they wanted to stick with the program, showing that they appreciated being nudged.

Observe.