Incentives

Incentives

Prices and incentives matter in choice architecture, alongside factors often neglected in traditional economic analysis. This is as good a time as any to state for the record that we believe in supply and demand. If the price of a product goes up, suppliers will usually produce more of it and consumers will usually want less of it. So choice architects must think about incentives when they design a system. Sensible architects will align the incentives of the most important decision makers. One way to start to think about incentives is to ask four questions about a particular choice architecture:

• Who chooses?
• Who uses?
• Who pays?
• Who profits?

When one person chooses, uses, and pays for a good or service provided by a single supplier, things are pretty simple and incentives are well aligned. If you go somewhere for lunch, you choose what you want and pay for it. If you don’t like what you got, you can pick something else next time, or go to a different restaurant. It is a little more complicated when a group dines together and shares the bill. If the group is large, some people might be inclined to order more expensive items than they would alone, since they are paying only a small share of the extra cost. Others might do just the opposite: they might avoid ordering a pricey wine they would enjoy because they feel bad about sticking someone else with part of the bill. When the answers to the first three questions above all point to one person, markets tend to work reasonably well, at least so long as people have adequate information and are not suffering from behavioral biases.

The opposite is true of the notorious U.S. health care system. In this system, patients receive health care services that are often chosen by their physicians and (for most people) paid for by an insurance company or government. The fees are then divided up among a host of providers, from medical workers to equipment manufacturers to hospitals to drug companies to malpractice lawyers. Two patients receiving identical services can pay wildly different prices. It is no surprise, then, that the United States has the most expensive health care system in the world, with only mediocre health care outcomes.

Aligning incentives is very standard economics. But as usual, it is possible to elaborate and enrich the standard analysis by remembering that the agents in the economy are Humans. Incentives are a fundamental driver of behavior across all animals, including humans. We naturally move toward rewards and take steps to avoid punishment. Yet our evaluation of any incentive’s value is filtered through a biased lens shaped by self-esteem, personal narratives, and physiological states. As Charlie Munger put it, “Never, ever, think about something else when you should be thinking about the power of incentives.” Given this pervasive power, one might expect that financial incentives would reliably reduce judgment biases—after all, if accuracy becomes rewarding, people should be motivated to think more carefully and overcome error. The evidence on anchoring, however, tells a different story. Multiple studies have found that offering monetary payments for accuracy does not meaningfully reduce the anchoring effect. Tversky and Kahneman (1974) reported that payment for accuracy failed to reduce anchoring, and Wilson et al. (1996) found that a prize for accuracy decreased perceived influence of the anchor but did not actually diminish the anchoring effect itself. Even when a study found a marginal reduction, it involved public posting of accuracy scores as well as payment (Wright and Anderson, 1989, p < .09). These findings suggest that rewarding accuracy need not give people an effective way to correct anchoring. The effects of incentives on behavior are not always straightforward or rational.

The most important modification that must be made to a standard analysis of incentives is salience. Do choosers actually notice the incentives they face? In free markets, the answer is usually yes, but in important cases it can be no. Consider the example of members of an urban family deciding whether to buy a car. Suppose their choices when they are unable to walk or bike are to take taxis, ride-share services, and public transportation or to spend a significant amount of their savings to buy a used car, which they can park on the street in front of their home for a small fee. Once they acquire the car, the only salient costs of owning will be the stops at the gas station, occasional repair bills, and a yearly insurance bill. The opportunity cost of money spent on the car is likely to be neglected. (In other words, once they purchase the car, they tend to forget about the up-front payment and stop treating it as money that could have been spent on something else.) In contrast, every time the family uses a taxi, the cost will be in their face, with the meter clicking every few blocks. So a behavioral analysis of the incentives of car ownership will predict that people will underweight the opportunity costs of car ownership and possibly other less salient aspects, such as depreciation, and may overweight the very salient costs of using a taxi.

An analysis of choice architecture systems must make similar adjustments. It is common to use the tax system to alter incentives—but which taxes are salient? In many countries, retirement savings are encouraged through tax incentives, such as making contributions and earnings tax-free until the money is withdrawn. Do such incentives work? The study discussed in this account found that they had little effect, especially when compared to (drum roll, please) defaults! Rich savers are more likely to pay attention to tax incentives, in part because they have hired advisers to attend to such things, so they will switch money into tax-exempt accounts, but that is mostly just moving money around rather than increasing savings. Worse, these kinds of incentives given via tax breaks are not very visible to the public. In the United States they are called tax expenditures, but no one gets a bill for the money that the government is not collecting. Behaviorally sound public policy would evaluate policies based on how efficient they are at achieving their goals and also on the visibility of their costs. Unfortunately, politicians often do not find it in their best interest to make the costs of their activities transparent. The importance of salience was vividly demonstrated when Ronald Reagan, as governor of California, opposed a bill to switch from annual lump-sum income tax payments to paycheck withholding. He famously said, “Taxes should hurt,” recognizing that the pain of paying is a salient brake on government spending.

Salience can be manipulated, and good choice architects can take steps to direct people’s attention to incentives. In some domains, people may want the salience of gains and losses treated asymmetrically. For example, no one would want to go to a health club that charged its users on a “per step” basis on the treadmill. At the same time, many treadmill users enjoy watching the “calories burned” meter while they work out (especially since those meters seem to give generous estimates of calories actually burned). For some people, even better might be a pictorial display that indicates the calories one has burned in terms of food: after ten minutes, a user would only see a small bag of carrots, but after forty minutes, a large cookie.

Beyond salience, effective incentive design often requires aligning the interests of the people who make the critical decisions. A striking example comes from organ donation. Although paying living donors is widely prohibited, it is possible to augment any system with additional incentives. In the policies described in the book, Israel implemented a policy that grants priority on the waiting list to candidates who had registered as organ donors at least three years prior, and even higher priority to candidates whose first-degree relatives had donated organs at their death. This second factor directly rewards the family members who are ultimately making the donation decision. The book describes Singapore as using a complementary loss frame: opting out of its presumed-consent system reduces a person’s priority if they later need a donated organ. In the five years after Israel changed its policy, the family authorization rate rose from 45 percent to 55 percent, and registration increased. These examples suggest that who receives an incentive and how it is framed matter alongside its size. The before-and-after figures alone do not establish how much of the change came from the priority rule.