Misbehaving: The Making of Behavioral Economics (53 page)

Yet the lack of consensus on what constitutes the core “rational” macroeconomic model does not imply that behavioral economics principles cannot be profitably applied to big-picture policy issues. Behavioral perspectives can add nuance to macroeconomic issues even in the absence of a clear null hypothesis to disprove or build on. We should not need smoking guns to get busy collecting evidence.

One important macroeconomic policy begging for a behavioral analysis is how to fashion a tax cut aimed at stimulating the economy. Behavioral analysis would help, regardless of whether the motive for the tax cut is Keynesian—to increase demand for goods—or supply side—aimed at getting “job creators” to create even more jobs. There are critical behavioral details in the way a tax cut is administered, details that would be considered SIFs in any rational framework. If Keynesian thinking motivates the tax cut, then policy-makers will want the tax cut to stimulate as much spending behavior as possible. And one supposedly irrelevant detail these policy-makers should consider is whether the cut should come in a lump sum or be spread out over the course of the year. Without evidence-based models of consumer behavior, it is impossible to answer that question. (When the goal is to stimulate spending, my advice would be to spread it out.
‡
Lump sums are more likely to be saved or used to pay down debts.)

The same questions apply to a supply-side tax cut. Suppose we are contemplating offering a tax holiday to firms that bring money home to the U.S. instead of keeping it stashed in foreign subsidiaries to avoid taxation. To design and evaluate this policy we need an evidence-based model that will tell us what firms will do with the repatriated money. Will they invest it, return it to shareholders, or hoard it, as many U.S. firms have been doing since the financial crisis? This makes it hard to predict what firms would do if they found themselves with a greater share of that cash held domestically. More generally, until we better understand how real firms behave, meaning those run by Humans, we cannot do a good job of evaluating the impact of key public policy measures. I will have a bit more to say about that later.

Another big-picture question that begs for more thorough behavioral analysis is the best way to encourage people to start new businesses (especially those who might be successful). Economists on the right tend to stress reducing marginal tax rates on high-income earners as the key to driving growth. Those on the left tend to push for targeted subsidies for industries they want to encourage (such as clean energy) or increased availability of loans from the Small Business Administration, a government agency whose mission is to encourage the creation and success of new enterprises. And both economists and politicians of all stripes tend to favor exemptions from many government regulations for small firms, for whom compliance can be costly. All of these policies are worth consideration, but we rarely hear much from economists about mitigating the
downside
risk to entrepreneurs if a new business fails, which happens at least half if not more of the time.
§
We know that losses loom larger than gains to Humans, so this might be an important consideration. Here is one such suggestion along those lines, offered during an impromptu television interview (so pardon the grammar):

What we need to do in this country is make it a softer cushion for failure. Because what [those on the right] say is the job creators need more tax cuts and they need a bigger payoff on the risk that they take. . . . But what about the risk of, you’re afraid to leave your job and be an entrepreneur because that’s where your health insurance is? . . . Why aren’t we able to sell this idea that you don’t have to amplify the payoff of risk to gain success in this country, you need to soften the damage of risk?

This idea did not come from an economist, not even a behavioral economist. It came from comedian Jon Stewart, the host of
The
Daily Show,
during an interview with Austan Goolsbee, my University of Chicago colleague who served for a while as the chairman of President Obama’s Council of Economic Advisors. Economists should not need the host of a comedy news show to point out that finding ways to mitigate the costs of failures might be more effective at stimulating new business startups than cutting the tax rate on people earning above $250,000 a year, especially when 97% of small business owners in the U.S. earn less than that amount.

B
ehavioral macroeconomics is on the top of my wish list, but virtually every field of economics could benefit from giving greater scrutiny to the role of Humans. Along with finance, development economics is probably the field where behavioral economists are having the greatest impact, in part because that field has been revitalized by an influx of economists who are testing ideas in poor countries using randomized control trials. Some poor African country is not going to turn into Switzerland overnight, but we can learn how to make things better, one experiment at a time.

We need more evidence-based economics, which can be either theoretical or empirical. Prospect theory is, of course, the seminal evidence-based theory in behavioral economics. Kahneman and Tversky began by collecting data on what people do (starting from their own experiences) and then constructed a theory whose goal was to capture as much of that behavior as possible in a parsimonious way. This is in contrast to expected utility theory, which, as a normative theory of choice, was derived from rationality axioms. Prospect theory has now been repeatedly and rigorously tested with data taken from a wide variety of settings, from the behavior of game show contestants to golf professionals to investors in the stock market. The next generation of behavioral economic theorists, such as Nicholas Barberis, David Laibson, and Matthew Rabin (to name just three), also start with facts and then move to theory.

To produce new theories we need new facts, and the good news is that I am now seeing a lot of creative evidence collection being published in top economics journals. The growing popularity of randomized control trials, starting with the field of development economics, nicely illustrates this trend, and shows how experimentation can increase economists’ tool kit, which often has had a single tool: monetary incentives. 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.

A good example of a domain where field experiments run by economists are having an impact is education. Economists do not have a theory for how to maximize what children learn in school (aside from the obviously false one that all for-profit schools are already using the best methods). One overly simplistic idea is that we can improve student performance by just by giving financial incentives to parents, teachers, or kids. Unfortunately, there is little evidence that such incentives are effective, but nuances matter. For example, 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. I find this result intuitively appealing because the students most in need do not know how to become better students. It makes sense to reward them for doing things that educators believe are effective.

Another interesting result comes directly from the behavioral economics playbook. The team of Fryer, John List, Steven Levitt, and Sally Sadoff has found that the framing of a bonus to teachers makes a big difference. Teachers who are given a bonus at the beginning of the school year that must be returned if they fail to meet some target, improve the performance of their students significantly more than teachers who are offered an end-of-year bonus contingent on meeting the same goals.
¶

A third positive result even further from the traditional tool kit of financial incentives comes from a recent randomized control trial conducted in the U.K., using the increasingly popular and low-cost method of text reminders. This intervention involved sending texts to half the parents in some school in advance of a major math test to let them know that their child had a test coming up in five days, then in three days, then in one day. The researchers call this approach “pre-informing.” The other half of parents did not receive the texts. 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. This program also belies the frequent claim, unsupported by any evidence, that nudges must be secret to be effective.

Public schools, like remote villages in poor countries, are challenging environments for experimenters. That we are learning important lessons about how to teach our children and keep them motivated should embolden others outside of education and development economics to try collecting data too. Field experiments are perhaps the most powerful tool we have to put the evidence in evidence-based economics.

M
y wish list for non-economists has a similar flavor. Considering that schools are one of the oldest of society’s institutions, it is telling that we have not figured out how to teach our children well. We need to run experiments to figure out how to improve, and have only just started doing so. What should that tell us about creations much newer than schools, such as modern corporations? Is there any reason to think we know the best way to run them? It is time for everyone—from economists to bureaucrats to teachers to corporate leaders—to recognize that that they live in a world of Humans and to adopt the same data-driven approach to their jobs and lives that good scientists use.

My participation in the making of behavioral economics has taught me some basic lessons that, with due caution, can be adopted across circumstances. Here are three of them.

Observe.
Behavioral economics started with simple observations. People eat too many nuts if the bowl is left out. People have mental accounts—they don’t treat all cash the same. People make mistakes—lots of them. To paraphrase an earlier quote, “There are Humans. Look around.” The first step to overturning conventional wisdom, when conventional wisdom is wrong, is to look at the world around you. See the world as it is, not as others wish it to be.

Collect data.
Stories are powerful and memorable. That is why I have told so many in this book. But an individual anecdote can only serve as an illustration. To really convince yourself, much less others, we need to change the way we do things: we need data, and lots of it. As Mark Twain once said, “It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.” People become overconfident because they never bother to document their past track record of wrong predictions, and then they make things worse by falling victim to the dreaded confirmation bias—they only look for evidence that confirms their preconceived hypotheses. The only protection against overconfidence is to systematically collect data, especially data that can prove you wrong. As my Chicago colleague Linda Ginzel always tells her students: “If you don’t write it down, it doesn’t exist.”

In addition, most organizations have an urgent need to
learn how to learn
, and then commit to this learning in order to accumulate knowledge over time. At the very least this means trying new things and keeping track of what happens. Even better would be to run actual experiments. If no one in your organization knows how to go about running a proper experiment, hire a local behavioral scientist. They are cheaper than lawyers or consultants.

Speak up.
Many organizational errors could have been easily prevented if someone had been willing to tell the boss that something was going wrong.

One vivid example of this comes from the high-stakes world of commercial aviation, as chronicled by Atul Gawande, a champion of reducing Human error, in his recent book
The Checklist Manifesto
. Over 500 people lost their lives in a 1977 runway crash because the second officer of a KLM flight was too timid to question the authority of the captain, his “boss.” After mishearing instructions about another plane still on the same runway, the captain continued to speed the plane forward for takeoff. The second officer tried to warn him but the captain dismissed his warning, and the second officer remained quiet from then on—until the two planes collided. Gawande aptly diagnoses the cause to be an organizational failure: “[The airline was] not prepared for this moment. They had not taken the steps to make themselves a team. As a result, the second officer never believed he had the permission, let alone the duty, to halt the captain and clear up the confusion. Instead the captain was allowed to plow ahead and kill them all.”