Standard theory starts with the assumption that people are saving the exact right amount (not to mention investing intelligently).
Even if an economist did want to help out on such a project, she would only have one policy tool to play with, namely the after-tax financial return on savings. The standard theories of saving such as those offered by Milton Friedman or Franco Modigliani implicitly make the very strong prediction that no other policy variable can matter, since the other factors that determine a household’s saving—such as age, income, life expectancy, and so forth—are not controlled by the government.
Higher rates of return mean that it takes less saving to achieve a given retirement savings goal. Someone who is trying to accumulate a specific nest egg can achieve that goal with less saving if rates of return go up.
One of the problems in determining the effect of a change in the tax law is that to qualify for the low tax rate investors have to satisfy other rules, such as putting the money in a special account, possibly with penalties for withdrawals before retirement. The special account may facilitate saving in two ways. First, the penalty for withdrawal acts as an inducement to leave the money invested. Second, a mental account that is designated as “retirement saving” is less tempting to dip into than a simple savings account.
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 give them that same choice tomorrow at 2 p.m., few would be able to wait until 2:15. They are present-biased.
By comparing the choices of people who joined before automatic enrollment with those who came after, Madrian and Shea were able to show that some employees would have selected a higher saving rate if left to their own devices. In particular, many employees had heretofore picked a 6% savings rate—the rate at which the employer stopped matching contributions. After automatic enrollment came in, there were fewer people choosing 6% and more choosing 3%. This is the downside of automatic enrollment.
Participation rates depended strongly on the ease with which employees could learn about the program and sign up.
Bulk of the saving generated by automatic saving plans is “new.” When someone moves to a company with a more generous retirement saving plan and automatically starts saving more via that plan, there is neither a discernible decrease in savings in other categories nor an increase in debt. In a world of Econs this result would be surprising because Econs treat money as fungible and are already saving just the right amount, so if an employee is forced or nudged into saving more in one place, she would just save less or borrow more somewhere else.
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.”
“Asymmetric Paternalism.” They defined their concept this way: “A regulation is asymmetrically paternalistic if it creates large benefits for those who make errors, while imposing little or no harm on those who are fully rational.”
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. But we all enjoy having the right to choose for ourselves, even if we sometimes make mistakes. Are there ways to make it easier for people to make what they will deem to be good decisions, both before and after the fact, without explicitly forcing anyone to do anything?
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.
A nudge is some small feature in the environment that attracts our attention and influences behavior. Nudges are effective for Humans, but not for Econs, since Econs are already doing the right thing. Nudges are supposedly irrelevant factors that influence our choices in ways that make us better off.
What if we could design policies that were equally easy to create “user-centered” choice environments?
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!
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%.
The official mission of the Behavioural Insights Team (BIT) was left broad: to achieve significant impact in at least two major areas of policy; to spread understanding of behavioral approaches across government; and to achieve at least a tenfold return on the cost of the unit. The basic idea was to use the findings of behavioral science to improve the workings of government.
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.†
Everyone received a reminder letter explaining how their bill could be paid, and aside from the control condition, each letter contained a one-sentence nudge that was some variation on Cialdini’s basic theme that most people pay on time. Some examples:
The great majority of people in the UK pay their taxes on time.
The great majority of people in your local area pay their taxes on time.
You are currently in the very small minority of people who have not paid their taxes on time.
Ethical nudges must be both transparent and true.
All the manipulations helped, but the most effective message combined two sentiments: most people pay and you are one of the few that hasn’t. This letter increased the number of taxpayers who made their payments within twenty-three days§ by over five percentage points. Since it does not cost anything extra to add a sentence to such letters, this is a highly cost-effective strategy. It is difficult to calculate exactly how much money was saved, since most people do pay their taxes eventually, but the experiment sped up the influx of £9 million in revenues to the government over the first twenty-three days.
I found myself repeating two things so often they became known as team mantras.
If you want to encourage someone to do something, make it easy.
The first step in getting people to change their behavior as “unfreezing.” One way to unfreeze people is to remove barriers that are preventing them from changing, however subtle those barriers might be.
We can’t do evidence-based policy without evidence.
An equally important innovation was the insistence that all interventions be tested using, wherever possible, the gold-standard methodology of randomized control trials (RCTs)—the method often used in medical research. In an RCT, people are assigned at random to receive different treatments (such as the wording of the letters in the tax study), including a control group that receives no treatment (in this case, the original wording).
Potential pitfalls of randomized controlled trials in field settings. Such experiments are expensive, and lots of stuff can go wrong. When a lab experiment gets fouled up, which happens all too often in labs run by Humans, a relatively small amount of money paid to subjects has been lost, but the experimenter can usually try again. Furthermore, smart experimenters run a cheap pilot first to detect any bugs in the setup. All of this is hard in large-scale field experiments, and to make matters worse, it is often not possible for the experimenters to be present, on site, at every step along the way. Of course, scientists skilled at running RCTs can reduce the risks of errors and screw-ups, but these risks will never disappear.
It is also crucial to understand that many improvements may superficially appear to be quite small: a 1 or 2% change in some outcome. That should not be a reason to scoff, especially if the intervention is essentially costless. Indeed, there is a danger of falling into a trap similar to the “big peanuts” fallacy exhibited by the game show contestants. A 2% increase in the effectiveness of some program may not sound like a big deal, but when the stakes are in billions of dollars, small percentage changes add up.
These savings interventions combined three important ingredients that greatly increase the chance that a program will achieve its stated goal. First, the program designers have a good reason to believe that a portion of the population will benefit by making some change in their behavior. In this case, with many people saving little or nothing for retirement, that was an easy call. Second, the target population must agree that a change is desirable. Here, surveys indicated that a majority of employees thought they should be saving more. Third, it is possible to make the change with one nearly costless action (or in the case of automatic enrollment, no action at all). I call such policies “one-click” interventions. Simply by ticking a box, someone who signs up for Save More Tomorrow sets himself on a course that will increase his saving rate over time, with no need to do anything else.
Those looking for behavioral interventions that have a high probability of working should seek out other environments in which a one-time action can accomplish the job. If no one-time solution yet exists, invent one!
In a study in Ghana, the nonprofit Innovations for Poverty Action ran a randomized control trial testing whether text message reminders to take malaria medication helped people follow through with the medical regimen. Not only did they find these texts to be effective, but they also found that the most effective messages were brief; it was the reminder, not any additional information, which mattered.
most of economic theory is not derived from empirical observation. Instead, it is deduced from axioms of rational choice, whether or not those axioms bear any relation to what we observe in our lives every day. A theory of the behavior of Econs cannot be empirically based, because Econs do not exist.
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.
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.¶
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.
Observe. Behavioral economics started with simple observations.
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.”
The only protection against overconfidence is to systematically collect data, especially data that can prove you wrong.
The ideal organizational environment encourages everyone to observe, collect data, and speak up. The bosses who create such environments are risking only one thing: a few bruises to their egos. That is a small price to pay for increasing the flow of new ideas and decreasing the risks of disasters."