2 REVIEW OF ECONOMIC STUDIES
One reason that so little is known about these important decisions is that researchers do not
generally have the power to randomize people into treatments that compel them to, say, quit
their jobs or leave their spouses. Even if it were possible to choose 1,000 married couples from
the general population and randomly force 500 of those couples to divorce, it would not be
particularly informative. Such a study would tell us about the average treatment effect of divorce.
What we really care about, however, is the impact on the marginal decision maker. It would not
be surprising if getting a divorce would have a devastating impact on the infra-marginal married
person. A much more interesting question is whether divorce, ex post, will be the right choice for
someone teetering on the edge of ending a relationship.
Even if one found such a group of individuals who are close to indifferent between remaining
married and getting divorced, an ex post comparison of the happiness of those who do and do not
make a change still would not have an easy causal interpretation, because the people who make
a change will systematically differ from those who do not on many dimensions. To convincingly
answer the question, a researcher would not only need to ﬁnd large numbers of these marginal
individuals, but also, through some sort of randomization, inﬂuence their important life choices.
That is what I do in this study. I created a website called FreakonomicsExperiments.com.
On the website, individuals who are having a difﬁcult time making a life decision are asked to
answer a series of questions concerning the decision they are struggling with. Users are presented
with a wide range of questions to choose from (see Supplementary Appendix A for the full set
of questions offered) or invited to create their own question. One choice (e.g. “go on a diet”) is
assigned to heads and the other choice (in this case “don’t go on a diet”) is assigned to tails. The
outcome of the coin toss is randomized and the user is shown the outcome of the coin toss. The
coin ﬂippers are then re-surveyed two and six months after the initial coin toss. Additionally, prior
to the randomization, coin ﬂippers are encouraged to identify a third party (a friend or family
member) to verify their outcomes. The third parties are also surveyed two and six months after
the coin toss.
While it might seem implausible that anyone would come to such a website and ﬂip a coin,
much less follow the dictate of the coin toss, the results obtained speak to the contrary. In the year
of data collection, over 20,000 coins were ﬂipped. A number of results emerge from the analysis.
First, two months into the study participants show a bias towards the status quo, in the sense
that people report making a change less frequently than they predicted they would before the coin
toss. Six months after the coin toss, however, this bias is gone.
Second, those who report making a change in follow-up surveys are substantially happier
than those who do not make a change, and they are more likely to say they would make the same
decision if they were to choose again. This is true for virtually every question asked both two
and six months later. This correlation does not, of course, necessarily imply causality. Those who
make a change differ from those who do not make a change on many dimensions.
Third, the outcome of the coin toss appears to inﬂuence the actions taken. Those who ﬂipped
heads were approximately 25% more likely to report making a change than those who got tails.
The coin toss had a roughly equal impact on decisions across the entire range of self-stated ex ante
likelihoods of making a change (i.e. the coin toss matters whether before the toss the coin-ﬂipper
says he/she has a 20%, 50%, or 90% likelihood of making the change). The coin toss was roughly
equally inﬂuential on men and women, the old and the young, and across income levels. The coin
minor decisions [e.g. what quality of baseball card to offer (List, 2002), whether to respond to a solicitation letter from a
charity (Falk, 2007), and when to make mail-order catalogue purchases (Anderson and Simester, 2003)].
2. To answer questions like that, previous research has typically had to rely on correlational studies (e.g.
Kalmijn et al., 2009; Pedersen and Schmidt, 2014) or natural experimental variation (e.g. Gruber and Mullainathan, 2005;
Meier and Stutzer, 2007), with the usual challenges to causal inference.
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