Many of our decisions, both inside and outside the investment world, are often based on anecdotal information, anomalies, emotions, or existing opinions. SuperFreakonomics illustrates how applying an economic approach can help us change this. Investors can use the tools described in this book, including better and more prevalent use of data, along with an an understanding of the power of incentives to make better decisions.
In this chapter, the authors describe several instances of how the use of data can be employed to make better decisions. The authors start by explaining some strange anomalies with respect to one's birth month. Those who believe in horoscopes can point to many cases where birth months appear to play an abnormal role in predicting an outcome; the authors delve into the data to explain why.
For example, babies born in a certain month of the year (for 2010, it would be May) are 20% more likely to have a learning disability. This is because of religious fasting during the month of Ramadan; when pregnant mothers fast, their children are more likely to have disorders.
In other examples, a US-born child is 50% more likely to play professional baseball if he is born in August (vs July), whereas a British-born child is far more likely to play pro soccer if he is born in January (vs December). This is because the cut-off dates for little league baseball and youth soccer are July 31st and December 31st respectively, and so kids that excel under that format are given the most encouragement, confidence and playing time. (That is, a four-year old born in August will be much more able than his teammate born almost a year later in July.)
The authors then detail how such data mining can be applied to help improve our lives. Mining medical records can help identify the best (and worst) doctors. Mining banking data can help identify terrorists.
Using outed terrorists as a guide, the authors sought to create a banking profile to help identify terrorists still living among us. This is a challenging exercise for many reasons, not the least of which is the fact that because there are so few terrorists, inaccuracies in prediction are liable to yield scores of false positives, the investigation of which would overburden authorities. The authors provide some examples of the behaviours of terrorists (and omit some, because of the top secret nature of this work), including the fact that terrorists do not buy life insurance, do not have savings accounts, and were unlikely to use an ATM on a Friday afternoon (potentially due to prayers). This work is ongoing, but the authors believe the progress and future potential of this work is strong.