Freakonomics summary

We are all familiar with attempts to incentivize behavior. Whether it is parents offering small treats to their children for doing schoolwork or companies paying bonuses to employees who hit their sales targets, everyone has had incentives dangled in front of them.

However, influencing behavior by adding incentives is often a more complicated affair than it might first seem. Incentives often operate in an environment where small changes can have a dramatic impact, and not always in the way those initiating the changes would hope.

Have you ever robbed a bank? Probably not, since there are a variety of disincentives (e.g., prison, loss of social stature, a guilty conscience) that keep you from trying. And yet, some people do rob banks even though they face the same disincentives. Why?

Because different people react differently to the same incentives.

Everyone needs the advice of an expert from time to time. Whether you are having something repaired, making a big purchase or dealing with a legal issue, you rely on someone with specialist knowledge to help you navigate through unfamiliar territory.

Experts have access to a wealth of information that the layperson does not, meaning an information asymmetry exists. Although the experts are usually paid a fee or commission for their expertise, they can also use their informational advantage to cheat laypeople for additional gain.

The unknown can be pretty scary. In any transaction in an area you have little knowledge or information about, you will likely be worried and anxious. Experts frequently leverage this fear for financial gain.

This can happen in a number of ways: A car salesman can convince you not to buy a cheaper model by instilling the fear that it is unsafe. A real estate agent can play on your fear of missing out on your dream house to get you to put in a higher bid. A stockbroker can tell you that if you don’t invest in a certain stock now, you’ll miss the boat and have to live with the regret for the rest of your life.

In the 1990s, the price of life insurance fell dramatically. There was no similar trend in other forms of insurance, or any significant shifts in the life insurance business or customer base itself. So, why this sudden drop in prices?

The answer lies with the emergence of the Internet, or more specifically of price comparison websites. These websites enabled customers to compare insurance prices offered by dozens of different companies in mere seconds. Price information that had been extremely time-consuming to gather just a few years earlier was available at the click of a mouse. As the policies were fairly similar in nature, the more expensive companies had no choice but to lower their prices, driving down the overall price of the policies.

One of the side effects of a culture of information asymmetry is that even a lack of information – real or perceived – can have a powerful effect.

For example, it is commonly understood that once a new car is bought it will instantly lose as much as a quarter of its value. Someone who had paid $20,000 for a car yesterday can expect to get less than $15,000 for it today.

When it comes to assessing risks, we are far less rational than we would like to believe.

One factor that disproportionately influences our assessment is how readily we can imagine the risk in question. Although they are in fact quite rare, we can easily imagine plane crashes, gun crime or terrorist attacks occurring due to their excessive coverage in the media. This leads us to over-assess the risk of these threats.

Despite having similar populations, the city of Washington DC has three times the number of police officers as Denver and eight times the number of homicides. Would you assume that the additional officers are causing the higher rate of homicide?

When we see that an increase in a certain factor, X, corresponds with an increase in another factor, Y, it is tempting to think that the relationship is causal and that the increase in X caused the increase in Y. This is a human tendency: we assume causality when in fact there may only be correlation.

In addition to jumping to conclusions about causality between two events, we also tend to look for that causality in the most immediate and obvious places, ignoring more distant or indirect causes.

Consider crime: At the end of 1989 in the United States, crime figures seemed to be going through the roof. Violent crime had increased by 80% over the previous 15 years, and experts were predicting the situation would only get worse. So it came as something of a surprise when crime figures suddenly and dramatically dropped in the early 1990s.