Freaknomics: A Rogue Economist Explores the Hidden Side of Everything is the book for readers who run screaming at the thought of cracking open a book with the word “economics” in the title. When most readers think economics, they think advanced math, complicated models, and subjects like unemployment, the stock market, and the trade deficit.
In Freakonomics, authors Steven D. Levitt and Stephen J. Dubner take a different approach. They use economics to, as the title says, “explore the hidden side of everything.” They argue that economic motivations determine much of human behavior, and demonstrate that economic analysis can be applied to a wide range of problems in the real world.
This approach to economics is empirical, not theoretical.
Throughout, the authors emphasize some key themes.
Simply put, human beings are self-motivated creatures. They are driven by their desire to get what they want, and they will always look for ways to achieve their ends by the easiest or least-costly means.
Therefore, proper incentive design is key, and poorly designed incentives often misfire. For example, it might seem like a great idea to hold individual teachers accountable for their students’ scores on standardized tests—until that incentive drives those same teachers to start cheating on behalf of their students!
Not all incentives are purely monetary. There are economic, social, and moral incentives, and they all exert a powerful grip on human behavior. The key for economists and for those who wish to move public policy in a certain direction is to harness the power of incentives to make them work toward some desired goal.
Conventional wisdom is easy to understand, comforting, and confirms our own preconceived notions—it’s not necessarily correct. Conventional wisdom held that violent crime would keep soaring through the...
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Are we right to accept the opinions and recommendations of experts? Are there perhaps deeper mechanisms at work that might explain why human beings behave the way they do and why events unfold as they do? In fact, much of the conventional wisdom we have come to accept is false.
As the decade of the 1990s dawned, the US airwaves were awash with experts claiming that a new crime wave (far more terrifying than the just-passed crime wave of the 1980s) was on the horizon, to be driven by a fresh cohort of violent, amoral young “superpredators,” who would soon be entering the prime years of their criminal careers.
Except that the crime wave just...never happened. In fact, a historic drop in crime would be a hallmark of the period beginning in the 1990s and lasting through to the present day. In a later chapter, we’ll discuss how data and analysis were used to unpack this misconception.
At their most basic, incentives are stimuli that encourage “good” behavior and discourage “bad” behavior. (Think of how you might give a dog a treat for sitting when she’s told to). As we’ll see throughout Freakonomics,...
There are three main kinds of incentives:
This is the type we’re most familiar with. Economic incentives provide tangible rewards to people for engaging in productive behavior and penalties on people for destructive behavior. Getting a raise at work for exceeding expectations, a speeding ticket, or even being sentenced to prison (which deprives one of one’s freedom and livelihood), are all economic incentives.
Social incentives are rooted in our natural desire to be looked upon favorably by others. On the flip side, we fear being shamed and looked upon disfavorably by our peers. To harness social incentives, many jurisdictions in the US have started publishing the names and photos of people arrested for prostitution-related offences—likely a far stronger deterrent than a $500 fine!
Most of us want to do what we consider to be “right,” and avoid doing things that we consider to be “wrong.” Thus, there is a powerful moral incentive against committing crimes or engaging in any behavior that causes harm to others. Uniquely, **moral incentives are...
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If you define the incentive in a situation, you’ll understand the human behavior better.
A bank decides to increase its profit margins and motivate its workforce by creating a new incentive program: employees receive bonuses for having customers open new accounts or sign up for additional products and services. Employees are also threatened with termination if they do not meet predetermined quotas assigned by management. Describe how this incentive structure could end up encouraging bad or unethical behavior.
As the world has become more specialized and complex, people have come to rely more and more on experts to guide them through major life decisions. This is largely driven by fear of making a wrong decision that might result in financial ruin or even physical harm.
As we’ve discussed above, however, experts are hardly neutral arbiters of truth who are selflessly devoted to guiding you through the trials and tribulations of a staggeringly complex world. Rather, they are often self-interested, equally fallible humans who seek to use their superior information to gain an advantage on you. This unequal distribution of information between parties to a transaction is known as information asymmetry.
Think of the mechanic who tells you that you need to replace parts of your engine that you’ve never heard of to be able to pass your vehicle inspection. Or the doctor who orders that MRI that you’re not quite sure you needed. Or the car salesperson who insists that you need all of those pricey add-on safety features. All of these experts know perfectly well that you know nothing about their business. Often, **your information deficit is...
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In information asymmetry, whoever has more information has more power.
Can you think of a major public issue or event where information asymmetry is a factor? Explain how an information gap is at play.
Experts are major purveyors of conventional wisdom. Conventional wisdom comprises those beliefs or ideas that are commonly accepted, regardless of their truth or predictive value. It is an impediment to actual knowledge and discovery, often motivated by a mix of self-interest, convenience, and a desire to avoid dealing with the actual complexity of the world. Conventional wisdom is often anecdotal and is rarely backed up by hard, quantitative data.
Let’s set the stage. The 1980’s were marked by a tide of rising crime in The United States. A great deal of the violence in the inner cities was driven by crack-cocaine. According to one study, crack accounted for a shocking 25 percent of homicides in New York City in 1988.
It was in this context that the conventional wisdom of “crack millionaires” was born. The myth was largely propagated by police officers, criminologists, and others in the law enforcement community. They argued that dealers were making money hand-over-fist and living extravagant, luxurious lifestyles. Furthermore, these experts warned, the profits from crack dealing were...
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Be skeptical of conventional wisdom - what everyone believes might be wrong.
What’s a piece of conventional wisdom that you used to believe in? How did you come to realize that it was incorrect?
(Shortform note: this chapter discusses the dramatic and unexpected fall in US crime rates beginning in the early 1990s, examining a number of popular theories for why this happened before proposing an alternative, and quite counterintuitive, explanation. As we’ll discuss later, the authors’ theory has become controversial since the initial publication of Freakonomics.)
We discussed the crack-fueled violence of the 1980s in the preceding chapter. But crack was just one part of an alarming rise in violent crime that had begun in the 1950s and continued for a generation. In 1989, violent crime in America was at a peak, having risen 15 percent since 1975.
Criminologists and law enforcement personnel warned the public that these trends would continue well into the 1990s and beyond. But this also proved to be wrong.
Beginning in the mid-1990s, crime began an unexpected and precipitous decline, dropping to levels not seen since the 1950s. The much-predicted (and much-feared) rise in murders, rapes, burglaries, and assaults simply failed to...
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When two things happen together, it’s tempting to believe that one caused the other. For example, in the previous chapter, we saw that the economy improved while crime rates dropped. This seems like a satisfying explanation, until the data show the economy couldn’t have had a large effect.
In reality, many correlated phenomena are correlated purely by chance. This gives rise to the well-known saying, “correlation does not imply causation.”
(Shortform note: this underlies a lot of popular superstitions, like people who wear their “lucky hats” to baseball games because they think it helps their team win.)
A great demonstration of the correlation/causation trap can be found in the proliferation of popular theories about how “best” to raise children. For years, childcare experts have advocated contradictory and ever-changing theories:
Because the stakes are so high,...
Sometimes, one thing that causes another turns out to be completely unrelated.
Have you ever thought that something was caused by one thing, only to discover that the true cause was actually something else? Describe the situation.
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(Shortform note: this chapter is meant to illustrate what we can learn from applying statistical tools like regression analysis to a rich and large dataset. Like other chapters in Freakonomics, it employs an unusual, counterintuitive, and decidedly non-economic story—in this case, the effect on life outcomes of individuals with black-sounding names—as a framing device for economic principles. When reading the book, it can be easy to get lost in the minutiae of the individual stories, so we reinforce the larger, overarching ideas.)
Economics is not an easy field in which to conduct randomized controlled experiments. Since much of economics focuses on quantities that are very large (national GDP, effects of fiscal policy on employment) and involves the study of individual decisions made by billions of people, it’s rare for economists to be able to test their hypotheses in a lab.
For this reason, economists must observe events in the real world that happen create “variable” and “control” groups that can substitute for the classical design of a laboratory experiment. These real world events are called natural experiments....
(Shortform note: This section is composed of a series of articles from the New York Times Magazine, including a “Freakonomics” column that ran in the magazine from 2005-2006, as well as select entries from the “Freakonomics” blog that were added to the revised and expanded 2006 edition to the book. These should be viewed as supplemental readings only. They aren’t necessary for understanding the larger themes of the main Freaknomics text, and many of them simply restate or summarize the case studies featured in the original book.
From these articles, you should gain a deeper understanding of why the author’s focus on the stories that they do, how they define their approach to economics, what they think are the main drivers of behavior (incentives), and how data analysis can provide powerful insights into how the world actually works.)
This article discusses Levitt’s focus on applying an economic lens and economic tools to explain events and behaviors that are traditionally outside the realm...
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Think like an economist in everyday life.
In a few sentences, explain how experts use information asymmetry and correlation/causation fallacies to mislead people. Please cite examples from Freakonomics.