Preventing Crime Waves
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Policy Finance & Accounting Jan 1, 2011

Preventing Crime Waves

Why harsh punishments for all offenses may exacerbate crime

Criminal questioned by authority

Good_Studio via iStock

Based on the research of

Philip Bond

Kathleen Hagerty

In the twelve hours that spanned the night of April 15, 2010 and the wee morning hours of April 16, gun violence in Chicago claimed the lives of seven people and wounded fifteen.

But that one night was merely a footnote in a violent crime wave that was gripping the city. Things had gotten so bad that earlier in the month gunshots interrupted the Chicago police superintendent’s press conference about the spike in gun violence. Residents and elected officials called for assistance from the Illinois National Guard. It was not the first time violent crime had spiraled out of control in the city, nor would it be the last.

Gun violence eventually returned to more “normal” levels without the help of the National Guard. (Chicago’s homicide rate is three times New York’s, so normal is a relative term.) Still, the violent month of April 2010 again raised a perennial question in law enforcement: how to best respond to crime waves, or even normal levels of crime?

One answer may come from the world of finance, which has its own criminal element. Researchers Kathleen Hagerty, a professor of finance at the Kellogg School of Management, and Philip Bond, a professor at the University of Pennsylvania, used their expertise in financial regulation enforcement to study crime waves and determine how authorities can best prevent them. Typical proposals have included increasing monitoring, ratcheting up investigations, and imposing stiffer penalties, and Hagerty and Bond looked at them all. Politicians favor tough penalties because they are cheap and psychologically reassuring, but in reality, harsh penalties may make things worse.

Strict Theories
Politicians are not alone in recommending harsh penalties to combat crime. Many theories advocate imposing maximum penalties, which can include massive fines, life sentences, or even the death penalty—it does not really matter, so long as the hypothetical penalty is harsh enough to deter all but the most criminal element. Though such punishments are often unrealistic (or morally questionable), these theories draw on some sound logic: enforcing laws and conducting investigations are expensive, but deterring crime through harsh penalties is cheap, which according to existing theories, makes them the most efficient deterrent.

“But you don’t see penalties like this in real life,” Hagerty points out. The reasons for this are sundry, but include mankind’s propensity to make mistakes, society’s general sense of fairness, and the impossibility of implementing certain maximum penalties, such as sufficiently large fines, she adds.

“Deterring crime through harsh penalties is cheap, which according to existing theories, makes them the most efficient deterrent.”

“One thing you do see is marginal penalties,” Hagerty says. “Marginal penalties are ‘different crimes get different penalties.’ That’s the thing you see most often. Little crimes get little penalties and big crimes get big penalties.” It is a sensible approach, one that is generally accepted throughout the world. But economists and other academics lacked a convincing theory as to why maximum penalties are the exception rather than the rule.

So Hagerty and Bond tested the marginal penalty problem the humane way—through mathematical modeling. Their equations revealed that while maximum penalties suppress crime in most circumstances, they create an all-or-nothing world for criminals—one in which crime either ceases to exist or exists ceaselessly.

The paradox of maximum penalties stems from both human nature and the realities of law enforcement. People have different tendencies toward criminal behavior, something that was not recognized in much of the earlier literature that promoted maximum penalties. At one extreme are people who have zero taste for crime; nothing will entice them to commit a crime. At the other end are those for whom no penalty is severe enough to deter them. They pose a significant problem. Namely, they guarantee that no society will ever be free of crime, no matter how draconian the punishment.

The people in the middle, however, are wild cards. Their decisions influence crime rates greatly. They might commit a crime if the corresponding penalty were mild or the probability of getting caught were sufficiently low. Eventually, some in that group will commit minor offenses, and under a system of marginal penalties they will be sentenced accordingly.

But under a system of maximum penalties, these small-time criminals might decide to up the ante. After all, by committing any crime, they would already be facing the maximum penalty, so they would be inclined to commit a crime that would bring them the maximum benefit, like robbing a bank instead of a convenience store. As more criminals committed more egregious offenses, law enforcement would become overwhelmed. This, in turn, would decrease the chances of the criminals’ getting caught, which would encourage more people to commit crimes. A crime wave would ensue.

Lighten Up
To break a crime wave under a system of maximum penalties, Hagerty and Bond recommend not more patrols or more investigations, but rather less severe punishments. People with a moderate taste for crime would return to committing smaller offenses, as the penalty schedule would no longer encourage them to move up the criminal ladder. With fewer severe crimes to investigate, law enforcement could regain its balance.

The difficulty for law enforcement is determining what “normal” crime rates should look like. Marginal penalties could entice some people to commit small crimes, so penalties should not be too light, and penalties that are too severe could encourage criminals to commit more egregious acts. Unfortunately, the paper does not offer any advice on this matter.

What it does provide are guidelines for how enforcement agencies should allocate their limited resources. The answer is deceptively simple—know the people they are monitoring. For example, police officers would be more effective if they spent more time getting to know the people on their beat. That way, when a crime is committed, the officers would have a better sense for who might have done it and could more efficiently direct an investigation. Hagerty cites community policing, whereby officers walk the streets rather than patrol them from squad cars, as an example of effective monitoring behavior.

Though few systems employ maximum penalties—North Korea may come close, but even there sentences vary in length—the three-strikes laws many states have on their books are a step in that direction. Under such laws, the first two crimes a person commits receive the normal sentences, but the third is punished more severely. This can cause criminals to escalate the severity of their third crime, much as small-time criminals do under threat of a maximum penalty.

Though Hagerty and Bond’s paper is a theoretical exercise, it does inch experts closer to understanding which penalties can best deter crime. It also frees theory of the assumption that law enforcement can dynamically scale its enforcement resources up or down to varying levels of criminal activity. “When enforcement resources are limited, the decision to commit a crime depends on what everyone else is doing. That comes up in a lot of settings,” Hagerty says, from participating in riots to speeding to cheating on your income taxes.

Related reading on Kellogg Insight

Reward Now, Punish Later: Punishment alone may not change social norms

Judging the Jury Vote: Explaining the pitfalls of total agreement

Featured Faculty

Provost of Northwestern University; First Chicago Professorship in Finance, Kellogg School of Management; Professor of Finance

About the Writer
Tim De Chant was science writer and editor of Kellogg Insight between 2009 and 2012.
About the Research

Bond, Philip, and Kathleen Hagerty. 2010. Preventing Crime Waves. American Economic Journal: Microeconomics. 3(2): 1-24.

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