Economics, Econometrics, and Police Brutality

If you live in the U.S., you are probably aware of the high-profile police brutality cases that have incited uproar across the nation. From an economist’s perspective, this is a difficult topic to analyze; there are many reasons why these cases occur and using general principles to understand these events seems impossible. But all is not lost. The key to using economic principles to understand brutality lies in the types of questions we ask. In general, economists can answer two kinds of questions: (1) is it likely that A caused B?  and (2) what reactions should we expect from rational agents in this context?

Do police brutality cases tend to increase or decrease crime? Do police brutality cases affect crime rates differently depending on the race of the officer or victim? Do police enforce the law differently after a brutality case? Do rates of police brutality vary by race of officer or victim? These are all examples of cause and effect questions. Though economists can never be sure whether their answers to any of these questions are correct, econometrics can show how likely a particular causal relationship is. For instance, one way of understanding the effect of police brutality cases on crime rates could be to compare the average crime rate in a city before and after a brutality event occurred. Answering this question would require data about the timing of brutality events in multiple cities.

Unfortunately, this is not a good way of answering the causal question of interest. There could be reasons other than the brutality incident that caused systematic changes in crime rates after an event. For example, what if cities that tend to have more police brutality also have larger police forces, so police curb increased crime in these cities more efficiently. Thus, police force size (or another characteristic that is correlated with police brutality) could confound the effect of interest. This issue is called the endogeneity problem, and good econometricians have to circumvent this problem to answer almost every interesting cause and effect question.

But how do economists narrow the field of possible cause and effect relationships to look for? This question is linked to the second type of question that economists can answer: what reactions should we expect from rational agents in this context? Economists (or non-behavioral microeconomists at least) base their causal predictions on what is called Rational Choice Theory. Rational Choice Theory assumes that people make decisions to maximize their well-being (“utility” in economics) and that they can decide between alternatives.

How does that help us understand police brutality? We can see the power of Rational Choice Theory if we imagine the decisions that a police department faces after an officer uses excess force. If this is a high-profile or racially charged case, how might we expect the police department to respond? If this event puts media and public scrutiny on the department, the cost of another mistake could be extremely high. Furthermore, if civilians are frustrated with the police department, the chance of retaliatory behavior against the police may go up. Rational Choice Theory dictates that police officers will “ease up” in an attempt to reduce high-cost errors (for an empirical analysis of this theory see this paper by Lan Shi). Further, police may not police as aggressively because the chance of being attacked goes up. Rational Choice Theory provides the insight required to predict how humans make decisions when costs and benefits change.

Now that we understand the types of questions that economists can answer, we have a framework for understanding the causes and effects of police brutality more clearly. I intend to propose multiple ways to analyze police brutality in a series of posts following this one. In particular, I will use the insights of Rational Choice Theory to decide whether certain proposed causal theories make sense, and then I will discuss testable predictions and the type of data that might be useful for supporting or falsifying that theory. Through this, I hope to both articulate my opinions and clarify my research ideas on this topic. I am not so confident as to think that I can fully understand these issues, so I appreciate any and all comments or corrections.


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