Summary of Week 2
The historic crime rise during the 1980s and an equally drastic crime decrease in the 1990s challenged and motivated many economists to look for explanations. We saw the set of explanations offered by economist Steven Levitt, who points out the increase in the number of police, the rising prison population, the receding crack cocaine epidemic, and the legalization of abortion as four main factors that caused crime to fall during the 1990s.
After decades of research, we grew more confident of our understanding of an individual’s criminal decision. For example, holding all else equal, we expect that high-risk individuals would become more likely to commit crime when their potential earnings from legitimate jobs decrease. However, expanding this argument to predict the link between national economic and crime trends is difficult, because the idea of “holding all else equal” is unlikely to hold when looking at the crime trend at the national level.
For example, during severe recessions, many people would lose well-paying jobs and their potential earnings would fall. But at the same time, many other crime-relevant changes may take place at the national level: Government may provide more generous income support to low-income households, law enforcement may pay more attention to monitoring and catching thieves, and cash-strapped local governments may have to cut down the number of police officers. Each of these potential changes can have important effects on an individual’s criminal decision, and separating the causal effect of a job loss on crime from all these other changes would be very much difficult.
In the following weeks, we will spend more time understanding individuals’ criminal decisions. In Week 3, we will see whether having more police causes individuals to commit fewer crimes. To test this prediction, ideally we would like to run a randomized experiment in which some places receive more police officers than other places with similar background characteristics. However, running such an experiment is not going to be easy, and we will learn about clever research strategies used by economists to discover the causal effect of police on crime without actually having to run the experiment.
We also talked about linear regression this week. Linear regression is an important tool used by economists to test their theoretical predictions using actual data. The results from simple linear regression usually do not tell us the causal relationship we are interested in, but researchers can improve their study design by focusing on the within-unit variation over time using panel data, instead of across-unit variation using cross-sectional data. In the following weeks, we will see other analytic techniques economists use to make the causal interpretation of regression results more credible.
I hope you enjoyed Week 2 of the course. See you next week.
© Songman Kang, Hanyang University