Modeling Crime Trends: a Criminal Opportunity Perspective

Abstract

A macrodynamic social indicator framework is used to demonstrate how accurate crime rate forecasts can be produced. Trends in reported robbery, burglary, larceny, and auto theft rates for the United States are modeled, using annual data for the years 1947-72. The poverty ratio and unemployment rate, two variables considered important predictors of crime in the traditional criminological literature, fail to account for the index crime trends in this analysis. A "criminal opportunity perspective" is used to formulate several substantively meaningful "social produc tion functions" for the above crime rate trends, showing how relatively moderate social changes can generate rather dramatic increments in the crime rate. For example, I consider how the participation of women in the labor force, the incidence of persons living alone, and the presence of lightweight durable goods provide offenders with opportunities favorable for carrying out the above illegal acts. Stochastic equations estimating these production functions indicate that the null hypothesis of no autocorrelation of disturbances is consistently accepted. Ex post forecasts of 1973-75 reported crime rates used to gauge the accuracy of the models usually err within a few percentage points. The data presented here indicate the efficacy of including criminal opportunity factors in crime rate forecasting designed to supply policy makers with technical information relevant to organizational goal criteria.