Predicting Homicide Clearances in Chicago

Abstract

Few studies examine homicide clearances despite trends across many jurisdictions that show a decrease in homicides cleared each year. This study addresses that research gap using Chicago homicide data from 1991 to 2002 to identify predictive factors in homicide clearances. Across five logistic regression models we found that victim and incident characteristics were significant predictors of homicide clearances, although the number and types of significant predictors were varied across the analyses. Moreover, we found differences in the factors that significantly predicted clearances for three specific homicide circumstances: expressive, instrumental, and gang-related.