Crime Clearance and Temporal Variation in Police Investigative Workload: Evidence from National Incident-Based Reporting System (NIBRS) Data - Roberts, Aki (Journal of quantitative criminology, 2016)
Objectives Police workload’s relationship with crime clearance has been studied widely. In the challenging environment now facing police, even small and possibly temporary changes in investigative workload could harm clearance. However existing workload-clearance research either used only a yearly average that obscures temporal variability in caseload, or explored proxy rather than direct measures of workload’s short-term variation. Our improved workload measures capture caseload’s daily changes as crimes are reported, cleared, or remain uncleared but reach the end of active investigation. We examine relationships between clearance and both long- and short-term variability in workload. Methods Using NIBRS and LEMAS data, we calculated between-agency (typical or long-term) and time-varying, within-agency (daily fluctuating or short-term) workload measures. We used these and other agency/jurisdiction- and incident-level variables in multi-level survival analysis of clearance by arrest for serious violent incidents from 2007 NIBRS. Results Both workload measures were significantly and negatively related to the clearance hazard rate; higher long- and short-term workloads are associated with reduced chance of a case being cleared. The estimated relationship between longterm workload and clearance became progressively stronger (more negative) as the crime incident’s legal seriousness decreased. However, estimates indicated greater sensitivity of the clearance hazard to short-term workload fluctuations for more serious crimes, though the workload-clearance relationship remained negative for all crime types. Conclusion Crime clearance should be considered by police agency planners when addressing workload through staffing decisions. Refinement of our workload measures will require additional information, and should be considered in future agency- and incident-level data collection.