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Reintroducing "Time" Into the Time Series Analysis of the Police Size-Crime Relationship: An Error Correction Approach

NCJ Number
224949
Journal
Policing: An International Journal of Police Strategies & Management Volume: 31 Issue: 3 Dated: 2008 Pages: 499-513
Author(s)
Mitchell B. Chamlin; Beth A. Sanders
Date Published
2008
Length
15 pages
Annotation
This paper examined the causal relationship between crime rate measures and police force size within Milwaukee, WI, using annual data covering the years 1930 to 2004.
Abstract
The findings point to the conclusion that, when studying causal processes that operate over time, one must be careful to remove long run information from the data in the attempt to control for the spurious effects of autocorrelation. How one attempts to eliminate the specious effects of time can affect whether or not one finds a statistically significant association between two time series. As the current investigation makes clear, this is particularly true for the macro-social relationship between measures of police size and crime. Consistent with past research, the bivariate ARIMA (time series models) analyses yield no evidence of a short-term association between police force size and crime. However, the parameter estimates from error correction models indicate that changes in the level of crime have a longer-term impact on police force strength. In a first attempt, this paper specifies error correction models to examine the bivariate association between police size and total, property, and personal crime rates for a large midwestern city, Milwaukee, WI. Tables, references

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