Evaluating US state police performance using data envelopment analysis

1. Evaluating US state police performance using data envelopment analysis

2. Outline • Abstract • Previous studies • Objective • Methodology • Empirical application • Conclusions

3. Abstract • In this paper, we evaluate the efficiency of state police services in the continental United States using a multiple-stage data envelopment analysis model. • Using multiple inputs and multiple outputs to characterize police service provision, technical and scale efficiency are calculated for the 49 continental states. • Given the complex nature of service provision, we allow for environmental factors of production and control for these non-discretionary inputs. • Our results indicate that most states are technically efficient, but nearly half are operating at less than optimal scale size.

4. Previous studies(1/2)

5. Previous studies(2/2)

6. Objective • We assess police efficiency of all US states using an expanded set of DMUs and a broader set of inputs and outputs, with improved three-stage DEA techniques to account for multiple environmental variables.

7. Methodology(1/4) • First Stage

8. Methodology(2/4) • Second Stage-regression

9. Methodology(3/4) • Forth Stage • Third Stage

10. Methodology(4/4) • Sixth Stage • Fifth Stage

11. Empirical application • Collect data from… • US Department of Justice • Bureau of Justice Statistics • US Federal Bureau of Investigation Uniform Crime Reporting • US Bureau of Census • DMUs are selected from 49 state police force from year 2000 in US.

12. Empirical application • Output for crime type • Murders • Other violent crimes • Total property crimes • Discretionary input • The number of sworn officers • The number of other employees • The number of vehicles

13. Empirical application • Environmental factor • The percent of single mother • The poverty rate • The percent of individuals in the labor force • Population • Population per square mile

14. Empirical application • Use the outputs and discretionary input in first-stage. • To decomposed the effect, use second-stage where the index from first-stage regressed on the environmental variables. • Calculate CI(cost index), the overall cost of providing police services. • Then solving fifth-stage, allows calculate SE(scale efficiency).

15. Empirical application • An improved policing environment for more dense populations. • High correlation (0.59) between the poverty rate and the percent of single mothers. • The percent of single mothers is positively correlated with the population and the population per square mile.

16. Empirical application

17. Empirical application • 123

18. Empirical application-Efficient states • California, Florida, Illinois, Michigan, New York, Ohio, Pennsylvania, and Texas have the largest cost indices and, with the exception of Texas, are technically efficient. • Illinois and Texas are observed operating on the increasing returns to scale portion of the production frontier.

19. Empirical application -Inefficient states • Arizona, Arkansas, Colorado, Delaware, Kansas,Kentucky, Louisiana, Maryland, Minnesota, Missouri,Nebraska, New Mexico, Oklahoma, Tennessee,and Texas from a diverse crosssection of population and landmass size andgeographic regions, but tend to be of larger landmasswith fewer major cities. • Arizona and Kentucky are operating at increasing returns of scale.

20. Conclusions • Most of the states are technically efficient and that the location of the frontier depends crucially on environmental factors beyond the control of the states. • To inefficient SKUs… There are opportunities for cost reduction via contraction of observed input usage. • To Arizona and Kentucky… There are opportunities for lowering average costs by changing the level of inputs.