Simon Hakim Temple University Brian Meehan Berry College Erwin Blackstone Temple University The Problem of Police Burglar Alarm Response Police burglar alarm response is 9499 percent to false alarms resulting from users and technological errors ID: 915870
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Slide1
Enhancing Police Productivity Using Private Security: The Case of Verified Response In Salt Lake City.
Simon Hakim,
Temple
University
Brian
Meehan, Berry
College
Erwin Blackstone, Temple University
Slide2The Problem of Police Burglar Alarm Response
Police burglar alarm response is 94-99 percent to false alarms, resulting from users’ and technological errors.
It is estimated that city police departments allocate 20-30 percent of their time to false alarm response. (Blackstone et al. 2005).
This is a significant drain on public sector resources.
Slide3Alarm Response is a Quasi-Public Good
Police response to a valid alarm, which results in arrest or apprehension, decreases the pool of potential burglars, reducing the probability of burglary or break in to the surrounding community.
These spillover benefits from valid alarm response are somewhat non-excludable.
But this is only 1-6 percent of alarm response!
Slide4False Alarm Response is a Private Good
Response to a false alarm does not decease this pool of criminals, instead it allocates police resources away from potentially productive uses.
False alarm response is a private good.
Given that 94-99 percent of response is to false alarms we shouldn’t treat alarm response as a pure public good.
Slide5Potential Policy Solution: Verified Response
Salt Lake City ordinance put in place Dec. 2000.
VR requires physical or visual verification of an actual or attempted penetration before police are dispatched.
Thus, alarm owners designate individuals, including themselves, or private response companies to physically attend the premises, or using cameras and smartphones visually check the premises.
Slide6Slide7Synthetic Control Comparison
Slide8Synthetic Predictors Constructed from Donor Pool for Alarm Response Model
Treated
Synthetic
Number of
free police responses
4
3.529
Population
175901
176258.3
City Police
Officers
404.33
405.191
Unemployment Rate
3.2667
3.927
Income
Per Capita (2015 $)
37421.51
37415.42
2000 Alarm
Responses
9439
9448.33
1998 Alarm
Responses
10542
9837.8
Slide9City Weights From Donor Pool Used to Construct Alarm Response Synthetic Control
City
Weight used to construct Synthetic SLC
Fayetteville, NC
.216
Honolulu, HI
.102
Asheville, NC
.563
Leawood, KS
.023
Orlando, FL
.096
Evansville, IN
0
Irvine, CA
0
Greensboro, NC
0
Stockton, CA
0
Slide10Slide11Synthetic Control Model for Burglaries
Slide12Synthetic Predictors Constructed from Donor Pool for Burglary Model
Treated
Synthetic
Number of
free police responses
4
3.529
Population
175901
176258.3
City Police
Officers
404.33
405.191
Unemployment Rate
3.2667
3.927
Income
Per Capita (2015 $)
37421.51
37415.42
2000 Burglaries
2169
2172.342
1998 Burglaries
2831
2834.946
Slide13City Weights Used to Construct Burglary Synthetic Control
City
Weight used to construct Synthetic SLC
Fayetteville, NC
.017
Honolulu, HI
.003
Asheville, NC
0
Leawood, KS
0
Orlando, FL
.236
Evansville, IN
.412
Irvine, CA
0
Greensboro, NC
.061
Stockton, CA
.272
Slide14Slide15Abadie, A. Diamond, A., and Hainmueller. 2010. Synthetic Control Methods for Comparative Case Studies of Aggregate Interventions: Estimating the Effect of California’s Tobacco Control Program. Journal of the American Statistical Association 105(400). 493-505