|Benefit-Cost Summary Statistics Per Participant|
|Taxpayers||$70,181||Benefits minus costs||$438,679|
|Participants||$0||Benefit to cost ratio||$5.32|
|Others||$485,819||Chance the program will produce|
|Indirect||($15,713)||benefits greater than the costs||100 %|
|Net program cost||($101,608)|
|Benefits minus cost||$438,679|
|Meta-Analysis of Program Effects|
|Outcomes measured||Treatment age||No. of effect sizes||Treatment N||Adjusted effect sizes(ES) and standard errors(SE) used in the benefit - cost analysis||Unadjusted effect size (random effects model)|
|First time ES is estimated||Second time ES is estimated|
Crime elasticity: property
Change in property crime rates in response to a particular policy change.
|Detailed Monetary Benefit Estimates Per Participant|
|Affected outcome:||Resulting benefits:1||Benefits accrue to:|
|Crime elasticity: property||Crime||$70,181||$0||$485,819||$35,090||$591,091|
|Program cost||Adjustment for deadweight cost of program||$0||$0||$0||($50,804)||($50,804)|
|Detailed Annual Cost Estimates Per Participant|
|Annual cost||Year dollars||Summary|
|Program costs||$90,927||2011||Present value of net program costs (in 2018 dollars)||($101,608)|
|Comparison costs||$0||2011||Cost range (+ or -)||20 %|
Benefits Minus Costs
Benefits by Perspective
Taxpayer Benefits by Source of Value
|Benefits Minus Costs Over Time (Cumulative Discounted Dollars)|
|The graph above illustrates the estimated cumulative net benefits per-participant for the first fifty years beyond the initial investment in the program. We present these cash flows in discounted dollars. If the dollars are negative (bars below $0 line), the cumulative benefits do not outweigh the cost of the program up to that point in time. The program breaks even when the dollars reach $0. At this point, the total benefits to participants, taxpayers, and others, are equal to the cost of the program. If the dollars are above $0, the benefits of the program exceed the initial investment.|
Evans, W.N., & Owens, E.G. (2007). COPS and crime. Journal of Public Economics, 91(1-2), 181.
Levitt, S.D. (2002). Using electoral cycles in police hiring to estimate the effects of police on crime: Reply. The American Economic Review, 92(4), 1244-1250.
Lin, M. (2009). More police, less crime: Evidence from US state data. International Review of Law and Economics, 29(2), 73-80.
McCrary, J. (2002). Using electoral cycles in police hiring to estimate the effect of police on crime: Comment. The American Economic Review, 92(4), 1236-1243.
Shi, L. (2009). The limit of oversight in policing: Evidence from the 2001 Cincinnati riot. Journal of Public Economics, 93(1), 99-113.
Worrall, J.L., & Kovandzic, T.V. (2010). Police levels and crime rates: An instrumental variables approach. Social Science Research, 39(3), 506-516.