|Benefit-Cost Summary Statistics Per Participant|
|Taxpayers||$70,385||Benefits minus costs||$473,637|
|Participants||$0||Benefit to cost ratio||$5.96|
|Others||$511,398||Chance the program will produce|
|Indirect||($12,586)||benefits greater than the costs||100 %|
|Net program cost||($95,560)|
|Benefits minus cost||$473,637|
|Detailed Monetary Benefit Estimates Per Participant|
|Benefits from changes to:1||Benefits to:|
|Adjustment for deadweight cost of program||$0||$0||$0||($47,716)||($47,716)|
|Detailed Annual Cost Estimates Per Participant|
|Annual cost||Year dollars||Summary|
|Program costs||$90,927||2011||Present value of net program costs (in 2015 dollars)||($95,560)|
|Comparison costs||$0||2011||Cost range (+ or -)||20 %|
|Estimated Cumulative Net Benefits Over Time (Non-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 non-discounted dollars to simplify the “break-even” point from a budgeting perspective. 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.|
|Meta-Analysis of Program Effects|
|Outcomes measured||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||n/a||0||-0.763||0.116||28||-0.351||0.123||28||0.000||0.001|
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.