mates of the benefits of crime reduction has just begun to be explored. A program of research to improve bottom-up measures, refine and apply CV methods, and compare the results of these two approaches is in order.

As observed above, Monte Carlo analysis offers a way to address the uncertainty of estimates of benefits and costs. Monte Carlo analysis strengthens the credibility of a study’s findings and needs to be a routine element of future BCAs. The WSIPP model under development (Aos and Drake, 2010), which will have the capacity to execute Monte Carlo simulations, will substantially improve BCAs of criminal justice programs.

Finally, because evaluations of juvenile justice interventions understandably focus on changes in criminal behavior, they generally do not collect data on changes in noncriminal behavior, such as education or employment attributable to an intervention. Because these changes typically have positive benefits, ignoring them underestimates an intervention’s total benefits. A more complete BCA would quantify the economic value of changes in these outcomes as well as in ones directly related to crime. Future impact evaluations and BCAs of juvenile justice programs would be stronger if they routinely measured changes in important noncrime outcomes, quantified their economic value whenever possible, and included them in the benefit-cost calculations in addition to the benefits directly related to crime.



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement