Benefit-Cost Analysis of Drug Abuse Prevention Programs: A Macroscopic Approach

Abstract

To date, benefit-cost analysis has rarely been used to justify the drug abuse prevention field. However, there is an increasing demand for this type of analysis as the field of substance abuse prevention enters a new phase—a phase characterized by a competitive marketplace, an increased demand for accountability, and the desire to measure return on the money invested in prevention. In response, an effort is made to stimulate discussion and further research on the topic. This article first determines the overall strategy for initiating benefit-cost analysis (BCA), followed by definitions of BCA and cost-effectiveness analysis (CEA). This is followed by the determination of some of the major variables used in BCA along with the algorithm for determining the benefit-cost efficiency ratio (R) as it applies to the macro level analysis. In estimating a value for the R, a decision has been made to incorporate uncertainty into the BCA. In a macroscopic approach to BCA, four independent variables are identified for computing R. These independent and dependent variables are assumed to be random variables with normal distributions. The population means and standard deviations pertaining to these independent variables are estimated from the existing literature. In order to incorporate uncertainty into the computation of R, ten measurements have been randomly selected for each of the four independent variables. Following this procedure, fifteen benefit-cost efficiency ratios are calculated by selecting one of the ten values at random per variable used in the R equation. In this way, we were able to determine the most likely population benefit-cost efficiency ratio of 15:1, indicating that there is a $15 savings on every dollar spent on drug abuse prevention. The 95 percent confidence level pertaining to the R has an interval from $13.7 to $16.1. This indicates that the population R resides within the range 95 percent of the time.