Substance Abuse Treatment as a Mechanism to Reduce Drug Abuse and Crime

Statement of the Problem: With drug abuse escalating over the past decade, as indicated by a more-than-doubling of drug-overdose deaths, understanding the degree to which interventions can reduce its public-health costs is of major concern. Federal spending in this area has primarily targeted drug-supply-reducing policies, including law enforcement, interdiction, and international relations. However, research suggests that these types of policies are largely ineffective at reducing drug abuse and related criminal activities. Substance-abuse treatment (SAT) offers a promising alternative. To date, most of what is known about the causal effects of SAT is based on randomized control trials, which have strong internal validity but are unlikely to be conducted on a large enough scale or with long-enough follow-up periods to be able to document effects on extreme and/or rare outcomes of particular interest, such as violent crimes involving firearms. They also will understate the effects of SAT if there are positive spillovers on the broader community. We propose to more fully document the effects of SAT on drug abuse and related crimes by investigating the extent to which these outcomes change in a community in response to changes in access to SAT. Subjects: The analysis will involve secondary data analysis focusing primarily on communities covered by the National Incident Based Reporting System, representing approximately 29% of the US population. Research Design and Methods: This study will use county-level data on the number of SAT facilities constructed using County Business Patterns data to consider the degree to which rates of substance abuse and crime change when SAT facilities open and close. Measures of drug abuse and crime in each county and year will be constructed using the National Incident Based Reporting System and the National Center for Health Statistics Multiple Cause of Death Data. Analysis: Our panel data will allow us to estimate regression models that include a rich set of fixed effects (county and state-by-year) and control variables (measures of demographics and economic conditions) so that the estimates are identified based on plausibly exogenous variation. We conduct several ancillary analyses in support of the validity of this research design, including an analysis that demonstrates that outcomes in a county change after but not before the number of facilities change. Products: The results of the proposed study will be submitted for publication in peer-reviewed journals and presented at professional meetings. ca/ncf