Collaborative Research: Unintended Consequences of Law Enforcement Disruptions to Illicit Drug Networks

The use of illicit substances remains an ongoing and widespread part of society, with the illegal drug market estimated to be one percent of total global trade. Although the true scale of this market is unknown because of its illicit, underground nature, the overarching US drug policy and strategy has consistently been to allocate large amounts of resources to law enforcement agencies to dismantle illicit distribution networks. The motivation for narcotics enforcement is that it imposes negative consequences on the supply side that both deter people from distributing illicit drugs and decrease the drugs' availability to vulnerable consumers. Yet little evidence suggests the negative consequences disrupt the macro-level drug supply to an extent that alters the price of drugs, and very little is known about the meso level effects of these interdictions on violence in the broader community and drug consumers at risk of fatal overdose. Therefore, the purpose of this Disrupting Operations of Illicit Supply Networks (D-ISN) project is to utilize national integrated data to develop an understanding of the impact of law enforcement disruptions to the illicit drug supply on both public health (e.g., overdoses) and public safety (e.g., violent crime) outcomes.

In order to understand the relationship between drug market disruptions and outcomes of interest, the study team will construct a county level dataset across 10 diverse states using three sources of administrative data: (1) National Incident-Based Reporting System (NIBRS), (2) National Center for Health Statistics (NCHS) detailed Multiple Cause of Death (MCOD) data, and (3) the US Census. NIBRS captures characteristics of crime incidents, including detailed information on drug seizures, crime offenses, and other incident characteristics in a nationally systematic way. The MCOD data will provide unsuppressed, county-level, mortality information based on death certificates and include information on cause, decedent demographics, month of death, and county urbanicity. Data from the Census will be used to calculate rates of drug seizures and control for county level population characteristics and resource deprivation. The county-level integrated dataset will include the date of overdoses (by type of drug), public safety events (e.g., murder/nonnegligent manslaughter, robbery, and aggravated assault), and the county where the outcomes occurred. The study team will employ methods of spatial temporal causal modeling, which allows for the modeling of causal relationships between time- and space-persistent features. Lastly, the integrated datasets will be shared with researchers interested in utilizing these data with information on the data integration process, how to access the data, and how to customize it to fit the needs of their research. The team will also engage with relevant stakeholders to identify or create strategies that may mitigate the unintended consequences of drug market disruptions (e.g., reduce overdoses and community violence), reduce disparities in these outcomes in communities of color, inform local and state law enforcement drug interdiction strategies, and inform the allocation of resources for drug interdiction.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.