The need for dynamic drug policy.
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Biomedical subjects
Publications and source records attributed to Jonathan P Caulkins.
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Data on illicit drug purity and prices are invaluable but challenging. Academic purists argue they are unsuitable for economic analysis [Manski, C.F., Pepper, J.V., Petrie, C.V., 2001. Informing America's Policy on Illegal Drugs: What We Don't Know Keeps Hurting Us. National Academy Press, Washington, DC], but in practice they are used frequently [ONDCP, 2001a. What America's Users Spend on Illegal Drugs. Office of National Drug Control Policy, The White House, Washington, DC; ONDCP, 2001b. The Price of Illicit Drugs: 1981 Through the Second Quarter of 2000. Office of National Drug Control Policy, The White House, Washington, DC; ONDCP, 2004. The Price and Purity of Illicit Drugs: 1981 Through the Second Quarter of 2003. Office of National Drug Control Policy, The White House, Washington, DC; Grossman, M., 2004. Individual behaviors and substance use: the role of price. Plenary Address for the 24th Arne Ryde Symposium on Economics of Substance Abuse at Lund University, Lund, Sweden, August 13-14, 2004]. This paper reviews data and conceptual issues that people producing, analyzing, and consuming drug price and purity series should understand in order to reduce the likelihood of misinterpretation. It also identifies aspects of drug markets that are both poorly understood and relevant to some of these issues. They constitute a useful research agenda for health and law enforcement communities who would benefit from better data on the supply, availability, and use of illicit drugs.
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Cost-of-illness (COI) studies seemingly provide a solid foundation for quantifying the potential benefits of illicit drug policy interventions that reduce drug use at the population level. However, their usefulness is severely limited. In this paper, we suggest several improvements to substance abuse COI studies. The first set of improvements can be implemented with little change to the current framework: developing estimates that reflect the best available information, rather than using lower bound estimates that represent 'conservative' figures; dealing with uncertainty explicitly by developing estimation ranges; and disaggregating social costs by particular illicit drug types. The second set of improvements address key conceptual problems in transferring a health approach to a 'condition' where healthcare costs are a minor component: dealing with the intangible costs of drug dependence; valuing property crime; including systemic crime; and considering the spillover effects of drug abuse on human capital formation. COI studies can become valuable sources of policy-relevant information if their authors improve the current approach by making changes such as those identified here.
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Everingham and Rydell's Markov chain model of cocaine demand is modified and updated in light of recent data. Key insights continue to hold, e.g., that the proportion of cocaine demand stemming from heavy vs. light users changed dramatically over the 1980s. New insights emerge, e.g., pertaining to the average duration of a career of heavy use (about 12 years) and the negative relationship between levels of heavy use and epidemic "infectivity" or the number of new initiates per current user per year. This illustrates how simple modeling can yield insights directly relevant to managing complex drug control policy questions.
School-based drug prevention is a central component of drug control strategies. This paper assesses quantitatively its contributions in the United States from a social policy perspective. The social benefits per participant stemming from reduced drug use ( approximately $840 from tobacco, alcohol, cocaine and marijuana) appear to exceed the economic costs of running the programs ( approximately $150 per participant); while the benefits associated with reduced cocaine use alone ( approximately $300) exceed the costs, the corresponding figure for marijuana ( approximately $20) is small. Even if prevention reduced the use of other illicit drugs (e.g. heroin) by as much as it reduced use of cocaine, the majority of benefits would still stem from reductions in use of tobacco and alcohol, which has implications for how school-based drug prevention is funded and whether it is perceived more as a weapon in the war on illicit drugs or as a public health measure. Specific numeric results are subject to considerable uncertainty, but the basic character of the conclusions appears to be robust with respect to parameter uncertainty. The greatest uncertainties concern the permanence of prevention's effects and how to value instances of initiation being deferred but not completely prevented.
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This paper explores the idea that drug markets may be chaotic in a mathematical sense by considering a discrete-time model of populations of drug users and drug sellers for which initiation into either population is a function of relative numbers of both populations. The structure of the system follows that considered in an arms control context by Behrens, Feichtinger and Prskawetz (Behrens D.A., Feichtinger G., & Prskawetz A. (1997). Complex Dynamics and Control of Arms Race. European Journal of Operations Research, 100, 192-215). The model presented in this paper summarizes prerequisites for possible chaotic behavior of the number of addicts and drug dealers frequenting a local drug market. Interestingly, even if the uncontrolled market dynamics do not exhibit chaotic patterns, a static intervention like removing a constant fraction of addicts each time period can easily create chaos--but even if static control would create chaos, dynamic controls can be crafted that avoid it. Especially OGY controls seem to work well for this example.
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