HOW TO EXPAND THE ‘SAMPLE SIZE’ OF STUDIES OF DRUG MARKET DISRUPTIONS

Cunningham et al.[1] make a valuable contribution by adding purity to the list of outcomes US methamphetamine precursor regulations have been shown to affect, and by showing that Canadian precursor regulations also affected US methamphetamine purity. One minor clarification is that the natural outcome of interest is not purity, but purity-adjusted price. In many markets, the distribution chain changes purity-adjusted price primarily by changing purity, so purity acts almost as a sufficient statistic for purity-adjusted price [2]. Sometimes, however, purity itself is unresponsive to supply shifts. Apparently that is the case with methamphetamine in Hawaii, where the preference for ‘ice’ ensures that purity is always high. In those markets, stable purity should not be seen as ‘possibly limiting its sensitivity to precursor regulation’ unless one verifies, first, that price per gram has also not changed. The more important question is: how can the research community expand the number of market disruptions that have been studied in this way? There are many papers on the Australian heroin drought and now a small collection (due mainly to Cunningham, Liu and co-authors [1]) on North American methamphetamine precursor controls. There are a few reports on the cocaine market disruptions of 1989/90 and 1995 (notably Crane et al.[3]), some discussion of effects of the Taliban opium ban (e.g. Paoli et al.[4]) and a handful of other events, such as Operation Intercept [5]. This may be enough for someone to start building a unified theory of drug market disruptions, but building theory on a larger empirical base would clearly be preferable. Arguably, the literature describes so few market disruptions primarily because so few major market disruptions have occurred, but the key word is ‘major’. It seems plausible, if not likely, that for every major disruption there may be a number of disruptions that are too brief to detect with annual or even quarterly time–series. (Note: there are many potential causes of market disruptions besides precursor regulation.) The key to capturing smaller disruptions is having higher-frequency indicator series. The traditional emphasis on prevalence estimation skews attention towards infrequent, expensive surveys. To study market disruptions, high-frequency administrative data sets are more useful. Purity is particularly promising because it is related so directly to supplier behavior, and because it is available in many places. Economists studying drug prices have relied almost exclusively on the United States Drug Enforcement Administrations System to Retrieve Information from Drug Evidence (STRIDE) database, as do Cunningham et al. here. STRIDE is one of the very few data sets with high-frequency data on purity-adjusted prices per se. However, many agencies in the United States and abroad send seizure samples to forensic laboratories even if they do not make many undercover purchases. Hence, high-frequency purity series are available for many jurisdictions where it is harder to monitor prices directly. Furthermore, where price per raw gram is relatively stable and price adjustments take the form of purity adjustments, a high-frequency purity series is almost as useful as a high-frequency time–series of purity-adjusted prices [2]. Cunningham, Liu and co-authors have studied one set of market disruptions using an impressive range of high-frequency data indicators (hospitalizations, arrests, treatment demand, reported mode of ingestion and now purity). What the literature needs next is for researchers to comb through such data series looking for additional market disruptions. Some care is required to avoid false positives (seeing disruptions where there is nothing but random noise). However, if several independent indicators register spikes (or troughs, depending on the indicator) at the same time, that is strong circumstantial evidence of some real disruption in the market. Having identified a set of disruptions, one can then ask what caused them and how costly it is to create the disruptions relative to any benefits they might bring. None.

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