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The USA forms the world’s largest market for illegal drugs (Reuter, 2000), and has the most well-developed data resources and research literature in the area.

General-population individual-level data on drug use are available through the National Household Survey on Drug Abuse (NHSDA)21 (see OAS, 2004). There are particularly rich data from surveys focusing specifically on the young, including the Monitoring the Future survey (MtF), the National Longitudinal Survey of Youth (NLSY), the National Parents’ Resource Institute for Drug Education survey (PRIDE) and the Youth Risk Behavior Survey (YRBS). More targeted sources focusing on high-prevalence groups include the survey of Substance Abuse among Probationers and Inmates, conducted by the Bureau of Justice Statistics, and state- and federal-level surveys of inmates in correctional facilities. The Arrestee Drug Abuse Monitoring (ADAM), established by the National Institute of Justice in 1987 was the core of the international ADAM programme.

The USA also has extensive data on drug prices and purity, through the “System to Retrieve Information on Drug Evidence” (STRIDE), compiled by the Drug Enforcement Administration (DEA). This provides data on laboratory analyses of street-level drug purchases and of drugs removed from the marketplace where DEA participated in the seizure(s). STRIDE also provides chemical analyses to determine the geographic origins of drugs, and is the source for data on long-term trends in the prices and purities of drug exhibits.

US experience underlines the uncertainty inherent in measurement of market size. Reuter (2005), in his review of strategies for estimating the size of the global drugs market, notes that even in the US, which currently has the most complete and sophisticated data on drug use and prices, expenditure estimates could range between $40 billion and $100 billion.

The US DEA produces estimates of drug production by other nations. These reports, published annually in the International Narcotics Control Strategy Report are based on reports of growing areas, crop per acre and refining yield of the raw product in source countries. Reuter (1996) enumerates several problems with these estimates, including inadequate methodology, unresolved discrepancies between different series of data, and generally implausible figures. However, he expresses a much greater degree of confidence in the estimates produced by the US Office of National Drug Control Policy, published as the series

What America’s Users Spend on Illegal Drugs. The latest in the series covers the years 1988- 2000 (ONDCP 2001) and draws data from a range of sources, including those listed above, to estimate total aggregate expenditures on drugs.

Table 4.1 reproduces the series of estimated aggregate expenditures for a range of drug categories. As an example of the way the ONDCP estimate is built up, Table 4.2 gives more detail for marijuana, which shows a decline in market value of 14 per cent over the period. This estimated fall in market size is the result of a 28 per cent fall in price, outweighing the 20 per cent increase in prevalence. The fall in price is shared by the other drugs included in the ONDCP analysis and is apparent in the UK also.

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Table 4.1: Estimated total expenditures on iIllicit drugs, USA 1994-2000 ($ billions) 1994 1995 1996 1997 1998 1999 2000 Cocaine 42.8 40.0 39.2 34.7 34.9 35.6 35.3 Heroin 13.2 13.2 12.8 11.4 11.1 10.1 10.0 Methamphetamine 7.6 9.2 10.1 9.3 8.0 5.8 5.4 Marijuana 12.2 10.2 9.5 10.5 10.8 10.6 10.5 Other drugs 2.6 2.7 2.7 2.5 2.3 2.6 2.4 Total 78.4 75.2 74.3 68.4 67.2 64.6 63.7

Columns may not add due to rounding error. Estimates for 2000 are projections. Prices in 2000 dollar equivalents

Source: Taken from What America’s Users Spend on Illegal Drugs 2001, Table 11.

Table 4.2: Calculation of total marijuana consumption, USA 1994-2000

1994 1995 1996 1997 1998 1999 2000

Number of users (millions)

10.1 9.8 10.1 11.1 11.0 11.9 12.1

Joints used per month 18.7 18.7 18.7 18.7 18.7 18.7 18.7

Weight of a joint (ounces)

0.0136 0.0136 0.0136 0.0136 0.0136 0.0136 0.0136

Price per ounce, 1/3 ounce purchase

$397 $340 $309 $311 $322 $292 $284

Total real expenditure $12.2 $10.2 $9.5 $10.5 $10.8 $10.6 $10.5

Tonnes 874 848 874 960 952 1028 1047

Expenditures are in $ billion dollars, in 2000 dollar equivalents. Estimates for 2000 are projections.

Sources: Table taken from What America’s Users Spend on Illegal Drugs 2001, Table 9. Figures in the table are based on information from NHSDA and the STRIDE database.

The ONDCP estimates for marijuana are quite straightforward: a relatively large proportion of marijuana users are to be found in the general population as sampled by the NHSDA survey, and because the use of marijuana is more widespread than other drugs, and the penalties for use less severe, there is less of a problem with under-reporting. For other drugs, the estimation process is more complicated. For example, the heaviest users of heroin are likely to be missed from a household-based survey, and thus, calculations incorporate other sources of data, including arrestee surveys and emergency-room admissions. Rates of methamphetamine use vary widely between areas, so instead of using household-level data, information on drug treatment admissions was used.

Several US studies exist on markets for individual drugs, although most are concerned with modelling behaviour rather than measuring aggregate market size. This research nevertheless has important implications for aggregate measurement. For example, Caulkins and Pacula (2002) examine the nature of marijuana markets and acquisition using data from the 2001 NHSDA. They estimate that in the US there are around 400 million retail marijuana purchases annually, and that the average purchase size is small, of the order of 6-7 joints’ worth. They also find that a minority of users is responsible for a majority of purchases. This has implications for the use of purchase data in the measurement of market size: if a large proportion of marijuana purchases are sold on to friends, there may be double counting in the data.