International Journal of Drug Policy 25 (2014) 235–243

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International Journal of Drug Policy journal homepage: www.elsevier.com/locate/drugpo

Research paper

Risks, prices, and positions: A social network analysis of illegal drug trafficking in the world-economy Rémi Boivin a,b,∗ a b

School of criminology, University of Montreal, C.P. 6128, succursale Centre-ville, Montréal, QC, H3C 3J7, Canada International Centre for Comparative Criminology, C.P. 6128, succursale Centre-ville, Montréal, QC, H3C 3J7, Canada

a r t i c l e

i n f o

Article history: Received 17 December 2012 Received in revised form 29 November 2013 Accepted 9 December 2013 Keywords: Drug prices Drug trafficking Risks and prices model World-system perspective Network analysis Drug law enforcement

a b s t r a c t Background: Illegal drug prices are extremely high, compared to similar goods. There is, however, considerable variation in value depending on place, market level and type of drugs. A prominent framework for the study of illegal drugs is the “risks and prices” model (Reuter & Kleiman, 1986). Enforcement is seen as a “tax” added to the regular price. In this paper, it is argued that such economic models are not sufficient to explain price variations at country-level. Drug markets are analysed as global trade networks in which a country’s position has an impact on various features, including illegal drug prices. Methodology: This paper uses social network analysis (SNA) to explain price markups between pairs of countries involved in the trafficking of illegal drugs between 1998 and 2007. It aims to explore a simple question: why do prices increase between two countries? Using relational data from various international organizations, separate trade networks were built for cocaine, heroin and cannabis. Wholesale price markups are predicted with measures of supply, demand, risks of seizures, geographic distance and global positioning within the networks. Reported prices (in $US) and purchasing power parity-adjusted values are analysed. Results: Drug prices increase more sharply when drugs are headed to countries where law enforcement imposes higher costs on traffickers. The position and role of a country in global drug markets are also closely associated with the value of drugs. Price markups are lower if the destination country is a transit to large potential markets. Furthermore, price markups for cocaine and heroin are more pronounced when drugs are exported to countries that are better positioned in the legitimate world-economy, suggesting that relations in legal and illegal markets are directed in opposite directions. Conclusion: Consistent with the world-system perspective, evidence is found of coherent world drug markets driven by both local realities and international relations. © 2013 Elsevier B.V. All rights reserved.

Illegal drugs are extremely valuable: some types of drugs are literally worth their weight in gold (Reuter & Greenfield, 2001). There is, however, considerable variation in value depending on place and market (Caulkins & Reuter, 1998; Wilson & Stevens, 2008). For example, a kilo of cocaine that is worth less than $US 1000 in Bolivia could easily sell for more than $US 100 000 in the streets of the United States, Australia, or France (UNODC, 2011). Traditional economic models provide a partial explanation of why drugs are more expensive in some countries than others because in many ways, drug markets act as trade networks; buyers and sellers

∗ Correspondence address: University of Montreal, School of criminology, C.P. 6128, succursale Centre-ville, Montreal, Quebec, Canada H3C3J7. Tel.: +1 514 343 6111x2473. E-mail address: [email protected] 0955-3959/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.drugpo.2013.12.004

willingly collaborate in an effort to exchange a commodity (Caulkins & Reuter, 2007, 2010; Naylor, 2003). However, a clear difference between drug markets and other trade networks is the legal status of the commodity. It has long been recognized that the price and value of illegal commodities cannot be fully explained by ordinary laws of supply and demand. The main proposition of the current paper is that a country’s position within global markets affects wholesale prices of illegal commodities, a proposition underexploited in previous explanations. Drawing on the larger literature on legal trade, this paper uses social network analysis (SNA) to explain price markups between pairs of countries involved in the trafficking of illegal drugs. It draws on Reuter & Kleiman’s risks and prices model and Wallerstein’s worldsystem perspective to analyse contemporary illegal drug markets. An empirical analysis of wholesale prices of cocaine, heroin, and cannabis for a sample of 173 countries from different parts of the world is presented and discussed.

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Prices, costs and risks The price of any commodity is expected to continually increase as the commodity moves from source to user. The first owner will sell his product at a price high enough to cover his own costs and eventually make some profit. That buyer will likely sell at a higher price, again to cover his own costs (which include the costs of the first owner) and make some profit, and so on. Costs are passed on to the next buyer, who passes them on to the next, etc. until the commodity reaches the final buyer – the user. In other words, prices are in part determined by costs incurred by previous sellers who are not involved in a given transaction. Where buyers are positioned in the chain is closely associated to the purchase cost of commodities. In addition to other expenses, traders of illegal commodities incur specific costs. A prominent framework for the study of illegal drugs is the “risks and prices” model (Reuter & Kleiman, 1986). Enforcement is seen as a “tax” – an additional cost added to the regular price. The model is conceptualized as a sequence of related effects. First, enforcement imposes costs on drug dealers, in the form of drug and asset seizures, compensation for risk of prison, and compensation for risk of violence from other participants of illegal markets (Caulkins & Reuter, 1998). Second, “drug dealers are in business to make money, so they pass [their] costs on to users in the form of higher prices” (Caulkins & Reuter, 2010, p. 215). Because it is assumed that the main objective of drug law enforcement is to reduce drug consumption, Reuter & Kleiman’s third and final proposition is that higher prices reduce consumption. In the strictest sense, enforcement efforts are successful only if they cause a significant reduction in drug consumption. Such a theory requires considerable empirical testing, and more than 25 years of research has brought important insights on all three propositions. The most disappointing finding is that law enforcement is rarely able to disrupt or seriously damage drug markets (proposition 1; Layne et al., 2001; Mazerolle, Soole, & Rombouts, 2001). At best, law enforcement interventions may have a confined or temporary impact on specific markets, without redefining global markets. The focus of this paper is on the second proposition of the risks and prices model. While the existence of additional costs related to drug enforcement is undisputed, how and when they are passed to users is still a matter of discussion (Caulkins, 1994; Caulkins & Reuter, 1998; DeSimone, 2006). The value of illegal drugs increases almost exponentially after production, while price increases are more modest for legal commodities (Reuter & Greenfield, 2001). A version of this argument can be used to explain wholesale price variations at country-level. It is expected that illegal drug prices will be higher where costs imposed on drug traffickers are higher. The context in which traffickers operate is crucial: if enforcement efforts are weak or fairly easy to avoid, additional costs imposed on traffickers are low and drug prices should be lower. On the contrary, if the risks associated with trafficking are high, costs are high, and prices will be high. The corollary is that traffickers who acquire drugs at lower prices and assume fewer costs are able to sell at lower prices; at country-level, it means that traffickers operating where enforcement has minimal effects on drug prices have lower costs than traffickers operating in high-risk countries. Consequently, the structure of transnational drug trafficking is a key element in a better understanding of price variations.

Drug trafficking in the world-economy Structure may, however, have a more subtle effect on commodity prices. It is expected that some countries will have more wealth than others due to differential access to raw materials, more effective production means, lower wages, etc. The

world-system perspective argues that today’s world-economy is a global trade network “built” on unequal political and economic agreements (Chase-Dunn, 1989, 2002; Wallerstein, 1974, 1979). The world-system argument is thus not only that some countries have more wealth than others, but that they have it at the expense of others. Proponents of the world-system perspective hold that the core-periphery hierarchy does not necessarily refer to geographic regions but rather to countries that occupy similar positions in the world-economy. Examples of core countries are the United States and Japan, and of peripheral countries, Togo and Senegal. Semi-peripheral countries, such as New Zealand and Argentina, are less dominant but still occupy an important position in the world-economy (Chase-Dunn, 1989; Mahutga, 2006; Smith & White, 1992; Snyder & Kick, 1979; Wallerstein, 1974, 1979). In the legal world-economy, peripheral and semi-peripheral countries are not able to produce necessary specialized commodities and must depend on core countries, which sell such commodities at a high price. It has been argued that the situation is reversed for illicit drugs (Boivin, 2013): core countries are not able to produce enough – if any – drugs to meet national demand and are forced to import from more peripheral countries. The situation is most obvious for cocaine and heroin, which are produced in a limited number of non-dominant countries. Position in the system is thus a direct function of means of production. An important structural consequence is that, all other things being equal, prices are higher than they should be in importing countries. In other words, because core countries depend largely on more peripheral countries for their supply of illegal drugs, prices are expected to increase more rapidly when the trade is directed towards the core of the world-economy.

Trade networks A major contribution of the world-system perspective was to shift the focus of analysis from individual countries to the relations between them. Empirical tests of the world-system perspective then quickly used tools of social network analysis (SNA). A similar trend can be observed for drug trafficking: recent editions of the World Drug Report, a widely-cited annual publication by the United Nations Office on Drugs and Crime (UNODC), include a discussion of drug “flows” and “routes” between countries. In a recent publication, Paoli, Greenfield, and Reuter (2009) used the network terminology to describe the world heroin market as a trade network in which distant regions can affect aspects of local markets, but grounded their analysis in traditional economics. However, Paoli et al.’s work is a notable exception: drug trafficking is usually not analysed in relational terms. Farrell, Mansur, and Tullis (1996), who analysed cocaine and heroin trafficking in Europe during the 1980s and 1990s, still provide the most comprehensive examination of wholesale prices. Their analysis is interesting because it shows how European drug markets evolved over a 10year period (1983–1993). It also introduces the idea that countries had steady roles in the market throughout the period and that a wide array of factors explain that situation. Farrell et al. observed that wholesale prices were lower in the countries that serve as gateways to the European markets – Spain, Portugal, and Turkey. In neighbouring countries, drugs were a little more expensive, but still cheaper than in most other countries, which is consistent with the idea that prices increase with distance. They also observed that drugs were expensive in Switzerland and concluded that it reflected a more general pattern: everything was more expensive in Switzerland. Finally, Farrell et al. suggested that the level of risk for importers was associated to wholesale prices, citing the example of the Netherlands, a country that was thought to be more lenient about drugs and where lower than expected prices were observed.

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Despite its descriptive nature, Farrell et al. (1996) offer an important contribution to the field for at least two reasons. First, they provide one of the few analyses of transnational drug trafficking based on empirical data gathered by the UNODC. The UNODC data has limitations but provides conveniently accessed information unavailable anywhere else (Caulkins, 2007). Second, their study was an attempt to build a general model of high-level trafficking that tries to account for price variations without focusing on local and anecdotal explanations. Their tentative results demonstrated that country-level analyses are instructive and deserve further research. Furthermore, while their results are consistent with economic principles, Farrell et al. suggested that prices reflect the role of a country within the global drug trade. They find evidence of a coherent European market driven by both local realities and international relations. Since the exploratory analysis of European drug prices by Farrell et al. (1996), there has been virtually no attempt to formulate a general model of country-level price variations. Costa Storti and De Grauwe (2009a, 2009b) did use a theoretical model to explore the impact of globalization on drug prices, but their analysis was aimed at explaining variations of retail prices over time. This paper offers an empirical test of the “risks and prices” model (Reuter & Kleiman, 1986) combined with propositions from the world-system perspective to explain illegal drug price increases at country-level. Methodology Data and network construction The primary data for this study was gathered together by the UNODC and covers a 10-year period from 1998 to 2007. The UNODC releases an overview of various indicators of drug trafficking and consumption in its annual report, World Drug Report. Most indicators are collected through an annual survey, the Annual Questionnaire Reports (ARQ), which is filled out by officials in different countries. In instances in which ARQ data are not available, the UNODC complements with data from other sources, such as INTERPOL (Chandra, Barkell, & Steffen, 2011). In most cases, the data is released without further test of its validity. The average value for the whole period is used, except when there is a specific mention. Other data sources are detailed below. An almost undisputed feature of illegal drugs is that they are exchanged in separate markets, although some convergence is found (UNODC, 2011). For example, a single region (the Andes) produces all the cocaine consumed in the world, while three regions produce heroin (the Golden Crescent and Golden Triangle in Asia and South/Central America) and cannabis is grown virtually everywhere. The number of sources is strongly associated with the availability of drugs; it also seems plausible to expect different price variations and determinants in different markets. The first step in this analysis was to build drug trade networks. Two types of relational data were used to build separate trade networks for plant-based drugs (cocaine, heroin, and cannabis) at country-level. The first data is a collection of seizures of “significant” quantities of drugs that occurred between 1998 and 2007.1 This dataset provides detailed information on a large number of cases, including origin and/or destination countries (n = 20 527 dyads). When Spanish authorities seize drugs coming from Venezuela and headed to France, they collect information on a network of three nodes (Spain, Venezuela, and France) and two relations (Venezuela–Spain, Spain–France). The accumulation of

1 The defined thresholds of significant quantities used by UNODC are as follows: 1 kg or more for cannabis and 100 g or more for cocaine and heroin.

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such information allows the construction of a network that covers the world. However, seizures are reported to the UNODC on a voluntary basis; as a consequence, key players in the drug trade are not included in the UNODC dataset. Observational data reported by various international organizations involved in drug trafficking surveillance or control was therefore used to complete the networks.2 Overall, most countries of the world are covered (n = 173). More details on the construction of the drug trafficking networks are available elsewhere (Boivin, 2011). Two softwares were used to build and analyse the networks (UCINET and Pajek); statistical analyses were conducted using SPSS 20. Dependent variables Fig. 1 illustrates investigated relations between dependent and independent variables. As discussed above, the focus of this paper is on the actual relations between countries rather than country attributes. Relational measures are used to test slightly different research questions (e.g., “Is the price increase higher when two countries are more distant?”). The unit of analysis is thus the dyad, which relates to a pair of countries (n = 2124). Wholesale is defined as the price for a kilogram of drugs in US dollars.3 Prices are estimated by experts (e.g., law enforcement agents) from various national organizations and then reported to the UNODC. When available the reported average value is used; otherwise, the mean of the reported maximum and minimum price is used as a proxy (Chandra et al., 2011). Consequently, wholesale prices are likely to be noisy and should be interpreted as raw estimates of the price of drugs at higher levels of trafficking. Researchers and analysts have nevertheless used the data because they constitute the most systematic source of information on drug prices in various countries (Caulkins, 2007; Chandra et al., 2011). In order to compare prices from different countries, reported prices use a common currency ($US). However, prices in US dollars would be perfectly comparable only if money had the same value everywhere in the world, which is certainly not the case (Gottschalk & Smeeding, 1997). Farrell et al. (1996) expressed this idea when they suggested that drugs were more expensive in Switzerland simply because everything was more expensive there. Prices were adjusted to capture the “real” value of a kilo of drugs, using the following procedure: adjusted pricex = pricex ×



GNP − PPPx median value of GNP − PPP



where GNP − PPP stands for a country’s gross nation product per capita adjusted for purchasing power parity in 2002 (IMF, 2002). Algeria had the median value, with $5900/capita, which means that the wholesale price of drugs in Algeria is equal to its adjusted value. Luxembourg had the highest factor of adjustment (10.19) and Malawi had the lowest (0.09); if drug prices were the same worldwide, the adjusted value of drugs worth $1000 in Algeria would be $10 190 in Luxembourg and $90 in Malawi.

2 A systematic review of information contained in 48 annual reports and country overviews published by the UNODC, the Bureau of International Narcotics and Law Enforcement Affairs (BINLEA), the International Narcotics Control Board (INCB) and the European Monitoring Center for Drugs and Drug Addiction (EMCDDA) was conducted. 3 Prices are not adjusted for purity. Some authors (e.g. Caulkins, 2007) have suggested that quality-adjusted prices should be analysed instead of raw prices because price changes may manifest through quality changes rather than selling price changes. However, the purity of drugs requires chemical tests that are not routinely done in most countries of the world. Furthermore, there are a number of important issues (e.g. selection bias) related to purity tests that are well-beyond the scope of this paper. Finally, substantial quality variations have been observed at retail-level (Caulkins et al., 2004; Darke, Topp, Kaye, & Hall, 2002), but the situation is less clear at higher levels of drug trafficking.

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Fig. 1. Dependent and independent variables.

Price markups were calculated by subtracting wholesale prices in sending countries from wholesale prices in destination countries. The result is analysed as the price or value increase incurred during the exchange of drugs from one country to the other. Markups were calculated for both reported and adjusted prices. Independent variables The nature of the dependent variables requires relational independent variables. However, many available country-level features are attributes, i.e., characteristics of individual countries. Two types of variables are used: attributive and relational. Attributive variables Consistent with the risks and prices model, two measures of risk are used. The level of corruption in a country is estimated by the Corruption Perception Index (CPI) developed by Transparency International. Hundreds of experts are asked annually, by survey, to estimate the level of corruption in a number of countries. While the measure is certainly imperfect, it gives an order of magnitude that allows comparing countries (Lambsdorff, 2007). It is also highly correlated with other measures of corruption (Lederman, Layza, & Soares, 2005; Van Dijk, 2008). The CPI is the average value of the estimates, on a scale of 10 points; original values were inverted so that high values mean high levels of corruption and vice versa. The 2008 edition of the CPI is used in this analysis. The second measure of risk is the number of police agents per 1000 inhabitants taken from the UNODC’s crime trends surveys. The average value of editions 7–10 (1998–2006) is used.4 This measure has been used to estimate the risk of interception (Keefer, Loayza, & Soares, 2008; Soares, 2004). The third attributive variable is derived from social network analyses. Some countries are used as transit between others. They import a certain amount that is destined for exportation. It is expected that, in those countries, drugs are more available, which could reduce prices. “Flow betweeness” is our indicator of centrality in a network. High values of flow betweeness indicate that a country is located on many paths between other countries; scores

4 Bahrain was excluded from the analysis because it had a value more than eight times higher than the average.

are higher if nodes are located on every path between two countries (de Nooy, Mrvar, & Batagelj, 2005; Freeman, Borgatti, & White, 1991; White & Borgatti, 1994). Flow betweeness is different from more traditional measures of centrality (e.g. betweeness centrality) because it includes every possible path, while other measures are based solely on the shortest paths (Borgatti, 2005). Standardized values are used for cocaine and heroin. Because cannabis is grown almost everywhere in the world it is almost impossible to trace it back to source; consequently, flow betweeness could not be calculated for cannabis. An estimate of the number of potential consumers is also included in the analysis, to test the proposition that large markets are more competitive and prices could therefore be lower. It is given by the product of active population size (age 15–64) multiplied by the prevalence of consumption reported in the 2009 edition of the World Drug Report (UNODC, 2009). A measure of potential markets is also included to better account for transit positions. For example, Spain is a well-known turntable for moving South American cocaine into Europe. Spain itself is not a very large drug market compared to more populated countries, but it allows drugs to enter a multi-million user market. Therefore, Spain plays a crucial role: not only are drugs exported from Spain to a large number of countries but those destination countries are populated with a large number of drug users. “Potential buyers” is a measure of the number of users in countries connected to destination countries. In the case of heroin, the number of “potential buyers” (from other countries) and “potential consumers” (from the country of destination) is highly correlated (r = 0.625; p < 0.001) and related to price markups in a similar way. For cocaine, both measures of transit are slightly less correlated (r = 0.473; p < 0.001) and have opposite effects on price markups. Therefore, both measures were included in models predicting cocaine price markups. Relational variables It has been recognized for decades that geographic distance is an important determinant of the pattern of legal trade (Beckerman, 1956; Disdier & Head, 2008). The volume of trade between distant countries is expected to be lower because of higher transport costs, among other costs, and prices, of course, are expected to be higher where costs are higher (Deardroff, 1998). Consequently, the geographic distance between countries was included in the models predicting price markups.

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A key proposition of the world-system perspective is that prices are related to the means of production of a country and the degree of dependence with other countries. For example, peripheral countries that are not able to produce their own specialized commodities (e.g., machinery) are forced to import them from more developed countries at high prices. In legal markets, core/developed countries are able to trade their commodities to peripheral/less developed countries at high prices because the latter have limited access to such commodities. It is hypothesized that the situation is reversed in the case of illegal drugs. Cocaine and heroin are produced in a handful of semi-peripheral and peripheral countries; core countries necessarily import them from less developed countries. A simple method was used to determine the global position of a country in the world-economy. Mahutga (2006) estimated the position of 53 countries for the year 2000. We initially had information on 166 countries, which meant that using only his classification would result in a great number of missing cases. However, Mahutga’s classification is strongly correlated to a country’s total exports (in $US) for the year 2000.5 Consequently, four categories of countries were created: (1) core (annual exports of $100 000 M or more; n = 16), (2) strong semi-periphery ($50–100 000 M; n = 11), (3) weak semiperiphery ($10–50 000$; n = 28), and (4) periphery ($10 000 M or less; n = 111). We identified pairs of countries in which the trade was directed towards the core of the world-economy; for example, cases where drugs were traded from a peripheral country (category 4) to a strong semi-peripheral country (2), or from a weak semiperipheral country (3) to a core country (1). A dichotomous variable indicates the direction of the trade (1 = towards core; 0 = same category or towards a more peripheral country). Limitations As in any macro-level study, all variables are very rough measures of the concepts of interest. Furthermore, most data on drugs was collected through various annual surveys completed on a voluntary basis. Biases and errors (deliberate or not) are expected but difficult to document. Also, there is an important number of missing cases, especially for price data. In general, developed countries tend to provide more information than less developed countries, but there are exceptions. It was possible to calculate markups only when prices from both countries were available. Cases where prices decreased in a pair (negative markups) were also excluded from the analysis (17.8% of available markups). From all pairs of countries identified (n = 2124), a total of 539 price markups and 536 adjusted price markups are analysed. In addition, many countries provide answers to the surveys submitted by the UNODC only irregularly, which creates another kind of problem of missing data. For example, Canada did not provide information on drug seizures for the year 2008 but did so for 2007. Multiple years were analysed to reduce potential sample bias. The UNODC tries to complement reported information through different methods (e.g., observations, reports from other international organizations) but the data is certainly not ideal, a situation that could explain its limited use in academic research (Caulkins, 2007). However, the uniqueness of the data provided by the UNODC is undisputed because it is a worldwide annual compilation of information. It is noteworthy that the level of corruption is strongly correlated to the GNP − PPP (r = −0.816; p < 0.001). Consequently, both measures cannot be included in the same statistical models without

5 The non-parametric correlation between Mahutga’s classification and exports (in US$) is nearly perfect (Spearman’s rho = −0.967; p < 0.001; n = 53). The core of the world-economy is constituted by the top exporting countries in the world; the less exports, the further from the core.

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Table 1 Bivariate correlations between reported cocaine, heroin and cannabis wholesale prices (US$), 1998–2007 (coefficient: Pearson’s R).

Cocaine (ln) Heroin (ln) Cannabis (ln) * **

Cocaine (ln)

Heroin (ln)

Cannabis (ln)

– 0.28* (n = 69) 0.49** (n = 77)

– – 0.46** (n = 81)

– – –

p < 0.05. p < 0.01.

breaking a key assumption of OLS regression analysis (no multicollinearity). While economic contributions are acknowledged, corruption was preferred over GNP because the paper is aimed at providing a criminological and sociological explanation for price variations rather than an economic analysis. However, these measures are expected to have opposite effects on drug prices: prices should be higher in richer countries, while richer countries have lower levels of perceived corruption. At the same time, the possibility of avoiding seizures and arrests by “buying a way out” is a significant source of savings. Lower risks mean lower compensation, which in turn should be associated with lower prices. Adjusted prices are expected to better capture the effect of corruption on the value of illegal drugs because the effect of relative wealth is included directly in the dependent variable. An important assumption should also be mentioned. In many cases, the situation of a whole country – and all the traffickers operating within its borders – is summarized by a single value. Therefore, throughout the analysis, it is assumed that wholesale prices are indicative of the value of drugs in a country after importation and before exportation to another country; it was not possible to assess possible price increases within countries.6 For example, according to the 2008 edition of the World Drug Report, a kilo of cocaine sold for $31 580 in Canada (UNODC, 2008). During the same period, the Royal Canadian Mounted Police – the federal police – reported that a kilo of cocaine could be bought in British Columbia (the far western part of Canada) for $20 000, while in Saskatchewan (a more central province) it cost $70 000 (RCMP, 2007). The value reported by the UNODC is somewhere in-between but not perfectly representative of the situation in either British Columbia or Saskatchewan. Results and discussion Consistent with the observations of Farrell et al. (1996) there is a statistically significant and positive correlation between prices for cocaine, heroin, and cannabis in a country; as a general rule, when one drug is high-priced in a given country, other drugs are also high-priced, suggesting that there is a set of common factors that explain drug prices. However, the correlations are sufficiently low to require further examination. Consequently, analyses are provided for three types of plant-based illegal drugs: cocaine, heroin, and cannabis (Table 1). Preliminary analyses revealed that sending country features were unrelated to price markups but that destination country features were. Thus corruption, police per capita, potential consumers,

6 Prices are expected to increase within a country for various reasons. Most notably, there are costs associated with stocking drugs for future transactions (Caulkins & Reuter, 1998). Drugs that are not moving can nevertheless be detected and seized by local enforcement agencies; stocking drugs are thus likely to be associated with additional risk compensation, even if it is only a moderately risky activity. Caulkins (1995) and Caulkins and Padman (1993) also demonstrated that distance from the entry point is strongly related to drug prices within a country. Drugs tend to be least expensive near their source and more expensive farther away. Local traffickers are also in business to make money and also pass their costs on to the next buyers.

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Table 2 Descriptive statistics, price markups. Unadjusted prices (US$)

Adjusted values

Cocaine

Heroin

Cannabis

Cocaine

Heroin

Cannabis

N Average Median Standard deviation Minimum

226 37 431 30 598 33 950

212 31 986 18 471 40 575

110 2756 1691 3570

214 185 660 172 578 166 070

220 144 622 74 220 204 250

111 12 149 4258 18 761

300 (Dom. Republic – Haiti)

8 (Colombia – Panama)

8 (Croatia – Slovenia)

154 (Colombia – Ecuador)

1 (Guinea – Mali)

Maximum

145 688 (Peru – Australia)

217 964 (Pakistan – Australia)

16 984 (Ghana – Japan)

679 100 (Peru – Australia)

134 (Afghanistan – Tajikistan) 1 025 456 (Pakistan – Australia)

97 017 (South Africa – Ireland)

Table 3 OLS regression models, price markups, 1998–2007 (coefficient: beta). Cocaine

N pairs of countries Destination: corruption Destination: police per 1000 inhabitants Destination/transit: flow betweeness Destination/transit: potential buyers (ln) Destination: number of users (ln) Geographic distance (kms; ln) Towards core R squared † * **

Heroin

Cannabis

1

2

3

4

5

6

Markups ($US)

Markups (adjusted prices)

Markups ($US)

Markups (adjusted prices)

Markups ($US)

Markups (adjusted prices)

225 −0.204** −0.007 0.235** −0.325** −0.101† 0.518** 0.080† 0.692**

213 −0.450** 0.122** 0.220** −0.246** −0.024 0.292** 0.198** 0.758**

212 −0.240** 0.153* −0.251** – 0.085 0.407** −0.003 0.275**

220 −0.660** 0.265** −0.112* – 0.102† 0.212** 0.104* 0.616**

102 −0.379** 0.115 – – −0.269** 0.452** 0.133 0.463**

103 −0.731** 0.212** – – −0.063 0.135 0.087 0.616**

p < 0.1. p < 0.05. p < 0.01

potential buyers, and flow betweeness measures are included in models predicting price markups, but for destination countries only. In other words, if drugs are sent from Haiti to Canada, the analysis considers Canada’s features, but not Haiti’s; in the case of drugs sent from Canada to Australia, Australia’s attributes are considered, but not Canada’s.7 As expected, cross-national analyses of cocaine, heroin, and cannabis wholesale prices reveal that prices are higher in countries where costs and risks are higher (see Appendix 1). Subsequent analyses explore a less obvious question, at least for the literature on drug policy: why do prices increase between two countries? Table 2 presents descriptive statistics for unadjusted and adjusted price markups. As expected, average and median markups for cocaine and heroin are higher than for cannabis, reflecting their higher value per kilo. However, relatively speaking, markups for cannabis are considerably higher. The ratio between median unadjusted prices for cocaine (median markup = $US 30 598; median price = $US 38 526) and heroin (median markup = $US 18 471; median price = $US 23 985) are comparable (respectively 79.4% and 77.0%), but markups are more than four times higher for cannabis (median markup = $US 1691; median price = $US 477; ratio = 354.2%). This may be explained by the recent reduction in transnational cannabis trafficking, due in large part to the high risks of detection and low expected profits (i.e., low mass density, distinctive smell, easy to grow, low retail prices). Cannabis trafficking

7 An anonymous reviewer pointed out that there could be an issue with the assumption of statistical independence because countries could be repeated in the dataset; for example, the fact that the U.S. appears four times as a receiving country means that its features appear four times in the models. Consistent with the literature, supplementary analyses using robust errors and cross-random effect terms did show differences in standard errors but the estimates remained roughly the same.

has become largely unnecessary as domestic cultivation increases (Weisheit, 1992): countries still relying on cannabis imports therefore pay higher prices. Also, a specialized market for varieties of cannabis has emerged: foreign consumers may be willing to pay higher prices for Jamaican Ganja or Quebec Gold (Bouchard, Potter, & Decorte, 2011). Geographic distance appears to be crucial as well in understanding price markups. The lowest price increases for all drugs are observed between countries that share ground borders, and the highest price increases are observed between distant countries. Isolated countries also seem to pay higher prices: for example, Australia is not only an island located at the far end of the world, it is apparently never used as a transit to other destinations. There is no clear distinction between the impact of distance and position on price markups: multivariate analyses are needed for further discussion (Table 3). It was argued that adjusted prices give a better estimation of the “real” value of drugs in a country; only adjusted price markups are interpreted below (models 2, 4, and 6). In all cases, results from unadjusted price markups models are consistent with adjusted price markups. As expected, distance has a strong positive impact on price markups. The more distance there is between two countries, the higher the markup. A pragmatic interpretation of this result is that, as in legal markets, transport costs increase with distance (Deardroff, 1998). However, traffickers usually report that transport costs are negligible compared to revenues (Zaìtch, 2002a, 2002b). Another interpretation is that distance is associated with risk compensation. Longer journeys involve higher risks of being detected at some point; it takes more time to cover longer distance. Scholars have also suggested that distance is closely related to means of transportation and that different means are associated with specific risks (Farrell et al., 1996; Reuter & Kleiman, 1986).

R. Boivin / International Journal of Drug Policy 25 (2014) 235–243

Both explanations are equally plausible, but further research is needed. Both aggregate measures of risk are significantly related to adjusted price markups. Contrary to cross-national comparisons (Appendix 1), police per capita in destination countries has a significant positive effect on price increases between two countries, for all types of drugs. This result suggests that research findings based solely on cross-national comparisons should not conclude that wholesale prices are not related to law enforcement efforts. Furthermore, corruption is again a strong predictor of price markups. Markups are lower when drugs are headed to corrupt countries. Two opposite effects are observed when drugs are sent to transit countries in the cocaine and heroin markets. On the one hand, price markups are lower if the destination is a transit to potentially large markets for cocaine and if the destination is a transit for heroin. This effect could be explained by the fact that supply greatly exceeds demand in transit countries. An alternative explanation could be that transit countries enjoy a “discount” for their role as brokers in drug trafficking. Social network analyses have often found that offenders acting as middlemen in their networks had higher earnings and lower risks of arrest (Bouchard & Ouellet, 2011; Morselli, 2009). Our results suggest that this idea of strategic positioning could also be applied in macro-level analyses. On the other hand, markups are greater when cocaine is exported to countries that are positioned on many paths between other countries (“flow betweeness”). Recall that from 1998 to 2007, cocaine was only produced in three South-American countries (UNODC, 2009). Other countries had to import from one of these countries and the number of possible trafficking routes was limited. This was not the case for heroin, which could be imported from three distant regions, with a larger number of routes. Surprisingly, the size of destination markets does not have a significant impact on price markups. For example, other things being equal, markups are similar whether drugs are exported towards the United States (more than 300 million inhabitants) or Iceland (approximately 300 000 inhabitants). Finally, we find support for the world-system hypothesis. Cocaine and heroin price markups are more pronounced when the trade is directed towards the core of the world-economy. Prices increase more sharply when a country exports drugs to a betterpositioned country. Cocaine and heroin are scarce commodities produced in a limited number of countries; cannabis is not. Many countries could be self-sufficient in cannabis, in the sense that imports are not necessary to insure drug supply within the country. In general, countries are less dependent on others for their supply in cannabis than cocaine and heroin.

Conclusion Drug trafficking is an illegal activity that consists of multilateral exchanges of prohibited goods between producers, distributors, and consumers in a market-like context (Naylor, 2003). At the global level, drug trafficking is best conceived as a series of relations between countries. Network analysis naturally fits this relational definition; knowledge about drug trafficking is gained from the combination of cross-national and relational analyses. This paper aimed to test propositions derived from two apparently unrelated theoretical perspectives. The risks and prices model (Caulkins & Reuter, 2010; Reuter & Kleiman, 1986) states that law enforcement imposes additional costs on traffickers that are passed on to the next buyers of the drugs in the form of higher prices. The model predicts that prices will be higher where costs are higher. Most costs are due to risk compensation for possible prison sentences and violence (Caulkins & Reuter, 1998).

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The world-system perspective (Wallerstein, 1974) suggests that the world-economy is based on unequal economic and political relations that result in a hierarchy of countries. In legal markets, countries forming the core of the system collect more wealth at the expense of others, mainly because they have extensive means of production. Core countries do not rely on others for their supply of various commodities, in contrast to peripheral and, to a lesser extent, semi-peripheral countries, which are more or less dependent on others for their supply of specialized and transformed commodities. It is argued that the situation is reversed for illegal drugs: due to various contextual factors (e.g., tougher drug law enforcement, temperate climate), industrialized countries are not able to produce enough drugs to meet national demand and are forced to import from less developed countries. In the cases of cocaine and heroin, the whole supply is based on imports. Because the relation of dependence is reversed for cocaine and heroin, prices are expected to increase more sharply when the trade is directed towards the core of the world-economy. The results presented above support both perspectives. First, evidence is found that the level of corruption and the ratio of police per capita are related to price markups in drug exchanges between two countries. Cocaine, heroin, and cannabis are more expensive in stricter countries, and markups are sharper when drugs are exported to countries in which the risks of detection and seizure are higher. Second, price markups for cocaine and heroin only are more pronounced when drugs are exported to countries that are better positioned in the legitimate worldeconomy. Higher price markups indicate unequal exchanges: in legal markets, when countries come together to exchange products in the world market, the exchange results in a net transfer of value towards the core (Chase-Dunn, 1989; Chase-Dunn & Grimes, 1995). In this case, there is evidence of unequal exchanges in global cocaine and heroin markets, but in the opposite direction. Markups are to the advantage of more peripheral countries. For cocaine and heroin, the trade network appears to be turned upsidedown. The model presented may provide unexpected insights into new drug markets. The recent emergence of synthetic drugs and the widespread domestic production of cannabis certainly question the idea that the core depends on the periphery for its supply in drugs; a number of core countries are now self-sufficient or even exporters of illegal drugs (UNODC, 2011). Current explanations suggest that domestic production may be explained, among others, by increased demand, technological innovations (i.e. hydroponics), precursor availability, social tolerance towards consumption, inefficient law enforcement, etc. According to the world-system perspective, the emergence of domestic production should rather be seen as a mean to increase competitiveness and reduce dependency. The capacity to produce popular commodities is necessary to maintain a favourable position in the world-economy, regardless of the legal status of commodities. A more general conclusion is that global drug markets are structured, but not necessarily because of a vast criminal conspiracy. The structure imposes itself as a series of logical, rational, but independent choices motivated by a common appetite for profit and risk management (Benson & Decker, 2010; Williams, 1998). Illegal drug prices and price markups are explained by local contexts (Hobbs, 1998) but also by global features. On one hand, drugs are more expensive where additional costs are imposed on traffickers by law enforcement. On the other hand, distance is also a strong predictor of price markups, simply because long distance trade costs more. The position and role of a country in global drug markets are also closely associated with the value of drugs in that country. Retail markets are embedded in and influenced by larger trade networks and a better understanding of global drug markets improves knowledge about local contexts.

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Table A1 OLS regression models, wholesale prices, 1998–2007 (coefficient: beta). Cocaine

N countries Corruption Police per 1000 inhabitants Geodesic distance from source Networks: flow betweeness Networks: import Networks: export America Africa Europe R squared † * **

Heroin

Cannabis

1

2

3

4

5

6

Prices ($US; ln)

Adjusted prices (ln)

Prices ($US; ln)

Adjusted prices (ln)

Prices ($US; ln)

Adjusted prices (ln)

58 −0.347** 0.011 0.508** −0.229* – – – – – 0.639**

58 −0.684** 0.095 0.300** −0.140† – – – – – 0.743**

82 −0.317** 0.192* 0.118 −0.318** – – 0.173† – – 0.389**

82 −0.670** 0.198** 0.100 −0.214** – – 0.178* – – 0.718**

104 −0.402** 0.064 – – 0.112† −0.191** – −0.234** 0.310** 0.716**

104 −0.514** 0.079† – – 0.110* −0.160** – −0.229** 0.268** 0.817**

p < 0.1. p < 0.05. p < 0.01.

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