Elsevier

European Economic Review

Volume 120, November 2019, 103306
European Economic Review

Cannabis prices on the dark web

https://doi.org/10.1016/j.euroecorev.2019.103306Get rights and content

Abstract

This paper examines prices of cannabis sold over the anonymous internet marketplace AlphaBay. We analyze cannabis prices of 500 listings from about 140 sellers, originating from 18 countries. We find that both listing characteristics and country characteristics matter. Cannabis prices are lower if sold in larger quantities, so there is a clear quantity discount. Cannabis prices increase with perceived quality. Cannabis prices are also higher when the seller is from a country with a higher GDP per capita or higher electricity prices. The internet based cannabis market seems to be characterized by monopolistic competition where many sellers offer differentiated products with quality variation causing a dispersion of cannabis prices and sellers have some control over the cannabis prices.

Introduction

Drug policies around the world have been relatively strict since the adoption of the Single Convention on Narcotic Drugs in 1961. However, in recent years many countries started adopting more liberal policies towards consumption of cannabis. European countries such as Portugal, Czech Republic and Germany pursued decriminalization policies, while the Netherlands has quasi-legalized cannabis use and retail sale. In 2013, Uruguay was the first country in the world to legalize recreational cannabis use, followed by Canada in 2018. In the US, at a federal level cannabis is still illegal but several states have legalized medical cannabis use, decriminalized or even legalized recreational cannabis use. Nevertheless, recreational cannabis remains an illegal drug in many countries.

Related to the illegal nature, information and therefore studies on quality, prices and quantities of cannabis consumed or supplied are limited. Nisbet and Vakil (1972) is a pioneering work analyzing cannabis prices using self-reported data of US university students. Many studies focused on estimation of participation elasticities, i.e. the effect of prices on the extensive margin of cannabis use (see for example Pacula, Grossman, Chaloupka, O’Malley, Johnston, Farrelly, 2001, Cameron, Williams, 2001, DeSimone, Farrelly, 2003, Williams, 2004, Williams, Pacula, Chaloupka, Wechsler, 2004, Zhao, Harris, 2004, Clements, Zhao, 2009 and Clements et al., 2010). Van Ours and Williams (2007) study the price sensitivity of cannabis use dynamics among young Australians finding that low prices stimulate the uptake of cannabis while reducing quitting from cannabis use. There are also a few studies on the supply side of the cannabis market investigating the determinants of cannabis prices.

Cannabis prices vary both between and within countries. Statistics Canada for example reported that in 2017 cannabis prices ranged from a low US $5.02 in the province of Manito to US $7.18 in the territories.1 These numbers are in line with the 2018 cannabis price index that provides cannabis prices by city, where for Canada the price range in US dollars per gram was from 6.15 in Montreal to 7.82 in Toronto.2 The 2018 cannabis price index for US cities ranged from a low US $7.58 per gram in Seattle to US $18.08 per gram in Washington DC. The overall range in the 2018 cannabis price index was from US $1.34 per gram in Quito (Ecuador) to US $32.66 per gram in Tokio (Japan).3 Cannabis prices vary across countries for various reasons, i.e. differences in legal regime with respect to the consumption and/or production of cannabis, quality of the cannabis, market power of cannabis suppliers. There is also within country variation of cannabis prices for similar reasons as also within a country the legal regime may differ like for example in the US where recreational cannabis use is legalized in some states but prohibited in others.

Previous studies on the determinants of drug prices mainly focus on quantity discounts and quality effects. An early study is Brown and Siverman (1974) who analyze data of heroin prices based on purchases by US undercover narcotics agents. They find a quantity discount while purity of the heroin is found to have a positive effect on the heroin price. Caulkins and Padman (1993) analyze price data collected by US undercover narcotics agents for various illicit drugs including cannabis. They find for most drugs quantity discounts and quality premiums. Clements (2006) studies the price of cannabis using Australian data collected through undercover buys finding a quantity discount. Caulkins and Pacula (2006) use data from a US household survey to investigate the variation in cannabis prices finding a quantity discount. Lakhdar et al. (2016) analyzes cannabis prices using information from regular French cannabis users finding a significant quantity discount and significant positive quality effects. Lahaie et al. (2016) study heroine prices in France using data on heroin samples and surveys of heroin users finding significant quantity discounts and positive purity effects. Smart et al. (2017) study cannabis prices of retail transactions from Washington state where cannabis sales are legal. They find are a significant quantity discount and significant positive price effects of quality.

Online illegal drug markets with cannabis transactions as an important element are a recent phenomenon. Through the so called Dark Web, sellers and buyers of cannabis interact anonymously. Soaka and Christin (2015) present a descriptive analysis of various online anonymous market places including the first successful one, Silk Road, which was online from 2011 to 2013 when it was taken down by law enforcement and its operator was arrested. They conclude that these online market places are resilient to law enforcement take-downs as within a month after shutdown ‘a novel incarnation’ of Silk Road was online. Van Hout and Bingham (2013) conclude on the basis of anonymous online interviews among users that transactions are experienced to be safer than negotiating in a street-level drug market. Décary-Hétu et al. (2016) study the risk-taking behavior of drug sellers at the Dark Web concluding that compared to traditional drug market transactions the risk of violence is reduced as the face-to-face transactions are eliminated. Aldridge et al. (2018) argue that online illegal markets may reduce the harm of drug use through the increase in quality and safety of the drugs sold and because in the course of the transaction there is less conflict and violence. Furthermore, it is easier to build a reputation because of customer feedback. Barratt and Aldridge (2016) argue that higher drug quality may be the most important reason for buyers to prefer transactions through the Dark Web rather than via face-to-face interactions.

Aldridge and Décary-Hétu (2016) study data of the first major internet based illegal market, Silk Road 1. In their analysis of more than thousand drug-selling vendors from about 40 countries, they conclude that drug sales accounted for about a quarter of the total revenue on the market, with ecstasy-type drugs dominating the wholesale activity. Bhaskar et al. (2019) in their analysis of more than 1.5 million online drug sales focus on moral hazard, analyzing seller reputation and performance. They find that only a small proportion of on line drug deals received a bad rating from buyers. For Dark Web vendors, bad ratings lead to big reductions in sales and to market exit. The authors also note that compared to street sales of illegal drugs, Dark Web marketplaces suffer less from problems of drug adulteration and low quality. According to Bhaskar et al. (2019) a substantial share of drug users buy their drugs online through the Dark Web. Duxbury and Haynie (2018) analyze the network structure of opioid distribution through the Dark Web. They arrive to similar conclusions as Bhaskar et al. (2019), finding that vendors’ trustworthiness explains more variation in the overall network structure than the affordability of vendor products or the diversity of vendor product listings.

Our paper presents an analysis based on unique data on cannabis prices. Whereas usually information on cannabis prices is based on undercover operations or consumer surveys we use information from cannabis sellers. We exploit data from the Dark Web site called AlphaBay, a website that operated between December 2014 and July 2017. According to Christin (2017) during that time among the online drug market places it became the leading one. From an analysis of 27 scrapes from AlphaBay Christin (2017) concludes that half of the drug sellers specialized in one drug. From a descriptive analysis of the cannabis sales he also concludes that most transactions are for 1g, 5g and 10g while there is a modest volume discounting, i.e. larger quantities are sold at a lower price.

We use data collected from AlphaBay for two weeks early October 2015. At the time, AlphaBay was still in its initial stage on a steep expansion curve. Paquet-Clouston et al. (2018) for example report about 700 vendors in the drug sections of AlphaBay end of September 2015. Five months later the number of vendors was more than doubled.4 We have information about 500 cannabis prices from about 140 sellers in 18 countries. The nature of our data allows us to exploit the detailed information about the quality of a particular cannabis strain, measured both by its potency (active ingredient content) and popularity among users. Our main contribution to the literature on the analysis of cannabis markets is threefold. First, we study the relationship between cannabis prices, quantities and qualities whereas often quality is not part of the analysis. Second, we use information from the Dark Web to provide a quantitative analysis whereas thus far mostly descriptive studies are available. Third, because our data allow us to include seller characteristics – including their country of origin – in the analysis we can investigate to what extent seller have some influence over cannabis prices.

The remainder of our paper is organized as follows. Section 2 outlines the Dark Web in relation to information about cannabis transactions. In the spectrum of drugs available through the Dark Web, cannabis is special in the sense that the legal status of the drug varies a lot. In many countries, cannabis is an illicit drug but in other countries or parts of countries (states in the US) cannabis use is legalized or quasi-legalized. Section 3 presents our data and some summary statistics. Section 4 discusses the set-up of our empirical analysis. Section 5 presents our parameter estimates and Section 6 concludes.

Section snippets

TOR Software

The advent of sophisticated cryptographic algorithms and distributed networks gave rise not only to a better privacy on the internet, but also sparked the creation of many so-called “Dark Web” websites. Thanks to The Onion Router (TOR) software, internet users are able to hide and transmit their communication anonymously. The core principle behind the TOR was developed as a project at the US Naval Research Laboratory and Defense Advanced Research Projects Agency (DARPA) in the early 1990s.

Data and summary statistics

Our dataset contains information about 511 cannabis prices, sold by 141 different vendors from 18 countries.13 We collected data from the “Cannabis and Hashish” subsection of the site between 29 September and 12 October 2015 that were still active. The focus of our analysis is on active listings due to the fact that the listings sold out in

Determinants of cannabis prices

Studies on the determinants of drug prices go back to Brown and Siverman (1974) who use a price equation with a multiplicative functional form, i.e. linear in log prices, log quantities and other variables. This example is followed in many later studies. Brown and Siverman (1974) relate US heroin prices to quantity and purity of the purchases. The finding of a negative effect of transaction quantity on price is related to the usual quantity discount but also related to the risk for the seller

Parameter estimates

Table 3 reports our parameter estimates. To account for the fact that we have repetitive observations per seller we cluster standard errors at the level of the seller. The upper part of the table shows the effects of the offer characteristics, the lower part of the table shows the effects of the country characteristics. For the sake of comparison, we estimate the model defined in equation (1) in its simplest form, relating prices to quantities only. This also allows us to use all 511 prices

Conclusions

It is possible to buy and sell illegal drugs in a rather anonymous way through internet. Improvements in the internet security over the past decade transitioned a small proportion of black markets to the already popular and fast-growing e-commerce. Thanks to the highly sophisticated cryptographic algorithms and a user-friendly platform, the almost untraceable nature of the network on the Dark Web allows users to safely engage in trade of illicit drugs. Compared to traditional street

References (33)

  • J.P. Caulkins

    Estimated cost of production for legalized cannabis

    Working Paper Series

    (2010)
  • J.P. Caulkins et al.

    Marijuana markets: inferences from reports by the household population

    J. Drug Issues

    (2006)
  • J.P. Caulkins et al.

    Quantity discounts and quality premia for illicit drugs

    J. Am. Stat. Assoc.

    (1993)
  • N. Christin

    An EU-Focused Analysis of Drug Supply on the AlphaBay Marketplace

    (2017)
  • K.W. Clements

    Pricing and packaging: the case of marijuana

    J. Bus.

    (2006)
  • K.W. Clements et al.

    The demand for marijuana, tobacco and alcohol: inter-commodity interactions with uncertainty

    Empir. Econ.

    (2010)
  • Cited by (12)

    • Uncertainty and risk: A framework for understanding pricing in online drug markets

      2022, International Journal of Drug Policy
      Citation Excerpt :

      Przepiorka et al. (2017) observe discounts on “poor quality” cannabis. Červený and van Ours (2019) find no association between price and self-described THC content in cannabis, but find some strains sold at a premium. While the reputation system has been studied exhaustively, and there is a growing literature on country-level variation, the role of escrow payment and status rankings for price formation is less scrutinized.

    • Cannabis flower prices and transitions to legal sources after legalization in Canada, 2019–2020

      2022, Drug and Alcohol Dependence
      Citation Excerpt :

      In the non-medical market, Canadians are permitted to purchase up to 30 g of dried flower in a single transaction (Government of Canada, 2018a). Indeed, quantity discounts are considerable in illegal markets, where purchasing in larger quantities tend to be cheaper per gram than smaller quantities (Ben Lakhdar et al., 2016; Caulkins, 2007; Caulkins and Padman, 1993; Červený and van Ours, 2019; Clements, 2006; Wadsworth et al., 2020). Canada had an established illegal and medical cannabis market prior to legalization (Mahamad and Hammond, 2019; Capler et al., 2017).

    • Illicit drug prices and quantity discounts: A comparison between a cryptomarket, social media, and police data

      2021, International Journal of Drug Policy
      Citation Excerpt :

      A few studies have examined quantity discounts online. Przepiorka et al. (2017) found a mean quantity discount of −0.2 across nine drug types while (Červený & van Ours, 2019) found −0.19 for herbal cannabis. Smart, Caulkins, Kilmer, Davenport, & Midgette, 2017 analysed 36,001,228 transactions in Washington's legal cannabis market and found a mean quantity discount of −0.06.

    View all citing articles on Scopus
    View full text