Illicit drug prices and quantity discounts: A comparison between a cryptomarket, social media, and police data
Bibliographic record
Abstract
BACKGROUND: Illicit drugs are increasingly sold on cryptomarkets and on social media. Buyers and sellers perceive these online transactions as less risky than conventional street-level exchanges. Following the Risks & Prices framework, law enforcement is the largest cost component of illicit drug distribution. We examine whether prices on cryptomarkets are lower than prices on social media and prices reported by law enforcement on primarily offline markets. METHODS: Data consists of online advertisements for illicit drugs in Sweden in 2018, scraped from the cryptomarket Flugsvamp 2.0 (n = 826) and collected with digital ethnography on Facebook (n = 446). Observations are advertisements for herbal cannabis (n = 421), cannabis resin, hash (n = 594), and cocaine (n = 257) from 156 sellers. Prices are compared with estimates from Swedish police districts (n = 53). Three multilevel linear regression models are estimated, one for each drug type, comparing price levels and discount elasticities for each platform and between sellers on each platform. RESULTS: Price levels are similar on the two online platforms, but cocaine is slightly more expensive on social media. There are quantity discounts for all three drug types on both platforms with coefficients between -0.10 and -0.21. Despite the higher competition between sellers on cryptomarkets, prices are not lower compared to social media. Online price levels for hash and cocaine are similar to those reported by police at the 1 g level. CONCLUSION: Mean prices and quantity discounts are similar in the two online markets. This provides support for the notion that research on cryptomarkets can also inform drug market analysis in a broader sense. Online advertisements for drugs constitute a new detailed transaction-level data source for supply-side price information for research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".