Uncertainty and risk: A framework for understanding pricing in online drug markets
Bibliographic record
Abstract
BACKGROUND: The pricing of illicit drugs is typically approached within the risks and prices framework. Recent sociological and economic studies of prices in online drug markets have stressed the centrality of reputation for price formation. In this paper, we propose an account of price formation that is based on the risks and prices framework, but also incorporates internal social organization to explain price variation. We assess the model empirically, and extend the current empirical literature by including payment methods and informal ranking as influences on drug pricing. METHODS: We apply our model to estimate the prices of cannabis, cocaine, and heroin in two online drug markets, cryptomarkets (n = 92.246). Using multilevel linear regression, we assess the influence of product qualities, reputation, payment methods, and informal ranking on price formation. RESULTS: We observe extensive quantity discounts varying across substances and countries, and find premia and discounts associated with product qualities. We find evidence of payment method price adjustment, but contrary to expectation we observe conflicting evidence concerning reputation and status. We assess the robustness of our findings concerning reputation by comparing our model to previous approaches and alternative specifications. CONCLUSION: We contribute to an emerging economic sociological approach to the study illicit markets by developing an account of price formation that incorporates cybercrime scholarship and the risks and prices framework. We find that prices in online drug markets reflect both external institutional constraint and internal social processes that reduce uncertainty.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".