Uruguay's Cannabis Natural Experiment: Quality Drives Market Adoption
Bibliographic record
Abstract
Description: This paper analyzes Uruguay's cannabis market (2017-2025) as a natural experiment that isolates product quality as the primary driver of legal market adoption. Uruguay became the first nation to fully legalize recreational cannabis in 2013, offering an 85% price discount over illicit markets. Despite this advantage, adoption remained below 10% through 2021—until systematic quality improvements triggered dramatic market shifts. Research Design: Uruguay systematically increased legal cannabis potency from 3% to 20% THC across four distinct periods while holding price, regulations, and access constant. This provides rare quasi-experimental conditions for testing the Consumer-Driven Black Market Displacement (CBDT) Framework—a behavioral-utility model predicting legal market share based on weighted policy dimensions. Key Results: Quality drives adoption: Between 2022-2023, improving THC from 9% to 15% increased legal sales 84% (1,774→3,258 kg) while price remained constant Strong statistical validation: Linear regression shows β₁ = 17.82 (p = 0.036, R² = 0.929), with CBDT predictions within 2.0 percentage points across all eras Minimum viable threshold: 15-18% THC represents critical inflection point where legal products achieve functional parity with illicit alternatives Cross-national consistency: Framework validated in U.S. (MAE 5.0%), Canada (MAE 1.1%), and Uruguay (MAE 2.0%) Policy Implications: Demonstrates that price discounts alone cannot overcome quality deficits. Countries implementing legalization (Germany, Thailand, Mexico) should prioritize quality standards over price competition. Findings challenge conventional economic assumptions about price-sensitive consumers and establish quality as non-negotiable threshold for black market displacement.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".