Ex-Ante Analysis of the Costs and Benefits of Legalizing Cannabis Markets in the Czech Republic
Bibliographic record
Abstract
Abstract Cannabis is the most commonly used illicit drug worldwide. In countries with repressive drug policies, the costs of its prohibition plausibly outweigh the benefits. We conduct a cost–benefit analysis of cannabis legalization and regulation in the Czech Republic, taking into consideration alternative scenarios designed using parameters from the known effects of cannabis legalization in selected U.S. states, Canada, and Uruguay. Our analysis focuses on tax revenues, law enforcement costs, the cost of treatment and harm reduction, and the value of Quality Adjusted Life Years (QALYs). Under all the projected scenarios, the identified benefits of legalizing cannabis for personal use exceed the potential costs. The estimated net social benefit of legalization is in the range of 34.4 to 107.6 million EUR per year (or between 3.2 and 10.1 EUR per capita), depending on the size of the cannabis market and the development of cannabis prices after legalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".