Cannabis Taxation as a Revenue Source for Development: Opportunities and Challenges
Bibliographic record
Abstract
Recent years have seen an increasing number of countries across the globe establish legal markets for the production, distribution, and consumption of medicinal or recreational cannabis. With this has come the expectation that more markets will follow suit. Malta has legalised recreational cannabis in 2021, Germany has recently presented an outline of how its legalisation process will look like. The global legal cannabis market is currently estimated at over GBP 20 billion and expected to quadruple in the coming decade. Notably, while much of the attention has originally been directed toward high income consumption markets, like the US and Canada, there have been movements towards legalisation within low- and middle-income countries (LMICs) which traditionally have been large production centres supplying illegal markets across the globe: Morocco, Lesotho, Mexico, Colombia, to mention just a few. In this latter group of countries, the discourse on the development potential of cannabis legalisation was at times hyper-optimistic, citing the potential for job creation, rural development, lowering crime and raising tax income. A huge range of estimates on the size and potential of the cannabis industry have further fanned this enthusiasm. In South Africa it was estimated that the sector could contribute up to 130,000 jobs and be worth as much as USD1.6 billion – about half of the minimum value that has been estimated for the Mexican market (USD3 billion). While this might seem huge figures, there is evidence of substantial investments into these sectors in LMICs. Between 2015 and 2019, the medical cannabis sector in Colombia received more than USD600 million in investment, while Lesotho received more than USD40 million in 2020 alone.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".