Measuring the effects of the new ECOWAS and WAEMU tobacco excise tax directives
Bibliographic record
Abstract
BACKGROUND: In December 2017, the 15-member ECOWAS (Economic Community of West African States) and the 8-member WAEMU (West African Economic and Monetary Union, a subset of ECOWAS) passed new Tobacco Tax Directives. Both Directives increased the minimum ad valorem excise tax rate to 50%. In addition the ECOWAS Directive introduced a minimum specific tax (US$ 0.02/stick), but the WAEMU Directive did not. This paper examines the likely effects of these new Directives on cigarette prices, sales volumes and revenues. METHOD: Tax simulation models using comparable data were constructed for each of the 15 countries to estimate the effects of the ECOWAS and WAEMU Directives. RESULTS: If the 15 ECOWAS members implement the ECOWAS Directive it would substantially increase the retail price of cigarettes (unweighted average 51%, range: 12% to 108%), decrease sales volumes (22%, range: -8% to -39%) and increase tax revenue (373%, range: 10% to 1243%). The impact of the WAEMU Directive on WAEMU countries' cigarette prices (unweighted average +2%), sales volumes (-1%) and revenue (+17%) is likely to be minimal. CONCLUSIONS: The 2017 ECOWAS Directive, which adds a specific excise tax per pack, along with an increase in the ad valorem tax, substantially improves its members' cigarette tax structure. The specific tax overcomes the weakness of the ad valorem excise tax, since it does not depend on import or ex-factory values, which comprise only a small part of the retail price in ECOWAS countries. We recommend that WAEMU countries adopt the ECOWAS Directive, rather than the WAEMU Directive.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".