Effect of cigarette prices on cigarette consumption in Ghana
Bibliographic record
Abstract
Introduction: Noncommunicable diseases are on the rise globally, with tobacco consumption being a major risk factor. Reducing tobacco consumption is an important step towards reducing the incidence and prevalence of many noncommunicable diseases. Tax and price measures have been proposed as tobacco control tools. This study investigated the link between cigarette prices and cigarette consumption in Ghana. Methods: Annual time series data for the period 1980-2016 were used. The data came from diverse sources, including WHO, World Bank, and tobacco industry documents. Dynamic Ordinary Least Squares (DOLS), cointegration techniques, and three-stage least squares (3SLS) were used to analyze the data. Results: After controlling for education, income, and population growth, we estimated that the price elasticity of cigarette demand is between -0.35 and -0.52 and statistically significant at 1% level. In the short run, the price elasticity is -0.1. Another variable that significantly reduced cigarette consumption during the period was education, with an elasticity between -1.7 and -2.7. Conclusion: Cigarette demand in Ghana is influenced by cigarette prices and education. We conclude that tobacco taxes that significantly raise retail prices of cigarettes and higher education (including health education) will help reduce cigarette consumption.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".