Price Elasticity of Demand for Tobacco Consumption in Eritrea: an Exploratory Study
Bibliographic record
Abstract
Smoking is a major cause of premature death and morbidity around the world.In Eritrea, the National Non-communicable Disease Risk Factors Baseline Survey of the Ministry of Health and WHO, conducted in 2005, noted that tobacco use is the main risk factor for non-communicable diseases such as respiratory infection, diabetes, cardio-vascular diseases and lung cancer in the country (8).This paper is an exploratory study into the sensitivity or responsiveness of increases in price of cigarettes on its consumption levels in Eritrea.The study used the Engle-Granger Ordinary Least Square (OLS) econometric modelling to estimate the (long-run) price elasticity of demand for tobacco.The model controls for per capita GDP, legislation for tobacco control (introduced as dummy variable) and past consumption.It is based on annual time series data from 1998 to 2012.The estimated long-run price elasticity of demand for cigarettes ranges from -0.8232 to -2.8122, with an average elasticity of -1.67.A comparison of the average price elasticity of demand for cigarettes in Eritrea with similar studies in other low-and middle income countries around the world reveals that the price elasticity of demand for Eritrea is more elastic.This could be attributed to the low income of people.As the demand is more price-elastic, increases in the prices of cigarettes could be more effective in reducing cigarette consumption.Seen from the point of the health benefits of overall reductions in cigarette consumption, the prices of cigarettes should be continuously monitored so that increases in prices of cigarettes (through increases in excise taxes) could be made to offset any future affordability of cigarettes due to increments in incomes in the population.The study could not assess the impact of increase in the prices of cigarettes on tax revenue due to data limitations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".