The elasticity of tobacco products in BiH – macrodata analysis
Bibliographic record
Abstract
Tobacco consumption continues to be behavior engaged in by a large percentage of Bosnia & Herzegovina (BiH) citizens. According to the official statistics, nearly half of the state’s adults, that is about 1,200,600 people, consume tobacco product on a daily bases. The state excise policy is one of the main available tool for reducing smoking prevalence because the cigarette prices are under direct impact of this policy. The specific excise on cigarettes introduced in BiH in 2009 and have increased every year so it was the main driver of cigarettes price growth. In order to provide research-based evidence for more effective tobacco taxation policies in BIH, in this paper we estimate the price elasticity of demand for cigarettes using the macro level data for the period 2008 to 2017, on a semi-annual basis. The results have shown that increase in prices of cigarettes have statistically significant impact on cigarettes consumption, at a significance level of 1%. The estimated price elasticity coefficient is in the range from -0.71 to -0.83, depends on the selected control variables used in the model. It means that increase in real cigarettes prices for 10% led to the decrease in cigarettes consumption in the range from 7.1% to 8.3%. Results of our analysis suggest that the state excise policy is an effective tool for reducing smoking prevalence in BiH. If policy-makers in BiH continue with the policy of increasing excise taxes, the consumption of cigarettes will decrease.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".