Quality and quantity of price elasticity of cigarette in Iran
Bibliographic record
Abstract
OBJECTIVE: Effectiveness of tax policies to control cigarette consumption largely depends on the sensitivity of cigarette demand due to price change. Price elasticity is the measurement of this responsiveness. The main objective of this study is to measure quantity, and quality price elasticity of demand (PED) and cross-price elasticity of demand (XED) for Iranian and non-Iranian cigarette brands in Iran. METHODS: This study used data from the 2017 Iranian household income and expenditures survey conducted in all 31 provinces of Iran. A total of 39,864 households were included in the survey. PED of quantity and quality and XED were estimated using restricted, unrestricted and quintile regression models. RESULTS: Our results s show that the Iranian and non-Iranians brands cigarettes were price inelastic and elastic, respectively. XED between Iranian and non-Iranian brands was positive suggesting households' preference for Iranian brands of cigarettes over non-Iranian brands. Quintile regression results suggest that PED varied between -1.20 and -0.91 across the distribution of quantity demanded. CONCLUSION: Imposing tax could be a useful policy tool to control smoking initiation and intensity in Iran. However, the effectiveness of such policy would depend on the better governance of taxation imposed on different brands of cigarettes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".