Price elasticity of tobacco products among economic classes in India, 2011–2012
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study are to: (1) examine the pattern of price elasticity of three major tobacco products (bidi, cigarette and leaf tobacco) by economic groups of population based on household monthly per capita consumption expenditure in India and (2) assess the effect of tax increases on tobacco consumption and revenue across expenditure groups. SETTING: Data from the 2011-2012 nationally representative Consumer Expenditure Survey from 101,662 Indian households were used. PARTICIPANTS: Households which consumed any tobacco or alcohol product were retained in final models. PRIMARY OUTCOME MEASURES: The study draws theoretical frameworks from a model using the augmented utility function of consumer behaviour, with a two-stage two-equation system of unit values and budget shares. Primary outcome measures were price elasticity of demand for different tobacco products for three hierarchical economic groups of population and change in tax revenue due to changes in tax structure. We finally estimated price elasticity of demand for bidi, cigarette and leaf tobacco and effects of changes in their tax rates on demand for these tobacco products and tax revenue. RESULTS: Own price elasticities for bidi were highest in the poorest group (-0.4328) and lowest in the richest group (-0.0815). Cigarette own price elasticities were -0.832 in the poorest group and -0.2645 in the richest group. Leaf tobacco elasticities were highest in the poorest (-0.557) and middle (-0.4537) groups. CONCLUSIONS: Poorer group elasticities were the highest, indicating that poorer consumers are more price responsive. Elasticity estimates show positive distributional effects of uniform bidi and cigarette taxation on the poorest consumers, as their consumption is affected the most due to increases in taxation. Leaf tobacco also displayed moderate elasticities in poor and middle tertiles, suggesting that tax increases may result in a trade-off between consumption decline and revenue generation. A broad spectrum rise in tax rates across all products is critical for tobacco control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".