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Record W1911651913 · doi:10.1002/hec.3004

Smokers' Strategic Responses to Sin Taxes: Evidence from Panel Data in Thailand

2013· article· en· W1911651913 on OpenAlexfundno aff
Justin S. White, Hana Ross

Bibliographic record

VenueHealth Economics · 2013
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersNational Cancer InstituteCanadian Institutes of Health Research
KeywordsExcisePrice elasticity of demandEconomicsConsumption (sociology)Panel dataTobacco controlProduct (mathematics)Demographic economicsPublic economicsPublic healthMicroeconomicsMedicineEconometricsMacroeconomics

Abstract

fetched live from OpenAlex

In addition to quitting and cutting consumption, smokers faced with higher cigarette prices may compensate in several ways that mute the health impact of cigarette taxes. This study examines three price avoidance strategies among adult male smokers in Thailand: trading down to a lower-priced brand, buying individual sticks of cigarettes instead of packs, and substituting roll-your-own tobacco for factory-manufactured cigarettes. Using two panels of microlevel data from the International Tobacco Control Southeast Asia Study, collected in 2005 and 2006, we estimate the effects of a substantial excise tax increase implemented throughout Thailand in December 2005. We present estimates of the marginal effects and price elasticities for each of five consumer behaviors. We find that, controlling for baseline smoking characteristics, sociodemographics, and policy variables, quitting is highly sensitive to changes in cigarette prices, but so are brand choice, stick-buying, and use of roll-your-own tobacco. Neglecting such strategic responses leads to overestimates of a sin tax's health impact, and neglecting product substitution distorts estimates of the price elasticity of cigarette demand. We discuss the implications for consumer welfare and several policies that mitigate the adverse impact of consumer responses.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.297
GPT teacher head0.390
Teacher spread0.093 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2013
Admission routes1
Has abstractyes

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