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Record W1992604828 · doi:10.2202/1558-9544.1187

Do Changes In Cigarette Taxes Impact Youth Smoking? Evidence from Canadian Provinces

2010· article· en· W1992604828 on OpenAlexaffabout
Anindya Sen, Hideki Ariizumi, Daciana Driambe

Bibliographic record

VenueForum for Health Economics & Policy · 2010
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsWilfrid Laurier UniversityUniversity of Waterloo
Fundersnot available
KeywordsExciseLogitEconomicsDemographic economicsYouth smokingDemographyGeographyEconometricsMedicineTobacco controlPublic health

Abstract

fetched live from OpenAlex

Recent U.S. studies report much smaller youth smoking participation elasticities compared to research based on 1980s and 1990s data. We exploit the considerable time-series variation available within and across Canadian provinces. In particular, we study the dramatic (50%) reduction in cigarette excise taxes that occurred in February 1994 in most eastern provinces in Canada as well as significant increases within most provinces between 1994 and 2006. OLS and logit estimates from a variety of surveys suggest participation elasticities from –0.1 to –0.3 for teens aged 15 to 19 years, which are lower than traditional estimates. However, children aged 10 to 14 are significantly more tax elastic than older peers, with participation elasticities between -1.5 and -2. Finally, employing different sub-samples, we find that sharp hikes and reductions generate similar cigarette tax elasticities.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.039
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.008
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.044
GPT teacher head0.361
Teacher spread0.318 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2010
Admission routes2
Has abstractyes

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