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Record W1572156687

Revisiting an important Canadian natural experiment with new methods: an evaluation of the impact of the 1994 tax decrease on smoking

2014· preprint· en· W1572156687 on OpenAlexaboutno aff
François Gardes, Philip Merrigan

Bibliographic record

VenueRePEc: Research Papers in Economics · 2014
Typepreprint
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsPrice elasticity of demandPopulationPublic economicsEconometricsCurrent Population SurveyElasticity (physics)Tax policySample (material)Economic impact analysisDemographic economicsMicroeconomicsTax reformMedicineEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

The panel structure of the Survey on Smoking in Canada (1994-95) and novel methods are used to estimate the impact of an important decrease in the levels of taxation of cigarettes occurring in five out of the ten Canadian provinces that intended to eradicate black market sales of cigarettes in the spring of 1994. Given that black market sales have recently increased substantially because of new taxes, a complete and thorough analysis of the 1994 policy is of particular importance for policy makers. We revisit the issue with new econometric methods to address this evaluation problem as well as focus on particular sub-groups in the Canadian population. The large sample permits precise estimation of the impact of the policy by sub-group showing that females, young males, the poorly educated and separated or divorced individuals were particularly sensitive to these dramatic changes in cigarette prices. We also compute under realistic assumptions a price-elasticity for the probability of smoking and a lower bound on the price-elasticity for the quantities of cigarettes smoked.

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)0.099
metaresearch head score (Gemma)0.185
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.526

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0990.185
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0040.005
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.440
Teacher spread0.367 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicCanadian Policy and GovernanceFrench-language works237,207