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Record W4376131374 · doi:10.1186/s12954-023-00790-1

The impact of JUUL market entry on cigarette sales: evidence from a major chain retailer in Canada

2023· article· en· W4376131374 on OpenAlexaffabout
Yingying Xu, Anindya Sen, Tengjiao Chen, Christopher M. Harris, Shivaani Prakash

Bibliographic record

VenueHarm Reduction Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElectronic cigaretteMarket shareBusinessAdvertisingPanel dataConsumption (sociology)EconomicsMarketingEconometricsMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Electronic nicotine delivery systems (ENDS), such as the JUUL system, are nicotine products for adults who currently smoke cigarettes but are looking for an alternative to combustible cigarettes. Sales of ENDS products were legislatively acknowledged and authorized federally in Canada with the Royal Assent of the Tobacco and Vaping Products Act in 2018. METHODS: With the unique dataset from a major chain retailer in Canada, we evaluated the impacts of JUUL market entry on cigarette sales across Canada from January 2017 to August 2019 using two-way fixed effects panel regression models by leveraging on the entry time variation at the city level. We conducted various robustness checks and a permutation test to validate our results. RESULTS: Our estimates suggested that JUUL market entry was, on average, significantly correlated with a 1.65% per-month decrease in cigarette sales during the initial months, and with a potentially larger impact on urban areas. Our results were robust across various specifications and tests. These findings implied that JUUL and combustible cigarettes act as economic substitutes during the study time period in Canada. CONCLUSIONS: These results suggested that local availability of ENDS products, such as JUUL, has the potential to reduce local cigarette consumption.

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)0.001
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 score0.284
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.037
GPT teacher head0.312
Teacher spread0.275 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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