MétaCan
Menu
Back to cohort

Price smoking participation elasticity in Colombia: estimates by age and socioeconomic level

2020· article· en· W3005908948 on OpenAlexfundno aff
Juan Miguel Gallego, Susana Otálvaro-Ramírez, Paul Rodríguez‐Lesmes

Bibliographic record

VenueTobacco Control · 2020
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsSocioeconomic statusElasticity (physics)Price elasticity of demandEnvironmental healthDemographic economicsDemographyEconomicsMedicineSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Tobacco prevalence in Colombia is small compared with other Latin America despite the nation's tobacco taxes being among the lowest in the region. However, tobacco taxes have increased several times during the last decade, and large increases in 2010 and 2016 impacted consumer prices. OBJECTIVE: This paper aims to estimate the price smoking participation elasticity (PPE) in Colombia, with specific reference to regional increases in consumer prices after 2010 tax policy changes. METHODS: The PPE is computed using logistic regression based on individual-level data from the National Psychoactive Substances Consumption Survey for 2008 and 2013. Our specific focus is state-level variation in Colombian cigarette prices between 2008 and 2013 induced by the tax hike in 2010. RESULTS: The estimated PPE in Colombia is around -0.66 (p value=0.046). We find almost no differences across socioeconomic level, but price sensitivity was greater for women than men, and for relatively older individuals (ages 51-64). CONCLUSIONS: PPE for Colombia is above estimates for comparable middle-income countries such as Mexico. As a result, current estimates for health gains of tax policies are likely to be underestimated. Moreover, in contrast with the literature, we find that the PPE for the youth (≤25 years) is lower than older age groups, and there is no evidence of a prominent socio-economic status (SES) gradient.

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 score0.005
Threshold uncertainty score0.410

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 head0.040
GPT teacher head0.297
Teacher spread0.257 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueTobacco ControlSame topicSmoking Behavior and CessationFrench-language works237,207