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The Economics of Smoking Prevention

2018· reference-entry· en· W2803842597 on OpenAlexaffabout
Philip DeCicca, Donald Kenkel, Michael Lovenheim, Erik Nesson

Bibliographic record

VenueOxford Research Encyclopedia of Economics and Finance · 2018
Typereference-entry
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHarmSocioeconomic statusSmoking prevalenceFellDemographic economicsSmoking cessationMedicineEnvironmental healthDemographyEconomicsPsychologyGeographyPopulationSociologySocial psychology

Abstract

fetched live from OpenAlex

Abstract Smoking prevention has been a key component of health policy in developed nations for over half a century. Public policies to reduce the physical harm attributed to cigarette smoking, both externally and to the smoker, include cigarette taxation, smoking bans, and anti-smoking campaigns, among other publicly conceived strategies to reduce smoking initiation among the young and increase smoking cessation among current smokers. Despite the policy intensity of the past two decades, there remains debate regarding whether, and to what extent, the observed reductions in smoking are due to such policies. Indeed, while smoking rates in developed countries have fallen substantially over the past half century, it is difficult to separate secular trends toward greater investment in health from actual policy impacts. In other words, smoking rates might have declined in the absence of these anti-smoking policies, consistent with trends toward other healthy behaviors. These trends also may reflect longer-run responses to policies enacted many years ago, which also poses challenges for identification of causal policy effects. While smoking rates fell dramatically over this period, the gradient in smoking prevalence has become tilted toward lower socioeconomic status (SES) individuals. That is, cigarette smoking exhibited a relatively flat SES gradient 50 years ago, but today that gradient is much steeper: relatively less-educated and lower-income individuals are many times more likely to be cigarette smokers than their more highly educated and higher-income counterparts. Over time, consumers also have become less price-responsive, which has rendered cigarette taxation a less effective policy tool with which to reduce smoking. The emergence of tax avoidance strategies such as casual cigarette smuggling (e.g., cross-tax border purchasing) and purchasing from tax-free outlets (e.g., Native reservations in Canada and the United States) have likely contributed to reduced price sensitivity. Such behaviors have been of particular interest in the last decade as cigarette taxation has roughly doubled cigarette prices in many developed nations, creating often large incentives to avoid taxation for those who continue to smoke. Perhaps due to the perception that traditional policy has been ineffective, recent anti-smoking policy has focused more on the direct regulation of cigarettes and smoking behavior. The main non-price-based policy has been the rise of smoke-free air laws, which restrict smoking behavior in workplaces, restaurants, and bars. These regulations can reduce smoking prevalence and exposure to secondhand smoke among nonsmokers. However, they may also shift the location of smoking in ways that increase secondhand smoke exposure, particularly among children. Other non-tax regulations focus on the packaging (e.g., the movement towards plain packaging), advertising, and product attributes of cigarettes (e.g., nicotine content, cigarette flavor, etc.), and most are attempts to reduce smoking by making it less desirable to the actual or potential smoker. Perhaps not surprisingly, research in the economics of smoking prevention has followed these policy developments, though strong interest remains in both the evaluation of price- and non-price policies as well as any offsetting responses among smokers that may undermine the effectiveness of these regulations. While the past two decades have provided fertile ground for research in the economics of smoking, we expect this to continue, as governments search for more innovative and effective ways to reduce smoking.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.342
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2018
Admission routes2
Has abstractyes

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