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Why Do Europeans Smoke More than Americans?

2009· book-chapter· en· W1526718515 on OpenAlexaboutno aff
David Cutler, Edward L. Glaeser

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)Quarter (Canadian coin)American exceptionalismDemographic economicsExceptionalismDemographySmokePolitical scienceEconomicsEnvironmental healthDevelopment economicsGeographyMedicineSociologySocial scienceLaw

Abstract

fetched live from OpenAlex

Abstract This chapter examines three potential explanations for the low level of smoking in the United States relative to other developed countries. First, it asks whether the effective price of cigarettes, which reflects both taxes and other regulations on tobacco, is higher in the United States. Second, it looks at whether higher American income levels might explain the lower level of U.S. cigarette consumption, if better health is a luxury good. Third, it considers whether differences in beliefs about the consequences of smoking might be responsible for American exceptionalism. After analyzing the data, the researchers firmly reject the first hypothesis, that differences in cigarette prices and regulations explain differences in smoking rates between Europe and the United States. Moving on to the second hypothesis, the researchers find that income differences explain no more than one quarter of the difference between European and American smoking rates. The most important factor appears to be differences in beliefs about the health consequences of smoking. While 91 percent of Americans think that cigarettes cause cancer, only 84 percent of Europeans share that view.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.015

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 head0.100
GPT teacher head0.436
Teacher spread0.337 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations29
Published2009
Admission routes1
Has abstractyes

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