Behavioral Welfare Economics and FDA Tobacco Regulations
Bibliographic record
Abstract
The U.S. 2009 Tobacco Control Act opened the door for new anti-smoking policies by giving the Food and Drug Administration broad regulatory authority over the tobacco industry.We develop a behavioral welfare economics approach to conduct cost-benefit analysis of FDA tobacco regulations.We use a simple two-period model to develop expressions for the impact of tobacco control policies on social welfare.Our model includes: nudge and paternalistic regulations; an excise tax on cigarettes; internalities created by period 1 versus period 2 consumption; and externalities from cigarette consumption.Our analytical expressions show that in the presence of uncorrected externalities and internalities, a tax or a nudge to reduce cigarette consumption improves social welfare.In sharp contrast, a paternalistic regulation might either improve or worsen social welfare.Another important result is that the social welfare gains from new policies do not only depend on the size of the internalities and externalities, but also depend on the extent to which current policies already correct the problems.We link our analytical expressions to the graphical approach used in most previous studies and discuss the information needed to complete cost-benefit analysis of tobacco regulations.Finally, we use our model as a framework to reexamine the evidence base regarding the size of the relevant internalities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".