Economic Approaches to Estimating Benefits of Regulations Affecting Addictive Goods
Bibliographic record
Abstract
The question of how to evaluate lost consumer surplus in benefit−cost analyses has been contentious. There are clear health benefits of regulations that curb consumption of goods with health risks, such as tobacco products and foods high in fats, calories, sugar, and sodium. Yet, if regulations cause consumers to give up goods they like, the health benefits they experience may be offset by some utility loss, which benefit−cost analyses of regulations need to take into account. This paper lays out the complications of measuring benefits of regulations aiming to curb consumption of addictive and habitual goods, rooted in the fact that consumers’ observed demand for such goods may not be in line with their true preferences. Focusing on the important case of tobacco products, the paper describes four possible approaches for estimating benefits when consumers’ preferences may not be aligned with their behavior, and identifies one as having the best feasibility for use in applied benefit−cost analyses in the near term. The question of how to evaluate lost consumer surplus in benefit−cost analyses has been contentious. There are clear health benefits of regulations that curb consumption of goods with health risks, such as tobacco products and foods high in fats, calories, sugar, and sodium. Yet, if regulations cause consumers to give up goods they like, the health benefits they experience may be offset by some utility loss, which benefit−cost analyses of regulations need to take into account. This paper lays out the complications of measuring benefits of regulations aiming to curb consumption of addictive and habitual goods, rooted in the fact that consumers’ observed demand for such goods may not be in line with their true preferences. Focusing on the important case of tobacco products, the paper describes four possible approaches for estimating benefits when consumers’ preferences may not be aligned with their behavior, and identifies one as having the best feasibility for use in applied benefit−cost analyses in the near term.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".