Cost–benefit analysis involving addictive goods: contingent valuation to estimate willingness‐to‐pay for smoking cessation
Bibliographic record
Abstract
The valuation of changes in consumption of addictive goods resulting from policy interventions presents a challenge for cost-benefit analysts. Consumer surplus losses from reduced consumption of addictive goods that are measured relative to market demand schedules overestimate the social cost of cessation interventions. This article seeks to show that consumer surplus losses measured using a non-addicted demand schedule provide a better assessment of social cost. Specifically, (1) it develops an addiction model that permits an estimate of the smoker's compensating variation for the elimination of addiction; (2) it employs a contingent valuation survey of current smokers to estimate their willingness-to-pay (WTP) for a treatment that would eliminate addiction; (3) it uses the estimate of WTP from the survey to calculate the fraction of consumer surplus that should be viewed as consumer value; and (4) it provides an estimate of this fraction. The exercise suggests that, as a tentative first and rough rule-of-thumb, only about 75% of the loss of the conventionally measured consumer surplus should be counted as social cost for policies that reduce the consumption of cigarettes. Additional research to estimate this important rule-of-thumb is desirable to address the various caveats relevant to this study.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".