Bibliographic record
Abstract
Abstract Some policy makers justify cigarette taxes by arguing that they actually make smokers better off. This argument has been hard to evaluate because behavioral data, such as that showing reduced cigarette consumption following a tax hike, cannot resolve the issue of whether smokers are made better off by the reduction or not. In this paper, we directly assess the effect of cigarette taxes on well-being, using subjective well-being data. We model the differential impact of excise taxes on those with a propensity to smoke, relative to others, in order to control for omitted correlations between happiness and excise taxation. Using US data on happiness and state-level changes in excise taxes, we find consistent evidence that excise taxes make those who have a propensity to smoke happier. To assess robustness, we repeat the exercise using Canadian data, which has independent information on well-being and also much larger tax changes, and find the exact same pattern. Moreover, these impacts are present for cigarette excise taxes, but not for other excise taxes. These results suggest that the welfare effects of cigarette taxation are far more complex than simple rational economic models might predict.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".