Excise Tax Avoidance: The Case of State Cigarette Taxes
Bibliographic record
Abstract
In this paper we contribute new empirical results about consumers' decisions to avoid cigarette excise taxes, and a new applied welfare economic analysis of optimal excise taxation with tax avoidance.We examine direct measures of consumer excise tax avoidance in novel individual-level data from the 2003 and 2006 -2007 Tobacco Use Supplements to the U.S. Current Population Survey.We estimate reduced-form models and a structural endogenous switching regression model.In the structural border-crossing equation, the decision to cross the border depends on the difference between the endogenous homeand border-state prices.The reduced-form and structural results show that the probability of cross-border cigarette purchases responds in predictable ways to the economic incentives created by the distance to the border and state tax differentials.To our knowledge, we are also the first study to extend the formula for optimal Pigouvian corrective taxation to incorporate excise tax avoidance.Taking into account tax avoidance implies the optimal tax is substantially below the simple Pigouvian tax that internalizes external costs.In illustrative calculations for 2003, we find that in 20 states the optimal tax that accounts for tax avoidance is at least 20 percent smaller than the simple Pigouvian tax.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".