How Standards Drive Taxes: The Political Economy of Tailpipe Pollution
Bibliographic record
Abstract
Abstract To control tailpipe pollution, governments often use environmental product standards and consumption taxes in conjunction (for example, the use of fuel economy standards and gasoline taxes to restrict automobile pollution in the US). Further, the choice of standards and consumption taxes is often independently influenced by special interests. For example, domestic producers have the incentive to influence environmental product standards, and likewise, domestic consumers have the incentive to influence the choice of the consumption tax. In this paper we explore the political link between environmental standards and consumption taxes in the presence of independent special interests. We find that despite the independence of special interests, the political outcome is inextricably linked. This political link is different from the welfare maximizing second-best link usually expected between two related policies, and is crucial in correctly anticipating policy outcomes. Specifically, we find that the government's choice of an environmental standard influences political incentives in the choice of the consumption tax. As the environmental standard falls, a higher demand for the environmentally damaging product develops. This higher demand increases the incentives for consumers to lobby for lower consumption tax. Under certain conditions, this political link is large enough to result in a complementary relationship between the two policies in equilibrium. The complementary relationship implies that a lower standard results in a lower consumption tax and vice versa when the standard is higher.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".