The Incidence of Taxes on Sugar-Sweetened Beverages: The Case of Berkeley, California
Bibliographic record
Abstract
Obesity and diet-related chronic disease are increasing problems worldwide.In response, many governments have enacted or are considering taxes on energy-dense foods.Perhaps the most commonly-recommended policy is a tax on sugar-sweetened beverages (SSBs).This paper estimates the extent to which a tax on SSBs is passed through to consumers in the form of higher prices.We examine the first city-level tax on SSBs in the U.S., which was enacted by the voters of Berkeley, California in November, 2014.We collected the prices of various brands and sizes of SSBs and other beverages before and after the implementation of the tax from a near-census of convenience stores and supermarkets in Berkeley, California.We also collected prices from stores in a control city: San Francisco, where in a similar voter referendum failed despite majority support.Estimates from difference-in-differences models indicate that, across all brands and sizes of products examined, 43.1 percent (95 percent confidence interval: 27.7 percent -58.4 percent) of the Berkeley tax was passed on to consumers.The estimates also are consistent with cross-border shopping.For each mile of distance between the store and the closest store selling untaxed SSBs, pass-through rose 33.3 percent for 2-liter bottles and 25.8 percent for 12-packs of 12-ounce cans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".