Overview of State Laws Restricting Alcohol Outlet Density in the United States
Bibliographic record
Abstract
OBJECTIVES: Complementing the extensive research literature demonstrating that increased alcohol outlet density is associated with excessive alcohol consumption and related harms, this article synthesizes information on the types of alcohol outlet density restrictions in US state-level laws. DESIGN: Statutes and regulations related to alcohol outlet density in all 50 states and the District of Columbia in effect as of January 1, 2022, were collected using Westlaw. State-level density restrictions were coded according to 4 variables and overlaid with existing research on state-specific local authority to regulate outlet density. Alcohol outlet density laws in Michigan and Massachusetts were analyzed in detail as case studies. SETTING: United States. MAIN OUTCOME MEASURE: US state-level licensing laws restricting alcohol outlet density. RESULTS: Thirty-three states and the District of Columbia have state-level licensing laws that limit alcohol outlet density. Of those, 25 have population-based restrictions, 8 have distance-based restrictions, 7 have quotas, and 6 require the licensing agency to consider density-related factors. Within the same group of 34 jurisdictions, 22 apply restrictions to both on- and off-premises outlets, 5 apply them only to on-premises outlets, and 7 apply them only to off-premises outlets. Among the 32 states where localities lack authority to license alcohol outlets, two-thirds have state-level laws restricting outlet density. State-level density restrictions also exist in approximately two-thirds of the states where localities have licensing authority. Case studies of Michigan and Massachusetts highlight how state-level density restrictions operate in practice. CONCLUSIONS: Two-thirds of jurisdictions have state-level alcohol outlet density restrictions, with population-based restrictions being the most common. In addition, outlet density restrictions may exist regardless of limits on local control and whether localities with authority to enact density restrictions have done so. Policymakers and others can reference this assessment to identify examples and opportunities to strengthen the alcohol policy environment in any given state.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".