Economic Effects of Smoke-Free Laws on Rural and Urban Counties in Kentucky and Ohio
Bibliographic record
Abstract
INTRODUCTION: Numerous empirical studies have examined the influence of smoke-free legislation on economic activity, with most finding a null effect. The influence could possibly differ in rural areas relative to urban areas due to differing rates of smoking prevalence and access to prevention and treatment programs. Furthermore, the discussion of the effectiveness of smoke-free laws has been extended to consider local ordinances relative to statewide laws. This study examines these issues using 21 local laws in Kentucky and the Ohio statewide smoke-free law. METHODS: The number of employees, total wages paid, and number of reporting establishments in all hospitality and accommodation services in Kentucky and Ohio counties were documented, beginning the first quarter 2001 and ending the last quarter of 2009. A generalized estimating equation time-series design is used to estimate the impact of local and state smoke-free laws in Kentucky and Ohio rural and urban counties. RESULTS: There is no evidence that the economies in Kentucky counties were affected in any way from the implementation of local smoke-free laws. There was also no evidence that total employment or the number of establishments was influenced by the statewide law in Ohio, but wages increased following the implementation of the law. Furthermore, there is no evidence that either rural or urban counties experienced a loss of economic activity following smoke-free legislation. CONCLUSIONS: The study finds no evidence that local or state smoke-free legislation negatively influences local economies in either rural or urban communities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".