Would Stronger Seat Belt Laws Reduce Motor Vehicle Crash Deaths? A Semi-Bayesian Analysis
Bibliographic record
Abstract
Background: For policy questions where substantial empirical background information exists, conventional frequentist policy analysis is hard to justify. Bayesian analysis quantitatively incorporates prior knowledge, but is not often used in applied policy analysis. Methods: We combined 2000-2016 data from the Fatal Analysis Reporting System with priors based on past empirical studies and policy documents to study the impact of mandatory seat belt laws on traffic fatalities. We used a Bayesian data augmentation approach to combine information from prior studies with difference-in-differences analyses of recent law changes to provide updated evidence on the impact that upgrading to primary enforcement of seat belt laws has on fatalities. Results: After incorporating the evidence from past studies, we find limited evidence to support the hypothesis that recent policy upgrades affect fatality rates. We estimate that upgrading to primary enforcement reduced fatality rates by 0.37 deaths per billion vehicle miles traveled (95% posterior interval -0.90, 0.16), or a rate ratio of 0.96 (95% posterior interval 0.91, 1.02), and increased the proportion of decedents reported as wearing seat belts by 7 percentage points (95% posterior interval 5, 8), or a risk ratio of 1.18 (95% posterior interval 1.13, 1.24). Conclusion: Bayesian methods can provide credible estimates of future policy impacts, especially for policy questions that occur in dynamic environments, such as traffic safety.
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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 | 0.027 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".