Explaining state-to-state differences in seat belt use : an analysis of socio-demographic variables.

Yang, Yang; Dasgupta, Kohinoor; Eby, David W.; Molnar, Lisa J.; Nair, Vijayan N.; Pollock, Stephen · 2011 · ROSA P / University of Michigan. Transportation Research Institute

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Summary

This study investigates the socio-demographic factors that explain significant state-to-state variations in seat belt use rates across the United States. Despite evidence that seat belts reduce fatal injury risk by 45 percent, usage rates in 2009 ranged from 68 percent in Wyoming to 98 percent in Michigan. While previous research identified influences such as enforcement laws, fines, gender, age, and vehicle type, this project aimed to determine if additional socio-demographic variables—specifically education, racial composition, median household income, political leaning, and religiosity—could further account for these geographic disparities. The goal was to identify these factors to help develop more effective belt use promotion programs. The researchers analyzed 2008 data from the Fatality Analysis Reporting System (FARS), which records all vehicle crashes resulting in at least one fatality. Although FARS data likely underestimates general population usage due to the higher mortality risk of unbelted occupants, the study focused on relative differences between states rather than absolute rates. The analysis included drivers of automobiles and pickup trucks, excluding records with missing belt use data. To validate the dataset, the authors first confirmed that FARS data reflected known trends, such as higher usage among females, in urban areas, and in states with primary enforcement laws or fines exceeding $30. They then incorporated state-level socio-demographic data from the US Census, American Community Survey, election records, and Gallup polls into a logistic regression model. The analysis identified three significant socio-demographic predictors: religiosity, racial composition (percentage of White population), and political leaning (percentage of Democratic voters). Education and median household income were found to be statistically insignificant. Model selection using Akaike’s Information Criterion and hold-out validation confirmed that a model including religiosity, race, and political leaning, alongside traditional factors like law type and fines, best explained the variation. Specifically, higher percentages of Democratic voters were associated with increased seat belt use, while higher religiosity and higher percentages of White populations were associated with decreased use. The final model significantly reduced unexplained geographic clustering in the data, with most states falling within 10 percent of their predicted usage rates. The findings suggest that socio-demographic factors, particularly cultural indicators like religiosity and political leaning, are effective in explaining state-level seat belt use variations. The authors note that these results are preliminary and require confirmation with other datasets, as the study relied on a single year of fatality data. However, the identification of these factors provides a basis for developing targeted interventions. Future research should explore causal relationships and identify more precise measures for these socio-demographic traits to better understand the mechanisms influencing safety behaviors.

Key finding

State-level seat belt use rates are significantly predicted by religiosity, racial composition, and political leaning, while education and income levels do not significantly influence these rates.

Methodology

dataset

Sample size: 19090

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• seat belt use
• demographic disparities

Information type

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• Empirical Findings: observational prevalence