Regional economic conditions and crash fatality rates – a cross-county analysis

• Published in: Journal of safety research Vol. 39; no. 1; pp. 33 - 39
• Main Author: Traynor, Thomas L.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 2008; Elsevier Science Ltd
• Subjects: Accidents, Traffic - economics; Accidents, Traffic - mortality; Accidents, Traffic - statistics & numerical data; Alcohol use; Analysis; Automobile Driving - statistics & numerical data; Automobiles - statistics & numerical data; City county; Correlation analysis; Cross-country; Cross-Sectional Studies; Economic conditions; Economics; Fatalities; Fatality rate; Geography; Humans; Income; Interaction effect; Models, Economic; Ohio; Per capita; Regional; Regional economic conditions; Regional traffic fatalities; Risk Factors; Roads & highways; Safety; Safety - economics; Safety - statistics & numerical data; Socioeconomic Factors; Speed limits; Studies; Traffic accidents & safety; Ohio; USA, Ohio; Safety and Accidents; Public Health; Transportation; Income; Interaction effect; Economic conditions; Regional; Regional economic conditions; R41; Fatality rate; Regional traffic fatalities; I18; Fatalities; Safety; Cross-country
• ISSN: 0022-4375; 1879-1247
• DOI: 10.1016/j.jsr.2007.10.008

Most studies that evaluate the relationship between economic conditions and traffic fatalities focus on the time-series relationship between the two factors. This analysis considers the cross-sectional perspective by estimating the cross-county correlation between per capita income and fatalities per vehicle mile traveled (VMT) in Ohio. Method: The empirical model employed in this analysis allows for interaction effects between per capita income and highway usage, in the determination of fatality rates. Results: The resultant least squares estimates indicate that a significant interaction effect exists between per capita income and the percentage of highway VMT, indicating a nonlinear correlation between per capita income and fatality rates. This correlation rises as the proportion of VMT on highways rises, such that there is an inverse relationship with fatality rates when the highway share of county VMT is low and a direct relationship with fatality rates when the highway share of county VMT is high. Additionally, population density, the presence of interstate highways in rural counties, the prior prevalence of severe alcohol abuse, and the proportion of teen drivers all proved to be significant correlates with county fatality rates. Conclusions: These observations suggest factors that state and federal policy makers should consider when allocating resources that impact (whether directly or indirectly) traffic fatalities.