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


METHODS
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.
Traynor
Thomas L
TL
Department of Economics, Wright State University, Dayton, Ohio 45435, USA. thomas.traynor@wright.edu
D000063:Accidents, Traffic / Q000191:economics* / Q000401:mortality / Q000706:statistics & numerical data; D001334:Automobile Driving / Q000706:statistics & numerical data*; D001336:Automobiles / Q000706:statistics & numerical data*; D003430:Cross-Sectional Studies; D004467:Economics; D005843:Geography; D006801:Humans; D007182:Income; D018803:Models, Economic; D009820:Ohio; D012307:Risk Factors; D012449:Safety / Q000191:economics* / Q000706:statistics & numerical data; D012959:Socioeconomic Factors