A sturdy values analysis of motor vehicle fatalities

Understanding the major determinants of crash fatalities continues to be an important topic of investigation for safety researchers. Regression models using a vast number of explanatory variables are often used which result in a huge array of specifications. Results often vary among studies based on size of estimated coefficients and significance levels. To address this, we explore both significance and model sturdiness in regression models using Leamer’s s-values. This Bayesian technique allows us to address estimation uncertainty and model ambiguity over all possible subset regressions so as to evaluate the effect of key variables which we focus on as contributors to crash fatalities. These include cell phone use, fleet modernization, suicidal behavior, alcohol use, and speed limits.