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This paper analyzes the impact of obesity on the probability of a motor vehicle fatality (highway death rate) and on its component probabilities: the probability of a fatality, given a crash (vulnerability rate) and the probability of a crash (crash rate).

Using state-level data for 1995–2015, the paper estimates models explaining all three rates. Explanatory factors include obesity and a representative set of potential determinants.

Results indicate that obesity has a statistically significant positive relationship with the highway death rate and the crash rate. Also having a statistically significant positive association with at least one of the three rates are the proportions of young and old drivers, alcohol consumption, the ratio of rural to urban vehicle miles and temperature. Factors with a statistically significant negative relationship with at least one of the rates include primary seat belt laws and precipitation. In 2016, a total of 928 traffic fatalities could have been avoided if obesity rates decreased by one percentage point.

Seat belts and crash dummies should be better designed to fit and represent those with higher BMIs, and education efforts to increase seat belt use should be supplemented with information about the adverse impact of obesity on highway safety.

This paper uses 21 years of state-level information, including socio-economic and regulation data, and contributes to the existing research on the relationship between obesity and highway safety.

This paper examines variable selection among various factors related to motor vehicle fatality rates using a rich set of panel data. Four Bayesian methods are used. These include Extreme Bounds Analysis (EBA), Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), and Bayesian Additive Regression Trees (BART). The first three of these employ parameter estimation, the last, BART, involves no parameter estimation. Nonetheless, it also has implications for variable selection. The variables examined in the models include traditional motor vehicle and socioeconomic factors along with important policy-related variables. Policy recommendations are suggested with respect to cell phone use, modernization of the fleet, alcohol use, and diminishing suicidal behavior.

Universal helmet laws (UHLs) are widely believed to be effective in reducing motorcycle fatalities. In this chapter, we further investigate the effectiveness of such policies by focusing on their long-term impact as well as their effect on motorcycle use. Using state-level longitudinal data from 1975 to 2005, we estimate how the adoption and repeal of UHLs influence motorcycle safety. Our results confirm earlier findings that adoption of UHLs prevents fatalities, whereas repeals lead to higher fatality rates. We provide evidence that UHLs operate as intended, decreasing fatalities mainly by improving safety rather than by reducing motorcycle riding. Finally, using dynamic specifications, we show that the long-term effects of both adoption and repeal persist in the years beyond the policy change.

The United States government is widely acknowledged as an inveterate collector and disseminator of statistical information. The various agencies of the government gather statistics on virtually every aspect of American life, as well as world affairs. The impact and use of these data are immeasurable, since U.S. government statistics on such topics as economics, society, and labor supply the bases for all forms of statistical research and analysis.