Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: State traffic safety and highway related laws have been
modified numerous times over the years. Some policy changes are in
direct response to federal pressure, and some changes are pursued by
states pushing policy boundaries. An oft-stated goal of many traffic
safety laws is the reduction of fatalities. One policy change –
increasing the speed limit – runs counter to the trend in reducing risk
factors for fatalities (Saffer and Grossman 1987; Wagenaar, Streff, and
Schultz 1990; Baum, Wells, and Lund 1991; O’Malley and Wagenaar 1991;
Houston, Richardson, and Neeley 1995, 1996). Our interest is not only
in this relationship, but more importantly to understand how different
speed limits for different vehicle types on the same roadway impacts
the fatality rate.
We will develop a model to test the impact of differential speed limits
for tractor trailers and passenger vehicles in reducing fatalities for
all drivers. The National Highway Traffic Safety Administration (NHTSA)
maintains data on all fatal crashes in the Fatality Analysis Reporting
System (FARS) dataset. Lower speed limits should reduce the number of
fatalities, but it is difficult to operationalize a measure of average
speeds across the states for the entire time period. The only measure
that is consistently available across the data set is the maximum speed
limits on interstate highways. Previous research has suggested that the
adoption of a 65 mph maximum speed limit increases fatality rates. To
determine the impact of differential speed limits, we control for a
number of other public policies as well as known correlates of traffic
fatalities. We will include control variables for other traffic safety
laws (such as seat belt requirements, drinking and driving policies,
and graduated licensing for teenagers), vehicle miles traveled annually
in a state, population density, percent of young adults, per capita
income, climate, percent of cars with airbags, the mix of vehicle
types, per capita alcohol consumption, and enforcement efforts. For the
evaluation of policy effects, we will employ cross-sectional time
series methods for all fifty states for a 25-year period up to 2000. We
will treat all policy-related variables and covariates described above,
such as drinking and driving laws, climate, personal income and
demographic and economic factors, as explanatory variables. The models
allow policy and covariate effects to be fixed or random, linear or
nonlinear and time-independent or time-dependent. The approach has the
advantage that it produces estimates of the state-specific temporal
trends of the response variables. For estimation of policy and
covariate effects, we will use maximum likelihood estimation.

Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: I am using ethnographic methods to research Intermestic impacts of trade policy. Sector and individual level research is currently missing in the field.

Publication Type: Conference Paper/Unpublished Manuscript
Abstract: Through extensive fieldwork and interviews this paper examines the responses of long-haul truck drivers to remote monitoring of their driving performance. Surprisingly, despite the tremendous limitations the technology placed on driver behavior, drivers had much less negative responses than anticipated given the cultural norms surrounding the occupation. In part this finding is explained by these workers’ very limited expectations of autonomy in the workplace and by the reduction of self-sweating that followed introduction of the technology.

Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: We examine state’s policy efforts related to the motor freight industry including common road traffic factors such as speed limits, height and weight restrictions. We also examine tax structures affecting this industry (fuel tax and other fees). While concern for safety and the impact of these policies on a state’s roadways often dominates discussion of policy goals, we also examine the economic livelihood of a state and the impact that the transportation and manufacturing sectors may have on policy adoption. Do states that rely heavily on these sectors shift safety and economic concerns to passenger vehicles? What pressures may exist to entice states to adopt policies counter to this industry’s concerns? We utilize a cross-sectional time series analysis to model policy adoption in the U.S. States over a twenty year time frame.