OFFSETTING OR ENHANCING BEHAVIOR: AN EMPIRICAL ANALYSIS OF MOTORCYCLE HELMET SAFETY LEGISLATION
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1 OFFSETTING OR ENHANCING BEHAVIOR: AN EMPIRICAL ANALYSIS OF MOTORCYCLE HELMET SAFETY LEGISLATION Jonathan Lee East Carolina University Department of Economics
2 Theory of Offsetting Behavior Peltzman (1975), Blomquist (1986) Tradeoff between driving intensity and driver fatality risks P(Death) Driving Intensity
3 Theory of Offsetting Behavior Peltzman (1975), Blomquist (1986) Technological safety improvements P(Death) No Helmet Helmet Driving Intensity
4 Theory of Offsetting Behavior Peltzman (1975), Blomquist (1986) Technological safety improvements P(Death) No Helmet P(Death H=0,DI=DI NH ) Helmet P(Death H=1,DI=DI NH ) DI NH Driving Intensity
5 Theory of Offsetting Behavior Peltzman (1975), Blomquist (1986) Increased driving intensity P(Death) No Helmet P(Death H=0,DI=DI NH ) Helmet P(Death H=1,DI=DI H ) P(Death H=1,DI=DI NH ) DI NH DI H Driving Intensity
6 Theory of Enhancing Behavior Thaler and Sunstein (2008) Laws can nudge people. Alternatively individuals may have biases regarding risk probabilities. P(Death) No Helmet P(Death H=0,DI=DI NH ) Helmet P(Death H=1,DI=DI NH ) P(Death H=1,DI=DI H ) DI H DI NH Driving Intensity
7 Research Outline Test for increased (offset) or decreased (enhance) driving intensity post helmet law using two alternative datasets and estimation strategies. I. State-level motorcycle crash data Do motorcycle crash counts increase or decrease post mandatory helmet law? II. Individual police accident report (PAR) crash data Are motorcyclists in mandatory helmet law states more or less likely to engage in risky driving behavior?
8 Empirical Strategy Estimate the following: lncrashes j,t = α + SC j,t γ + S j + T t + β helmet_law j,t + ε j,t lncrashes j,t = natural log of motorcycle crash count in state j in year t SC j,t = vector of all observable state characteristics including laws for skills tests, rider education, education prior to licensing, daytime headlights, and maximum speed limits. SC j,t also includes temperature, precipitation, vmt, population, alcohol consumption and natural log of registered motorcycles. S j = state specific fixed effects T t = year fixed effects
9 Empirical Strategy Estimate the following: lncrashes j,t = α + SC j,t γ + S j + T t + β helmet_law j,t + ε j,t helmet_law j,t = 1 for states with a mandatory universal coverage motorcycle helmet law, and = 0 otherwise ε j,t = random error term clustered at the state level.
10 Empirical Strategy Estimate the following: lncrashes j,t = α + SC j,t γ + S j + T t + β helmet_law j,t + ε j,t helmet_law j,t = 1 for states with a mandatory universal coverage motorcycle helmet law, and = 0 otherwise ε j,t = random error term clustered at the state level.
11 Results Natural log state motorcycle crashes is the dependent variable (n=1,239) Helmet Law *** (-19.0%) Skill Test Rider Education Rider Education Licensing *** (-11.6%) Daytime Headlight ** (-11.4%) Temperature 0.018** Ln Alcohol Consumption Ln Registered Motorcycles 0.149** *,**,*** Denote significance at 10%, 5%, and 1% levels respectively
12 Research Outline Test for increased (offset) or decreased (enhance) driving intensity post helmet law using two alternative datasets and estimation strategies. I. State-level motorcycle crash data Do motorcycle crash counts increase or decrease post mandatory helmet law? II. Individual police accident report (PAR) crash data Are motorcyclists in mandatory helmet law states more or less likely to engage in risky driving behavior?
13 Empirical Strategy Estimate the following system of equations: violation j,c = α + IC j,c γ + CC c δ + β helmet j,c + ε j helmet j,c = α + IC j,c γ + CC c δ + β helmet_law c + ε j violation j,c = dummy variable equal to 1 if individual j received a traffic ticket for reckless driving (speeding, alcohol, failure to stop, etc.) IC j,c = vector of all observable individual characteristics including motorcyclists age, gender, and seating position CC c = vector of crash characteristics including manner of collision, and vehicles/objects involved in collision
14 Empirical Strategy Estimate the following system of equations: violation j,c = α + IC j,c γ + CC c δ + β helmet j,c + ε j helmet j,c = α + IC j,c γ + CC c δ + β helmet_law c + ε j helmet j,c = dummy variable equal to 1 if individual j was wearing a protective helmet at the time of crash helmet_law j = dummy variable equal to 1 if the crash occurred in a state with a mandatory motorcycle helmet law
15 Estimated Difference in Probability of Violation Individual traffic citation is the dependent variable (n=13,610) IV Control function probit Bivariate probit Helmet ** * * F-test/χ 2 1,029.97*** *** *** *,**,*** Denote significance at 10%, 5%, and 1% levels respectively
16 Possible Explanations Omitted Variable / Simultaneity Bias Non-classical measurement error - All crashes are not observed. Only police accident reported crashes are observed Motorcyclists ride less frequently following helmet law adoption, and the number of registered motorcycles is an imperfect proxy for motorcycle utilization Helmets make riders more visible to other motorists Enhancing behavior - Helmet laws induce motorcyclists to take additional safety precautions
17 Future Research: Identifying Source of Enhancing Behavior Helmet laws encourage safety conscious behavior among motorcyclists Sadiq & Graham (2014) risk reducing measures and risk perception AMA focuses considerable attention on alcohol use and rider conspicuity as contributing factors Motorcyclists have biased opinions regarding helmet inefficacy Cox (2014) and Freling et al. (2014) confirmation bias and anecdotal bias ABATE propagates belief that helmets are ineffective and may actually increase risk of serious neck injuries Motorcyclists believe helmets increase crash propensity
18 Questions/Comments Thank you!
19 Multinomial probit estimated difference in probability of fatality and injury Injury Severity is the dependent variable Control function probit Bivariate probit Injury Fatality Injury Fatality Helmet ** *** ** *** χ *** *** *,**,*** Denote significance at 10%, 5%, and 1% levels respectively
20 Table 9. Motorcycle Helmet Effectiveness Using Bivariate Multinomial Probit Specification. Number of obs. Predicted Mean Probability of Injury Predicted Mean Probability of Death Panel A: Technological Effectiveness: Universal Helmet Use 13, No Helmet Use 13, Percentage change in mean predicted probabilities with helmet use -6.88% % Panel B: Helmet Law Effectiveness: States with a Universal Helmet Law States without Universal Helmet Laws Percentage Change in Mean probabilities from Adopting a Universal Helmet Law 6, , % % Panel C: 100% Compliance Helmet Law Effectiveness: Universal Helmet Use in Non-helmet Law States States without Universal Helmet Laws Percentage Change in Mean Probabilities from Adopting a Universal Helmet Law with 100% compliance 7, , % %
21 Table 9. Motorcycle Helmet Effectiveness Using Bivariate Multinomial Probit Specification. Number of obs. Predicted Mean Probability of Injury Predicted Mean Probability of Death Panel A: Technological Effectiveness: Universal Helmet Use 13, No Helmet Use 13, Percentage change in mean predicted probabilities with helmet use -6.88% % Panel B: Helmet Law Effectiveness: States with a Universal Helmet Law States without Universal Helmet Laws Percentage Change in Mean probabilities from Adopting a Universal Helmet Law 6, , % % Panel C: 100% Compliance Helmet Law Effectiveness: Universal Helmet Use in Non-helmet Law States States without Universal Helmet Laws Percentage Change in Mean Probabilities from Adopting a Universal Helmet Law with 100% compliance 7, , % %
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