Teenage Driving, Mortality, and Risky Behaviors

Huh, Jason; Reif, Julian

doi:10.1257/aeri.20200653

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…Dates for Pill Mill laws were obtained from the Prescription Drug Abuse Policy System (2023). Dates for minimum driving age laws were obtained fromHuh and Reif (2021) and the Insurance Institute for Highway Safety (2023). Dates for seat belt laws were also obtained from the Insurance Institute for Highway Safety (2023).19 It is worth nothing that any potential cross-state impacts of drug-related factors could introduce a bias that may understate our results.20 This outcome distinguishes between initiators and non-initiators among drivers with drug involvement.…”

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confidence: 99%

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The effect of opioid use on traffic fatalities

Beland,

Huh,

Kim

2024

Health Economics

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We use a difference‐in‐differences design to study the effect of opioid use on traffic fatalities. Following Alpert et al., we focus on the 1996 introduction and marketing of OxyContin, and we examine its long‐term impacts on traffic fatalities involving Schedule II drugs or heroin. Based on the national fatal vehicle crash database, we find that the states heavily targeted by the initial marketing of OxyContin (i.e., non‐triplicate states) experienced 2.4 times more traffic fatalities (1.6 additional deaths per million individuals) involving Schedule II drugs or heroin during 2011–2019, when overdose deaths from heroin and fentanyl became more prominent. We find no difference in traffic fatalities until after the mid‐2000s between states with and without a triplicate prescription program. The effect is mainly concentrated in fatal crashes with drug involvement of drivers ages between 25 and 44. Our results highlight additional long‐term detrimental consequences of the introduction and marketing of OxyContin.

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confidence: 99%

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The effect of opioid use on traffic fatalities

Beland,

Huh,

Kim

2024

Health Economics

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“… Also, Gri th (2022) warns that policies aimed at reducing obesity (such as corrective taxes) may result in additional costs without any benefit if people with high internalities or externalities do not respond.29 Our estimates align with previous findings on the e ectiveness of such policies. For example,Huh and Reif (2021) estimate for the US that a one-year increase in minimum driving age could reduce motor vehicle fatalities by up to 44%. Likewise,Bostwick and Severen (2022) estimate that increasing the minimum full-privilege driving license age induces a reduction of fatal car accidents among 16-years-old ranging between 17% and 34%.…”

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confidence: 99%

Depowering Risk: Vehicle Power Restrictions and Teens’ Driving Accidents in Italy

Balia,

Brau,

Nieddu

2023

The Journal of Law and Economics

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This paper investigates the road safety impact of a power restriction on novice drivers. The restriction, introduced in Italy in 2011, prevents drivers from using high-performance vehicles during the first license year. To estimate the e ect on teen tra c accidents we leverage the between-cohorts di erence in the exposure to the reform. We find that, when prevented from using high-powered cars, teens are 13% less likely to cause an accident and 28% less likely to cause a fatal accident, mainly because of fewer speed violations. This e ect persists even after the one-year restriction expires. Our findings highlight the importance of targeted policiesdirected at those generating the largest harm -limiting exposure to specific high-risk settings. These policies stand out as an e ective, yet feasible alternative to deterrence-based strategies and screening mechanisms, which are often di cult to enforce and sustain.

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“…(16) find that female drug overdoses spike at the minimum driving age, suggesting that vehicle access may causally increase drug use, perhaps by making it easier to purchase or consume drugs 9. In Figure A.10, we use the toxicology reports from the FARS(17) to plot the race-specific positivity rates for four classes of drugs: Cannabis (panel a), opioids (panel b), stimulants (panel c) and all other drugs (panel d).…”

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confidence: 99%