“Trucks and SUVs in the Twin Cities Metropolitan Area: How Dangerous Are They?”
Bibliographic record
Abstract
Light or heavy trucks and sport-utility vehicles (SUVs) are increasingly popular, in part because they provide improved protection to their own passengers in an accident. However, when an accident occurs, these vehicles also cause greater injury to passengers of other vehicles, pedestrians, bicyclists, and motorcyclists. Because the costs of these injuries are external costs in no-fault auto-liability systems such as those used in Minnesota, consumers may have inefficiently high incentives to purchase light/heavy trucks and SUVs. This study investigated the extent of injury costs associated with light/ heavy trucks and SUVs in the Twin Cities metropolitan area. We estimated the relationship between the type of vehicle in accidents involving one light/heavy truck or SUV and one standard passenger car and the level of vehicle damage caused, the likelihood of hospital admissions resulting from the accident, and the hospital charges for those hospitalized from the accident. The analysis showed that the likelihood of hospital admission was highest for occupants of standard cars and lowest for occupants of light/heavy trucks or SUVs. For accidents involving hospitalization, occupants of standard cars also incurred higher hospitalization charges on average compared with occupants of light/heavy trucks or SUVs. These findings suggest that light/heavy trucks and SUVs benefit their occupants in terms of the likelihood of a hospital admission and hospital charges stemming from an accident, but do so at the expense of standard-car occupants. We suggest several policy changes that would internalize the costs that light/heavy trucks and SUVs impose on occupants of other vehicles and pedestrians, and would lead to a more optimal mix of vehicle types in the nation’s vehicle fleet. The research upon which this article is based was supported by a grant from CURA’s Faculty Interactive Research Program. Motor-vehicle accidents are the leading cause of injury-related death for all age groups in Minnesota and the nation. According to the Minnesota Department of Health’s Injury and Violence Prevention Unit, 50% of serious traumatic brain injuries and 60% of spinal cord injuries in Minnesota result from a motor-vehicle accident. Among motor-vehicle accident victims, pedestrians, young people (15–24 year olds), the elderly, and male drivers, as well as unbelted passengers and unrestrained children, are at higher risk of injury or death.1 Minnesota has a “no-fault” autoliability system which, in contrast to the more common “tort system,” limits a driver’s liability for their actions. Evidence is mixed about whether or not no-fault liability systems alter driving behavior and reduce incentives to drive carefully.2 1 Minnesota Department of Health, Injury and Violence Prevention Unit, Ten Leading Causes of Nonfatal Hospitalized Injury by Age Group and Gender, Minnesota (St. Paul: Minnesota Department of Health, 2001), www.health.state.mn.us/injury/pub /ed2001/ed01cause23.pdf. 2 Several studies found little or no evidence that accidents rates are different in no-fault states—see P.S. Kochanowski and M.V. Young, “Deterrent Aspects of No-Fault Automobile Insurance: Some Empirical Findings,” Journal of Risk and Insurance 52,2 (1985): 269–288; D.S. Loughran, The Effect of No-Fault Automobile Insurance on Driver Behavior and Automobile Accidents in the United States (Santa Monica, CA: RAND Corporation, 2001); R.A. Derrig This concern is particularly important given the change in the composition of motor vehicles on the roads during the last few decades. According to the National Highway Traffic Safety Administration, the proportion of light or heavy trucks and sport-utility vehicles (SUVs) has increased from 22% of motor vehicles on the road in 1980 to 39% in 2000. Research attributes their popularity to the fact that they provide improved protection to et al., “The Effect of Population Safety Belt Usage Rates on Motor Vehicle-Related Fatalities,” Accident Analysis and Prevention 34,1 (2002): 101–110; P. Heaton and E. Helland, “No-Fault Insurance and Automobile Accidents,” unpublished paper, April 2010, works.bepress.com/psheaton/7/. Other studies point to empirical evidence that no-fault laws lead to significant increases in traffic fatalities—see E.M. Landes, “Insurance, Liability, and Accidents: A Theoretical and Empirical Investigation of the Effect of No-Fault Accidents,” Journal of Law and Economics 25,1 (1982): 49–65; R.A. Devlin, “Liability versus No-Fault Automobile Insurance Regimes: An Analysis of the Experience in Quebec,” in G. Dionne (ed.), Contributions to Insurance Economics (Norwell, MA: Kluwer Academic Publishers, 2010), pp. 499–520; J.D. Cummins et al., “The Incentive Effects of No-Fault Automobile Insurance,” Journal of Law and Economics 44 (2001): 427–464; and A. Cohen and R. Dehejia, “The Effect of Automobile Insurance and Accident Liability Laws on Traffic Fatalities,” Journal of Law and Economics 47 (2004): 357–359. © Frnces Tw ittyStockp hoo .com
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".