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논문 기본 정보

자료유형
학술대회자료
저자정보
Yong-Kyun Bae (Pusan National University) Hugo Benítez-Silva (SUNY-Stony Brook)
저널정보
한국산업경제학회 한국산업경제학회 정기학술발표대회 초록집 한국산업경제학회 2014년도 춘계국제학술발표대회 논문집
발행연도
2014.5
수록면
483 - 518 (36page)

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초록· 키워드

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Using data on correction rates for vehicle recalls in the United States from 2007 to 2010, we investigate information transmission from manufacturers to owners regarding the defects of recalled vehicles. We pay special attention to the role of the language manufacturers use to convey each recall’s seriousness in the letters they send to owners to explain the nature of the defects in their vehicles, and the possible consequences if the defects are not fixed. We find that recalls linked to riskier defects, defined by the type of equipment affected in the vehicles, are associated with higher correction rates. Interestingly, the content of recall notification letters plays an important role in increasing correction rates because the letters convey information to owners above and beyond baseline information about which part of their vehicles can present problems. We also find that, in a number of cases, the language that manufacturers use to explain the risks to owners are worryingly milder than the descriptions the National Highway Traffic Safety Administration (NHTSA) use, resulting in significantly lower correction rates. We conclude that information transmission to owners regarding recalls should be more clearly regulated since the language affects drivers’ likelihood of taking their cars to be fixed. We advocate that the NHTSA return to the pre-2001 practice of assigning hazard levels to all recalls, and that the agency consider making sure manufacturers clearly communicate recall rating information to vehicle owners. Our results indicate that these practices would result in higher correction rates, remove faulty cars from the roads, and, consequently, save lives.

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Abstract
1. Introduction
2. Vehicle Recalls, Correction Rates, and Information Transmission
3. Recall Data and Sample Construction
4. Analysis and Estimation of Correction Rates
5. Estimation Results
6. Conclusions and Policy Implications
References

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