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The value of a statistical life for transportation regulations: A test of the benefits transfer methodology

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Abstract

Policy applications of the value of a statistical life (VSL) often make a benefits transfer assumption that the VSL from one market context is broadly applicable to other contexts. The U.S. Department of Transportation’s estimate of $9.2 million is based on labor market estimates of VSL. This article examines whether there are any significant differences in labor market estimates of the VSL by the nature of the fatality, utilizing two different approaches that distinguish between fatalities resulting from transportation events and vehicle-related sources based on the Census of Fatal Occupational Injuries (CFOI) data. The labor market estimates of VSL generalize across transport and non-transport contexts so that it is appropriate to use labor market estimates of VSL to value the benefits of transport regulations. This result holds even after accounting for the level and composition of nonfatal job injuries.

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Notes

  1. Viscusi (2014) reviews the VSL estimates used in almost 100 U.S. regulatory analyses and finds substantial convergence in the VSL levels used. Viscusi (2009) provides more detail on the source of many of these VSL figures, which are based either entirely on labor market estimates of the VSL or based predominantly on these values in conjunction with a meta-analysis of stated preference estimates.

  2. Income elasticity adjustments for the VSL are used to account for rising income levels across time but not for income differences across population groups at any point in time.

  3. Lindhjem et al. (2011) provide a recent review of this literature. They find an average VSL level of $7.4 million and a median level of $2.4 million in 2005 dollars (in 2008 dollars, an $8.2 million mean and a $2.7 million median). Our estimates are comparable to their mean but are above their median values.

  4. The median VSL based on the U.S. studies reviewed in Viscusi and Aldy (2003) was $7 million in 2000 dollars, or $8.75 million in 2008 dollars, which is higher than the median for stated preference studies reported in Lindhjem et al. (2011).

  5. There may, however, be transportation situations in which equity concerns are salient, as with the valuation of risks to passengers on airplanes who have higher income levels than the average person killed in traffic accidents. Viscusi (1993) examines these issues in a study prepared for the Federal Aviation Administration.

  6. Examples of reasons people are with a job but not at work include illness, vacation, bad weather, child care problems, etc.

  7. For the CPS data, we dropped the census codes associated with military industries and occupations. For the CFOI data used to create fatality rates, workers within the North American Industry Classification System (NAICS) code 928110 and Standard Occupational Classification codes beginning with 55 are excluded.

  8. A worker is defined as having union status if she either reports being a member of a labor union or a similar employee association or reports being covered by a union or employee association contract.

  9. We also include an indicator variable indicating whether the worker received a doctorate or professional degree.

  10. Potential experience is constructed by using age and subtracting years of education and an additional 5 years in order to account for age upon entering school. A squared experience term is also included in the regressions.

  11. A worker is classified as reporting herself as full-time if she has a full-time schedule, is part-time for economic reasons but usually full-time, is not at work but usually full-time, or is part-time for non-economic reasons but usually full-time.

  12. For example, the variation in vehicle fatality rates is 0.85 for white-collar workers and 4.78 for blue-collar workers.

  13. The procedure followed in the hours-based methodology is described in U.S. Department of Labor (2007a, footnote 2).

  14. The data before 2003 use the Standard Industrial Classification system, instead of the 2002 North American Industry Classification System. The 2009 data use the 2007 NAICS, so data from 2003–2008 were used for this project to have a consistent industry classification system. The BLS creates 52 aggregated industries, but we exclude workers within the agricultural and armed forces industries, leaving 50 industries. The full sample fatality rate measure is based on 50 industries and 10 occupations.

  15. The vehicle proportion is calculated by dividing the vehicle-specific incidence rate by the sum of all source incidence rates. The non-vehicle proportion is calculated by dividing the non-vehicle-specific incidence rate by the sum of all source incidence rates. The transportation/non-transportation proportions are constructed similarly.

  16. Goodman (1960) derives the exact variance of the product of two random, independent variables. This allows us to construct the exact variance of the product \( {\widehat{\gamma}}_1\times \overline{Wage}\times 2,000\times 100,000 \).

  17. The blue-collar mean wage is $17.21, while the full sample mean wage is $20.67.

  18. The DOT calculates the VSI as a proportion of the VSL ($9.2 million), depending on the severity of the injury (U.S. Department of Transportation 2014). The following are the VSIs associated with each level, with the proportion of VSL in parentheses: The value of a minor injury is $27,600 (0.003), a moderate injury is $432,400 (0.047), a serious injury is $966,000 (0.105), a severe injury is $2.4 million (0.266), and a critical injury is $5.5 million (0.593).

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Authors and Affiliations

  1. Vanderbilt University, 131 21st Avenue South, Nashville, TN, 37203, USA

    W. Kip Viscusi & Elissa Philip Gentry

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  1. W. Kip Viscusi
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  2. Elissa Philip Gentry
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Correspondence to W. Kip Viscusi.

Additional information

This paper used fatal injury data that were obtained with restricted access to the BLS Census of Fatal Occupational Injuries Research File.

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Table 9

Table 9 Descriptive statistics
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Viscusi, W.K., Gentry, E.P. The value of a statistical life for transportation regulations: A test of the benefits transfer methodology. J Risk Uncertain 51, 53–77 (2015). https://doi.org/10.1007/s11166-015-9219-2

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