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October 2013

Using data from the Census of Fatal Occupational Injuries to estimate the “value of a statistical life”

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Table 4. Labor market estimates of the value of a statistical life (VSL), based on data from the Census of Fatal Occupational Injuries (CFOI)
StudyCFOI measureWorker sampleRepresentative VSL estimates (millions of dollars)
Viscusi (2003)Industry–race, 6-year averageCPS (1997), 20 equations$21.5 Whites (full sample)
10.3 Blacks (full sample)
Leeth and Ruser (2003)Occupation–gender–race, 3-year averageCPS (1996–1998), 28 equations6.0 (risks, by occupation, men)
Jennings and Kinderman (2003)IndustryOccupational Employment Statistics survey, 1992–1999, 1 equation at industry levelNo significant effect
Viscusi (2004)Industry, occupation, and industry–occupation annual and 6-year averagesCPS (1997), 80 equations6.7 (full sample)
10.0 (blue-collar men)
12.2 (blue-collar women)
Kniesner and Viscusi (2005)Industry–occupation, 6-year averageCPS (1997), 6 equations6.7 (full sample)
6.9 (male sample)
Kniesner, Viscusi, and Ziliak (2006)Industry–occupationPSID (1997), 10 equations12.8 (base case with industry controls)
Viscusi and Aldy (2007)Industry–age, 6-year averageCPS (1998), 20 equations7.8 (ages 55–62)
16.4 (ages 35–44)
Viscusi and Hersch (2008)Industry–age–gender, 6-year averageCPS (1996), 4 equations9.8 (nonsmokers)
9.7 (smokers)
Aldy and Viscusi (2008)Industry–ageCPS (1993–2000), 8 equations6.4 (full sample)
5.0 (ages 18–24)
12.8 (age 35–44)
4.6 (age 55–62)
Kniesner, Viscusi, and Ziliak (2010)Industry–occupationPSID (1993–2001), 5 quantile equations9.8 (median)
Evans and Schaur (2010)Industry–ageHRS (1994-1998), 5 quantile equations, 1 ordinary least squares equation20.7 (mean for 50-year-olds)
Hersch and Viscusi (2010)Industry–occupation–age–immigrant status, 3-year averageNew Immigrant Survey (2003); CPS (2003), 22 equations11.0 (native-born U.S. workers),
6.6 (immigrant workers)
Kochi and Taylor (2011)Accident or homicide, by metropolitan statistical area, for driversCPS (1996–2002), 13 equations6.1–8.4 range
Scotton and Taylor (2011)Industry–occupation, 6-year averageCPS (1996–1998), 9 equations12.3 (undifferentiated deaths)
Kniesner, Viscusi, Woock, and Ziliak (2012)Industry–occupation, annual and 3-year averagesPSID (1993–2001), 59 quantile equations11.4 (static first differences)
Kniesner, Viscusi, and Ziliak (2012)Industry–occupation, annual and 3-year averagesPSID (1993–2001), 40 equations13.0 (first difference for job changers, 3-year average risk)

Note: CPS = Current Population Survey; PSID = Panel Study of Income Dynamics. All VSL estimates are in 2012 dollars. Authors and years cited reference the following works in the literature: Viscusi (2003): W. Kip Viscusi, “Racial differences in labor market values of a statistical life,” Journal of Risk and Uncertainty, December 2003, pp. 239–256; Leeth and Ruser (2003): John D. Leeth and John Ruser, “Compensating wage differentials for fatal and nonfatal injury risk by gender and race,” Journal of Risk and Uncertainty, December 2003, pp. 257–277; Jennings and Kinderman (2003): William P. Jennings and Albert Kinderman, “The value of a life: new evidence of the relationship between changes in occupational fatalities and wages of hourly workers, 1992 to 1999,” Journal of Risk and Insurance, September 2003, pp. 549–561; Viscusi (2004): W. Kip Viscusi, “The value of life: estimates with risks by occupation and industry,” Economic Inquiry, January 2004, pp. 29–48; Kniesner and Viscusi (2005): Thomas J. Kniesner and W. Kip Viscusi, “Value of a statistical life: relative position vs. relative age,” American Economic Review, May 2005, pp. 142–146; Kniesner, Viscusi, and Ziliak (2006): Thomas J. Kniesner, W. Kip Viscusi, and James P. Ziliak, “Life-cycle consumption and the age-adjusted value of life,” Contributions to Economic Analysis & Policy, vol. 5, no. 1, 2006, pp. 1–34; Viscusi and Aldy (2007): W. Kip Viscusi and Joseph E. Aldy, “Labor market estimates of the senior discount for the value of statistical life,” Journal of Environmental Economics and Management, May 2007, pp. 377–392; Viscusi and Hersch (2008): W. Kip Viscusi and Joni Hersch, “The mortality cost to smokers,” Journal of Health Economics, July 2008, pp. 943–958; Aldy and Viscusi (2008): Joseph E. Aldy and W. Kip Viscusi, “Adjusting the value of a statistical life for age and cohort effects,” Review of Economics and Statistics, August 2008, pp. 573–581; Kniesner, Viscusi, and Ziliak (2010): Thomas J. Kniesner, W. Kip Viscusi, and James P. Ziliak, “Policy relevant heterogeneity in the value of statistical life: new evidence from panel data quantile regressions,” Journal of Risk and Uncertainty, February 2010, pp. 15–31; Evans and Schaur (2010): Mary F. Evans and Georg Schaur, “A quantile estimation approach to identify income and age variation in the value of a statistical life,” Journal of Environmental Economics and Management, May 2010, pp. 260–270; Hersch and Viscusi (2010): Joni Hersch and W. Kip Viscusi, “Immigrant status and the value of statistical life,” Journal of Human Resources, Summer 2010, pp. 749–771; Kochi and Taylor (2011): Ikuho Kochi and Laura O. Taylor, “Risk heterogeneity and the value of reducing fatal risks: further market-based evidence,” Journal of Benefit–Cost Analysis, August 2011, pp. 1–28; Scotton and Taylor (2011): Carol R. Scotton and Laura O. Taylor, “Valuing risk reductions: incorporating risk heterogeneity into a revealed preference framework,” Resource and Energy Economics, May 2011, pp. 381–397; Kniesner, Viscusi, Woock, and Ziliak (2012): Thomas J. Kniesner, W. Kip Viscusi, Christopher Woock, and James P. Ziliak, "The Value of a Statistical Life: Evidence from Panel Data," Review of Economics and Statistics, February 2012, pp. 74–87; Kniesner, Viscusi, and Ziliak (2012): Thomas J. Kniesner, W. Kip Viscusi, and James P. Ziliak, "Willingness to Accept Equals Willingness to Pay for Labor Market Estimates of the Value of a Statistical Life," IZA Discussion Paper 6816, Journal of Risk and Uncertainty, forthcoming, June 2014.

The average overall level of the VSL from the more conventional econometric analyses is in the general range of previous VSL estimates. Using risk measures extant before any CFOI data were available indicates that the median labor market estimate based on a meta-analysis of the literature was $9.3 million.9 The estimates in table 4 are fairly similar, with some being higher than that value and some lower. The VSL is not a universal constant but reflects the heterogeneity of worker preferences. Thus, the estimates will vary across different samples and over time for similar samples. Differences in econometric specifications, such as the inclusion of nonfatal risk variables in the analysis, also may be influential. Many differences in VSL levels found across studies arise from differences in the job match analyzed, because the estimated risk–money tradeoff rates will embody the influence of differences in worker preferences as well as differences in the marginal cost of providing greater safety across jobs.

The range of estimates based on the CFOI data is narrower than in the literature generally, particularly for studies that include detailed sets of controls to isolate the wage–risk premium. The greater credibility of VSL studies based on CFOI data has begun to be reflected in government practices with respect to the valuation of mortality reduction benefits. The U.S. Department of Transportation now bases its assessments of mortality risks on VSL estimates that use results from the CFOI data.10 More specifically, the Department established its VSL estimate by averaging a set of CFOI VSL estimates from nine of the studies listed in table 4 and concluded that the most pertinent VSL for mortality risk assessment is $9.2 million. This estimate is near the median value from the previous literature but higher than most VSL levels currently used in the federal government. Part of our refined understanding of the VSL is that these new results also make it possible to narrow the range of VSL estimates, reducing the range of uncertainty for this key statistic.

Perhaps the principal dividend derived from using the CFOI data is not in estimating an average value for the VSL but rather in greatly expanding the range of our understanding of the functioning of labor markets with respect to job risks. The final column of table 4 indicates that studies have generated VSL estimates not just for an economywide average but also for specific groups within the labor market.

Use of the CFOI with panel data

Panel data offer the opportunity to control for time-invariant fixed effects as well as differences in wage growth over time. In addition, panel data make it possible to explore the locus of compensating differentials in the workplace. Do these risk premiums arise for changes in risk affecting workers within their current jobs, or do workers receive premiums largely for differences in riskiness when changing jobs? Panel data from the Panel Study of Income Dynamics (PSID) also make it possible to explore possible sources of the relatively high VSL estimates found in some cross-sectional studies that used the PSID data and to control for person-specific effects that may give rise to VSL outliers.

Notes

9 W. Kip Viscusi and Joseph E. Aldy, “The value of a statistical life: a critical review of market estimates throughout the world,” Journal of Risk and Uncertainty, August 2003, pp. 5–76.

10 “Guidance on treatment of the economic value of a statistical life” (U.S. Department of Transportation, May 6, 2013), http://www.dot.gov/office-policy/transportation-policy/guidance-treatment-economic-value-statistical-life.

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About the Author

W. Kip Viscusi
kip.viscusi@vanderbilt.edu

W. Kip Viscusi is University Distinguished Professor of Law, Economics, and Management, Vanderbilt University, Nashville, TN