Using data from the Census of Fatal Occupational Injuries to estimate the “value of a statistical life”
The advent of the Census of Fatal Occupational Injuries has enabled researchers to reduce measurement error in fatality rate estimates; in turn, estimates of the “value of a statistical life” that are based on labor market data have become less uncertain.
Occupational fatality rate data sometimes have been used to estimate the tradeoff between worker wages and fatality risks. In this regard, a key question is, “Controlling for other aspects of the job and characteristics of the worker, what additional pay do workers receive for bearing greater risks?” This tradeoff rate, which has come to be known as the value of a statistical life (VSL), equals the extra amount of wages workers require per expected workplace fatality. For the past three decades, government agencies have used VSL estimates to monetize the mortality reduction benefits of health, safety, and environmental regulations.1 Labor market estimates of the VSL provide a measure of workers’ revealed preferences for the valuation of risk. Estimating the VSL on the basis of actual risk-taking behavior yields potentially more meaningful estimates than stated preferences with respect to hypothetical risks described in a survey context.
The extensive labor market literature generating estimates of the VSL has utilized several fatality rate measures, which typically are matched to employment information on individual workers that is reported in large datasets. These fatality rates have included various Bureau of Labor Statistics (BLS) fatality rates by industry, fatality rates from the National Traumatic Occupational Fatality database of the National Institute of Occupational Safety and Health, insurance company fatality rates by occupation, and fatality rates derived from workers’ compensation records. The most common approach has been to match fatality rates by industry to the industry reported by the worker in the survey, although some studies instead have used occupational risk data to match risks to workers by occupation. The compensating differential studies utilize a wage equation or the logarithm of a wage equation to estimate the additional premium workers receive for risk, controlling for other wage determinants. This premium per unit risk is the VSL.
Ideally, the fatality rate measure used in any analysis should reflect the riskiness of the worker’s job. However, the fatality rate variables used in the VSL studies typically have been imperfect proxies for the worker’s risk level. In addition, there are several sources of measurement error in the fatality rate data, and this error in turn may create a bias in wage equation estimates of the VSL.2 First, the fatality rate measure may not pertain only to job-related risks, as happened in the case of studies that used mortality rates of people in different occupations, independently of whether the death was job related. Second, early BLS estimates of fatality rates by industry relied on voluntary reporting and a limited sample of firms, potentially influencing the estimates. Finally, even if the industry fatality rate is measured accurately, not all workers in that industry face the same fatality rate, so occupational differences in risk also should be taken into account.
The advent of the BLS Census of Fatal Occupational Injuries (CFOI) alleviated these and related shortcomings of fatality rate measures. The CFOI data series is a comprehensive census of all job-related fatalities, which are verified through multiple sources of information, such as accident reports, coroners’ reports, and workers’ compensation records. The information on each fatality includes diverse personal characteristics data, as well as details regarding the nature of the incident and the type of injury. As a result, it is possible to develop much more refined measures of the fatality rate than would be possible on the basis of an overall industry average. Also, these measures can be tailored to the concerns of the particular study by, for example, conditioning the fatality rate on gender or age. Note that, because accidents are more readily monitored than job-related illnesses, particularly those with a long latency period for which causality may be difficult to determine, the CFOI data are best suited to analyses of acute fatality risks.
1 See W. Kip Viscusi, Fatal tradeoffs: public and private responsibilities for risk (New York: Oxford University Press, 1992), for a discussion of the U.S. Department of Labor’s disagreement with the U.S. Office of Management and Budget over a proposed Occupational Safety and Health Administration hazard communication regulation.
2 Among the more recent thorough explorations of some of the measurement error problems are Dan A. Black and Thomas J. Kniesner, “On the measurement of job risk in hedonic wage models,” Journal of Risk and Uncertainty, December 2003, pp. 205–220; and Orley Ashenfelter, “Measuring the value of a statistical life: problems and prospects,” Economic Journal, February 2006, pp. C10–C23.