Can you put a number on the value of a human life? How much are we willing to spend to reduce the risk of fatality in a given population? Economists can answer this question by calculating the value of a statistical life as a measure of the local trade-off between fatality risk and money spent. The figure that they estimate is used to help policymakers and regulators perform cost-benefit analyses in fields such as workplace health and safety regulations.
In a forthcoming paper in Quantitative Economics entitled “A Note on the Estimation of Job Amenities and Labor Productivity”, Arnaud Dupuy, Professor in Economics at the Faculty of Law, Economics and Finance at the University of Luxembourg and co-author Alfred Galichon (NYU, Sciences-Po), build on their previous research to introduce a maximum likelihood estimator of the value of job amenities and labor productivity in a single matching market based on the observation of equilibrium matches and wages.
While the proposed model can be used to solve a wide range of assignment problems in both the field of family economics (marriage market) and labour economics (labour market), the researchers have chosen to illustrate the usefulness of their methodology by applying it to the estimation of compensating wage differentials for the risk of fatal injury on the job. Past approaches to this calculation have used classic hedonic regression techniques, whereas the researchers’ method adds a structural estimation of preferences for risky jobs that explicitly considers the matching of workers to jobs while estimating the equilibrium hedonic wage equation.
Using recent data from the United States (Current Population Survey, Census of Fatal Occupational Injuries), the researchers were able to quantify the extent to which US workers dislike risky jobs, finding that US workers’ utility (job satisfaction) drops 2.3% as the probability of fatal injury on the job increases by one standard deviation (ie: 13.05 per 100,000). This coefficient allows the researchers to then estimate the value of statistical life, obtaining a figure of $6.3 million ($2017). This estimate is $3 million lower than the one obtained through a classic approach, suggesting that not accounting explicitly for the sorting of workers to jobs can lead to biases in the estimation of the value of statistical life.