Structural Uncertainty and the Value of Statistical Life in the Economics of Catastrophic Climate Change
Using climate change as a prototype motivating example, this paper analyzes the implications of structural uncertainty for the economics of low-probability high-impact catastrophes. The paper shows that having an uncertain multiplicative parameter, which scales or amplifies exogenous shocks and is updated by Bayesian learning, induces a critical "tail fattening" of posterior-predictive distributions. These fattened tails can have strong implications for situations (like climate change) where a catastrophe is theoretically possible because prior knowledge cannot place sufficiently narrow bounds on overall damages. The essence of the problem is the difficulty of learning extreme-impact tail behavior from finite data alone. At least potentially, the influence on cost-benefit analysis of fat-tailed uncertainty about the scale of damages -- coupled with a high value of statistical life -- can outweigh the influence of discounting or anything else.
Department of Economics, Harvard University, Cambridge, MA 02138 (e-mail: firstname.lastname@example.org). For helpful detailed comments on earlier drafts of this paper, but without implicating them for its remaining defects, I am grateful to Frank Ackerman, Roland Benabou, Richard Carson, Daniel Cole, Stephen DeCanio, Don Fullerton, Olle Haggstrom, Robert Hahn, Karl Lofgren, Michael Mastrandrea, Robert Mendelsohn, William Nordhaus, Cedric Philibert, Richard Posner, John Reilly, Richard Tol, Gary Yohe, and Richard Zeckhauser. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research.