Structural Uncertainty and the Value of Statistical Life in the Economics of Catastrophic Climate Change

Abstract

Using climate change as a prototype motivating example, this paper analyzes the implications of structural uncertainty for the economics of 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. Such fattened tails 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. At least potentially, the impact on cost-benefit analysis of fat-tailed uncertainty about the scale of damages'coupled with a high value of statistical life'outweighs the importance of discounting or anything else.