Structural Uncertainty and the Value of Statistical Life in the Economics of Catastrophic Climate Change. Weitzman, M. National Bureau of Economic Research Working Papers, 2007. ![Structural Uncertainty and the Value of Statistical Life in the Economics of Catastrophic Climate Change [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 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.
@article{weitzman_structural_2007,
series = {{NBER} {Working} {Paper} {No}. {W13490}},
title = {Structural {Uncertainty} and the {Value} of {Statistical} {Life} in the {Economics} of {Catastrophic} {Climate} {Change}},
url = {http://www.nber.org/papers/w13490},
abstract = {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.},
number = {13490},
journal = {National Bureau of Economic Research Working Papers},
author = {Weitzman, M.},
year = {2007},
keywords = {GA, Untagged},
}
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