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.
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number
13490.
Length: Date of creation: Oct 2007 Date of revision: Handle: RePEc:nbr:nberwo:13490
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Find related papers by JEL classification: Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters
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