Working Paper: NBER ID: w13490
Authors: Martin Weitzman
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.
Keywords: climate change; structural uncertainty; value of statistical life; catastrophic risks
JEL Codes: Q54
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Structural uncertainty (D89) | Expected utility analysis (D81) |
| Uncertain climate sensitivity (Q54) | Tail behavior of damages (K13) |
| Structural uncertainty (D89) | Tail behavior of damages (K13) |
| Structural uncertainty (D89) | Exogenous shocks (F41) |
| Exogenous shocks (F41) | Tail behavior of damages (K13) |