Working Paper: NBER ID: w16353
Authors: Robert S. Pindyck
Abstract: Climate policy is complicated by the considerable compounded uncertainties over the costs and benefits of abatement. We don't even know the probability distributions for future temperatures and impacts, making cost-benefit analysis based on expected values challenging to say the least. There are good reasons to think that those probability distributions are fat-tailed, which implies that if social welfare is based on the expectation of a CRRA utility function, we should be willing to sacrifice close to 100% of GDP to reduce GHG emissions. I argue that unbounded marginal utility makes little sense, and once we put a bound on marginal utility, this implication of fat tails goes away: Expected marginal utility will be finite even if the distribution for outcomes is fat-tailed. Furthermore, depending on the bound on marginal utility, the index of risk aversion, and the damage function, a thin-tailed distribution can yield a higher expected marginal utility (and thus a greater willingness to pay for abatement) than a fat-tailed one.
Keywords: No keywords provided
JEL Codes: D81; Q51; Q54
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| temperature increases (T) (O39) | consumption (C) (E20) |
| consumption (C) (E20) | social welfare (U) (I38) |
| temperature increases (T) with fat-tailed distribution (C46) | marginal utility (U_C) approaches infinity (D11) |
| bounded marginal utility (U_C) (D11) | expected marginal utility remains finite (D11) |
| consumption (C) with thin-tailed distribution (C46) | expected marginal utility (U_C) higher than fat-tailed (D81) |