Abstract: A critical issue in climate-change economics is the specification of the so-called "damages function" and its interaction with the unknown uncertainty of catastrophic outcomes. This paper asks how much we might be misled by our economic assessment of climate change when we employ a conventional quadratic damages function and/or a thin-tailed probability distribution for extreme temperatures. The paper gives some numerical examples of the indirect value of various GHG concentration targets as insurance against catastrophic climate-change temperatures and damages. These numerical examples suggest that we might be underestimating considerably the welfare losses from uncertainty by using a quadratic damages function and/or a thin-tailed temperature distribution. In these examples, the primary reason for keeping GHG levels down is to insure against high-temperature catastrophic climate risks.
Comments: I think this is the most constructive of Weitzman's string of papers on catastrophic risk, perhaps because the final result (which is actually the absence of a result) doesn't rest on infinite negative losses. I was most struck by his case that the quadratic loss function assumed by Nordhaus et al. is insufficiently flexible to express large aversions to catastrophic events. I think this is an example of a seemingly innocuous, esoteric assumption made twenty years ago coming back to bite us later.
Jarrod Welch, University of California, San Diego Jennifer Alix-Garcia, University of Wisconsin-Madison
Craig McIntosh, University of California, San Diego
Katharine Sims, Amherst College
Abstract: We study the consequences of poverty alleviation programs for environmental degradation in Mexico. We exploit the community-level eligibility discontinuity for Oportunidades (a conditional cash transfer program) to identify the impacts of income increases on local deforestation, and use random variation in the initial program phase to explore household responses. We find that additional income significantly increases demand for resource-intensive consumption goods. The corresponding production response increases deforestation but is localized only where communities have poor road infrastructure. The results suggest that better access to markets simply disperses environmental harm; the true impacts are only observable in infrastructure poor areas.
Comments: I really like what this paper is trying to do, the data they are willing to integrate and the importance of the findings. It has all the makings of a great paper. My only concern is whether the absence of deforestation in communities with high road densities is robust. In general, its far harder to demonstrate a "null" result than to show that some sort of non-zero relationship exists, since failing to reject a null-hypothesis doesn't tell you much. I think that in order to make the claim described in the last two sentences of the abstract, they'll have to do substantially more work with the NDVI data they are using and/or look at data on trade or other markets.
Jeff Vincent, Duke University Maximilian Auffhammer, University of California, Berkeley
Abstract: Recent research indicates that monsoon rainfall became less frequent but more intense in India during the latter half of the Twentieth Century, thus increasing the risk of drought and flood damage to the country’s rice crop. Our statistical analysis of state-level Indian data confirms that drought and extreme rainfall negatively affected rice yield during 1966-2002. Using Monte Carlo simulation, we find that yield would have been 2% higher on average if monsoon characteristics, especially drought frequency, had not changed since 1960. Yield would have received an additional boost of nearly 5% if two other meteorological changes (warmer nights, lower surface radiation) had not occurred. Climate change has evidently already negatively affected India’s hundreds of millions of rice producers and consumers.
Comments: I think their Monte Carlo method will become standard in the next few years. Its a nice way at getting at "climate" in comparison to "weather".
Maximilian Auffhammer, University of California, Berkeley Brian Wright, UC Berkeley
Seung-Jick Yoo, Korea Energy Institute
Abstract: We identify two issues with a standard time series approach to reconstruction of past climate fluctuations from paleoclimatic data series, one related to specification of the estimated relationship between climate and the paleoclimatic index, the other to the methodology of estimation. We show that the standard approach provides biased estimates of the reconstructed climate series and underestimates the true variability of historical climate. We demonstrate that inversion of the estimated response function between tree ring growth and climate indicators provides consistent estimates of historical climate. The inversion method results in an overestimation of the variance. We show analytically as well as using Monte Carlo experiments and actual tree ring data, that use of the new specification and reconstruction procedure can be crucial for inferences about the nature of past climate and interpretation of recent climate variations.
Comments: I think this paper is going to have a big impact. And I think the idea of having statisticians from different fields check one another's work is brilliant. I hope this is implemented more in the future and I hope economists invite non-economists to check their math, so the exchange is bidirectional.
Matthew Neidell, Columbia University Joshua Graff Zivin, UC San Diego
Abstract: In this paper we estimate the impacts of climate change on the allocation of time using econometric models that exploit plausibly exogenous variation in daily temperature over time within counties. We find large reductions in U.S. labor supply in industries with high exposure to climate and similarly large decreases in time allocated to outdoor leisure. We also find suggestive evidence of short-run adaptation through temporal substitutions and acclimatization. Given the industrial composition of the US, the net impacts on total employment are likely to be small, but significant changes in leisure time as well as large scale redistributions of income may be consequential. In developing countries, where the industrial base is more typically concentrated in climate-exposed industries and baseline temperatures are already warmer, employment impacts may be considerably larger.
