Current Research

Massetti, E., and R. Mendelsohn. “Do Temperature Thresholds Threaten American Farmland?” Submitted.

Abstract: Estimated Ricardian models of climate change impacts on agriculture have been criticized because they rely on mean temperatures and do not explicitly include extreme temperatures. This paper compares the results using the entire distribution of temperature versus just the mean temperature in a Ricardian model. Including all temperatures does not increase damage. Weather panel studies find high temperatures are particularly harmful but these temperatures have only modest effects on farmland values. The results are robust to alternative model specifications and data sets.

Cattaneo, C. and E. Massetti. “Migration as an Adaptation to Climate: Evidence from Rural Households in Nigeria.” Submitted.

Abstract: This paper analyses whether migration is an adaptation strategy that households employ to cope with climate change in Nigeria. For households that operate farms, we find that the relationship between climate and migration is non-linear. In particular, climates with closer to ideal farming conditions are associated with a higher propensity to migrate, whereas in the least favorable climatic conditions, the propensity to migrate declines. The marginal effect of rainfall and temperature changes on migration varies by season. We estimate the impact of climate change in 2031-2060 and 2071-2100, ceteris paribus. Climate change increases migration in roughly half of the Local Government Areas and reduces migration in the other half. Households that already live under the worst climate conditions will migrate less if climate conditions worsen.

Appendix available here.

Massetti, E. “Statistical Learning for Climate Change Impact Research: an Application to US Agriculture.” Presented at the International Workshop on Climate Change Economics.

Abstract: We apply “statistical learning” methods to estimate how climate and weather affect US agriculture using the largest dataset of climate variables ever used in the literature. The goal of this paper is to provide a more precise estimation of the relationship between agricultural productivity and climate. This will lead to a clearer understanding of the potential impacts of climate change on US agriculture. The paper will also tests several statistical learning methods in the literature that studies the effect of climate on the economy. The analysis can be replicated in other sectors and in other countries, provided a rich dataset of climate variable is available.

Massetti, E. 2016.  "How do heat waves, cold waves, droughts, hail and tornadoes affect US agriculture?"

Abstract: We estimate the impact of extreme events on corn and soybeans yields, and on agricultural land values in the Eastern United States. We find the most harmful event is a severe drought but that cold waves, heat waves, and storms all reduce both corn and soybean yields. Over 80% of the damage from extreme events is caused by droughts and cold waves with heat waves causing only 6% of the damage. Including extreme events in a panel model of weather alters how temperature affects yields, making cold temperature more harmful and hot temperatures less harmful. Extreme events have no effect on farmland values probably because American farmers are buffered from extreme events by subsidized public crop insurance.

Massetti, E. 2017. "Can the Long-Difference Method Reveal Adaptation to Climate Change?"

Abstract: Burke and Emerick (2016) find that farmers in U.S. counties that have experienced a positive warming trend over 1982-2002 have not reduced the vulnerability of crops to extreme temperatures. The authors suggests that this is evidence of the limited role that adaptation may play against future climate change. However, a careful analysis of climate data reveals that the observed trends could not be anticipated by farmers because they were driven by short-term, mostly mean-reverting, random weather variations. Not much can be learned about adaptation to climate change from Burke and Emerick’s analysis.

Massetti, E. 2016. “Investments in Climate Change Mitigation: Evidence from 300 IAMs Scenarios.”

Massetti, E.. 2015.“Chaos in Climate Change Impact Estimates.”

Abstract: Global Circulation Models incorporate chaotic dynamics to reflect real-world weather patterns. This implies that extremely small perturbations of the climate system may generate very different weather patterns. Here I show that the SRES climate change scenarios generated by the Coupled Model Intercomparison Project phase 3 (CMIP3) - ubiquitous in the impact literature - display strong chaotic dynamics at regional and sub-regional level, at least until 2065. Chaos is triggered by changes to historic forcing in the year 2000 to reflect different emissions trajectories. This suggests that large uncertainty exists on how to link local climate change and global forcing. Furthermore,  short- and mid-term differences in local climate change across different SRES emission scenarios reflect chaotic dynamics rather than different forcing patterns. I show that the "chaos" in the climate scenarios generates a "chaotic" relationship between exogenous forcing and local economic impacts. "Perturbed exogenous forcing" model ensemble would resolve this uncertainty.

Supplementary material
Maps of 2011-2030 temperature anomalies for the US
Maps of 2046-2065 temperature anomalies for the US
Maps of 2011-2030 precipitations anomalies for the US
Maps of 2046-2065 precipitations anomalies for the US
Impacts of climate change at county level in the US
ppt presentation