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Global Warming and the American Economy

A Regional Assessment of Climate Change Impacts

Edited by Robert Mendelsohn

The impact of climate change on seven regions of the United States is studied in this new and accessible collection. The study examines how the different regions of the United States may be affected by climate change. In particular, the study explores whether warming would be beneficial to the northern (colder) regions but harmful to the economies of the southern (warmer) regions.
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Chapter 3: Agriculture: A Ricardian analysis

Robert Mendelsohn


Robert Mendelsohn INTRODUCTION The impacts of warming on agriculture could well be the most important market effect of climate change. The possibility that changing climates could damage large agricultural zones has motivated considerable research on this topic (Reilly et al. 1996). Agricultural research has followed two major approaches: experimental simulation and cross-sectional studies. The simulation models begin with basic agronomic results showing crop yields changing under various climatic conditions. These results are then fed into an agroeconomic model that predicts how farm production and prices will change. Chapter 2 presents an example of this approach applied regionally to US agriculture. Cross-sectional models follow an empirical approach to this same problem. They examine farm outcomes across space and measure the correlation between climate and farm value. Using these empirical relationships, the approach then predicts how farm welfare will change under alternative climate scenarios. This chapter explores the cross-sectional approach for a regional analysis of US agriculture. The cross-sectional approach was first suggested by Mendelsohn et al. (1994), who examined counties across the United States. That study regressed value per acre on a number of climates and control variables, and discovered that the climate variables had a quadratic relationship with farm value and that climate could be captured by seasonal measures from four evenly spaced months. The study also discovered that it was important to control for other variables that could explain spatial farm values. Some of these effects could be dealt with by including a battery of control variables capturing soil...

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