Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/253587 
Year of Publication: 
2020
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 11 [Issue:] 4 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2020 [Pages:] 1391-1429
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
We study potential impacts of future climate change on U.S. agricultural productivity using county-level yield and weather data from 1950 to 2015. To account for adaptation of production to different weather conditions, it is crucial to allow for both spatial and temporal variation in the production process mapping weather to crop yields. We present a new panel data estimation technique, called mean observation OLS (MO-OLS) that allows for spatial and temporal heterogeneity in all regression parameters (intercepts and slopes). Both forms of heterogeneity are important: We find strong evidence that production function parameters adapt to local climate, and also that sensitivity of yield to high temperature declined from 1950-89. We use our estimates to project corn yields to 2100 using 19 climate models and three greenhouse gas emission scenarios. We predict unmitigated climate change will greatly reduce yield. Our mean prediction (over climate models) is that adaptation alone can mitigate 36% of the damage, while emissions reductions consistent with the Paris targets would mitigate 76%.
Subjects: 
Climate change
crop yield
production function
large panel datamodels
JEL: 
C23
C54
D24
Q15
Q51
Q54
Q55
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

Files in This Item:
File
Size
5.22 MB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.