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Fezzi, Carlo
Bateman, Ian
Year of Publication: 
Series/Report no.: 
CSERGE Working Paper 2012-02
Ricardian models predicting the impact of climate change on agriculture are typically estimated on data aggregated across counties and assuming additively separable effects of temperature and precipitation. We investigate the potential bias induced by such assumptions by using a large panel of farm-level data and estimating a semi-parametric specification. Consistent with the agronomic literature, we observe significant non-linear interaction effects, with more abundant precipitation being a mitigating factor for heat stress. This interaction disappears when the same data is aggregated in the conventional manner, leading to predictions of climate change impacts which are severely distorted.
Aggregation Bias
Climate Change
Ricardian Analysis
Semi-Parametric Models
Document Type: 
Working Paper

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