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The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farm Land Values

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  • Carlo Fezzi

    (Department of Economics, University of California and CSERGE (School of Environmental Sciences), University of East Anglia)

  • Ian Bateman

    (CSERGE (School of Environmental Sciences), University of East Anglia)

Abstract

Ricardian (hedonic) analyses of the impact of climate change on farmland values typically assume additively separable effects of temperature and precipitation. Model estimation is implemented on data aggregated across counties or large regions. We investigate the potential bias induced by such approaches by using a large panel of farm-level data. Consistent with the literature on plant physiology, we observe significant non-linear interaction effects, with more abundant precipitation acting as a mitigating factor for increased heat stress. This interaction disappears when the same data is aggregated in the conventional manner, leading to predictions of climate change impacts which are significantly distorted.

Suggested Citation

  • Carlo Fezzi & Ian Bateman, 2013. "The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farm Land Values," Working Papers 2013.94, Fondazione Eni Enrico Mattei.
    • Handle: RePEc:fem:femwpa:2013.94
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    More about this item

    Keywords

    Climate Change; Agriculture; Ricardian Analysis; Aggregation Bias; Semi-Parametric Models;
    All these keywords.

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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