The Use of Panel Models in Assessments of Climate Impacts on Agriculture

TitleThe Use of Panel Models in Assessments of Climate Impacts on Agriculture
Publication TypeJournal Article
Year of Publication2017
JournalReview of Environmental Economics and Policy
Volume11
Number2
Pages258-279
Date Published07/2017
Abstract / Summary

Assessments of climate change impacts on agriculture are increasingly relying on panel models to examine the relationship between agricultural outcomes and weather fluctuations. This article reviews the strengths and weaknesses of such models. We argue that panel models are ideal for assessing climate impacts on agriculture because they use group fixed effects to absorb all time-invariant variation and thus rely on weather deviations from the mean that are random and exogenous. Using this random and exogenous source of variation is crucial to identifying a causal relationship between agricultural outcomes and weather. In addition, the large number of observations offered by a panel data set allows the identification of a nonlinear response function, which is an important step in modeling the effects of climate change, as the response can be highly nonlinear. Despite these strengths of panel models, they may still suffer from omitted variable biases of time-varying variables, such as pollution shocks, which are correlated with the weather shocks. Moreover, because group fixed effects absorb a lot of the signal in the weather variables, the signal:noise ratio might decrease. Thus researchers should be careful when constructing the weather variables in order to avoid having noise in the data that causes downward biases in the coefficients.

URLhttp://doi.org/10.1093/reep/rex016
DOI10.1093/reep/rex016
Funding Program: 
Journal: Review of Environmental Economics and Policy
Year of Publication: 2017
Volume: 11
Number: 2
Pages: 258-279
Date Published: 07/2017

Assessments of climate change impacts on agriculture are increasingly relying on panel models to examine the relationship between agricultural outcomes and weather fluctuations. This article reviews the strengths and weaknesses of such models. We argue that panel models are ideal for assessing climate impacts on agriculture because they use group fixed effects to absorb all time-invariant variation and thus rely on weather deviations from the mean that are random and exogenous. Using this random and exogenous source of variation is crucial to identifying a causal relationship between agricultural outcomes and weather. In addition, the large number of observations offered by a panel data set allows the identification of a nonlinear response function, which is an important step in modeling the effects of climate change, as the response can be highly nonlinear. Despite these strengths of panel models, they may still suffer from omitted variable biases of time-varying variables, such as pollution shocks, which are correlated with the weather shocks. Moreover, because group fixed effects absorb a lot of the signal in the weather variables, the signal:noise ratio might decrease. Thus researchers should be careful when constructing the weather variables in order to avoid having noise in the data that causes downward biases in the coefficients.

DOI: 10.1093/reep/rex016
Citation:
Blanc, E, and W Schlenker.  2017.  "The Use of Panel Models in Assessments of Climate Impacts on Agriculture."  Review of Environmental Economics and Policy 11(2): 258-279.  https://doi.org/10.1093/reep/rex016.