Estimating the Effects of Climatic Change on Grain Production: Spatial Versus Non-Spatial Models

This paper aims to estimate the effects of climatic change on grain production in China by doing a cross-sectional analysis based on county-level dataset with variables measuring the total output of grain production, climate, and other economic and geographical data for over 2200 counties of China in the year of 2000. A non-spatial model, using Ordinary Least Squares(OLS) approach, was firstly built to measure the effects of climatic change on grain production, and then a spatial econometric model including the spatially weighted values of the explained and the explanatory variables to obtain more consistent and efficient estimates was developed by using the Maximum Likelihood Estimation approach. We find that spatial lag and spatial error models built on the rationale of spatial econometrics might be serving as an alternative to capture the effects from the dependent variables as well as the independent variables when we explore the impacts of climate change on the grain production in China.