DETERMINANTS OF VIRTUAL WATER TRADE OF CEREAL CROPS IN SAUDI ARABIA

Abstract

In this research, we used a gravity model to investigate whether water scarcity variables influence agricultural trade of cereal crops for Saudi Arabia. We compare the OLS, Fixed effects, Random effects, and Poisson Pseudo-Maximum Likelihood (PPML) estimators to determine the best model. The AIC, and multicollinearity, heteroskedasticity, and autocorrelation tests assist in determining estimation procedures and the final model. We cluster the errors by distance to improve the specific country effect variables, such as economic mass. We find that water-related variables influence virtual water imports of cereals, millet, corn, barely, and sesame.