Global Food Trade in a Spatially Correlated Climate

Global scale events, such as climate change, generate winners and losers across the planet. The consequences of such global inequality depends on the ability of trade to smooth away spatial differences. When the climate is more spatially correlated, a location surrounded by similarly affected neighbors must overcome larger trade costs in order to trade with distant, anti-correlated locations. We empirically test this idea by examining features of the global food market under planetary-scale climatic events experienced between 1960-2010. The El Nino Southern Oscillation (ENSO) periodically organizes local temperature and rainfall shocks of opposing sign into two contiguous regions of the planet such that under an El Nino event, cereal output is lower in the tropics and higher in the mid-latitudes while the opposite occurs during a La Nina event. We find that El Nino drives cereal exports from more distant locations towards the tropics but trade is insufficient to prevent price increases there. Using historically estimated ENSO effects, we calibrate a spatial model of global food trade and simulate future scenarios of food redistribution under climate change.

Speaker: Kyle Meng, UC Santa Barbara

Room 111