Modeling Food Commodity Prices and Market Dynamics

Abstract

This dissertation examines two distinct but related drivers of food commodity prices: global climate shocks and regional transportation costs. The first chapter, "Exploring the Dynamics of the El Niño Southern Oscillation and Food Commodity Prices," investigates how ENSO-related weather anomalies influence the prices of wheat, corn, rice, and soybean products. Using data from 1980–2022 and nonlinear econometric methods (Smooth Transition Autoregressive and Smooth Transition Vector Error Correction models), the analysis uncovers asymmetric effects of ENSO shocks. A ±1.5°C deviation in ENSO-related temperatures generates price shifts of 10–20 percent. Warmer El Niño phases tend to depress prices, while cooler La Niña phases raise them. These dynamics matter most for low-income food-deficit countries (LIFDCs), where higher prices exacerbate food insecurity. The second chapter, "Down the Mississippi: How Barge Rates Affect Corn Basis," turns to transportation and spatial market linkages in the U.S. corn market. Using weekly data from 2014 to 2024 across 14 Mississippi River markets, a Spatial Durbin Model with spatial and time fixed effects reveals that higher barge freight rates reduce local corn basis values by roughly five cents per bushel per one-dollar increase in barge rates. Rising barge costs, driven by factors such as river depth and diesel prices, also generate spillovers that transmit price effects to neighboring markets. Together, the two studies show how food commodity prices are shaped both by global climate variability and by regional supply chain constraints. The findings highlight the importance of policies that strengthen resilience to climate shocks while also addressing transportation bottlenecks in agricultural markets.