The Influence of State-Level Production Outcomes upon U.S. National Corn and Soybean Production: A Novel Application of Correlated Component Regression

Abstract

The relative importance of key state-level outcomes upon U.S. national corn and soybean production was examined using correlated component regression, a recently developed regression technique for application to multicollinear and sparse data sets. Standardized coefficients were used to rank the states' relative importance. A Herfindahl-Hirschman Index was used to measure the degree of concentration among the top ranked states. The empirical analysis looked at two time periods: a pre-Genetic Modification (1975-1995) and a post-Genetic Modification (1996-2017) period. The results indicate that U.S. corn production is becoming less geographically concentrated in terms of state-level importance while the opposite holds true for soybean production.