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 technique for application to multicollinear sparse data sets. Standardized coefficients were used rank the states’ importance. A Herfindahl-Hirschman Index measure degree concentration among top ranked states. empirical analysis looked at two time periods: pre-Genetic Modification (1975–1995) post-Genetic (1996–2017) period. results indicate that is becoming less geographically concentrated in terms while opposite holds true production.