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

David Bullock ()

Journal of Agricultural and Applied Economics, 2021, vol. 53, issue 1, 55-74

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.

Date: 2021
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Working Paper: The Influence of State-Level Production Outcomes Upon U.S. National Corn and Soybean Production: A Novel Application of Correlated Component Regression (2019)
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