DEMAND FOR HERBICIDE IN CORN: AN ENTROPY APPROACH USING MICRO-LEVEL DATA
Price responsiveness of herbicide demand in corn for farmers in Indiana's White River Basin using cross-section data from individual farms is estimated. Particular attention is paid to appropriate treatment of binding nonnegativity constraints. Estimation was first attempted using an approach to demand systems estimation suggested by Lee and Pitt. However, analytical and computational difficulties effectively preclude estimation by the Lee and Pitt approach. As an alternative, a maximum entropy (ME) approach is presented and discussed. Results from the ME estimator tentatively indicate limited response of herbicide demand to changes in own prices. The maximum entropy approach to demand systems estimation appears to have merit and warrants further attention.