Business & Economics
; Mathematical Methods In Social Sciences
英文摘要:
This study makes two key contributions to the agricultural productivity literature. First, it demonstrates, using US agricultural state-level data, how a random-parameters stochastic frontier model can be used to account for environmental heterogeneity across decision-making units. Second, it uses the estimated parameters of the model to compute and decompose a productivity index that satisfies several key axioms from index theory. Because the decomposition explicitly accounts for both observed and unobserved environmental effects, we are able to obtain a more realistic and flexible assessment of productivity growth. We find substantial differences between productivity results generated using a model with random slope parameters and those generated using a more conventional model with constant slope parameters.
1.Univ Connecticut, Dept Agr & Resource Econ, Storrs, CT 06269 USA 2.Univ Talca, Dept Agr Econ, Talca, Chile 3.Univ Queensland, Sch Econ, St Lucia, Qld, Australia
Recommended Citation:
Njuki, Eric,Bravo-Ureta, Boris E.,O',et al. Decomposing agricultural productivity growth using a random-parameters stochastic production frontier[J]. EMPIRICAL ECONOMICS,2019-01-01,57(3):839-860