Decomposing agricultural productivity growth using a random-parameters stochastic production frontier
Boris E. Bravo-Ureta and
Christopher J. O’Donnell
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Boris E. Bravo-Ureta: University of Connecticut
Christopher J. O’Donnell: University of Queensland
Empirical Economics, 2019, vol. 57, issue 3, No 5, 839-860
Abstract 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.
Keywords: Random parameters; Stochastic production frontier; Total factor productivity; US agriculture (search for similar items in EconPapers)
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