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Decomposing agricultural productivity growth using a random-parameters stochastic production frontier

Eric Njuki, 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: 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)
Date: 2019
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DOI: 10.1007/s00181-018-1469-9

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