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Empirical Economics

, Volume 57, Issue 3, pp 839–860 | Cite as

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

  • Eric NjukiEmail author
  • Boris E. Bravo-Ureta
  • Christopher J. O’Donnell
Article

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 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Eric Njuki
    • 1
    Email author
  • Boris E. Bravo-Ureta
    • 1
    • 2
  • Christopher J. O’Donnell
    • 3
  1. 1.Department of Agricultural and Resource EconomicsUniversity of ConnecticutStorrsUSA
  2. 2.Department of Agricultural EconomicsUniversity of TalcaTalcaChile
  3. 3.School of EconomicsUniversity of QueenslandSt. LuciaAustralia

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