Empir Econ https://doi.org/10.1007/s00181-018-1469-9 Decomposing agricultural productivity growth using a random-parameters stochastic production frontier 1 1,2 Eric Njuki · Boris E. Bravo-Ureta · Christopher J. O’Donnell Received: 15 September 2017 / Accepted: 8 May 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract This study makes two key contributions to the agricultural productiv- ity 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 real- istic 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 1 Introduction Historically, economists have investigated agricultural productivity change in order to identify sources of growth and subsequently to inform public policy and guide decision makers
Empirical Economics – Springer Journals
Published: May 30, 2018
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