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

Decomposing agricultural productivity growth using a random-parameters stochastic production... 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Empirical Economics Springer Journals

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

Loading next page...
 
/lp/springer_journal/decomposing-agricultural-productivity-growth-using-a-random-parameters-IpoHc70QxY
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Economics; Econometrics; Statistics for Business/Economics/Mathematical Finance/Insurance; Economic Theory/Quantitative Economics/Mathematical Methods
ISSN
0377-7332
eISSN
1435-8921
D.O.I.
10.1007/s00181-018-1469-9
Publisher site
See Article on Publisher Site

Abstract

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

Journal

Empirical EconomicsSpringer Journals

Published: May 30, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off