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Economic elasticities of input substitution using data envelopment analysis

The use of elasticities of substitution between inputs is a standard method for addressing the effect of a change in the mix of inputs used for production from a technical or cost standpoint. Most estimation methods use parametric production or cost functions or frontiers to estimate these elasticit...

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Detalles Bibliográficos
Autores principales: Miller, Noah J., Bergtold, Jason S., Featherstone, Allen M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6687138/
https://www.ncbi.nlm.nih.gov/pubmed/31393895
http://dx.doi.org/10.1371/journal.pone.0220478
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author Miller, Noah J.
Bergtold, Jason S.
Featherstone, Allen M.
author_facet Miller, Noah J.
Bergtold, Jason S.
Featherstone, Allen M.
author_sort Miller, Noah J.
collection PubMed
description The use of elasticities of substitution between inputs is a standard method for addressing the effect of a change in the mix of inputs used for production from a technical or cost standpoint. Most estimation methods use parametric production or cost functions or frontiers to estimate these elasticities. A potentially useful nonparametric alternative is data envelopment analysis (DEA). The purpose of this paper is to derive elasticities of input substitution for both technical and cost frontiers using DEA, extending the use of this approach in the field of economics and associated fields. The paper provides derivations for both Hicksian (production and cost frontier) and Morishima (cost frontier) elasticities of input substitution, as well as a parsimonious method for estimating them using DEA. The derivations are presented using an agricultural example form Kansas, USA.
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spelling pubmed-66871382019-08-15 Economic elasticities of input substitution using data envelopment analysis Miller, Noah J. Bergtold, Jason S. Featherstone, Allen M. PLoS One Research Article The use of elasticities of substitution between inputs is a standard method for addressing the effect of a change in the mix of inputs used for production from a technical or cost standpoint. Most estimation methods use parametric production or cost functions or frontiers to estimate these elasticities. A potentially useful nonparametric alternative is data envelopment analysis (DEA). The purpose of this paper is to derive elasticities of input substitution for both technical and cost frontiers using DEA, extending the use of this approach in the field of economics and associated fields. The paper provides derivations for both Hicksian (production and cost frontier) and Morishima (cost frontier) elasticities of input substitution, as well as a parsimonious method for estimating them using DEA. The derivations are presented using an agricultural example form Kansas, USA. Public Library of Science 2019-08-08 /pmc/articles/PMC6687138/ /pubmed/31393895 http://dx.doi.org/10.1371/journal.pone.0220478 Text en © 2019 Miller et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Miller, Noah J.
Bergtold, Jason S.
Featherstone, Allen M.
Economic elasticities of input substitution using data envelopment analysis
title Economic elasticities of input substitution using data envelopment analysis
title_full Economic elasticities of input substitution using data envelopment analysis
title_fullStr Economic elasticities of input substitution using data envelopment analysis
title_full_unstemmed Economic elasticities of input substitution using data envelopment analysis
title_short Economic elasticities of input substitution using data envelopment analysis
title_sort economic elasticities of input substitution using data envelopment analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6687138/
https://www.ncbi.nlm.nih.gov/pubmed/31393895
http://dx.doi.org/10.1371/journal.pone.0220478
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