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Reconstructing Nonparametric Productivity Networks

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models...

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Autores principales: Bostian, Moriah B., Daraio, Cinzia, Färe, Rolf, Grosskopf, Shawna, Izzo, Maria Grazia, Leuzzi, Luca, Ruocco, Giancarlo, Weber, William L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764256/
https://www.ncbi.nlm.nih.gov/pubmed/33322452
http://dx.doi.org/10.3390/e22121401
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author Bostian, Moriah B.
Daraio, Cinzia
Färe, Rolf
Grosskopf, Shawna
Izzo, Maria Grazia
Leuzzi, Luca
Ruocco, Giancarlo
Weber, William L.
author_facet Bostian, Moriah B.
Daraio, Cinzia
Färe, Rolf
Grosskopf, Shawna
Izzo, Maria Grazia
Leuzzi, Luca
Ruocco, Giancarlo
Weber, William L.
author_sort Bostian, Moriah B.
collection PubMed
description Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown.
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spelling pubmed-77642562021-02-24 Reconstructing Nonparametric Productivity Networks Bostian, Moriah B. Daraio, Cinzia Färe, Rolf Grosskopf, Shawna Izzo, Maria Grazia Leuzzi, Luca Ruocco, Giancarlo Weber, William L. Entropy (Basel) Article Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown. MDPI 2020-12-11 /pmc/articles/PMC7764256/ /pubmed/33322452 http://dx.doi.org/10.3390/e22121401 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bostian, Moriah B.
Daraio, Cinzia
Färe, Rolf
Grosskopf, Shawna
Izzo, Maria Grazia
Leuzzi, Luca
Ruocco, Giancarlo
Weber, William L.
Reconstructing Nonparametric Productivity Networks
title Reconstructing Nonparametric Productivity Networks
title_full Reconstructing Nonparametric Productivity Networks
title_fullStr Reconstructing Nonparametric Productivity Networks
title_full_unstemmed Reconstructing Nonparametric Productivity Networks
title_short Reconstructing Nonparametric Productivity Networks
title_sort reconstructing nonparametric productivity networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764256/
https://www.ncbi.nlm.nih.gov/pubmed/33322452
http://dx.doi.org/10.3390/e22121401
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