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Marginal values and returns to scale for nonparametric production frontiers

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journal contribution
posted on 2016-01-05, 15:06 authored by Victor PodinovskiVictor Podinovski, Robert G. Chambers, Kazim Baris Atici, Iryna D. Deineko
We present a unifying linear programming approach to the calculation of various directional derivatives for a very large class of production frontiers of data envelopment analysis (DEA). Special cases of this include different marginal rates, the scale elasticity and a spectrum of partial and mixed elasticity measures. Our development applies to any polyhedral production technology including, to name a few, the conventional variable and constant returns-to-scale DEA technologies, their extensions with weight restrictions, technolo gies with weakly disposable undesirable outputs and network DEA models. Furthermore, our development provides a general method for the characterization of returns to scale (RTS) in any polyhedral technology. The new approach effectively removes the need to develop bespoke models for the RTS characterization and calculation of marginal rates and elasticity measures for each particular technology.

History

School

  • Business and Economics

Department

  • Business

Published in

Operations Research

Volume

64

Issue

1

Pages

ii-iv, 1-272

Citation

PODINOVSKI, V.V., 2016. Marginal values and returns to scale for nonparametric production frontiers. Operations Research, 64(1), pp. 236-250.

Publisher

© INFORMS (Institute for Operations Research and Management Sciences)

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

2015-10-01

Publication date

2016-01-11

Notes

This paper was accepted for publication in the journal Operations Research and the definitive published version is available at http://dx.doi.org/10.1287/opre.2015.1457.

ISSN

1526-5463

Language

  • en