EconPapers    
Economics at your fingertips  
 

Nonparametric analysis of technology and productivity under non-convexity: a neighborhood-based approach

Jean-Paul Chavas () and Kwansoo Kim ()

Journal of Productivity Analysis, 2015, vol. 43, issue 1, 59-74

Abstract: This paper investigates the nonparametric analysis of technology under non-convexity. The analysis extends two approaches now commonly used in efficiency and productivity analysis: data envelopment analysis where convexity is imposed; and free disposal hull (FDH) models. We argue that, while the FDH model allows for non-convexity, its representation of non-convexity is too extreme. We propose a new nonparametric model that relies on a neighborhood-based technology assessment which allows for less extreme forms of non-convexity. The distinctive feature of our approach is that it allows for non-convexity to arise in any part of the feasible set. We show how it can be implemented empirically by solving simple linear programming problems. And we illustrate the usefulness of the approach in an empirical application to the analysis of technical and scale efficiency on Korean farms. Copyright Springer Science+Business Media New York 2015

Keywords: Technology; Productivity; Nonparametric; Non-convexity; C6; D2; Q12 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1007/s11123-014-0383-1 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:jproda:v:43:y:2015:i:1:p:59-74

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/11123/PS2

DOI: 10.1007/s11123-014-0383-1

Access Statistics for this article

Journal of Productivity Analysis is currently edited by William Greene, Chris O'Donnell and Victor Podinovski

More articles in Journal of Productivity Analysis from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-19
Handle: RePEc:kap:jproda:v:43:y:2015:i:1:p:59-74