Decomposing the Luenberger–Hicks–Moorsteen Total Factor Productivity indicator: An application to U.S. agriculture
Frederic Ang and
Pieter Jan Kerstens
European Journal of Operational Research, 2017, vol. 260, issue 1, 359-375
This paper introduces a decomposition of the additively complete Luenberger–Hicks–Moorsteen Total Factor Productivity indicator into the usual components: technical change, technical inefficiency change and scale inefficiency change. Our approach is general in that it does not require differentiability or convexity of the production technology. Using a nonparametric framework, the empirical application focuses on the agricultural sector at the state-level in the U.S. over the period 1960–2004. The results show that Luenberger–Hicks–Moorsteen productivity increased substantially in the considered period. This productivity growth is due to output growth rather than input decline, although the extent depends on the convexity assumption of the technology. Technical change is the main driver, while the role of technical inefficiency change and scale inefficiency change also depends on the convexity assumption of the technology.
Keywords: Data Envelopment Analysis; Luenberger–Hicks–Moorsteen Total Factor Productivity indicator; decomposition; Non-convex technology; Additive completeness, (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:260:y:2017:i:1:p:359-375
Access Statistics for this article
European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati
More articles in European Journal of Operational Research from Elsevier
Series data maintained by Dana Niculescu ().