A Comparison of Panel Data Models in estimating Technical Efficiency

Masoomeh Rashidghalam, Almas Heshmati (), Ghader Dashti () and Esmail Pishbahar ()
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Ghader Dashti: Department of Agricultural Economics,University of Tabriz, Tabriz, Iran
Esmail Pishbahar: Department of Agricultural Economics,University of Tabriz, Tabriz, Iran

No 433, Working Paper Series in Economics and Institutions of Innovation from Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies

Abstract: The purpose of this paper is two-fold. First, it compares the performance of various panel data models in estimating technical efficiency in production. Second, it applies various stochastic frontier panel data models to estimate the technical efficiency of Iran’s cotton production and to provide empirical evidence on the sources of technical inefficiency of cotton production using 2000-2012 panel data from Iran's 13 cotton producing provinces. The results indicate that labor and seeds are determinants of cotton production. Further, an investigation of the sources of technical inefficiency reveals that inorganic fertilizers result in reducing technical efficiency. The mean technical efficiency according to most of the models is found to be around 80 per cent. The empirical results show evidence of variations in the distribution of estimated efficiency amongst the different models. We also find a large difference in technical efficiency levels between provinces, which shows that geography and management’s impacts on technical efficiency are quite different among the provinces.

Keywords: Technical efficiency; panel data modeling; time-variant; persistent inefficiency; individual heterogeneity; model comparison; cotton production; Iran. (search for similar items in EconPapers)
JEL-codes: C23 D24 Q12 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2016-04-06
New Economics Papers: this item is included in nep-agr, nep-cwa and nep-eff
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: A Comparison of Panel Data Models in Estimating Technical Efficiency (2016)
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