A Bayesian stochastic frontier: an application to agricultural productivity growth in European countries
Economic Change and Restructuring, 2012, vol. 45, issue 4, 247-269
This paper measures and compares total factor productivity (TFP) growth in agriculture for the European Union (EU) countries and candidate countries (CC), in order to distinguish and investigate cross-country differences in agricultural productivity growth rates from 1993 to 2006. A stochastic production frontier model is estimated using a Bayesian approach capturing country-specific time-invariant heterogeneity and country-specific time-varying inefficiency. Agricultural productivity growth is found to be mostly driven by technological change. The TFP growth rates of the EU-12 countries and CC are about twice the EU-15 growth rate. Catch-up in productivity levels is observed between EU-15 and EU-12 as well as between EU-15 and CC. The results are compared for a situation in which country-specific time-invariant heterogeneity is not taken into account. Copyright The Author(s) 2012
Keywords: Bayesian inference; Stochastic production frontier; Time-varying technical inefficiency; Total factor productivity growth; European agriculture; C15; D24; O47 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
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:kap:ecopln:v:45:y:2012:i:4:p:247-269
Ordering information: This journal article can be ordered from
http://www.springer. ... nt/journal/10644/PS2
Access Statistics for this article
Economic Change and Restructuring is currently edited by George Hondroyiannis
More articles in Economic Change and Restructuring from Springer
Bibliographic data for series maintained by Sonal Shukla ().