Journal of Productivity Analysis

, Volume 27, Issue 1, pp 57–72

Technical efficiency in farming: a meta-regression analysis

  • Boris E. Bravo-Ureta
  • Daniel Solís
  • Víctor H. Moreira López
  • José F. Maripani
  • Abdourahmane Thiam
  • Teodoro Rivas
Article

Abstract

A meta-regression analysis including 167 farm level technical efficiency (TE) studies of developing and developed countries was undertaken. The econometric results suggest that stochastic frontier models generate lower mean TE (MTE) estimates than non-parametric deterministic models, while parametric deterministic frontier models yield lower estimates than the stochastic approach. The primal approach is the most common technological representation. In addition, frontier models based on cross-sectional data produce lower estimates than those based on panel data whereas the relationship between functional form and MTE is inconclusive. On average, studies for animal production show a higher MTE than crop farming. The results also suggest that the studies for countries in Western Europe and Oceania present, on average, the highest levels of MTE among all regions after accounting for various methodological features. In contrast, studies for Eastern European countries exhibit the lowest estimate followed by those from Asian, African, Latin American, and North American countries. Additional analysis reveals that MTEs are positively and significantly related to the average income of the countries in the data set but this pattern is broken by the upper middle income group which displays the lowest MTE.

Keywords

Meta-Regression Frontier Models Technical Efficiency International Agriculture 

JEL Classifications

Q12 D24 

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Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Boris E. Bravo-Ureta
    • 1
    • 2
  • Daniel Solís
    • 3
  • Víctor H. Moreira López
    • 4
  • José F. Maripani
    • 2
    • 5
  • Abdourahmane Thiam
    • 6
  • Teodoro Rivas
    • 7
  1. 1.Office of International AffairsUniversity of ConnecticutStorrsUSA
  2. 2.Department of Agricultural Resource EconomicsUniversity of ConnecticutStorrsUSA
  3. 3.Division of Marine Affairs and Policy, Rosenstiel School of Marine and Atmospheric ScienceUniversity of MiamiMiamiUSA
  4. 4.Department of Agricultural EconomicsUniversity Austral de ChileValdiviaChile
  5. 5.Department of Business and EconomicsUniversity of MagallanesPunta ArenasChile
  6. 6.Ecole Nationale d’Economie AppliquéeDakarSenegal
  7. 7.Department of Agricultural Policies in the Studies and Agrarian Policies BureauMinistry of AgricultureSantiagoChile

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