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. Copyright Springer Science+Business Media, LLC 2007Meta-Regression, Frontier Models, Technical Efficiency, International Agriculture, Q12, D24,

This article reviews and critiques the frontier literature dealing with farm level efficiency in developing countries. A total of 30 studies from 14 different countries are examined. The country that has received most attention is India, while rice has been the most studied agricultural product. The average technical efficiency (TE) index from all the studies reviewed is 72%. The few studies reporting allocative and economic efficiency show an average of 68% and 43%, respectively. These results suggest that there is considerable room to increase agricultural output without additional inputs and given existing technology. Several of the studies reviewed have sought to explain farm level variation in TE. The variables most frequently used for this purpose have been farmer education and experience, contacts with extension, access to credit, and farm size. With the exception of farm size, the results reveal that these variables tend to have a positive and statistically significant impact on TE. This paper shows that considerable effort has been devoted to measuring efficiency in developing country agriculture using a wide range of frontier models. Despite all this work, the extent to which efficiency measures are sensitive to the choice of methodology remains uncertain.

The objective of this article is to examine the association between agricultural subsidies and dairy farm technical efficiency in the European Union, and in so doing we make novel contributions to the literature. We include in the analysis nine diverse western European Union (EU) countries over an 18‐year period (1990–2007) encompassing the various Common Agricultural Policy (CAP) reforms enacted since the inception of the EU. Further, we account for input endogeneity using an original method of moments estimator. Our results show that the effect of subsidies on technical efficiency may be positive, null, or negative, depending on the country. The analysis reveals that the introduction of decoupling with the 2003 CAP reform weakens the effect that subsidies have on technical efficiency.

This article brings together the stochastic frontier framework with impact evaluation methodology to compare technical efficiency (TE) across treatment and control groups using cross-sectional data associated with the MARENA Program in Honduras. A matched group of beneficiaries and control farmers is determined using propensity score matching techniques to mitigate biases stemming from observed variables. In addition, possible self-selection arising from unobserved variables is addressed using a selectivity correction model for stochastic frontiers recently introduced by Greene (J Prod Anal 34:15-24, 2010). The results reveal that average TE is consistently higher for beneficiary farmers than the control group while the presence of selectivity bias cannot be rejected. TE ranges from 0.67 to 0.75 for beneficiaries and from 0.40 to 0.65 for the control depending on whether biases were controlled or not. The TE gap between beneficiaries and control farmers decreases by implementing the matching technique and the sample selection framework decreases this gap even further. The analysis also suggests that beneficiaries do not only exhibit higher TE but also higher frontier output.