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Technical efficiency in farming: a meta-regression analysis

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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.

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Notes

  1. Several other papers were found in the databases and journals included in the search but they did not contain all the variables needed; thus, they had to be excluded from the analysis.

  2. Independent t-tests and One-Way ANOVA are used to compare AMTEs. The former is used to compare means between two groups whereas the latter is applied when comparing more than two groups (Field 2005).

  3. Based on the World Bank (2005), the LICs that are included in this study are Bangladesh, Cameroon, Cote d’Ivoire, Ethiopia, Ghana, India, Kenya, Lesotho, Mali, Nepal, Nicaragua, Nigeria, Pakistan, Papua New Guinea, Tanzania, Uganda and Vietnam. The LMICs include Brazil, China, Dominican Republic, Ecuador, Guatemala, Indonesia, Iran, Jamaica, Paraguay, Philippines, Sri Lanka and the Ukraine. The UMICs include Costa Rica, Malaysia and Turkey. The HICs comprise Australia, Canada, Finland, France, Germany, Greece, Italy, Netherlands, New Zealand, Poland, Portugal, Republic of Korea, Slovenia, Spain, Sweden, United Kingdom and the United States.

  4. As suggested by one of the referees, it would be interesting to include the orientation of the efficiency measures (i.e., input- versus output-oriented) and, for panel data studies, whether TE is time variant or not, and if time variant then the specification used. The former factor is not included, however, because very few studies are input oriented and almost all of them are non-parametric so not much would be gained by including a separate variable for orientation. In terms of the second comment, the models already have a control for panel data but the inclusion of further related effects would be more appropriate if the analysis was restricted to panel data studies with sufficient variability in the behavior of TE overtime.

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Acknowledgments

The authors gratefully acknowledge comments from two anonymous referees and from Spiro Stefanou. This work was partially supported by the United States Agency for International Development (USAID) under the Peanut CRSP Grant LAG-G-00-96-90013-00.

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Authors and Affiliations

  1. Office of International Affairs, University of Connecticut, 843 Bolton Road, U-1182, Storrs, CT, 06269-1182, USA

    Boris E. Bravo-Ureta

  2. Department of Agricultural Resource Economics, University of Connecticut, Storrs, CT, USA

    Boris E. Bravo-Ureta & José F. Maripani

  3. Division of Marine Affairs and Policy, Rosenstiel School of Marine and Atmospheric Science, University of Miami, 4600 Rickenbacker Causeway, Miami, FL, 33149, USA

    Daniel Solís

  4. Department of Agricultural Economics, University Austral de Chile, Valdivia, Chile

    Víctor H. Moreira López

  5. Department of Business and Economics, University of Magallanes, Punta Arenas, Chile

    José F. Maripani

  6. Ecole Nationale d’Economie Appliquée, Dakar, Senegal

    Abdourahmane Thiam

  7. Department of Agricultural Policies in the Studies and Agrarian Policies Bureau, Ministry of Agriculture, Santiago, Chile

    Teodoro Rivas

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  1. Boris E. Bravo-Ureta
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  3. Víctor H. Moreira López
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Correspondence to Boris E. Bravo-Ureta.

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Bravo-Ureta, B.E., Solís, D., Moreira López, V.H. et al. Technical efficiency in farming: a meta-regression analysis. J Prod Anal 27, 57–72 (2007). https://doi.org/10.1007/s11123-006-0025-3

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