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Efficiency in the European agricultural sector: environment and resources

Environmental Science and Pollution Research volume 25, pages 17927–17941 (2018)Cite this article

Abstract

This article intends to compute agriculture technical efficiency scores of 27 European countries during the period 2005–2012, using both data envelopment analysis (DEA) and stochastic frontier analysis (SFA) with a generalized cross-entropy (GCE) approach, for comparison purposes. Afterwards, by using the scores as dependent variable, we apply quantile regressions using a set of possible influencing variables within the agricultural sector able to explain technical efficiency scores. Results allow us to conclude that although DEA and SFA are quite distinguishable methodologies, and despite attained results are different in terms of technical efficiency scores, both are able to identify analogously the worst and better countries. They also suggest that it is important to include resources productivity and subsidies in determining technical efficiency due to its positive and significant exerted influence.

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Notes

  1. Belgium, Bulgaria, Czech Republic, Denmark, Germany, Estonia, Ireland, Greece, Spain, France, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Malta, Netherlands, Austria, Poland, Portugal, Romania, Slovenia, Slovakia, Finland, Sweden, and United Kingdom.

  2. For this sector in particular, it includes de net value added of all units involved in agricultural production, also if the units have more economic important activities as well and if the purpose of the units is not commercial. Kitchen garden (producing for own consumption only) is not included. The data considers: growing of non-perennial crops; growing of perennial crops; plant propagation; animal production; mixed farming; support activities to agriculture and post-harvest crop activities; hunting, trapping and related service activities.

  3. In tonnes per capita. Considering only biomass, DMC measures the total amount of biomass directly used by the economy, being defined as the annual quantity extracted from the domestic economy, plus all physical imports minus all physical exports.

  4. The code was implemented by us in MATLAB (R2009a) software.

  5. See Wasserstein and Lazar (2016) for an important discussion on the use of p values.

  6. The authors use the share of livestock subsidies in total subsidies (%), the share of the sum of subsidies on crops, intermediate consumption and external factors in total subsidies (%), and the share of total subsidies in total farm income (%).

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Acknowledgements

This work was supported in part by the Portuguese Foundation for Science and Technology (FCT—Fundação para a Ciência e a Tecnologia), through CIDMA—Center for Research and Development in Mathematics and Applications, within project UID/MAT/04106/2013 and by the Research Unit on Governance, Competitiveness and Public Policy—GOVCOPP (project POCI-01-0145-FEDER-008540), funded by FEDER funds through COMPETE2020—Programa Operacional Competitividade e Internacionalização (POCI), and by national funds through FCT—Fundação para a Ciência e a Tecnologia. We thank the comments received from the reviewers and the Editor of this article which helped us to improve this final version. Any remaining errors and shortcomings are our own responsibility.

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Affiliations

  1. GOVCOPP - Research Unit in Governance, Competitiveness and Public Policy, and Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal

    Victor Moutinho, Mara Madaleno & Margarita Robaina

  2. CIDMA - Center for Research and Development in Mathematics and Applications, Department of Mathematics, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal

    Pedro Macedo

  3. CEFAGE – Center of Advanced Studies in Management and Economics Portugal and Department of Management, University of Évora, Évora, Portugal

    Carlos Marques

Authors
  1. Victor Moutinho
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  2. Mara Madaleno
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  3. Pedro Macedo
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  4. Margarita Robaina
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  5. Carlos Marques
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Correspondence to Mara Madaleno.

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Responsible editor: Philippe Garrigues

Appendix

Appendix

Table 3 DEA efficiency estimates for 27 European countries, by year
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Table 4 SFA with GCE efficiency estimates for 27 European countries, by year
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Table 5 DEA and SFA with GCE rankings for the sixth highest and lowest scores for the 27 European countries, by year
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Table 6. Results of fixed effects and quantile regression estimates (DEA method)
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Table 7. Specification and diagnosis tests
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Moutinho, V., Madaleno, M., Macedo, P. et al. Efficiency in the European agricultural sector: environment and resources. Environ Sci Pollut Res 25, 17927–17941 (2018). https://doi.org/10.1007/s11356-018-2041-z

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Keywords

  • Agriculture resources productivity
  • European subsidies
  • Common agricultural policy (CAP)
  • Data envelopment analysis (DEA)
  • Stochastic frontier analysis (SFA)
  • Generalized cross-entropy (GCE)