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.
CIDMA - Center for Research and Development in Mathematics and Applications, Department of Mathematics, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal.
Environ Sci Pollut Res Int. 2018 Jun;25(18):17927-17941. doi: 10.1007/s11356-018-2041-z. Epub 2018 Apr 22.
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.
本文旨在使用数据包络分析（DEA）和随机前沿分析（SFA）结合广义交叉熵（GCE）方法，计算 2005-2012 年间 27 个欧洲国家的农业技术效率得分，并进行比较。之后，我们将使用这些得分作为因变量，应用分位数回归，使用一组可能影响农业部门的变量来解释技术效率得分。结果表明，尽管 DEA 和 SFA 是两种截然不同的方法，而且从技术效率得分来看，两者的结果也有所不同，但它们都能够同样地识别出最差和最好的国家。此外，研究结果还表明，由于资源生产力和补贴对技术效率具有正向和显著的影响，因此在确定技术效率时，将这些因素纳入考虑是很重要的。
