Measuring the Efficiency of Managerial and Technical Performances in Forestry Activities by Means of Data Envelopment Analysis (DEA)

Abstract The relation between the most productive scale size for particular input and output mixes and returns to scale for multiple-input multiple-output situations is explicitly developed. Data Envelopment Analysis (DEA) has been extensively applied in a range of empirical settings to identify relative inefficiencies, and provide targets for improvements. It accomplishes this by developing peer groups for each unit being operated (Decision Making Unit: DMU) This paper introduces the technique and focuses on some of the key issues that arise in applying DEA in practice. Some illustrations of the practical applications of these results to the estimation of most productive scale sizes and returns to scale for Forest Owner’s Associations (FOAs) at the local level in Japan are also provided to emphasize the advantage of this method in examining specific segments of the efficient production surface.