Sustainable tourism and agriculture multifunctionality by PAR: A variable selection approach

Abstract

Data Envelopment Analysis (DEA) is a popular non-parametric method used to measure efficiency. It uses linear programming to identify points on a convex hull defined by the inputs and outputs of the most efficient Decision Making Units (DMUs). Two critical elements account for the strength of the DEA approach: (1) no a priori structure is placed on the production process of the firm, and (2) the models can yield a measure of efficiency even with a very small number of data points. The first point is particularly important because the measure of efficiency is based upon the best practice of the DMUs at any of the levels of output observed. Data envelopment analysis measures efficiency and is very sensitive to the choice of variables for two reasons: the number of efficient DMUs is directly related to the number of variables, and the selection of the variables greatly affects the measure of efficiency when the number of DMUs is few and/or when the number of explanatory variables needed to compute the measure of efficiency is too large. Our approach advises which variables should be included in a DEA model. Hence, a variable selection method is presented for the deterministic DEA approach. First, a definition of different measures of efficiency and the various DEA models used to measure efficiency is provided, and then a variable selection method is proposed. The Azorean agricultural system is used as an example to illustrate the method.