Testing the accuracy of DEA estimates under endogeneity through a Monte Carlo simulation

Endogeneity, and the distortions on the estimation of economic models that it causes, is a usual problem in the econometrics literature. Although non-parametric methods like Data Envelopment Analysis (DEA) are among the most used techniques for measuring technical efficiency, the effects of such problem on efficiency estimates have received little attention. The aim of this paper is to alert DEA practitioners about the accuracy of their estimates under the presence of endogeneity. For this, first we illustrate the endogeneity problem and its causes in production processes and its implications for the efficiency measurement from a conceptual perspective. Second, using synthetic data generated in a Monte Carlo experiment we evaluate how different levels of positive and negative endogeneity can impair DEA estimations. We conclude that, although DEA is robust to negative endogeneity, a high positive endogeneity level, i.e., the existence of a high positive correlation between one input and the true efficiency level, might bias severely DEA estimates.