Dealing with the Endogeneity Problem in Data Envelopment Analysis
AbstractEndogeneity, and the distortions on the estimation of economic models that it causes, is a familiar 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 endogeneity on such efficiency estimates have received little attention. The aim of this paper is twofold. First, we further illustrate the endogeneity problem and its causes in production processes like the correlation between one input and the efficiency level. Second, we use synthetic data generated in a Monte Carlo experiment to analyze 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., a high positive correlation between one input and the true efficiency level, significantly and severely biases DEA performance.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 47475.
Date of creation: Apr 2013
Date of revision:
Technical efficiency; DEA; Endogeneity; Monte Carlo.;
Find related papers by JEL classification:
- C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
- C9 - Mathematical and Quantitative Methods - - Design of Experiments
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-06-16 (All new papers)
- NEP-ECM-2013-06-16 (Econometrics)
- NEP-EFF-2013-06-16 (Efficiency & Productivity)
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