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Dealing with the Endogeneity Problem in Data Envelopment Analysis

Author

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  • Cordero, José Manuel
  • Santín, Daniel
  • Sicilia, Gabriela

Abstract

Endogeneity, 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.

Suggested Citation

  • Cordero, José Manuel & Santín, Daniel & Sicilia, Gabriela, 2013. "Dealing with the Endogeneity Problem in Data Envelopment Analysis," MPRA Paper 47475, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:47475
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    File URL: https://mpra.ub.uni-muenchen.de/47475/1/MPRA_paper_47475.pdf
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    Cited by:

    1. Kristof de Witte & Laura López-Torres, 2015. "Efficiency in Education. A Review of Literature and a Way Forward," Working Papers 1501, Departament Empresa, Universitat Autònoma de Barcelona, revised Apr 2015.
    2. KONISHI Yoko & NISHIMURA Yoshihiko, 2013. "A Note on the Identification of Demand and Supply Shocks in Production: Decomposition of TFP," Discussion papers 13099, Research Institute of Economy, Trade and Industry (RIETI).
    3. repec:oup:apecpp:v:39:y:2017:i:3:p:428-440. is not listed on IDEAS
    4. repec:eee:soceps:v:61:y:2018:i:c:p:16-28 is not listed on IDEAS
    5. David J. Mayston, 2015. "Data envelopment analysis, endogeneity and the quality frontier for public services," Discussion Papers 15/05, Department of Economics, University of York.

    More about this item

    Keywords

    Technical efficiency; DEA; Endogeneity; Monte Carlo.;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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