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Dealing with the Endogeneity Problem in Data Envelopment Analysis

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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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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. David J. Mayston, 2017. "Data envelopment analysis, endogeneity and the quality frontier for public services," Annals of Operations Research, Springer, vol. 250(1), pages 185-203, March.
    3. Kristof De Witte & Laura López-Torres, 2017. "Efficiency in education: a review of literature and a way forward," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 339-363, April.
    4. Cordero, Jose M. & Polo, Cristina & Santín, Daniel & Simancas, Rosa, 2018. "Efficiency measurement and cross-country differences among schools: A robust conditional nonparametric analysis," Economic Modelling, Elsevier, vol. 74(C), pages 45-60.
    5. Ioanna G. Gkiza & Stefanos A. Nastis, 2017. "Health and Women’s Role in Agricultural Production Efficiency," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 39(3), pages 428-440.
    6. Juan Aparicio & Jose Manuel Cordero & Carlos Díaz-Caro, 2020. "Efficiency and productivity change of regional tax offices in Spain: an empirical study using Malmquist–Luenberger and Luenberger indices," Empirical Economics, Springer, vol. 59(3), pages 1403-1434, September.
    7. Khoshnevis, Pegah & Teirlinck, Peter, 2018. "Performance evaluation of R&D active firms," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 16-28.
    8. Daniel Santín & Gabriela Sicilia, 2018. "Using DEA for measuring teachers’ performance and the impact on students’ outcomes: evidence for Spain," Journal of Productivity Analysis, Springer, vol. 49(1), pages 1-15, February.
    9. 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.
    10. Iyad Dhaoui, 2019. "Healthcare system efficiency and its determinants: A two-stage Data Envelopment Analysis (DEA) from MENA countries," Working Papers 1320, Economic Research Forum, revised 21 Aug 2019.
    11. Nirmalkumar Singh Moirangthem & Barnali Nag, 2020. "Developing a Framework of Regional Competitiveness Using Macro and Microeconomic Factors and Evaluating Sources of Change in Regional Competitiveness in India Using Malmquist Productivity Index," International Journal of Global Business and Competitiveness, Springer, vol. 15(2), pages 61-79, December.

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    More about this item

    Keywords

    Technical efficiency; DEA; Endogeneity; Monte Carlo.;
    All these keywords.

    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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