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Dealing with the Endogeneity Problem in Data Envelopment Analysis

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

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.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 47475.

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Date of creation: Apr 2013
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Handle: RePEc:pra:mprapa:47475
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  1. Schlotter, Martin & Schwerdt, Guido & Woessmann, Ludger, 2010. "Econometric Methods for Causal Evaluation of Education Policies and Practices: A Non-Technical Guide," IZA Discussion Papers 4725, Institute for the Study of Labor (IZA).
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  5. Bifulco, Robert & Bretschneider, Stuart, 2003. "Response to comment on estimating school efficiency," Economics of Education Review, Elsevier, vol. 22(6), pages 635-638, December.
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  7. Ruggiero, John, 2003. "Comment on estimating school efficiency," Economics of Education Review, Elsevier, vol. 22(6), pages 631-634, December.
  8. Bifulco, Robert & Bretschneider, Stuart, 2001. "Estimating school efficiency: A comparison of methods using simulated data," Economics of Education Review, Elsevier, vol. 20(5), pages 417-429, October.
  9. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
  10. Carlos D. Mayen & Joseph V. Balagtas & Corinne E. Alexander, 2010. "Technology Adoption and Technical Efficiency: Organic and Conventional Dairy Farms in the United States," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(1), pages 181-195.
  11. Nataraja, Niranjan R. & Johnson, Andrew L., 2011. "Guidelines for using variable selection techniques in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 215(3), pages 662-669, December.
  12. Hédi Essid & Pierre Ouellette & Stéphane Vigeant, 2013. "Small is not that beautiful after all: measuring the scale efficiency of Tunisian high schools using a DEA-bootstrap method," Applied Economics, Taylor & Francis Journals, vol. 45(9), pages 1109-1120, March.
  13. Daniel Solís & Boris E. Bravo-Ureta & Ricardo E. Quiroga, 2007. "Soil conservation and technical efficiency among hillside farmers in Central America: a switching regression model ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 51(4), pages 491-510, December.
  14. repec:zwi:journl:v:43:y:2012:i:1:p:55-72 is not listed on IDEAS
  15. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
  16. Ruggiero, John, 2004. "Performance evaluation when non-discretionary factors correlate with technical efficiency," European Journal of Operational Research, Elsevier, vol. 159(1), pages 250-257, November.
  17. John Ruggiero, 2005. "Impact Assessment Of Input Omission On Dea," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 4(03), pages 359-368.
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