IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this article

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

Listed author(s):
  • Cordero, José Manuel
  • Santín, Daniel
  • Sicilia, Gabriela

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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.sciencedirect.com/science/article/pii/S0377221715000351
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Elsevier in its journal European Journal of Operational Research.

Volume (Year): 244 (2015)
Issue (Month): 2 ()
Pages: 511-518

as
in new window

Handle: RePEc:eee:ejores:v:244:y:2015:i:2:p:511-518
DOI: 10.1016/j.ejor.2015.01.015
Contact details of provider: Web page: http://www.elsevier.com/locate/eor

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as
in new window


  1. 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.
  2. 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.
  3. Peyrache, Antonio & Coelli, Tim, 2009. "Testing procedures for detection of linear dependencies in efficiency models," European Journal of Operational Research, Elsevier, vol. 198(2), pages 647-654, October.
  4. Eva Crespo-Cebada & Francisco Pedraja-Chaparro & Daniel Santín, 2014. "Does school ownership matter? An unbiased efficiency comparison for regions of Spain," Journal of Productivity Analysis, Springer, vol. 41(1), pages 153-172, February.
  5. Timo Kuosmanen, 2008. "Representation theorem for convex nonparametric least squares," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 308-325, 07.
  6. 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.
  7. Martin Schlotter & Guido Schwerdt & Ludger Woessmann, 2011. "Econometric methods for causal evaluation of education policies and practices: a non-technical guide," Education Economics, Taylor & Francis Journals, vol. 19(2), pages 109-137.
  8. Ruggiero, John, 2003. "Comment on estimating school efficiency," Economics of Education Review, Elsevier, vol. 22(6), pages 631-634, December.
  9. 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.
  10. Sergio Perelman & Daniel Santin, 2011. "Measuring educational efficiency at student level with parametric stochastic distance functions: an application to Spanish PISA results," Education Economics, Taylor & Francis Journals, vol. 19(1), pages 29-49.
  11. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
  12. Boris Bravo-Ureta & William Greene & Daniel Solís, 2012. "Technical efficiency analysis correcting for biases from observed and unobserved variables: an application to a natural resource management project," Empirical Economics, Springer, vol. 43(1), pages 55-72, August.
  13. 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.
  14. Bifulco, Robert & Bretschneider, Stuart, 2003. "Response to comment on estimating school efficiency," Economics of Education Review, Elsevier, vol. 22(6), pages 635-638, December.
  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. 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.
  17. Dyson, R. G. & Allen, R. & Camanho, A. S. & Podinovski, V. V. & Sarrico, C. S. & Shale, E. A., 2001. "Pitfalls and protocols in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 245-259, July.
  18. repec:zwi:journl:v:43:y:2012:i:1:p:55-72 is not listed on IDEAS
  19. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:244:y:2015:i:2:p:511-518. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.