EconPapers    
Economics at your fingertips  
 

Benchmarking the benchmarks – Comparing the accuracy of Data Envelopment Analysis models in constant returns to scale settings

Sebastian Kohl and Jens O. Brunner

European Journal of Operational Research, 2020, vol. 285, issue 3, 1042-1057

Abstract: Despite the massive use of Data Envelopment Analysis (DEA) models for efficiency estimations in scientific applications, no paper cared about identifying the DEA model, which is able to provide the most accurate efficiency estimates, so far. We develop an established method based on a Monte Carlo data generation process to create artificial data. As we use a Translog production function instead of the commonly utilized Cobb Douglas production function, we are able to construct meaningful scenarios for constant returns to scale. The decision-making units resulting from the generated data are then used to calculate DEA estimators using different DEA models. Finally, the quality of the resulting efficiency estimates is evaluated by five performance indicators and summarized in benchmark scores. With this procedure, we can postulate general statements on parameters that influence the quality of DEA studies in a positive/negative way and determine which DEA model operates in the most accurate way for a range of scenarios. Here, we can show that the Assurance Region and Slacks-Based-Measurement models outperform the CCR (Charnes–Cooper–Rhodes) model in constant returns to scale scenarios. We therefore recommend a reduced utilization of the CCR model in DEA applications.

Keywords: Data Envelopment Analysis; Monte Carlo experiments; Artificial data (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221720301661
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:285:y:2020:i:3:p:1042-1057

DOI: 10.1016/j.ejor.2020.02.031

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2021-06-30
Handle: RePEc:eee:ejores:v:285:y:2020:i:3:p:1042-1057