Skip to main content

Evaluating Efficiency in Nonhomogeneous Environments

  • Chapter
  • First Online:
Advances in Efficiency and Productivity II

Abstract

The conventional DEA methodology is generally designed to evaluate the relative efficiencies of a set of comparable decision-making units (DMUs). An appropriate setting is one where all DMUs use the same inputs, produce the same outputs, experience the same operating conditions, and generally operate in similar environments. In many applications, however, it can occur that the DMUs fall into different groups or categories, where the efficiency scores for any given group may be significantly different from those of another group. Examples include sets of hospitals with different patient mixes, groups of bank branches with differing customer demographics, manufacturing plants where some have been upgraded or modernized and others not, and so on. In such settings, if one wishes to evaluate an entire set of DMUs as a single group, this necessitates modifying the DEA structure such as to make allowance for what one might deem different environmental conditions or simply inherent inequities. Such a modification is presented herein and is illustrated using a particular example involving business activities in Mexico. While we do carry out a detailed analysis of these businesses, it is important to emphasize that this paper’s principal contribution is the methodology, not the particular application to which the methodology is applied.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Aparicio, J., & Santin, D. (2018). A note on measuring group performance over time with pseudo-panels. European Journal of Operational Research, 267(1), 227–235.

    Article  Google Scholar 

  • Banker, R., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale efficiencies in data envelopment analysis. Management Science, 30, 1078–1092.

    Article  Google Scholar 

  • Banker, R. D., & Morey, R. (1986). The use of categorical variables in data envelopment analysis. Management Science, 32(12), 1613–1627.

    Article  Google Scholar 

  • Battase, G., Rao, D., & O'Donnell, C. (2004). A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Journal of Productivity Analysis, 21(1), 91–103.

    Article  Google Scholar 

  • Byrnesa, P. E., & Storbeck, J. E. (2000). Efficiency gains from regionalization: Economic development in China revisited. Socio-Economic Planning Sciences, 34, 141–154.

    Article  Google Scholar 

  • Charnes, A., & Cooper, W. W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9, 67–88.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.

    Article  Google Scholar 

  • Cook, W. D., Liang, L., & Zhu, J. (2010). Measuring performance of two-stage network structures by DEA: A review and future perspective. Omega, 38, 423–430.

    Article  Google Scholar 

  • Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)-thirty years on. European Journal of Operational Research, 192(1), 1–17.

    Article  Google Scholar 

  • 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, 132, 245–259.

    Article  Google Scholar 

  • Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences, 42(3), 151–157.

    Article  Google Scholar 

  • Fried, H. O., Lovell, C. A. K., Schmidt, S. S., & Yaisawarng, S. (2002). Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis, 17(1–2), 157–174.

    Article  Google Scholar 

  • Instituto Nacional de Estadistica y Geografáa - INEGI. (2009). Glosario de Censos Económicos. Aguascalientes, Mexico. Retrieved from: https://www.inegi.org.mx/app/glosario/default.html?p=ce09

  • Liu, J. S., Lu, L. Y. Y., & Lu, W. M. (2016). Research fronts in data envelopment analysis. Omega, 58, 33–45.

    Article  Google Scholar 

  • Liu, J. S., Lu, L. Y. Y., Lu, W. M., & Lin, B. J. Y. (2013). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega, 41(1), 3–15.

    Article  Google Scholar 

  • Paradi, J., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis. Omega-International Journal of Management Science, 41, 61–79.

    Article  Google Scholar 

  • Parra Leyva, G. (2000). Economic growth in Mexico: A regional approach. (PhD.), Cornell University United States.

    Google Scholar 

  • Seiford, L., & Zhu, J. (2003). Context-dependent data envelopment analysis-measuring attractiveness and progress. Omega, 31(5), 397–408.

    Article  Google Scholar 

  • Tong, L., & Liping, C. (2009). Research on the Evaluation of Innovation Efficiency for China’s Regional Innovation System by Utilizing DEA. Paper presented at the 2009 International Conference on Information Management, Innovation Management and Industrial Engineering. http://0ieeexplore.ieee.org.millenium.itesm.mx/stamp/stamp.jsp?tp=&arnumber=5369923

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sonia Valeria Avilés-Sacoto .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Avilés-Sacoto, S.V., Cook, W.D., Güemes-Castorena, D., Zhu, J. (2020). Evaluating Efficiency in Nonhomogeneous Environments. In: Aparicio, J., Lovell, C., Pastor, J., Zhu, J. (eds) Advances in Efficiency and Productivity II. International Series in Operations Research & Management Science, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-030-41618-8_3

Download citation

Publish with us

Policies and ethics