Skip to main content
Log in

Cost efficiency measurement with price uncertainty: a data envelopment analysis

  • Original Research
  • Published:
Mathematical Sciences Aims and scope Submit manuscript

Abstract

Data envelopment analysis (DEA) technique is commonly utilized for efficiency assessment in a variety of fields for both theoretical and applicational purposes. In classic cost efficiency measurement models, the input and output data and input prices should be known for each decision-making unit (DMU). However, in real-life markets the input prices are not precisely defined for DMUs. In this paper, we shed light on the fact that fixed prices assumption cannot reflect the reality of situations, because market will force lower prices if greater amounts of a product are purchased. It can be said that discounts are automatically considered in these circumstances. To this end, an innovative idea is considered to modify the classic cost efficiency DEA model in order to investigate the situations of real-life markets. Then, by an empirical example, a comparison between the proposed approach and the classic cost efficiency model is provided.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Farrell, M.J.: The measurement of productive efficiency. J. R. Stat. Soc. 120, 253–81 (1957)

    Google Scholar 

  2. Fare, R., Grosskopf, S., Lovell, C.A.K.: The Measurement of Efficiency of Production. Kluwer Academic Publisher, Dordrecht (1985)

    Book  Google Scholar 

  3. Tone, K.: A strange case of the cost and allocative efficiencies in DEA. J. Oper. Res. Soc. 53, 1225–31 (2002)

    Article  Google Scholar 

  4. Cooper, W.W., Thompson, R.G., Thrall, R.M.: Extensions and new developments in DEA. Ann. Oper. Res. 66, 3–45 (1996)

    MathSciNet  MATH  Google Scholar 

  5. Lee, C.C., Huang, T.H.: Cost efficiency and technological gap in Western European banks: a stochastic meta frontier analysis. Int. Rev. Econ. Finance 48, 161–178 (2017)

    Article  Google Scholar 

  6. Cesaroni, G.: Industry cost efficiency in data envelopment analysis. Socio-Econ. Plan. Sci. 61, 37–43 (2018)

    Article  Google Scholar 

  7. Ashrafi, A., Kaleibar, M.M.: Cost, revenue and profit efficiency models in generalized fuzzy data envelopment analysis. Fuzzy Inf. Eng. 9, 237–246 (2017)

    Article  MathSciNet  Google Scholar 

  8. Ajayi, V., Weyman-Jones, T., Glass, A.: Cost efficiency and electricity market structure: a case study of OECD countries. Energy Econ. 65, 283–291 (2017)

    Article  Google Scholar 

  9. Geng, A., Zhang, H., Yang, H.: Greenhouse gas reduction and cost efficiency of using wood flooring as an alternative to ceramic tile: a case study in China. J. Clean. Prod. 166, 438–448 (2017)

    Article  Google Scholar 

  10. Preciado-Pérez, O.A., Fotios, S.: Comprehensive cost-benefit analysis of energy efficiency in social housing. Case study: Northwest Mexico. Energy Build. 152, 279–289 (2017)

    Article  Google Scholar 

  11. Hasançebi, O.: Cost efficiency analyses of steel frameworks for economical design of multi-storey buildings. J. Constr. Steel Res. 128, 380–396 (2017)

    Article  Google Scholar 

  12. Blagojević, M., Ralević, P., Šarac, D.: An integrated approach to analysing the cost efficiency of postal networks. Util. Policy (2020). https://doi.org/10.1016/j.jup.2019.101002

    Article  Google Scholar 

  13. Sapci, A., Miles, B.: Bank size, returns to scale, and cost efficiency. J. Econ. Bus. (2019). https://doi.org/10.1016/j.jeconbus.2019.04.003

    Article  Google Scholar 

  14. Obenga, K., Sakano, R.: Effects of government regulations and input subsidies on cost efficiency: a decomposition approach. Transp. Policy 91, 95–107 (2020)

    Article  Google Scholar 

  15. AliAl-Khasawneh, A., Essaddam, N., Hussain, T.: Total productivity and cost efficiency dynamics of US merging banks: a non-parametric bootstrapped analysis of the fifth merger wave. Q. Rev. Econ. Finance (2020). https://doi.org/10.1016/j.qref.2020.02.002

    Article  Google Scholar 

  16. Camanho, A.S., Dyson, R.G.Dyson: Cost efficiency measurement with price uncertainty: a DEA application to bank branch assessments Dordrecht MA. Eur. J. Oper. Res. 161, 432–446 (2005)

    Article  Google Scholar 

  17. Jahanshahloo, G.R., Soleimani-damaneh, M., Mostafaee, A.: Cost ef ficiency analysis with ordinal data: a theoretical and computational view. Int. J. Comput. Math. 84, 553–562 (2007)

    Article  MathSciNet  Google Scholar 

  18. Kuosmanen, T., Post, T.: Measuring economic efficiency with incomplete price information: With an application to European commerical banks. Eur. J. Oper. Res. 134, 43–58 (2001)

    Article  Google Scholar 

  19. Kuosmanen, T., Post, T.: Measuring economic efficiency with incomplete price information. Eur. J. Oper. Res. 144, 454–457 (2003)

    Article  MathSciNet  Google Scholar 

  20. Mostafaee, A., Saljooghi, F.H.: Cost effficiency measures in data envelopment analysis with data uncertainty. Eur. J. Oper. Res. 202, 595–603 (2010)

    Article  Google Scholar 

  21. Grieco, P., Pinkse, J., Slade, M.: Brewed in North America: mergers, marginal costs, and efficiency. Int. J. Ind. Organ. 59, 24–65 (2017)

    Article  Google Scholar 

  22. Timilsina, G.R., Sikharulidze, A., Karapoghosyan, E., Shatvoryan, S.: Development of marginal abatement cost curves for the building sector in Armenia and Georgia. Energy Policy 108, 29–43 (2017)

    Article  Google Scholar 

  23. Batarce, M.: Estimation of urban bus transit marginal cost without cost data. Transp. Res. Part B: Methodol. 90, 241–262 (2016)

    Article  Google Scholar 

  24. Biskas, P.N., Bakirtzis, G.A., Chatziathanasiou, V.: Computation of strict long-run marginal cost for different HV consumers. Electr. Power Syst. Res. 128, 66–78 (2015)

    Article  Google Scholar 

  25. Cook, D.W., Zhu, J.: Piecewise linear output measure in DEA. Eur. J. Oper. Res. 197, 312–319 (2009)

    Article  Google Scholar 

  26. Lotfi, F.H., Rostay-Malhkalife, M., Moghaddas, Z.: Modified piecewise linear DEA model. Eur. J. Oper. Res. 205, 729–733 (2010)

    Article  Google Scholar 

  27. David Cummins, J., Rubio-Misas, M., Vencappa, D.: Competition, efficiency and soundness in European life insurance markets. J. Financ. Stabil. 28, 66–78 (2017)

    Article  Google Scholar 

  28. Eling, M., Schaper, P.: Under pressure: how the business environment affects productivity and efficiency of European life insurance companies. Eur. J. Oper. Res. 258, 1082–1094 (2017)

    Article  MathSciNet  Google Scholar 

  29. Biener, C., Eling, E., Hendrik Wirfs, J.: The determinants of efficiency and productivity in the Swiss insurance industry. Eur. J. Oper. Res. 248, 703–714 (2016)

    Article  Google Scholar 

  30. Wanke, P., Pestana Barros, C.: Efficiency drivers in Brazilian insurance: a two-stage DEA meta frontier-data mining approach. Econ. Model. 53, 8–22 (2016)

    Article  Google Scholar 

  31. Jarray, B., Bouri, A.: Optimal production plan and profit efficiency in European non-life insurance companies. Procedia Econ. Finance 13, 69–81 (2014)

    Article  Google Scholar 

  32. Hu, H.H., Qi, Q., Yang, C.H.: Analysis of hospital technical efficiency in China: effect of health insurance reform. China Econ. Rev. 23, 865–877 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Amirteimoori.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lotfi, F.H., Amirteimoori, A., Moghaddas, Z. et al. Cost efficiency measurement with price uncertainty: a data envelopment analysis. Math Sci 14, 387–396 (2020). https://doi.org/10.1007/s40096-020-00349-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40096-020-00349-2

Keywords

Mathematics Subject Classification

Navigation