An Additive Model for Estimation Return to Scale in Regulated Environment with Quasi-Fixed Inputs in Data Envelopment Analysis (DEA)

Subject Areas : International Journal of Data Envelopment Analysis

Farshid Emami ¹ , Toktam Nasirzade Tabrizi ²

1 - Department of Mathematics, Shahid Rajaee Teacher Training University, Lavizan, Tehran, Iran
2 - Department of Mathematics, Science and Research Branch, Islamic Azad University

Received: 2016-10-04 Accepted : 2016-10-04 Published : 2016-01-01

Keywords: Returns to scale, Regulation, Quasi-fixed inputs,

Abstract :

The measurement of RTS amounts measures a relationship between inputs and outputs in a production structure. There are many different ways to calculate RTS in primal or dual space. But in more realistic cases, governments usually intervene on DMU’s behavior as regulatory agency, this clearly represent a set of limitations and restrictions on behaviors of DMUs, So very few decisions in DMUs are made without intersecting some regulations. Therefore it is essential to be able to assess the impact of regulation on the behavior of the DMUs, and this would be ideally done by estimating returns to scale with and without the effect of the regulation. In this paper we use additive model to provide an alternative approach for estimating returns to scale in regulated environments. The proposed model is developed to determining returns to scale in the presence of quasi-fixed inputs in Data Envelopment Analysis.

References:

 

 [1] Banker, R.D., 1984. Estimating most productive scale size using data envelopment analysis. European Journal of Operational Research 17 (1), 35–44.

[2] Banker, R. D., Charnes, A., Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.

 

[3] Banker, R.D., Morey, R.C., 1986. The use of categorical variables in data envelopment analysis. Management Science 32, 1613–1627.

 

[4] Banker, R.D., Thrall, R.M., 1992. Estimation of returns to scale using data envelopment analysis. European Journal of Operational Research 62, 74–84.

 

[5] Charnes, A., Cooper, W. W., Golany, B., Seiford, L., Stutz, J. (1985). Foundation of data envelopment analysis for pareto-koopmans efficient empirical production functions. Journal of Econometrics, 30, 91–107.

 

[6] Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2 (6), 429–444.

 

[7] Cooper, W.W., Seiford, L.M., Tone, K., 2007. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software (Second Edition). New York, Springer Science+Business Media: Publisher.

 [8] Färe, R., Grosskopf, S. (1994). Estimation of returns to scale using data envelopment analysis: A comment. Journal of Operational Research, 79, 379–382.

 

[9] Khodabakhshi, M., Gholami, Y., Kheirollahi, H. (2010). An additive model approach for estimating returns to scale in imprecise data envelopment analysis. Applied Mathematical Modelling, 34, 1247–1257.

 

[10] Ouellette , P. Quesnel , J-P. Vigeant, G. (2012). Measuring returns to scale in DEA models when the firm is regulated, European Journal of Operational Research .220 (2012) 571–576.

[11] Ouellette, P., Vierstraete, V., 2004. Technological change and efficiency in the presence  of quasi-fixed inputs: A DEA application to the hospital sector. European Journal of Operational Research 154, 755–763.

 

[12] Ouellette, P., Vigeant, S., 2001. On the existence of a regulated production function. Journal of Economics 73, 193–200