Endogenous Generalized Weights under DEA Control

Non-parametric efficiency analysis, such as Data Envelopment Analysis (DEA) relies so far on endogenous local or exogenous general weights, based on revealed preferences or market prices. However, as DEA is gaining popularity in regulation and normative budgeting, the strategic interest of the evaluated industry calls for attention. We offer endogenous general prices based on a reformulation of DEA where the units collectively propose the set of weights that maximize their efficiency. Thus, the sector-wide efficiency is then a result of compromising the scores of more specialized smaller units, which also gives a more stable set of weights. The potential application could be to precipitate collective bargaining on cost efficiency for non-marketed resources and products. The models are applied to paneldata from 285 Danish district heating plants, where the open evaluation of multiple non-priced out- puts is relevant. The results show that sector wide weighting schemes favor input/output combinations that are less variable than would individual units

[1]  Peter Bogetoft,et al.  Efficiency and Merger Gains in the Danish Forestry Extension Service , 2001 .

[2]  Emmanuel Thanassoulis,et al.  Estimating preferred target input−output levels using data envelopment analysis , 1992 .

[3]  P. Andersen,et al.  A procedure for ranking efficient units in data envelopment analysis , 1993 .

[4]  Francisco Pedraja-Chaparro,et al.  On the Role of Weight Restrictions in Data Envelopment Analysis , 1997 .

[5]  B. Golany,et al.  Controlling Factor Weights in Data Envelopment Analysis , 1991 .

[6]  Abraham Charnes,et al.  Cone ratio data envelopment analysis and multi-objective programming , 1989 .

[7]  R. Dyson,et al.  Reducing Weight Flexibility in Data Envelopment Analysis , 1988 .

[8]  Per J. Agrell,et al.  DEA and Dynamic Yardstick Competition in Scandinavian Electricity Distribution , 2005 .

[9]  B. Golany A note on including ordinal relations among multipliers in data envelopment analysis , 1988 .

[10]  Emmanuel Thanassoulis,et al.  Simulating Weights Restrictions in Data Envelopment Analysis by Means of Unobserved Dmus , 1998 .

[11]  M. Pollitt,et al.  Benchmarking and regulation: international electricity experience , 2000 .

[12]  L. Seiford,et al.  Strict vs. weak ordinal relations for multipliers in data envelopment analysis , 1991 .

[13]  Joe Zhu Data Envelopment Analysis with Preference Structure , 1996 .

[14]  F. Førsund,et al.  Generalised Farrell Measures of Efficiency: An Application to Milk Processing in Swedish Dairy Plants , 1979 .

[15]  J. Wallenius,et al.  A Value Efficiency Approach to Incorporating Preference Information in Data Envelopment Analysis , 1999 .

[16]  John E. Beasley,et al.  Restricting Weight Flexibility in Data Envelopment Analysis , 1990 .

[17]  Lawrence M. Seiford,et al.  Recent developments in dea : the mathematical programming approach to frontier analysis , 1990 .

[18]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[19]  Per Joakim Agrell,et al.  Incentive plans for Productive Efficiency, Innovation and Learning , 2002 .

[20]  Peter Bogetoft,et al.  Estimating the Potential Gains from Mergers , 2005 .

[21]  Per Joakim Agrell,et al.  Economic and environmental efficiency of district heating plants , 2005 .