Transcript Diplomarbeit zum Thema slot-Allokation Slot

EE²
Benchmarking
With An Application to Electricity Distribution
GAP Workshop
14 December 2005, Berlin
Astrid Cullmann , DIW Berlin
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Agenda
1. Overview - Benchmarking Methodologies
2. Application in the Electricity Sector
3. Transfer to the Airports
Literature
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Overview of Benchmarking Techniques
Benchmarking
Partial
Approaches
(onedimensional)
Multi-dimensional Approaches
Average Approaches
Frontier Approaches
Non-Parametric
Data
Performance Envelopment
Indicators
Analysis
(DEA)
Stochastic
DEA
(SDEA)
Parametric
Stochastic
Frontier
Analysis
(SFA)
Parametric
Modified
Corrected
Ordinary
Ordinary
Least Squares Least Squares
(MOLS)
(COLS)
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Induced
Approach
Ordinaray
Total Factor
Least Squares Productivity
(OLS)
(TFP)
Data Envelopment Analysis (DEA) – (I)
max u ,v (u´ yi / v´xi ),
Y
e.g.
units
sold
Efficiency Frontier
DEA CRS
C B
u´ yi / v´xi  1, j  1, 2,...N
u, v  0
max  , ( ´, yi ),
A
v´xi  1
Efficiency Frontier
DEA VRS
´ yi   ´xi  0, j  1, 2..., N
 ,  0,
min ,  ,
0
X
e.g. labour, network size
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 yi  Y   0
 xi  X   0
 0
Data Envelopment Analysis (II)
Advantages:
-
Identifies a set of peer firms (efficient firms with similar input and
output mixes) for each inefficient firm.
-
Can easily handle multiple output.
-
Does not assume a functional form for the frontier or a
distributional form for the inefficiency error term.
Drawbacks:
- May be influenced by noise.
- Traditional hypothesis tests are not possible.
-
Requires large sample size for robust estimates, which may not
be available early on in the life of a regulator.
→ Sensitivity Analysis by Bootstrapping
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Stochastic Frontier Analysis (SFA) (I)
ln( yi )  xi   vi  ui
SFA Assumption about the residuals
- vi are random variables
PSFA = f2(Y)
assumed to be iid, independent of the
Y
- ui usually assumed to be half normal
distributed (truncated)
POLS =
α+f1(Y)
B
accounting for technical inefficiency
E
Efficiency of firm ESFA = EF/BF
exp(ui ) 
yi
ln( xi   ui ) y
 TEi 

exp( xi  )
ln xi 
y
E[ui | vi  ui ]
0
F
X
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Stochastic Frontier Analysis (SFA) (I)
Specify production (or cost) function:
1) Cobb Douglas
ln(Qi )  0  1 ln( Ki )  2 ln( Li )  (Vi  Ui )
2) Translog Functional Form
ln(Qi )   0 ln( Ki )   2 ln( Li )   3 ln( Ki ) 2   4 ln( Li ) 2
  5 ln( Ki ) ln( Li )  (Vi  U i )
Shortcoming;
Can handle only one output:
→ Aggregation
→ Distance Functions
-
The decomposition of the error term into noise and efficiency component
may be affected by the particular distributional forms specified.
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Agenda
1. Overview - Benchmarking Methodologies
2. Application in the Electricity Sector
2. Transfer to the Airports
Literatur
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Efficiency Analysis in the Electricity Distribution
1) Efficiency Analysis of German Local Distribution Utilities
2) Efficiency Analysis of East European Distribution Companies (Poland,
Hungary, Czech Republic, Slovakia) in Comparison to Germany
The Issue:
- Increased use of efficiency analysis in the regulation of network industries
- Reform of the electricity sector: Incentive based regulation
- EU Directive 2003/54/EC and German Energy Law (July 2005)
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Choice of Variables
Inputs
Outputs
LABOR: number of employees
UNITS SOLD (in MWh)
NETWORK LENGTH: approximation
for capital input (factored: high-,
medium- and low-voltage lines;
5;1.6;1)
NUMBER OF CUSTOMERS
(residential)
INVERSE DENSITY INDEX: (supplied
area in square kilometres per
inhabitants)
• Number of customers is determined by industry and households within the supply area
can be considered as a given date
• Demand of the end users is quite inelastic and must be satisfied
Output is fix, input has to be minimized
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Our Empirical Application
I) We analyze technical efficiency (no cost data is available, VDEW data 2001)
DEA is applied as main productivity analysis technique:
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Constant Returns to Scale (Variable Returns to Scale for verification)
-
Input-orientated approach
Input distance function approach with SFA for verification
II) Specify a translog functional form, general unrestricted form
Truncated normal distribution for the technical inefficiency random
variables
Specification of Battese and Coelli, 1995
Maximum likelihood method to estimate the parameters (Frontier Version
2.1, Coelli)
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Selected Results
Efficiency in %
DEA, Model 2, CRS
- German local
distribution:
100%
80%
- East German
Utilities more
efficient
60%
40%
20%
0%
1
19
37 55
73 91 109 127 145 163 181 199 217 235 253 271 289 307
utility num ber
- East European
regional
Distribution
- Poland features
by far the lowest
efficiency scores
- Scale inefficient
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Measurement of Scale Efficiency
• Difference Model 2,
DEA: VRS – CRS
70%
60%
50%
40%
30%
20%
10%
0%
301
286
271
256
241
226
211
196
181
166
151
136
121
106
91
76
61
46
16
31
• Economies of Scale
seem to be limited, “big
is not necessarily
beautiful”
1
Efficiciency Change in
percent points
Difference Results DEA, Model 2, VRS-CRS
utility number
DEA Model 1 - Scale Efficiency East European countries
Poland (1-33), Slovak Rep. (34-36), Czech Rep. (37-43), Hungary (44-47)
1,00
0,90
scale efficiency scores
0,80
0,70
0,60
• Evidence for
economies of scale in
Poland (area of
increasing returns to
scale)
0,50
0,40
0,30
0,20
0,10
0,00
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
Firm number
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• Slovakia: scale
inefficiency due to
decreasing returns to
scale
Agenda
1. Overview - Benchmarking Methodologies
2. Application in the Electricity Sector
3. Transfer to the Airports
Literatur
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Transfer to Airport Benchmarking
- Decide which methodologies to use:
Stochastic Frontier Analysis not widely used. Integrate SFA, at least for
verification and validation method
- Focus on technical efficiency or allocative efficiency?
- Dynamic analysis with panel data?
Special Issue → technical change
Panel Data Models
- Choose appropriate input and output factors
Difficult task → many activities, heterogeneous
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Literature
Aigner, Dennis J., Lovell Ashley C., Schmidt Peter, 1977. Formulation and Estimation of stochastic Frontier Production
Function Models. Journal of Econometrics 6/1, 21-37.
Christensen, L.R., Jorgensen, D.W. and Lau, L.J. 1971. Conjugate Duality and the Transcendental Logarithmic Production
Function. Econometrica 39, 225-256
Coelli, Tim, Prasada Rao, Dodla S., Battese, George E., 1998. An Introduction to Efficiency and Productivity Analysis.
Kluwer Academic Publishers, Bostron/Dordrecht/London,
Coelli, Tim, 1996. A Guide to Frontier Version 4.1: A Computer Program for Stochastic Frontier Production and Cost Function
Estimation. CEPA Working Paper 96/7, Department of Econometrics, University of New England, Armidale NSW Australia.
Estache, Antonio, Rossi Martin A., Ruzzier Christian A., 2004. The Case for International Coordination of Electricity
Regulation: Evidence from the Measurement of Efficiency in South America. Journal of Regulatory Economics 25/3, 271-295.
EBRD, Transition Report 2004, London.
Filippini, Massimo, Hrovatin, Nevenka, Zoric, Jelena, 2004. Regulation of the Slovenian Electricity Distribution Companies.
Energy Policy 32, 335-344.
Jamasb, Tooraj, Pollitt, Michael, 2003. International Benchmarking and Yardstick Regulation: An Application to European
Electricity Distribution Utilities. Energy Policy 31, 1609-1622.
Kocenda, Evzen, Cabelka, Stepan, 1999. Liberalization in the Energy Sector in the CEE-Countries: Transition and Growth.
Osteuropa-Wirtschaft 44/1, 196-225.
Shephard, Ronald W., 1970. Theory of Cost and Production Functions. Princeton University Press, Princeton.
Frontier Economics, and Consentec (2003) Netzpreisaufsicht in der Praxis, Abschlussbericht für VIK und BDI, London.
Riechmann, C. (2000) Kostensenkungsbedarf bei Deutschen Stromverteilern, Wirtschaftswelt Energie, 55, 6-8.
Schiffer, H-W. (2002) Energiemarkt Deutschland, 8. Auflage, Köln, TÜV-Verlag GmbH.
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