In this paper, we used two DEA models in order to measure efficiencies of electric power
companies; CCR and BCC models. In both models we used the same variables: input variables are
capital, labor, fuel , and other materials; and output variable is the megawatt-hour electricity sold
to the customers. In CCR4 and BCC4 models, we disaggregated the output to 4 outputs
(megawatt-hour electricity sold to the residential, commercial, industrial and other customers).
We would like to recall the model definitions before introducing empirical results.
CCR: The efficiency level of a DMU (a company) by using CCR model with one output (Totalaggregated
megawatt- hours sold to the end users).
CCR4: The efficiency level of a DMU (a company) by using CCR model with 4 outputs
(megawatt-hours sold to the residential, commercial, industrial and other end users).
BCC: The efficiency level of a DMU (a company) by using BCC model with one output (Totalaggregated
megawatt- hours sold to the end users).
BCC4: The efficiency level of a DMU (a company) by using BCC model with 4 outputs (megawatthours
sold to the residential, commercial, industrial and other end users).
DCCR: The growth rate of efficiency level of DMU (company), measured by CCR model with
one output (Total-aggregated megawatt- hours sold to the end users).
DCCR4: The growth rate of efficiency level of DMU (company), measured by CCR model with
4 outputs (megawatt-hours sold to the residential, commercial, industrial and other end users).
DBCC: The growth rate of efficiency level of DMU (company), measured by BCC model with
one output (Total-aggregated megawatt- hours sold to the end users).
DBCC4: The growth rate of efficiency level of DMU (company), measured by CCR model with
4 outputs (megawatt-hours sold to the residential, commercial, industrial and other end users).
We used IDEAS software to solve the constrained optimization problems describing our model.
IDEAS has three basic steps to perform the DEA analysis: The first step is the entering input output
data in a spreadsheet; The second step is that selecting the model (CCR, BCC etc) type and
orientation (input or output). In the last stage, the analysis is performed, and the results are
obtained. Since the DEA measures efficiencies of DMUs by comparing each indivudual DMU
(company), we had to run the program separately for each time period (1986-1990, 1991-1995
and 1996-2000). The number of DMUs, number of efficient units and average iteration in solving
the problem is shown in Table 2.
Table 2. General Statistics of Performing DEA Models
The number of DMUs changes from period to period because of missing data points for some
utility companies. As we mentioned earlier, the models with four outputs (CCR4 and BCC4)
almost double the efficient number of companies, comparing with the models with one output
models (CCR and BCC); respectively. The reason is that some utility companies might focus on
some individual customers (residential, commercial, industrial and other). The number of iteration
increases when the number of input and output increase.
We reached the feasible solutions while running the program. Z-value in the objective function
represents the efficiency level of a particular DMU. In the emprical results section of our paper,
we denotes it as CCR, CCR4, BCC and BCC4. The slack variables are the necessary input
reduction for a DMU in order to reach an efficient company as shown in Table 9.
4.1. The DEA Results
Descriptive statistics of DEA scores are shown in Table 3. Because of the model differences and
output variable differences between single and multiple output sets, the means of the efficiency
Periods
DEA
Model
Number
of DMUs
Solved
Number of
Efficient
DMUs
Average
Number of
Iterations
CCR 370 19 11
CCR4 370 48 21
BCC 370 48 15
BCC4 370 93 29
CCR 365 25 11
CCR4 365 56 26
BCC 365 50 16
BCC4 365 115 41
CCR 330 27 8
CCR4 330 54 18
BCC 330 50 15
BCC4 330 106 32
1986-1990
1991-1995
1996-2000
scores and the growths are different. Kurtosis and skewness values are away from the normal
distribution assumptions. The differences of CCR and CCR4 or BCC and BCC4 models are based
on the output selection. The CCR and the BCC models have a single output variable of aggregated
output; on the other hand, the CCR4 and the BCC4 models have four outputs; MWh sold to the
residential, commercial, industrial, and other consumers.
Table 3. Descriptive Statistics of DEA Results
Table 4 shows the 15 years averages of the DEA scores and growth rates. According to the DEA
Scores, Idaho Power, Kentucky Power, Kentucky Utilities, Entergy New Orleans, and Puget Sound
have the best DEA scores in 15 year time period. However, Arizona Public Service Company,
Consolidated Edison Company of New York, Duquesne Light and KeySpan Generation had the
lowest DEA scores in overall.
In the growth of DEA point of view, Commonwealth Edison, Ohio Edison, Orange & Rockland,
Pacific Gas & Electric, and the United Illuminating Company have the biggest growth in terms of
DEA efficiencies; on the other hand, Bangor Hydro-Electric, Black Hills Power, Cleco Power, the
Empire District Electric, Hawaiian Electric, Southern Indiana Gas & Electric, and Wisconsin Power
& Light have the lowest DEA growth rates.
Table 5 and Table 6 show the average DEA scores and growth rates, respectively, in three time
periods among overall industry, large, and small company perspectives. On average, the DEA
scores did not change very much; however, the growth rates in the 1991-1995 period are more
less than other two periods. The efficiency scores in the BCC model are higher than the CCR
model because of the variable returns to scale assumption and convex hull frontier line. Both DEA
models with four output variables score higher than the corresponding single output DEA models.
Mean Std. Dev. Kurtosis Skewness
CCR 0.7138 0.1679 -0.8233 -0.0568
CCR4 0.7964 0.1575 -0.9770 -0.3221
BCC 0.7892 0.1672 -0.7306 -0.5076
BCC4 0.8698 0.1452 0.0480 -1.0479
DCCR 0.0129 0.1006 21.1642 0.2058
DCCR4 0.0114 0.0881 8.9795 0.9154
DBCC 0.0103 0.0938 8.9200 1.0282
DBCC4 0.0082 0.0830 9.8075 0.8489
Table 4. 15-Year-Averages of DEA Scores and Growth Rates
DEA Scores DEA Growth Rates
UTILITY CCR CCR4 BCC BCC4 DCCR DCCR4 DBCC DBCC4
Alabama Power Co 0.68 0.72 0.90 0.93 0.0040 -0.0001 0.0083 0.0091
Appalachian Power Company 0.87 0.93 0.97 0.98 0.0010 0.0022 0.0083 0.0079
Arizona Public Service Company 0.47 0.57 0.54 0.69 0.0237 0.0233 0.0242 0.0125
Entergy Arkansas, Inc. 0.65 0.67 0.66 0.68 0.0229 0.0240 0.0219 0.0212
Baltimore Gas and Electric Company 0.66 0.77 0.77 0.92 0.0282 0.0207 0.0218 0.0101
Bangor Hydro-Electric Co 0.75 0.90 0.79 0.92 -0.0023 -0.0078 -0.0096 -0.0088
Black Hills Power, Inc. 0.63 0.69 0.85 0.91 -0.0146 -0.0114 -0.0071 -0.0062
Boston Edison Company 0.51 0.71 0.52 0.83 0.0198 0.0123 0.0224 -0.0063
Carolina Power & Light Company 0.61 0.65 0.80 0.86 0.0172 0.0127 0.0173 0.0119
Central Hudson Gas & Elec Corp 0.52 0.58 0.55 0.61 -0.0028 -0.0053 -0.0051 -0.0088
Central Illinois Light Company 0.78 0.82 0.82 0.85 0.0052 0.0142 0.0049 0.0125
Central Illinois Public Service Company 0.75 0.80 0.76 0.80 0.0374 0.0282 0.0360 0.0280
Cleco Power LLC 0.71 0.83 0.73 0.86 -0.0041 -0.0047 -0.0056 -0.0076
Central Maine Power Company 0.79 0.84 0.82 0.86 0.0202 0.0180 0.0195 0.0180
Central Power and Light Company 0.77 0.83 0.79 0.84 -0.0017 0.0023 -0.0043 0.0024
Central Vermont Public Service Corporation 0.73 0.89 0.80 0.91 0.0176 0.0143 -0.0011 0.0020
Cincinnati Gas & Electric Company, The 0.68 0.78 0.73 0.82 -0.0054 -0.0040 0.0032 0.0011
Citizens Utilities Co 0.70 0.84 0.97 0.98 0.0917 0.0576 0.0027 0.0050
Commonwealth Edison Company 0.55 0.69 0.83 0.99 0.0707 0.0553 0.0532 0.0072
Consolidated Edison Company of New York, Inc. 0.37 0.59 0.45 0.82 0.0081 -0.0040 0.0045 -0.0196
Consumers Energy Company 0.74 0.75 0.83 0.91 0.0042 0.0063 -0.0005 0.0118
The Dayton Power and Light Company 0.65 0.80 0.66 0.82 0.0093 0.0035 0.0095 0.0079
Delmarva Power & Light Company 0.65 0.68 0.66 0.68 0.0161 0.0170 0.0156 0.0183
The Detroit Edison Company 0.60 0.65 0.71 0.84 0.0220 0.0179 0.0143 0.0162
Duke Energy Corporation 0.77 0.78 0.99 0.99 0.0026 0.0042 0.0030 0.0027
Duquesne Light Company 0.51 0.61 0.52 0.66 0.0348 0.0151 0.0337 0.0075
The Empire District Electric Company 0.81 0.91 0.89 0.96 -0.0098 -0.0083 -0.0115 -0.0057
Florida Power Corporation 0.76 0.95 0.83 0.98 0.0172 0.0084 0.0141 0.0102
Florida Power & Light Company 0.80 0.92 0.96 0.98 0.0278 0.0138 0.0083 0.0062
Green Mountain Power Corporation 0.86 1.00 0.97 1.00 0.0270 0.0008 0.0000 0.0000
Gulf Power Company 0.88 0.97 0.89 0.97 0.0021 0.0046 0.0029 0.0043
Hawaiian Electric Company, Inc. 0.61 0.63 0.61 0.64 -0.0396 -0.0320 -0.0396 -0.0329
Reliant Energy HL&P 0.72 0.75 0.90 0.99 -0.0011 0.0036 -0.0051 -0.0056
Idaho Power Company 1.00 1.00 1.00 1.00 0.0000 0.0000 0.0000 0.0000
Illinois Power Company 0.67 0.76 0.71 0.81 0.0122 0.0116 0.0077 0.0078
Indiana Michigan Power Company 0.71 0.76 0.77 0.82 0.0149 0.0136 0.0164 0.0220
Indianapolis Power & Light Company 0.81 0.89 0.82 0.92 -0.0060 0.0024 -0.0069 0.0038
Interstate Power Company 0.81 0.86 0.84 0.90 0.0012 0.0127 -0.0011 0.0076
Kentucky Power Company 0.97 0.99 0.98 1.00 0.0034 0.0026 0.0023 0.0012
Kentucky Utilities Company 0.95 0.98 0.96 0.98 0.0131 0.0083 0.0127 0.0080
KeySpan Generation, LLC 0.45 0.55 0.48 0.59 0.0238 0.0286 0.0264 0.0305
Entergy Louisiana, Inc. 0.92 0.95 0.95 0.98 0.0081 0.0079 0.0110 0.0053
Madison Gas and Electric Company 0.73 0.99 0.81 1.00 0.0093 0.0026 0.0071 0.0014
Maine Public Service Company 0.86 0.94 0.98 0.99 0.0217 0.0009 0.0000 0.0000
Minnesota Power, Inc. 0.90 0.96 0.90 0.96 0.0199 0.0241 0.0179 0.0212
Entergy Mississippi, Inc. 0.85 0.89 0.91 0.94 0.0324 0.0211 0.0386 0.0223
Montana Power Company, The 0.90 0.96 0.92 0.96 0.0000 0.0000 0.0000 0.0000
Entergy New Orleans, Inc. 0.94 1.00 0.95 1.00 0.0084 0.0000 0.0076 0.0000
Niagara Mohawk Power Corporation 0.57 0.60 0.70 0.80 0.0158 0.0197 -0.0002 -0.0046
Northern Indiana Public Service Company 0.64 0.78 0.65 0.86 0.0217 0.0216 0.0239 0.0240
Northwestern Public Service 0.57 0.70 0.97 0.98 0.0195 0.0213 0.0078 0.0072
Ohio Edison Company 0.56 0.58 0.60 0.62 0.0446 0.0438 0.0416 0.0416
Oklahoma Gas and Electric Company 0.84 0.92 0.89 0.99 0.0104 0.0077 0.0098 0.0053
Orange and Rockland Utilities, Inc. 0.47 0.51 0.52 0.55 0.0599 0.0588 0.0483 0.0475
Otter Tail Power Company 0.76 0.83 0.84 0.89 0.0138 0.0111 0.0179 0.0137
Pacific Gas & Electric Co 0.53 0.63 0.85 0.98 0.0462 0.0597 0.0415 0.0175
PPL Electric Utilities Corporation 0.58 0.61 0.73 0.77 0.0226 0.0221 0.0176 0.0142
Potomac Electric Power Company 0.64 0.89 0.71 0.97 0.0227 0.0139 0.0196 0.0023
Public Service Company of New Mexico 0.47 0.65 0.51 0.66 0.0226 0.0154 0.0211 0.0164
Public Service Electric and Gas Company 0.52 0.73 0.75 0.91 0.0315 0.0375 0.0529 0.0241
Puget Sound Energy, Inc. 0.99 1.00 0.99 1.00 0.0027 0.0000 0.0016 0.0000
Rochester Gas and Electric Corporation 0.49 0.59 0.51 0.65 0.0503 0.0415 0.0420 0.0787
South Carolina Electric & Gas Company 0.67 0.70 0.70 0.74 -0.0050 -0.0034 0.0001 0.0003
Southern Indiana Gas and Electric Company 0.87 0.92 0.93 0.96 -0.0122 -0.0023 -0.0084 0.0000
Tampa Electric Company 0.68 0.80 0.70 0.83 -0.0002 -0.0019 0.0012 -0.0028
TXU Electric Company 0.78 0.92 0.99 1.00 -0.0021 0.0072 0.0000 0.0000
Texas-New Mexico Power Company 0.83 0.89 0.84 0.90 -0.0073 0.0000 -0.0072 0.0000
Tucson Electric Power Company 0.59 0.64 0.62 0.66 0.0032 0.0029 0.0006 0.0010
Union Electric Co 0.68 0.73 0.83 0.90 0.0194 0.0214 0.0133 0.0109
The United Illuminating Company 0.47 0.54 0.49 0.56 0.0551 0.0466 0.0485 0.0460
West Texas Utilities Company 0.80 0.94 0.86 0.96 0.0012 0.0221 0.0056 0.0191
Wisconsin Electric Power Company 0.82 0.84 0.91 0.97 -0.0042 -0.0051 0.0035 0.0086
Table 5. Average DEA Scores in Three Time Periods among All, Large, and Small Companies
Table 6. Average DEA Growth Rates in Three Time Periods among All, Large, and Small Companies
As a next step we focus on whether these DEA scores and growth rates are statistically
significantly different over the three time periods, 1986-1990, 1991-1995, and 1996-2000. As
mentioned earlier, we did not find very high DEA score changes (growth) between time periods.
The null hypothesis that there are no significant differences in DEA scores in three time periods.
This null hypothesis has been tested for all 74 utility companies, large, and small companies. The
insignificant p-values of the DEA scores support the previous thoughts of the average DEA scores
are not different in three time periods. In Table 7, the growth of all industry and large companies
in the CCR and BCC models are significantly different in different time periods.
DCCR DCCR4 DBCC DBCC4
Overall 1986-1990 0.0135 0.0177 0.0177 0.0158
1991-1995 0.0114 0.0041 0.0035 0.0016
1996-2000 0.0225 0.0185 0.0154 0.0098
Large Companies 1986-1990 0.0181 0.0183 0.0239 0.0196
1991-1995 0.0034 0.0048 0.0038 0.0006
1996-2000 0.0231 0.0169 0.0136 0.0079
Small Companies 1986-1990 0.0065 0.0165 0.0082 0.0102
1991-1995 0.0160 0.0021 0.0031 0.0036
1996-2000 0.0093 0.0104 0.0077 0.0096
CCR CCR4 BCC BCC4
Overall 1986-1990 0.70 0.79 0.79 0.87
1991-1995 0.72 0.80 0.79 0.88
1996-2000 0.72 0.80 0.79 0.86
Large Companies 1986-1990 0.68 0.76 0.77 0.87
1991-1995 0.68 0.77 0.77 0.87
1996-2000 0.70 0.78 0.78 0.86
Small Companies 1986-1990 0.73 0.83 0.81 0.87
1991-1995 0.77 0.84 0.82 0.89
1996-2000 0.76 0.84 0.81 0.88
Table 7. P-Value Test Statistics* of DEA Efficiency Scores in three Time Periods among All, Large, and
Small Companies
After analyzing the companies as whole industry, large, and small individually, we investigate
whether there is a significant difference between large and small companies in each time period.
Table 8 shows that the DEA efficiencies of large and small companies are significantly different than
each other in each of the time periods, except for the BCC model with four output set. The
average efficiency scores of small companies are higher than large companies. The lower efficiency
scores of large companies could come from more bureaucracy in the large companies.
Table 8. ANOVA Table for Large/Small Company Differences in three Time Periods
On the other hand, the growths of these DEA models are not significantly different at the 0.05
level. We conclude that the efficiency score changes of large and small companies had the same
growth rate in the same time period. However, large and small company efficiency scores are
significantly different in the same time periods, except for the four-output-DEA model. Small utility
companies are more efficient than large companies. Deregulation of the electricity market didn't
make any difference on large and small company behavior of efficiency.
The output slack variables show how much additional output could be produced with the efficient
CCR 1986-1990 0.274 10.121 0.002
CCR 1991-1995 0.689 25.056 0.000
CCR 1996-2000 0.279 10.541 0.001
CCR4 1986-1990 0.391 15.739 0.000
CCR4 1991-1995 0.554 23.044 0.000
CCR4 1996-2000 0.329 13.638 0.000
BCC 1986-1990 0.198 7.275 0.007
BCC 1991-1995 0.244 8.702 0.003
BCC 1996-2000 0.107 3.915 0.049
BCC4 1986-1990 0.001 0.064 0.800
BCC4 1991-1995 0.032 1.461 0.228
BCC4 1996-2000 0.022 0.991 0.320
Companies
All Large Small
CCR 0.049 0.069 0.066
CCR4 0.494 0.398 0.562
BCC 0.270 0.856 0.122
BCC4 0.326 0.477 0.534
DCCR 0.000 0.000 0.130
DCCR4 0.001 0.004 0.120
DBCC 0.003 0.010 0.167
DBCC4 0.002 0.019 0.089
* At the 0.05 significance level
level of input; and the input slack variables reveals how much proportional deduction is needed to
achieve efficiency level. Major inefficiencies came from labor (Emp) and capital (NUP) inputs.
Inefficiencies went down in the second period, 1991-1995, but material inefficiency went up. Labor
and fuel inefficiencies went down during this time period (Table 8).
Table 9. Average Slack Variables by Time Periods
OUTPUT SLACK VARIABLES INPUT SLACK VARIABLES
,where RMWHS, CMWHS, IMWHS, OMWHS are the electricity sold to the residential,
commercial, industrial and other consumers as megawatthours, respectively; and Emp, NUP, QF
and QO are the number of employees (Labor) worked at the electricity generation units, net
utility plant (capital), quantity of fuel, and quantity of other expenditures including material,
respectively.
4.2. Regulatory Effect on Efficiency Results
We used two different reports in order to measure regulatory effect on efficiency; one is the
rankings of state regulatory commissions, published by Duff & Phelps, called friendliness; and the
other is the report of what the states' position is in deregulation and restructuring, called the Retail
Energy Deregulation (RED) Index. The difference between two reports is that the former
measures the state commission's success in regulating the industry, and latter is the measurement
of efforts in deregulating the electric power industry in the U.S.
Table 10 shows that the DEA scores (CCR, CCR4, and BCC, BCC4) are significantly different
between more friendly and less friendly regulatory environments on average. The average
productivity and efficiency levels of electric power companies in high friendliness states to the end
users are higher than in a low friendliness states. It means tighter the state commission in a
regulated environment is, higher the productivity and efficiency scores are. We do not detect any
significant difference between more friendly and less friendly environments on average growth of
productivity and efficiency.
Years RMWHS CMWHS IMWHS OMWHS Emp NUP QF QO
1986-1990 3.55 1.09 1.39 0.47 220.09 1995.59 100.56 60.28
1991-1995 1.90 1.38 0.69 0.46 235.28 1261.25 59.46 117.30
1996-2000 0.18 0.78 2.62 1.42 166.31 1671.23 33.99 49.19
Table 10. Test Statistics for Productivity Differences in More Friendly and Less Friendly States,
1986-1991
In the deregulation era from 1997 to 2000, in addition to the DEA scores, the changes in DEA
(DCCR, DCCR4, DBCC, and DBCC4) of the utilities in higher the RED index scored states are
significantly different than the utilities in lower RED index scored states (Table 11). The utility
companies which operate in highly regulated states have higher productivity level and growth on
average. This could be due towo reasons: early stages of the deregulation may cause the
uncertainty for the near future and the differences of states in terms of accepting the level of
restructuring.
Table 11. Test Statistics for Productivity Differences in High and Low RED Index Scored States, 1997-
2000
Finally, we investigate whether there is a significant difference in between two time periods, 1986-
1991 and 1997-2000 on average. Except for the BCC and BCC4 models of DEA, the other
efficiency level scores are significantly different in two time periods on average (Table 12). The
utility companies in a deregulated time period (1997-2000) are more productive than in a
regulated time period of 1986-1991. Even though not all states in the U.S. accepted to change the
market structure in electric power industry, they scored better in the period of 1997-2000 on
average when we compare with the time period 1986-1991. This could be because of
technological change, political influences, and financial reasons. Technological change in the electric
power industry does not occur very fast, but solving the technical difficulties and learning-by-doing
could be an effect on high productivity.
squares F P-value
CCR 0.2590 9.395 0.002
CCR4 0.2680 11.352 0.001
BCC 0.2530 9.272 0.002
BCC4 0.1730 8.978 0.003
DCCR 0.0012 0.095 0.758
DCCR4 0.0016 0.203 0.653
DBCC 0.0005 0.056 0.813
DBCC4 0.0000 0.003 0.958
squares F P-value
CCR 0.2590 9.395 0.002
CCR4 0.2680 11.352 0.001
BCC 0.2530 9.272 0.002
BCC4 0.1730 8.978 0.003
DCCR 0.0012 0.095 0.758
DCCR4 0.0016 0.203 0.653
DBCC 0.0005 0.056 0.813
DBCC4 0.0000 0.003 0.958
Table 12. Test Statistics for Productivity Differences in Two Time Periods (1986-1991 and 1997-2000)
The states in regulated industries are under considerable pressure in their decision making
processes as whether to change the market structure to a competitive market. Since those states
have the lowest electricity rates in the U.S. electricity market, the legislators are more favor on
delaying to accept the competitiveness in the electric power market. In addition, possibility of
changing the market structure in the near future could cause an increase in productivity of power
companies to compete with other companies if any market structure changes.
5. Conclusion
In this study, efficiencies of the U.S. electric power companies were measured by using data
envelopment analysis (DEA). We used the CCR model, introduced by Charnes et al. (1978) and
the BCC model, introduced by Banker et al. (1984) in order to measure technical efficiencies of
the utility companies.
DEA measures the efficiency of the decision making units (DMUs) by a linear programming
technique to draw a piecewise frontier line. The DEA models that we used in this study the CCR
and the BCC models have two possible orientation; input and output. We used input oriented
DEA models with two different perspectives; one is the single output of aggregated all MWh sales
to the customer, and the other one is the outputs of MWh sold to the residential, commercial,
industrial, and other consumers.
We found that there is no significant difference in terms of efficiency scores, except for overall
technical efficiency (CCR) for all companies in the industry; however, the growth rates are
significant for the industry and large companies in each time period (1986-1990, 1991-1995, and
1996-2000). This means that technical efficiency growth may result in significant differences in
average efficiency in the near future.
The efficiency scores of large and small companies are significant in a particular time period on
average, but, we cannot conclude the significant differences of the growth rates among large and
small companies.
The changes in output mix are not subject to regulatory control. Utility companies have their
squares F P-value
CCR 0.2290 8.328 0.004
CCR4 0.1390 5.786 0.016
BCC 0.0404 1.478 0.224
BCC4 0.0184 0.919 0.338
DCCR 0.1340 11.051 0.001
DCCR4 0.1270 14.903 0.000
DBCC 0.1130 11.861 0.001
DBCC4 0.0791 10.791 0.001
unique customer choices of residential, commercial, industrial, and other customer type. In this
study, we used two types of output mix in the efficiency analysis: one is the aggregated output,
and the other is MWh sold to the residential, commercial, industrial, and other consumers. In DEA,
the efficiency model with four outputs has more efficient decision making units (DMUs) as we
expected. The findings of efficiency differences between different time periods show that the
efficiency growth in single output models (CCR and BCC) have a positive difference in the
deregulated time period when we compare with other two time periods; however, the efficiency
model with four outputs (CCR4 and BCC4) has the positive change in the deregulated time
period, comparing with the second time period. This may be interpreted as utility companies focus
on some customer types; therefore, when we disaggragate the sales of the all customer to the
individual customers as residential, commercial, industrial, and other, more utility companies are
found efficient.
Regulatory agency influence is very vital in the electric power industry, because, state and federal
commissions have big impact on price, environmental, loading, and investment of the power
companies. Although, the utility companies have higher efficiency scores between 1996 and 2000,
we looked at which companies made the difference. As mentioned earlier, half of the states did
not pass the deregulation laws. The states which accepted the restructuring efforts have higher
electricity cost to the consumers. Our findings support that the higher efficiency in time period of
1996-2000 occured because of the utility companies which are still highly regulated in their states.
There may be small increase on efficiency when the states deregulate the electric power industry,
but, it is too early to make a strong conclusion about the benefits of the restructuring are achieved.
