1 IJAEST Volume No 1 Issue No 2 a Parametric and Non Parametric Approach for Performance Appraisal of Indian Power Generating Companies

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Shafali Jain,Research scholar, ElectricalDepartment, MANIT-Bhopal,India-462003

[email protected],

Tripta Thakur,Associate Professor, ElectricalDepartment, MANIT-Bhopal,India-462003

[email protected],

Arun Shandilya,Professor, Electrical Department,

MANIT-Bhopal

India-462003

[email protected]

Abstract — This work aims at evaluating the productivity and

efficiency analysis of Indian electricity generation companies

(GENCOs) for the time period 2002-03 to 2007-08.The

performance analysis or benchmarking of generation companies

is evaluated using two methodologies- a non-parametricapproach to frontier analysis commonly known as Data

Envelopment Analysis (DEA) and a parametric approach known

as Stochastic Frontier Analysis (SFA) using panel data of six

years. Both methodology evaluates and uses total factor

productivity (TFP) change as a benchmarking gauge. The total

TFP change is decomposed into technical change and efficiency

change and the efficiency analysis is investigated taking scale

effect into account so as to separate pure technical efficiency and

scale efficiency. In addition, the SFA allows analyzing the effect

of restructuring in Indian electric power sector. This work in the

field of generation sector of power will identify whether the

companies/utilities are efficient or not, create benchmark for

inefficient utilities by identifying their shortcomings and set the

targets. This benchmarking will pressurize the generationcompanies to provide better services to customers. Such an

analysis would offer valuable lessons to ensure that the new

structure being adopted is better than the regulatory and

legislative framework designed a few decades back. Efficiency

measurement can form the core for introduction of the incentive

based regulatory regimes and in promoting yardstick

competition amongst a number of utilities.

Index Terms — Benchmarking, Generation companies,

Restructuring, Return to scale, Scale efficiency, Technical

efficiency, Total factor productivity change.

I. I NTRODUCTION

The electric power industry which had been maintained as a

vertically integrated system in the past, the restructuring of

electric power industry in many countries in the world has

been performed in the way so as to raise efficiency by

introducing competition [1]. The restructuring of electric

power industry in India kept pace with the worldwide trend

and started with the purpose of decreasing the electricity price

and to bridge the demand-supply gap through the introduction

of competition and improvement of efficiency, but did not

proceed as it planned. At this stage it is essential to have

documentation of the effects of such reforms. Such

documentation has been done in developed countries,

however from a few case studies: the experience of

developing countries remains much less researched. This

documentation can be made by performance evaluation for the

structural change in electric power industry. We will be able

to find out the direction of the structural change in electric

power industry in India by analyzing the efficiency level of

power generation companies in India. Such a review of

performance of existing utilities is a need for the success of

any reform program. Based on efficiency analysis,

benchmarks can be set, and targets for improvement may be

identified. The efficiency evaluation is also necessary for

generating competition and for sector regulation.

Efficiency measurement can form the core for introduction

of the incentive based regulatory regimes and in promoting

yardstick competition amongst a number of utilities. Since the

country has not reached a mature stage in the development of

electricity infrastructure unlike the case of developed

countries, there is a very good opportunity to learn from

mistakes and adopt a suitable model for the country. Internal

efficiency improvements are always win-win options for the

existing utilities as benchmarking the operational and financial

aspects can free up resources, which can bring down the

overall resource requirement for utilities [4]. All of this would

however, require application of formal benchmarking

techniques to evaluate performance at regular intervals.

Benchmarking is the practice of comparing indicators of

performance. Benchmarking has proven to be powerful way in

pressurizing utilities to provide better services to customers.

The performance evaluation can be through by a number of

approaches. Among many possible efficiency measurement

methods, Data Envelopment Analysis (DEA) and Stochastic

Frontier analysis (SFA) are most widely used for

benchmarking. DEA has been used especially for the

complicated systems with lots of inputs and outputs since its

introduction by Charnes, Cooper and Rhodes in 1978 based

on previous work by Farrell on production efficiency. This

A Parametric and Non-Parametric Approach for Performance

Appraisal of Indian Power Generating Companies

Shafali Jain et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIESVol No. 1, Issue No. 2, 064 - 078

ISSN: 2230-7818 @ 2010 http://www.ijaest.iserp.org. All rights Reserved. Page 64



paper presents a case study which provides productivity and

efficiency analysis of generation utilities for the period 2003

to 2008, so that they can rank themselves, identify their

shortcomings, set targets and tries to achieve those targets.

II. METHODOLOGY

1) DEA

In DEA, the data are enveloped by a piecewise linear frontier in such a way that radial distances to the frontier are

minimized. The basic model of DEA, the CCR model, was

proposed by Charnes, Cooper and Rhodes (1978). The CCR

model was formulated as a linear programming (LP) problem

concerned with, say, n decision making units (DMUs), electric

utilities in the present analysis, which use varying quantities

and combination of inputs Xi (i=1,…s) to produce varying

quantities and combinations of outputs Y j (j=1,…m).The most

common form of measurement of efficiency in case of a single

output and single input framework is the ratio output/input [8].

In case of multiple outputs and inputs, it is a weighted

combination of outputs to weighted combination of inputs,

known as virtual outputs and virtual inputs, where the weightsare derived from data instead of being fixed in advance.

Efficiency of each DMU is measured and hence n

optimization exercises are carried out. The following problem

is solved to obtain the values of input weights (vi) and output

weights (ur ) as variables:

max ioii

ror r o

xv

yu

s.t. 1................

................

11

11

mjm j

sjs j

xv xv

yu yu

(1)

where j=1,…,n

v1, v

2,…. v

m≥0,

u1, u2,…. us ≥0,

The constraints imply that the ratio of ―virtualoutput‖ to

―virtualinput‖ should not exceed 1 for every DMU. The

objective is to obtain weights vi and ur that maximize the ratio

for DMUo. The optimal objective value θ* is at most 1.

However, multiple solutions might exist for the above

problem. Hence it is transformed into a linear programming

problem using transformation developed by Charnes and

Cooper. To allow for variable returns to scale Banker,

Charnes and Cooper (1984) added the convexity constraint to

the optimization problem and proposed variable return model

(BCC).

2) SFA

The stochastic Frontier approach (SFA) specifies a functional

form of the cost, profit or production relationship among

inputs, outputs, and environmental factors and allow for

random error. Hence it is also called parametric approach. It

gives composite error model decomposed into two terms, a

symmetric component representing statistical noise and an

asymmetric one representing inefficiency.

A stochastic production function defined by Battese and

Coelli (1995)

it it it it uvt x f y In ,, (2)

Whereit

is the output of the i-th firm in the t-th year;

it x denotes a (1×K) vector of inputs;

. is a functional form;

t is a time trend representing technical change; is a vector of unknown parameters to be estimated;

the svit are random errors, to be independent and identically

distributed (i.i.d) with mean zero and variance 2

v ,

independent of the uits;

the suit are the technical inefficiency effects which are

assumed to be defined by

iit uT t u exp ,

i =1,2,……N; t=1,2,……T and η is scalar parameter which

accounts for time-varying effects.

The technical efficiency measure are obtained as

it

it

it e

u

E TE exp (3)

where eit = vit - uit is the total error term which can be used to

calculate the efficiency change component.

3) Malmquist Productivity Index (MPI)

The DEA and SFA techniques can be used to calculate

Malmquist Index of productivity change over time, assuming

the underlying technology is constant returns to scale (CRS)

(Coelli et al., 1998). The Malmquist total factor productivity

(TFP) index measures the TFP change between two data

points by calculating the ratio of the distances of each point

relative to a common technology. The distance function in

terms of the above analysis can be defined as{Dt(xt,yt)}-1 = θt

2.1 SFA Model Specification

The translogarithmic and the cobb-douglas production

functions are the two most common functional forms, which

have been used, in empirical studies on production, including

frontier analysis [13] The cobb-douglas and translog

production function models are defined in equations (4) and

(5)

it it t it it it uvt X X Y )ln()ln()ln(22110

(4)

it it tt t it it

it t it it it

it it it it

uvt t t X

t X X X X

X X X Y

2

22

112112

2

222

2

11122110

)ln(

)ln()]ln()[ln()][ln(

)][ln()ln()ln()ln(

(5)

The above model does not include the environmental

variables. As pointed out by Coelli, Perelman and Romano

(1999), measuring net efficiency is an important matter as it

allows one to predict how companies would be ranked if they

were able to work in equaling environments. Therefore, the





most general function to be estimated including six additional

environmental variables.

After including environmental variables, the model becomes

it it

it it

it t it it it

uvFUELTYPE

REGULATION Z Z

Z t X X Y

)(

)()ln()ln(

)ln()ln()ln()ln(

5

43322

1122110

(7)

it it it

it it tt t it it

it t it it it

it it it it

uvFUELTYPE REGULATION Z

Z Z t t t X

t X X X X

X X X Y

)()()ln(

)ln()ln()ln(

)ln()]ln()[ln()][ln(

)][ln()ln()ln()ln(

5433

2211

2

22

112112

2

222

2

11122110

(8)

Where ln = logarithm

it Y = units generated by the power station (GWh)

it X 1= installed capacity (MW)

it X 2 = coal consumption (MT)

it Z 1 = plant load factor (%)

it Z 2 = Energy losses (GWh)

it Z 3 = Per capita consumption (GWh)

T = time trend

i and

i are unknown parameters to be estimated.

The two dummies are included in the model

namely, REGULATION and FUELTYPE .

,1 REGULATION if utility is unbundled, otherwise 0;

,1FUELTYPE

if GENCO uses coal as primary fuel,

otherwise 0;

2.2 Hypothesis testing

As suggested by Coelli (1996), the alternative models would

be estimated and the preferred model would be selected using

Likelihood Ratio (LR) test. The generalized likelihood ratio

test was conducted on certain hypotheses relating to the

estimated parameters such as: (1) The production function is

specified by a Cobb-Douglas functional form (that is, H0: β jk

=0); (2) There is absence of inefficiency effects that is there is

stochastic effects in the production (H0: γ = 0); (3) The half normal model is adequate representation of the data (H0: µ =

0); (4) Technically inefficiency effects are absent from the

production function model i.e. model is equivalent to the

average response function (Full Frontier Model), which can be

efficiently estimated by ordinary least square (OLS)

regression (H0: γ = δ0=δ1= δ2= δ3= δ4= δ5= δ6= 0); (5) Panel

data is not applicable to the model (H0: η = 0).

The final hypothesis although not tested with generalized

likelihood ratio test but based on the assumption, that the

selected Cobb-Douglas functional form is characterized by

constant return to scale and fixed elasticity of output with

respect to production inputs. The generalized likelihood ratio

test, which is defined by the test statistic, chi-square (χ 2) is

defined as:

χ 2=-2[L(H0)–L(Ha)]

(9)

The χ 2

has a mixed chi-square distribution with the degree of freedom equal to the number of parameters excluded in the

restricted model; L (H0) is the log – likelihood value of the

restricted model. While L (Ha) is the Log- likelihood value of

the un-restricted model. Maximum likelihood estimation

procedure is used to estimate the parameters of the stochastic

frontier equation 1. The parameters to be estimated include β

and variance parameters such as σ2 = σu2 + σv

2 and γ = σu2 /σ2.

Where σ2 is the sum of the error variance, while γ measures

the total variation of output from the frontier attributed to the

existence of random noise or inefficiency as γ is bounded

between zero and one, where if γ = 0, inefficiency is not

present, hence deviation from the frontier is entirely due to

random noise and if γ = 1, indicates that the deviation is dueentirely to inefficiency (Battese & Coelli, 1995). The

FRONTIER 4.1 version (Coelli, 1996) was used to obtain the

maximum likelihood estimates (MLE) for the study.

2.3 Input-output selection and data source

There can be a number of input/output variables for evaluating

the efficiency of electric utilities. The most important job in

this efficiency analysis is the right selection of inputs and

outputs. No universally applicable rational template is

available for selection of variables. In the context of efficiency

measurement, the inputs must reflect the resources used and

the outputs chosen must represent the activity levels of theutilities. A study of standard literature reveals significant

insights into the choice of variables. The most widely used

variables based on international experience have been outlined

in the literature. Input variables chosen has been shown in

table I. DEA and SFA were used to derive the benchmarks

based on the comparison of the 30 generation companies

(GENCOs) in which 8 entities were the SEBs, 7 entities

comprised various electricity departments (EDs), and 15

entities comprised the unbundled state-owned electric utilities

(SOEUs). The physical data for various states were obtained

for the different years from ―General Review‖ published by

Central Electricity Authority (CEA) [10].

3.1 DEA based MPI

Following Fare et al. (1994), the Malmquist input oriented

TFP change index between period s and period t is given by:

2/1

,

,

,

,

,

,),,,(

ss

t

i

ss

s

i

t t

t

i

t t

s

i

t t

s

i

t t

t

it t ssi

x yd

x yd

x yd

x yd

x yd

x yd x y x ym

where the first ratio on the right hand side measured change in

efficiency between periods s and t. The remaining part of the

index in the equation measures technical change, so that

tfpch = effch × techch





t t

s

i

t t

t

i

x yd

x yd effch

,

,

2/1

,

,

,

,

ss

t

i

ss

s

i

t t

t

i

t t

s

i

x yd

x yd

x yd

x yd techch

TABLE I

PERFORMANCE PARAMETERS

DEA model SFA model

Inputs Output Inputs Output

Installed capacity (MW) Units generated (GWh) Installed capacity (MW) Units generated (GWh)

Coal consumption (MT) Coal consumption (MT) Environmental variables

Plant load factor (%)

System losses (GWh)

Per capita consumption

(GWh)

Dummy Regulation

Dummy Fuel type

where, tfpch signifies change in total productivity, which is

caused by the joint influence of effch, i.e. the change in

efficiency from period s to t and techch, the geometric mean

of the shift in technology between the two periods, evaluated

at xt and also at xs. The value of the indices greater than one

signifies increase in productivity.

2.3.2 SFA based MPI

Efficiency change between two adjacent period s and t for the

ith firm /utility can be obtained as effch= TE it /TE is and technical

change index between period s and t for the ith firm can be

calculated directly from the estimated parameters.

2/1

,, ,1

,1

t

t x f

s

s x f techch

it is

III. A NALYSIS OF THE R ESULTS

1) DEA results

CCR model measures the overall efficiency which is the

efficiency measured against the CRS frontier. The results are

presented in Table III. It is evident from Table II that Indian

GENCOs display significant variations in efficiency levels.The total efficiency had a mean score of 0.6 for all the

utilities and almost half of the utilities lie below this average

value. Only two utilities turned out to be the best practices and

the remaining 28 utilities exhibited varying degree of

inefficiencies. It is also observed that all the companies, with

the exception of the best practices and one utility (Nagaland),

exhibited decreasing returns to scale suggesting that the

utilities exceeded their most productive scale size. This

outcome supports the unbundling policy of the GoI, as

envisaged in the Electricity Act. The management of the

utilities, in general, does not have control over their scale of

operation. Therefore, it is quite appropriate to assess

efficiency relative to the VRS frontier. So, the technical

efficiency of utilities is measured against the VRS frontier.

To explore the scale effects, the BCC formulation that

assumes a VRS by taking into consideration the sizes of

utilities was employed. This formulation ensures that similar

sized utilities are benchmarked and compared with each other.

The average technical efficiency is 0.772. The results indicate

the possibility of restructuring of several utilities that display

low scale efficiencies (Table III). The low value of scale

efficiencies and the fact that these utilities exhibit decreasing

returns to scale indicate that these have considerable scope for

improvements in their efficiencies by resizing (downsizing)

their scales of operations to the optimal scale defined by more

productive utilities in the sample.

2) SFA Results

2.1 Hypotheses test results

The results of the likelihood ratio tests are presented in TableIII The first hypothesis is conducted to find whether the Cobb

Douglas is the right functional form. The first hypothesis that

the Cobb-Douglas functional form was the best-fit functional

form for the data was accepted. The second hypothesis that

there was absence of inefficiency effects in the production





TABLE II

R ESULTS OF DEA MODEL

S.No UtilityTotal Efficiency

(CRS)

Technical

Efficiency

(VRS)

Scale

Efficiency (SE)

Returns to

Scale (RTS)Benchmarks

1 Haryana 0.569 0.802 0.709 DRS 16 9 8

2 Himachal Pradesh 0.97 0.97 1 - 16 18

3 Jammu & Kashmir 0.588 0.588 1 - 18 16

4 Punjab 0.668 0.978 0.683 DRS 16 9 8

5 Rajasthan 0.701 0.981 0.714 DRS 9 18

6 Uttar Pradesh 0.552 0.773 0.714 DRS 9 18

7 Uttrakhand 0.787 0.787 1 -

16 18

8 Delhi 0.703 1 0.703 DRS 8

9 Gujarat 0.713 1 0.713 DRS 9

10 Madhya Pradesh 0.532 0.745 0.714 DRS9 18

11 Chhattisgarh 0.51 0.713 0.715 DRS 9 18

12 Maharashtra 0.616 1 0.616 DRS 12

13 Goa 0.971 0.971 1 - 16 18

14 Andhra Pradesh 0.566 0.97 0.584 DRS 9 15 12

15 Karnataka 0.53 1 0.53 DRS 15

16 Kerala 1 1 1 -16

17 Tamil Nadu 0.503 0.962 0.523 DRS 15 12

18 Puducherry 1 1 1 - 18

19 Bihar 0.067 0.124 0.543 DRS 16 8 18

20 Jharkhand 0.361 0.516 0.698 DRS 16 9 8

21 Orissa 0.548 0.929 0.59 DRS 8 16 9

22 West Bengal 0.517 0.724 0.714 DRS 9 18

23 Sikkim 0.449 0.449 1 - 16 18

24 Assam 0.86 0.86 1 - 18 16

25 Manipur 0.063 0.063 1 -

16 18

26 Meghalaya 0.741 0.741 1 - 16 18

27 Nagaland 0.409 1 0.409 IRS 27

28 Tripura 0.934 0.934 1 - 16 18

29 Arunachal Pradesh 0.438 0.438 1 - 16 18

30 Mizoram 0.158 0.158 1 - 18 16

Mean 0.6 0.772 0.795





TABLE III

HYPOTHESES TEST R ESULTS

process was rejected, while the third hypothesis that the half

normal representation is correct distributional form for the

data is also rejected. The fourth hypothesis that the

inefficiency effects are absent from the production function

model i.e. model is equivalent to the average response

function (Full frontier model), which can be efficiently

estimated by ordinary least square (OLS) regression is also

rejected for the model. The last hypotheses that the panel data

is not applicable for the model is also rejected that means the

panel data can be applied to the model.

3.2 Parameter estimation and interpretation

The estimates of the stochastic frontier production function for

cobb-douglas (CD) and translog (TR) form are presented in

Table IV. The estimated coefficients of the explanatory

variables showed that installed capacity and coal consumption

had positive effect on the change in output. This means an

increase in the installed capacity and coal consumption

increases plant output and vice-versa. Both coefficients are

significant at 1% level and 5% of significance respectively for

the accepted cobb-douglas form. The negative variables of the

inefficient function mean positive impact on technical

efficiency, and vice-versa. All environmental factors are

significant except dummy regulation. The energy losses has

unexpected negative sign, so here we can conclude that in this

particular analysis the inefficiency cannot be reduced by

reducing the energy losses. The output elasticities are shown

in Table VII. It is clear that the production elasticity is

dominated by the capital or installed capacity elasticity.

3.3 Comparative efficiency and productivity analysis

The yearly DEA efficiencies for CRS and VRS, and SFA

efficiencies for both CD and TR models are shown in table V.

The average efficiencies are also shown for the GENCOs in

table. It is quite clear that DEA VRS > SFA TR > SFA CD >

DEA CRS. Gujarat generating company is having the highest

average efficiency over the period of six years with CRS,

VRS, SFA CD and SFA TR of 0.667, 1, 0.953 and 0.956

respectively. The mean CRS, VRS, SFA CD and SFA TR

efficiencies are 0.541, 0.730, 0.627 and 0.647 as seen from

Table VI. Table VIII shows TFP changes for the generating

companies. Nagaland is having the highest SFA based TFP

change and Manipur is having the highest DEA based TFP

change, though these companies are technically as well as

scale inefficient companies. The comparative analysis of

average efficiencies is shown in fig 1 and the comparative

productivity results are shown in fig 2. There are differences

between the SFA and DEA results. In case of DEA results of

productivity, 21utilities are having TFP change value greater

than 1 that means showing technical progress. While in SFA-

CD form, 22 (almost same) utilities have TFP change greater

than 1. SFA translog form shows that almost all the generation

companies are having TFP change of value greater than 1.

IV CONCLUSIONS

From the comparison of SFA and DEA model, the average

CRS TE, VRS TE and SFA TE efficiency scores are 0.541,

0.73 and 0.627 respectively. We can conclude that VRS TE >

SFA TE> CRS TE. Himachal Pradesh is having the highest

average CRS efficiency score of 0.862 and Gujarat is having

the highest SFA efficiency score of 0.953 and Mizoram is

having least CRS and SFA efficiency score. For the SFA

model, the production elasticities are dominated by installed

capacity elasticity which is equal to 0.817 while the fuel

elasticity is 0.1144. The RTS value is 0.9314 indicating that

the utilities are exhibiting decreasing returns to scale (DRS).

This supports unbundling policy of government of India. The

γ parameter is 0.998 that means 99.8 % deviations are due to

inefficiency effects and 0.2 % is noise effects.

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H0: β jk =0 12.59 11.58 H0: Accepted

H0: γ = 0 5.138 204.4 H0: Rejected

H0: µ = 0 7.045 213.64 H0: Rejected

H0: γ = δ0=δ1= δ2= δ3= 0 1.37 -366.16 H0: Rejected

H0: η = 0 3.84 547.8 H0: Rejected





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TABLE IV

SFA PARAMETER ESTIMATIONS

Variables ParametersCobb Douglas (CD) Translog (TR)

Coefficient t-ratio Coefficient t-ratio

Production factors

Intercept β0 0.7578* 4.83 0.8429** 2.98

In(Installed capacity) β1 0.8170* 52.426 0.8399* 14.545

In(Coal consumption) β2 0.1144* 5.398 0.0873** 2.183

In(Installed capacity)*ln(installed capacity) β11 0.0703** 2.971

In(coal consumption)*ln(coal consumption) β22 0.0009 -0.561

In(Installed capacity)*ln(coal consumption) β12 0.0377** -2.36

In(Installed capacity)*time β1t 0.0051 -0.048

In(coal consumption)*time β2t -0.0022 0.43

time βt 0.0048 0.648 0.0383 1.13

time*time βtt -0.0051 1.055

Inefficiency factors

Intercept δ0 -4.8666* -4.621 -5.1128* -5.64

In(Plant load factor) δ1 -0.9471** -3.215 -0.8651* -5.392

In(Energy losses) δ2 -1.1250* -6.203 -1.1422* -6.256

In(Per capita consumption) δ3 3.0131* -6.059 -3.0584* -10.103

Dummy (Regulation) δ4 0.0576** 2.214 0.0353 1.335

Dummy (Fuel type) δ5 -0.5815* -3.445 -7.09** -2.483

Variance factors

Sigma squared σ2 2.1342* 5.548 2.1275* 5.748

Gamma γ 0.9983* 993.63 0.9987* 1418.78

Loglikelihood function LLF -63.34 -57.55*

Note: This value is obtained from table 1 of Kodde and Palm (1986) which gives critical values for the tests of null hypotheses.

*,**,*** Estimate is significant at 1%, 5%, 10% level of significance respectively





TABLE V

SFA A ND DEA EFFICIENCIES

S.No Utility

2002-03 2003-04

DEA CRS DEA VRS SFA CD SFA TR DEA CRS DEA VRS SFA CD SFA TR

1 Haryana 0.605 0.917 0.888 0.91 0.644 1 0.915 0.928

2 Himachal Pradesh 0.755 0.755 0.608 0.670.857

0.857 0.866 0.927

3 Jammu & Kashmir 0.212 0.212 0.158 0.180.396

0.396 0.361 0.42

4 Punjab 0.607 0.995 0.954 0.96 0.626 1 0.964 0.965

5 Rajasthan 0.687 1 0.961 0.97 0.597 0.947 0.892 0.905

6 Uttar Pradesh 0.596 0.915 0.879 0.91 0.583 0.933 0.854 0.875

7 Uttrakhand 1 1 0.808 0.83 1 1 0.955 0.971

8 Delhi 0.442 0.721 0.64 0.64 0.621 1 0.907 0.901

9 Gujarat 0.624 1 0.95 0.96 0.605 0.997 0.936 0.932

10 Madhya Pradesh 0.615 0.896 0.922 0.940.576

0.909 0.871 0.876

11 Chhattisgarh 0.692 1 0.943 0.95 0.667 1 0.918 0.907

12 Maharashtra 0.593 1 0.941 0.92 0.594 1 0.944 0.914

13 Goa 0.864 0.864 0.806 0.84 0.586 0.586 0.565 0.574

14 Andhra Pradesh 0.541 0.871 0.858 0.860.499

0.893 0.81 0.784

15 Karnataka 0.498 0.859 0.785 0.79 0.506 1 0.836 0.812

16 Kerala 1 1 0.737 0.59 1 1 0.677 0.549

17 Tamil Nadu 0.531 0.866 0.889 0.87 0.462 0.818 0.793 0.752

18 Puducherry 1 1 0.957 0.96 1 1 0.952 0.934

19 Bihar 0.122 0.229 0.193 0.19 0.088 0.15 0.143 0.139

20 Jharkhand 0.295 0.498 0.47 0.47 0.321 0.511 0.496 0.489

21 Orissa 0.314 0.473 0.466 0.48 0.468 0.856 0.745 0.746

22 West Bengal 0.566 0.872 0.853 0.88 0.58 0.93 0.854 0.873

23 Sikkim 0.144 0.144 0.144 0.14 0.122 0.122 0.124 0.119

24 Assam 0.399 0.399 0.321 0.35 0.293 0.293 0.291 0.32

25 Manipur 0.003 0.003 0.003 0 0.003 0.003 0.003 0.003

26 Meghalaya 0.734 0.734 0.548 0.66 0.619 0.619 0.543 0.647

27 Nagaland 0.096 1 0.1 0.09 0.1 1 0.102 0.09

28 Tripura 0.582 0.582 0.443 0.53 0.72 0.72 0.625 0.732

29 Arunachal Pradesh 0.088 0.088 0.082 0.08 0.102 0.102 0.105 0.099

30 Mizoram 0.036 0.036 0.036 0.04 0.038 0.038 0.04 0.037

Mean 0.508 0.697 0.838 0.62 0.509 0.722 0.636 0.641





S.No Utility

2004-05 2005-06

DEA CRS DEA VRS SFA CD SFA TR DEA CRS DEA VRS SFA CD

SFA

TR

1 Haryana 0.456 0.667 0.665 0.6695 0.659 0.857 0.87 0.881

2 Himachal Pradesh 1 1 0.836 0.9107 1 1 0.805 0.894

3 Jammu & Kashmir 0.362 0.362 0.275 0.3225 0.421 0.421 0.307 0.369

4 Punjab 0.553 0.856 0.898 0.8763 0.682 0.932 0.962 0.961

5 Rajasthan 0.649 0.951 0.933 0.9439 0.77 1 0.962 0.973

6 Uttar Pradesh 0.524 0.769 0.76 0.7723 0.538 0.724 0.724 0.737

7 Uttrakhand 1 1 0.681 0.7174 0.977 0.977 0.754 0.814

8 Delhi 0.693 1 0.951 0.9491 0.726 0.969 0.931 0.92

9 Gujarat 0.68 1 0.97 0.9714 0.706 1 0.959 0.961

10 Madhya Pradesh 0.567 0.829 0.847 0.8446 0.558 0.725 0.74 0.754

11 Chhattisgarh 0.695 1 0.934 0.9203 0.83 1 0.968 0.968

12 Maharashtra 0.592 1 0.935 0.8977 0.623 1 0.919 0.879

13 Goa 1 1 0.914 0.9271 1 1 0.936 0.949

14 Andhra Pradesh 0.545 0.974 0.878 0.8443 0.528 0.863 0.799 0.763

15 Karnataka 0.468 1 0.78 0.7448 0.488 0.779 0.767 0.729

16 Kerala 0.858 1 0.77 0.639 1 1 0.884 0.759

17 Tamil Nadu 0.457 0.84 0.786 0.7347 0.465 0.814 0.757 0.7

18 Puducherry 1 1 0.946 0.918 1 1 0.9 0.854

19 Bihar 0.041 0.077 0.068 0.0669 0.04 0.063 0.066 0.067

20 Jharkhand 0.302 0.439 0.456 0.4456 0.351 0.464 0.49 0.481

21 Orissa 0.524 0.981 0.823 0.8197 0.457 0.63 0.66 0.661

22 West Bengal 0.613 1 0.934 0.9332 0.696 1 0.983 0.983

23 Sikkim 0.201 0.201 0.199 0.1899 0.1 0.1 0.095 0.096

24 Assam 0.171 0.36 0.347 0.3645 0.194 0.351 0.333 0.357

25 Manipur 0.004 0.004 0.004 0.0037 0.006 0.006 0.005 0.006

26 Meghalaya 0.753 0.753 0.571 0.6844 0.616 0.616 0.46 0.559

27 Nagaland 0.015 1 0.015 0.0132 0.015 1 0.014 0.013

28 Tripura 0.881 0.881 0.676 0.7944 0.709 0.709 0.532 0.64


30 Mizoram 0.016 0.016 0.014 0.0147 0.027 0.027 0.024 0.026

Mean 0.52 0.732 0.631 0.63 0.438 0.705 0.624 0.63





S.No Utility

2006-07 2007-08

DEA CRS DEA VRS SFA CD SFA TR DEA CRS DEA VRS SFA CD SFA TR

1 Haryana 0.664 1 0.894 0.9139 0.569 0.802 0.774 0.809

2 Himachal Pradesh 0.93 0.93 0.728 0.8194 0.97 0.97 0.874 0.947

3 Jammu & Kashmir 0.579 0.579 0.411 0.5025 0.588 0.588 0.475 0.57

4 Punjab 0.63 0.99 0.945 0.9455 0.668 0.978 0.954 0.965

5 Rajasthan 0.643 0.976 0.9 0.9189 0.701 0.981 0.938 0.962

6 Uttar Pradesh 0.551 0.825 0.765 0.7852 0.552 0.773 0.745 0.782

7 Uttrakhand 0.869 0.869 0.682 0.7121 0.787 0.787 0.741 0.727

8 Delhi 0.646 1 0.877 0.8721 0.703 1 0.898 0.908

9 Gujarat 0.651 1 0.942 0.9434 0.713 1 0.962 0.973

10 Madhya Pradesh 0.573 0.867 0.799 0.8185 0.532 0.745 0.733 0.765

11 Chhattisgarh 0.684 1 0.906 0.8995 0.51 0.713 0.716 0.733

12 Maharashtra 0.589 1 0.906 0.8662 0.616 1 0.912 0.898

13 Goa 1 1 0.929 0.9624 0.971 0.971 0.772 0.865

14 Andhra Pradesh 0.51 0.919 0.798 0.7681 0.566 0.97 0.798 0.848

15 Karnataka 0.551 1 0.882 0.8529 0.53 1 0.882 0.826

16 Kerala 1 1 0.889 0.7952 1 1 0.889 0.918

17 Tamil Nadu 0.485 0.948 0.817 0.7581 0.503 0.962 0.817 0.785

18 Puducherry 1 1 0.924 0.9186 1 1 0.924 0.851

19 Bihar 0.021 0.046 0.041 0.0453 0.067 0.124 0.041 0.12

20 Jharkhand 0.429 0.653 0.608 0.6043 0.361 0.516 0.608 0.511

21 Orissa 0.552 0.982 0.825 0.8374 0.548 0.929 0.825 0.838

22 West Bengal 0.673 1 0.905 0.9322 0.517 0.724 0.905 0.747

23 Sikkim 0.105 0.105 0.1 0.1044 0.449 0.449 0.1 0.423

24 Assam 0.375 0.375 0.269 0.323 0.86 0.86 0.269 0.888

25 Manipur 0.016 0.016 0.015 0.0163 0.063 0.063 0.015 0.059

26 Meghalaya 0.476 0.476 0.342 0.4351 0.741 0.741 0.342 0.754

27 Nagaland 0.131 1 0.128 0.1215 0.409 1 0.128 0.376

28 Tripura 0.807 0.807 0.584 0.7363 0.934 0.934 0.584 0.921


30 Mizoram 0.028 0.028 0.024 0.0277 0.158 0.158 0.024 0.15

Mean 0.544 0.752 0.632 0.65 0.6 0.772 0.632 0.71

TABLE VII

SFA ELASTICITIES

With respect to Estimated elasticity

Installed capacity (E1) 0.817

Fuel (E2) 0.114

Time 1.0048

Returns to scale 0.931





TABLE VI

SFA A ND DEA AVERAGE EFFICIENCIES

S.No Utility DEA CRS DEA VRS SFA CD SFA TR

1 Haryana 0.588 0.874 0.834 0.852

2 Himachal Pradesh 0.862 0.919 0.786 0.862

3 Jammu & Kashmir 0.523 0.426 0.331 0.395

4 Punjab 0.584 0.959 0.946 0.945

5 Rajasthan 0.66 0.976 0.931 0.946

6 Uttar Pradesh 0.596 0.823 0.788 0.81

7 Uttrakhand 0.866 0.939 0.77 0.796

8 Delhi 0.68 0.948 0.867 0.865

9 Gujarat 0.667 1 0.953 0.956

10 Madhya Pradesh 0.595 0.829 0.819 0.833

11 Chhattisgarh 0.634 0.952 0.898 0.896

12 Maharashtra 0.636 1 0.926 0.897

13 Goa 0.841 0.904 0.82 0.853

14 Andhra Pradesh 0.61 0.915 0.824 0.811

15 Karnataka 0.514 0.94 0.822 0.793

16 Kerala 0.891 1 0.808 0.709

17 Tamil Nadu 0.573 0.875 0.81 0.767

18 Puducherry 0.911 1 0.934 0.905

19 Bihar 0.223 0.115 0.092 0.105

20 Jharkhand 0.291 0.514 0.521 0.5

21 Orissa 0.46 0.809 0.724 0.73

22 West Bengal 0.568 0.921 0.906 0.891

23 Sikkim 0.286 0.187 0.127 0.179

24 Assam 0.366 0.44 0.305 0.434

25 Manipur 0.047 0.016 0.008 0.015

26 Meghalaya 0.555 0.657 0.468 0.624

27 Nagaland 0.228 1 0.081 0.118

28 Tripura 0.657 0.772 0.574 0.725

29 Arunachal Pradesh 0.252 0.156 0.11 0.159

30 Mizoram 0.068 0.051 0.027 0.048

Mean 0.541 0.730 0.627 0.647





Fig 2 SFA and DEA TFP changes

0

0. 2

0. 4

0. 6

0. 8

1

Utility

T F P

c h a n g e s

DEA CRS DEA VRS SFA CD SFA TR

Fig 1 S FA and DEA Efficiencies

0. 5

0. 9

1.3

1.7

2. 1

2. 5

2. 9

Utility

E f f i c i e n c y S c o r e

DEA-TFPCH S FA (CD)-TFPCH S FA (TR)-TFPCH





TABLE VIII

SFA A ND DEA TFP CHANGES

S.No Utility

DEA SFA CD SFA TR

Efficienc

y Change

Technical

Change

TFP

Change

Efficiency

Change

Technical

Change

TFP

Change

Efficienc

y Change

Technica

l Change

TFP

Chang

e

1 Haryana 0.988 1 0.987 0.992 1.0048 0.997 0.996 1.028 1.024

2 Himachal Pradesh 1.051 1.038 1.092 1.092 1.0048 1.097 1.065 1.053 1.121

3 Jammu & Kashmir 1.226 1.034 1.268 1.332 1.0048 1.338 1.195 1.055 1.2617

4 Punjab 1.019 1 1.019 1.001 1.0048 1.006 1.006 1.024 1.030

5 Rajasthan 1.004 1 1.004 0.997 1.0048 1.001 1.001 1.025 1.026

6 Uttar Pradesh 0.985 1 0.984 0.969 1.0048 0.973 0.986 1.022 1.008

7 Uttrakhand 0.953 1.039 0.991 0.999 1.0048 1.003 1.007 1.051 1.059

8 Delhi 1.097 1 1.097 1.082 1.0048 1.087 1.054 1.036 1.092

9 Gujarat 1.027 1 1.027 1.003 1.0048 1.008 1.009 1.020 1.028

10 Madhya Pradesh 0.971 1 0.971 0.957 1.0048 0.962 0.979 1.025 1.003

11 Chhattisgarh 0.941 1 0.941 0.951 1.0048 0.955 0.97 1.030 1.000

12 Maharashtra 1.008 1 1.007 0.994 1.0048 0.999 1.001 1.015 1.016

13 Goa 1.023 1.013 1.036 1.033 1.0048 1.038 1.025 1.067 1.094

14 Andhra Pradesh 1.009 1 1.009 1.002 1.0048 1.007 1.005 1.020 1.024

15 Karnataka 1.012 1 1.012 1.017 1.0048 1.022 1.011 1.023 1.034

16 Kerala 1 1.096 1.096 1.059 1.0048 1.064 1.064 1.048 1.115

17 Tamil Nadu 0.989 1 0.989 0.987 1.0048 0.992 0.995 1.019 1.014

18 Puducherry 1 1 1 0.988 1.0048 0.993 0.997 1.069 1.066

19 Bihar 0.888 1 0.887 1.109 1.0048 1.114 1.023 1.043 1.067

20 Jharkhand 1.041 1 1.041 1.024 1.0048 1.029 1.02 1.031 1.052

21 Orissa 1.117 1 1.117 1.146 1.0048 1.152 1.084 1.030 1.116

22 West Bengal 0.982 1 0.982 0.973 1.0048 0.978 0.988 1.024 1.011

23 Sikkim 1.256 0.999 1.254 1.586 1.0048 1.594 1.288 1.068 1.375

24 Assam 1.166 1.051 1.225 1.266 1.0048 1.272 1.164 1.051 1.223

25 Manipur 1.818 1.007 1.83 2.009 1.0048 2.019 1.574 1.067 1.680

26 Meghalaya 1.002 1.025 1.028 1.05 1.0048 1.055 1.033 1.060 1.095

27 Nagaland 1.337 1 1.337 2.864 1.0048 2.877 1.816 1.070 1.944

28 Tripura 1.099 1.023 1.124 1.118 1.0048 1.123 1.079 1.062 1.145

29 Arunachal Pradesh 1.379 1.001 1.381 1.469 1.0048 1.476 1.266 1.067 1.352

30 Mizoram 1.347 1.006 1.354 1.898 1.0048 1.907 1.434 1.067 1.530

Mean 1.347 1.006 1.354 1.2 1.0048 1.205 1.1 1.04 1.154





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