Journal of Indian Research (ISSN: 2321-4155)
Vol.3, No.3, July-September, 2015, 42-52
Engonomics
MEASURING TECHNICAL, SCALE, COST
AND ALLOCATIVE EFFICIENCY IN THE
MANUFACTURE OF BASIC METALS IN INDIA
USING DATA ENVELOPMENT ANALYSIS
Dr. M. Manonmani*
ABSTRACT
7KHUH DUH WZR DSSURDFKHV IRU HVWLPDWLRQ RI HI¿FLHQF\ YL] WKH 6WRFKDVWLF )URQWLHU
Approach (SFA) and Data Envelopment Approach (DEA). While the SFA (econometric
DSSURDFKHVWLPDWHVWKHHI¿FLHQF\RIWKH¿UPVE\HVWLPDWLQJWKHSURGXFWLRQIXQFWLRQ
the DEA technique involves the use of mathematical programming to estimate the
HI¿FLHQF\RIWKH¿UPVLQGXVWU\)RUWKHSHULRGWRWKHFDOFXODWLRQV
RQ WKH HI¿FLHQF\ RI 'HFLVLRQ 0DNLQJ 8QLWV '08V LQ WKH PDQXIDFWXUH RI EDVLF
metals in India have been done. The paper demonstrates that the technical, scale,
FRVW DQG DOORFDWLYH HI¿FLHQW '08V ZHUH PRUH XQGHU 9DULDEOH 5HWXUQV WR 6FDOH
(VRS) production technology in comparison with Constant Returns to Scale (CRS)
production technology.
Keywords: $OORFDWLYHHI¿FLHQF\%&&02'(/&&50RGHOFRVWHI¿FLHQF\'HFLVLRQ
0DNLQJ8QLWV'08VFDOHHI¿FLHQF\9DULDEOH5HWXUQVWR6FDOH956
INTRODUCTION
India’s manufacturing sector is vital for its economic progress. The contribution of
manufacturing to overall GDP is meager 17.2 per cent (2014-15). The government has realized
the importance of this sector to the country’s industrial development, and has taken a number
of proactive steps to further enhance the industry. Manufacturing Industry in India has gone
through various phases of development over the period of time.
Since independence in 1947, the Indian manufacturing sector has traveled from the initial
phase of building the industrial foundation in 1950’s and early 1960’s, to the license–permit
Raj during the period of 1965–1980, to a phase of liberalization of 1990’s, emerging into the
*Dr. M. Manonmani is Professor in Economics at the Avinashilingam Institute For Home Science And Higher
Education For Women,Coimbatore-43,E-mail:[email protected].
42
Journal of Indian Research
Vol.3, No.3, July-September, 2015
current phase of global competitiveness. It has grown at a robust rate over the past ten years
and has been one of the best performing manufacturing economy. Studies have estimated that
every job created in manufacturing has a multiplier effect, creating 2–3 jobs in the services
sector. In a country like India, where employment generation is one of the key policy issues,
this makes manufacturing a critical sector to achieve inclusiveness in growth.
The metal sector is a key part of manufacturing. It is highly sensitive to changes in the
business cycle. It is considered a capital- (basic metals), labour- (fabricated metal products)
and energy-intensive industry, producing a wide range of products e.g. basic metals, tanks,
steam generators, cutlery, tools, light metal packaging, wires etc. The metal industry is an
important component of the world economy when measured by its share of GDP worldwide.
In sub-branches such as metal production, non-electrical machinery, electrical machinery and
transport equipment, it employs some 70 million workers worldwide, who account for nearly
half of the goods produced in the manufacturing sector and more than half of all merchandise
exported worldwide (in terms of value).Consequently, the metal industry is both a driving
IRUFHRIWKHZRUOGHFRQRP\DQGLVLQÀXHQFHGWRDODUJHH[WHQWE\WKHRYHUDOOZRUOGHFRQRPLF
climate.
METHODOLOGY
1. Data Base of the Study
7KHEDVLFGDWDVRXUFHRIWKHVWXG\RQ¿[HGFDSLWDO wages, net value added and number
of workers was Annual Survey of Industries (ASI) published by the Central Statistical
Organisation (CSO), Government of India. All the referred variables were normalised by
DSSO\LQJ*URVV6WDWH'RPHVWLF3URGXFW*6'3GHÀDWRU7KH*6'3DWFXUUHQWDQGFRQVWDQW
prices were obtained by referring to the Economic Survey, published by the Government of
India, Economic Division of the Ministry of Finance, New Delhi .The reference period chosen
for the study covers post- liberalization period between 2000-01 and 2011-12.The availability
RIGDWDLVFRQ¿QHGRQO\XSWRWKLVSHULRG
2. Tools of Analysis
DEA Model
7KHUH DUH EDVLFDOO\ WZR DSSURDFKHV IRU HVWLPDWLRQ RI HI¿FLHQF\ YL] WKH 6WRFKDVWLF
Frontier Approach (SFA) and Data Envelopment Approach (DEA). While the Stochastic
)URQWLHU$SSURDFKHFRQRPHWULFDSSURDFKHVWLPDWHVWKHHI¿FLHQF\RIWKH¿UPVE\HVWLPDWLQJ
the production function, the DEA technique involves the use of mathematical programming
WR HVWLPDWH WKH HI¿FLHQF\ RI WKH ¿UPV LQGXVWU\ '($ LV D QRQSDUDPHWULF GHWHUPLQLVWLF
PHWKRGRORJ\IRUGHWHUPLQLQJUHODWLYHO\HI¿FLHQWSURGXFWLRQIURQWLHUEDVHGRQWKHHPSLULFDO
data on chosen inputs and outputs of a number of entities called Decision Making Units
'08V )URP WKH VHW RI DYDLODEOH GDWD '($ LGHQWLI\ UHIHUHQFHSRLQWV UHODWLYHO\ HI¿FLHQW
'08VWKDWGH¿QHHI¿FLHQWIURQWLHUDVWKHEHVWSUDFWLFHSURGXFWLRQWHFKQRORJ\DQGHYDOXDWH
WKHLQHI¿FLHQF\RIRWKHULQWHULRUSRLQWVUHODWLYHO\LQHI¿FLHQW'08VWKDWDUHEHORZWKHIURQWLHU
(Saon Ray, 2004).
7KH '($ SURYLGHV D PHDVXUH RI HI¿FLHQF\ WKDW DOORZV LQWUD¿UP FRPSDULVRQ DV WKH
Dr. M. Manonmani
43
Journal of Indian Research
Vol.3, No.3, July-September, 2015
HI¿FLHQF\PHDVXUHLVDSXUHQXPEHU7KHPDLQDGYDQWDJHRI'($LVWKDWXQOLNH6)$LWGRHV
not require a priority assumption about the analytical form of the production function. Instead,
it constructs the best practice production solely on the basis of observed data and therefore the
SRVVLELOLW\RIPLVVSHFL¿FDWLRQRIWKHSURGXFWLRQWHFKQRORJ\LVPLQLPL]HG,QWKHFDVHRI6)$
WKHSDUDPHWHUHVWLPDWHVDUHVHQVLWLYHWRWKHFKRLFHRIWKHSUREDELOLW\GLVWULEXWLRQVSHFL¿HGIRU
the disturbance term.
7KHUHDUHWZRDSSURDFKHVWRHVWLPDWLQJWKHHI¿FLHQF\RIWKH¿UPLQWKH'($DSSURDFK
YL]WKHRXWSXWRULHQWHGHI¿FLHQF\DQGWKHLQSXWRULHQWHGHI¿FLHQF\,QWKHRXWSXWRULHQWHG
DSSURDFK HI¿FLHQF\ LV GHWHUPLQHG E\ PD[LPXP RXWSXW WKDW FDQ EH SURGXFHG IURP DQ
LQSXWEXQGOH,QWKHLQSXWEDVHGPHDVXUHWKHWHFKQLFDOHI¿FLHQF\RIWKH¿UPLVHYDOXDWHG
by the extent to which all inputs could be proportionally reduced without a reduction in
the output. Among number of DEA models, the two most frequently used ones (inputoriented) are, CCR model (after Charnes, Cooper, Rhodes, 1978) and BCC model (after
Banker, Charnes and Cooper, 1984), both of which are used in the study. The DEA model
LV XVHG WR HVWLPDWH WKH WHFKQLFDO VFDOH FRVW DQG DOORFDWLYH HI¿FLHQF\ RI WKH LQGXVWULHV
under study.
I. TECHNICAL EFFICIENCY
(i) CCR Model ( based on constant returns to scale)
&KDUQHV&RRSHUDQG5KRGHVLQWURGXFHGDPHDVXUHRIHI¿FLHQF\IRUHDFK'08WKDW
is obtained as a maximum of ratio of weighted outputs to weighted inputs. The weights for the
ratio are determined by a restriction that the similar ratios for every DMU have to be less than
or equal to unity, thus reducing multiple inputs and outputs to single “virtual” output without
requiring pre-assigned weights.
7KH HI¿FLHQF\ PHDVXUH LV WKHQ D IXQFWLRQ RI ZHLJKWV RI WKH ³YLUWXDO´ LQSXWRXWSXW
FRPELQDWLRQ)RUPDOO\WKHHI¿FLHQF\PHDVXUHIRUWKH'08FDQEHFDOFXODWHGE\VROYLQJWKH
following mathematical programming problem:
S
∑u Y
r r o
r =1
S
max h0(u,v) =
∑v x
……………………….(1)
i io
i =1
S
∑u
Subject to
∑v
i =1
44
r
r =1
m
i
Yr
x
j
i j
≤ 1, j = 1,2......, j o ,......, n........................................(2)
ur”U V vi•M P Journal of Indian Research
Vol.3, No.3, July-September, 2015
where the observed amount of input of the ith type of the DMU > 0,
i = 1,2,......n, j = 1,2,.....n) and = the observed amount of output of the rth type for the jth
DMU (Yrj > 0, r = 1,2,.....s, j = 1,2,...n).
The variables Ur , and Vi are the weights to be determined by the above programming
SUREOHP+RZHYHUWKLVSUREOHPKDVLQ¿QLWHQXPEHURIVROXWLRQVVLQFHLIXYLVRSWLPDO
WKHQIRUHDFKSRVLWLYHVFDODUĮĮXĮYLVDOVRRSWLPDO)ROORZLQJWKH&KDUQHV&RRSHU
transformation (1962), one can select a representative solution (u,v) for which to obtain a
linear programming problem that is equivalent to the linear fractional programming problem
7KXVGHQRPLQDWRULQWKHDERYHHI¿FLHQF\PHDVXUHK0 is set to equal one and the
transformed linear problem for DMU can be written.
m
∑v x
i =1
i i o
=1
........................... (5)
S
max z0 =
∑u
Yr o
r
r =1
......................... (6)
S
Subject to
∑ urYr
r =1
m
∑v
m
j
−∑ vi xi j ≤ 0, j = 1,2,..., n......................(7)
r =1
xi o = 1
i
r =1
ur•U V
...................... (8)
0, i = 1,2,...., m
...................... (10)
For the above linear programming problem, the dual can be written (for the given
DMU) as:
min z0 Ĭo
Subject to
..................... (11)
n
∑
λr Y •\ro r = 1,2,...,s
r j
................ (12)
j =1
n
ĬR[io –
∑λ
j
xij ≥ 0, i = 1,2,..., m
...................... (13)
j =1
Ȝj •M Q Dr. M. Manonmani
45
Journal of Indian Research
Vol.3, No.3, July-September, 2015
%RWKRIWKHDERYHOLQHDUSUREOHPV\LHOGWKHRSWLPDOVROXWLRQĬZKLFKLVWKHHI¿FLHQF\
VFRUH VRFDOOHG 7HFKQLFDO HI¿FLHQF\ RU &&5 HI¿FLHQF\ IRU WKH SDUWLFXODU '08 DQG
repeating them for each DMUjM QHI¿FLHQF\VFRUHVIRUDOORIWKHPDUHREWDLQHG
7KHYDOXHRIĬLVDOZD\VOHVVWKDQRUHTXDOWRXQLW\VLQFHZKHQWHVWHGHDFKSDUWLFXODU
DMU is constrained by its own virtual input-output combination too). DMUs for which
ĬDUHUHODWLYHO\LQHI¿FLHQWDQGWKRVHIRUZKLFKĬ DUHUHODWLYHO\HI¿FLHQWKDYLQJ
their virtual input-output combination points lying on the frontier. The frontier itself
FRQVLVWVRIOLQHDUIDFHWVVSDQQHGE\HI¿FLHQWXQLWVRIWKHGDWDDQGWKHUHVXOWLQJIURQWLHU
production function (obtained with the implicit constant returns to scale assumption) has
no unknown parameters.
(ii) BCC Model ( based on Constant Returns to Scale )
6LQFHWKHUHDUHQRFRQVWUDLQWVIRUWKHZHLJKWVȜj , other than the positivity conditions in the
problem (11) - (14), it implies constant returns to scale. For allowing variable returns to scale,
LWLVQHFHVVDU\WRDGGWKHFRQYH[LW\FRQGLWLRQIRUWKHZHLJKWVȜj, i.e. to include in the model
(11) - (14) the constraint:
n
∑λ
j
=1
.................... (15)
j =1
The resulting DEA model that exhibits variable returns to scale is called BCC model, after
Banker, Charnes and Cooper (1984). The input-oriented BCC model for the DMU0 can be
written formally as:
min z0 ĬR
Subject to
n
∑λ
r
j =1
Yr j ≥ Yr o r = 1,2,..., s
n
Ĭoxio –
n
∑λ
j
∑λ
j =1
j
.................... (17)
xi j ≥ 0, i = 1,2,..., m
..................... (18)
=1
..................... (19)
j =1
Ȝj •M Q 5XQQLQJWKHDERYHPRGHOIRUHDFK'08WKH%&&HI¿FLHQF\VFRUHVDUHREWDLQHGZLWK
similar interpretation of its values as in the CCR model). These scores are also called
³SXUH WHFKQLFDO HI¿FLHQF\ VFRUHV´ VLQFH WKH\ DUH REWDLQHG IURP WKH PRGHO WKDW DOORZV
YDULDEOHUHWXUQVWRVFDOHDQGKHQFHHOLPLQDWHWKH³VFDOHSDUW´RIWKHHI¿FLHQF\IURPWKH
DQDO\VLV *HQHUDOO\ IRU HDFK '08 WKH &&5 HI¿FLHQF\ VFRUH ZLOO QRW H[FHHG WKH %&&
46
Journal of Indian Research
Vol.3, No.3, July-September, 2015
HI¿FLHQF\VFRUHZKDWLVLQWXLWLYHO\FOHDUVLQFHLQWKH%&&PRGHOHDFK'08LVDQDO\]HG
“locally” (i.e. compared to the subset of DMUs that operate in the same region of returns
to scale) rather than “globally”.
II. SCALE EFFICIENCY
Following the scale properties of the above two models, (Cooper et al., 2000) the scale
HI¿FLHQF\LVGH¿QHGDVIROORZV)RUDSDUWLFXODU'08WKHVFDOHHI¿FLHQF\LVGH¿QHGDVD
UDWLRRILWVRYHUDOOWHFKQLFDOHI¿FLHQF\VFRUHPHDVXUHGE\WKH&&5PRGHODQGSXUHWHFKQLFDO
HI¿FLHQF\VFRUHPHDVXUHGE\WKH%&&PRGHO
III. COST EFFICIENCY
7KHVWDQGDUGPHDVXUHRIFRVWHI¿FLHQF\LVREWDLQHGYLDWZRVWDJHSURFHVV
(i) Estimate the minimum price-adjusted resource usage given technological constraints;
DQGLL&RPSDUHWKLVPLQLPXPWRDFWXDOREVHUYHGFRVWV&RVWHI¿FLHQF\FDQEHPHDVXUHGLI
input prices are available in addition to output and input data. Let x =(x1, ....xkİ5+k denotes
a vector of inputs and y = (y1, ....ymİ5+m denote vector of outputs. Formally, the cost
HI¿FLHQF\PRGHOFDQEHVSHFL¿HGDV
m
Minz,x
∑w
VW
]<”\
j =1
j o
xj
..................... (21)
0
][”[0
]L•
n
∑z
i
=1
i =1
where Y is an n x m matrix of observed outputs for n industries and x is an n x k matrix
of inputs for each industry. z is a l x n vector of intensity variables and w = (w1,...wk)
İ5+k GHQRWHGLQSXWSULFHV7KHFRQVWUDLQWVRIWKHPRGHOGH¿QHWKHLQSXWUHTXLUHPHQW
set given by:
n
/\ []\•\ ][”[]i•
0
∑z
i
= 1 ) ..................... (22)
i =1
7KHLQSXWUHTXLUHPHQWVHWVSHFL¿HVDFRQYH[WHFKQRORJ\ZLWK9DULDEOH5HWXUQVWR6FDOH
n
(VRS), which is imposed by the constraint
∑z
i
= 1 . Leaving the constraint out of the model
i =1
changes the technology to Constant Returns to Scale (CRS).
Dr. M. Manonmani
47
Journal of Indian Research
Vol.3, No.3, July-September, 2015
IV. ALLOCATIVE EFFICIENCY
$OORFDWLYHHI¿FLHQF\LVGH¿QHGDVDUDWLRRIFRVWHI¿FLHQF\VFRUHWRWHFKQLFDOHI¿FLHQF\
VFRUH%RWKXQGHU&56SURGXFWLRQWHFKQRORJ\DQG956SURGXFWLRQWHFKQRORJ\WKLVHI¿FLHQF\
score was estimated for the present study.
RESULTS AND DISCUSSION
$7HFKQLFDO(I¿FLHQF\
7KH UHVXOWV UHJDUGLQJ WHFKQLFDO HI¿FLHQF\ VFRUHV RI WKH VHOHFWHG LQWHUPHGLDU\ JRRGV
industries are presented in Table-1.
7DEOH7HFKQLFDO(I¿FLHQF\7((VWLPDWHV
DMUs
CRS*
VRS**
2002-03
0.324
1.000
2003-04
0.470
1.000
2004-05
0.595
1.000
2005-06
0.959
1.000
2006-07
0.669
0.865
2007-08
0.853
0.872
2008-09
1.000
1.000
2009-10
0.736
0.785
2010-11
0.778
0.814
2011- 12
0.780
0.791
$YHUDJH7HFKQLFDO(I¿FLHQF\
0.716
0.913
$YHUDJH7HFKQLFDO,QHI¿FLHQF\
0.397
0.095
1RRI7HFKQLFDOLQHI¿FLHQW'08V
1
5
CRS*- Constant Returns to scale; VRS*- Variable Returns to scale;
(Source: Calculations based on ASI data)
8QGHU &RQVWDQW 5HWXUQV WR 6FDOH &56 SURGXFWLRQ WHFKQRORJ\ WHFKQLFDO HI¿FLHQF\
between 2002-03 and 2011- 12 was 0.716. This implied that the industry would have needed
RQO\SHUFHQWRIWKHLQSXWVFXUUHQWO\EHLQJXVHG,QWHUPVRIDYHUDJHLQHI¿FLHQF\LWZRXOG
have needed 28.4 percent more inputs to produce the same output, which meant waste of
resources to the extent mentioned above.
8QGHU956SURGXFWLRQWHFKQRORJ\WKHQXPEHURIHI¿FLHQW'08VH[FHHGHGWKHQXPEHURI
HI¿FLHQW'08VXQGHU&56SURGXFWLRQWHFKQRORJ\8QGHU956SURGXFWLRQWHFKQRORJ\KLJKHU
DYHUDJHHI¿FLHQF\ZDVDOZD\VUHFRUGHG,WPD\EHGXHWRWKHUHDVRQWKDW'08VWKDWZHUH
48
Journal of Indian Research
Vol.3, No.3, July-September, 2015
HI¿FLHQWXQGHU&RQVWDQW5HWXUQVRI6FDOH&56ZHUHDFFRPSDQLHGE\WKHQHZHI¿FLHQW'08V
that might operate under increasing or decreasing return to scale. Higher degree of average
WHFKQRORJ\LQHI¿FLHQF\SDUWLFXODUO\XQGHUFRQVWDQWUHWXUQWRVFDOHSURGXFWLRQWHFKQRORJ\FDQ
EH DWWULEXWHG WR WKH IDFW WKDW WKH LQGXVWU\ PD\ QRW EH XVLQJ WKH PRVW HI¿FLHQW WHFKQRORJ\
available to transform the input into outputs due to differences in products, the industry was
likely to have different best practice frontiers; relatively small regional spheres of operation
RIWKHLQGXVWU\PD\KDYHUHVXOWHGLQLQHI¿FLHQFLHVDQGVWUXFWXUHGSUREOHPVUHJDUGLQJVWDII
HI¿FLHQF\DQGRSHUDWLQJHI¿FLHQF\PD\KDYHSUHYHQWHGWKH¿UPIURPLPSURYLQJLWVHI¿FLHQF\
OHYHO,WFDQEHFRQFOXGHGWKDWWKRXJKWKHHI¿FLHQF\RIWKH¿UPVYDULHGFRQVLGHUDEO\RQDFFRXQW
RIWKHYDULRXVUHDVRQVPHQWLRQHGWKH¿UPZDVHVWLPDWHGWREHRQWKHIURQWLHUVDWOHDVWRQFH,Q
RWKHUZRUGVERWKXQGHU&56DQG956WHFKQRORJ\WKHQXPEHURIHI¿FLHQF\VFRUHVRUOHYHOV
GXULQJWKHHQWLUHSHULRGZDVLQGLFDWLYHRIWKHIDFWWKDWWKHHI¿FLHQF\RI¿UPZDVQRWVWURQJO\
LQÀXHQFHGE\WKHVL]HRISURGXFWLRQ
%6FDOH(I¿FLHQF\
7KHVFDOHHI¿FLHQF\VFRUHVLVSUHVHQWHGLQ7DEOH
7DEOH6FDOH(I¿FLHQF\6((VWLPDWHV
DMU
CRS*(TE)
VRS(TE) 6FDOH(I¿FLHQF\
RTS**
(CRS(TE) / RS(TE)
2002-03
0.324
1.000
0.324
IRS***
2003-04
0.470
1.000
0.470
IRS
2004-05
0.595
1.000
0.595
IRS
2005-06
0.959
1.000
0.959
IRS
2006-07
0.669
0.865
0.774
IRS
2007-08
0.853
0.872
0.978
IRS
2008-09
1.000
1.000
1.000
CRS
2009-10
0.736
0.785
0.938
IRS
2010-11
0.778
0.814
0.955
IRS
2011- 12
0.780
0.791
0.986
IRS
$YHUDJH6FDOH(I¿FLHQF\
0.716
0.913
0.798
$YHUDJH6FDOH,QHI¿FLHQF\
0.716
0.913
0.253
5
1
1RRI6FDOH,QHI¿FLHQW'08V 1
CRS* – Constant Returns to Scale; RTS** - Returns to Scale; IRS*** - Increasing
5HWXUQVWR6FDOH$YHUDJHVFDOHLQHI¿FLHQF\ (Source: Calculations based on ASI data)
Dr. M. Manonmani
49
Journal of Indian Research
Vol.3, No.3, July-September, 2015
'($UHVXOWVDSSOLHGWRNQRZWKHVFDOHHI¿FLHQF\RILQGXVWULHVIRUWKHHQWLUHSHULRGUHYHDOHG
WKDWWKHLQGXVWULHVZHUHQRWRSHUDWLQJDWDQRSWLPXPVFDOH7KHDYHUDJHVFDOHHI¿FLHQF\ZDV
SHUFHQW,QWHUPVRIDYHUDJHLQHI¿FLHQF\LWFRXOGLQFUHDVHDGGLWLRQDOSURGXFWLRQWRWKH
extent of 15.4 percent, by taking advantage of their scale characteristics. DEA allows to assess
ZKHWKHUD¿UPOLHVLQWKHUDQJHRILQFUHDVLQJFRQVWDQWDQGGHFUHDVLQJUHWXUQVWRVFDOH,QRWKHU
ZRUGVLWUHYHDOHGWKHVFDOHFKDUDFWHULVWLFVRI'08V,IPDUNHWFRQWDLQV¿UPVVFDOHPDUNHW
HI¿FLHQF\ FDQ EH LQFUHDVHG LI PRUH '08V DWWDLQ FRQVWDQW UHWXUQV WR VFDOH EHFDXVH IHZHU
resources are wasted. The measurement of economies of scale, therefore, helps assess at the
VDPHWLPHZKHWKHUKLJKHUPDUNHWFRQFHQWUDWLRQVKRXOGEHHQFRXUDJHGWRLPSURYHHI¿FLHQF\$
'08PD\EHVFDOHLQHI¿FLHQWLILWH[SHULHQFHVGHFUHDVLQJUHWXUQVWRVFDOHRULILWKDVQRWWDNHQ
IXOODGYDQWDJHVRILQFUHDVLQJUHWXUQVWRVFDOH,QGHHGPRVWRIWKHLQHI¿FLHQW'08VSUHVHQWHG
increasing returns to scale characteristics which indicated that industries can increase the scale
WRHIIHFWLYHO\LPSURYHWKDWHI¿FLHQF\
C. &RVWHI¿FLHQF\
7DEOHJLYHVGHWDLOVUHJDUGLQJFRVWHI¿FLHQF\VFRUHVRIVHOHFWHGLQGXVWULHVIRUWKHUHIHUHQFH
period under study.
7DEOH&RVW(I¿FLHQF\&((VWLPDWHV
DMU
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
2011- 12
$YHUDJH&RVW(I¿FLHQF\
CRS*
0.269
0.405
0.548
0.882
0.643
0.798
1.000
0.703
0.650
0.668
0.657
VRS**
1.000
0.999
0.975
1.000
0.825
0.860
1.000
0.737
0.674
0.676
0.875
$YHUDJH&RVW,QHI¿FLHQF\
1RRI&RVWHI¿FLHQW'08V
0.522
1
0.142
3
CRS*- Constant Returns to scale;
VRS**- Variable Returns to scale;
$YHUDJHFRVWLQHI¿FLHQF\ (Source: calculations are based on ASI data)
50
Journal of Indian Research
Vol.3, No.3, July-September, 2015
8QGHU&RQVWDQW5HWXUQVWR6FDOH&56WHFKQRORJ\WKHLQGXVWU\ZDVHI¿FLHQWWRWKHH[WHQW
of 65.7 percent. Under Variable Returns to Scale (VRS) production technology the industry
ZDVPRUHHI¿FLHQWWRWKHH[WHQWRISHUFHQW7KHFRVWHI¿FLHQW'08VLWZDVIRXQGWREH
PRUHXQGHU956SURGXFWLRQWHFKQRORJ\7KHDYHUDJHFRVWLQHI¿FLHQF\ZDVPRUHXQGHU&56
production technology than under VRS production technology.
'$OORFDWLYHHI¿FLHQF\
$OORFDWLYHHI¿FLHQF\VFRUHVRIWKHLQGXVWULHVXQGHUWKHUHIHUHQFHSHULRGLVSUHVHQWHGLQ
Table.4.
7DEOH$OORFDWLYH(I¿FLHQF\$((VWLPDWHV
DMU
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
2011- 12
$YHUDJH$OORFDWLYH(I¿FLHQF\
CRS
0.831
0.861
0.921
0.920
0.961
0.936
1.000
0.956
0.836
0.857
0.908
VRS
1.000
0.999
0.975
1.000
0.954
0.986
1.000
0.939
0.828
0.855
0.875
$YHUDJH$OORFDWLYH,QHI¿FLHQF\
0.101
0.143
1RRI$OORFDWLYHHI¿FLHQW'08V
,QHI¿FLHQW'08V
CRS*- Constant Returns to scale; VRS**Variable Returns to scale
$YHUDJH$OORFDWLYHLQHI¿FLHQF\ (Source: Calculations are based on ASI data)
1
3
Estimates revealed that over the study period, the industries under CRS production
WHFKQRORJ\ KDG RQ DQ DYHUDJH DOORFDWLYH HI¿FLHQF\ OHYHO RI SHUFHQW LPSO\LQJ WKDW
WKH LQGXVWULHV ZHUH SHUFHQW LQHI¿FLHQW UHVSHFWLYHO\ ,Q WKH FDVH RI 956 SURGXFWLRQ
WHFKQRORJ\DQDYHUDJHDOORFDWLYHHI¿FLHQF\RISHUFHQWKDVEHHQPHDVXUHGLPSO\LQJ
WKDW WKH LQGXVWULHV ZHUH RQ DQ DYHUDJH SHUFHQW LQHI¿FLHQW 0RUH HI¿FLHQW '08V
were observed in VRS production technology in comparison with the CRS production
technology.
Dr. M. Manonmani
51
Journal of Indian Research
Vol.3, No.3, July-September, 2015
CONCLUSION
)RUWKHHQWLUHSHULRGWHFKQLFDOVFDOHFRVWDQGDOORFDWLYHHI¿FLHQW'08VZHUHPRUHXQGHU
Variable Returns to Scale (VRS) production technology in comparison with Constant Returns
WR6FDOH&56SURGXFWLRQWHFKQRORJ\,WLVYHU\FOHDUWKDWLQHI¿FLHQF\FRXOGEHGXHWRWKH
existence of either increasing or decreasing returns to scale.
REFERENCES
1. Banker.R.D Charnes.A & Cooper.W.W (1984). Some Models For Estimating Technical
$QG 6FDOH ,QHI¿FLHQFLHV ,Q 'DWD (QYHORSPHQW $QDO\VLV Management Science,
Volume. 30, pp.1078-2092.
2. &KDUQHV$ &RRSHU :: 5KRGHV( 0HDVXULQJ 7KH (I¿FLHQF\ 2I 'HFLVLRQ
Making Units, European Journal Of Operation Research,Volume.2, pp.429-444. Available
online at http://www.utdallas.edu/~ryoung/phdseminar/CCR1978.pdf
52