Efficiency of India s intermediate goods industries in the liberalized regime.
Manonmani, M. ; Ramya, M.
Intermediate Goods
Manufacturing is an organized activity devoted to the
transformation of raw materials into marketable goods. In technical
parlance, marketable goods are known as economic goods; they cannot be
obtained without paying a price. This is in contrast to free goods,
which are available at no cost. The manufacturing system usually employs
a series of value adding processes to convert raw materials into more
useful forms and eventually into finished products. The outputs from one
manufacturing system may be utilized as the inputs to another. A
manufacturing system is, therefore, a typical input-output system, which
produces outputs (economic goods) through activities that transform the
inputs (raw materials). In an industrialized country, the manufacturing
industries are the backbone of the national economy because it is mainly
through their activities that the real wealth is created.
A crucial characteristic of the production process in the
industrialized countries is that many goods are produced in one industry
and used as input in other industries, in both home country and the
trade partner countries. The goods are called intermediate goods and,
are produced goods which, through the production processes, are
transformed into goods of a greater value, whether another intermediate
good or the final good.
Because of high degree of specialization in production processes,
and the optimal use of the production factors in the single split
processes, it is important that intermediate goods, to a great extent,
fulfill the connected specific needs of factors. In every link of the
production processes there is precisely defined need for intermediate
goods according to, certain kind of specification. The production
technology for the group of intermediate goods being considered is taken
to be non-combinable. This means that the single producer of the final
good cannot obtain characteristics in proportions not represented among
the available intermediate goods by buying more of such goods and using
them in combination consequently.
The production of a specific variety of the final good requires one
specific variety of intermediary goods, the ideal intermediate goods.
Therefore, the number of ideal intermediate good varieties is identical
with the produced number of final goods varieties. Among the
intermediary goods industries, the following industries were selected
based on their significant contributions to the economy.
Chemical Industry
The chemical industry is an important constituent of the Indian
economy with an estimated turnover at around US$ 35 billion,
constituting 1.5 per cent of the global chemical industry estimated at
US$ 2,400 billion. The total investment in the sector is nearly US$ 60
billion and the employment is about one million. The industry accounts
for 1314 percent of the total exports and 8 -9 percent of the total
imports into the country. Gujarat dominates with 51 percent of the total
share of major chemicals produced in the country (Eleventh Five Year
Plan 2007-12).
Increased competition resulting from globalization is driving the
chemical industry towards consolidation, cost reduction, location of
manufacturing bases close to raw materials, cheaper energy sources,
lower tax regimes, increased use of information technology (IT), and
intensification of R&D activities. At the same time, the industry is
responding to the increased environment consciousness worldwide. Over
the last decade, the Indian chemical industry has evolved from being a
chemical producer to becoming an innovative industry. With increasing
investments in R&D, the industry is registering significant growth
in the knowledge sector comprising speciality chemicals, and
pharmaceuticals. Broadly, the share of basic, knowledge, and speciality
chemicals is 57 per cent, 18 per cent, and 25 per cent, respectively.
Leather Industry
The importance of the Indian leather industry is derived from the
fact that it is labour-intensive and contributes substantially to
exports. Artisans, micro enterprises, and SSIs account for 60-65 percent
of the total production. The manufacturing activity provides full-time
employment to 1 million persons and activities connected with the
recovery of hides from carcasses provides part-time employment to
another 0.8 million. The turnover of the industry was Rs 25,000 crore in
2004-05, out of which Rs 10,800 crore (43 per cent) was exported.
Exports have risen in recent years from US$ 1.9 billion in 2006-07. The
composition of exports of leather and leather goods has been moving
increasingly towards leather footwear, but the share (32 per cent in
2006-07) still falls far short of the 65 per cent share of footwear in
the world export of leather and leather products. The Inter-Ministerial
Group constituted to evolve a comprehensive strategy for the development
of the leather sector has assessed that India has the potential to
expand exports from the level of US$ 2.7 billion in 200506 to US$ 7
billion in 2011-12.
Paper Industry
Paper industry is one of the 35 high-priority industries in India
and is presently growing at 6.3 per cent per annum. The turnover is
nearly Rs 17,000 crore per annum and its contribution to the national
exchequer is around Rs 2,500 crore. The industry employs 0.3 million
people directly and is estimated to employ 1 million people indirectly.
The per capita consumption of paper in India is 7.2 kg, which is far
lower than that in other emerging economies, for example it is 45 kg in
China, 15-20 kg in other East Asian countries, and much higher level
that exists in the US and Europe. The consumption of paper is likely to
increase manifold with the rise in literacy.
At the end of the Tenth Five Year Plan there were about 666
industrial units with the total installed capacity of 8.50 million tons
of paper and paperboard. However, 98 units with a capacity of 1.1
million tons have been closed due to environmental problems. The
industry produces 5.80 million tons of paper and paperboard. It has made
significant progress after independence with government support and
fiscal incentives. The country is almost self-sufficient in most
varieties of paper and paperboard, and imports are taking place only of
certain speciality items such as coated paper, cheque paper, etc.
However, the industry has failed to keep pace with the technological
advances and is beset with major difficulties such as high production
cost, pollution problems, and finished paper quality not conforming to
international standards.
Mining Sector
Accelerated growth rate of the Indian economy needs rapid
development of the mining sector, on which most of the basic industries
depend. The efforts for locating minerals over the last 55 years have
enhanced reserves of various minerals. The mining sector was opened to
FDI in 1993 and 100 per cent FDI has been allowed since 2000. During the
Eleventh Five Year Plan however, the actual flows for prospecting have
been minimal in the absence of policies conducive to FDI. Attracting FDI
for exploration and prospecting will require a revision of the current
non-investor-friendly mining regime and adoption of a multi-disciplinary
approach, embracing the legal framework, technology, sustainability,
infrastructure, and procedural streamlining.
There is an increasing recognition of the necessity to assess the
efficiency of performance of the manufacturing sector. Efficiency is a
very important factor for productivity growth especially in developing
economies, where resources are scarce and opportunities for developing
and adopting better technology have lately started dwindling. Past
studies showed that productivity can be raised by improving efficiency,
which usually is neglected, without increasing the resource base or
without developing new technologies.
The Data
The data for the current study was collected from secondary sources
like the economic survey and the annual survey of the industries
(various issues). The reference period chosen for the study is from
1991-92 to 2005-06.
DEA Model
There are basically two approaches for estimation of efficiency,
viz., the Stochastic Frontier Approach (SFA) and the Data Envelopment
Approach (DEA). While the stochastic frontier approach (econometric
approach) estimates the efficiency of the firms by estimating the
production function, the DEA technique involves the use of mathematical
programming to estimate the efficiency of the firms / industry. DEA is a
non-parametric, deterministic methodology for determining relatively
efficient production frontier, based on the empirical data on chosen
inputs and outputs of a number of entities called Decision Making Units
(DMUs). From the set of available data, DEA identifies reference points
(relatively efficient DMUs) that define efficient frontier (as the best
practice production technology) and evaluate the inefficiency of other
interior points (relatively inefficient DMUs) that are below the
frontier.
The DEA provides a measure of efficiency that allows intra-firm
comparison, as the efficiency measure is a pure number. The main
advantage of DEA is that unlike SFA, it does not require the a priori
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 possibility of misspecification of
the production technology is minimized. In the case of SFA, the
parameter estimates are sensitive to the choice of the probability
distribution specified for the disturbance term.
There are two approaches to estimating the efficiency of a firm by
DEA viz., the output-oriented efficiency and the input-oriented
efficiency. In the output oriented approach, efficiency is determined by
maximum output that can be produced from an input bundle. In the
input-based measure, the technical efficiency of a firm is evaluated by
the extent to which all inputs could be proportionally reduced without a
reduction in the output. Among a number of DEA models, the two most
frequently used ones (input oriented) the CCR model (Charnes, Cooper
& Rhodes Model) and BCC model (Banker, Charnes & Cooper Model)
have been used in the study. The DEA model has been used to estimate the
technical, scale, cost and allocative efficiency of the industries under
study.
I. Technical Efficiency
(i) CCR Model: Charnes, Cooper and Rhodes (1978) introduced a
measure of efficiency for each DMU that 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.
The efficiency measure is then a function of weights of the
"virtual" input-output combination. Formally, the efficiency
measure for the DMU can be calculated by solving the following
mathematical programming problem:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)
subject to
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)
where [x.sub.ij] is the observed amount of input of the ith type of
the DMU (xij > 0, i = 1, 2, ... n, j = 1, 2, ... n) and yrj = 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 [u.sub.r] and [v.sub.i] are the weights to be
determined by the above programming problem. However, this problem has
infinite number of solutions since if ([u.sup.*], [v.sup.*]) is optimal
then for each positive scalar a (a[u.sup.*], a[v.sup.*]) is also
optimal. Following the above one can select a representative solution
(u, v) for which
[m.summation over (i=1)][v.sub.i][x.sub.io] = 1 (5)
to obtain a linear programming problem that is equivalent to the
linear fractional programming problem (1)-(4). Thus, denominator in the
above efficiency measure h0 is set to equal one and the transformed
linear problem for DMU can be written:
max [z.sub.0] = [s.summation over (r=1)] [u.sub.r][Y.sub.ro] (6)
subject to
[s.summation over (r=1)] [u.sub.r][Y.sub.rj] - [m.summation over
(r=1)]
[v.sub.i][x.sub.ij] [less than or equal to] 0, j = 1, 2, ..., n (7)
[m.summation over (r=1)] [v.sub.i][x.sub.io] = 1 (8)
[u.sub.r] [??] 0, r = 1, 2, ..., s (9)
[v.sub.i] [??] 0, i = 1, 2, ..., m (10)
For the above linear programming problem, the dual can be written
(for the given DMU) as:
min [z.sub.0] = Eo (11)
subject to
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (12)
E [ox.sub.io] - [n.summation over (j=1)] [[lambda].sub.j][x.sub.ij]
[greater than or equal to] 0, i = 1, 2, ..., m (13)
[[lambda].sub.j] 0, j = 1, 2, ..., n (14)
Both the above linear problems yield the optimal solution
[E.sup.*], which is the efficiency score (so-called technical efficiency
or CCR efficiency) for the particular DMU and repeating them for each
DMUj , j = 1, 2, ... n efficiency scores for all of them are obtained.
The value of E is always less than or equal to unity (since when tested,
each particular DMU is constrained by its own virtual input-output
combination too). DMUs for which [E.sup.*][]1 are relatively inefficient
and those for which [E.sup.*][] = 1 are relatively efficient, having
their virtual input-output combination points lying on the frontier. The
frontier itself consists of linear facets spanned by efficient units of
the data and the resulting frontier production function (obtained with
the implicit constant returns to scale assumption) has no unknown
parameters.
ii. BCC Model: Since there are no constraints for the weights ej,
other than the positivity conditions in the problem (11)-(14), it
implies constant returns to scale. For allowing variable returns to
scale, it is necessary to add the convexity condition for the weights
ej, i.e. to include in the model (11)-(14) the constraint:
[n.summation over (j=1)] [[lambda].sub.j] = 1 (15)
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 [z.sub.0] = Eo (16)
subject to
[n.summation over (j=1)] [[lambda].sub.r][Y.sub.rj] [greater than
or equal to] r = 1, 2, ..., s (17)
Eo[X.sub.10] - [n.summation over (j=1)][[lambda].sub.j][x.sub.ij]
[greater than or equal to] 0, i = 1, 2, ..., m (18)
[n.summation over (j=1)][[lambda].sub.j] =1 (19)
[[??].sub.j] [??] 0, j = 1, 2, ..., n (20)
Running the above model for each DMU, the BCC efficiency scores are
obtained (with similar interpretation of its values as in the CCR
model). These scores are also called "pure technical efficiency
scores", since they are obtained from the model that allows
variable returns to scale and hence eliminates the "scale
part" of the efficiency from the analysis. Generally, for each DMU
the CCR efficiency score will not exceed the BCC efficiency score, what
is intuitively clear since in the BCC model each DMU is analysed
"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 efficiency is defined as follows. For a particular
DMU, the scale efficiency is defined as the ratio of its overall
technical efficiency score (measured by the CCR model) and pure
technical efficiency score (measured by the BCC model).
III. Cost Efficiency
The standard measure of cost efficiency is obtained via a two stage
process: i) estimate the minimum price-adjusted resource usage given
technological constraints, and (ii) compare this minimum to actual,
observed costs. Cost efficiency can be measured if input prices are
available in addition to output and input data. Let x = ([x.sub.1], ....
[x.sub.k]) [??]R + k denotes a vector of inputs and y = (y1, .... Ym)
[??]R + m denote vector of outputs. Formally, the cost efficiency model
can be specified as :
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (21)
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) [??]R + [sup.k] denoted input
prices. The constraints of the model (21) define the input requirement
set given by:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (22)
The input requirement set specifies a convex technology with
Variable Returns to Scale (VRS)n, which is imposed by the constraint
[n.summation over (i=1)] [z.sub.i] = 1. Leaving the constraint out of
the model changes the technology to Constant Returns to Scale (CRS).
IV. Allocative Efficiency
Allocative efficiency is defined as a ratio of cost efficiency
score to technical efficiency score. This efficiency score was estimated
for the present study both under CRS production and VRS production
technologies.
Results & Discussion
Technical Efficiency: The results regarding technical efficiency
scores of the selected intermediary goods industries are presented in
Table 1.
Under Constant Returns to Scale (CRS) production technology,
technical efficiencies during the period 1991-92 to 2005-06 were 0.834,
0.677, 0.820 and 0.815 respectively for industries manufacturing
Chemical & Chemical Products, Paper & Paper Products, Leather
& Leather Products and Non-Metallic Mineral Products. This implies
that the industries needed only 83.4 percent, 67.7 per cent, 82.0 per
cent, and 81.5 percent of the inputs currently being used. In terms of
average inefficiency they would have need 19.9 per cent, 47.7 per cent
21.9 per cent and 22.6 per cent more inputs to produce the same output,
which implies waste of resources to the extent mentioned above.
Under VRS production technology, the number of efficient DMUs
exceeded the number of efficient DMUs under CRS production technology.
Under VRS production technology, higher average efficiency was always
recorded. It may be due to the reason that DMUs that were efficient
under Constant Returns of Scale (CRS) were accompanied by new efficient
DMUs that could operate under increasing or decreasing returns to scale.
Higher degree of average technology inefficiency, particularly under CRS
production technology, can be attributable to the fact that the
industries may not be using the most efficient technology available to
transform the inputs into outputs. Due to differences in products
produced, the industries were likely to have different best practice
frontiers; relatively small regional spheres of operation of the
industries may have resulted in inefficiencies; and structured problems
regarding staff efficiency and operating efficiency may have prevented
the firms from improving their efficiency levels. It can be concluded
that though the efficiency of the firms varied considerably on account
of the various reasons mentioned, all the firms were estimated to be on
the frontiers at least once. In other words, both under CRS and VRS
technologies, the efficiency scores or levels during the entire period,
are indicative of the fact that the efficiency of firms was not strongly
influenced by the size of production.
Scale Efficiency: The scale efficiency scores of all the industries
selected, for the present study are presented in Table 2.
DEA results applied to know the scale efficiency of industries for
the entire period revealed that the industries were not operating at an
optimum scale. The average scale efficiency of manufacturing Paper and
Paper Products was maximum (97.9 percent), followed by Leather and
Leather Products (92.4 percent), Chemical and Chemical Products (92.1
per cent) and Non-Metallic Mineral Products (84.4 per cent). In terms of
average inefficiency, production could increase to the extent of 2.1 per
cent, 8.2 per cent and 18.4 per cent respectively in the above
industries, by taking advantage of their scale characteristics. DEA
allows assessment of whether a firm lies in the range of increasing,
constant or decreasing returns to scale. In other words, it reveals the
scale characteristics of DMUs. Market efficiency can be increased if
more DMUs attain constant returns to scale, because fewer resources are
wasted. The measurement of economies of scale, therefore, helps assess,
at the same time whether higher market concentration should be
encouraged to improve efficiency. A DMU may be scale inefficient, if it
experiences decreasing returns to scale or if it has not taken full
advantages of increasing returns to scale. Indeed most of the
inefficient DMUs presented increasing returns to scale characteristics
which indicates that industries can increase the scale to effectively
improve that efficiency. It is clear that inefficiency can be due to the
existence of either increasing or decreasing returns to scale.
Cost Efficiency: Table 3 gives details regarding cost efficiency
scores of selected industries for the reference period under study.
Under Constant Returns to Scale (CRS) technology, industries
manufacturing Chemicals and Chemical Products, Paper and Paper Products,
Leather and Leather Products and Non-Metallic Mineral Products were
efficient to the extent of 73.9 percent, 52 .9 per cent, 65.3 per cent
and 79 per cent respectively. Under Variable Returns to Scale (VRS)
production technology the same industries were more efficient to the
extent of 87.2 per cent, 58.9 per cent, and 77.8 percent and 83.8
percent respectively. The cost efficient DMU's were more under VRS
production technology. The average cost inefficiency was more under CRS
production technology than under VRS production technology.
Allocative Efficiency: Allocative efficiency scores of the
industries for the reference period is presented in Table 4.
Over the study period, the industries under CRS production
technology had on an average, allocative efficiency level of 89.0 per
cent, 75.0 per cent, 79.8 percent and 97.0 per cent for Chemical and
Chemical Products, Paper and Paper Products, Leather and leather
Products, and Non-metallic Mineral Products respectively implying that
the industries were 11 per cent, 25 per cent, 20.2 percent and 3 per
cent inefficient respectively. In the case of VRS production technology,
an average allocative efficiency of 92.5 per cent, 78.7 per cent, 85.4
per cent and 86.9 per cent could be observed by industries manufacturing
Chemical and Chemical Products, Paper and Paper Products, Leather and
Leather Products and Non-Metallic Mineral Products respectively implying
that the industries were on an average 7.5 per cent, 21.3 per cent, 14.6
per cent and 13.1 per cent inefficiency respectively in these
industries. More efficient DMUs were observed under VRS production
technology compared to CRS production technology. The average
inefficiency scores were more for Paper and paper Products both under
CRS and VRS Production technologies.
Conclusion
For the entire period, technical, scale, cost and allocative
efficient DMUs were more under Variable Returns to Scale (VRS)
production technology than under Constant Returns to Scale (CRS)
production technology. Inefficiency could be due to the existence of
either increasing or decreasing returns to scale. Technical efficiency
was more in industries manucaturing Chemical and Chemical Products both
under CRS and VRS production technologies. Technical inefficiency was
more in industries manucaturing Paper and Paper Products under CRS and
VRS production technologies. Cost efficiency both under CRS and VRS
production technologies, was more in industries manufacturing
Non-Metallic Mineral Products, while cost inefficiency was more in
industries manufacturing Paper and Paper Products under CRS production
technology. Industries manufacturing Paper and Paper Products were found
to be cost inefficient, when compared to other industries under VRS
production technology. Allocative efficiency under CRS and VRS
technology was observed more in industries manufacturing Non-Metallic
Mineral Products. On the contrary, high allocative inefficiency was
observed in industries manufacturing Paper and Paper Products under CRS
production technology, while Paper and Paper Products proved as an
allocatively inefficient industry under VRS production technology when
compared to the others.
References
Central Statistical Organisation (CSO), Annual Survey of Industries
(ASI),various issues, Government of India, New Delhi.
Eleventh Five Year Plan (2007-12) Agriculture, Rural Development,
Industry, Services and Physical Infrastructure, Volume III, Government
of India.
Ministry of Finance, Economic Survey, various issues, Government of
India, New Delhi.
Charnes, A. Cooper, W. W. & Rhodes E. (1978), "Measuring
the Efficiency of Decision Making Units", European Journal Of
Operation Research, 2: 429-44.
Banker, R. D. Charnes, A. & Cooper, W.W. (1984), "Some
Models for Estimating Technical & Scale Inefficiencies in Data
Envelopment Analysis", Management Science, 30:1078-2092.
M. Manonmani is Associate Professor (E-mail: manomyil@yahoo.com)
& M.Ramya (E-mail: punitha.mramya0@gmail.com) is Research Scholar in
the Department of Economics, Avinashilingam Deemed University for Women,
Coimbatore-641043
Table 1: Technical Efficiency (TE) Estimates
Chemical and Paper and
Industry Chemical Products Paper Products
DMU CRS VRS CRS VRS
1991-92 0.793 1.000 0.968 1.000
1992-93 0.942 1.000 0.934 0.968
1993-94 0.982 1.000 1.000 1.000
1994-95 0.886 0.949 0.815 0.997
1995-96 0.886 0.910 1.000 1.000
1996-97 0.840 0.868 0.905 1.000
1997-98 0.677 0.822 0.793 0.951
1998-99 0.725 0.907 0.598 0.704
1999-00 0.681 0.893 0.393 0.396
2000-01 0.763 1.000 0.427 0.427
2001-02 0.689 0.882 0.432 0.433
2002-03 0.870 0.939 0.411 0.415
2003-04 0.876 0.983 0.478 0.483
2004-05 1.000 1.000 0.475 0.480
2005-06 1.000 1.000 0.524 0.536
Average 0.834 0.944 0.677 0.719
Technical
Efficiency
(1991-92
to 2005-06)
Average 0.199 0.059 0.477 0.30.219
Technical
Inefficiency
1991-92 to
2005-06
No. of 2 6 2 4
Technical
Inefficient
DMUs
(1991-92 to
2005-06)
Leather and Non-Metallic
Industry Leather Products Mineral Products
DMU CRS VRS CRS VRS
1991-92 0.863 1.000 1.000 1.000
1992-93 0.735 0.998 0.738 0.988
1993-94 1.000 1.000 0.747 1.000
1994-95 0.693 0.855 0.792 0.999
1995-96 0.712 0.863 1.000 1.000
1996-97 0.731 0.876 0.709 0.970
1997-98 0.957 1.000 0.775 1.000
1998-99 0.946 0.759 0.750 0.966
1999-00 0.770 0.814 0.731 0.958
2000-01 0.766 0.810 0.728 0.865
2001-02 0.832 0.906 0.752 0.960
2002-03 0.783 0.882 0.708 0.762
2003-04 0.902 0.964 0.795 1.000
2004-05 0.807 0.886 1.000 1.000
2005-06 1.000 1.000 1.000 1.000
Average 0.820 0.907 0.815 0.965
Technical
Efficiency
(1991-92
to 2005-06)
Average 0.102 0.226 0.036
Technical
Inefficiency
1991-92 to
2005-06
No. of 2 4 4 7
Technical
Inefficient
DMUs
(1991-92 to
2005-06)
Note: Calculations based on ASI data. CRS-Constant
Returns to Scale. VRS-Variable Returns to scale
Table 2: Scale Efficiency (SE) Estimation
Industry Chemical RTS Paper RTS Leather RTS Non- RTS
and and and Metallic
Chemical Paper Leather Mineral
Products Products Products Products
1991-92 0.793 IRS 0.968 IRS 0.899 IRS 1.000 CRS
1992-93 0.942 IRS 0.944 IRS 0.910 IRS 0.806 IRS
1993-94 0.982 IRS 1.000 CRS 1.000 CRS 0.795 IRS
1994-95 0.922 IRS 0.933 IRS 0.934 IRS 0.823 IRS
1995-96 0.953 IRS 1.000 CRS 0.859 IRS 1.000 CRS
1996-97 0.939 IRS 1.000 CRS 0.861 IRS 0.722 IRS
1997-98 0.974 IRS 1.000 CRS 1.000 CRS 0.777 IRS
1998-99 0.900 IRS 1.000 CRS 1.000 CRS 0.762 IRS
1999-00 0.899 IRS 0.935 IRS 0.920 IRS 0.741 IRS
2000-01 0.780 IRS 0.978 IRS 0.950 IRS 0.824 IRS
2001-02 0.905 IRS 0.950 IRS 0.882 IRS 0.757 IRS
2002-03 0.923 IRS 0.990 IRS 0.824 IRS 0.852 IRS
2003-04 0.909 IRS 0.982 IRS 0.940 IRS 0.795 IRS
2004-05 1.000 CRS 1.000 CRS 0.882 IRS 1.000 CRS
2005-06 1.000 CRS 1.000 CRS 1.000 CRS 1.000 CRS
Average 0.921 -- 0.979 -- 0.924 -- 0.844 --
Scale
Efficiency
(1991-92 to
2005-06)
Average 0.085 -- 0.021 -- 0.082 -- 0.184 --
Scale
Inefficiency
(1991-92 to
2005-06)
No. of 2 7 -- 4 -- 4 --
Scale
Inefficient
DMUs (1991-
92 to 2005-
06) to
2005-06)
Note: Calculations based on ASI data. RTS--Returns to Scale.
IRS--Increasing Returns to Scale. DRS--Decreasing Returns to
Scale. CRS--Constant Returns to Scale.
Table 3: Cost Efficiency (CE) Estimates
Industry Chemical and Paper and
Chemical Products Paper Products
DMU CRS VRS CRS VRS
1991-92 0.507 1.000 0.735 1.000
1992-93 0.651 0.997 0.752 0.692
1993-94 0.739 1.000 0.898 1.000
1994-95 0.726 0.923 0.678 0.722
1995-96 0.794 0.900 1.000 1.000
1996-97 0.722 0.833 0.708 0.732
1997-98 0.639 0.729 0.613 0.649
1998-99 0.711 0.783 0.589 0.619
1999-00 0.680 0.743 0.305 0.358
2000-01 0.700 0.757 0.323 0.360
2001-02 0.656 0.707 0.234 0.264
2002-03 0.801 0.840 0.250 0.268
2003-04 0.844 0.870 0.266 0.285
2004-05 1.000 1.000 0.267 0.285
2005-06 0.910 1.000 0.321 0.325
Average 0.739 0.872 0.529 0.589
Cost
Efficiency
(1991-92
to 2005-06)
Average 0.353 0.146 0.890 0.697
Cost
Inefficiency
(1991-92 to
2005-06)
No. of Cost 1 4 1 3
Inefficient
DMUs
(1991-92
to 2005-06)
Industry Leather and Non-Metallic
Leather Products Mineral Products
DMU CRS VRS CRS VRS
1991-92 0.724 1.000 0.907 1.000
1992-93 0.689 0.904 0.678 0.935
1993-94 1.000 1.000 0.703 0.833
1994-95 0.634 0.695 0.759 0.820
1995-96 0.596 0.630 0.970 1.000
1996-97 0.583 0.601 0.697 0.731
1997-98 0.782 0.862 0.765 0.791
1998-99 0.636 0.715 0.744 0.764
1999-00 0.581 0.694 0.727 0.745
2000-01 0.566 0.692 0.725 0.737
2001-02 0.588 0.725 0.725 0.726
2002-03 0.550 0.643 0.700 0.712
2003-04 0.642 0.809 0.766 0.778
2004-05 0.560 0.699 0.996 0.998
2005-06 0.671 1.000 1.000 1.000
Average 0.653 0.778 0.790 0.838
Cost
Efficiency
(1991-92
to 2005-06)
Average 0.531 0.285 0.265 0.193
Cost
Inefficiency
(1991-92 to
2005-06)
No. of Cost 1 3 1 3
Inefficient
DMUs
(1991-92
to 2005-06)
Note: Calculations based on ASI data. CRS--Constant
Returns to Scale. VRS--Variable Returns to Scale.
Table 4: Allocative Efficiency (AE) Estimates
Industry Chemical and Paper and
Chemical Products Paper Products
DMU CRS VRS CRS VRS
1991-92 0.640 1.000 0.760 1.000
1992-93 0.691 0.997 0.804 0.993
1993-94 0.753 1.000 0.898 1.000
1994-95 0.819 0.973 0.832 0.725
1995-96 0.897 0.989 1.000 1.000
1996-97 0.898 0.960 0.783 0.732
1997-98 0.944 0.887 0.774 0.683
1998-99 0.980 0.863 0.985 0.880
1999-00 0.998 0.832 0.776 0.903
2000-01 0.928 0.755 0.756 0.844
2001-02 0.953 0.802 0.541 0.609
2002-03 0.993 0.892 0.607 0.645
2003-04 0.962 0.886 0.556 0.590
2004-05 1.000 1.000 0.563 0.595
2005-06 0.910 1.000 0.611 0.607
Average 0.890 0.925 0.750 0.787
Allocative
Efficiency
(1991-92 to
2005-06)
Average 0.123 0.081 0.333 0.270
Allocative
Inefficiency
(1991-92 to
2005-06)
No. of Allocative 1 4 1 3
Inefficient
DMUs
(1991-92 to
2005-06)
Industry Leather and Non-Metallic
Leather Products Mineral Products
DMU CRS VRS CRS VRS
1991-92 0.839 1.000 0.907 1.000
1992-93 0.937 0.906 0.910 0.945
1993-94 1.000 1.000 0.941 0.833
1994-95 0.914 0.813 0.958 0.821
1995-96 0.837 0.729 0.970 1.000
1996-97 0.797 0.687 0.983 0.754
1997-98 0.817 0.762 0.987 0.791
1998-99 0.852 0.942 0.991 0.791
1999-00 0.754 0.853 0.995 0.778
2000-01 0.740 0.855 0.996 0.852
2001-02 0.707 0.800 0.951 0.756
2002-03 0.703 0.729 0.989 0.934
2003-04 0.712 0.840 0.964 0.778
2004-05 0.694 0.789 0.996 0.998
2005-06 0.671 1.000 1.000 1.000
Average 0.798 0.854 0.970 0.869
Allocative
Efficiency
(1991-92 to
2005-06)
Average 0.253 0.170 0.030 0.150
Allocative
Inefficiency
(1991-92 to
2005-06)
No. of Allocative 1 3 1 3
Inefficient
DMUs
(1991-92 to
2005-06)
Note: Calculations based on ASI data. CRS--Constant
Returns to Scale. VRS--Variable Returns to Scale.
