Are our export-oriented industries technically more efficient?
Mahmood, Tariq ; Ghani, Ejaz ; ud Din, Musleh 等
This paper makes a comparison of technical efficiency scores
between groups of exporting and non-exporting industries. Using data
from Census of Manufacturing Industries in Pakistan (2005-06), technical
efficiency scores of 102 large scale manufacturing industries are
estimated. Stochastic Frontier Analysis as well as Data Envelopment
Analysis technique are used to estimate technical efficiency scores. In
Stochastic Frontier Analysis Translog and Cobb-Douglass Production
Functions are specified, whereas in Data Envelopment Analysis technique,
efficiency scores are computed under the assumptions of Constant Returns
to Scale as well as Variable Returns to Scale. Industries showing high
technical efficiency include Tobacco Products, Refined Petroleum
Products, Carpets and Rugs, and Meat and Meat Products. Industries
showing low technical efficiency include Refractory Ceramic Products,
Electricity Distribution and Control Apparatus, Fish and Fish Products,
Basic Precious Metals and Aluminum and its Products. Comparison of mean
efficiency scores between exporting and non-exporting industries does
not indicate any significant difference between efficiency scores across
types of industries.
JEL Classification: D24, L6, 014, F14
Keywords: Manufacturing Industries, Technical Efficiency,
Stochastic Frontier Analysis, Data Envelopment Analysis, International
Trade
1. INTRODUCTION
It is generally believed that export-oriented industries are better
able to exploit economies of scale due to widening of markets and their
exposure to international competition is a major driving force in their
adoption of advanced production and marketing techniques. Opportunity
cost of idle capacity for these industries is higher, which induces
managers to use inputs up to full capacity. On the other hand
non-exporting industries (industries with relatively smaller proportion
in national exports) work in relatively more protected environment in
the form of tariffs and quotas, have small domestic market to sell their
products, and their production and marketing techniques are not well
up-to-date. These factors may make export-oriented industries more
efficient than import-substitution industries.
These arguments seem plausible but the superiority of
export-oriented industries in terms of technical efficiency is an
empirical question. The theory of international trade suggests that
international trade is driven by factors like comparative advantage and
relative factor endowments and factor intensities across countries. On
the other hand technical efficiency determines how optimally a producer
uses inputs in the production of outputs in a group of producers,
usually within a country. Therefore the only way to check whether
exporting industries in a country are comparatively more efficient than
non-exporting industries is to test the hypothesis against real data.
Empirical evidence contrary to above hypothesis is not difficult to find
[see for example Walujadi (2004)]. In this paper we aim to
estimate/compute technical efficiency scores for large-scale
manufacturing industries in Pakistan. Once these scores are obtained,
statistical techniques can be applied to test the hypothesis that
export-oriented industries are technically more efficient.
The objective of this paper is two-fold: First, it aims to provide
a comparison between technical efficiency scores between groups of
exporting industries and non-exporting industries. Second, it identifies
the most efficient and least efficient industries in terms of technical
efficiency among all manufacturing industries reported in the Census of
Manufacturing Industries in Pakistan. More specifically, we compute the
technical efficiency scores for the large scale manufacturing industries
in Pakistan and employ statistical techniques to test the hypothesis
that export-oriented industries are technically more efficient. (1) In
the literature technical efficiency is typically estimated/computed by
comparison of input-output combination of a Decision Making Unit
(industry in this case) with reference to a production frontier, which
can be found through various techniques including Stochastic Production
Frontier and Data Envelopment Analysis.
The remainder of this paper is structured as follows: Section 2
presents a theoretical review of efficiency measurement. Recent
empirical literature on efficiency of manufacturing firms and industries
is reviewed in Section 3. In Section 4 methodology and data are
discussed. Empirical results are given in Section 5, and Section 6
concludes the discussion.
2. A THEORETICAL REVIEW OF EFFICIENCY MEASUREMENT
Koopmans (1951, p. 60) defines a producer as technically efficient
if an increase in any output requires a reduction in at least one other
output or an increase in at least one input, and if a reduction in any
input requires an increase in at least one other input or a reduction in
at least one output. In other words, with a given technology a producer
is technically efficient if it is not possible to produce more output
from the same inputs nor the same output with less of one or more inputs
without increasing the amount of other inputs. Debreu (1951) and Farrell
(1957) define technical efficiency as one minus the maximum
equi-proportionate reduction in all inputs that still allows continued
production of given outputs (or alternatively, equi-proportionate
expansion in outputs with given inputs). A score of unity would imply
that the producer is technically efficient and a score of less than one
would indicate the extent of technical inefficiency.
Although Koopman's definition is theoretically more stringent,
in empirical studies the definition proposed by Debreau and Farrell is
more commonly used. The reason is that technical efficiency thus defined
can be described in terms of a distance function. (2)
An output distance function is defined as:
Do (x, y) = min ([gamma] : y/[gamma][epsilon] P(y)}
Where x and y are input and output vectors respectively, and P(y)
is the feasible production set. In other words output distance function
measures how much outputs can be radially expanded for given level of
inputs while still remaining within the feasible production set.
Similarly input distance functions can be defined as follows:
Di (y, x) = max{[delta] : x/[delta][epsilon] L(y)}
Where x and y are again input and output vectors respectively, and
L(y) is the input requirement set. This function measures radial
contraction in inputs for a given level of output while still remaining
within the input requirement set.
Estimation of Technical Efficiencies
The pioneering work for measurement of technical efficiency was
done by Farrell (1957). (3) This measurement involves the estimation of
a frontier against which the performance of productive units can be
compared. Following these early works, many writers tried different
techniques to estimate/compute the production frontier and efficiencies.
Broadly, these techniques can be divided in two major groups:
* Parametric Techniques, and
* Non-Parametric Techniques
Choice of Techniques
Parametric Techniques are based on econometric regression models.
Usually a stochastic production, cost, or profit frontier is used, and
efficiencies are estimated with reference to that frontier. Parametric
techniques require a functional form, and random disturbances are
allowed for in the model. Usual tests of significance can be performed
in these models. Non-parametric techniques on the other hand do not
require a functional form; do not allow for random factors; and all
deviations from the frontier are taken as inefficiencies. Consequently,
inefficiencies in non-parametric techniques are expected to be higher
than those in parametric techniques. Moreover, tests of significance
cannot be performed in non-parametric techniques.
The commonly used parametric efficiency techniques are the
stochastic frontier analysis (SFA), the thick frontier approach (TFA),
and the distribution-free approach (DFA). Whereas, among non-parametric
techniques, data envelopment analysis (DEA) and free disposable hull
(FDH) are more commonly used. Unlike SFA, which can be applied on
cross-sectional as well as on panel data, DFA requires panel data for
estimation. Since data on manufacturing industries in Pakistan is not a
panel dataset, DFA becomes unsuitable. Likewise FDH is quite stringent
regarding input substitution. As pointed out by Berger and Humphrey
(1997):
"DEA presumes that linear substitution is possible between
observed input combinations on an isoquant (which is generated from the
observations in piecewise linear forms). In contrast, FDH presumes that
no substitution is possible so the isoquant looks like a step function
formed by the intersection of lines drawn from observed (local)
Leontief-type input combinations."
Since we are using industry-level data, the assumption of no
substitution between inputs would not be quite reasonable. The major
issue with Thick Frontier Technique (TFA) is that it does not provide a
set of individual efficiency scores, which is, in fact, one of the key
objectives of this paper. With these considerations, this study uses two
most commonly used techniques, one parametric and one non-parametric
technique viz. Stochastic Frontier Analysis (SFA), and Data Envelopment
Analysis (DEA). These techniques are explained below, but first we shall
briefly review the concepts of Input-and Output-Orientation of technical
efficiency measurement.
Output- and Input-Orientations
Technical efficiency can be defined either with input-orientation
or with an output-orientation. The input-oriented approach defines
technical efficiency in terms of proportional reduction in inputs while
holding output level constant. The output-oriented approach, on the
other hand measures technical efficiency in terms of proportional
increase in output while holding input levels constant. This study uses
output oriented measure of technical efficiency.
Graphical Representation of Technical Efficiency
Technical efficiency measures how optimally a producer is using
inputs in relation to output. In Figure 1 the curve represents the
production frontier. For production point A, the output-oriented measure
of technical efficiency is given by:
Technical Efficiency = aA/ ab
[FIGURE 1 OMITTED]
This measure of technical efficiency equals the output distance
functions [Coelli, et al. (2005), pp. 53,56].
Stochastic Frontier Analysis
The SFA is an econometric technique introduced independently by
Aigner, Lovell, and Schmidt (1977) and Meeusen and Broeck (1977). In
this technique the error term of the model is divided into two
components, random noise and inefficiency component. Being a parametric
technique, SFA requires a functional form, and usual tests of
significance can be performed with this technique.
A stochastic production frontier model can be written in general
form as:
y = f([x.sub.0] [beta]) + [v.sub.i] - [u.sub.i]
Where:
[y.sub.i] is the observed scalar output of the producer i, i =
1,..I,
[x.sub.i] is a vector of N inputs used by the producer i,
f([x.sub.0], [beta]) is the production frontier,
[beta] is a vector of technology parameters to be estimated,
[v.sub.i] is the random error, and
[u.sub.i] is the non-negative random variable associated with
technical inefficiency.
In literature different assumptions have been used about
distribution of inefficiency term, [u.sub.i]. Afriat (1972) assumes
[u.sub.i] to have a gamma distribution; Stevenson (1980) uses truncated
normal distribution; and Greene (1990) uses two-parameter gamma
distribution. Exponential distribution was suggested by Aigner, Lovell,
and Schimidt (1977), and Meeusen and Broeck (1977). Flowever, as pointed
by Coelli, et al. (2005), p. 252, rankings of predicted technical
efficiencies are quite often robust to distributional choice. In this
study we assume [u.sub.i], to follow exponential distribution. (4)
The Ordinary Least Square estimation of the above model provides
consistent estimates of, slope parameters but not of intercept. More
importantly, we cannot obtain efficiency estimates through OLS
[Kumbhakar and Lovell (2000), p. 73]. This issue is resolved by applying
maximum likelihood estimation technique to obtain consistent parameter
estimates as well as efficiency scores. The estimated model forms the
basis for computing a predictor of technical efficiencies. The estimates
of technical efficiency are obtained as a mean of the conditional
distribution of [u.sub.i] given [[epsilon].sub.i], where
[[epsilon].sub.1] = [v.sub.i]-[u.sub.i] [Kumbhakar and Lovell (2000), p.
82].
The next step is to check the significance of inefficiencies
estimated by the model, i.e. to test the null hypothesis of no
inefficiencies against the alternative hypothesis that inefficiencies
are present. As suggested by Coelli (1996), a one-sided likelihood ratio
test with a mixed chi-square distribution ([[bar.[chi]].sup.2] =
[[chi].sup.2.sub.0] + 1/2 + [[chi].sup.2.sub.1]) is appropriate here.
Therefore, the null hypotheses will be rejected if LR >
[[bar.[chi]].sup.2]
Once technical efficiency scores are obtained, we can test whether
mean efficiency scores of exporting and non-exporting industries are
statistically same or not. We can divide industries in two groups i.e.
exporting and non-exporting industries. Then the following t-test can be
applied to test the equality of mean efficiency score of these two
groups.
t = ([[bar.x].sub.1]- [[bar.x].sub.2])/[square root of
([[S.sup.2.sub.p]/[n.sub.1] + [S.sup.2.sub.p]/[n.sub.2]])]
Where [s.sup.2.sub.p] is the pooled variance of two groups, given
by the formula:
[s.sup.2.sub.p] = {([n.sub.1], -1)[S.sup.2.sub.1] + ([n.sub.2] -
2)[S.sup.2.sub.2]} /([n.sub.1] + [n.sub.2] - 2)
[[bar.x].sub.1] and [[bar.x].sub.2] are average efficiency scores
of two groups, [s.sup.2.sub.1] and [s.sup.2.sub.2] are variances of
average efficiency scores of two groups, and [n.sub.1] and [n.sub.2] are
respective number of industries in two groups.
Data Envelopment Analysis
The Data Envelopment Analysis (DEA) is a mathematical programming
technique for the construction of a production frontier. It is an
alternative technique for efficiency measurement and possesses certain
advantages of its own. It can handle multiple outputs and multiple
inputs, and it places no restriction on the functional form of the
relationship among inputs and outputs. DEA has some limitations as well.
Being a non-parametric technique, DEA is not amenable to direct
application of tests of significance and statistical hypothesis testing,
and statistical noise is not allowed for.
The DEA models differ in the assumptions that are made about the
technology set. The most important assumptions are: free disposability,
convexity, returns to scale, and additivity. The free disposability
assumption implies that unnecessary inputs and unwanted outputs can be
freely discarded. The assumption of convexity assumption implies that
any convex combination of feasible production points is feasible as
well. The assumption of returns to scale implies possibility of
rescaling. The additivity assumption implies that when some production
plans are feasible, their sum will also be feasible. (5)
We have applied DEA under two possible returns to scale
assumptions: (i) Constant returns to scale, and (ii) Variable returns to
scale.
The constant returns to scale model is attributed to Charnes,
Cooper, and Rhodes (1978). The model was modified by Banker, Charnes,
and Cooper (1984) by imposing an additional convexity constraint to
obtain VRS model.
Data Envelopment Analysis can be employed by adopting either of two
approaches, viz. output-oriented approach or input-oriented approach.
The efficiency scores obtained from these two alternative approaches are
identical if constant returns to scales (CRS) are assumed, but are
different under the assumption of variable returns to scale (VRS)
[Coelli, et al. (2005), p. 180], Moreover, "output- and
input-oriented DEA will estimate exactly the same frontier and
therefore, by definition, identify the same set of firms as being
efficient. It is only the efficiency measures associated with the
inefficient firms that may differ between the two methods." [Coelli
(2005), p. 181].
[FIGURE 2 OMITTED]
Figure 2 depicts production frontiers under the assumption of CRS
and VRS. These are in fact optimal combinations of inputs and outputs.
For an industry producing at point b, technical efficiency under CRS
will be the ratio ab/ad. Whereas under the assumption of VRS, the
technical efficiency measure will be the ratio ab/ac. VRS model gives
higher efficiency scores since the frontier fits data more tightly than
in the case of CRS.
It is assumed that there are n industries (J = 1,2, ..., n), each
using m different inputs (h = 1,2, ..., m) and producing a single
output. Moreover, it is assumed that [x.sub.hj] > 0 and [y.sub.j]
[greater than or equal to] 0 so that each industry uses at least one
positive input and produces positive output. The analysed industry is
indicated with subscript i. The objective and the constraint of the
industry i are given by:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
The vectors u and v represent weights with the restriction that
these weights are non-negative. Consequently, neither an output nor an
input can be negative. These weights are computed in such away that the
efficiency of the analysed industry i is at a maximum and becomes
smaller for any other value of u and v. The above objective function is
not actually used to compute technical efficiencies. Rather, it is
converted into the following linear programming problem:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
The duality property of linear programming can be used to convert
the above problem into the following envelopment form:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Where [PHI] is a scalar, and X is a vector of constants. X and Y
represent input and output matrices for all industries. The scalar [PHI]
is the largest factor by which all outputs of industry i can be raised.
The reciprocal of [PHI] is the technical efficiency of the z'th
industry. It represents the proportional increase in output that could
be achieved by the ith industry, with inputs being held constant.
The above programme is for CRS model. For VRS additional convexity
constraint (e'[lambda]=1) is imposed in the model. The VRS model is
written as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Where e' is a vector of ones.
The convexity constraint ensures that an inefficient industry is
only "benchmarked" against industry of a similar size. That
is, the projected point for that industry on the DEA frontier is a
convex combination of observed industries [Coelli, (2005), p. 172],
3. A REVIEW OF EMPIRICAL LITERATURE
A detailed review of studies regarding performance of manufacturing
sectors in developing countries has been done by Tybout (2000). In the
following pages we shall present a brief review of some recent empirical
studies, which specifically address the issue of efficiency of
manufacturing industries.
Mukherjee and Ray (2004) analyse state level data to study the
efficiency dynamics of individual states in India. The study uses data
from Annual Survey of Industries for the period 1986-87 to 1999-00. Data
Envelopment Analysis technique is used to construct super-efficiency
ranking the states in terms of their performance. Stability of
efficiency ranking is checked as well as effect of economic reforms
introduced in the 1990s. Although considerable variations in efficiency
scores are found across the states, no major change is observed in the
efficiency ranking of states after the reforms. The study also finds
that there is no evidence of convergence in the distribution of
efficiency in the post-reform period.
Tripathy (2006) examines efficiency gap between foreign and
domestic firms in eleven manufacturing industries of India during
1990-2000. Two different techniques, i.e. SFA and DEA are used to
measure efficiency of the firms. The study assumes a Cobb-Douglas
technology and estimates stochastic production and cost frontier in each
industry to measure technical efficiency and cost efficiency of each
firm as well as to obtain some inference on allocative efficiency.
Alvarez and Crespi (2003) explore differences in technical
efficiency in Chilean manufacturing firms applying Data Envelopment
Analysis technique on plant level. The study uses a sample of 1,091
observations covering all industrial sectors in Chilean Industry
according to ISIC three digits. The firms are classified in small,
medium and large categories in terms of their annual sales. The
efficiency scores indicate that medium firms perform better than the
small or large firms. "Professional and scientific equipment"
and "Non-metallic mineral products" turn out to be most
efficient, whereas, "Agro-industry" and "Textiles"
are least efficient. Further, regression analysis is performed to
identify some determinants of firms' efficiency. Firms'
characteristics like experience are not found to be related with
efficiency. On the other hand input quality variables, such as worker
experience, product differentiation, and modernisation of capital, are
found to positively affect the efficiency of firms.
Ikhsan-Modjo (2006) examines the patterns of total factor
productivity growth and technical efficiency changes in Indonesia's
manufacturing industries over the period 1988-2000. The study uses the
data incorporating both the liberalisation years and the crisis/post
crisis years sourced from an annual panel survey of manufacturing
establishments. A translog frontier production function is estimated.
Gross output is regressed on inputs like the cost of capital, wages,
intermediate inputs and energy, and the study finds that technical
progress is the most important factor in explaining TFP growth in the
Indonesian manufacturing sector.
Kneller and Stevens (2006) investigate whether absorptive capacity
helps to explain cross-country differences in the level of technical
efficiency. The study uses stochastic frontier technique to estimate a
frontier. Industries' output is assumed to depend on four inputs
viz. physical capital, effective labour supply (the number of workers
adjusted for average hours per week), the stock of human capital and the
stock of knowledge. Inefficiency effects are modelled as dependent
variable and the independent variables are the level of investment in
research and development, level of human capital and country specific
dummies. The data consist of a sample of nine manufacturing industries
in 12 OECD countries over the period 1973-91. The results indicate
differences across countries in efficiencies. It is found that human
capital plays a significant and quantitatively important role in
explaining these differences.
Din, et al. (2007) analyse the efficiency of large scale
manufacturing sector in Pakistan using the stochastic frontier as well
as data envelopment analysis. The study compares the efficiency scores
for the years 1995-96 and 2000-01. The results show that there has been
some improvement in the average efficiency of the large scale
manufacturing sector from the year 1995-96 to 2000-01. Stochastic
frontier technique shows an improvement from 0.58 to 0.65, while for
data envelopment analysis the efficiency scores increase from 0.23 to
0.42 (under the assumption of constant returns to scale) and 0.31 to
0.49 (under the assumption of variable returns to scale). However
results are mixed at the disaggregated level. Whereas a majority of
industrial groups have gained in terms of technical efficiency, some
industries have shown deterioration in their efficiency levels including
transport equipment, glass and glass products, other nonmetallic mineral
products, and other manufacturing.
Burki and Khan (2005) analyse the implications of allocative
efficiency for resource allocation and energy substitutability. The
study covers the period 1969-70 to 1990-91 and utilises pooled time
series data from Pakistan's large scale manufacturing sector to
estimate a generalised translog cost function. The study also computes
factor demand elasticities and elasticities of substitution by using the
parameters of the estimated generalised cost function. The results
indicate strong evidence of allocative inefficiency leading to over- or
under-utilisation of resources and higher cost of production. Input-mix
inefficiency takes the form of over-utilisation of raw material and
capital vis-a-vis labour and energy. The study finds that allocative
inefficiency of firms has on average decreased the demand for labour by
0.19 percent and increased the demand for energy by 0.12 percent. Own
price elasticities of factors of production imply that the demand for
capital is much more sensitive to its own price than the demand for
labour. However, the elasticity of substitution between all factors is
found out to be positive, which implies that they are substitutes. This
is attributed to installation of new but more energy-efficient capital.
The new machinery and plants, although more energy-intensive and raw
material saving, leave the share of capital and labour unchanged.
Some studies have utilised the Data Envelopment Analysis (DEA) to
explore the question of industrial efficiency. Jajri and Rahmah (2006)
analyse trend of technical efficiency, technological change and TFP
growth in the Malaysian manufacturing sector. The data come from the
Industrial Manufacturing Survey of 1984 to 2000 collected by the
Department of Statistics, Malaysia. Input variables are capital and
labour whereas value added is used as output. It is found that Total
Factor Productivity Growth is mainly driven by technical efficiency. The
industries that experienced high technical efficiency are food, wood,
chemical and iron products. Analysis by industry shows that there is no
positive relationship between capital intensity and efficiency,
technological change and Total Factor Productivity growth.
Lee and Kim (2006) analyse the effects of research and development
(R&D) on Total Factor Productivity growth in manufacturing
industries, using a sample of 14 OECD countries (6) for the years
1982-1993. With the assumption of constant returns to scale technology,
the Malmquist Productivity Index and its components are computed using
two traditional inputs i.e. labour and capital; then the exercise is
repeated with the stock of R & D capital as an additional input.
Inclusion of R & D capital is found to be statistically significant
and the introduction of R & D capital as an additional input reduces
the TFP measures on average by 10 percent. This is attributed to
"costly" R&D capital formation as opposed to
"costless" productivity growth when only labour and fixed
capital are considered. It is also found that it is technological
progress rather than efficiency catch up that is driven by the
accumulation of R & D capital. Spillovers of R & D capital are
tested using regression analysis. Two types of spillovers are considered
viz. domestic R&D spillovers across industries and international
spillovers within a single industry. Domestic R&D capital stocks and
foreign R&D capital stocks for different industries are used for
this purpose. It is found that productivity gains in manufacturing
industries depend significantly on R & D spillovers, especially for
an economy that is more open to international trade.
4. METHODOLOGY AND DATA
This study uses both SFA and DEA techniques to measure technical
efficiencies. For stochastic frontier two functional forms are tried
viz. Translog and Cobb-Douglass production functions. The purpose is to
check the sensitivity of the efficiency scores with reference to the
functional form/estimation technique.
Model 1
The Stochastic Production Frontier of Translog form is given below:
Ln [Y.sub.i] = [[beta].sub.0] + [[beta].sub.1] In [L.sub.i] +
[[beta].sub.2] ln[K.sub.i] + [[beta].sub.3] ln [RM.sub.i] +
[[beta].sub.4] ln [Ener.sub.i] + [[beta].sub.5] ln [NIC.sub.i] + 1/2
[[beta].sub.6] [(ln [L.sub.i]).sup.2] + [[beta].sub.7] [(ln
[K.sub.i]).sup.2] + 1/2 [[beta].sub.8] [(ln [RM.sub.i]).sup.2] + 1/2
[[beta].sub.9] [(ln [Ener.sub.i]).sup.2] + 1/2 [[beta].sub.10] [(ln
[NIC.sub.i]).sup.2] + [[beta].sub.11] ln [L.sub.i] In[K.sub.i] +
[[beta].sub.12] in [L.sub.i] ln [RM.sub.i] + [[beta].sub.13] ln
[L.sub.i] ln [Ener.sub.i] + [[beta].sub.14] ln [L.sub.i] ln [NIC.sub.i]
+ [[beta].sub.15] In[K.sub.i] ln [RM.sub.i] + [[beta].sub.16]
In[K.sub.i] ln [Ener.sub.i] + [[beta].sub.17] In[K.sub.i] ln [NIC.sub.i]
+ [[beta].sub.18] ln [RM.sub.i] ln [Ener.sub.i] + [[beta].sub.19] ln
[RM.sub.i] In [NIC.sub.i] + [[beta].sub.20] ln [Ener.sub.i] ln
[NIC.sub.i] + [v.sub.i] - [u.sub.i]
Where:
[Y.sub.i] is the value of output,
[L.sub.i] is the average number of persons engaged,
[K.sub.i] is the amount of capital used
[RM.sub.i] is the value of raw material used,
[Ener.sub.i] is the value of energy consumed,
[NIC.sub.i] is the non-industrial cost,
[v.sub.i] and [u.sub.i] are two components of the error term with
following distributional assumptions [Kumbhakar and Lovell (2000),
p.80].
(i) [v.sub.i] ~ iidN (0, [[sigma].sup.2.sub.v])
(ii) [u.sub.i] ~ iid with exponential distribution
(iii) u/and v, are distributed independently of each other, and of
the regressors. The symmetric error term w is the usual noise component
to allow for random factors like measurement errors, weather, strikes
etc. The non-negative error term w, is the technical inefficiency
component. Subscript i stands for ith industry.
Model 2
The Cobb-Douglass function has the following form:
Ln [Y.sub.i] = [[alpha].sub.0] + [[alpha].sub.0] ln [L.sub.i] +
[[alpha].sub.2] ln[K.sub.i] + [[alpha].sub.3] ln [RM.sub.i] +
[[alpha].sub.4] ln [Ener.sub.i] + [[alpha].sub.5] ln [NIC.sub.i] +
[v.sub.i] - [u.sub.i]
The variables names and distributional assumptions of the composite
random term are the same as in the case of the translog function.
The data are obtained from the Census of Manufacturing Industries
(2005-06), (7) In all, 102 large-scale manufacturing industries are
selected.
The following is a brief description of the variables:
Output
CMI reports value added as well as contribution to GDP. Value added
reported in CMI does not allow for non-industrial costs. So we have used
contribution to GDP as output which equals value of production minus
industrial cost minus net non-industrial cost.
Capital
Capital consists of land and building, plant and machinery and
other fixed assets, which are expected to have a productive life of more
than one year and are in use by the establishment for the manufacturing
activity.
Labour
Labour includes employees, working proprietors, unpaid family
workers and home workers. Labour data have been adjusted to allow for
number of shifts as reported in CMI.
Raw Materials
As defined in CMI (2005-06) "Raw-materials include raw and
semi-finished materials, assembling parts etc., which are physically
incorporated in the products and by-products made. Chemicals, lubricants
and packing materials, which are consumed in the production and spare
parts charged to current operating expenses are included. Raw-materials
given to other establishment for manufacturing goods (semi-finished and
finished) on behalf of the establishment are included, whereas raw
material supplied by others for manufacturing goods is excluded."
Energy
This input is obtained by adding cost on fuel and cost on
electricity. Fuel is defined as "firewood, coal, charcoal, kerosene
oil, petrol, diesel, gas and other such items which are consumed in
generating heat and power."
Non-industrial Costs
These consist of payments for transport, insurances, copy
rights/royalties, postage, telephone, fax and internet charges, printing
and stationery, legal and professional services, advertising and selling
services, traveling, etc.
Exporting and Non-exporting Industries
The distinction between exporting and non-exporting industries is
made on the basis of shares of industries in total exports for the year
2005-06. The CMI data are based on ISIC classification. Data on exports
could not be obtained in this classification. Exports Receipts, June
2006, (8) published by State Bank of Pakistan are used to identify
exporting industries. These industries are manually matched with ISIC
classification. List of all industries covered in this study is given in
Appendix with top twenty exporting industries marked with
"Ex". These twenty industries constitute the group of
"exporting industries". Remaining industries are treated as
"non-exporting industries". "Exporting industries"
cover more than 88 percent of total exports.
Main focus of this paper is to determine whether major exporting
manufacturing industries are technically more efficient than other
industries. For this purpose industries are divided in two groups.
Twenty exporting industries constitute group 1, and remaining industries
constitute group 2. Separate mean efficiency scores and standard
deviations of technical efficiency scores are computed for these groups
of industries. Finally, t-test outlined in Section 2 is used to check
the following null hypotheses:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Where MTE stands for mean technical efficiency score. Subscripts 1
and 2 denote two groups, and superscripts Trans, CD, DEACR and DEAVR
indicate the techniques used i.e. Stochastic Frontier Translog,
Stochastic Frontier Cobb-Douglass, Data Envelopment Analysis under
constant returns to scale, and Data Envelopment Analysis under variable
returns to scale respectively. The above four hypotheses are tested
against the alternative hypotheses that mean efficiency scores are not
equal, i.e. two-tail tests will be used to test the hypotheses.
Two different computer packages are used to obtain efficiency
scores. For SF model the computer package STATA 99 is used, and for DEA
model Win4DEAP (10) (Version 1.1.2) is used. Identification of output
and inputs is same in both techniques.
5. RESULTS
Results of regression equation for SF are given in Tables 1 and 2.
The results for Translog specification show that Raw Material and
Non-Industrial Costs are highly significant in explaining output.
Non-Industrial Costs variable is significant at almost 100 percent
level, whereas significance of Raw Material is about 98 percent. Labour
and Capital are significant at about 92 percent level. Significance of
Energy is rather low, but it is still a relevant variable. Sign of
capital turns out to be negative whereas square term of capital has a
positive sign. This might be an indication of threshold point beyond
which capital starts contributing positively to the output. Signs of
product terms indicate complementarity among inputs. The variances of
two error terms Vi and u, are denoted by [[sigma].sup.2.sub.v] and
[[sigma].sup.2.sub.u] respectively. In the log likelihood, they are
parameterised as In [[sigma].sup.2.sub.v] and In [[sigma].sup.2.sub.u]
respectively. The estimate of the total error variance which is sum of
these two variances is denoted by [[sigma].sup.2] (i.e. [[sigma].sup.2]
= [[sigma].sup.2.sub.v] + [[sigma].sup.2.sub.u]). The parameter [lambda]
stands for the ratio of the variance of these two error terms (i.e.
[lambda] = [[sigma].sub.u], [[sigma].sub.v]). These two
parameterisations indicate relative importance of the two components of
error term.
Mean Efficiency score is 0.7401 with standard deviation of 0.1346.
Likelihood-ratio test indicates that the use of stochastic frontier
approach is justified. The results of a likelihood-ratio test are
reported at the bottom of the above Table. Here the null hypothesis is
that there is no technical inefficiency component in the model, i.e.
[H.sub.0] : [[sigma].sub.u] = 0
Against the alternative hypothesis
[H.sub.1] : [[sigma].sub.u] > 0
The acceptance of null hypothesis would have implied that the
stochastic frontier model reduces to an OLS model with normal errors.
However in our case evidence is strong enough to reject the null
hypothesis. The hypothesis of no technical inefficiency component in the
model is rejected at less than 0.01 level of significance.
In Cobb-Douglass specification (Table 2), all inputs are highly
significant except Eneri. Mean Efficiency score is 0.7412 with standard
deviation of 0.1014. Again, the hypothesis of no technical inefficiency
component in the model is rejected, however at a lesser level of
significance than that of translog model. Here level of significance is
about 0.06 for rejection of null hypothesis of no technical
inefficiencies. Mean of efficiency scores and their standard deviation
are found to be very close to those of translog model.
Efficiency scores obtained from SF models are reported in Appendix
(along with those of DEA model). In Cobb-Douglass as well as translog
models of stochastic frontier, average efficiency is found to be about
0.74 with standard deviations of 0.13 and 0.10 respectively. This shows
that efficiency scores of most of the industries cluster around the mean
value in a very narrow band with a very small number of observations
going to either extremes (Figures 4 and 5).
[FIGURE 4 OMITTED]
[FIGURE 5 OMITTED]
Efficiency scores of most efficient industries are reported in
Table 3. As the scores indicate, most of the industries efficient in
Translog Model are also efficient in Cobb-Douglass Model. These are
Carpets and Rugs, Tobacco Products, Meat and Meat Products, Sound/video
Apparatus of TV and Radio and Vegetable and Animal Oils and Fats, and
Refined Petroleum Products.
Efficiency scores of least efficient industries are reported in
Table 4. Refractory Ceramic Products happens to be the least efficient
industry by a wide margin in both models; its efficiency score being
only 0.11. This indicates a very non-optimal utilisation of inputs. Next
in the list are Electricity Distri. and Control Apparatus, Fish and Fish
Products, and Basic Precious Metals and Aluminum and its Products; all
these industries are relatively less efficient according to the both
models.
DEA model has been applied under two assumptions; (i) Constant
returns to scale, and (ii) Variable returns to scale. Mean efficiency in
DEA models turns out to be 0.43 and 0.51 with standard deviations of
0.27 and 0.29 respectively under these two assumptions. These scores are
slightly less than that of SF models due to different assumptions
regarding the inefficiency term. Industry-wise technical efficiency
scores are given in Appendix. Like the SF case, we observe the pattern
of clustering of efficiency score in a narrow band around the mean value
in DEA models as well (Figures 6 and 7).
[FIGURE 6 OMITTED]
[FIGURE 7 OMITTED]
Ten most efficient industries in DEA models under assumption of
constant returns to scale and variable returns to scale are reported in
Table 5. Since DEA model does not allow for random error, the most
efficient industries are likely to lie exactly on the frontier. All such
industries reported in Table 5 have efficiency score of 1. Meat and Meat
Products, Tobacco Products, Carpets and Rugs, Refined Petroleum
Products, Cement, Lime and Plaster, Basic Iron and Steel, Ovens,
Furnaces and Furnace Burners, are the sectors with relatively high
efficiency scores under both the assumptions of DEA model. It should be
noted that Meat and Meat Products, Tobacco Products, Carpets and Rugs,
and Refined Petroleum products are efficient industries common in all
models. and Control Apparatus, Basic Precious Metals and Aluminum and
its Products are relatively less efficient industries under both the
assumptions of scale. Refractory Ceramic Products, Fish and Fish
products, Electricity Distribution and Control Apparatus, and Basic
Precious Metals and Aluminum and its products are relatively less
efficient in all the four models.
In general the efficiency scores computed through SFA turn out to
be higher than those computed through DEA. This is due to the fact that
SFA allows for random noise while estimating the frontier. Within DEA
technique efficiency scores under CRS are, generally, lower than those
under VRS. This occurs because under VRS assumption the frontier
encloses the observations in a more compact way. So, observations become
closer to the frontier. As pointed out by Din, et al. (2007), this is in
line with the evidence suggested in the literature, e.g. Lin and Tseng
(2005). This consistency of efficiency rankings again confirms that
results are not sensitive to the technique employed. A direct comparison
of theses individual efficiency scores with previous studies is not
possible. As mentioned before Burki and Khan (2005) do not provide
individual efficiency scores. Din, et al. (2007) do provide individual
efficiency scores but they use a different industrial classification and
aggregation level. So their efficiency scores are not directly
comparable with the present study.
Next, we turn to the efficiency of exporting industries. Mean
efficiency scores of exporting industries are compared with those of
non-exporting industries by using t-test. The results of these tests are
summarised in Table 7.
As the t-values suggest, there is no significant difference between
mean efficiency scores of exporting and non-exporting industries.
Therefore we do not reject the null hypotheses of equality of mean
efficiency scores across exporting and non-exporting industries. In
other words exporting industries are not performing better than
non-exporting industries in terms of technical efficiency in a
significant way. Rather, as the Table shows, mean efficiency score in
all the four models is slightly less for exporting industries (though
not in a significant way). This is against the common perception that
exporting industries must be the most efficient ones. This may be an
indication of inherent comparative advantage of exporting industries
rather than more efficient performance as the main factor for exports.
On the other hand it also indicates a significant margin for improvement
in export performance if only technical efficiency of manufacturing
industries could be improved through better use of given inputs.
Limitations of the Paper
The paper uses data of 102 industries groups defined at 4-digits
level of aggregation. At this level of aggregation, many diversified
industries are lumped within a broader industrial group, thus masking
important characteristics specific to an industry. Benefits of broader
analysis notwithstanding, an analysis based upon a more disaggregated
dataset could bring these differences into focus. The second limitation
is about the methodology. The estimated models provide technical
efficiency scores, but do not go beyond any further. There remain
unanswered questions about causes of differences in efficiency scores
among different industrial groups. Many factors like protection,
concentration, human resource development, institutional strengthening
etc. are responsible for differences in technical efficiencies.
Empirical testing is needed to determine direction and size of their
respective effects. These limitations indicate potential for future work
in this area.
6. SUMMARY AND CONCLUSIONS
In this paper technical efficiency levels of manufacturing
industries are estimated by using SFA and DEA techniques. SFA technique
is used to estimate Cobb-Douglass as well as translog production
frontier. DEA technique is used under the assumptions of constant
returns to scale and variable returns to scale. The results suggest that
the overall efficiency of manufacturing industries is low and there is a
substantial room for improvement. Industries showing high technical
efficiency include Tobacco Products, Refined Petroleum Products, Carpets
and Rugs, and Meat and Meat Products. Industries showing low technical
efficiency include Refractory Ceramic Products, Electricity Distribution
and Control Apparatus, Fish and Fish Products, Basic Precious Metals and
Aluminum and its Products.
Efficiency scores of exporting industries are statistically not
better than other industries. This indicates that there is a scope for
improving technical efficiency to gain a competitive edge in export
markets.
APPENDIX
Efficiency Scores of Industries
Industry
S. No. Codes Industries
1 1511 Meat and meat products
2 1512 Fish and fish products (Ex)*
3 1513 Fruits, vegetables and edible nuts
4 1514 Vegetable and animal oils and fats
5 1520 Dairy products
6 1531 Grain mill products (Ex)
7 1532 Starches and starch products (Ex)
8 1533 Animal feeds (Ex)
9 1541 Bakery products
10 1542 Sugar
11 1543 Cocoa, chocolate and sugar confectionery
12 1549 Other farinaceous products n.e.c.
13 1551 & Spirits; ethyl alcohol
1553 & Malt liquors and malt
1554 Soft drinks; mineral water
14 16 Tobacco products
15 1711 Spinning of textiles (Ex)
16 1712 Textile fabrics (Ex)
17 1713 Finishing of textiles (Ex)
18 1721 Made-up textile articles, not apparel (Ex)
19 1722 Carpets and rugs (Ex)
20 1723 Cordage, rope, twine and netting (Ex)
21 1729 Other textiles n.e.c. (Ex)
22 1730 Knitted and crocheted fabrics
23 1810 & Wearing apparel, except fur apparel
1820 Articles of fur (Ex)
24 1911 Tanning and dressing of leather (Ex)
25 1912 Luggage, saddlery and harness (Ex)
26 1920 Footwear (Ex)
27 2010 Sawmilling and planking of wood
28 2021 Plywood, panels and boards
29 2023 & Wooden containers
2029 Other products of wood
30 2101 Pulp, paper and paperboard
31 2102 Containers of paper and paperboard
32 2109 Other articles of paper and paperboard
33 2211 & Printing and publication of books etc.
2212 Publishing of newspapers and journals
34 2213 & Publishing of music
2219 Other publishing
35 2221 Printing
36 2222 Service activities of printing
37 232 Refined petroleum products (Ex)
38 2411 Basic chemicals
39 2412 Fertilisers and Nitrogen compounds
40 2413 Plastics and synthetic rubber (Ex)
41 2421 Pesticides and agrochemical products
42 2422 Paints, varnishes, printing ink
43 2423 Pharmaceuticals
44 2424 Soaps and detergents
45 2429 & Other chemical products
2430 Man-made fibres (Ex)
46 2511 Rubber tyres and tubes; retreading
47 2519 Other rubber products
48 2520 Plastic products
49 2610 Glass and glass products
50 2691 Non-refractory ceramic ware
51 2692 Refractory ceramic products
52 2693 Structural clay and ceramic products
53 2694 Cement, lime and plaster
54 2695 Articles of concrete, cement and plaster
55 2696 Cutting, shaping and finishing of stone
56 2699 Other non-metal lie mineral products
57 2711 Basic iron and steel
58 2712 Tubes and tube fittings
59 2713 Other first processed iron and steel
60 2721 & Basic precious metals
2722 Aluminium and its products
61 2724 Copper products
62 2731 Casting of iron and steel
63 2811 Structural metal products
64 2812 Tanks and containers
65 2892 & Treating and coating of metals
2893 Cutlery and general hardware
66 2899 Other fabricated metal products n.e.c
67 2911 Engines and turbines
68 2912 Pumps, compressors, taps and valves
69 2913 Driving elements
70 2914 Ovens, furnaces and furnace burners
71 2915 & Lifting and handling equipment
2919 Other general-purpose machinery
72 2921 Agricultural and forestry machinery
73 2922 Manufacture of machine tools
74 2923 & Machinery for metallurgy
2924 Mining and quarrying machinery
75 2925 Machinery for food and tobacco processing
76 2926 Textile and leather production machinery
77 2927 Weapons and ammunition
78 2929 Other special-purpose machinery
79 2930 Electric domestic appliances
80 3110 DC motors, generators and transformers
81 3120 Electricity distri. and control apparatus
82 3130 Insulated wire and cables
83 3140 Accumulators, cells and batteries
84 3150 Electric lamps and lighting equipment
85 3190 Other electrical equipment n.e.c.
86 321 Electronic valves and tubes etc.
87 322 TV, radio and telegraphy apparatus
88 323 Sound/video apparatus of TV and radio
89 3311 Medical/surgical/orthopaedic equipment (Ex)
90 3312 Measuring instruments and appliances
91 332 & 333 Watches and clocks
92 3410 Motor vehicles
93 3420 Bodies for motor vehicles and trailers
94 3430 Parts and accessories for motor vehicles
95 3511 & Building and repair of ships and boats
3520 & Railway locomotives and rolling stock
3530 Aircraft and spacecraft
96 3591 Motorcycles
97 3592 Bicycles and invalid carriages
98 3610 Furniture
99 3691 & Jewellery and related articles
3692 Musical instruments
100 3693 & Sports goods
3694 Games and toys (Ex)
101 3699 Other manufacturing n.e.c
102 37 RECYCLING
Mean Efficiency Scores
Technical Efficiency Scores
SFA SFA DEA
Cobb Trans
S. No. CRS VRS
1 0.86 0.90 1.00 1.00
2 0.49 0.26 0.14 0.14
3 0.73 0 78 0.23 0.25
4 0.85 0.88 0.99 1.00
5 0.72 0.79 0.19 0.37
6 0.71 0.44 0.32 0.37
7 0.81 0.89 0.46 0.50
8 0.80 0.78 0.46 0.47
9 0.69 0.67 0.17 0.17
10 0.79 0.69 0.51 0.79
11 0.67 0.67 0.19 0.20
12 0.76 0.83 0.67 0.75
13 0.79 0.84 0.41 0.47
14 0.89 0.90 1.00 1.00
15 0.72 0.71 0.29 1.00
16 0.72 0.82 0.23 0.96
17 0.59 0.63 0.13 0.29
18 0.69 0.73 0.20 0.23
19 0.85 0.91 1.00 1.00
20 0.80 0.82 0.59 0.65
21 0.74 0.69 0.35 0.35
22 0.71 0.76 0.22 0.27
23 0.70 0.80 0.20 0.57
24 0.66 0.68 0.19 0.22
25 0.73 0.62 0.27 0.29
26 0.79 0.83 0.38 0.45
27 0.74 0.68 0.49 0.51
28 0.73 0.77 0.24 0.24
29 0.49 0.65 0.14 1.00
30 0.81 0.88 0.52 0.60
31 0.75 0.79 0.35 0.35
32 0.70 0 43 0.27 0.28
33 0.70 0.69 0.20 0.21
34 0.77 0.81 0.62 0.68
35 0.81 0.76 0.94 0.99
36 0.73 0.70 0.28 0.31
37 0.86 0.87 1.00 1.00
38 0.74 0.81 0.27 0.28
39 0.80 0.51 0.79 0.95
40 0.80 0.84 0.48 0.48
41 0.65 0.62 0.18 0.19
42 0.78 0.74 0.54 0.55
43 0.74 0.76 0.24 0.63
44 0.75 0.76 0.28 0.30
45 0.66 0.64 0.17 0.30
46 0.79 0.84 0.35 0.36
47 0.71 0.70 0.23 0.25
48 0.75 0.76 0.34 0.36
49 0.69 0.68 0.22 0.25
50 0.70 0.78 0.30 0.30
51 0.13 0.11 0.03 0.03
52 0.75 0.73 0.30 0.33
53 0.84 0.86 1.00 1.00
54 0.70 0.72 0.24 0.25
55 0.84 0.89 0.79 0.79
56 0.64 0.72 0.17 0.18
57 0.84 0.82 1.00 1.00
58 0.77 0.75 0.39 0.41
59 0.77 0.59 0.41 0.47
60 0.64 0.60 0.16 0.19
61 0.83 0.73 0.87 0.88
62 0.70 0.73 0.23 0.23
63 0.79 0.72 0.51 0.56
64 0.72 0.78 0.25 0.25
65 0.79 0.84 0.37 0.38
66 0.81 0.86 0.54 0.63
67 0.78 0.83 0.46 0.60
68 0.71 0.69 0.23 0.28
69 0.73 0.72 0.26 0.27
70 0.86 0.81 1.00 1.00
71 0.80 0.82 0.49 0.58
72 0.76 0.79 0.46 0.50
73 0.87 0.79 1.00 1.00
74 0.77 0.80 0.40 0.42
75 0.81 0.84 0.54 0.62
76 0.83 0.86 0.66 0.69
77 0.72 0.82 0.27 0.27
78 0.67 0.67 0.29 0.30
79 0.75 0.79 0.27 0.30
80 0.71 0.76 0.21 0.23
81 0.54 0.19 0.15 0.16
82 0.85 0.82 1.00 1.00
83 0.72 0.64 0.22 0.33
84 0.65 0.65 0.14 0.16
85 0.65 0.68 0.16 0.17
86 0.68 0.67 0.22 0.23
87 0.84 0.88 0.83 0.97
88 0.87 0.90 1.00 1.00
89 0.73 0.73 0.25 0.28
90 0.82 0.82 0.56 0.68
91 0.49 0.75 0.14 0.15
92 0.83 0.78 0.50 0.81
93 0.83 0.84 0.70 0.76
94 0.77 0.78 0.38 0.43
95 0.80 0.76 0.51 0.54
96 0.72 0.59 0.24 0.25
97 0.82 0.82 0.63 0.76
98 0.76 0.83 0.42 0.44
99 0.63 0.83 0.20 1.00
100 0.77 0.80 0.36 0.39
101 0.71 0.71 0.23 0.27
102 0.87 0.75 1.00 1.00
0.7412 0.7401 0.4300 0.5050
* (Ex) indicates an exporting industries.
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(1) Burki and Khan (2005) and Din, et at. (2007) address the issue
of technical efficiency but these studies do not test for differences
between exporting and non-exporting industries.
(2) Distance functions were introduced by Malmquist (1953) and
Shephard (1953). For a detail discussion on use of distance function for
efficiency measurement, see Shephard (1970), and Russell (1985, 1990).
The description given here is adapted from Coelli, et al. (2005), pp.
47-49.
(3) Farrell actually proposed measurement of input-oriented
technical efficiency (explained below). Fie also introduced the idea of
"allocative efficiency", which involves production decisions
given output prices. The "technical efficiency" and
"allocative efficiency" combined are termed as "economic
efficiency" [Coelli, et al. (2005), p. 51].
(4) Other distributions have also been tried but results from
exponential distribution are found to be better in terms of parameter
estimates and likelihood ratio test.
(5) For details on these assumptions, see Bogetoft and Otto (2010),
pp. 85-86.
(6) The sample consists of Canada, Denmark, Finland, France,
Germany, Italy, Japan, Korea, Netherlands, Norway, Spain, United
Kingdom, and United States.
(7) This is the latest available published CMI.
(8) Now this publication is named as "Export of Goods and
Services".
(9) STATA programme is a general-purpose statistical software
package, developed by STATA Corp.
(10) Win4DEAP is a free software developed by Michel Deslierres.
(Departement d'economie Universite de Moncton). It is available at
http:Avww.umoncton.ca/desliem/dea. This package is an extension of the
computer programme DEAP, developed by Professor T. Coelli (for detail
see "A guide to DEAP version 2.1: A Data Analysis Computer
Programme." CEPA Working Paper 96/08).
Tariq Mahmood <
[email protected]> is Senior Research
Economist, Ejaz Ghani <
[email protected]> is Dean Faculty of
Economics and Musleh ud Din <
[email protected]> is Joint
Director, Pakistan Institute of Development Economics (PIDE), Islamabad.
Table 1
Translog Production Frontier Results
(for Overall Dataset Covering 102 Industries)
Coeff z P>z
Constant 4.75 1.47 0.141 L*K
L 2.54 2.95 0.003 L*RM
K -2.71 -3.09 0.002 L*Ener
RM 0.71 1.41 0.159 L*NIC
Ener 0.80 1.63 0.104 K*RM
NIC .41 0.57 0.567 K*Ener
[L.sup.2] 0.18 2.29 0.022 K*NIC
[K.sup.2] 0.14 1.97 0.049 RM*Ener
[RM.sup.2] 0.16 2.89 0.004 RM*NIC
[Ener.sup.2] 0.16 2.36 0.018 Ener*NIC
[NIC.sup.2] 0.21 2.63 0.009
ln [[sigma].sup.2.sub.v] -1.99 -8.32 0.000
ln [[sigma].sup.2.sub.u] -2.22 -5.07 0.000
[[sigma].sub.v] 0.37 .0442
[[sigma].sub.u] 0.33 .0721
[[sigma].sup.2] 0.24 .0421
[lambda] 0.89 .1018
Coeff z P>z
Constant -0.11 -1.28 0.202
L -0.11 -0.85 0.395
K -0.03 -0.24 0.809
RM -0.12 -0.95 0.344
Ener 0.04 0.41 0.681
NIC -0.22 -2.12 0.034
[L.sup.2] 0.13 1.15 0.249
[K.sup.2] -0.01 -0.08 0.938
[RM.sup.2] -.36 -2.57 0.010
[Ener.sup.2] -.10 -1.05 0.296
[NIC.sup.2]
ln [[sigma].sup.2.sub.v]
ln [[sigma].sup.2.sub.u]
[[sigma].sub.v]
[[sigma].sub.u]
[[sigma].sup.2]
[lambda]
Likelihood-ratio test of [[sigma].sub.u] = 0
[[bar.[chi]].sup.2] = 7.34
Prob [greater than or equal to] [[bar.[chi]].sup.2] = 0.003
Mean Efficiency score = 0.7401
SD of Efficiency scores = 0.1346.
Table 2
Cobb-Douglass Production Frontier Results
(for Overall Dataset Covering 102 Industries)
Independent Variables Coefficients z P>z
Constant 2.51 4.63 0.00
[L.sub.i] 0.15 1.73 0.08
[K.sub.i] 0.16 1.76 0.08
[RM.sub.i] 0.17 2.34 0.02
[Ener.sub.i] 0.08 1.37 0.17
[NIC.sub.i] 0.40 4.47 0.00
ln [[sigma].sup..sub.v] -1.14 -5.56 0.00
ln [[sigma].sup..sub.u] -2.31 -3.58 0.00
[[sigma].sub.v] 0.57
[[sigma].sub.u] 0.31
[[sigma].sup.2] 0.42
[lambda] 0.56
Likelihood-ratio test of [[sigma].sub.u] = 0
[[bar.[chi]].sup.2] = 2.31
Prob [greater than or equal to] [[bar.[chi]].sup.2] = 0.064
Mean Efficiency score = 0.7412
SD of Efficiency scores = 0.1014.
Table 3
Most Efficient Industries (by SF Model)
Translog Frontier Efficiency Scores
Carpets and Rugs 0.91
Tobacco Products 0.90
Meat and Meat Products 0.90
Sound/Video Apparatus of TV and Radio 0.90
Starches and Starch Products 0.89
Cutting, Shaping and Finishing of Stone 0.89
Vegetable and Animal Oils and Fats 0.88
TV, Radio and Telegraphy Apparatus 0.88
Pulp, Paper and Paperboard 0.88
Refined Petroleum Products 0.87
Cobb-Douglass Frontier Efficiency Scores
Tobacco Products 0.89
Sound/video Apparatus of TV and Radio 0.87
Recycling 0.87
Manufacture of Machine Tools 0.87
Ovens, Furnaces and Furnace Burners 0.86
Refined Petroleum Products 0.86
Meat and Meat Products 0.86
Carpets and Rugs 0.85
Insulated Wire and Cables 0.85
Vegetable and Animal Oils and Fats 0.85
Table 4
Least Efficient Industries (by SF Model)
Translog Frontier
Industries Efficiency
Scores
Refractory Ceramic Products 0.11
Electricity Distri. and Control Apparatus 0.19
Fish and Fish Products 0.26
Other Articles of Paper and Paperboard 0.43
Grain Mill Products 0.44
Fertilisers and Nitrogen Compounds 0.51
Other First Processed Iron and Steel 0.59
Motorcycles 0.59
Basic Precious Metals and Aluminum and its Products 0.60
Luggage, Saddlery and Harness 0.62
Cobb-Douglass Frontier
Industries Efficiency
Scores
Refractory Ceramic Products 0.13
Watches and Clocks 0.49
Fish and Fish Products 0.49
Other Products of Wood 0.49
Electricity Distri. and Control Apparatus 0.54
Finishing of Textiles 0.59
Musical Instruments 0.63
Basic Precious Metals and Aluminum and its Products 0.64
Other Noil-Metallic Mineral Products 0.64
Other Electrical Equipment n.e.c. 0.65
Table 5
Most Efficient Industries by DEA Model
Constant Returns to Scale Variable Returns to Scale
Meat and Meat Products Meat and Meat Products
Tobacco Products Vegetable and Animal Oils
and Fats
Carpets and Rugs Tobacco Products
Refined Petroleum Products Spinning of Textiles
Cement, Lime and Plaster Carpets and Rugs
Basic Iron and Steel Other Products of Wood
Ovens, Furnaces and Refined Petroleum Products
Furnace Burners
Manufacture of Machine Tools Cement, Lime and Plaster
Insulated Wire and Cables Basic iron and Steel
Sound/Video Apparatus of Ovens, Furnaces and Furnace
TV and Radio Burners
Least efficient industries under DEA model under the
assumptions of Constant Returns to Scale and Variable
Returns to Scale are given in Table 6. Again, Refractory
Ceramic Products turned out to be least efficient industry
with a very small score of 0.03. Fish and Fish Products,
Electric Lamps and Lighting Equipment, Electricity
Distribution
Table 6
Least Efficient Industries by DEA Model
Constant Returns to Scale
Industries Efficiency Scores
Refractory Ceramic Products 0.03
Finishing of Textiles 0.13
Fish and Fish Products 0.14
Other Products of Wood 0.14
Electric Lamps and Lighting Equipment 0.14
Watches and Clocks 0.14
Electricity Distri. and Control Apparatus 0.15
Basic Precious Metals 0.16
Aluminum and its Products
Other Electrical Equipment n.e.c. 0.16
Bakery Products 0.17
Variable Returns to Scale
Industries Efficiency Scores
Refractory Ceramic Products 0.03
Fish and Fish Products 0.14
Watches and Clocks 0.15
Electric Lamps and Lighting Equipment 0.16
Electricity Distri. and Control Apparatus 0.16
Other Electrical Equipment n.e.c. 0.17
Bakery Products 0.17
Other Non-Metal lie Mineral Products 0.18
Basic Precious Metals Aluminum 0.19
and its Products
Pesticides and Agrochemical Products 0.19
Table 7
Comparison of Mean Efficiency Scores between Exporting
and Non-Exporting Industries
Technique t-Values
Stochastic Frontier (CD) -0.49
Stochastic Frontier (Translog) -0.57
DEA (CRS) -1.05
DEA (VRS) -0.14
