TOTAL FACTOR PRODUCTIVITY AND EFFICIENCY IN INDIAN MEAT PROCESSING INDUSTRY Jabir Ali 1 Abstract This study evaluates the performance of meat processing industry and role of technology in acceleration of growth. Productivity and efficiency improvement of the processing industry is the key for sustainable growth. Malmquist TFP index is used for measuring productivity change in Indian meat processing industry. Malmquist productivity index is defined as the ratio of two output distance functions (Caves et al., 1982). Input oriented variable returns to scale (VRS) DEA model has been used for measuring technical and scale efficiency. The input-output variables used include capital, labour, raw material consumed, fuel consumed and gross value of output. Malmquist TFP index and efficiency scores have been obtained by using DEAP software (version 2.1) developed by Coelli (1996). Although the level of meat processing is extremely low, it has been increasing. The growth in processed meat segment has been drastic during 1990s (12.8%) as compared to 1980s (3.3%). Most of this occurred due to input growth. The contribution of technology was negligible during 1980s as well as 1990s. On an average TFP grew at a rate of 1.01 percent during 1980-81 to 1999-2000. The average technical efficiency score is estimated to be 0.59 under CRS model and 0.93 under VRS model. The efficiency indices values equal to unity imply that the industry is on frontier while values below unity imply that the industry is below the frontier or technically inefficient. On the other hand, average scale efficiency for the entire period is 0.64. There was considerable under utilization of input resources during 1980s. Nevertheless, over time resource - utilization has improved perhaps due to rising market trends. This had significant positive impact on labour absorption as well as labour productivity. While the capital investment in industry improved, capital productivity has remained stagnant. JELClassification: L66, N55, O30, O47, Q1 Keywords: Technical Efficiency, TFP, Meat Processing, DEA, India 1 Assistant Professor, Agriculture Management Centre (AMC), Indian Institute of Management, Lucknow – 226 013 Email: [email protected]
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TOTAL FACTOR PRODUCTIVITY AND EFFICIENCY IN INDIAN MEAT PROCESSING INDUSTRY
Jabir Ali1
AbstractThis study evaluates the performance of meat processing industry and role of technology in
acceleration of growth. Productivity and efficiency improvement of the processing industry is the key for sustainable growth. Malmquist TFP index is used for measuring productivity change in Indian meat processing industry. Malmquist productivity index is defined as the ratio of two output distance functions (Caves et al., 1982). Input oriented variable returns to scale (VRS) DEA model has been used for measuring technical and scale efficiency. The input-output variables used include capital, labour, raw material consumed, fuel consumed and gross value of output. Malmquist TFP index and efficiency scores have been obtained by using DEAP software (version 2.1) developed by Coelli (1996).
Although the level of meat processing is extremely low, it has been increasing. The growth in processed meat segment has been drastic during 1990s (12.8%) as compared to 1980s (3.3%). Most of this occurred due to input growth. The contribution of technology was negligible during 1980s as well as 1990s. On an average TFP grew at a rate of 1.01 percent during 1980-81 to 1999-2000. The average technical efficiency score is estimated to be 0.59 under CRS model and 0.93 under VRS model. The efficiency indices values equal to unity imply that the industry is on frontier while values below unity imply that the industry is below the frontier or technically inefficient. On the other hand, average scale efficiency for the entire period is 0.64. There was considerable under utilization of input resources during 1980s. Nevertheless, over time resource - utilization has improved perhaps due to rising market trends. This had significant positive impact on labour absorption as well as labour productivity. While the capital investment in industry improved, capital productivity has remained stagnant.
TOTAL FACTOR PRODUCTIVITY AND EFFICIENCY IN INDIAN MEAT PROCESSING INDUSTRY
1. Introduction
Food processing is a sunrise industry of the Indian economy and has been
identified as thrust area for development. Food processing sector covers a wide range
of items like fruits and vegetables; meat and poultry; milk and milk products, alcoholic
beverages, fisheries, plantation, grains, confectionery, chocolates and cocoa products,
mineral water, high protein foods etc. Based on the basic raw material usage, food
industry can broadly be claasified into plant based and animal based. Meat industry is
one of the important segments of food processing industry in general and
livestock/animal based industry in particular. India has immense potential for
production, consumption and export of meat due to sufficient resources, available
markets and huge livestock population.
Over the last two decades, the value of meat output has been increasing at a
rate of about 6 percent a year. Rising demand for meat has been the driving force
behind it. Between 1980 and 2000, while per capita consumption of foodgrains
increased by 4 percent, consumption of milk and meat increased by 50 percent and 25
percent respectively. In quantitative terms, per capita milk consumption increased from
40 kgs in 1980 to 66 kgs in 2000, and meat consumption increased from 4 kgs to 5 kgs
during this period. Most of the meat output (96%) is consumed domestically, yet per
capita meat consumption in India is much less compared to many developed (77 kgs)
and developing (27 kgs) countries. The demand for meat is expected to grow faster
with sustained economic growth, rising per capita incomes, strengthening urbanization
trends and increasing awareness of the nutritive value of meat and meat products
(Kumar, 1998; Bhalla and Hazell, 1998 and Delgado, 1999). These opportunities can
be capitailised for the benefit of producers as well as consumers and would largely be
determined by the pace of development and diffusion of the technologies in processing
of livestock based products (Mishra, 1995).
1
The increase in demand has been accompanied by increase in production. Total
meat production increased from 2.7 million tonnes in 1980 to 4.7 million tonnes in
2000 with annual growth of 3.41 percent. The growth in meat production has largely
been number driven, as yield growth is negligible in case of almost all the species.
Cattle, buffalo, goat, sheep, pigs and poultry are important meat species. While goat,
sheep, pig and poultry are exclusive meat animals, cattle and buffalo provide meat as an
adjunct to milk. Animals slaughtered are of poor quality. The structure of meat
production, however, is undergoing a gradual shift from ruminant to non-ruminant (pig
and poultry) meat production. The share of non-ruminant increased from 15 percent in
1980 to 23 percent in 1999.
The growth of meat industry is constrained by a number of socio-cultural and
economic factors at different levels of production, processing, handling and marketing.
Meat production is mainly constrained due to lack of productivity augmenting
technologies since the major quantity of meat is being produced in unorganized
slaughterhouses, where resource utilization is very limited. These slaughterhouses are
old, unhygienic and lack basic facilities like water, light, ventilation, drainage, waste
disposal and effluent treatment which contribute to poor meat quality and low recovery
of various by-products such as hides, blood, bonemeal, internal organs and trimmings.
Therefore, technology is the key to improvement in growth and efficiency in meat
processing sector. Empirical evidences on contribution of technology to growth of meat
processing industry in India are scarce. However, the evidences from food industry as a
whole indicate varied contribution of technology to growth of food processing industry
(Goldar, 1986; Ahluwalia, 1991; Mitra et al., 1998; Mitra, 1999; Goldar and Kumari,
2002; Trivedi et al., 2002 and Pattnayak and Thangavelu, 2003).
2. Data and Methodology
The data on input and output related to registered/ organized meat
manufacturing units is compiled from Annual Survey of Industries published by the
Central Statistical Organisation, Minstry of Statistics and Programme Planning,
Government of India. The study evaluates the performance of meat processing industry
and role of technology in acceleration of growth of this industry. Malmquist TFP index
2
is used to measure productivity change in Indian meat processing industry, which is
defined as the ratio of two-output distance functions (Caves et al., 1982). Input oriented
variable returns to scale (VRS) DEA model is used to measure technical and scale
efficiency in Indian meat processing industry. The input-output variables used include
capital, labour, raw material consumed, fuel consumed and gross value of output.
Malmquist TFP index and efficiency scores are obtained by using DEAP software
(version 2.1) developed by Coelli (1996).
2.1. Analytical Approach
2.1.1 Total Factor Productivity (TFP) Change
The simplest indicators of productivity are partial productivity measures derived
by dividing the output by relevant input. Most commonly used measures are labour
productivity i.e. output/ labour ratio and capital productivity i.e. the output/ capital
ratio. However, these ratios can be misleading as improvement in productivity cannot
be attributed to any single factor input individually. Therefore, an integrated model for
measuring productivity is desirable which considers all the factor inputs in aggregate
and explains interacting economic relationship.
In the present study, Malmquist TFP index is used to measure productivity
change in Indian meat processing industry. Malmquist productivity index is defined as
the ratio of two output distance functions (Caves et al., 1982). Distance functions are
functional representations of multiple-output and multiple-input technology which
requires data only on input and output quantities. Malmquist index has several
advantages over Fisher and Tranquist index as it does not require assumptions
regarding market structure and economic behaviour.
Malmquist TFP index decomposes productivity change into technical change
and technical efficiency change. Fare et al (1994) specifies an output based Malmquist
productivity change index as:
(1)
3
This represents the productivity of the production point relative to
the production point . A value greater than one will indicate positive TFP growth
from period t to t+1. This index is the geometric mean of two output based Malmquist
TFP indices. The input thus employs distance functions from two different periods or
technologies, and ; and two pairs of input-output vectors,
and . Caves et al. (1982) assume that =
implying that own-period observations are technically efficient in the sense of Farrell
(1957).
The Malmquist index can be decompose into two components namely technical
efficiency change (EFFCH) and technical change (TECHCH), defined as:
(2)
where the ratio of outside the square bracket measures the change in relative efficiency
between t and t+1. The geometric mean of the two ratios inside the square bracket
captures the shift in technology between the two periods. These may be given as:
(3)
(4)
The Malmquist index can further be explained in diagrammatic form (Figure 1).
In the figure, St and St+1 denote the technologies in period t and t+1 respectively. The
input-output vectors and are feasible in their own periods, but
does not belong to St. In the figure, =Oa/Ob and
=Od/Oe. Thus the term outside the square bracket in equation 2 equals:
4
(5)
Similarly, the term inside the square bracket in equation 2 is given as:
(6)
The last expression shows that the ratio of term inside the square bracket in
equation 6 measures shift in technology at input levels x t and xt+1 respectively. This
indicates technical change as the geometric mean of two shifts, which is of the same
form as Fisher Ideal Index (Hossain and Bhuyan, 2002).
2.1.2 Technical and Scale Efficiency
The non-parametric approach introduced as Data Envelopment Analysis (DEA)
by Charnes, Cooper and Rhodes (1978) is a method of measuring efficiency of
Decision Making Units (DMUs)/ firms through linear programming techniques, which
‘envelop’ observed input – output vectors as tightly as possible (Boussofiane et al.,
1991). The DEA is a methodology directed to frontiers rather than central tendencies
(Seiford and Thrall, 1990). The DEA is also capable of handling multiple inputs and
outputs at the same time. This study employs input oriented variable returns to scale
(VRS) DEA to measure technical and scale efficiency in Indian meat processing
O Xt Xt+1
Yt=d
e
f
c
Yt+1=a
b
Y
X
St
St+1
Figure 1: MalmquistOutput-Based TFP Index
Source: Hossain and Bhuyan (2002)
5
industry using DEAP computer software (version 2.1) developed by Coelli (1996). The
input-output variables used include capital, labour, raw material consumed, fuel
consumed and gross value of output.
The original model developed by Charnes, Cooper and Rhodes (CCR model)
was applicable when technologies were characterized by constant returns to scale
(CRS). It is assumed that there are ‘N’ DMUs with K inputs and S outputs on each
DMU. That is, consumes amount of input i and
produces amount of output r, where and . The mathematical
programming involves the selection of optimal weights that maximize the objective
function of the ratio of outputs to inputs for each DMU being evaluated. The constant
returns to scale (CRS) DEA model is only appropriate when firm is operating at an
optimal scale (Coelli et al., 1998).
In the method originally proposed by Charnes, Cooper and Rhodes (1978)
relative efficiency of the DMUs can be measured by input oriented DEA model as:
subject to
(7)
Where;
= the amount of the ith input at DMUj,
= the amount of rth output from DMUj,
= the input technical efficiency (TE) score,
6
= vector of weight which defines the linear combination of the peers of
DMUj
The value of gives efficiency score for a particular DMU, which satisfies
. The DMUs for which <1 are inefficient while for =1 are on frontiers and
hence efficient.
Imperfect competition may cause a DMU not to operate at optimal scale (Coelli,
1996). Banker, Charnes and Cooper (1984) extended the CCR model to account for
technologies that show variable returns to scale (VRS). The Banker, Charnes and
Cooper (BCC) model can be developed by adding the convexity constraint to the
constant returns to scale (CRS) linear programming problem.
i.e. (8)
The CRS technical efficiency scores can be decomposed into pure technical
efficiency and scale efficiency. This can be done by applying both CRS and VRS DEA
on the same model. The difference between CCR model and BCC model can be
illustrated as follows. We shall assume one input and one output situation. The CRS
and VRS frontiers have been drawn in Figure 2.
7
The inefficient DMU is represented by the point P. Under input orientation
model, the technical inefficiency of DMU ‘P’ is mp in CRS and bp in VRS. The
difference between these two measures is expressed as scale inefficiency (SE). In ratio
form, technical efficiency in CRS is qm/qp and in VRS it is qb/qp. Scale efficiency is
qm/qb. Further,
(9)
Thus, technical efficiency (TE) obtained from CRS can be decomposed into
‘pure’ technical efficiency and scale efficiency. The point such as ‘c’ on the frontier is
scale efficient.
The concept of scale efficiency constitutes two technologies i.e. constant return
to scale (CRS) and variable return to scale (VRS). The VRS technology in the figure 2
is presented by the single input - single output production function. The scale efficiency
measure corresponding to input Xt is given by:
Scale Efficiency (10)
O
Y
X
VRS
CRS
Figure 2: CRS, VRS and Scale Efficiency
Source: Coelli, 1996
a
bp
mq
c
d
r
s
Xt
8
3. Indian Meat Industry: An Overview
The structure of meat industry is highly unorganized and only a meagre quantity
of meat is processed for value addition. Most of the meat produced in the country
comes from traditional slaughterhouses. There are about ten thousand slaughterhouses
in the country of which 60 percent are unregistered. Most of these slaughterhouses have
poor hygiene and sanitation facilities resulting in poor meat quality and environmental
degradation. The organized sector of meat industry constitutes very few modern meat
processing units in the country. The country has 9 modern abattoirs and 171 meat
processing units licensed under Meat Products Order. Annual Survey of Industries
(ASI) data shows that only 37 meat processing units are registered under Factories Act.
A few modern pork processing plants are also coming up in the country. Poultry
processing is still in its infancy. There are only seven modern integrated poultry
processing plants. However, there are a good number of small poultry processing units
engaged in production of poultry meat products.
Table 1: Major manufacturers of processed meat products in India
Company Major Products BrandsFrigo Refico Allana Limited, Kulaba, Mumbai
Frozen buffalo meat Allana
Frigo Refico Allana Limited, Kulaba, Mumbai
Canned meat Allana
Hind Industries Limited, New Delhi Frozen buffalo meat Sibaco, EatcoHind Industries Limited, New Delhi Chilled/Frozen sheep and Goat meat Sibaco, EatcoAlkabeer Exports Limited, Mumbai Frozen buffalo meat AlkabeerAlkabeer Exports Limited, Mumbai Chilled/Frozen sheep and Goat meat AlkabeerP.M.L. Industries, Chandigarh Frozen buffalo meat PMLU.P. Pashudhan Udyog Nigam Ltd. Uttar Pradesh
Pork and other meat products CDF
U.P. Pashudhan Udyog Nigam Ltd. Uttar Pradesh
Canned meat manufactures CDF
A.P. Meat & Poultry Corporation, Hyderabad
Pork and other meat products APSMPC
Pigpo, Jorbagh Market, New Delhi Pork and other meat products PigpoMAFCO, Mumbai Pork and other meat products MAFCORanchi Bacon Factory, Ranchi Pork and other meat products Rajasthan Meat and Wool Marketing Federation, Alwar
Canned meat Manufactures
Venkateshwara Hatcheries, Pune Poultry products Venky’s FoodDeeJay, Bangalore Poultry products
Source: Ministry of Food Processing Industries, GOI
The performance of Indian meat indurstry has been measured by expotential
growth rate in terms of production, value of output from meat, domestic counsumption
9
and export earnings during last two decades. Meat production in India has increased
significantly over the last two decades at a rate of 3.41 percent a year. The growth in
contributions from different species, however, varied widely. Maximum growth
occurred in poultry meat (10.04 percent) followed by pork (4.04 percent), beef and veal
Table 7 provides results on target inputs and the estimated slack inputs in Indian
meat processing industry. Target inputs refer to what a particular DMU ought to have
consumed if it was on the efficient frontier. The slack inputs are excess inputs. The
slack is calculated as the difference between actual inputs consumed minus the target
input a DMU ought to have consumed. An efficient DMU will have zero input output
slacks. In meat processing units, slacks in capital use are showing mixed trends. The
highest slack in capital use was recorded in 1998-99 to the extent of Rs. 158.8 lakhs. If
the industry was to qualify for an efficient DMU in 1998-99, Rs. 158.8 lakhs of capital
had to be reduced. Labour inputs are efficiently used in meat processing units. No
slacks in labour used were recorded except in 1982-83, where about 11 labours were
excessively employed. The slack in materials and fuel consumed also showed mixed
trends but in most of the years these inputs were efficiently used.
5. Conclusions and Recommendations
Meat industry in India experienced tremendous growth during last two decades.
The growth is largely number driven causing doubts on its sustainability. The
processing of meat for value addition is meager and most of the production takes place
in unorganized slaughterhouses. The empirical analysis of productivity and efficiency
in meat processing units indicate that there are significant possibilities of enhancing the
performance of these units. Total Factor Productivity (TFP) change is negligible and
the increase in meat output is basically due to increase in input use.
19
The average technical efficiency score is estimated to be 0.59 under CRS model
and 0.93 under VRS model. The value of efficiency indices equal to unity implies that
the industry is on frontier while values below unity show that the industry is below the
frontier or is technically inefficient. The analysis indicates that the average technical
inefficiency could be reduced by 41 percent under constant returns to scale and 7
percent under variable returns to scale. The average scale efficiency for the entire
period is 0.64 which shows that the potential of increasing scale efficiency in meat
processing units is to the extent of 36 percent. There was considerable under utilization
of input resources during 1980s, which has improved overtime. This had significant
positive impact on labour absorption as well as labour productivity. While the capital
investment in industry improved, capital productivity has remained stagnant.
The analysis of input slacks in meat processing industry suggest that the
industry is labour intensive and the effects of expansion of meat industry on labour
employment and productivity appears to be favourable. The raw material which
constitutes more that 80 percent of the production cost is raw meat which is also
inefficiently used. Market infrastructure for live animals used for slaughtering is poor
and unorganized. There are a number of intermediaries acting between the producers
and slaughterhouses/ processors. Meat processing units are often located in the urban
areas and thus the transportation cost of live animals is high. Animals are transported
from distant markets, causing weight loss and hence meat yield is low. The meat
processing industry has not taken much initiative to strengthen backward linkages with
the farmers/ producers and largely depends on the intermediaries for its requirement of
raw materials. This leads to irregular supply of animals for slaughter. Therefore, proper
methods of sourcing quality animals for meat production should be adopted to shorten
the supply chain of meat processing industry.
Meat processing industry has also been scale inefficient due to slack in capital
use in recent the years. In order to improve the industry’s productivity and efficiency,
there is a need for investment in technological know-how and improvement in
managerial capabilities of meat processors. This would help to improve capacity
utilization and expansion. The scale inefficiency in meat processing shows that a proper
coordination/ combination of inputs is required which is lacking at present.
Nevertheless the industry is becoming more capital intensive. Thus while introducing
20
meat processing technologies it should be kept in mind that the technological change
should have minimum adverse effect on employment.
21
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