Issued for Discussion
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ACKNOWLEDGEMENTS
It is a pleasure to acknowledge the support we received from the
various quarters during the course of this study. We would like to extend
our sincere thanks to the Reserve Bank of India for enabling the interaction
between academicians and the policy makers through the Development
Research Group (DRG). We are particularly thankful to Dr. Narendra
Jadhav, the then Principal Adviser and Chief Economist, Department of
Economic and Policy Research (DEPR) whose academic support was crucial
at the initial stages of this study. We also acknowledge our thanks to the
participants (for the comments and suggestions) at the seminar
during which this study was proposed. We record our appreciation to each
and everyone who was associated with the DRG, DEPR, Reserve Bank of
India, since the inception of this study and has been instrumental in
providing the logistic support necessary for the completion of this study.
We would particularly like to thank Dr. Nishita Raje, Dr. Ramesh Golait,
Dr. Snehal Herwadkar and Shri Gopal Prasad. We have used Tim Coelli’s
softwares, DEAP (2.1 version) and SFPF (Front 4.1 version) and we are
grateful to him for enabling researchers, including us, to use his software.
We also express our gratitude to the anonymous referee whose valuable
suggestions and comments have helped us to sharpen the focus of the
study.
Not only during the course of this study but otherwise as well, Pushpa
Trivedi was fortunate enough to discuss the various facets of productivity
with Prof. K.L. Krishna, Prof. Subhash Ray, Prof. Biswanath Goldar, Prof.
Arup Mitra, Dr. Deb Kusum Das and Dr. Abhiman Das, at various seminars
and workshops. She expresses her gratitude to each one of them for sharing
their expertise with her. We will be failing in our duties if we do not thank
Amlendu Dubey and Jaison for their help, as and when needed. During
the course of this study, Pushpa Trivedi’s productivity registered a sharp
decline, due to her ill-health. She, therefore, thanks all those associated with the study for being patient with her and enabling her to complete
this study. She would like to record her gratitude to Prof. Devang Khakhar
(Director) and Prof. Rangan Bannerjee (Dean, Research and Development)
of the Indian Institute of Technology Bombay, for ensuring a conducive
environment for promoting research.
Needless to mention, the responsibility for errors, if any, is entirely
our own.
Pushpa Trivedi
L. Lakshmanan
Rajeev Jain
Yogesh Kumar Gupta
ABBREVIATIONS
Abbreviation |
Full Form |
ASI |
Annual Survey of Industries |
CD |
Cobb-Douglas Production Function |
CEA |
Central Electricity Authority |
CMIE |
Centre for Monitoring Indian Economy |
CRS |
Constant Return to Scale |
DEA |
Data Envelopment Analysis |
DME |
Directory Manufacturing Establishment |
E |
Energy input at constant prices |
EME |
Emerging Market Economies |
EXg |
Export Growth |
FBT |
Food, Beverages and Tobacco |
FK |
Fixed Capital |
FPLL |
Fuel, Power, Light and Lubricant |
GAA |
Growth Accounting Approach |
GCI |
Global Competitiveness Index |
GCR |
Global Competitiveness Report |
GCIR |
GCI rank |
GCIS |
GCI score |
GoI |
Government of India |
GVA |
Gross value added |
I |
Intermediate inputs (materials and energy) |
ILO |
International Labour Office |
I-O |
Input- output absorption matrix |
K |
Real capital stock |
KI |
Kendrick Index |
L |
Labour input |
L1 |
Production workers |
L2 |
Non-production workers = Total Employees-L1 |
M (N) |
Real material inputs, Real total inputs |
MFG |
Manufacturing |
MI |
Malmquist Index |
N |
Total Inputs |
NAS |
National Accounts Statistics |
NDME |
Non-directory Manufacturing Establishment |
NIC |
National Industrial Classification |
NSSO |
National Sample Survey Office |
NVA |
Net Value Added |
O |
Real Output |
OAME |
Own-account Manufacturing Establishment |
pcpa |
Percent per annum |
PFA |
Production Function Approach |
RBI |
Reserve Bank of India |
RGO (Q) |
Real Gross output (used in empirical estimation) |
RVA |
Real value added |
RVASD |
Real value added obtained by single deflation method |
RVADD |
Real value added obtained by double deflation method |
S |
Services input at constant prices |
SFPF |
Stochastic Frontier Production Function |
SI |
Solow Index |
SSIs |
Small Scale Industries |
TECRS |
Technical Efficiency Constant Return to Scale |
TEVRS |
Technical Efficiency Variable Return to Scale |
TFPG |
Total Factor Productivity Growth |
TI |
Tornqvist Index |
TL |
Translog |
VA |
Value Added (Nominal) |
WK |
Working Capital |
Abbreviation |
State |
AP |
Andhra Pradesh |
BIH* |
Bihar and Jharkhand |
DEL |
Delhi |
GUJ |
Gujarat |
HAR |
Haryana |
KAR |
Karnataka |
KER |
Kerala |
MAH |
Maharashtra |
MP* |
Madhya Pradesh and Chhattisgarh |
ORI |
Orissa |
PUN |
Punjab |
RAJ |
Rajasthan |
TN |
Tamil Nadu |
UP* |
Uttar Pradesh and Uttarakhand |
WB |
West Bengal |
EXECUTIVE SUMMARY
India’s development strategy placed a heavy emphasis on the creation
of a well-diversified industrial base to realise the dream of industry-led
development. In order to maximise growth from limited resources, the
importance of increasing productivity, efficiency and competitiveness
needs no justification. It may not be out of place that though the concepts
of productivity, efficiency and competitiveness are indicators of
performance, these need not necessarily move in tandem with each other.
However, improving these indicators should be conceived merely a means
to an end (i.e., social welfare) and certainly not as an end in itself.
This study focuses on the performance of manufacturing sector by
taking a disaggregate view of it. It examines the regional dimensions1 (for
18 states of India), component industries2 (6 industries within
manufacturing sector), organised versus unorganised segments of the
manufacturing sector, etc. The period of study is 1980-81 to 2003-04 for
the disaggregated analysis. However, we have extended the study to
include the period up to 2004-05 for the ‘Selected Public Ltd.
Manufacturing Companies’ and up to 2007-08 in the case of overall
organised manufacturing sector. The study uses both parametric and
non-parametric methods to estimate productivity and efficiency of India’s
manufacturing sector.
Before we report the major findings of the study, we deem it necessary
to report a few stylised facts pertaining to India’s manufacturing sector.
These are as follows. First, the average share of manufacturing sector in
real GDP has marginally increased from about 13 per cent during 1970-
75 to about 15.6 per cent in 2007-08, i.e., approximately by about 2.6
percentage points over a period of almost four decades. Despite the emphasis on manufacturing sector in India’s planning process, the
contribution of this sector, at best, is modest. It needs to increase so as
to absorb more workers and to enable people to improve their standard
of living. Second, the employment and output generation within the
manufacturing sector exhibits a major imbalance. According to the latest
available data, the unorganised sector accounts for about 80% of
employment and only about 33 % of income of the manufacturing sector.
Third, as regards the position of manufacturing sector of the various
states, Maharashtra, Tamil Nadu and Gujarat are the states which
consistently rank as the first three topmost states in terms of both output
and employment generation in the organised manufacturing sector.
Deterioration of Bihar* and West Bengal, and ascent of Haryana,
Karnataka, Punjab and Rajasthan is noticeable in these regards. Fourth,
over the period of the study, ‘Metal’ and ‘Machinery & Transport
Equipment’ industries accounted (each of them) for almost one-fifth of
gross value added (GVA) of the organised manufacturing sector. These
industries are followed by the chemical industry which accounted for
about 13 percent of GVA of the organised manufacturing sector. However,
in terms of job provision, these are not the topmost industries. Textiles
and Food (including beverages & tobacco) industries together account
for about 41 per cent of jobs in the organised manufacturing sector.
Fifth, during 2000-01 to 2008-09, the growth rate of exports (in US $
terms) of Metal and Engineering goods has been highest at about 24 per
cent per annum (pcpa) as against the overall growth of exports of about
17 pcpa and that of manufactured products of about 15 pcpa. Growth
rates of exports of Textiles and Leather (LEATH) industry have been quite
low ranging between 6 and 7 pcpa. Lastly, it is not too much to expect
that, with the growth of manufacturing sector, workers would benefit in
terms of rising per capita real wages. However, the worrisome feature of
the organised manufacturing sector in India is stagnancy of per capita
real wages of workers. The plight of workers in unorganised sector is
much worse, as the wage differentials between organised and unorganised
manufacturing sectors are rather sharp.
The major findings of this study are as follows.
-
As regards the organised manufacturing sector, the estimates
of Total Factor Productivity Growth (TFPG) are sensitive to the
methodology applied. For the period 1980-81 to 2003-04, we
get the estimates of TFPG for total organised manufacturing
sector as 0.92 pcpa by Growth Accounting Approach (GAA) which
is almost half of that obtained by the Production Function
Approach (PFA), i.e., 1.81 pcpa. Hence, the contribution of TFPG
to growth of output by these two approaches lies between 13 to
25 percent. The RBI dataset on public limited companies gives
us estimate of TFPG of about 1.5 pcpa for the period 1993-94 to
2004-05.
-
The industry-wise performance of organised manufacturing in
terms of TFPG as measured by the GAA indicates the worst
performance by Food, Beverages and Tobacco (FBT) industry,
followed by the Textiles (TEX) industry. The best performers are
Machinery and Transport Equipment (MTE) and Chemical
(CHEM) industries. The TFPG varied between -0.41 (for FBT)
and 1.47 pcpa (for MTE). As indicated earlier, the estimates of
TFPG are sensitive to the methodology used. The PFA estimates
provide us with the range of TFPG between 3.05 for TEX and
0.97 for Leather (LEATH) pcpa. Also, we do not see complete
consistency in the ranking of the industries based on these two
alternative approaches.
-
The inter-state performance of TFPG of organised manufacturing
sector across the states (as measured by the GAA) indicates
that Bihar*, Rajasthan and AP turn out to be best performers
while the worst performers are Tamil Nadu, Gujarat and Punjab.
Bihar* exhibits the highest TFPG of 1.55 pcpa and Tamil Nadu
exhibits the lowest TFPG of 0.65 pcpa. This is not surprising if
we combine this information with the fact that Bihar* witnessed a negative growth rate of employment (-1.8 pcpa) and Tamil
Nadu witnessed growth rate of employment of 2.5 pcpa in
comparison to the corresponding national figure of 0.5 pcpa.
Bihar* and West Bengal witnessed growth rates of output in
their manufacturing sectors which were lowest across the states,
for the former it was 3.7 pcpa and for the latter, it was 3.4 pcpa.
That is precisely why, we caution against using TFPG figures
unconditionally as indicator of welfare of the masses in general
and labourers in particular. It is quite possible that if output
growth is low and this low growth rate is accompanied by ‘falling
levels of employment’, it can show up as high TFPG.
-
It is not only TFPG, but also the level of TFP that should be paid
attention to. Maharashtra features as the best performing state
in terms of level of TFP in the organised manufacturing sector
and Gujarat ranks as the best performer in terms of TFPG, as
per the PFA estimates. These results are not unexpected, given
the extent of industrialisation of these two states.
-
Though we also attempted estimating efficiency using the ASI
unit level data for organised manufacturing and used stochastic
frontier production function (SFPF) methodology so as to avoid
the outliers affecting our results, the problem of measurement
of capital stock cannot be resolved unless the identification codes
are provided to the users. Though this is possible only for the
census sector. We have reported these results more for the sake
of highlighting the data limitations, rather than as findings on
efficiency based on the unit level ASI data.
-
Using the RBI dataset, we have estimated efficiency of 449
companies for the period 1993-94 to 2004-05, using both data
envelopment (DEA) and SFPF approaches. The mean efficiency
levels by the former approach range from 0.72 to 0.78 and by
the latter approach from 0.66 to 0.67, under the alternative
assumptions. These figures highlight the fact that there existsan ample scope for improving efficiency in the Indian
manufacturing sector. The SFPF approach also highlights the
lowest mean efficiency of FBT and TEX. These results neatly
coincide with the empirical evidence on TFPG emanating from
GAA. Moreover, the TFPG measured using the Malmquist Index
for this dataset also identifies the FBT and TEX as the poorest
performers with TFPG of the former being -1.5 pcpa and of the
latter being 0.5 pcpa. Top performers are the MTE and (METAL)
and their productivity estimates range between 2.2 and 2.4 pcpa.
This throws some light as to why the TEX has been one of the
poorest performers on exports front and the Metal and
Engineering goods industries have been the top performers.
-
The estimation of TFPG for the unorganised sector was
constrained due to the data limitations we faced in the
construction of capital stock series. In view of this, we estimated
labour productivity for the unorganised sector and compared it
with that of the organised manufacturing sector. We observe
that labour productivity in both organised and unorganised
sectors has increased over time. Labour productivity in various
component industries of the organised manufacturing sector
ranged from 1.6 to 2.2 times in 2000-01 as compared to that in
1989-90. The respective range for the unorganised sector was
1.7 to 2.8 times. In other words, growth of labour productivity
in unorganised sector has increased more or less in tandem
with that in the organised sector during the above-mentioned
period. However the disparity in the levels of labour productivity,
are rather sharp and have perpetuated. Organised
manufacturing sector had labour productivity which was 13,
14 and 15 times of that witnessed by its unorganised counterpart
in years 1989-90, 1994-95 and 2000-01, respectively.
-
Considerable research has been done on the issue of whether
policy reforms and liberalization have led to improvement inTFPG and in order to do so, much of the literature depends on
use of dummy variables for demarcating ‘pre’ and ‘post’ reforms
period. Two major problems arise in this context. First, it is
difficult to isolate the impact of reforms from the other factors
which affect TFPG. Second, there could be time-lags only after
which the impact of reforms could be felt on TFPG. By the use
of dummy variable for the pre and post-reform period, we have
tried to capture whether TFPG for the organised manufacturing
sector (as it was not possible to test it with the other databases
used in this study) was higher or lower in the two periods for
the various industries and states. It turns out that TFPG has
witnessed either deceleration or no acceleration across industries
(except for Metal industry) and across states (barring West
Bengal and Haryana) as per the GAA. The policy dummy in PFA
indicates a shift in production function only for Maharashtra.
However, when we take the averages of TFPG over shorter periods
(see Table 5.9A and 5.9B), there seems to be revival of TFPG in
the post-nineties. Except Bihar*, Madhya Pradesh, Orissa and
Rajasthan, all states have witnessed higher TFPG during the
post nineties, compared to the preceding quinquennium.
Similarly, all industries except metal industry have witnessed
revival of TFPG post-nineties period as compared to the preceding
quinquennium. In some sense, one could interpret it as an
evidence of a J-curve effect of the policy reforms.
-
We also tried to isolate the impact of one of the components of
the policy reforms and used it along with the other potential
determinants of productivity to explain the TFPG of the organised
manufacturing sector. Trade barriers (as measured by the ratio
of import duties to import payments) turned out to be a
significant determinant of TFPG of organised manufacturing
sector with a negative sign, indicating that the dismantling of
the trade barriers has had a positive impact on TFPG. The competition to export (as captured by the growth of exports)
also turned out to be positively associated with TFPG of the
organised manufacturing sector.
-
We also tried correlating competitivenss scores (which are for
the states as such and not for the manufacturing sector) and
TFPG of the organised manufacturing sector at the state level.
We could not find a neat relationship between competitivenss
of states and TFPG of its organised manufacturing sector.
-
Based on the following findings of the study, we reiterate the
need to improve productivity and efficiency of India’s
manufacturing sector. The contribution of TFPG to output growth
for organised manufactuing sector ranges between 13 and 25
percent using alternative methodologies. The mean efficiency
levels for the RBI dataset ranges between 0.66 and 0.78.
Moreover, the ratio of labour productivity of organised to
unorganised sector ranges between 13 and 15 combined with
the fact that about 80 % of the workers in India’s manufacturing
sector are in the unorganised sector. The states like Bihar* and
West Bengal and industries like Food and Textiles need urgent
attention. Needless to mention that this should be achived with
growing and not with falling employment if the development
process in the country needs to be ‘regionally balanced’ and
‘inclusive’
___________________________
PRODUCTIVITY, EFFICIENCY AND
COMPETITIVENESS OF THE INDIAN
MANUFACTURING SECTOR
Pushpa Trivedi#, L. Lakshmanan, Rajeev Jain, Yogesh K Gupta
1. Introduction
As a reaction to the colonial past, India’s development strategy focused
on self-reliance. In pursuit of the same, it placed a heavy emphasis on the
creation of a well-diversified industrial1 base to realise the dream of
industry-led development. Though this strategy assigned the prime
responsibility of developing heavy industries to the public sector, private
sector was also allowed to play a supplemental role. Almost until the
beginning of the eighties, a myriad of measures to control the private
sector, such as, licensing requirement for installation of capacities,
quantitative and tariff restrictions on imported inputs, regulation of
monopolies and trade practices, foreign exchange regulation,
nationalisation of commercial banks, price controls, etc., constituted an
integral part of India’s industrial policy. The socialistic fervor in the minds
of policy makers got reflected in the policy measure, such as, reservation
of labour-intensive manufacturing products for the small scale industries
(SSIs), preferential treatment to the SSIs, stringent labour laws against
firing of labour in large firms, etc. The industrial policy was primarily
designed to protect the ‘infant’ industries from external competition.
Unfortunately, it inhibited internal competition as well. By the end of
seventies, Indian manufacturing suffered from high costs of production,
sub-standard quality of products and lack of competitiveness of its exports.
It is no surprise that the regulatory framework of the pre-1980s, inter alia,
has been held responsible for low growth rate of output and productivity
of India’s manufacturing sector (Ahluwalia, 1991).
The first bout of industrial policy reforms that were initiated in the
eighties attempted to lift the economy from industrial stagnation through
measures, such as, removal of hurdles on capacity expansion, enabling
availability of imported inputs, liberalization of price controls, etc. The
primary intent of these reforms was to unleash the growth potential of
India’s industrial sector. The second bout of reform process was initiated
in 1990-91 in the wake of macroeconomic crisis. Economic and institutional
reforms are being fine-tuned since then, depending on the unfolding of
situations both at the external and domestic fronts. It may also be worth
noting that the reforms of the eighties were centered primarily on industrial
and fiscal sectors, whereas, the reforms initiated in the early nineties
were more in the nature of comprehensive macro-economic reforms.
Stabilisation and structural adjustment process constituted the core of
reforms in the nineties and these were deemed to be pre-requisites for the
pursuit of growth and viable balance of payment. In brief, the reforms in
the nineties differed in their characteristics from those of the eighties. The
reforms in the eighties have been branded as ‘pro-business’, whereas, the
latter as ‘pro-market’ (Rodrik and Subramanian, 2005 and Kohli, 2006).
It has been argued by Ahluwalia (1991) that the reforms of the eighties
resulted in an upward shift in growth rate and productivity of the Indian
economy and in particular that of industrial/manufacturing sector. The
comprehensive reforms of the nineties gained wide publicity as these pulled
the economy from a crisis situation and succeeded in alleviating foreign
exchange constraint and controlling inflation. As substantial liberalisation
in terms of tariff reductions and removal of quantity restrictions on imported
inputs (needed for growth of manufacturing sector) took place during the
nineties, it was expected that these reforms would also enable the economy
to follow growth and productivity paths higher than those witnessed during
the eighties. However, as noted by Rodrik and Subramanian (2005), no
such structural break in either growth or productivity is evident after the
initiation of reform process of the nineties. Perhaps, the reforms of nineties targeted primarily the external and financial sectors, which have impacted
the real sector indirectly.
The emphasis that needs to be placed on productivity has been well
articulated in the literature (Krugman, 1994 and Young, 1995). A higher
growth path on account of higher productivity is considered to be a
preferable alternative as compared to that due to increased application of
inputs. The latter is deemed to be unsustainable due to supply constraints
and also due to the phenomenon of diminishing returns. However, this
can be a contentious issue, if it pertains to application of labour input,
especially so in the context of a labour abundant economy like India. If
increased productivity is attained by downsizing employment, it may not
bode well for the social fabric and it ought to be a cause of concern to the
policy makers. As the basic objective underlying the argument for increasing
productivity is to increase social welfare, a situation of rising productivity
coupled with shrinking employment may be neither socially desirable nor
politically sustainable. A higher growth path, enabled by productivity growth
and combined with ‘employment generation’ ought to be considered as an
ideal trajectory from the point of view of sustainable growth of an economy.
The link between productivity and social welfare (poverty alleviation) can
best operate through employment generation. The importance of
productivity in poverty reduction via employment generation has been
duly emphasised in the World Employment Report 2004-05 (International
Labour Office, 2005), by an apt choice of theme for the report, viz.,
‘Employment, Productivity and Poverty Reduction’. In other words, increase
in productivity needs to be conceived merely as a means to an end (i.e.,
social welfare) and certainly not as an end in itself.
Though the concepts of productivity, efficiency and competitiveness
are indicators of performance, these need not necessarily move in tandem
with each other. These terms have rather different conceptual
underpinnings and hence, the policy makers need to focus on movement
of each of these in accordance with the socio-economic objectives. As regards the two concepts of productivity, viz., labour productivity and total factor
productivity (TFP), these are pertinent for policy makers, since the former
has a direct link to standard of living and the latter indicates the economical
use of resources in the process of production. ‘Productivity’ per se is a
descriptive measure of performance and it can be estimated2 independently
for a decision making unit, whereas, measurement of ‘efficiency’ entails a
comparison with a peer group and is a normative measure of performance
(Ray, 2004). The concept of ‘competitiveness’ has many more dimensions
to it, as compared to the concepts of efficiency and productivity.
Competitiveness implies increasing share of market in relation to other
decision making units3. The Global Competitiveness Report (GCR) 2009-10
published by the World Economic Forum (2009) provides the ranks for
Global Competitiveness Index (GCI) and scores for various countries along
with their component indices. These indices try to capture both the micro
and macro determinants of global competitiveness. The pillars of GCI (as
outlined in GCR, 2009) are classified into the basic requirements
(institutions, infrastructure, macroeconomic stability, and, health &
primary education), efficiency enhancers (higher education and training,
goods and market efficiency, labour market efficiency, financial market
sophistication, technological readiness, market size) and innovation and
sophistication factors (business sophistication and innovation). Hence, the
policy makers have to ensure that these basic requirements are provided
within the macroeconomic policy frame. The State also has an additional
task of providing policy environment to firms that is conducive for
enhancing their efficiency and attaining technological and managerial
sophistication. In brief, the gains in terms of productivity, efficiency and
competitiveness, have to percolate across sectors, regions and income
groups so as to enhance the social welfare. The cross-country comparison shows that India is far lower in terms of GCI rank (GCIR) and GCI score
(GCIS) as compared with the other Emerging Market Economies (EMEs).
India’s GCIR is 49 among 133 countries and the GCIS is 4.3 in the 7 point
scale.4 In terms of the efficiency enhancers sub-index, which includes
pillars critical for countries in the efficiency-driven stage, India stands at
33rd place among 134 countries.
Though the study highlights some aspects of competitiveness, it
primarily focuses on productivity and efficiency of ‘manufacturing sector’
for the following reasons. First, manufacturing/industrial sector has
received much attention of the policy makers in India in terms of financial
allocations in planning process. Second, during the process of transition
(i.e., when growth of an economy is being driven by manufacturing and
tertiary sectors rather than by the primary sector), manufacturing sector
is known to generate employment for both unskilled and skilled labour
and the employment potential of manufacturing sector is higher as
compared to that of the tertiary sector. Third, the growth of manufacturing
sector is also necessary for the overall growth of the economy, as it can
supply inputs and provide market to other sectors. Lastly, we also view
that the solution to the agrarian crisis will also be found in the growth of
output and employment of manufacturing sector.
The motivation for this study was derived from the fact that the
Reserve Bank of India has displayed a keen interest in the arena of
productivity trends in the Indian economy (Reddy, 2005). Unlike many
other countries, India has not witnessed a stylised sectoral growth process
during her developmental process. India’s service sector led growth in the
recent years has been viewed with some apprehension in terms of fostering
inequalities across regions and sections of population. Concerns have also
been raised about the widening regional and sectoral dispersion of growth (Ahluwalia, 2000) and jobless growth in the context of economic reform
process that gathered momentum in the early nineties.
The objectives of this study are as follows: (i) To estimate productivity
and efficiency both at industry5 and state6 levels for India’s manufacturing
sector; and, (ii) To examine the path of efficiency and productivity in the
context of economic reforms undertaken by the Indian manufacturing
sector; (iii) To provide a comparative view of the performance in organised
and unorganised sectors of India’s manufacturing sector and, (iv) To
identify the determinants of growth and productivity of the Indian
manufacturing sector.
The remainder of this study is organised as follows. Section 2
provides some stylised facts about India’s manufacturing sector. Section
3 provides a synoptic review of the literature pertaining to efficiency and
productivity of India’s manufacturing sector. Section 4 presents the details
of the coverage of the study, methodology and data used in the study.
Section 5 presents the estimates of productivity growth and efficiency
obtained by the various methodologies for the different datasets. Section
6 provides the overall view of the results obtained in the study in a holistic
perspective and policy implications emanating from the previous sections
of the study.
2. Stylised Facts
The share of manufacturing sector in India’s real GDP has risen
over the years. However, this increase has not matched the expectations
for two main reasons. First, the expectations from manufacturing sector
were high due to the emphasis on heavy industries led development in the
planning process in India; and, second, the countries with similar levels of
development on the eve of planning in India, especially the East-Asian
Economies including China, have been able to make their presence
felt in the global market for manufacturing products to a far greater extent
than India.
In Box 2.1, we provide the description of concepts and definitions
used in the context of disaggregated data in India’s manufacturing sector.
Box 2.1: Classification of Manufacturing Sector in India
The manufacturing sector covers all manufacturing, processing and repair &
maintenance services units irrespective of their employment size, investment and location.
The first classification of the manufacturing sector is into two broad sub-sectors:
(i) registered or organised; and, (ii) unregistered or unorganised. The data on
manufacturing in the organised sector is collected through Annual Survey of Industries
(ASI), annually, whereas, the data on unorganised sector is collected by National Sample
Survey Organisation (NSSO).
Registered (Organised) manufacturing sector : The registered manufacturing
sector includes all factories covered under sections 2m (i) and 2m (ii) of the Indian Factories
Act (IFA), 1948 which refers to the factories employing 10 or more workers and using
power or those employing 20 or more workers but not using power on any day of the
preceding 12 months ( for the entire country except the states of Arunachal Pradesh,
Mizoram, and Sikkim and Union Territory of Lakshadweep). It also includes Bidi and
Cigar establishments registered under Bidi and Cigar Workers (Condition of Employment)
Act, 1966 with the same employment and use of power criteria as mentioned above. All
electricity undertakings engaged in generation, transmission and distribution of electricity
registered with the Central Electricity Authority (CEA) were covered under ASI irrespective
of their employment size, until 1997-98. Since 1998-99, the electricity units registered
with the CEA and the departmental units such as railway workshops, RTC workshops,
GOVT. MINTS, sanitary, water supply, gas storage, etc. are not covered under ASI.
Unregistered (Unorganised) manufacturing sector: The unregistered
manufacturing sub-sector, a complement set to the registered manufacturing sub-sector,
covers all the residual units which are not covered under the registered manufacturing sector. Thus, the unregistered manufacturing sector covers all the manufacturing,
processing, repair & maintenance services units employing less than 10 workers and
using power or less than 20 workers and not using power. The data on unorganised
sector is collected through periodic surveys by the NSSO. Further classification of the
unorganised sector is as indicated below.
1. Own Account Manufacturing Enterprise (OAME) : These enterprises are
engaged in manufacturing and/or repairing activities and are run without any
hired worker employed on a regular basis.
2. Establishment : An establishment employs at least one hired worker on a fairly
regular basis. Paid or unpaid apprentices, paid household member/servant/
resident worker in an enterprise are considered hired workers. Establishments
are further categorised into two types: non-directory and directory
manufacturing establishments.
2.1 Non-directory Manufacturing Establishment (NDME) : It is an establishment
engaged in manufacturing and/or repairing activities and employs less
than six workers (household and hired workers taken together).
2.2 Directory Manufacturing Establishment (DME): A directory establishment
engaged in manufacturing and/or repairing activities employs six or more
workers (household and hired workers taken together).
Source : http://mospi.gov.in/mospi_asi.htm (for ASI coverage)
http://mospi.nic.in/rept%20_%20pubn/ftest.asp?rept_id=nad09_2007&type=NSSO (for NAS coverage)
http://mospi.nic.in/rept%20_%20pubn/ftest.asp?rept_id=524&type=NSSO
(for unorganised manufacturing coverage)
2.1 Contribution of Manufacturing Sector to Real GDP
In Table 2.1, we provide a synoptic view of the importance of the
manufacturing sector and its two components, viz., registered and
unregistered manufacturing in India’s real GDP. It can be seen from Table
2.1 that the average share of manufacturing sector in real GDP increased
from about 13 per cent during 1970-75 to about 15.1 per cent during
2002-07, i.e., approximately by just about 2 percentage points over a period
of more than three decades. Even in the year 2009-10, the share of
manufacturing sector in India’s real GDP is just about 16.1 percent.
During 1970-75, India’s real GDP of manufacturing sector was more
or less equally distributed between its registered and unregistered segments.
Over the years, the growth of real income in the registered manufacturing
has been higher than that of unregistered manufacturing sector, resulting in the average contribution of the unregistered sector shrinking to almost
half of that of the registered sector during 2002-07. The situation has
remained more or less unchanged even during the post 2007 period.
Table 2.1: Contribution of Manufacturing Sector to India’s Real GDP |
Average GDP (in Rupees Crore at 1999-2000 Constant Prices) |
Period |
Average GDP of
Manufacturing
Sector |
Average GDP of
Registered
Manufacturing Sector |
Average GDP of
Unregistered
Manufacturing Sector |
1970-75 |
64405 |
33545 |
30786 |
|
(13.2) |
(6.9) |
(6.3) |
1975-80 |
81744 |
42547 |
39108 |
|
(13.9) |
(7.2) |
(6.6) |
1980-85 |
101412 |
55571 |
45841 |
|
(14.3) |
(7.8) |
(6.5) |
1985-90 |
133812 |
79756 |
54056 |
|
(14.7) |
(8.7) |
(6.0) |
1990-95 |
171233 |
109247 |
61987 |
|
(14.6) |
(9.3) |
(5.3) |
1995-2000 |
248504 |
162847 |
85657 |
|
(15.7) |
(10.3) |
(5.4) |
2000-05 |
316307 |
212370 |
103938 |
|
(15.1) |
(10.1) |
(5.0) |
2001-06 |
338105 |
228619 |
109486 |
|
(15.0) |
(10.2) |
(4.9) |
2002-07 |
367898 |
249583 |
118315 |
|
(15.1) |
(10.3) |
(4.9) |
2008-09 |
(15.6) |
(10.4) |
(5.2) |
Note: Figures in parentheses are % share of the respective sector in the Real GDP (1999-2000 prices), except for 2008-09. For this year, figures are based on GDP at 2004-05 constant prices.
Source: http://www.mospi.nic.in/mospi_cso_rept_pubn.htm, National Accounts Statistics – Back series 1950-51 to 1999-2000 and http://www.mospi.nic.in/mospi
|
Kochhar et al (2006) demonstrate that given the per capita GDP and
size of India, the share of manufacturing sector in GDP was in conformity
with the stylised growth pattern of other countries. According to them,
manufacturing sector in India underperformed since 1981 and this
perception about its underperformance is also due to its comparison with
China7. China was a significant positive outlier (i.e., China’s manufacturing sector contributed more to its national income as compared with the
historical evidence on countries of similar levels of development) in 1981.
To put it differently, India had approximately the normal share of output
and employment in manufacturing in 1981, if compared with countries at
a similar level of development and size. Over the next two decades, (when
reforms were implemented, so as to remove the constraints on
manufacturing sector), it failed to keep pace with the growth of
manufacturing sector in other countries with similar levels of development.
This is not to deny the fact that India has done reasonably well as compared
to its own past performance.
The composition of India’s manufacturing sector is also crucial from
the point of view of intra-sectoral equity. In Figure 2.1, we provide the
proportion of real GDP originating in unregistered manufacturing in the
total manufacturing sector.
The unorganised manufacturing sector accounts for about 80 percent
of the employment generated in manufacturing sector. However, its
contribution to the income generation or to real GDP of manufacturing (as
seen in Figure 2.1) is much less in proportion to its employment generation.
Furthermore, the relative income contribution of the unregistered sector
vis-à-vis registered sector in manufacturing has been consistently declining
over the years. In the first half of the eighties, this share was approximately
45 per cent and it has fallen to about 32 per cent during 2002-07. Even in
2008-09, the share of unorganised sector in real GDP of manufacturing
sector was about 33.2 percent. In brief, the income originating in
unregistered segment of India’s manufacturing sector is much lower than
the proportion of workforce it supports. This has implications for the
differences in labour productivity in registered and unregistered segments
of India’s manufacturing sector.
2.2 Contribution of Manufacturing Sector to Employment
A vast body of literature has accumulated over the problem of lack
of tandem between income and employment generation across various
sectors in India. Though the composition of GDP in India has undergone substantial changes over the years, the dependence of workforce on
manufacturing sector has hardly increased. Historically, during the
transition process, manufacturing sector has been the main absorber of
mass unskilled labour that gets released from agricultural sector. Unlike
the East Asian economies, India failed to draw employment from agriculture
into manufacturing in any significant magnitude (Kochhar et al, 2006).
 |
In Table 2.2, we provide the data on employment generation in the
industrial sector of India which includes manufacturing sector. In the
year 2000, agriculture in India accounted for almost 60 per cent of total
employment which is the highest in comparison to the countries listed in
the Table. It is even higher than the respective figure for the lower middle
income countries. Industry accounted for just about 18 per cent of total
employment, which means the contribution of manufacturing to
employment generation is even lower. Though, in the case of India, services
sector has been able to absorb much more labour than the manufacturing
sector, labour absorption by this sector is lowest as compared to other
countries and it is almost similar to that witnessed by the low income countries. In brief, the manufacturing sector in India has failed to generate
adequate employment in general and in organised manufacturing sector
in particular. This implies inequalities in inter-sectoral and intra-sectoral
distribution of purchasing power.
Table 2.2 Sectoral Employment as % of Total Employment |
Country/Countries |
Agriculture |
Industry |
Services |
1980 |
2000 |
2007 |
1980 |
2000 |
2007 |
1980 |
2000 |
2007 |
India |
68.1 |
59.3 |
50.2* |
13.9 |
18.2 |
20.4* |
18.6 |
22.4 |
29.4 * |
Brazil * |
29.3 |
24.2 |
19.0 |
24.7 |
19.3 |
21.0 |
46.1 |
56.5 |
59.0 |
China |
68.7 |
46.9 |
44.0 |
18.2 |
23.0 |
18.0 |
11.7 |
29.9 |
16.0 |
Indonesia |
55.9 |
45.3 |
41.0 |
13.2 |
17.3 |
19.0 |
30.2 |
37.3 |
40.0 |
Korea |
34.0 |
10.9 |
7.0 |
29.0 |
28.0 |
26.0 |
37.0 |
61.0 |
67.0 |
Malaysia |
37.2 |
18.4 |
15.0 |
24.1 |
32.2 |
29.0 |
38.7 |
49.5 |
57.0 |
Mexico |
23.5 |
17.5 |
14.0 |
26.5 |
26.9 |
26.0 |
49.0 |
55.2 |
60.0 |
Thailand |
70.8 |
48.8 |
42.0 |
10.3 |
19.0 |
21.0 |
18.9 |
32.2 |
37.0 |
Turkey |
43.0 |
34.5 |
26.0 |
34.9 |
24.5 |
26.0 |
22.1 |
40.9 |
48.0 |
Low income |
74.6 |
64.5 |
n.a |
8.7 |
12.3 |
n.a |
16.5 |
23.2 |
n.a |
Lower middle income |
64.0 |
43.2 |
n.a |
18.5 |
18.5 |
n.a |
16.4 |
38.3 |
n.a |
Source: Kochhar et al (2006) and www.worldbank.database.org.
Note: For Brazil, the latest data available is for 2006 and for Turkey it is for 2008 and these have been reported in the Table instead of data for 2007.
* Based on Current Daily Status (2006-07). |
In Table 2.3, we present a synoptic view of the employment generation
in India’s unorganised sector vis-à-vis that in the organised sector.
It can be seen from Table 2.3 that the contribution of unorganised
sector in employment generation in manufacturing sector hovers around
80 per cent or so. In other words, the share of organised sector in total
employment generation of manufacturing sector is just about one-fifth. This,
when put together with the data reported in Table 2.1, indicates that only
about 32 per cent of income of the total manufacturing sector was generated
in the unorganised sector, which employs almost 80 per cent of labour of
the manufacturing sector. In other words, we see not only the disproportion
between income and employment generation across sectors, but also within
the manufacturing sector, i.e., between its organised and unorganised
segments. Moreover, we also observe from Table 2.3 that economically backward states have much higher proportion of employment in unorganised
manufacturing in comparison to that in the organised manufacturing.
Table 2.3 Employment in Unorganised Sector vis-à-vis
Organised Manufacturing Sector in India |
State/Country |
Employment in Unorganised sector as a % of Total Employment in Manufacturing Sector |
|
1989-90 |
1994-95 |
2000-01 |
2005-06 |
Andhra Pradesh |
79.4 |
70.4 |
78.4 |
75.9 |
Bihar |
85.3 |
88.0 |
91.1 |
91.8 |
Delhi |
79.7 |
80.5 |
88.4 |
79.1 |
Gujarat |
70.0 |
69.9 |
66.4 |
69.6 |
Haryana |
63.3 |
52.1 |
58.3 |
60.5 |
Karnataka |
96.8 |
94.0 |
81.1 |
78.1 |
Kerala |
83.8 |
65.7 |
77.4 |
81.4 |
Madhya Pradesh |
76.3 |
74.4 |
84.7 |
87.2 |
Maharashtra |
67.3 |
64.6 |
71.7 |
71.4 |
Orissa |
93.7 |
94.1 |
94.5 |
93.3 |
Punjab |
59.6 |
55.2 |
67.5 |
60.6 |
Rajasthan |
83.2 |
76.0 |
83.2 |
82.8 |
Tamil Nadu |
78.0 |
71.2 |
75.2 |
72.7 |
Uttar Pradesh |
86.6 |
88.4 |
91.2 |
89.5 |
West Bengal |
89.2 |
85.4 |
91.1 |
91.4 |
India (excluding J. & K.) |
80.9 |
78.2 |
82.3 |
81.2 |
Total employment (’00) |
432383 |
423611 |
450685 |
448964 |
Source: Based on the data from the 45th, 51st, 56th and 62nd rounds of NSS (for unorganised manufacturing sector) and from ASI (for the organised manufacturing sector). |
2.3 Growth of India’s Organised Manufacturing Sector: A Profile
In this sub-section, we highlight various dimensions of growth of
India’s organised manufacturing sector. In Figure 2.2, we plot the average
growth rates of real output, real wages and real emoluments8 in India’s
organised manufacturing sector.
It can be seen from the Figure 2.2 that average growth of output of
the organised manufacturing sector during the various quinquennia spanning from 1980-85 to 2005-08 ranged between 6.9 and 12.7 pcpa.
The growth performance of the manufacturing sector had been rather
impressive since the eighties itself. The pro-business policies of the eighties,
according to Rodrik and Subramanian (2004), were responsible for a
markedly improved growth performance of the Indian economy during the
eighties as compared to the earlier three decades. There does not seem to
be a statistically significant ‘improvement’ in the growth performance of
the Indian manufacturing sector in the early nineties, i.e., after the initiation
of the next bout of economic reforms. Nonetheless, the growth momentum
of the eighties has been ‘maintained’ during the pro-market reforms of the
nineties. It can be seen from Figure 2.2 that the growth performance of
manufacturing sector has indeed been impressive in the last three years.
Hashim, et al (2009) argue that the major liberalization undertaken during
1990s led to structural transformation of the Indian economy. Therefore,
the enormity of change associated with transition from old inefficient
structure to a new globally more efficient structure was characterised
initially by a slowdown in GDP growth in sectors undergoing such
transition.
 |
|
 |
Figure 2.3 indicates that the ratio of emoluments to both gross value
added and value of output has registered a consistent decline since the
eighties. In the first half of the eighties, the average ratio of emoluments to
gross value added (value of output) was about 43 (9.3) per cent which
declined to about 25 (4.7) per cent during 2000-05 period. This has further
gone down to 19.5 (3.8) percent during 2005-08 period, despite an
impressive growth performance of the economy. In other words, the ratio
of wages and emoluments in income generated in organised manufacturing
has shrunk over the years.
Figure 2.4 highlights the employment situation in the organised
manufacturing sector of India. As can be seen from Figure 2.4, almost up
to 1986-87, employment in organised sector witnessed a declining trend.
After that almost for the next ten years, employment trend was positive.
Thereafter, again we see a period of not only jobless but job loss growth.
This situation has been arrested in the earlier years of the next decade
and the latter half of the next decade presents an impressive performance
on the employment front.
 |
One of the areas of concern regarding the reform process in Indian
manufacturing sector has been the deceleration in the rate of growth of
real emoluments (see Figure 2.2). Growth of real emoluments has been
shrinking over the years and it was in fact negative during the latter half
of the nineties. However, we see a revival in the same since the midnineties
and a ‘U’ shaped pattern in the growth of real emoluments can
be seen in Figure 2.2. Emoluments consist of wages to the shop-floor
workers (skilled and unskilled) and compensation to the ‘other supporting
staff’, including the managerial staff. However, the worrisome feature of
Indian manufacturing sector is stagnancy of per capita real wages. In
Figure 2.5, we plot the per capita real wages and per capita real
remuneration to staff other than workers. It can be seen that the growth
rate of compensation to supporting staff has increased since the late
nineties, in relation to the workers directly engaged in production process.
This can have an adverse effect on the motivation for the shop-floor
workers. It also explains as to why most of the engineering graduates do
not prefer to pursue their engineering skills on the shop-floor and instead
prefer to take up managerial positions. It is necessary for manufacturing sector to retain technologists who are engaged in production process
and for this the real per capita incomes to technologists have to move in
tandem with those for the other managerial staff. Productivity increases
depend both on technology as well as managerial improvements and we
can ill-afford to neglect either of these.
 |
Another concern pertaining to India’s manufacturing sector has
environmental point of view, i.e., the growth of manufacturing sector
has been material resource intensive. Figure 2.6 highlights this
phenomenon. Total value added constitutes barely 20 per cent of the
value of output in India’s organised manufacturing sector. The ratio of
material inputs to total value of output has ranged between 58 and 65
per cent and the respective range for the fuel inputs is between 6 and 7
per cent. If we compare it with the resource intensity in the United States
(see Figure 2.7), the proportion of value added in gross output is about
half as compared to about one fifth in India. Material inputs account for
about two-thirds of value of output in India in comparison to just about one-third in the United States. Fuel inputs account for about 5 to 7
percent of value of output in India as compared to merely 2 percent in
the United States. For Germany, the average ratio of value added to gross output was about one-third during 1998-2007, i.e., for the last 10
years for which the data are available9. It is true that the composition of
the manufacturing sector of the US or Germany and that of India are
quite different. However, if the Indian manufacturing sector is not able
to add as much value to the intermediate inputs, as is done by the other
countries, it will need restructuring in order to be globally competitive.
In other words, there is scope for improving efficiency and productivity
in Indian manufacturing sector, if we have to benchmark India with the
globally competitive advanced nations. Even in China, the average ratio
of value added to gross output of the industrial sector during 1998-2007
was about 29 per cent (China Statistical Yearbook, 2009), In other words,
benchmarking against the most competitive economies is mandated, if
we need to compete with the manufacturing giants in this era of
globalization.
 |
|
 |
In this study, we have selected six major industry groups (listed in
Table 2.4) across the eighteen states of India (these include the three states
which were created in the year 2000). In Table 2.4 and 2.5, we provide the
relative positions of the various industries and states in India’s organised
manufacturing sector. It may be mentioned here at the outset that we
have been able to do the aggregate country level analysis in this study for
the period 1980-81 to 2007-08. However, for the state by industry analysis
of organised manufacturing sector, the time-span of the study is 1980-81
to 2003-04 and we have used the ASI 2-3 digit level data. We also procured
the unit level data from ASI for the time-span 1993-94 to 2003-04 and
estimated productivity for this dataset as well10. For the company level
data obtained from the RBI, we have been able to do industry level analysis
from 1993-94 up to 2004-05.
It can be seen from Table 2.4 that ‘metal’ and ‘machinery &
transport equipment’ industries are the two major industries and each one of these accounted for about one-fifth of GVA in the organised
manufacturing sector. These industries are followed by the chemical
industry which accounts for about 13 percent of GVA of the total
organised manufacturing sector. However, in terms of job provision,
these are not the topmost industries. Textiles and Food industries
together account for about 38 per cent of jobs in the organised
manufacturing sector. Leather industry is the least important industry
from the chosen industries. We have included this industry in our
analysis despite its low share in value added in organised manufacturing
in India, because its contribution to export earnings has been higher
than its importance in the production contribution in the organised
manufacturing sector. Table 2.5 (and Tables 2.5a to 2.5f) highlights
the importance of various selected manufacturing industries in the
selected states. The following observations can be made regarding the
ranking of the organised manufacturing sector of the various states of
India (also refer to Annexure 2.1 and Annexure 2.2).
Table 2.4: Shares of Selected Industries in the Organised Manufacturing Sector (2007-08) |
Sr.
No. |
Industry |
NIC
2004
code |
As a % of Total Organised Manufacturing Sector |
Fixed
Capital |
Persons
Engaged |
Emolu-
ment |
Total
Inputs |
Gross
Output |
Gross
Value
Added |
1 |
Food and Beverages# |
15 & 16 |
8.36 |
18.38 |
10.34 |
13.70 |
12.67 |
8.53 |
2 |
Chemical & Chemical Products |
24 |
14.70 |
8.54 |
12.21 |
10.26 |
10.85 |
13.25 |
3 |
Leather & Leather Products |
19 |
0.48 |
2.12 |
1.23 |
1.03 |
0.86 |
0.17 |
4 |
Metal & Metal Products |
27 & 28 |
22.33 |
12.74 |
17.63 |
17.75 |
18.51 |
21.59 |
5 |
Machinery & Transport Equipment |
29-35 |
14.44 |
17.43 |
26.77 |
19.23 |
19.42 |
20.17 |
6 |
Textiles & Textile Products |
17 & 18 |
10.39 |
20.00 |
13.45 |
7.28 |
7.22 |
6.97 |
|
SMFG (total of the above) |
|
70.7 |
79.2 |
81.6 |
69.2 |
69.5 |
70.7 |
|
MFG @ |
|
845132 |
104525 |
105443 |
2225525 |
2775709 |
550184 |
Notes: For various industries and selected industries (SMFG) as a group, the data presented are as a
per
cent of all India total.
@ : All variables for MFG except Persons Engaged, are in Rs. crore. Persons Engaged are in numbers.
# : We have also covered Tobacco industry in this study, wherever possible.
Source: ASI, 2004-05 |
-
Looking at the average contribution to the national output of
organised manufacturing sector, the five topmost states (in the
descending order of importance) are: Maharashtra; Gujarat; Tamil
Nadu; West Bengal and UP. The five bottom states are: Delhi; Orissa;
Rajasthan; Kerala and Bihar*.
-
In 1980-81, Maharashtra, Gujarat, Tamil Nadu, West Bengal, and
UP* ranked topmost states in terms of their contribution to value of
output of organised manufacturing sector. Of these states,
Maharashtra, Gujarat, Tamil Nadu and West Bengal together
accounted for more than 50 per cent of value of output of India’s
organised manufacturing sector. Orissa, Delhi, Rajasthan and
Haryana were the states with lowest contribution to the output of
organised manufacturing sector in India.
-
Maharashtra topped in terms of output contribution in 1980-81 and
the contribution of Gujarat was almost half of that of Maharashtra.
However, by 2007-08, Gujarat has significantly caught up with
Maharashtra in terms of its share in the output.
-
In 2007-08, Maharashtra continues to be the topmost State in terms
of value of output of manufacturing sector in the country. West Bengal
lost its position as one of the topmost 5 states. Gujarat, Andhra
Pradesh, Karnataka, Haryana and UP* improved their shares
significantly in terms of output contribution in 2007-08 as compared
with 1980-81 position. Growth of Hyderabad and Bengaluru’s IT
industry in the recent years seems to be related to the improvement
of ranking of manufacturing sectors of the respective states.
-
The share of Maharashtra, West Bengal, Bihar*, UP*, MP* in total
employment in organised manufacturing declined over the period
1980-81 to 2007-08. As against this, the relative contributions of
employment of Tamil Nadu, Gujarat, AP, Karnataka, Punjab, Haryana
and Rajasthan increased over this period.
-
As regards industry-wise position of various states, Maharashtra,
UP*, AP, Gujarat and Tamil Nadu are states that top in the production
of food, beverages and tobacco, whereas, Orissa, Bihar*, Delhi,
Rajasthan and Haryana belong to the bottom (refer Table 2.5a).
-
As regards the chemical industry, Gujarat and Maharashtra are the
two major states which account for more than half of the national
output of this industry. Worth noting is the fact that the relative
contribution of Maharashtra to the national output of chemical
industry has registered a substantial decline over the period of the
study, whereas, exactly the opposite is true for Gujarat. The average
contribution of Tamil Nadu, UP* and AP to output ranged between 5
to about 8 per cent (refer Table 2.5b).
-
Leather industry is concentrated mainly in three states, viz., Tamil
Nadu, UP* and West Bengal. In 1980-81, these three states accounted
for about 84 per cent of the national output of leather industry and
the respective figure was 70 per cent in 2003-04. Tamil Nadu and
West Bengal have registered decline in terms of their relative
contributions to output of leather industry and the opposite is true
for UP*. Haryana also has emerged as one of the largest producers
of leather goods in the recent years (refer Table 2.5c).
-
Maharashtra, MP*, Bihar* and Gujarat accounted for about 47 per
cent of national output of metal industry in 2003-04. The other major
metal producing states are Tamil Nadu, UP*, West Bengal, and AP
(refer Table 2.1d).
-
Machinery and Transport Equipment industry is located mainly in
Maharashtra, Tamil Nadu, Haryana, UP*, Karnataka and Gujarat.
Of these states, only Maharashtra’s relative contribution to national
output of this industry has declined, whereas, reverse is true for all
the other states. Notable among these are Haryana, UP* and
Karnataka (refer Table 2.5e).
-
The leading states (in descending order of importance) in the case of
production of textiles industry during the period of study have been
Tamil Nadu, Maharashtra, Gujarat, Punjab and Rajasthan. In 1980-
81, the leading states were Maharashtra, Gujarat, West Bengal, Tamil
Nadu and Punjab. By 2003-04, Maharashtra lost its first position to
Tamil Nadu and West Bengal did not remain one of the five topmost
states. Gujarat and Punjab retained their second and fifth positions
respectively, whereas, Rajasthan climbed to the fourth position.
-
Two states which have performed poorly in terms of manufacturing
production in all industries are Bihar* and West Bengal. Both these
states slipped down by 5 notches in 2003-04 as compared with their
ranking in 1980-81. Two states which performed well consistently
in all industries are Haryana and UP*. The ascent of Haryana is
notable. It has moved from the rank 12 to the rank 7 as regards
overall manufacturing output. Though Maharashtra has retained
its top position, this is mainly due to its significantly larger production
compared to other states in 1980-81 (refer Annexure 2.1).
-
Maharashtra, West Bengal, Tamil Nadu, Gujarat UP* and Andhra
Pradesh were the topmost states in terms of employment generation
in 1980-81. By 2003-04, West Bengal did not occupy its place in one
of the five topmost states. Andhra Pradesh caught up as the third
topmost employment generating states. Maharashtra lost its first
rank to Tamil Nadu. Gujarat and UP* retained their fourth and fifth
positions, respectively.
-
The fall in positions of Bihar* and West Bengal in terms of employment
generation in manufacturing sector has been significant and it is
true for all industries. As against this, the rise of Haryana’s
manufacturing sector as employment provider is laudable. In brief,
Bihar* and West Bengal have performed poorly on both employment
and output counts, whereas, opposite is true in the case of Haryana
(refer Annexure 2.2).
Table 2.5: Profile of Organised Manufacturing Sector across Various States of India |
Employment in Various States as a % of Total Employment |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected States |
1980-81 |
8.0 |
4.3 |
1.5 |
8.7 |
2.1 |
4.7 |
3.2 |
16.4 |
3.5 |
1.3 |
2.6 |
1.9 |
9.3 |
8.4 |
11.8 |
87.8 |
2007-08 |
10.0 |
0.7 |
1.2 |
10.0 |
4.9 |
6.9 |
3.4 |
13.0 |
2.4 |
1.8 |
5.3 |
3.5 |
14.8 |
7.2 |
4.9 |
90.0 |
Average |
9.0 |
2.5 |
1.4 |
9.4 |
3.5 |
5.8 |
3.3 |
14.7 |
3.0 |
1.5 |
3.9 |
2.7 |
12.1 |
7.8 |
8.4 |
88.9 |
Emoluments in Various States as a % of Total Emoluments |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected States |
1980-81 |
4.9 |
5.1 |
1.5 |
7.9 |
2.0 |
4.1 |
2.3 |
21.2 |
3.6 |
1.6 |
1.8 |
1.8 |
8.4 |
5.8 |
14.5 |
86.5 |
2007-08 |
7.4 |
0.3 |
1.3 |
10.5 |
5.2 |
7.9 |
2.2 |
19.5 |
2.4 |
2.4 |
3.6 |
2.6 |
11.6 |
6.5 |
4.8 |
88.2 |
Average |
6.2 |
2.7 |
1.4 |
9.2 |
3.6 |
6.0 |
2.2 |
20.3 |
3.0 |
2.0 |
2.7 |
2.2 |
10.0 |
6.1 |
9.7 |
87.3 |
Total Inputs Used in Various States as a % of Total Inputs Used |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected States |
1980-81 |
5.1 |
4.7 |
2.1 |
11.7 |
2.9 |
3.8 |
3.4 |
22.2 |
3.5 |
1.6 |
4.1 |
2.3 |
10.6 |
5.8 |
9.1 |
92.9 |
2007-08 |
6.6 |
0.9 |
1.0 |
16.8 |
4.7 |
6.5 |
2.2 |
17.9 |
2.8 |
1.5 |
3.6 |
2.8 |
9.8 |
7.4 |
4.2 |
88.6 |
Average |
5.8 |
2.8 |
1.6 |
14.2 |
3.8 |
5.2 |
2.8 |
20.0 |
3.2 |
1.5 |
3.9 |
2.5 |
10.2 |
6.6 |
6.6 |
90.7 |
Value of Output of Various States as a % of Total Output |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected States |
1980-81 |
4.9 |
4.6 |
1.9 |
11.1 |
2.8 |
3.9 |
3.2 |
22.1 |
3.7 |
1.6 |
3.7 |
2.2 |
10.2 |
5.7 |
9.3 |
91.2 |
2007-08 |
6.6 |
0.8 |
1.0 |
16.1 |
4.5 |
6.6 |
2.0 |
18.7 |
2.8 |
1.7 |
3.5 |
2.8 |
9.6 |
7.0 |
3.9 |
87.6 |
Average |
5.7 |
2.7 |
1.4 |
13.6 |
3.6 |
5.3 |
2.6 |
20.4 |
3.3 |
1.7 |
3.6 |
2.5 |
9.9 |
6.3 |
6.6 |
89.4 |
Note: See list of abbreviations for the full name of the state.
Source: Calculated from the various issues of Annual Survey of Industries. |
Table 2.5a: Profile of Food, Beverages and Tobacco Industry Across Various States of India |
Employment in Various States as a % of Total Employment |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected States |
1980-81 |
22.0 |
3.7 |
0.6 |
5.2 |
1.2 |
5.8 |
7.8 |
12.9 |
3.2 |
0.8 |
2.9 |
1.1 |
9.4 |
17.6 |
4.6 |
98.7 |
2003-04 |
26.4 |
1.2 |
0.5 |
3.8 |
2.2 |
4.5 |
10.3 |
13.5 |
3.2 |
1.6 |
4.6 |
1.2 |
7.7 |
9.3 |
4.3 |
94.2 |
Average |
26.2 |
1.8 |
0.6 |
4.8 |
2.1 |
4.3 |
7.9 |
12.2 |
3.5 |
1.0 |
4.1 |
1.1 |
8.4 |
12.6 |
4.3 |
94.9 |
Emoluments in Various States as a % of Total Emoluments |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected States |
1980-81 |
18.9 |
2.9 |
1.4 |
6.0 |
1.3 |
4.9 |
4.7 |
17.1 |
2.1 |
0.5 |
3.6 |
1.1 |
7.8 |
15.3 |
7.0 |
94.6 |
2003-04 |
15.6 |
0.8 |
1.4 |
6.1 |
3.1 |
7.1 |
5.4 |
19.2 |
3.1 |
0.9 |
5.2 |
1.8 |
7.7 |
12.7 |
4.1 |
94.2 |
Average |
15.9 |
1.8 |
1.5 |
5.9 |
2.5 |
6.2 |
4.8 |
20.1 |
3.3 |
0.8 |
4.9 |
1.6 |
8.0 |
14.5 |
4.1 |
95.8 |
Total Inputs Used in Various States as a % of Total Inputs Used |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected States |
1980-81 |
10.7 |
1.9 |
3.2 |
11.9 |
1.9 |
5.1 |
4.0 |
16.5 |
3.4 |
0.9 |
6.5 |
2.3 |
9.4 |
10.5 |
6.7 |
94.8 |
2003-04 |
13.0 |
1.0 |
2.0 |
9.9 |
3.2 |
5.6 |
3.6 |
15.0 |
9.0 |
1.2 |
4.9 |
3.3 |
7.5 |
13.5 |
3.7 |
96.1 |
Average |
12.7 |
1.2 |
2.0 |
9.4 |
3.3 |
5.3 |
3.4 |
17.1 |
7.4 |
1.3 |
5.8 |
3.0 |
7.8 |
12.2 |
3.9 |
95.7 |
Value of Output of Various States as a % of Total Output |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected States |
1980-81 |
10.8 |
2.0 |
3.0 |
11.5 |
1.9 |
5.2 |
4.1 |
15.9 |
3.3 |
0.8 |
6.4 |
2.2 |
9.5 |
11.2 |
6.4 |
94.4 |
2003-04 |
11.9 |
1.1 |
1.9 |
9.4 |
2.8 |
6.4 |
3.6 |
15.1 |
8.1 |
1.1 |
5.2 |
3.1 |
7.4 |
13.7 |
3.7 |
94.3 |
Average |
11.5 |
1.4 |
1.9 |
8.9 |
3.2 |
5.8 |
3.5 |
17.5 |
6.9 |
1.2 |
6.0 |
2.8 |
7.9 |
12.6 |
3.9 |
95.0 |
Note: See list of abbreviations for the full name of the state.
Source: Calculated from the various issues of Annual Survey of Industries. |
Table 2.5b: Profile of Chemical Industry Across Various States of India |
Employment in Various States as a % of Total Employment |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
6.8 |
4.1 |
1.4 |
14.1 |
1.3 |
3.6 |
3.4 |
26.9 |
2.6 |
1.1 |
1.6 |
1.8 |
15.4 |
5.5 |
8.6 |
98.2 |
2003-04 |
7.8 |
0.7 |
1.0 |
23.4 |
1.9 |
3.9 |
2.3 |
17.2 |
3.5 |
1.3 |
1.6 |
2.3 |
18.3 |
5.7 |
4.1 |
95.1 |
Average |
6.6 |
2.5 |
1.2 |
19.5 |
1.5 |
3.4 |
3.1 |
20.9 |
3.1 |
1.3 |
1.9 |
1.9 |
18.1 |
6.4 |
5.6 |
97.2 |
Emoluments in Various States as a % of Total Emoluments |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
4.2 |
4.9 |
1.0 |
13.3 |
0.9 |
1.8 |
4.2 |
41.4 |
2.1 |
0.8 |
1.7 |
1.2 |
7.3 |
4.8 |
8.7 |
98.4 |
2003-04 |
5.6 |
0.8 |
0.9 |
26.3 |
1.8 |
4.1 |
3.4 |
26.7 |
2.4 |
1.1 |
2.2 |
1.9 |
8.0 |
5.1 |
2.7 |
92.9 |
Average |
5.4 |
2.0 |
0.9 |
23.0 |
1.5 |
3.3 |
3.9 |
30.6 |
2.5 |
1.2 |
2.2 |
1.7 |
8.2 |
5.8 |
4.4 |
97.5 |
Total Inputs Used in Various States as a % of Total Inputs Used |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
4.6 |
1.9 |
1.6 |
17.7 |
1.3 |
2.8 |
3.8 |
35.2 |
3.1 |
0.8 |
2.2 |
2.1 |
10.0 |
4.5 |
6.4 |
98.0 |
2003-04 |
5.9 |
0.4 |
0.9 |
31.2 |
1.7 |
3.2 |
1.8 |
19.4 |
3.2 |
1.3 |
2.5 |
3.4 |
6.3 |
6.2 |
4.7 |
91.9 |
Average |
5.7 |
1.0 |
1.0 |
28.6 |
1.6 |
2.6 |
2.6 |
23.4 |
3.4 |
1.5 |
2.6 |
2.8 |
7.4 |
6.7 |
3.7 |
96.2 |
Value of Output of Various States as a % of Total Output |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
4.5 |
1.6 |
1.5 |
17.6 |
1.3 |
2.9 |
4.0 |
36.3 |
2.9 |
0.7 |
2.4 |
1.9 |
9.9 |
4.4 |
6.1 |
98.0 |
2003-04 |
5.9 |
0.4 |
0.8 |
31.8 |
1.6 |
3.2 |
1.7 |
19.5 |
3.1 |
1.0 |
2.3 |
3.2 |
6.0 |
6.2 |
4.5 |
91.1 |
Average |
5.8 |
0.7 |
1.0 |
28.9 |
1.5 |
2.7 |
2.6 |
24.0 |
3.3 |
1.2 |
2.5 |
2.8 |
7.2 |
6.8 |
3.5 |
96.2 |
Note: See list of abbreviations for the full name of the state.
Source: Calculated from the various issues of Annual Survey of Industries. |
Table 2.5c: Profile of Leather Industry Across Various States of India |
Employment in Various States as a % of Total Employment |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
2.1 |
4.3 |
0.5 |
1.0 |
0.2 |
0.8 |
0.0 |
3.1 |
1.6 |
0.1 |
1.7 |
0.5 |
38.2 |
17.7 |
27.2 |
99.1 |
2003-04 |
1.0 |
1.2 |
3.0 |
0.4 |
5.1 |
2.0 |
1.0 |
1.8 |
1.5 |
0.0 |
3.7 |
2.2 |
40.0 |
25.1 |
10.2 |
98.1 |
Average |
2.2 |
2.3 |
1.8 |
0.7 |
3.4 |
3.8 |
0.7 |
3.1 |
1.9 |
0.1 |
2.9 |
0.8 |
47.1 |
16.6 |
11.2 |
98.6 |
Emoluments in Various States as a % of Total Emoluments |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
1.0 |
5.5 |
0.6 |
0.5 |
0.2 |
0.5 |
0.0 |
3.0 |
1.5 |
0.0 |
1.4 |
0.4 |
33.7 |
12.8 |
37.5 |
98.5 |
2003-04 |
0.6 |
2.3 |
3.4 |
0.4 |
6.5 |
2.0 |
1.6 |
1.9 |
3.3 |
0.0 |
3.6 |
2.1 |
33.3 |
19.9 |
17.5 |
98.2 |
Average |
1.3 |
3.0 |
2.7 |
0.7 |
5.3 |
3.5 |
1.2 |
3.9 |
3.1 |
0.1 |
3.3 |
1.1 |
40.0 |
14.6 |
15.1 |
98.5 |
Total Inputs Used in Various States as a % of Total Inputs Used |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
1.4 |
3.7 |
0.5 |
0.5 |
0.4 |
0.7 |
0.0 |
4.2 |
2.5 |
0.0 |
1.7 |
0.5 |
51.8 |
13.7 |
18.3 |
100.0 |
2003-04 |
0.5 |
0.5 |
4.1 |
0.1 |
5.8 |
1.4 |
2.9 |
2.0 |
4.0 |
0.0 |
3.4 |
0.4 |
39.6 |
19.5 |
12.5 |
96.6 |
Average |
1.4 |
1.3 |
4.4 |
0.5 |
4.8 |
3.7 |
2.4 |
2.8 |
3.8 |
0.0 |
3.0 |
0.4 |
44.2 |
15.6 |
10.1 |
98.8 |
Value of Output of Various States as a % of Total Output |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
1.5 |
3.9 |
0.6 |
0.5 |
0.5 |
0.7 |
0.0 |
4.0 |
2.2 |
0.0 |
1.7 |
0.5 |
50.4 |
13.4 |
20.0 |
99.9 |
2003-04 |
0.5 |
0.6 |
4.1 |
0.0 |
6.8 |
1.5 |
2.8 |
2.1 |
3.8 |
0.0 |
3.4 |
0.4 |
38.3 |
20.0 |
12.0 |
96.4 |
Average |
1.3 |
1.3 |
4.4 |
0.3 |
5.2 |
3.7 |
2.5 |
3.0 |
3.7 |
0.0 |
3.0 |
0.5 |
43.4 |
15.8 |
10.1 |
98.6 |
Note: See list of abbreviations for the full name of the state.
Source: Calculated from the various issues of Annual Survey of Industries. |
Table 2.5d: Profile of Metal Industry Across Various States of India |
Employment in Various States as a % of Total Employment |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected States |
1980-81 |
3.7 |
13.6 |
1.6 |
5.2 |
3.3 |
4.1 |
0.9 |
16.7 |
6.6 |
4.6 |
4.8 |
1.9 |
6.5 |
6.0 |
19.6 |
99.1 |
2003-04 |
5.6 |
9.3 |
1.1 |
7.5 |
3.1 |
3.8 |
1.1 |
14.7 |
9.2 |
6.0 |
4.8 |
2.3 |
9.4 |
8.2 |
10.8 |
96.8 |
Average |
5.4 |
12.9 |
1.4 |
6.4 |
3.1 |
4.3 |
1.0 |
14.7 |
8.9 |
5.3 |
4.6 |
2.3 |
7.0 |
6.1 |
14.9 |
98.3 |
Emoluments in Various States as a % of Total Emoluments |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
1.7 |
18.1 |
0.8 |
2.9 |
2.2 |
3.7 |
0.9 |
18.3 |
9.2 |
7.1 |
2.5 |
1.7 |
4.8 |
4.2 |
21.6 |
99.6 |
2003-04 |
6.4 |
17.1 |
0.6 |
3.7 |
2.0 |
3.3 |
0.8 |
13.0 |
14.9 |
10.1 |
2.0 |
1.4 |
8.3 |
4.4 |
10.5 |
98.5 |
Average |
5.5 |
17.9 |
0.7 |
3.8 |
2.0 |
3.9 |
0.9 |
15.0 |
12.6 |
7.7 |
2.3 |
1.8 |
6.6 |
4.4 |
13.6 |
99.0 |
Total Inputs Used in Various States as a % of Total Inputs Used |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected States |
1980-81 |
2.5 |
13.0 |
1.9 |
5.4 |
4.4 |
4.1 |
0.9 |
19.7 |
7.5 |
6.1 |
6.4 |
2.3 |
4.8 |
6.2 |
14.1 |
99.2 |
2003-04 |
5.4 |
6.8 |
0.7 |
8.9 |
5.0 |
4.0 |
0.8 |
19.2 |
8.6 |
4.8 |
5.0 |
2.5 |
6.7 |
6.8 |
7.0 |
92.2 |
Average |
5.9 |
10.2 |
1.2 |
8.2 |
4.6 |
4.2 |
0.8 |
17.2 |
9.2 |
5.1 |
5.3 |
2.8 |
6.2 |
6.8 |
8.2 |
97.4 |
Value of Output of Various States as a % of Total Output |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected States |
1980-81 |
2.5 |
13.1 |
1.7 |
4.9 |
4.2 |
4.2 |
0.9 |
19.8 |
9.1 |
6.2 |
5.7 |
2.4 |
4.8 |
5.8 |
14.2 |
99.3 |
2003-04 |
6.1 |
9.0 |
0.7 |
8.3 |
4.6 |
4.4 |
0.7 |
17.9 |
9.8 |
5.5 |
4.2 |
2.3 |
6.2 |
6.6 |
6.9 |
93.1 |
Average |
6.1 |
12.5 |
1.1 |
7.7 |
4.1 |
4.2 |
0.8 |
16.4 |
10.2 |
5.7 |
4.7 |
2.6 |
5.8 |
6.7 |
7.9 |
97.6 |
Note: See list of abbreviations for the full name of the state.
Source: Calculated from the various issues of Annual Survey of Industries. |
Table 2.5e: Profile of Machinery & Transport Equipment Industry
Across Various States of India |
Employment in Various States as a % of Total Employment |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
5.1 |
3.9 |
2.4 |
6.5 |
4.2 |
6.5 |
1.4 |
22.9 |
3.0 |
0.3 |
3.9 |
2.6 |
11.8 |
7.7 |
16.8 |
98.9 |
2003-04 |
6.1 |
2.0 |
1.8 |
8.0 |
9.4 |
9.0 |
1.5 |
20.0 |
3.4 |
0.3 |
5.8 |
1.9 |
13.3 |
8.6 |
4.9 |
95.9 |
Average |
5.6 |
3.7 |
2.5 |
7.3 |
6.4 |
7.7 |
1.4 |
20.4 |
3.3 |
0.4 |
5.1 |
2.2 |
13.0 |
8.8 |
10.5 |
98.2 |
Emoluments in Various States as a % of Total Emoluments |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
5.3 |
4.6 |
1.7 |
4.4 |
3.5 |
7.2 |
1.5 |
27.2 |
2.9 |
0.3 |
2.1 |
2.4 |
11.7 |
6.9 |
17.6 |
99.2 |
2003-04 |
5.6 |
2.6 |
1.0 |
5.3 |
10.5 |
11.0 |
1.5 |
25.6 |
3.2 |
0.2 |
3.1 |
1.4 |
13.3 |
8.7 |
4.3 |
97.3 |
Average |
5.6 |
3.5 |
1.8 |
5.2 |
7.7 |
8.2 |
1.5 |
27.2 |
3.3 |
0.2 |
3.2 |
1.8 |
12.0 |
8.2 |
7.6 |
98.3 |
Total Inputs Used in Various States as a % of Total Inputs Used |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
4.9 |
4.5 |
2.8 |
5.9 |
5.8 |
6.1 |
1.4 |
27.8 |
3.0 |
0.5 |
4.6 |
2.1 |
13.1 |
6.4 |
10.4 |
99.5 |
2003-04 |
3.3 |
2.1 |
1.3 |
6.0 |
14.5 |
9.2 |
0.6 |
23.7 |
2.7 |
0.2 |
3.4 |
1.4 |
13.3 |
11.0 |
2.0 |
94.9 |
Average |
3.7 |
2.6 |
2.1 |
6.7 |
11.8 |
7.6 |
0.8 |
25.6 |
3.2 |
0.3 |
4.4 |
1.6 |
12.3 |
9.4 |
3.7 |
97.6 |
Value of Output of Various States as a % of Total Output |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
4.8 |
4.9 |
2.5 |
5.7 |
5.5 |
6.5 |
1.5 |
28.2 |
3.0 |
0.5 |
4.1 |
2.1 |
12.6 |
6.5 |
11.0 |
99.4 |
2003-04 |
3.6 |
2.3 |
1.2 |
5.8 |
14.1 |
9.4 |
0.7 |
22.6 |
2.7 |
0.2 |
3.2 |
1.5 |
13.7 |
10.7 |
2.0 |
93.8 |
Average |
4.0 |
2.7 |
2.0 |
6.5 |
11.3 |
7.9 |
0.9 |
25.5 |
3.1 |
0.3 |
4.1 |
1.6 |
12.4 |
9.3 |
4.0 |
97.5 |
Note: See list of abbreviations for the full name of the state.
Source: Calculated from the various issues of Annual Survey of Industries. |
Table 2.5f : Profile of Textiles Industry Across Various States of India |
Employment in Various States as a % of Total Employment |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
3.9 |
0.8 |
1.9 |
18.5 |
1.6 |
3.8 |
1.6 |
20.1 |
4.7 |
0.7 |
2.8 |
3.0 |
10.8 |
5.6 |
18.8 |
98.5 |
2003-04 |
3.7 |
0.3 |
2.5 |
9.5 |
4.4 |
10.4 |
1.8 |
8.9 |
2.3 |
0.2 |
4.6 |
4.8 |
27.0 |
4.4 |
11.5 |
96.6 |
Average |
4.5 |
0.6 |
2.0 |
13.8 |
2.3 |
6.0 |
1.7 |
15.3 |
4.1 |
0.8 |
4.5 |
4.3 |
17.4 |
5.4 |
14.5 |
96.8 |
Emoluments in Various States as a % of Total Emoluments |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
2.5 |
0.6 |
1.9 |
18.7 |
1.3 |
2.5 |
1.8 |
23.1 |
4.0 |
0.6 |
1.9 |
2.3 |
8.8 |
5.6 |
19.8 |
95.5 |
2003-04 |
1.9 |
0.1 |
2.0 |
6.7 |
2.8 |
5.6 |
1.1 |
10.2 |
2.0 |
0.1 |
3.0 |
2.9 |
12.7 |
2.8 |
8.8 |
62.8 |
Average |
2.9 |
0.4 |
2.4 |
11.4 |
2.3 |
5.5 |
1.7 |
17.4 |
3.4 |
0.5 |
4.2 |
4.1 |
15.3 |
4.7 |
15.0 |
94.3 |
Total Inputs Used in Various States as a % of Total Inputs Used |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
3.1 |
0.4 |
2.8 |
20.9 |
2.5 |
2.2 |
1.9 |
22.7 |
2.8 |
0.4 |
6.2 |
3.9 |
9.9 |
4.8 |
10.8 |
95.3 |
2003-04 |
3.3 |
0.0 |
3.9 |
12.5 |
4.5 |
4.0 |
1.2 |
11.2 |
3.3 |
0.1 |
6.2 |
6.7 |
24.7 |
5.3 |
4.0 |
91.0 |
Average |
3.8 |
0.1 |
4.0 |
14.9 |
3.4 |
3.8 |
1.2 |
14.4 |
3.5 |
0.2 |
7.0 |
6.5 |
19.1 |
4.7 |
4.9 |
92.6 |
Value of Output of Various States as a % of Total Output |
State |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
Selected
States |
1980-81 |
3.0 |
0.4 |
2.5 |
20.4 |
2.2 |
2.4 |
1.9 |
23.1 |
3.2 |
0.4 |
5.3 |
3.8 |
10.1 |
4.8 |
12.0 |
95.6 |
2003-04 |
3.3 |
0.0 |
3.8 |
12.5 |
4.6 |
4.6 |
1.2 |
11.2 |
3.6 |
0.1 |
6.1 |
6.4 |
24.5 |
5.0 |
4.6 |
91.5 |
Average |
3.6 |
0.1 |
4.1 |
14.4 |
3.3 |
4.2 |
1.3 |
14.6 |
3.8 |
0.2 |
6.8 |
6.3 |
19.0 |
4.6 |
5.5 |
92.9 |
Note: See list of abbreviations for the full name of the state.
Source: Calculated from the various issues of Annual Survey of Industries. |
In other words, Maharashtra, Tamil Nadu, Gujarat and UP* are the
states which consistently appear in the first five topmost states in terms
of both output and employment generation in the organised manufacturing
sector. The deterioration of Bihar* and West Bengal and the ascent of
Haryana are noticeable.
2.4: Contribution of Manufacturing Sector to India’s Exports
In Table 2.6, we provide the contribution of manufacturing sector to
exports of the Indian economy. It can be seen from Table 2.6 that India’s exports have multiplied almost 4.2 times during 2008-09 over 2000-01.
Manufactured exports as well as exports of selected industries have
multiplied by 3.6 times during this time-span. Share of exports of
manufacturing sector as also leather, chemical and textiles industries11 in
India’s exports have declined especially during the post-2003-04 period
(which is mainly due to rise in exports of petroleum products) despite
their impressive compound growth of about 15.3 pcpa in US $ terms. The
exports of selected industries account for about half of India’s export
earnings. The major change which we see as regards composition of exports
of selected industries is in terms of decline in contribution of textiles and
leather (traditional) industries and a rise in share of engineering goods.
The compound rates of growth of exports (EXg) of traditional manufactured
goods, such as leather and textiles were quite low (about 7 pcpa) in
comparison to the engineering goods (24 pcpa) and chemical industry (16
pcpa).
Table 2.6: Composition of India’s Exports (in Mn US $) |
Sr
No. |
Commodity/Year |
2000-01 |
2001-02 |
2002-03 |
2003-04 |
2007-08 |
2008-09 |
EXg |
I. |
Primary products |
7126.2 |
7163.6 |
8706.1 |
9901.8 |
27530.8 |
25385.4 |
15.2 |
|
(16.0) |
(16.3) |
(16.5) |
(15.5) |
(16.9) |
(13.7) |
|
A. Agriculture and allied products |
5973.2 |
5901.2 |
6710 |
7533.1 |
18408.2 |
17603.0 |
12.8 |
(13.4) |
(13.5) |
(12.7) |
(11.8) |
(11.3) |
(9.5) |
|
B. Ores and minerals
(METAL) |
1153 |
1262.4 |
1996 |
2368.7 |
9122.6 |
7782.4 |
23.6 |
(2.6) |
(2.9) |
(3.8) |
(3.7) |
(5.6) |
(4.2) |
|
II. |
Manufactured goods |
34335.2 |
33369.7 |
40244.5 |
48492.1 |
102955.3 |
123221.2 |
15.3 |
|
(77.1) |
(76.1) |
(76.3) |
(76.0) |
(63.2) |
(66.5) |
|
C. Leather and manufactures (LEATH) |
1944.4 |
1910.1 |
1848.3 |
2163 |
3583.9 |
3520.6 |
6.8 |
(4.4) |
(4.4) |
(3.5) |
(3.4) |
(2.2) |
(1.9) |
|
D. Chemicals and related products (CHEM) |
5885.9 |
6051.8 |
7455.3 |
9445.9 |
21177.5 |
22791.3 |
16.2 |
(13.2) |
(13.8) |
(14.1) |
(14.8) |
(13.0) |
(12.3) |
|
E. Engineering goods (MTE) |
6818.6 |
6957.8 |
9033 |
12405.4 |
37305.0 |
47250.2 |
24.0 |
(15.3) |
(15.9) |
(17.1) |
(19.4) |
(22.9) |
(25.5) |
|
F. Textile and textile products (TEX) |
11285 |
10206.5 |
11617 |
12791.5 |
19385.6 |
20011.9 |
6.6 |
(25.3) |
(23.3) |
(22.0) |
(20.0) |
(11.9) |
(10.8) |
|
Sum (C to F) |
25933.9 |
25126.2 |
29953.6 |
36805.8 |
81452.0 |
93574.0 |
15.3 |
|
(58.2) |
(57.3) |
(56.8) |
(57.7) |
(50.0) |
(50.5) |
|
G. Gems and jewellery |
7384 |
7306.3 |
9029.9 |
10573.3 |
19711.4 |
27979.5 |
16.0 |
|
(16.6) |
(16.7) |
(17.1) |
(16.6) |
(12.1) |
(15.1) |
|
H. Handicrafts (excluding handmade carpets) |
661.5 |
549 |
785.3 |
499.7 |
n.a |
n.a |
n.a |
(1.5) |
(1.3) |
(1.5) |
(0.8) |
|
|
|
I. Other manufactured goods |
355.8 |
388.3 |
475.6 |
613.3 |
1791.9 |
1667.7 |
18.7 |
(0.8) |
(0.9) |
(0.9) |
(1.0) |
(1.1) |
(0.9) |
|
III. |
Petroleum products |
1869.7 |
2119.1 |
2576.5 |
3568.4 |
28345.3 |
26867.8 |
34.5 |
|
(4.2) |
(4.8) |
(4.9) |
(5.6) |
(17.4) |
(14.5) |
|
IV. |
Others |
1229.2 |
1174.3 |
1192.3 |
1880.3 |
4072.6 |
9820.6 |
26.0 |
|
(2.8) |
(2.7) |
(2.3) |
(2.9) |
(2.5) |
(5.3) |
|
Total Exports |
44560 |
43827 |
52719 |
63843 |
162904 |
185295 |
17.2 |
Source: Handbook of Statistics on Indian Economy, 2007-08, RBI
Note: (i) Figures in parentheses are percentage of total exports for the respective year.
(ii) EXg denotes compound annual growth rate (CAGR) of exports in percentage. |
2.5: Summary
-
The average share of manufacturing sector in real GDP has marginally
increased from about 13 per cent during 1970-75 to about 16.1 per
cent in 2009-10, i.e, approximately by about 3.1 percentage points
over a period of more than three decades. Thus, this increase has
not matched the expectations from this sector.
-
During 1970-75, India’s real GDP of manufacturing sector was more
or less equally distributed between its registered and unregistered
segments. Over the years, the growth of real income in the registered
manufacturing has been higher than that of unregistered
manufacturing sector, resulting in the average contribution of the
latter to real GDP of manufacturing sector shrinking to almost half
of that of the registered sector in 2007-08.
-
The average contribution of unregistered manufacturing sector to
real GDP of manufacturing has been consistently declining over the
years. In the earlier half of the eighties, this share was approximately
45 per cent and it has fallen to about 33 per cent in 2008-09.
-
The unorganised manufacturing sector employs (2005-06, the latest
year for which the data are available for unorganised sector) almost
80 per cent of workforce manufacturing sector. In other words, we
see the disproportion not only between income and employment
generation across sectors, but also within the two segments of
manufacturing sector, i.e., between its organised and unorganised
segments. Moreover, we also observe that economically backward
states, such as, Bihar* and West Bengal have much higher proportion
of employment in unorganised manufacturing in comparison to that
in the organised manufacturing.
-
There are six major concerns regarding the growth of manufacturing
sector. These pertain to growth of output, growth of employment,
real wages and emoluments, resource intensity, inequalities within
the manufacturing sector and regionally imbalanced growth of
manufacturing sector.
1. Despite the emphasis on manufacturing sector in India’s
planning process, the contribution of this sector, is at best,
modest and it needs to be increased so as to enable people to
improve their standard of living.
2. As regards employment in organised sector, it witnessed a
declining trend almost up to 1986-87. After that employment
trend was positive until mid-nineties. Thereafter, again we see
a period of not only jobless but jobloss growth. Though this
trend has been arrested in the present decade the employment
contribution of this sector should increase.
3. The discouraging side of the reform process is in terms of growth
of real wages and real emoluments in the organised
manufacturing sector, especially since the nineties. Growth of
real wages (i.e., to skilled and unskilled shop-floor workers)
has been shrinking over the years and it was in fact negative
during the latter half of the nineties. As against it, we see the
revival of growth rate of emoluments (i.e., compensation to
both workers and the ‘other supporting staff’, including the
managerial staff). To put it differently, the growth rate of
compensation to supporting staff has increased in relation to
the workers who are directly engaged in production process.
This can have an adverse effect on the morale of the shop-floor
workers, especially the skilled ones. This also explains the fact
that most of the engineering graduates do not prefer to pursue
their engineering skills on the shop-floor, instead prefer
managerial positions. The reform process cannot neglect this
issue, if productivity and innovations need to drive India’s
growth process. It would also be interesting to examine the
role of the labour market flexibility in employment generation.
This would be possible if the ASI provides the data on
employment of those workers and supporting staff who are
permanent and contracturally employed.
4. Growth process in the organised manufacturing sector has
been resource intensive. Total non-primary inputs constitute
almost 80 per cent of the value of output. The ratio of material
inputs to total inputs has ranged between 58 to 65 per cent
and the respective range for the fuel inputs is between 6-7
per cent. This has implications for both environmental
degradation as well as sustainability of growth of
manufacturing sector in the years to come. The proportion of
value added to the gross output in the US is more than half
and for Germany it is one third, as compared to India’s one fifth. It is almost 29 percent for China. This has implications
for competitiveness, especially in a globalised world where
the costs of material inputs are rising.
5. The employment and output generation in organised and
unorganised manufacturing sector exhibit a major imbalance.
The unorganised sector accounts for almost 80 per cent
employment and generates only about 33 per cent of output of
the manufacturing sector. This causes sharp inequality
between the per capita output (and also wages) between the
organised and unorganised segments of India’s manufacturing
sector.
6. Maharashtra, Tamil Nadu and Gujarat, are the states which
consistently appear in the first three topmost states in terms
of both output and employment generation in the organised
manufacturing sector. Deterioration of Bihar* and West Bengal,
whereas, ascent of Haryana, Karnataka, Punjab and Rajasthan
is noticeable.
-
‘Metal’ and ‘Machinery & Transport Equipment’ industries are the
two major industries in India and each one of these accounted for
almost one-fifth of GVA in the organised manufacturing sector during
the period of the study. These industries are followed by the chemical
industry which accounted for about 13 percent of GVA of the total
organised manufacturing sector. However, most of the jobs are
provided by Textiles and Food (including beverages & tobacco)
industries. These industries together account for about 41 per cent
of jobs in the organised manufacturing sector.
-
During 2000-01 to 2008-09, the growth rate of exports of Metal and
Engineering goods has been highest at about 24 pcpa as against the
overall growth of exports of about 17 pcpa and that of manufactured
products of about 15 pcpa. Growth rates of exports of Textiles and
leather industries are quite low ranging between 6 to 7 pcpa.
Annexure 2.1 : Ranks of Various States Based on their Output
Contribution in Respective Industry |
Ranks based on Output Contribution: 1980-81 |
State |
MFG |
FBT |
CHEM |
LEATH |
METAL |
MTE |
TEX |
AP |
6 |
4 |
5 |
8 |
12 |
9 |
9 |
BIH* |
7 |
13 |
12 |
5 |
3 |
8 |
14 |
DEL |
14 |
11 |
13 |
10 |
14 |
12 |
10 |
GUJ |
2 |
2 |
2 |
11 |
8 |
6 |
2 |
HAR |
12 |
14 |
14 |
12 |
10 |
7 |
12 |
KAR |
8 |
8 |
8 |
9 |
11 |
4 |
11 |
KER |
11 |
9 |
7 |
14 |
15 |
14 |
13 |
MAH |
1 |
1 |
1 |
4 |
1 |
1 |
1 |
MP* |
9 |
10 |
9 |
6 |
4 |
11 |
8 |
ORI |
15 |
15 |
15 |
15 |
5 |
15 |
15 |
PUN |
10 |
6 |
10 |
7 |
7 |
10 |
5 |
RAJ |
13 |
12 |
11 |
13 |
13 |
13 |
7 |
TN |
3 |
5 |
3 |
1 |
9 |
2 |
4 |
UP* |
5 |
3 |
6 |
3 |
6 |
5 |
6 |
WB |
4 |
7 |
4 |
2 |
2 |
3 |
3 |
Ranks based on Output Contribution: 2003-04 |
AP |
5 |
3 |
5 |
12 |
8 |
7 |
12 |
BIH* |
12 |
14 |
15 |
11 |
3 |
10 |
15 |
DEL |
15 |
13 |
14 |
5 |
14 |
13 |
10 |
GUJ |
2 |
4 |
1 |
14 |
4 |
6 |
2 |
HAR |
7 |
12 |
12 |
4 |
10 |
2 |
7 |
KAR |
6 |
7 |
7 |
10 |
11 |
5 |
8 |
KER |
13 |
10 |
11 |
8 |
15 |
14 |
13 |
MAH |
1 |
1 |
2 |
9 |
1 |
1 |
3 |
MP* |
8 |
5 |
9 |
6 |
2 |
9 |
11 |
ORI |
14 |
15 |
13 |
15 |
9 |
15 |
14 |
PUN |
10 |
8 |
10 |
7 |
12 |
8 |
5 |
RAJ |
11 |
11 |
8 |
13 |
13 |
12 |
4 |
TN |
3 |
6 |
4 |
1 |
7 |
3 |
1 |
UP* |
4 |
2 |
3 |
2 |
6 |
4 |
6 |
WB |
9 |
9 |
6 |
3 |
5 |
11 |
9 |
Ranks based on Average Output Contribution: 1980-81 to
2003-04 |
AP |
5 |
3 |
5 |
11 |
7 |
8 |
11 |
BIH* |
11 |
14 |
15 |
12 |
2 |
11 |
15 |
DEL |
14 |
13 |
14 |
5 |
14 |
12 |
9 |
GUJ |
2 |
4 |
1 |
14 |
5 |
6 |
3 |
HAR |
9 |
11 |
12 |
4 |
12 |
3 |
12 |
KAR |
6 |
8 |
9 |
6 |
11 |
5 |
8 |
KER |
13 |
10 |
10 |
10 |
15 |
14 |
13 |
MAH |
1 |
1 |
2 |
8 |
1 |
1 |
2 |
MP* |
7 |
6 |
7 |
7 |
3 |
10 |
10 |
ORI |
15 |
15 |
13 |
15 |
9 |
15 |
14 |
PUN |
10 |
7 |
11 |
9 |
10 |
7 |
4 |
RAJ |
12 |
12 |
8 |
13 |
13 |
13 |
5 |
TN |
3 |
5 |
3 |
1 |
8 |
2 |
1 |
UP* |
4 |
2 |
4 |
2 |
6 |
4 |
7 |
WB |
8 |
9 |
6 |
3 |
4 |
9 |
6 |
Annexure 2.2: Ranks of Various States Based on their Employment Generation in Respective Industry |
Ranks based on Employment Contribution: 1980-81 |
State |
MFG |
FBT |
CHEM |
LEATH |
METAL |
MTE |
TEX |
AP |
6 |
1 |
5 |
6 |
11 |
7 |
7 |
BIH* |
8 |
9 |
7 |
4 |
3 |
9 |
14 |
DEL |
14 |
15 |
13 |
11 |
14 |
13 |
11 |
GUJ |
4 |
7 |
3 |
9 |
7 |
5 |
3 |
HAR |
12 |
12 |
14 |
13 |
12 |
8 |
12 |
KAR |
7 |
6 |
8 |
10 |
10 |
6 |
8 |
KER |
10 |
5 |
9 |
15 |
15 |
14 |
13 |
MAH |
1 |
3 |
1 |
5 |
2 |
1 |
1 |
MP* |
9 |
10 |
10 |
8 |
4 |
11 |
6 |
ORI |
15 |
14 |
15 |
14 |
9 |
15 |
15 |
PUN |
11 |
11 |
12 |
7 |
8 |
10 |
10 |
RAJ |
13 |
13 |
11 |
12 |
13 |
12 |
9 |
TN |
3 |
4 |
2 |
1 |
5 |
3 |
4 |
UP* |
5 |
2 |
6 |
3 |
6 |
4 |
5 |
WB |
2 |
8 |
4 |
2 |
1 |
2 |
2 |
Ranks based on Employment Contribution: 2003-04 |
AP |
3 |
1 |
4 |
12 |
9 |
7 |
10 |
BIH* |
13 |
13 |
15 |
11 |
4 |
11 |
14 |
DEL |
15 |
15 |
14 |
6 |
14 |
13 |
11 |
GUJ |
4 |
9 |
1 |
14 |
7 |
6 |
4 |
HAR |
9 |
11 |
11 |
4 |
12 |
3 |
8 |
KAR |
7 |
7 |
7 |
8 |
11 |
4 |
3 |
KER |
10 |
3 |
9 |
13 |
15 |
14 |
13 |
MAH |
2 |
2 |
3 |
9 |
1 |
1 |
5 |
MP* |
11 |
10 |
8 |
10 |
5 |
10 |
12 |
ORI |
14 |
12 |
13 |
15 |
8 |
15 |
15 |
PUN |
8 |
6 |
12 |
5 |
10 |
8 |
7 |
RAJ |
12 |
14 |
10 |
7 |
13 |
12 |
6 |
TN |
1 |
5 |
2 |
1 |
3 |
2 |
1 |
UP* |
5 |
4 |
5 |
2 |
6 |
5 |
9 |
WB |
6 |
8 |
6 |
3 |
2 |
9 |
2 |
Ranks based on Average Employment Contribution: 1980-81 to 2003-04 |
AP |
3 |
1 |
4 |
9 |
8 |
8 |
7 |
BIH* |
9 |
12 |
10 |
8 |
3 |
10 |
15 |
DEL |
14 |
15 |
15 |
11 |
14 |
12 |
12 |
GUJ |
4 |
6 |
2 |
13 |
6 |
6 |
4 |
HAR |
12 |
11 |
13 |
5 |
12 |
7 |
11 |
KAR |
7 |
7 |
7 |
4 |
11 |
5 |
5 |
KER |
11 |
5 |
8 |
14 |
15 |
14 |
13 |
MAH |
1 |
3 |
1 |
6 |
2 |
1 |
2 |
MP* |
8 |
10 |
9 |
10 |
4 |
11 |
10 |
ORI |
15 |
14 |
14 |
15 |
9 |
15 |
14 |
PUN |
10 |
9 |
11 |
7 |
10 |
9 |
8 |
RAJ |
13 |
13 |
12 |
12 |
13 |
13 |
9 |
TN |
2 |
4 |
3 |
1 |
5 |
2 |
1 |
UP* |
6 |
2 |
5 |
2 |
7 |
4 |
6 |
WB |
5 |
8 |
6 |
3 |
1 |
3 |
3 |
3. Productivity and Efficiency in Indian Manufacturing
Sector: A Review of Literature
The available literature reviews on studies of productivity in the Indian
manufacturing sector have been undertaken by Krishna (1987) and Goldar
and Mitra (2002). The former review considered three major studies, viz.,
Brahmananda (1982), Ahluwalia (1985) and Goldar (1986), i.e.,
those studies published in the eighties. The review by Goldar and Mitra
(2002) encompassed the studies of earlier vintage as well as the recent
ones12. The studies reviewed by Goldar and Mitra (2002) have been broadly
classified by them into two groups: (i) studies published until 1991
(first group); and, (ii) studies published thereafter (second group).
The temporal coverage of the first group of studies varied between 1946
and 1985.
Keeping in view the fact that the temporal coverage of this study
begins with 1980-81 and that the studies encompassing the period before
1980-81 have already been reviewed competently by the studies mentioned
above, we will focus this review on the studies that cover mainly the timespan
of the eighties and thereafter.
The studies on productivity of Indian manufacturing sector can be
classified broadly as follows: (i) studies that provide estimates of productivity
growth either for aggregate manufacturing sector or at various levels of
disaggregation (states/industries), using alternative databases and
methodologies for different time-spans; (ii) studies that test the sensitivity
of productivity growth estimates to the alternative proxies for output, viz.,
real gross output (O) or real value added obtained by single deflation method
(RVASD) or real value added obtained by double deflation method (RVADD);
(iii) studies that deal with the turnaround of productivity growth, if any, in
response to policy reforms undertaken in the Indian economy (the first
bout initiated in the eighties13 and the second bout in the nineties); and,
(iv) studies that attempt to ascertain the determinants of productivity,such as, the role of infrastructure, investment climate, education, policy
reforms (usually examined in an inter-state perspective).
In Chart 3.1, we present a synoptic view of the criteria for classifying
the studies on the productivity of Indian manufacturing sector.
 |
The studies reviewed by us differ in respect of: (i) the database used
and the coverage (spatial/temporal/sectoral) of the study; (ii) the proxy
used for level of production; (ii) the inputs included and their measurement;
(iii) methodology used for estimation of productivity and efficiency; and,
(iv) time-span covered. Due to these differences, the various studies have
obtained different estimates/magnitudes of TFPG and efficiency levels.
The output of these studies can be classified in terms of: (a) The estimates
of TFPG; and, (b) the research question answered by the study, over and
above providing the estimates of TFPG. The main research questions that
have been raised in these studies are as follows. First, whether there has
been acceleration/deceleration or absence thereof in TFPG of manufacturing
sector as a consequence of economic reforms in the Indian economy?
Second, has trade liberalisation (which is only one of the components of
the overall economic policy reforms) made any impact on the TFPG of
manufacturing sector? Third, does the provision of infrastructure (social,
physical or investment climate) matter in determining the TFPG?
Fourth, whether the economic reforms have impacted different industries/
regions/states differently? Lastly, whether the growth of output of
Indian manufacturing sector has been driven by TFPG or by intensive use
of inputs?
3.1: Databases Used
As indicated in Chart 3.1, mainly five databases have been used in
the studies on productivity/efficiency in the Indian manufacturing sector.
The information on databases used by the various studies is given in
Table 3.1.
Manufacturing sector in National Accounts Statistics (NAS) includes
both registered and unregistered segments. Hence, the studies which use
NAS database have wider coverage as compared to the other databases.
The problem with this database is the non-availability of employment series
on an annual basis for unregistered manufacturing sector. Hence,
employment series used in the studies based on NAS data has to be intrapolated/extrapolated using different assumptions. Bosworth et al
(2007), Mohanty (1992) and Virmani (2004) have used the NAS data. They
had to make various assumptions so as to arrive at the estimate of labour
input in order to proceed with the estimates of productivity14.
Table 3.1 Databases Used and Coverage of the Studies |
Sr.
No. |
Study |
Database
(disaggregation level, if any) |
Temporal Coverage |
1 |
Ahluwalia (1991) |
ASI (industry groups at 3 digit levels & 4 use-based industries) |
1959-60 to 1985-86 |
2 |
Mohanty (1992) |
NAS (Registered & Unregistered) |
1970-71 to 1988-89 |
3 |
Balakrishnan & Pushpangadan (1994) |
ASI |
1970-71 to 1988-89 |
4 |
Dholakia & Dholakia (1994) |
ASI |
1970-71 to 1988-89 |
5 |
Majumdar (1996) |
CMIE & ASI |
1950-51 to 1992-93 |
6 |
Rao (1996a) |
ASI |
1973-74 to 1992-93 |
7 |
Rao (1996b) |
ASI |
1973-74 to 1992-93 |
8 |
Srivastava (1996) |
RBI data on Public Limited Companies (industries at 2 digit level) |
1980-81 to 1989-90 |
9 |
Krishna & Mitra (1998) |
CMIE, PROWESS, firm level data (industry groups) |
1986-1993 |
10 |
Pradhan & Barik (1998) |
ASI (8 polluting industries) |
1962-63 to 1992-93 |
11 |
Gangopadhyay & Wadhwa(1998) |
ASI (industries at 2 digit level) |
1973-74 to 1993-94 |
12 |
Pradhan & Barik (1999) |
ASI (8 polluting industries) |
1963-93 |
13 |
Mitra (1999) |
ASI (panel data for industry groups at state level) |
1976-77 to 1992-93 |
14 |
Hulten & Srinivasan (1999) |
ASI |
1973-93 |
15 |
Balakrishanan et al (2000) |
PROWESS (CMIE), firm level data |
1988-89 to 1997-98 |
16 |
Goldar (2000) |
ASI (2 digit li |
|
|
evel industries) |
1980-81 to 1997-98 |
|
17 |
Trivedi al (2000) |
ASI (2 digit level industries) |
1973-74 to 1997-98 |
18 |
Unni et al (2001) |
ASI (organised) and NSSO
(unorganised); 5 use-based industry groups arrived after aggregation of 2-3 digit level industries |
1978-79, 1984-85,
1989-90 & 1994-95 |
19 |
Aghion et al (2003) |
ASI |
1980-1997 |
20 |
Goldar & Kumari (2003) |
ASI |
1981-82 to 1997-98 |
21 |
Trivedi (2003) |
ASI (2 digit industries aggregated into 5 industry groups for selected states of India) |
1980-81 to 1997-98 |
Table 3.1 Databases Used and Coverage of the Studies (Concld.) |
Sr.
No. |
Study |
Database
(disaggregation level, if any) |
Temporal Coverage |
22 |
Tata Services Ltd (2003) |
ASI, PROWESS (CMIE),
Private mfg: all firms and Top 50 firms, Major Tata Companies |
1981-82 to
2001-2002 |
23 |
Unel (2003) |
ASI (industry groups) |
1979-80 to 1997-98 |
24 |
Das (2004) |
ASI (industries at 3 digit level
also aggregated into use-based industries) |
1980-81 to
1999-2000 |
25 |
Goldar (2004) |
ASI |
1979-80 to1990-2000
|
26 |
Pradhan & Barik (2004) |
ASI (8 polluting industries) |
1963-64 to 1994-95 |
27 |
Topalova (2004) |
PROWESS (CMIE), firm level data |
1989-2001 |
28 |
Trivedi (2004) |
ASI (industry groups, at state level) |
1980-81 to 2000-01 |
29 |
Virmani (2004) |
NAS |
1950-51 to 2003-04 |
30 |
Veeramani & Goldar (2005) |
ASI (selected states and industries) |
1980-2000 |
31 |
Bhaumik, et al (2006) |
ASI (3 digit firm level data for 15 states) |
1984-97 |
32 |
Raj & Duraiswamy (2006) |
NSSO |
1978-79, 1984-85, 1989-90, 1994-95
and 2000-01 |
33 |
Banga & Goldar (2007) |
ASI
(panel data for industry groups) |
1980-81 to
1999-2000 |
34 |
Bosworth et al (2007) |
NAS |
1960-61 to 2004-05 |
35 |
Ray (2002) |
ASI (selected states) |
1986-87 to 1995-96 |
36 |
Mukherjee & Ray (2004) |
ASI (selected states) |
1986-87 to 1999-2000 |
37 |
Sivadasan (2007) |
ASI Unit level |
1987-90 & 1994-95 |
38 |
Gupta (2008) |
ASI Industry level |
1973-2003 |
39 |
Raj & Mahapatra (2009) |
ASI & NSSO (3 states) |
1981 to 2003 |
40 |
Goldar et al (forthcoming) |
ASI and NSSO |
1989-90 to 2004-05 for ASI & 1989-90
to 2005-06 for NSS
data |
41. |
Kathuria (2010) |
ASI Unit level and NSSO |
1994-95, 2000-01
and 2005-06 |
As against it, ASI database provides a much more detailed industry/
state level classification and both primary and non-primary input details
are available on an annual basis. Hence, this database has been most
widely used among the researchers. However, this database includes only
registered manufacturing sector. We have already seen that the registered
sector accounts for merely 20 per cent of employment though its
contribution in terms of output is about 68 per cent of the total
manufacturing sector.
A large number of studies, such as, Aghion et al (2003), Ahluwalia
(1991), Balakrishnan & Pushpangadan (1994), Banga & Goldar (2007),
Das (2004), Dholakia & Dholakia (1994), Gangopadhyay & Wadhwa (1998),
Goldar (2004), Hulten & Srinivasan (1999), Majummdar (1996), Mitra
(1999), Mukherjee & Ray (2004), Pradhan & Barik (1998), Pradhan & Barik
(1999), Pradhan & Barik (2004), Rajesh & Mahapatra (2009), Rao (1996a),
Rao (1996b), Ray (2002), Tata Services Ltd (2003), Trivedi et al (2000),
Trivedi (2003), Trivedi (2004), Unel (2003), among others, have used the
ASI data. The studies which have dealt with the industry/state level issues
related to productivity have used disaggregated data up to two or three
digit level National Industrial Classification (NIC) for the various states or
for India as a whole.
The CSO has also started compiling unit level (micro) data for the
organised sector and it is available for most of the years since 1993-94.
However, this database has been rarely used by the researchers.
Construction of capital stock series (at replacement cost) for unit level data is a challenging task, as the estimates of capital stock in earlier years
are not available. One of the main reasons for underutilisation of this data
is that permanent serial numbers or identification codes for the units
included in the sample are not available partly due to the sampling
procedure and partly on the grounds of maintaining confidentiality. Hence,
it is not possible to construct either a balanced or an unbalanced panel
from this dataset, something that would be of interest to researchers when
using the micro level data. In view of this, despite its richness in terms of
information content, this dataset is also grossly underutilised. Only a few
researchers, viz., Sivadasan (2007), Bhaumik, et al (2006) and Kathuria,
et al (2010) have used the unit level ASI dataset sourced from the CSO .
PROWESS database of CMIE consists of data on companies and is
compiled from their financial statements. More often than not, this database
does not include labour input and hence, is not particularly suited for
productivity measurement. The problem of construction of labour input
series for this dataset is similar to that for the NAS data. The studies by
Balakrishanan et al (2000), Topalova (2004) and Krishna & Mitra (1998)
have used this database and had to make various assumptions for the
construction of labour input series. However, PROWESS database has the
advantage that it is micro level database and it is possible to construct a
panel from this database and also stratify performance of the companies
according to their size or some other criteria. This dataset encompasses
only a subset of organised manufacturing sector, as the financial statements
are issued mandatorily only for the listed companies. As can be seen from
Table 3.1, this database also has not been used as intensively by the
researchers as is the case with the ASI database.
The RBI dataset has advantages and limitations which are more or
less akin to the PROWESS dataset, as both the datasets are based on
information culled out from the audited annual accounts of the companies.
The RBI standardises the data items presented in annual accounts of
companies through a normalization process, which is based on accounting principles. However, the firm level data compiled by the RBI is not publicly
available. Despite the fact that the identification of firms is confidential,
the codes are given to companies and hence it is possible to construct a
balanced/unbalanced panel from the RBI dataset. Only Srivastava (1996)
and Srivastava (2000) studies have used this dataset.
More detailed data (at industry and state levels) on unorganised
manufacturing sector is available from the ‘reports’ of the various enterprise
surveys conducted by the NSSO. This dataset is available only for a few
years and at times not exactly comparable across the different surveys.
The enterprise level data is also maintained by the NSSO and it is an
enormously rich dataset, but again it is not possible to construct a panel
from this dataset. In addition, there is a problem of constructing a reliable
capital stock series for the enterprises included in this dataset. Unni
et al (2001), Raj & Mahapatra (2009), Raj & Duraiswamy (2006) and Goldar
& Mitra (2008), Kathuria, et al (2010) have used NSSO data for analysing
the performance of unorganised sector in terms of employment,
output and/or productivity growth rates. Most of these studies have
also used ASI data for organised sector so as to provide a comparison
between organised and unorganised segments of the Indian manufacturing
sector.
Jayadevan (1996), Mitra (1999), Unni et al (2001), Ray (2002), Sunil
Kumar (2003), Aghion (2003), Trivedi (2003), Mukherjee & Ray (2004),
Trivedi (2004), Raj & Duraiswamy (2006), Raj and Mahapatra (2009), and
Goldar and Mitra (2008) have used state-level disaggregation to investigate
the state level performances and/or determinants of productivity. Quite a
few studies, such as Ahluwalia (1991), Krishna & Mitra (1998), Pradhan &
Barik (1998), Gangopadhyay and Wadhwa (1998), Pradhan and
Barik (1999), Mitra (1999), Trivedi, et al (2000), Trivedi (2003), Unel
(2003), Das (2004), Pradhan & Barik (2004), Trivedi (2004), Veeramani
and Goldar (2005), etc., have estimated TFPG at industry levels using the
ASI dataset.
In brief, the research on productivity on Indian manufacturing sector
has been gaining popularity among researchers. As is evident from the above
discussion, ASI industry/state level database has been the most often used
database, since it provides the essential information required for estimation
of productivity. The data limitations for micro level data compiled both by
CSO and NSSO need to be addressed by the Ministry of Statistics and
Programme Implementation, GoI, so as to enable the researchers to exploit
the rich information content embedded in these datasets.
3.2: Output-Input Framework and Methodologies Used
Productivity estimates are sensitive to measurement of output and
inputs, besides that to the methodology used. There has been a debate over
the appropriate measure of output and the corresponding inputs to be used
for productivity measurement. The three alternatives proxies of output used
in the various studies are: (i) real value added obtained by single deflation
(RVASD); (ii) real value added obtained by double deflation (RVADD); and
(iii) real gross output (RGO). The NAS data is compiled using the RVASD
methodology. ASI provides data on nominal value added and prior to
Balakrishnan and Pushpangadan (1994) researcher in India mainly used
RVASD as a measure of output. Balakrishnan and Pushpangadan (1994)
refuted the claim of Ahluwalia (1991) regarding turnaround in TFPG of
organised manufacturing sector arguing that such a conclusion was arrived
at due to the use of inappropriate proxy for measurement of output, viz.,
RVASD. They recommended use of RVADD instead of RVASD. A number of
researchers, viz., Ahluwalia (1994), Dholakia and Dholakia (1994), Goldar
(1994), Pradhan and Barik (1998), Rao (1996a), Rao (1996b) and Shastry
(1994), etc., contributed to this lively debate. Since the use of RGO does not
involve the assumption of separability of production function, we find that
the studies after 1994 have taken a greater recourse to the use of RGO as
the measure of output and in view of this the input framework has been
extended to include material inputs as well.
Table 3.2 details the input-output and methodological framework
adhered to in the various studies on TFPG15.
The input framework in the case of use of RVASD and RVADD (as
proxies to output) has to be confined to labour and capital. The input
framework has to be modified to include material and other inputs in the
case of use of RGO as a proxy for production. As can be seen from Table
3.2, various studies have used input frameworks which are not the same.
The input frameworks used range from the use of two inputs, viz., K16 and
L (capital stock and labour, respectively) to five inputs, viz, K, L, E (energy),
M (material inputs), N (total inputs) and S (services). It is pertinent to note
here that some studies have used the two variants of labour inputs, viz.,
production workers (L1) and non-production staff (L2, which is obtained
by subtracting L1 from the total number of employees). Studies, such as,
Bosworth et al (2007), Veeramani (2005) have adjusted for labour quality
as well.
We have refrained from discussing the construction of capital input
series in the studies covered in this survey, not because of lack of its
importance, but due to the elaboration it requires. Usually capital stock
series are available at book value and need conversion into one at
replacement cost. Quite a few studies have attempted such conversions
adhering to the various assumptions about the capital stock in the
benchmark year, the rate of depreciation and the rate of inflation deemed
relevant to capital goods which have been proxied either by investment
deflator or the WPI of machinery and transport equipment. Then the
perpetual inventory method (PIM), is used which requires the estimates of
capital stock for the benchmark year and capital formation for the
successive years. However, some of the studies have simply used the capital
stock series at book values, which is inappropriate.
Table 3.2: Methodology Used and Details of Input-Output Framework |
Sr.
No. |
Study |
Methodology for
TFPG Estimation
(underlying form
of production
function) |
Output
Measure |
Inputs included |
Source of weights for
compiling Price
Deflator Index for
inputs, if RVADD or
RGO are used as
output proxies |
1 |
Ahluwalia (1991) |
GAA (TL) |
RVASD |
K & L |
– |
2 |
Mohanty (1992) |
PFA (CD) |
RVASD |
K & L |
– |
3 |
Balakrishnan & Pushpangadan
(1994) |
GAA (TL) |
RVADD |
K & L |
Coefficients of I-O
1973-74 & 1983-84
tables |
4 |
Dholakia and Dholakia (1994) |
GAA (TL) |
RVASD (RVADD) |
K & L (K, L & M) |
Coefficients of
I-O1973-74 table |
5 |
Majumdar (1996) |
DEA |
RGO |
FK, WK, L1 & L2 |
Material inputs/
energy/services not
included in estimation.
Only two forms of
capital and labour
treated as inputs. |
6 |
Rao (1996a) |
GAA (SI) |
RVASD, RVDD,
RVADD* |
K & L (K, L & M) |
Coefficients of
I-O 1973-74 table |
7 |
Rao (1996b) |
|
|
|
|
8 |
Srivastava (1996) |
GAA (TL), PFA*
(CD and TL) |
RGO |
K, L & M |
Tornqvist index for
inputs derived from the
Index for industrial raw
materials and index for
power and fuel |
9 |
Krishna & Mitra (1998) |
PFA* (CD) |
RGO |
K, L & M |
72-sector I-O table
obtained from the World
Bank |
10 |
Pradhan & Barik (1998) |
GAA (TL) |
RGO |
K, L & M |
Coefficients of
I-O 1991-92 table |
11 |
Gangopadhyay & Wadhwa (1998) |
GAA (TL), PFA
(CD and TL, with
and without
industry effects) |
RVASD |
K & L |
– |
12 |
Pradhan & Barik (1999) |
CF (TL) cost
function |
RGO |
K, L & M |
Coefficients of
I-O 1991-92 table |
13 |
Mitra (1999) |
Frontier PF (CD) |
RVADD |
K, L, E & M |
Coefficients of
I-O 1989-90 table |
Table 3.2: Methodology Used and Details of Input-Output Framework (Contd.) |
Sr.
No. |
Study |
Methodology for
TFPG Estimation
(underlying form
of production
function) |
Output Measure |
Inputs included |
Source of weights for
compiling Price
Deflator Index for
inputs, if RVADD or
RGO are used as
output proxies |
14 |
Hulten & Srinivasan (1999) |
GAA (TL) |
RVADD |
K, L & M |
Tornquist index for inputs price derived from input prices of 2-digit industry levels |
15 |
Balakrishanan et al (2000) |
PFA* |
RGO |
K, L & M |
Coefficients of I-O 1989-90 table |
16 |
Goldar (2000) |
GAA (TL) |
RVASD, RVADD
& RGO |
K& L and K,
L & I |
Coefficients of
I-O 1989-90 table |
17 |
Trivedi, et al (2000) |
GAA (TL) |
RVASD, RVADD
(RGO) |
L & K (L, K & N) |
Coefficients of
I-O 1991-92 table |
18 |
Unni et al (2001) |
GAA (SI) |
RVASD |
K & L |
– |
19 |
Aghion et al (2003) |
GAA (TL) |
RVASD |
L1, L2, K |
– |
20 |
Goldar & Kumari (2003) |
GAA (TL) |
RGO |
K, L & I |
Coefficients of I-O 1989-90 table |
21 |
Trivedi (2003) |
GAA (TL) |
RGO |
K, L & N |
Coefficients of I-O 1993-94 table |
22 |
Tata Services Ltd .(2003) |
GAA (TL) |
RGO |
K, L & M |
Coefficients of I-O 1993-94 table |
23 |
Unel (2003) |
GAA (TL) |
RVASD |
K & L |
– |
24 |
Das (2004) |
GAA (TL) |
RGO |
K, L, E & M |
Coefficients of I-O 1983-84, 1989-90
& 1993-94 table |
25 |
Goldar(2004) |
GAA (TL) |
RGO, RVASD |
K, L, E & M |
Coefficients of I-O 1993-94 table |
26 |
Pradhan & Barik (2004) |
GAA (TL) |
RGO |
K, L, E & M |
Coefficients of I-O 1991-92 table |
27 |
Topalova (2004) |
PFA (CD) @ |
RGO |
K, L, E & M |
Appropriate price deflators from NAS |
28 |
Trivedi (2004) |
GAA (TL) |
RGO |
K, L & N |
Coefficients of I-O 1978-79, 1983-84,
1989-90 & 1993-94
tables |
29 |
Virmani (2004) |
PFA (CD) |
RVASD |
K & L$ |
– |
Table 3.2: Methodology Used and Details of Input-Output Framework (Concld.) |
Sr.
No. |
Study |
Methodology for
TFPG Estimation
(underlying form
of production
function) |
Output Measure |
Inputs included |
Source of weights for
compiling Price
Deflator Index for
inputs, if RVADD or
RGO are used as
output proxies |
30 |
Veeramani & Goldar (2005) |
Multilateral
TFP index |
RVASD (RGO) |
K & L^
(K, L, E, M & S) |
Coefficients of
I-O 1993-94 table |
31 |
Bhaumik et al (2006) |
Olley-Pakes
algorithm |
RVASD |
K & L |
– |
32 |
Raj & Duraiswamy (2006) |
DEA
(Malmquist index) |
RVASD |
K & L |
– |
33 |
Banga and Goldar (2007) |
PFA (CD) |
RGO |
K, L, E, M & S |
Coefficients of I-O 1993-94 table |
34 |
Bosworth et al (2007)^ |
GAA (SI) |
RVASD |
K & L$ |
– |
35 |
Ray (2002) |
DEA, Malmquist
and Tornqvist
Index |
RGO |
K, L1, L2,
E & M |
WPI for FPLL for
deflating E and index of
industrial raw materials
price for deflating Mindustrial raw
materials price for
deflating M |
36 |
Mukherjee & Ray (2005) |
DEA |
RGO |
K, L1, L2, E & M |
Not specified |
37 |
Sivadasan (2007) |
PFA (CD)#, SI, |
RVASD (RGO) |
K, L1 & L2
(K, L1, L2 & M) |
Not specified |
38 |
Raj & Mahapatra (2009) |
GAA (TL),
MI & DEA |
RVASD |
K & L |
– |
39 |
Kathuria, Raj and Sen (2010) |
PFA (CD) |
RVADD |
K & L |
– |
40. |
Gupta (2008) |
GAA, Index
Number Approach
and DEA |
RVADD, RGO |
K & L |
– |
Notes: Refer to the list of Abbreviations for abbreviations used in this Table.
* Hall (1988) framework has been used to estimate productivity
@ refer Levinsohn and Petrin (2003), Aw, Chen, and Roberts (2001), Pavcnik (2002) and Fernandes (2003) for more details on methodology
$ Indicates data for L are from NSS and other sources, see Virmani (2004, pp 10-11) for more details on this.
^ Adjustment for quality of labour made
# Levinsohn and Petrin (2003) procedure has been used. |
The use of RVADD and RGO necessitates computation of material/
input price indices. As can be seen from Table 3.2, quite a few studies
have used only one input-output absorption matrix for obtaining weights
for material inputs/total inputs used by industries/manufacturing sector.
Fixed weights imply that the input structure does not change over time
even as technology and input prices change. This seems to be an untenable
assumption, especially if one is trying to assess the impact of policy reforms
on TFPG. Balakrishnan and Pushpangadan (1994) did test the sensitivity
of productivity estimates to the alternative weighting diagrams for material
inputs derived from 1973-74 and 1983-84 I-O absorption matrices and
demonstrated that it does not alter their conclusion of ‘no turnaround in
TFPG in the eighties’. Though the insensitivity of TFPG estimates or
turnaround to the two set of I-O coefficients may be true for the data set
analysed by them, this cannot be presumed on theoretical grounds. Whilst
constructing the input price index it is desirable to incorporate the changes
in weights of various inputs which are expected to take place over time. To
best of our knowledge Das (2004) and Trivedi (2004) are the only studies,
which have incorporated changing weights derived from the various
available I-O matrices. A few studies, such as, Srivastava (1996) and Hulten
and Srinivasan (1999) have used Tornquist indices of inputs for the
aggregation of inputs.
3.3: Methodologies Used
In Table 3.2, we have also provided a synoptic view of the
methodologies used by the various studies on the TFPG for the Indian
manufacturing sector17. As outlined in Chart 3.1, there are three major
alternative approaches, viz., production function Approach (PFA)/Cost
Function Approach (CFA), Growth Accounting Approach (GAA) and Data
Envelopment Analysis (DEA) for estimation of TFPG and/or efficiency levels
and changes. PFA/ CFA and Stochastic Frontier Production Function (SFPF) approaches are parametric approaches and involve estimation of
parameters. These approaches require assumptions about the underlying
production functions. As against it, GAA and DEA are non-parametric
approaches. The DEA does not require any prior assumption about the
underlying functional form for production technology used by the firms/
industry. This approach yields estimates of efficiency, i.e., it provides
estimates of efficiency of various firms in relation to the most efficient firm
for which efficiency is set at unity. Thus, as mentioned earlier, efficiency
is a relative concept. Malmquist index of productivity is also based on the
concept of the distance functions.
As can be seen from Table 3.2, most of the studies in India have
adhered to GAA and used the translog (TL) production function as the
underlying form of the production function. It collapses into Cobb-Douglas
(CD) production function, if the interaction terms are not significant. The
studies using GAA have also used factor shares as the weights for the
inputs. This implies the assumption of perfectly competitive market
structure. Restricting the sum of weights for factor shares to unity implies
constant returns to scale (CRS). A few studies, such as, Majumdar (1996),
Ray (2002), Mukherjee and Ray (2005), Raj and Duraiswamy (2006), Raj &
Mahapatra (2009) have used the DEA and have provided estimates of
efficiency. A couple of studies, such as, Bhaumik et al (2006) and Sivadasan
(2007) have also tried to overcome the problem of endogeneity in the use
of capital by following procedures laid down in Olley & Pakes and Levinsohn
& Petrin (2003)
3.4: Efficiency and Productivity Estimates
In the preceding three sections, we have detailed the differences in
the time-period, input-output frameworks and methodologies used in the
various studies. In view of this, it is natural that these studies yield
disparate estimates of efficiency and productivity and it is rather difficult
to arrive at some consensus regarding; (i) estimates of TFPG/efficiency; (ii)
Contribution of TFPG to growth; and, (iii) The turnaround in productivity growth in response to policy reforms.Tables 3.3A and 3.3B provide a
synoptic view of these aspects of the various studies surveyed in this section.
It is pertinent to note here that wherever possible, we have tried to estimate
the rates of growth of production as used in the respective studies and
the contribution of TFPG to growth from the information provided in
these studies so as to enable us some meaningful comparisons across
studies.
It can be seen from Tables 3.3A & 3.3B that, in general, the estimates
of TFPG obtained with RVADD as a proxy for output are much higher as
compared with those obtained with RVASD or RGO. In view of empirical
evidence provided by Pradhan and Barik (1998) about non-separability of
inputs in production function, the productivity estimates based on RGO
seem to be preferable to the alternative estimates. For the organised
manufacturing sector, TFPG estimates based on RGO confirm that the
contribution of TFPG to output growth has been rather modest.
As regards the unorganised manufacturing sector, the estimates of
TFPG seem to be rather unreliable in view of the data limitations. Unni et
al (2001) provide the estimates of TFPG and real value added for
unorganised manufacturing. If we extrapolate the contribution of TFPG to
growth from these data, the conclusions arrived at are rather bizarre. The
problem seems to lie in the inaccurate measurement of capital. Hence, it
would be better to focus on labour productivity rather than TFPG for
unorganised sector.
As regards the turnaround of TFPG, different cut-offs for demarcating
pre-reform from post-reform periods have been used in the various studies.
In general, the beginning of the eighties, mid-eighties and beginning of the
nineties have been considered cut-off for pre-reform and post-reform
periods.
3.5 Turnaround and Determinants of Productivity and Efficiency
In this section, we discuss the turnaround of productivity and
efficiency for the organised and unorganised manufacturing sectors. As can be seen from Table 3.3A and 3.3B, the studies on unorganised sector
have been very few.
Table 3.3A: Estimates of Productivity and Efficiency for Indian Organised
Manufacturing Sector |
Sr.
No |
Study |
Methodology for
TFPG Estimation
(underlying form of
production function) |
Proxy for
Production |
Period |
TFPG
(% p.a.)
or Mean
Efficiency |
Growth
Rate of
Production
(% p.a.) |
Contribution
of TFPG to
Growth of
Output (%) |
1 |
Ahluwalia (1991) |
GAA (TL)* |
RVASD |
1959-60 to 1985-86 |
-0.40 |
5.30 |
-7.55 |
1959-60 to 1965-66 |
0.20 |
9.10 |
2.20 |
1966-67to 1975-76 |
-0.20 |
4.70 |
-4.26 |
1965-66 to 1979-80 |
-0.30 |
5.00 |
-6.00 |
1980-81 to 1985-86 |
3.40 |
7.50 |
45.33 |
2 |
Mohanty (1992) |
PFA (CD) |
RVASD |
1970-71 to 1988-89 |
0.01 |
5.90 |
0.16 |
1970-71 to 1979-80 |
0.00 |
5.00 |
-0.06 |
1980-81 to 1988-89 |
-0.01 |
8.10 |
-0.08 |
3 |
Balakrishnan
&
Pushpangadan
[1994] |
GAA (TL) |
RVADD |
1970-71 to 1988-89 |
3.07 |
9.32 |
32.93 |
1970-71 to 1979-80 |
5.59 |
11.80 |
47.37 |
1980-81 to 1988-89 |
-0.11 |
5.93 |
-1.80 |
GAA (TL) |
RVASD |
1970-71 to 1988-89 |
0.45 |
5.40 |
8.33 |
1970-71 to 1979-80 |
0.02 |
5.09 |
0.39 |
1980-81 to 1988-89 |
2.42 |
6.94 |
34.87 |
4 |
Dholakia &
Dholakia [1994] |
GAA (TL) |
RVASD |
1970-71 to 1988-89 |
-0.11 |
5.27 |
-2.09 |
1970-71 to 1979-80 |
-1.69 |
3.13 |
-53.99 |
1980-81 to 1988-89 |
1.89 |
8.01 |
23.60 |
GAA (TL) |
RVADD |
1970-71 to 1988-89 |
1.58 |
7.59 |
20.82 |
1970-71 to 1979-80 |
0.56 |
5.85 |
9.57 |
1980-81 to 1988-89 |
2.86 |
9.81 |
29.15 |
5 |
Majumdar [1996]¥ |
DEA |
RGO |
1950-51 to 1992-93 |
0.87 |
|
|
1960-61 to 1992-93 |
0.84 |
|
|
1970-91 to 1992-93 |
0.86 |
|
|
1980-81 to 1992-93 |
0.92 |
|
|
6 |
Rao [1996a] |
GAA (SL) |
RGO |
1973-74 to 1992-93 |
1.30 |
|
|
1973-74 to 1980-81 |
-0.20 |
|
|
1981-82 to 1992-93 |
2.10 |
|
|
RVADD |
1973-74 to 1992-93 |
2.20 |
|
|
1973-74 to 1980-81 |
4.60 |
|
|
1981-82 to 1992-93 |
-0.20 |
|
|
RVADD* |
1973-74 to 1992-93 |
2.00 |
6.10 |
32.79 |
1973-74 to 1980-81 |
5.50 |
10.60 |
51.89 |
1981-82 to 1992-93 |
-2.20 |
2.30 |
-95.65 |
Table 3.3A: Estimates of Productivity and Efficiency for Indian Organised
Manufacturing Sector (Contd.) |
Sr.
No. |
Study |
Methodology for
TFPG Estimation
(underlying form of
production function) |
Proxy for
Production |
Period |
TFPG
(% p.a.)
or Mean
Efficiency |
Growth
Rate of
Production
(% p.a.) |
Contrib-
ution of
TFPG to
Growth of
Output (%) |
7 |
Rao [1996b] |
GAA (Solow Index)
applied at
disaggregate level
and then weighted |
RVADD* |
1973-74 to 1992-93 |
2.30 |
6.10 |
37.70 |
1973-74 to 1980-81 |
6.50 |
10.60 |
61.32 |
1981-82 to 1992-93 |
-2.80 |
2.30 |
-121.74 |
8 |
Srivastava [1996] |
GAA (TL) |
RGO |
1980-81 to 1989-90 |
-1.36 to-1.47 |
7.73 |
-17.6 to -19.0 |
1980-81 to 1984-85 |
-0.22 to -0.35 |
6.58 |
3.3 to 5.3 |
1985-86 to 1989-90 |
-2.27 to -2.37 |
8.64 |
-26.3 to -27.4 |
PFA@ (CD and TL) |
1980-81 to 1989-90 |
0 to 1.2 |
7.73 |
0.0 to 15.5 |
1980-81 to 1984-85 |
0 to -0.8 |
6.58 |
0.0 to -12.2 |
1985-86 to 1989-90 |
0.4 to 2.1 |
8.64 |
4.6 to 24.3 |
9 |
Pradhan, G. and K. Barik (1998) |
GAA (TL) |
RGO |
1962-63 to 1992-93 |
1.00 |
|
|
1963-71 |
-2.09 |
|
|
1972-81 |
3.06 |
|
|
1982-92 |
-1.23 |
|
|
10 |
Gangopadhyay & Wadhwa (1998) |
GAA (TL) |
RVASD |
1973-74 to 1993-94 |
3.61 |
|
|
1974-80 |
1.17 |
|
|
1981-85 |
5.44 |
|
|
1986-90 |
5.01 |
|
|
1991-93 |
3.88 |
|
|
PFA (TL, (without
fixed effects)
PFA (TL, with
industry effects) |
RVASD |
1973-74 to 1993-94 |
-0.20 |
|
|
|
-0.26 |
|
|
11 |
Pradhan &
Barik (1999) |
CFA (TL) |
|
1963-64 to 1992-93 |
0.074 |
|
|
1965-66 |
0.076 |
|
|
1975-76 |
0.085 |
|
|
1985-86 |
0.063 |
|
|
1992-93 |
0.051 |
|
|
Table 3.3A: Estimates of Productivity and Efficiency for Indian Organised
Manufacturing Sector (Contd.) |
Sr.
No. |
Study |
Methodology for
TFPG Estimation
(underlying form of
production function) |
Proxy for
Production |
Period |
TFPG
(% p.a.)
or Mean
Efficiency |
Growth
Rate of
Production
(% p.a.) |
Contrib-
ution of
TFPG to
Growth of
Output (%) |
12 |
Mitra [1999] |
Frontier PF (CD) |
RVADD |
1976-77 to 1992-93 |
3.43 |
|
|
1976-77 to 1984-85 |
0.76 |
|
|
1985-86 to 1992-93 |
5.57 |
|
|
1976-77 to 1992-93 |
0.46 |
|
|
1976-77 to 1984-85 |
0.47 |
|
|
1985-86 to 1992-93 |
0.46 |
|
|
13 |
Hulten & Srinivasan
[1999] |
GAA (TL) |
RVADD |
1973-93 |
2.20 |
7.10 |
31.00 |
1973-82 |
2.18 |
6.80 |
32.00 |
1983-92 |
2.10 |
7.50 |
28.00 |
1973-93 $ |
5.40 |
7.10 |
76.00 |
1973-82 $ |
5.00 |
6.80 |
74.00 |
1983-92 $ |
5.70 |
7.50 |
76.00 |
14 |
Goldar (2000) |
GAA (TL) |
RVASD |
1981-82 to 1997-98 |
3.36 |
|
|
1981-82 to 1989-90 |
4.52 |
|
|
1990-91to 1997-98 |
1.86 |
|
|
RVADD |
1981-82 to 1997-98 |
5.79 |
|
|
1981-82 to 1989-90 |
8.97 |
|
|
1990-91to 1997-98 |
2.92 |
|
|
RGO |
1981-82 to 1997-98 |
1.49 |
|
|
1981-82 to 1989-90 |
2.13 |
|
|
1990-91to 1997-98 |
0.90 |
|
|
15 |
Trivedi, et al [2000] |
GAA TL |
RGO |
1973-74 to 1997-98 |
0.99 |
7.80 |
12.69 |
RVASD |
|
2.61 |
7.20 |
36.25 |
RVADD |
|
4.37 |
9.00 |
48.56 |
16 |
Unni et al (2001) |
GAA (SL) |
RVASD |
1978-95 |
-0.10 |
6.60 |
-1.52 |
1978-90 |
1.13 |
5.90 |
19.15 |
1978-85 |
-0.26 |
4.60 |
-5.65 |
1985-90 |
4.00 |
7.50 |
53.33 |
1990-95 |
-1.28 |
8.20 |
-15.61 |
17 |
Goldar & Kumari
(2003)# |
GAA (TL) |
RGO |
1981-82 to 1997-98 |
1.40 (1.50) |
|
|
1981-82 to 1990-91 |
1.89 (1.60) |
|
|
1990-91 to 1997-98 |
0.69 (1.30) |
|
|
Table 3.3A: Estimates of Productivity and Efficiency for Indian Organised
Manufacturing Sector (Contd.) |
Sr.
No. |
Study |
Methodology for
TFPG Estimation
(underlying form of
production function) |
Proxy for
Production |
Period |
TFPG
(% p.a.)
or Mean
Efficiency |
Growth
Rate of
Production
(% p.a.) |
Contrib-
ution of
TFPG to
Growth of
Output (%) |
18 |
Trivedi (2003) |
GAA (TL) |
RGO |
1980-81 to 1997-98 |
1.60 |
8.00 |
20.00 |
19 |
TSL (2003) |
GAA (TL) |
RGO |
1981-82 to 1999-00 |
0.79 |
7.66 |
10.30 |
1981-82 to 1992-93 |
0.68 |
7.57 |
9.00 |
1993-94 to 1999-00 |
0.97 |
7.81 |
12.40 |
1991-92 to 2001-02 |
2.56 |
8.55 |
29.90 |
1991-92 to 2001-03 |
3.46 |
11.74 |
29.50 |
1981-82 to 2001-02 |
2.96 |
5.61 |
52.80 |
1981-82 to 1992-93 |
1.80 |
5.46 |
33.00 |
1993-94 to 2001-02 |
4.37 |
5.80 |
75.30 |
20 |
Unel (2004) |
GAA (TL) |
RVASD |
1979 -97 |
1.4(3.1)@ |
|
|
1979 -90 |
1.8( 3.2)@ |
|
|
1990-91 |
-8.8(-7.2)@ |
|
|
1991-97 |
2.5( 4.7)@ |
|
|
21 |
Das (2004) |
GAA (TL) |
RGO |
1980-81 to 1999-00 |
-3.88# |
|
|
1980-81 to 1989-90 |
7.3# |
|
|
1990-91 to 1999-00 |
-0.18# |
|
|
22 |
Goldar (2004) |
GAA (TL) |
RGO |
1981-82 to 1990-91 |
0.92 |
|
|
1991-92 to 1999-00 |
0.65 |
|
|
1980-81 to 1990-91 |
1.37 |
|
|
RVASD |
1979-80 to 1990-91 |
2.14 |
|
|
1991-92 to 1997-98 |
1.00 |
|
|
1991-92 to 1999-00 |
1.57 |
|
|
PFA (TL) |
RGO |
1979-80 to 1990-91 |
2.23 |
|
|
1991-92 to 1997-98 |
1.08 |
|
|
1991-92 to 1999-00 |
1.65 |
|
|
23 |
Pradhan & Barik (2004) |
PFA (TL)
GAA (TL) |
RGO |
1963-64 to 1994-95 |
0.56 |
6.59 |
8.50 |
|
0.59 |
6.81 |
8.66 |
24 |
Trivedi (2004) |
GAA (TL) |
RGO |
1980-81 to 2000-01 |
1.00 |
7.80 |
12.80 |
1980-81 to 1991-92 |
1.90 |
7.50 |
25.30 |
1992-93 to 2000-01 |
0.70 |
8.60 |
8.10 |
PFA (CD) |
1980-81 to 2000-01 |
0.80 |
7.80 |
10.30 |
Table 3.3A: Estimates of Productivity and Efficiency for Indian Organised
Manufacturing Sector (Contd.) |
Sr.
No. |
Study |
Methodology for
TFPG Estimation
(underlying form of
production function) |
Proxy for
Production |
Period |
TFPG
(% p.a.)
or Mean
Efficiency |
Growth
Rate of
Production
(% p.a.) |
Contrib-
ution of
TFPG to
Growth of
Output (%) |
25 |
Virmani (2004) |
PFA (augmented) |
RVASD |
1950-51 to 1979-80 |
-0.30 |
|
|
1950-51 to 1964-65 |
0.40 |
|
|
1965-66 to 1979-80 |
-0.80 |
|
|
1980-81 to 2003-04 |
2.00 |
|
|
1980-81 to 1991-92 |
1.30 |
|
|
1992-93 to 2003-04 |
2.80 |
|
|
26 |
Banga and
Goldar (2007) |
PFA(CD) |
RGO (CD) |
1980-81 to 1999-00 |
0.59 (0.80)£ |
7.63 |
7.70 |
1980-81 to 1989-90 |
0.88 (0.50)£ |
7.21 |
12.20 |
1989-90 to 1999-00 |
0.26 (1.30)£ |
8.12 |
3.20 |
27 |
Bosworth et al (2007) |
GAA (CD) |
RVASD |
1960-61 to 2004-05 |
0.90 |
|
|
1960-61 to 1979-80 |
0.20 |
|
|
1980-81 to 2004-05 |
1.50 |
|
|
1960-61 to 1973-74 |
1.10 |
|
|
1973-74 to 1983-84 |
-0.30 |
|
|
1983-84 to 1993-94 |
2.10 |
|
|
1993-94 to 1999-00 |
0.30 |
|
|
1999-00 to 2004-05 |
1.40 |
|
|
28 |
Ray (2002) |
MI |
RGO |
1986-87 to 1995-96 |
0.81 |
|
|
1986-87 to 1990-91 |
0.17 |
|
|
1991-92to 1995-98 |
1.45 |
|
|
TI |
RGO |
1986-87 to 1995-96 |
0.49 |
|
|
1986-87 to 1990-91 |
0.23 |
|
|
1991-92to 1995-98 |
0.74 |
|
|
29 |
Mukherjee & Ray
(2005)# |
DEA |
RGO |
1986-2000 |
0.95 |
|
|
1986-91 |
0.96 |
|
|
1991-2000 |
0.94 |
|
|
1991-96 |
0.96 |
|
|
1996-2000 |
0.91 |
|
|
30 |
Rajesh & Mahapatra (2009) |
GAA (TL) |
RVASD |
1981 to 2003 |
0.44 |
|
|
1981 to 1991 |
1.40 |
|
|
1992–2003 |
-0.52 |
|
|
DEA ¥ |
1981 to 2003# |
0.78 |
|
|
1981 to 1991# |
0.77 |
|
|
1992–2003# |
0.78 |
|
|
Table 3.3A: Estimates of Productivity and Efficiency for Indian Organised
Manufacturing Sector (Concld.) |
Sr.
No. |
Study |
Methodology for
TFPG Estimation
(underlying form of
production function) |
Proxy for
Production |
Period |
TFPG
(% p.a.)
or Mean
Efficiency |
Growth
Rate of
Production
(% p.a.) |
Contrib-
ution of
TFPG to
Growth of
Output (%) |
31 |
Kathuria, Raj and Sen (2010) |
PFA (CD) |
RVASD
(Levinsohm
and Petrin
methodology) |
1994-20012001-05 |
0.043.14 |
|
|
1994-2005 |
0.64 |
|
|
32 |
Gupta (2008) |
GAA |
RGO |
1971-80 |
2.8 |
|
|
1980-90 |
5.1 |
|
|
1991-97 |
2.5 |
|
|
|
6.9 |
|
|
1998-2003 |
4.1 |
|
|
1970-2003 |
|
|
|
RNVA |
1971-80 |
2.9 |
|
|
|
2.4 |
|
|
|
0.1 |
|
|
|
4.3 |
|
|
|
2.4 |
|
|
1980-90 |
|
|
|
1991-97 |
|
|
|
1998-2003 |
|
|
|
1970-2003 |
|
|
|
Notes: We have calculated the contribution of productivity to growth in the case of studies which report both TFPG and rate of growth of proxy of output used in the study.
* Though Ahluwalia (1991) has also estimated production function, she preferred the GAA estimates to PFA estimates. Hence, we have reported only the latter.
@ See Hall, R (1988) for further elaboration on methodology
¥ Indicate efficiency level
# Figures are corrected for capacity utilisation. |
Table 3.3B: Estimates of Productivity and Efficiency for Indian Unorganised Manufacturing Sector |
Sr.
No. |
Study |
Methodology for
TFPG Estimation
(underlying form of
production function) |
Output
Measure |
Period |
Estimate of
TFPG %
p.a. |
Growth
Rate of
Production
(% p.a.) |
Contrib-
ution of
TFPG to
Growth of
Output (%) |
1 |
Mohanty (1992) |
PFA (CD) |
RVASD |
1970-71 to 1988-89 |
0.0850 |
4.40 |
1.93 |
1970-71 to 1979-80 |
-0.0006 |
4.50 |
-0.01 |
1980-81 to 1988-89 |
0.0003 |
5.40 |
0.01 |
2 |
Unni et al (2001) |
GA (SI) |
RVASD |
1978-95 |
-2.47 |
6.40 |
-38.6 |
1978-90 |
-2.66 |
9.10 |
-29.2 |
1978-85 |
-14.57 |
15.50 |
-94.0 |
1985-90 |
11.37 |
1.50 |
758.0 |
1990-95 |
-3.13 |
0.40 |
-782.5 |
3 |
Raj & Duraiswamy
(2006) |
MI |
RVASD |
1978-85 |
-6.30 |
|
|
1985-90 |
7.80 |
|
|
1990-95 |
0.40 |
|
|
1995-01 |
1.70 |
|
|
1978-79 to 1989-90 |
-1.00 |
|
|
1989-90 to 2000-01 |
0.70 |
|
|
4 |
Rajesh & Mahapatra (2009) |
MI |
RVASD |
1978–2001 |
0.1000 |
|
|
1978-79 to 1989-90 |
-0.3000 |
|
|
1994–95 to 2000–01 |
0.6000 |
|
|
DEA (estimates of
Technical Efficiency) |
|
1978–2001 |
0.7090 |
|
|
1978-79 to 1989-90 |
0.6210 |
|
|
1994–95 to 2000–01 |
0.8420 |
|
|
5 |
Kuthuria, Raj and Sen (2010) |
PFA (CD) |
RVASD
(Levinsohm
and Petrin
methodology) |
1994-20012001-05 |
-4.01-16.0 |
|
|
1994-2005 |
-10.14 |
|
|
|
|
|
|
|
|
|
|
3.5.1 Organised Manufacturing Sector
Studies by Ahluwalia (1991), Dholakia and Dholakia (1994),
Majummdar (1996), Gangopadhyay & Wadhwa (1998), Srivastava (1996),
Mitra (1999), Tata Services Limited (TSL, 2003), Topalova (2004) and Unel
(2003) find that change in policy regime in India has been associated with
higher TFPG and/or efficiency. However, the timing of the change of policy
regime in these studies differs. Ahluwalia (1991), Dholakia and Dholakia
(1994), Majummdar (1996), Gangopadhyay and Wadhwa (1998), among
others, have used the eighties as the beginning of the liberalised policy
regime and compared it with the earlier period. Srivastava (1996) and
Mitra (1999) compared the post mid-eighties with the earlier period and
treat the former as a period of liberalised policy regime. Ray (2002), Unel
(2003) and Topalova (2004) treat the nineties as the period of reform period
and compare it with earlier period.
Mohanty (1992), Balakrishnan & Pushpangadan (1994), Rao (1996a),
Pradhan & Barik (1998), Hulten & Srinivasan (1999), Unni et al (2001),
Das (2004), Goldar (2004), Trivedi (2004), Banga and Goldar (2007), among
others, find that TFPG has decelerated during the post-reforms period.
Again, like the studies quoted in the above paragraph, these studies also
use different periodisation for the pre and post-reform periods. However,
except for a couple of studies, the mounting empirical evidence points out
to the deceleration of TFPG during the nineties, as compared to that in the
eighties. Some studies attribute it to the poor capacity utilisation during
1990s while others attribute it to the lag between reforms and impact on
productivity growth. It is also noteworthy that quite a few of these studies
have used policy dummy to demarcate the pre-reform from the post-reform
period, with the exception of a few studies which have used the specific
variables pertaining to tariff reduction, FII and FDI inflows, etc., as a proxy
for the liberalised regime.
3.5.2 Unorganised Manufacturing Sector
The studies by Mohanty (1992) and Unni, et al (2001) do not indicate
any acceleration in TFPG for the unorganised sector in the wake of
liberalisation, irrespective of the periodisation of reform period. As against
it, Raj and Duraiswamy (2006) and Raj and Mahapatra (2009) indicate
increase in TFPG and efficiency for the unorganised sector during the
reform period. In fact, a recent study by Kuthuria et al (2010) finds a
steady decline in TFPG in unorganised manufacturing sector over the period
1994-2005.
3.5.3 Determinants of TFPG across States
Besides the policy reforms, various state-specific characteristics have
also been used by the researchers to investigate the determinants of TFPG.
Jaydevan (1996), Ray (1997), Mitra (1999), Ray (2002), Kumar (2003),
Aghion (2003), Trivedi (2003), Trivedi (2004), Mukherjee and Ray (2004),
and Topalova (2004) are among a few studies which also deal with the
estimation and/or determinants of productivity across states. The estimates
of TFPG provided by some of these studies are provided below in Table 3.4.
It can be seen from Table 3.4 that TFPG estimates by Mitra (1999) are
rather high as compared to the other studies. This is mainly due to the
use of RVADD used as the output proxy. The differences between TFPG in
other studies are not too wide. Both Mitra (1999) and Ray (2002) indicate
improvements in TFPG in response to policy reforms. However, Mukherjee
and Ray (2004) do not indicate any substantial improvement in efficiency
at the all India level as a result of reforms and also do not indicate any
substantial changes in ranking of the states or convergence of efficiency
in response to economic reforms in India. The inter-state variations in
TFPG are high in the study by Mitra (1999). There is some problem in
ranking of states according to TFPG since in the case where output fall is
lower than fall in employment rate, it gets reflected in high or rising TFPG
and such a state can get a higher rank, despite its poor overall performance.The case of Bihar* has been pointed out by Ray (2002) and Trivedi (2003,
2004).
Table 3.4: Estimates of TFPG and Efficiency for Indian Organised Manufacturing Sector |
Study (year)→ |
Jaydevan
(1996) |
Ray
(1997) |
Mitra
(1999) |
Ray
(2002) |
Trivedi
(2003) |
Time-span → |
1976-87 |
1969-85 |
1976-84 |
1985-92 |
1976-92 |
1986-90 |
1991-95 |
1986-95 |
1980-97 |
Region/State ↓ |
TFPG |
TFPG |
TFPG |
TEFF |
TFPG |
TEFF |
TFPG |
TEFF |
TFPG |
TFPG |
TFPG |
TFPG |
AP |
1.10 |
-5.20 |
0.84 |
0.49 |
3.18 |
0.44 |
2.18 |
0.46 |
-0.10 |
1.80 |
1.00 |
1.80 |
BIH* |
1.20 |
-3.00 |
4.16 |
0.46 |
3.14 |
0.47 |
3.58 |
0.46 |
4.10 |
0.20 |
1.90 |
1.90 |
GUJ |
1.10 |
0.40 |
1.92 |
0.45 |
6.69 |
0.48 |
4.47 |
0.46 |
0.50 |
2.30 |
1.50 |
1.30 |
HAR |
1.00 |
-3.10 |
-0.51 |
0.48 |
4.26 |
0.44 |
1.66 |
0.46 |
0.00 |
1.30 |
0.70 |
– |
KAR |
0.90 |
-5.50 |
1.98 |
0.44 |
6.97 |
0.48 |
4.48 |
0.46 |
0.90 |
1.40 |
0.40 |
1.90 |
KER |
1.20 |
-4.80 |
3.50 |
0.43 |
6.69 |
0.49 |
5.32 |
0.46 |
0.20 |
-0.10 |
-0.20 |
– |
MP* |
1.40 |
-2.50 |
2.21 |
0.46 |
5.34 |
0.47 |
3.68 |
0.46 |
0.90 |
3.20 |
2.20 |
1.80 |
MAH |
1.00 |
-1.90 |
0.11 |
0.46 |
5.94 |
0.46 |
4.00 |
0.46 |
0.00 |
2.30 |
1.30 |
2.10 |
ORI |
0.30 |
-5.00 |
-5.18 |
0.53 |
5.84 |
0.42 |
0.70 |
0.47 |
1.70 |
1.40 |
1.30 |
– |
PUN |
1.00 |
0.30 |
11.61 |
0.58 |
9.36 |
0.40 |
1.13 |
0.49 |
-2.20 |
0.70 |
-0.60 |
– |
RAJ |
1.00 |
-2.70 |
-0.85 |
0.48 |
6.14 |
0.45 |
3.03 |
0.46 |
-0.20 |
2.90 |
1.10 |
2.30 |
TN |
1.00 |
-0.60 |
-3.70 |
0.48 |
8.57 |
0.45 |
n.a. |
n.a. |
-0.10 |
1.10 |
0.50 |
1.50 |
UP* |
1.50 |
-4.20 |
4.44 |
0.41 |
8.29 |
0.53 |
6.25 |
0.47 |
-1.30 |
2.50 |
0.90 |
1.90 |
WB |
0.60 |
-5.50 |
0.60 |
0.53 |
-0.09 |
0.49 |
0.28 |
0.47 |
-1.50 |
1.50 |
0.20 |
1.00 |
All India |
1.00 |
-2.90 |
0.76 |
0.47 |
5.57 |
0.46 |
3.43 |
0.46 |
0.20 |
1.50 |
1.50 |
1.60 |
Note: TFPG is pcpa and Technical Efficiency (TEFF) is in levels. |
Table 3.4: Estimates of TFPG and Efficiency for Indian Organised Manufacturing Sector (Concluded) |
Study → |
Kumar |
Trivedi |
Mukherjee and Ray |
(2003) |
(2004) |
(2004) |
Time-span → |
1969-
80 |
1980-
91 |
1991-
95 |
1969-
95 |
1980-
92 |
1992-
2001 |
1980-
2001 |
1986-
2000 |
1986-
91 |
1991-
2000 |
1991-
96 |
1996-
2000 |
Region/State ↓ |
TFPG |
TFPG |
TFPG |
TFPG |
TFPG |
TFPG |
TFPG |
(Ranks based on Technical Efficiency) |
AP |
1.10 |
0.72 |
-2.07 |
0.63 |
1.70 |
1.30 |
1.30 |
21 |
21 |
20 |
20 |
21 |
BIH* |
-2.79 |
2.52 |
-3.14 |
0.14 |
3.10 |
2.00 |
1.70 |
5 |
5 |
4 |
5 |
5 |
GUJ |
-0.23 |
0.32 |
2.03 |
0.24 |
0.30 |
2.20 |
0.70 |
10 |
13 |
10 |
15 |
6 |
HAR |
2.15 |
-0.85 |
-0.97 |
0.20 |
– |
– |
– |
19 |
20 |
19 |
21 |
18 |
KAR |
-1.91 |
1.99 |
3.95 |
0.73 |
2.70 |
-1.30 |
1.20 |
15 |
11 |
14 |
10 |
16 |
KER |
-1.08 |
3.36 |
-7.30 |
0.88 |
– |
– |
– |
13 |
12 |
13 |
14 |
12 |
MP* |
1.00 |
1.90 |
1.24 |
1.53 |
2.00 |
1.50 |
1.30 |
11 |
10 |
12 |
11 |
10 |
MAH |
-3.03 |
2.40 |
0.82 |
0.30 |
1.70 |
-0.10 |
0.90 |
6 |
6 |
7 |
7 |
8 |
ORI |
-3.86 |
2.61 |
-1.86 |
-1.10 |
– |
– |
– |
8 |
8 |
8 |
9 |
9 |
PUN |
0.25 |
2.14 |
2.07 |
1.46 |
– |
– |
– |
22 |
22 |
21 |
22 |
17 |
RAJ |
1.17 |
0.35 |
7.20 |
1.17 |
1.90 |
1.90 |
1.40 |
18 |
19 |
16 |
17 |
11 |
TN |
4.34 |
-7.00 |
-16.04 |
-3.88 |
1.80 |
0.40 |
0.60 |
16 |
15 |
15 |
12 |
14 |
UP* |
-2.22 |
4.06 |
2.16 |
1.62 |
2.50 |
-2.90 |
0.30 |
17 |
17 |
17 |
16 |
15 |
WB |
-1.59 |
-1.41 |
2.53 |
-1.17 |
0.80 |
1.20 |
0.60 |
20 |
18 |
22 |
18 |
22 |
All India |
-0.26 |
2.34 |
1.71 |
1.35 |
1.90 |
0.70 |
1.00 |
0.94 |
0.96 |
0.94 |
0.96 |
0.91 |
Note: TFPG is pcpa and Technical Efficiency (TEFF) is in levels. |
Some of the other studies have used the various characteristics
of states which are considered as pertinent to exploiting the advantage
of reforms at the state levels. These factors have been predominantly
geographical location of states which makes them more suitable for
water/surface transport, investment climate, labour regime as to
whether it is pro-labour or pro-business or neutral. These characteristics
have been provided in Table 3.5.
Table 3.5: Classification of states According to Various
Characteristics |
State |
Geographical Characteristic |
Labour Regime |
IC Ranks and Category |
2009 |
2002 |
AP |
C |
EF |
4 |
4 |
G |
BIH* |
L |
N |
13 |
14 |
NC |
DEL |
L |
- |
8 |
2 |
B |
GUJ |
C |
WF |
3 |
3 |
G |
HAR |
L |
N |
5 |
8 |
G |
KAR |
C |
EF |
1 |
5 |
G |
KER |
C |
EF |
2 |
10 |
P |
MAH |
C |
WF |
7 |
1 |
B |
MP* |
L |
EF |
13 |
9 |
P |
ORI |
C |
WF |
12 |
17 |
NC |
PUN |
L |
N |
10 |
6 |
G |
RAJ |
L |
EF |
16 |
16 |
NC |
TN |
C |
EF |
9 |
7 |
G |
UP* |
L |
N |
15 |
12 |
P |
WB |
C |
WF |
6 |
11 |
P |
EF : Employer Friendly, WF: Worker Friendly, N: Neutral
G : Good, B: Best, NC: Not Classified
C : Coastal and L: Land locked
Note: Abbreviations used in the table are as follows:
Source: Geographical indicators as reported in Topalova (2004)
Labour regime as reported in Topalova (2004) from Besley and Burgess (2002)
IC Ranks and Category (2002) from FACS 2002 (quoted in Veeramani, 2005) and
IC Ranks (2009) as reported in Iarossi, Giuseppe (2009) |
4. Coverage of the Study and Methodology
4.1. Coverage of the Study and Data Details
This study can be considered both a continuum and a complement to
the previous studies, viz., Trivedi et al (2000), and Trivedi (2004). These
studies encompassed only organised manufacturing sector. The present
study is more comprehensive as compared to the previous studies as
regards the datasets used and its coverage which are as follows:
-
Use of micro level data: Unlike the earlier studies, it uses four major
datasets for estimation18 of productivity. This study makes use of not
only the meso (industry) level data, as was the case with the earlier
studies, but also uses the micro (unit) level data.
-
Inclusion of data for Non-government Public Limited Companies: This dataset of manufacturing sector is compiled by the RBI
(henceforth referred to as the RBI dataset). This dataset enables us to
get a balanced panel of micro level data at the company level.
-
Inclusion of data on unorganised Sector: The study uses data
available for unorganised manufacturing sector so as to make
comparisons between organised and unorganised sectors.
-
Inclusion of more states of India: In Trivedi (2004), only 10 major
states of India (and the three bifurcated states) were included. In this
study, 15 major states (and the three bifurcated states) have been
included.
-
Inclusion of Food, Beverages and Tobacco Industry: Given the
importance of this industry for employment generation and output
produced, we have included this industry as well in our analysis in
this study. As can be seen from Section 2, this industry accounts for
more than 20 per cent of employment and about 13 per cent of gross output of India’s organised manufacturing sector. Thus, the industry
coverage of this study is much wider.
-
Extended Temporal Coverage: The temporal coverage of the study
has been extended to 2003-04 (to 2004-05 in case of RBI dataset). In
fact, for the aggregate manufacturing sector for India as a whole, we
have extended the coverage of the study up to 2007-08.
-
Use of Input-Output Absorption Matrices for Input Price Index
compilation: The input-Output absorption matrix 1998-99 has been
used to update the input price index series, during 1998-99 to 2003-
04, for industry and state level analysis. For the total organised
manufacturing sector for all India where we could extend the analysis
up to 2007-08, we have also used Input-Output Absorption Matrices
for 2003-04 and 2006-07 for the compilation of input price indices.
More details of the datasets used are given in Tables 4.1 and 4.2.
The study encompasses 18 major states of India, three of which were
bifurcated in November 2000. The bifurcated states are: Bihar, Madhya
Pradesh and Uttar Pradesh. Three new states, viz., Jharkhand, Chattisgarh
and Uttarakhand were carved out of Bihar, Madhya Pradesh and Uttar
Pradesh, respectively. To ensure the comparability of pre-bifurcation period
with the post-bifurcation period, we have added the data for the newly
created state to the respective state from which it was created. We have
marked the bifurcated States with ‘*’. Thus, the states included in this
study, arranged in alphabetical order, are: Andhra Pradesh (AP), Bihar*
(BIH*), Delhi (DEL), Gujarat (GUJ), Haryana (HAR), Karnataka (KAR), Kerala
(KER), Maharashtra (MAH), Madhya Pradesh* (MP*), Orissa (ORI), Punjab
(PUN), Rajasthan (RAJ), Tamil Nadu (TN), Uttar Pradesh* (UP*) and West
Bengal (WB).
The industry groups chosen for investigation, again arranged in
alphabetical order are: (i) Chemical and chemical products (CHEM); (ii)
Food, Beverages and Tobacco (FBT); (iii) Leather and leather products
(LEATH); (iv) Metal and metal products (METAL); (v) Machinery and transport equipment (MTE); and, (vi) Textiles and textile products (TEX).
In addition, we have investigated the performance of overall manufacturing
sector (MFG).
Table 4.1: Datasets and Methodology Used in the Study |
Dataset |
Temporal Span |
Coverage |
Characteristics of the Dataset |
Methodology Used |
Industry Coverage |
Spatial Disaggregation |
1) |
ASI |
1980-81 to 2003-04 |
Organised Manufacturing |
2-3 digit level industry data and also for the manufacturing sector |
Growth Accounting with Translog index used for measurement of Total Factor Productivity |
1. Food Products & Beverages
2. Textiles & Textile Products
3. Leather & Leather Products
4. Chemical & Chemical Products
5. Metal & Metal Products 6. Machinery & Transport Equipment, Total Manufacturing |
1. Andhra Pradesh
2. Bihar*
3. Delhi
4. Gujarat
5. Haryana
6. Karnataka
7. Kerala
8. Madhya Pradesh*
9. Maharashtra 10. Orissa
11. Punjab
12. Rajasthan 13. Tamil Nadu 14. Uttar Pradesh*
15. West Bengal All India |
2) |
ASI unit level data |
1993-94 to 2003-04 (except 1995-96) |
Organised Manufacturing |
Unit codes have not been given in a consistent manner across the years and hence, preparation of a panel data not possible |
Efficiency measurement for each year with stochastic frontier production function |
—”— |
All India |
3) |
RBI Data from Balance Sheets of Companies |
1993-94 to 2004-05 |
Non-government Public Limited Companies in Manufacturing Sector |
Balanced panel data for 6 industries and also for 449 companies |
Data Envelopment Analysis, SFPF, calculation of efficiency for each year and also Malmquist index of productivity change |
—”—
(except leather and Tobacco industries) |
All India |
Table 4.1: Datasets and Methodology Used in the Study (Concld.) |
Dataset |
Temporal Span |
Coverage |
Characteristics of the Dataset |
Methodology Used |
Industry Coverage |
Spatial Disaggregation |
4) |
Unit level
NSSO data |
45th Round
(1989-90),
51st Round
(1994-95)
and 56th Round
(2000-01) |
Unorganised
sector |
Unit codes have
not been given in
a consistent
manner across for
the three rounds
and hence,
preparation of panel
data was not
possible. Also
compilation of
series on real
capital stock is
problematic due to
discontinuous
nature of the data. |
Estimation of
Labour
productivity |
—”— |
All India |
Table 4.2: No. of Micro Units Covered in the Various Datasets |
|
No. of Units in ASI Unit Level Dataset |
YEAR |
CHEM |
FBT |
LEATH |
METAL |
MTE |
TEX |
1993-94 |
2777 |
4613 |
407 |
3098 |
4575 |
3808 |
1994-95 |
3526 |
7631 |
608 |
4840 |
7048 |
5311 |
1996-97 |
3650 |
7793 |
586 |
5065 |
7055 |
5585 |
1997-98 |
1480 |
3065 |
273 |
1866 |
3055 |
2957 |
1998-99 |
1489 |
2524 |
356 |
1845 |
3064 |
2715 |
1999-00 |
1623 |
2011 |
344 |
1985 |
3803 |
2921 |
2000-01 |
2219 |
2674 |
487 |
2592 |
4711 |
3909 |
2001-02 |
2485 |
2653 |
562 |
2605 |
5024 |
4072 |
2002-03 |
2430 |
3294 |
562 |
2703 |
5110 |
4084 |
2003-04 |
3222 |
4947 |
721 |
3936 |
7083 |
5073 |
No. of Companies included from The RBI dataset |
Period |
CHEM |
FBT* |
LEATH |
METAL |
MTE |
TEX |
1993-94 to 2004-05 |
78 |
60 |
- |
47 |
116 |
53 |
No. of Units in NSS Unit Level Dataset |
Year |
CHEM |
FBT |
LEATH |
METAL |
MTE |
TEX |
1989-90 (45th Round) |
922 |
22660 |
2140 |
4809 |
1928 |
15935 |
1994-95 (51st Round) |
1547 |
22885 |
2856 |
6917 |
2688 |
42246 |
2000-01 (56th Round) |
1977 |
32172 |
2543 |
9772 |
4896 |
50891 |
Note: * This dataset did not contain any company from Tobacco industry. |
Thus, this study is a major revision of the earlier studies, in terms of
the spatial coverage, temporal coverage, sectoral coverage and application
of alternative methodologies to examine the performance of the Indian
manufacturing sector.
The input price index in this study uses input weight for input prices
based on the CSO Input-Output tables 1978-79, 1984-85, 1989-90, 1993-
94 and 1998-9919 and hence this index is expected to reflect a more
adequately the movement in input prices as compared with the indices which have been used in the other studies. The advantage of using these
weights is that these indices also include the prices of ‘infrastructure
and other services’ as well. The extension of time-period in this study
has also enabled us to increase the length of the post-reform period in
our dataset. For the period of the study, three National Industrial
Classification (NIC) codes have been used for data collection by the Annual
Survey of Industries (ASI). The details of NIC codes (1970, 1987 and
1998) of the industries covered in this study have been provided in
Annexure 4.1. It may also be noted that the necessary adjustment has
been made in the data series for period 1980-81 to 1997-98 so as to
make it comparable to the ASI data reported after 1997-98 (as these do
not include electricity, etc.).
4.2 Measurement of Variables
Output: We have used gross output/value of output as the proxy for
output, which has been converted in real terms by deflating it by the WPI
for the respective industries. For the datasets which begin in 1993-94, we
have used WPI with 1993-94 base and for the ASI industry-state level
data which begins in 1980-81, the WPI series have been spliced and
arithmetically converted to 1981-82 base.
Labour: Total persons engaged/ total employees have been used as a
measure of labour input. We did not attempt to adjust for the quality of
the labour. It may be pertinent to note that Bosworth, et al (2007) did
adjust for the quality of labour and did not find any significant contribution
of education to productivity.
Inputs: As mentioned earlier, we have included total inputs which
comprise of material, fuel and inputs of other services and we have deflated
total inputs by a weighted index of input prices, wherein, the weights of
input prices have been assigned in proportion to the value of inputs used
to the total inputs used by the respective industry. The weights have been
derived from the absorption matrices of the various input-output tables
provided by the CSO. The industry codes in the various input-output matrices which are matched with the various NIC codes are provided in
Annexure 4.2.
Capital Stock: As the stock of capital is available at the historic cost,
we have converted the same to replacement cost, wherever possible. This
has been possible in the case of datasets 1 and 3. For datasets 2 and 4,
this conversion was not possible due to the lack of data on the units’
capital stock in years prior to 1993-94. Annexure 4.3 details the procedure
for compilation of capital stock series for dataset 1 and Annexure 4.4 does
so for the dataset 3.
4.3. Methodologies Used
Productivity and efficiency levels/changes can be measured using
either parametric or non-parametric methods. In Table 4.3, we outline the
main methodologies used in the literature to measure productivity and
efficiency levels/changes. We have used some of these methodologies and these are explained in the various sub-sections in the context of the dataset
used.
Table 4.3: Methodologies Used in the Literature to Measure Productivity and Efficiency Levels/Changes |
Estimation Approach |
Method |
Main Options |
Measure |
Parametric
Estimation |
Production
function |
Cobb-Douglas, Translog, Constant Elesticity
of Substitution (CES) |
Productivity
growth
(Descriptive) |
Stochastic
Frontier |
Cobb-Douglas, Translog, with alternative
assumptions about the distribution of random
variable (Ui) that capture inefficiency |
Efficiency level
(Normative) |
Non-
parametric
methods |
Index of
Productivity |
Discrete approximations, based on the various
functional forms of production functions, such
as, Cobb-Douglas, Translog, etc. |
Productivity
change
(Descriptive) |
Malmquist index based on distance functions |
Productivity and efficiency change
(Descriptive and
Normative) |
Data Envelopment Analysis (DEA) |
Input or Output orientations, Constant/ Non-constant/Variable Returns to Scale |
Efficiency level(Normative) |
4.3.1 ASI dataset for Industry-State Levels (Organised Manufacturing
Sector)
For this dataset, we have used Equation (4.1) to estimate ‘annual’
TFPG, which have later been used to construct TFP indices. The trend
growth rates of TFP for the entire time-span of the study have been
estimated by using the semi-log trend equation as specified in equation
(4.2A). Equation (4.2B) has been used for estimating trend rates of growth
of TFP for the pre-reform and post-reform periods, respectively.
 |
The notations used in the above equations are as follows. ‘O’ and ‘N’
denote value of output and raw materials at constant (1981-82) prices. ‘L’
and ‘K’ denote labour employed and real capital stock, respectively. In
equation (4.1), ‘w’ and ‘n’ are shares of wages and inputs (excluding factor
inputs) respectively, in nominal output. ‘ln’ indicates natural logarithm,
whereas, ‘t’ denotes time. We have used notation ‘T’, wherever we have
used time an explanatory variable. The weight of capital input in equation
(4.1) has been obtained as residual, i.e., by subtracting the sum of weights
of labour and inputs from unity.
 |
We have also included multiplicative or slope dummies in equation
4.3A (4.3B) which have been obtained by multiplying the state (industry) intercept dummies with the time variable. These dummies have been
indicted by ‘T’ and multiplied to the intercept dummies. Needless to mention
that one of the states/industries is treated as benchmark state/industry
for TFPG and the multiplicative dummy for the benchmark state/industry
is dropped by the software. The coefficients of slope dummies capture the
differences in TFPG of various states/industries in relation to the
benchmark state/industry. Equation 4.3A and 4.3B have been estimated
using the panel data. Data for 15 states/6 industries for the abovementioned
variables spanning the time-period 1980-81 to 2003-04 (24
years) constituted the balanced panel for each state/industry group. The
benchmark states/industries for intercept dummies and slope dummies
are indicated in the empirical results.
 |
As regards the measure of production, gross output22 has been
preferred to value added and hence, total inputs23 have been included in
the set of inputs. Data on value of output, number of employees, value of
material inputs and net fixed capital formation, fixed capital, etc., have
been drawn from the datasets mentioned above. All these data, barring
the number of employees, are in nominal terms. Nominal output has been
converted into real output by using the Wholesale Price Index (WPI) for the
relevant industry/industry groups. WPI series with base 1970-71, 1981-
82 and 1993-94 have been used for the relevant periods. The 1970-71 and
1993-94 WPI series have been arithmetically brought to a common base
year, i.e., 1981-82. We have converted the nominal inputs series into real
input series by deflating them by the input price index series constructed
for each of the industry groups. As mentioned above, five input-output
matrices have been used to obtain the weights for inputs used by the
selected industries. These are Input-Output (Commodity X Industry)
absorption matrices. The industries in the input-output absorption matrices
which broadly correspond to the industries selected in this study have
been reported in Annexure 4.2.
As both workers and supervisory/managerial staff changes can affect
productivity, we have preferred to use the number of employees over the
number of workers, as a proxy for labour input. For the period 1998-99 to
2001-02, the data on total persons engaged has been used. The data on
emoluments and of total inputs in nominal gross output, used to calculate
‘w’ and ‘n’, respectively (see Equation 4.1), have also been drawn from the
ASI and used for estimating TFPG for dataset 1.
The National Accounts Statistics (NAS) 1990 provides estimates of
net fixed capital stock (NFCS) for the registered manufacturing sector.
Using data from NAS and from ASI, we have constructed the capital stock
series. Details of compilation of capital stock series have been provided in
Annexure 4.3.
4.2.2 ASI dataset at Unit Level (Organised Manufacturing Sector)
As mentioned earlier, this dataset does not enable us to construct
either a balanced or unbalanced panel, as it is not possible to assign
identification codes for the units surveyed in the sample sector. In view of
this, the best we could do is to estimate efficiencies derived from the
stochastic frontier production function, for each year and for each industry
and report the mean efficiency levels for the same.
For this dataset, at best we could use a Cobb-Douglas and Translog
Production Frontier using cross-sectional data and assuming a truncated
normal distribution for the non-negative random variable (Ui) that capture
inefficiency. The Translog Production Frontier functional form underlying
this model is given in Equation (4.4) and the Cobb-Douglas function
excludes the square and interaction terms for inputs from this equation.
Here ‘i’ indicates the firm and Vi are random error term with a zero mean
and constant variance and are identically and independently distributed
(iid). The estimates of efficiency have been obtained using Front 4.1 version
of the software provided by Tim Coelli24.
4.2.3 RBI dataset at Unit Level for Non-government Public Limited
Companies
This dataset enabled us to construct a balanced panel for industries
and for all manufacturing. We have estimated the standard efficiency models within the framework of data envelopment analysis (henceforth,
DEA), with the assumptions of CRS (constant returns to scale) and VRS
(variable returns to scale) for each of the industries across years and also
estimated the output-oriented Malmquist index (MIo) for estimating
productivity change. As mentioned by Coelli (1996) DEA drawing upon the
works of Debreu (1951) and Koopmans (1951), Farrell (1957) outlined the
framework for measurement of levels of efficiency in the framework of
DEA. DEA method involves construction of a piecewise linear frontier (using
the linear programming models) for the decision making units (DMUs)
from their observed input-output data. In other words, these combinations
of inputs-outputs are based on the actual data and no functional form to
the underlying relationship between inputs and outputs is assumed. Thus,
the construction of the frontier is parameter free. Efficiency of a DMU is
measured in terms of how far it is from the frontier.
DEA can be either input-orientated or output-orientated. The input-orientated
(I-O) DEA method defines the frontier by seeking the maximum
possible proportional reduction (radial measure) in input usage for a given output for each DMU. This is shown in Figure 4.1 (A). In the case of output
orientated (O-O) DEA method, it measures the maximum proportional
(radial measure) increase in output production, for the input levels (Figure
4.1 (B)). (In)efficiency of a DMU is measured in terms of how far it is from
the frontier. The efficiency is bound between 0 and 1, with DMUs which
are on the frontier, the efficiency level will be equal to 1 and those outside
(inside) it in the case of I-O (O-O) will have efficiency levels less than 1.
The I-O and O-O measures of technical efficiency yield identical results in
the case of constant returns to scale (Fare and Lovell, 1978).
 |
The assumption of CRS is valid when all DMUs operate at optimal
scale. In the situations of market imperfections and distortions, the
companies have to deviate from operating at optimal scale. Adjustments
to CRS-DEA model to incorporate VRS were suggested by Fare, Grosskopf
and Logan (1983) and Banker, Charnes and Cooper (1984)25. Technical
efficiency (TE) measured with the assumption of CRS in the event of market
imperfections and distortions is confounded by scale efficiencies (SE). In
order to remove the effect of SE from TE, the use of VRS models is adhered
to. Figure 4.2 and equations 4.5 to 4.8 illustrate how the assumptions
regarding the returns to scale can alter the measurement of TE. Figure
4.2 depicts one output and one input case. We measure output quantity
(q) on Y axis and input quantity (x) on X axis. CRS and VRS frontiers have
been indicated along with the NIRS (non-increasing returns to scale) portion
of the VRS frontier.
The input oriented technical and scale efficiencies at point ‘P’ (Figure
4.2) can be estimated as follows.
In other words, the scale efficiency can be interpreted as the ratio of
the average product of the DMU operating at point Pv to the average product
at the point of technically optimal scale of production at point R.
We have used DEAP version 2.1 of the software provided by Tim Coelli 26
for measuring efficiency levels of DMUs and have reported only the mean
efficiency levels of companies for each industry in the following chapter.
We have used equation 4.4 and also the Cobb-Douglas specification
to estimate the efficiency of various industries. The Cobb-Douglas
specification excludes the quadratic and multiplicative terms of equation
4.4. Rest of the description of methodology remains the same as specified
in section 4.2.2.
Frontiers and technical-efficiency measures can be compared across
time by means of the Malmquist index. Malmquist productivity indices
were first introduced into the literature by Caves et al27 (1982) and were
empirically applied by Fare et al (1994). They demonstrated that the Malmquist output based productivity index can be decomposed in two
components: (i) representing the reletive efficiency change index under
the constant returns to scale and measures the degree of catching up to
the best-practice frontier for each observation between time period t and
time period t+1; and (ii) representing the technical change index which
measures the shift in the frontier of technology between two time periods
evaluated at Xt and Xt+1. The Malmquist index output oriented (MIo) index
of productivity change is the geometric mean of the two output based
Malmquist TFP indices. The former ratio uses the period t technology and
the latter ratio uses period t+1 technology. The four distance functions are
calculated for MIo.
 |
A value of MIo > 1 indicates positive TFP growth from period t to
period t+1, while a value less than one indicates a TFP growth decline. We
elaborate this using Figure 4.3.
In Figure 4.3, Ro and R1 indicate the CRS frontiers for the two times
period 0 and 1, whereas, the piecewise linear frontiers depict the VRS
frontiers. The lower frontier is for time period ‘0’ and the upper frontier is
for time period 128. To measure the Malmquist index of productivity change,
we have used DEAP 2.1 version, provided by Coelli.
4.2.4 Unit level NSSO data
As seen in Section 2, the unorganised manufacturing sector is more
important in terms of generation of employment rather than in terms of
contribution to the output. There were numerous problems with capital
stock series for the unit level data for unorganised sectors and we did not
deem it appropriate to use the same. In view of this, instead of solving the
huge number of linear programming models and examining the efficiencies
of DMUs or industries, we have estimated industry-wise average labour
productivity in unorganised sector and compared the same with the
organised sector. In other words, we can view the organised sector as
providing a benchmark to the unorganised sector or vice versa.
5. Productivity and Efficiency of Indian Manufacturing
Sector
This chapter details the estimates of TFP levels, TFPG and efficiency
of the Indian manufacturing sector. In section 5.1, we present the empirical
results pertaining to the organised manufacturing sector (industry and
state levels), using the both growth accounting and production function
approaches. The estimates presented in section 5.1 are for the period
1980-81 to 2007-08 for aggregate manufacturing sector and for the period
1980-81 to 2003-04 for ASI data at industry and state levels. In section
5.2, we present the efficiency estimates using the Stochastic Frontier
Production Function (SFPF) (TL specification) for the ASI unit level data.
The estimates presented in section 5.2 are for the period 1993-94 to 2003-
04 (barring the year 1995-96, due to the non-availability of data). Section
5.3.provides estimates of efficiency for the Public Ltd. Companies (RBI
dataset), using Data Envelopment Analysis (DEA), Stochastic Frontier
Production Function with both Cobb-Douglas and Translog specifications
and also productivity growth estimates using the Malmquist Index. The
estimates of productivity and efficiency provided in this section are for
the period 1993-94 to 2004-05. Section 5.4 provides estimates of labour
productivity for the unorganised manufacturing sector based on the NSSO
unit level data. This dataset is available for 1989-90, 1994-95, 2000-01.
A comparison of labour productivity between organised and unorganised
sectors has also been provided in section 5.4.
5.1 Total Factor Productivity in India’s Organised29 Manufacturing
Sector (MFG)
5.1.1 Growth Accounting Estimates of TFPG: Trends
In this sub-section, we first discuss the trend growth rates of TFP
obtained using the growth accounting framework (i.e., discrete approximation of the translog production function). As mentioned earlier, opinions have
differed over the inclusion of the year 1991-92 as a post or pre-reform year.
In view of this, we have estimated productivity with two alternative pre and
post reform periods. These are classification 1 with pre-reform period as
(1A): 1980-81 to 1990-91 and post-reform Period (1B): 1991-92 to 2003-04
and classification 2 with pre-reform period as (2A): 1980-81 to 1991-92 and
post-reform Period (2B): 1992-93 to 2003-04. This is to ensure that the choice
of cut-off period does not vitiate the comparison of TFPG during the pre and
post reform period. We also highlight the annual variations in TFPG which
allow us to view the fluctuations in TFPG over the span of the study.
Figure 5.1 gives a synoptic view of the TFP index and TFPG for the
organised manufacturing sector. It can be seen from Figure 5.1 that the TFP
index registered a rise in the eighties, though the rate of increase decelerated
as we approached the end of the eighties. Despite the fluctuations in TFPG
during the eighties, the trend rate of TFPG (refer section 5.1.2 for more
details) is about 2 pcpa during the pre-reform period irrespective of which of
the two alternative cut-off periods are considered. The deceleration in TFPG
during the post-reform period, especially in the nineties, is very much evident. The TFPG estimates for the post-reform period with the two cut-off periods
are 0.98 and 1.05.30 The deceleration in TFPG during the post-reform period
is statistically significant. However, one of the redeeming features of the
growth of manufacturing sector in India is that in the post-nineties, TFP
index seems to be on an ascent, as can be seen from Figure 5.1.
 |
Table 5.1 provides the trend rates of TFPG across the various industries.
It can be seen from Table 5.1 that TFPG for the manufacturing sector
for the period 1980-81 to 2007-08 works out to be about 0.99 pcpa. As
regards the rate of change of TFP (TFPG) for MFG as a whole, it witnessed
deceleration during the post-reform period.
For the period 1980-81 to 2003-04, CHEM and MTE industries
witnessed TFPG which were much higher than those witnessed by other
industries. As against it, FBT and TEX industries were responsible for
pulling down the TFPG of MFG as a whole. TFPG of METAL industry was
comparable to that witnessed by the MFG.
Both CHEM and TEX industries witnessed deceleration in TFPG.
The only shining industry seems to be the METAL industry during the post-reform period, which seems to be confined to the nineties alone. The
7 other industries, viz., FBT, MTE and LEATH did not witness either
acceleration or deceleration in TFPG during the post-reform period.
Table 5.1: Trend Rates of Growth of TFPG (pcpa): Industry-wise |
State |
Entire
time-span
of the study |
Pre-reform |
Post-reform |
Acceleration (A)/
Deceleration (D)
or
absence
thereof (-)
in
TFPG |
Period IA |
Period 2A |
Period IB |
Period 2B |
IB over IA |
2B over 2A |
CHEM(a) |
1.35 |
3.45 |
3.37 |
-0.23 |
-0.24 |
D * |
D * |
FBT(a) |
-0.41 |
0.55 |
0.07 |
0.01 |
-0.06 |
– |
– |
LEATH(a) |
1.17 |
0.78 |
0.76 |
1.18 |
1.03 |
– |
– |
METAL(a) |
1.08 |
-0.03 |
0.04 |
1.63 |
1.54 |
A * |
A * |
MTE(a) |
1.47 |
2.10 |
1.95 |
1.46 |
1.50 |
– |
– |
TEX(a) |
0.69 |
2.47 |
2.32 |
-0.03 |
0.12 |
D * |
D * |
MFG(a) |
0.92 |
2.07 |
1.88 |
0.61 |
0.66 |
D * |
D * |
MFG(b) |
0.99 |
2.07 |
1.88 |
0.98 |
1.05 |
D * |
D * |
Note: ‘**’, ‘*’ and @ indicate significance at 1 per cent, 5 per cent and 10 per cent levels, respectively.
(a) indicates estimates up to 2003-04 and (b) indicates estimates up to 2007-08. |
 |
In Figure 5.2, we provide a synoptic view of the yearly movements
of TFP index across the selected industries. We can see from Figure 5.2
that during the post-nineties, most of the industries, barring FBT have
demonstrated rising TFP levels. In other words, by and large the Indian
manufacturing sector productivity performance seems to have improved
in the recent years in comparison to that witnessed during the nineties.
Table 5.2 provides the trend rates of growth of TFPG for the
manufacturing sector across the various states of India.
It can be seen from Table 5.2 that, the TFPG has decelerated for the
total manufacturing sector as well as for most of the states. In the case of
AP, MP*, Orissa and Rajasthan neither acceleration nor deceleration in
TFPG was witnessed, irrespective of the cut-off year for pre and postreform
period. Incidentally these states, barring AP, are not the major contributors to the output of organised manufacturing sector in India.
Haryana and West Bengal have demonstrated acceleration in TFPG.
Acceleration in TFPG in Haryana is sensitive to cut-off year for the reform
period, whereas, for West Bengal, acceleration is more robust. In brief,
the TFPG in three major states of India, viz, Gujarat, Maharashtra and
Tamil Nadu was either below or equal to the TFPG for India as a whole
and these states witnessed deceleration in TFPG. Only West Bengal, which
ranked high in terms of output in 1980-81, witnessed acceleration in
TFPG. However, this is only a partial view of the industrial performance,
as will be explained shortly.
Table 5.2: Trend Rates of Growth of TFPG (pcpa) in Total MFG: State-wise |
State |
1980-81
to
2003-04 |
Pre-reform |
Post-reform$ |
Acceleration (A)/
Deceleration (D)
or
absence
thereof (-)
in TFPG |
Period IA |
Period 2A |
Period IB |
Period 2B |
IB over IA |
2B over 2A |
AP |
1.41 |
1.82 |
1.67 |
1.69 |
1.76 |
– |
– |
BIH* |
1.55 |
3.32 |
3.01 |
1.55 |
1.78 |
D ** |
D @ |
DEL |
1.04 |
1.89 |
1.84 |
0.93 |
1.12 |
D @ |
D # |
GUJ |
0.78 |
1.59 |
1.41 |
0.81 |
0.88 |
D @ |
D # |
HAR |
1.33 |
1.96 |
1.83 |
1.94 |
2.24 |
– |
A # |
KAR |
1.05 |
2.51 |
2.66 |
-0.28 |
-0.13 |
D * |
D * |
KER |
1.36 |
2.35 |
2.33 |
1.18 |
1.42 |
D * |
D * |
MAH |
0.88 |
1.95 |
1.67 |
0.76 |
0.75 |
D * |
D ** |
MP* |
1.37 |
1.56 |
1.39 |
1.76 |
1.78 |
– |
– |
ORI |
1.27 |
2.16 |
1.99 |
1.97 |
2.36 |
– |
– |
PUN |
0.79 |
1.72 |
1.60 |
0.59 |
0.69 |
D ** |
D ** |
RAJ |
1.43 |
2.06 |
1.87 |
1.86 |
2.01 |
– |
– |
TN |
0.65 |
1.90 |
1.73 |
0.63 |
0.85 |
D * |
D ** |
UP* |
1.14 |
2.25 |
2.12 |
1.00 |
1.17 |
D ** |
D ** |
WB |
1.05 |
1.12 |
0.91 |
2.07 |
2.25 |
A ** |
A * |
All India |
0.92 |
2.07 |
1.88 |
0.61 |
0.66 |
D * |
D * |
Note: ‘*’, ‘**’, ‘@’ and ‘#’as suffixes to A and D indicate significance at 1%, 5%, 10%, and 20% levels,
respectively. $ indicates that estimates are up to 2003-04. |
In what follows, we present the industry-wise TFPG (Table 5.3 to
Table 5.8) for each of the selected states of India. We provide the estimates
of TFPG for both pre and post-reform periods. It may also be noted that TFPG at industry levels in a few states could not be estimated due to data
problems in capital stock series31.
As mentioned earlier, Gujarat and Maharashtra are the two major
states which account for more than half of the national output of chemical
industry. Both these states witnessed TFPG (see Table 5.3) which was
lower than the TFPG for this industry for the country as a whole. Moreover,
both these states as well as many other states witnessed deceleration in
TFPG in this industry. Kerala, West Bengal and Orissa witnessed neither
acceleration nor deceleration in TFPG. However, in the former two states
this industry has the least presence, whereas, in West Bengal this industry
has a good presence.
As can be seen from Table 5.4, FBT industry witnessed negative
trend rate of TFPG over the entire span of the study and did not witness any acceleration or deceleration in TFPG during the post-reform period
over the pre-reform period, irrespective of the two cut-off periods chosen.
Maharashtra, UP*, AP, Gujarat and Tamil Nadu are the states in which
FBT industry has the maximum presence. In other states as well, FBT
industry has not witnessed acceleration in TFPG in the post-reform period.
It can be recalled that this is one of the industries which accounts for
about 21 per cent of employment in organised manufacturing industry. A
fall in productivity level in FBT industry coupled with no improvement in
growth of productivity in this industry during the post-reform period should
certainly be a cause of concern for policy makers.
Table 5.3: Trend Rates of TFPG (pcpa) in Chemicals (CHEM) Industry |
State |
1980-81 to 2003-04 |
Pre-reform |
Post-reform$ |
Acceleration (A)/
Deceleration (D)
or absence
thereof (-) in TFPG |
Period IA |
Period 2A |
Period IB |
Period 2B |
IB over IA |
2B over 2A |
AP |
1.75 |
3.66 |
4.10 |
-0.98 |
-0.90 |
D * |
D * |
BIH* |
2.06 |
5.04 |
5.74 |
-1.53 |
-1.15 |
D * |
D * |
DEL |
1.12 |
2.19 |
2.69 |
0.23 |
0.84 |
D # |
D # |
GUJ |
1.26 |
3.30 |
2.98 |
-0.16 |
-0.38 |
D * |
D * |
HAR |
2.06 |
6.22 |
5.63 |
0.98 |
1.28 |
D * |
D * |
KAR |
1.56 |
3.44 |
3.56 |
0.22 |
0.47 |
D * |
D * |
KER |
0.06 |
-0.31 |
0.01 |
-0.91 |
-1.02 |
– |
– |
MAH |
1.15 |
3.71 |
3.43 |
-1.13 |
-1.47 |
D * |
D * |
MP* |
2.24 |
3.77 |
4.42 |
-0.33 |
-0.07 |
D * |
D * |
ORI |
-0.004 |
-1.03 |
-0.69 |
0.39 |
0.60 |
– |
– |
RAJ |
2.05 |
2.27 |
2.13 |
1.93 |
1.80 |
D * |
– |
TN |
0.30 |
1.89 |
1.83 |
-0.16 |
0.14 |
D * |
D * |
UP* |
0.58 |
3.99 |
4.26 |
-1.82 |
-1.32 |
D * |
D * |
WB |
2.50 |
2.63 |
2.83 |
2.96 |
3.43 |
– |
– |
All India |
1.35 |
3.45 |
3.37 |
-0.23 |
-0.24 |
D * |
D * |
Note: ‘*’, ‘**’, ‘@’ and ‘#’as suffixes to A and D indicate significance at 1 %, 5 %, 10 %, and 20 % levels,
respectively. $ indicates that estimates are up to 2003-04. |
Table 5.4: Trend Rates of TFPG (pcpa) in Food, Beverages and
Tobacco (FBT) Industry |
State |
1980-81
to
2003-04 |
Pre-reform |
Post-reform $ |
Acceleration (A)/
Deceleration (D)
or
absence
thereof (-)
in TFPG |
Period IA |
Period 2A |
Period IB |
Period 2B |
IB over IA |
2B over 2A |
AP |
0.14 |
2.14 |
1.75 |
-0.55 |
-0.60 |
D ** |
D ** |
BIH* |
0.19 |
2.44 |
2.32 |
-1.02 |
-0.86 |
D ** |
D ** |
DEL |
0.09 |
1.00 |
0.40 |
0.43 |
0.16 |
– |
– |
GUJ |
0.36 |
1.75 |
1.21 |
0.65 |
0.61 |
D # |
– |
HAR |
-0.13 |
2.52 |
1.78 |
-0.57 |
-0.70 |
D * |
D ** |
KAR |
0.60 |
1.88 |
1.46 |
0.55 |
0.47 |
– |
– |
KER |
0.12 |
2.76 |
2.22 |
-0.40 |
-0.34 |
D * |
D * |
MAH |
0.75 |
4.12 |
3.11 |
-0.54 |
-1.06 |
D * |
D ** |
MP* |
-0.42 |
2.19 |
1.62 |
-1.08 |
-1.11 |
D * |
D ** |
ORI |
-0.03 |
1.51 |
1.03 |
-0.24 |
-0.36 |
D # |
D # |
PUN |
0.82 |
1.29 |
0.86 |
0.85 |
0.52 |
D # |
– |
RAJ |
0.51 |
1.78 |
1.40 |
0.47 |
0.44 |
D # |
– |
TN |
1.26 |
3.34 |
2.80 |
0.66 |
0.51 |
D * |
D ** |
UP* |
0.52 |
2.27 |
1.57 |
0.67 |
0.51 |
D # |
– |
WB |
0.28 |
2.06 |
1.42 |
0.55 |
0.52 |
D # |
– |
All India |
-0.41 |
0.55 |
0.07 |
0.01 |
-0.06 |
– |
– |
Note: ‘*’, ‘**’, ‘@’ and ‘#’as suffixes to A and D indicate significance at 1 %, 5 per cent, 10 % , and 20
% levels, respectively. $ indicates that estimates are up to 2003-04. |
Tamil Nadu, UP* and West Bengal are the three major states which
account for most of leather production in India. The TFPG of this industry
is above that witnessed by the manufacturing sector as a whole (Table 5.1). Though the TFPG rates for leather industry (Table 5.5) during the
post-reform period are slightly higher than those witnessed in the prereform
period, the acceleration is not statistically significant. West Bengal
did witness acceleration in TFPG over the post-reform period, but this
was not the case with other two states. Also TFPG in the two other states
was much below that witnessed by West Bengal, if the entire time-span
of the study is considered.
TFPG of metal industry was higher than the TFPG for the
manufacturing sector as a whole (Table 5.1). Moreover, TFPG for metal
industry accelerated during the post-reform period, unlike the other
industries. The dominating states in the case of metal industry are:
Maharashtra, West Bengal, Bihar* and MP*. Maharashtra displays almost
absence of growth of TFP in metal industry. The acceleration/deceleration
of TFPG is sensitive to the choice of the cut-off year for reform and is
significant only at 20 per cent level. Hence, Maharashtra does not account
for acceleration of TFPG of metal industry. Bihar* also did not show either
acceleration/deceleration in TFPG in metal industry, though it certainly
accounts for better overall performance of TFPG of metal industry. MP* and West Bengal witnessed TFPG in metal industry close to the national
rate and also the acceleration of TFPG in these two states in the postreform
period was statistically significant.
Table 5.5: Trend Rates of TFPG in Leather (LEATH) Industry |
State |
1980-81
to
2003-04 |
Pre-reform |
Post-reform$ |
Acceleration (A)/
Deceleration (D)
or absence
thereof (-) in TFPG |
Period IA |
Period 2A |
Period IB |
Period 2B |
IB over IA |
2B over 2A |
AP |
1.38 |
1.13 |
1.10 |
1.52 |
1.46 |
– |
– |
DEL |
-0.86 |
-1.23 |
-0.64 |
-1.73 |
-1.50 |
– |
– |
HAR |
1.55 |
0.15 |
0.11 |
2.81 |
2.77 |
A ** |
A ** |
KAR |
0.46 |
2.42 |
1.84 |
-0.05 |
-0.26 |
D # |
D # |
MAH |
0.91 |
2.39 |
2.15 |
0.58 |
0.67 |
D # |
– |
MP* |
1.44 |
1.69 |
1.39 |
1.66 |
1.48 |
– |
– |
ORI |
-0.004 |
-1.03 |
-0.69 |
0.39 |
0.60 |
– |
– |
TN |
1.20 |
0.72 |
0.79 |
0.91 |
0.72 |
– |
– |
UP* |
0.71 |
1.01 |
0.84 |
0.51 |
0.32 |
– |
– |
WB |
1.68 |
-0.26 |
-0.37 |
2.68 |
2.27 |
A ** |
A ** |
All India |
1.17 |
0.78 |
0.76 |
1.18 |
1.03 |
– |
– |
Note: ‘*’, ‘**’, ‘@’ and ‘#’as suffixes to A and D indicate significance at 1 %, 5 %, 10 %, and 20 % levels,
respectively. $ indicates that estimates are up to 2003-04. |
Table 5.6: Trend Rates of TFPG in Metals (METAL) Industry |
State |
1980-81
to
2003-04 |
Pre-reform |
Post-reform$ |
Acceleration (A)/
Deceleration (D)
or
absence
thereof (-)
in TFPG |
Period IA |
Period 2A |
Period IB |
Period 2B |
IB over IA |
2B over 2A |
BIH* |
1.94 |
1.92 |
1.82 |
2.15 |
2.11 |
– |
– |
DEL |
1.04 |
-0.67 |
-0.31 |
1.66 |
1.74 |
D * |
A * |
GUJ |
-0.51 |
0.17 |
0.83 |
-1.96 |
-1.53 |
D @ |
D @ |
HAR |
0.47 |
-0.74 |
-0.52 |
0.92 |
0.94 |
D * |
A * |
KAR |
0.55 |
0.79 |
1.07 |
-0.51 |
-0.51 |
D # |
D @ |
KER |
1.51 |
-0.12 |
0.40 |
1.98 |
2.22 |
D ** |
A ** |
MAH |
0.15 |
-0.33 |
-0.64 |
0.39 |
-0.05 |
A # |
D # |
MP* |
1.06 |
-0.74 |
-1.04 |
1.80 |
1.11 |
A ** |
A ** |
ORI |
6.52 |
1.34 |
1.44 |
15.70 |
17.59 |
A * |
A * |
PUN |
0.83 |
-0.67 |
-0.44 |
1.42 |
1.42 |
A * |
A * |
RAJ |
1.23 |
-0.42 |
-0.27 |
2.59 |
2.71 |
A * |
A * |
TN |
0.99 |
0.18 |
0.52 |
1.07 |
1.23 |
A ** |
A # |
UP* |
0.42 |
0.21 |
0.46 |
0.90 |
1.31 |
A # |
A @ |
WB |
1.00 |
-1.90 |
-1.45 |
2.54 |
2.67 |
A * |
A * |
All India |
1.08 |
-0.03 |
0.04 |
1.63 |
1.54 |
A * |
A * |
Note: ‘*’, ‘**’, ‘@’ and ‘#’as suffixes to A and D indicate significance at 1 %, 5 %, 10 %, and 20 % levels,
respectively. $ indicates that estimates are up to 2003-04. |
MTE industry demonstrated a better performance in terms of TFPG
as compared to the other industries (Table 5.1). This industry did not
witness any significant acceleration/deceleration in TFPG during the postreform
period. Machinery and transport equipment industry is located
mainly in Maharashtra, Tamil Nadu, Haryana, UP*, Karnataka and
Gujarat. Maharashtra and UP* have witnessed significant deceleration in
TFPG in MTE industry during the post-reform period. In contrast, Haryana
has witnessed acceleration in TFPG in MTE industry. Karnataka and Tamil
Nadu did not witness either acceleration or deceleration in TFPG of MTE
industry. Gujarat also did not witness acceleration in TFPG. In fact it
witnessed deceleration in TFPG, if we include the year 1991-92 in the
post-reform period.
Table 5.7: Trend Rates of TFPG in Machinery and Transport Equipment
(MTE) Industry |
State |
1980-81 to
2003-04 |
Pre-reform |
Post-reform$ |
Acceleration (A)/
Deceleration (D)
or absence
thereof (-) in TFPG |
Period IA |
Period 2A |
Period IB |
Period 2B |
IB over IA |
2B over 2A |
AP |
1.74 |
1.61 |
1.43 |
2.33 |
2.30 |
– |
– |
BIH |
1.39 |
3.30 |
2.74 |
1.59 |
1.66 |
D @ |
– |
DEL |
1.20 |
1.85 |
1.70 |
1.26 |
1.33 |
A # |
– |
GUJ |
1.27 |
1.93 |
1.75 |
1.42 |
1.50 |
D # |
– |
HAR |
1.26 |
1.13 |
1.12 |
1.92 |
2.13 |
A # |
A @ |
KAR |
1.34 |
1.52 |
1.60 |
1.35 |
1.51 |
– |
– |
KER |
0.55 |
-0.24 |
-1.43 |
2.36 |
1.35 |
– |
– |
MAH |
0.98 |
1.71 |
1.60 |
0.26 |
0.09 |
D * |
D * |
MP |
1.32 |
2.96 |
2.66 |
1.32 |
1.53 |
D ** |
D # |
ORI |
0.24 |
1.15 |
0.77 |
0.11 |
-0.07 |
D # |
D # |
PUN |
1.38 |
1.29 |
1.33 |
1.54 |
1.63 |
– |
– |
RAJ |
1.85 |
2.04 |
2.10 |
1.89 |
2.04 |
– |
– |
TN |
1.48 |
2.00 |
1.77 |
1.86 |
1.92 |
– |
– |
UP |
1.51 |
2.89 |
2.81 |
0.23 |
0.10 |
D * |
D * |
WB |
1.37 |
2.09 |
1.94 |
1.41 |
1.50 |
D # |
– |
All India |
1.47 |
2.10 |
1.95 |
1.46 |
1.50 |
– |
– |
Note: ‘*’, ‘**’, ‘@’ and ‘#’as suffixes to A and D indicate significance at 1 %, 5 %, 10 % , and 20 % levels,
respectively. $ indicates that estimates are up to 2003-04. |
Textiles industry is one of the major contributors to employment in
manufacturing sector. TFPG has been rather low and has also decelerated
in this industry (See Table 5.8) during the post-reform period, similar to
what has happened to FBT industry. Tamil Nadu, Maharashtra, Gujarat,
Punjab and Rajasthan have been the leading states in textiles industry.
Except for Rajasthan, TFPG in each of these states has been lower as
compared to that witnessed for the country as a whole. Moreover, all
these states including Rajasthan have witnessed deceleration in TFPG
during the post-reform period.
5.1.2 Growth Accounting Estimates of TFPG: Time-Path and Volatility
We now discuss the time paths of TFP indices, as these enable us to
observe the TFPG movements across states and industries in a less rigid
framework than in the framework of pre and post-reform period. In Figures 5.3 to 5.17, we present the annual time-path of TFP indices for the 15 selected
states of India32. The thin black smoothened curve is the fitted trend (of
polynomials of not more than 4th order) for the TFP indices. It can be seen
from these Figures that in most of the states (barring Kerala, MP* and
Rajasthan, wherein trend growth rate of TFP seems to be constant over
the entire span of the study), the curvature of the trend curve is sharper
in the post-nineties than during the nineties which is indicative of the
fact that the TFP has been rising at a higher rate during the recent years
as compared with that witnessed during the decade of the nineties.
Table 5.8: Trend Rates of TFPG in Textiles (TEX) Industry |
State |
1980-81
to
2003-04 |
Pre-reform |
Post-reform$ |
Acceleration (A)/
Deceleration (D)
or
absence
thereof (-)
in TFPG |
Period IA |
Period 2A |
Period IB |
Period 2B |
IB over IA |
2B over 2A |
AP |
0.54 |
2.39 |
2.14 |
-0.06 |
0.04 |
D * |
D * |
BIH* |
0.62 |
1.51 |
1.45 |
0.33 |
0.46 |
– |
– |
DEL |
0.45 |
3.51 |
3.37 |
-1.48 |
-1.37 |
D * |
D * |
GUJ |
0.62 |
1.94 |
1.69 |
0.48 |
0.58 |
D ** |
D @ |
HAR |
1.09 |
0.88 |
0.95 |
1.46 |
1.61 |
A # |
A @ |
KAR |
1.18 |
2.39 |
2.59 |
-0.46 |
-0.45 |
D * |
D * |
KER |
0.70 |
2.47 |
2.14 |
0.78 |
1.04 |
D * |
D @ |
MAH |
0.57 |
2.81 |
2.70 |
-0.41 |
-0.16 |
D * |
D * |
MP* |
0.83 |
1.95 |
1.97 |
0.41 |
0.65 |
D ** |
D @ |
ORI |
4.25 |
3.83 |
3.48 |
8.64 |
9.81 |
A @ |
A * |
PUN |
0.58 |
2.06 |
2.16 |
0.20 |
0.68 |
D ** |
D ** |
RAJ |
0.76 |
2.00 |
2.43 |
-0.59 |
-0.19 |
D * |
D * |
TN |
1.02 |
2.61 |
2.53 |
0.47 |
0.70 |
D * |
D * |
UP* |
0.60 |
1.78 |
1.71 |
-0.07 |
-0.01 |
D * |
D * |
WB |
1.41 |
2.00 |
1.92 |
1.53 |
1.68 |
– |
– |
All India |
0.69 |
2.47 |
2.32 |
-0.03 |
0.12 |
D * |
D * |
Note: ‘*’, ‘**’, ‘@’ and ‘#’as suffixes to A and D indicate significance at 1 %, 5 %, 10 %, and 20 % levels,
respectively. $ indicates that estimates are up to 2003-04. |
In Table 5.9A, we provide the average of annual growth rates of TFP
for the various time-periods and also the coefficient of variation (CV) as a
measure of volatility of TFPG for the various industries.
Table 5.9A: Quinquennial Average TFPG and Volatility in TFPG: Industry-wise |
Period |
Industries |
CHEM |
FBT |
LEATH |
METAL |
MTE |
TEX |
MFG |
1981-82 to 1984-85 |
5.04 |
8.53 |
5.85 |
-0.63 |
4.20 |
1.92 |
4.32 |
1985-86 to 1989-90 |
2.99 |
-3.37 |
-0.83 |
0.67 |
1.27 |
2.52 |
1.19 |
1990-91 to 1994-95 |
0.64 |
-0.18 |
1.57 |
1.10 |
0.92 |
1.11 |
0.27 |
1995-96 to 1999-2000 |
-0.44 |
-0.97 |
0.48 |
2.15 |
1.40 |
-1.40 |
0.24 |
2000-01 to 2003-04 |
2.03 |
0.64 |
2.94 |
1.29 |
2.82 |
1.53 |
2.21 |
AVG ANNUAL TFPG$ |
1.92 |
0.61 |
1.79 |
0.97 |
2.00 |
1.09 |
1.50 |
TREND TFPG$ |
1.35 |
-0.41 |
1.17 |
1.08 |
1.47 |
0.69 |
0.92 |
CV for ANNUAL TFPG$ |
1.80 |
8.34 |
3.03 |
3.40 |
1.55 |
3.80 |
1.58 |
Notes: (i) $ indicates that estimates are up to 2003-04.
(ii) TFPG is measured in terms of pcpa. |
It can be seen from Table 5.9A that except for metal industry, all
the component industries, as well as the manufacturing sector have
witnessed a revival of TFPG in the present decade. This is a welcome
feature of the Indian manufacturing sector, though much more is needed in terms of its overall performance. As can be seen from Table 5.9, FBT
and TEX are the industries with maximum fluctuations in TFPG, besides
being the lowest TFPG performers.
Table 5.9B: Quinquennial Average TFPG and Volatility in TFPG: State-wise |
States/
Country |
1981-82
to
1984-85 |
1985-86
to
1989-90 |
1990-91
to
1994-95 |
1995-96
to
1999-2000 |
2000-01
to
2003-04 |
AVG
ANNUAL
TFPG$ |
TREND
TFPG$ |
CV for
ANNUAL
TFPG$ |
AP |
5.51 |
0.15 |
1.38 |
0.90 |
3.24 |
2.05 |
1.41 |
1.70 |
BIH* |
7.33 |
2.17 |
-1.38 |
4.10 |
1.75 |
2.64 |
1.55 |
1.32 |
DEL |
4.46 |
0.28 |
2.12 |
0.62 |
1.77 |
1.74 |
1.04 |
1.38 |
GUJ |
4.18 |
0.20 |
0.20 |
0.81 |
1.86 |
1.32 |
0.78 |
3.15 |
HAR |
4.37 |
0.88 |
0.23 |
1.92 |
3.14 |
1.97 |
1.33 |
2.30 |
KAR |
4.15 |
2.03 |
1.30 |
-2.08 |
3.96 |
1.68 |
1.05 |
2.08 |
KER |
4.28 |
2.32 |
-0.87 |
1.69 |
1.87 |
1.75 |
1.36 |
1.52 |
MAH |
4.00 |
0.96 |
0.40 |
0.50 |
1.95 |
1.44 |
0.88 |
2.82 |
MP* |
2.40 |
1.54 |
0.22 |
2.40 |
1.47 |
1.58 |
1.37 |
2.23 |
ORI |
1.52 |
3.22 |
-1.37 |
3.06 |
2.76 |
1.81 |
1.27 |
2.40 |
PUN |
4.01 |
1.09 |
0.37 |
-0.74 |
3.00 |
1.38 |
0.79 |
1.91 |
RAJ |
4.70 |
1.10 |
0.92 |
2.25 |
1.29 |
1.97 |
1.43 |
1.92 |
TN |
3.96 |
0.90 |
-1.01 |
0.99 |
1.84 |
1.20 |
0.65 |
5.11 |
UP* |
3.52 |
1.53 |
0.23 |
0.40 |
2.37 |
1.49 |
1.14 |
2.77 |
WB |
3.21 |
-0.39 |
0.63 |
1.81 |
3.46 |
1.60 |
1.05 |
1.79 |
All India |
4.32 |
1.19 |
0.27 |
0.24 |
2.21 |
1.50 |
0.92 |
1.58 |
Notes: (i) $ indicates that estimates are up to 2003-04.
(ii) TFPG is measured in terms of pcpa. |
A perusal of Table 5.9B also indicates the revival of TFPG during
2000-01 to 2003-04 for all India as well as for most of the states barring
Bihar*, MP*, Orissa and Rajasthan.
5.1.3 Production Function Estimates of TFP and TFPG: 1980-81 to
2003-04
We have also used the Cobb-Douglas (CD) production function
approach for measurement of TFPG and the inter-state differences in TFP
levels and growth rates (see section 4.2.1 for methodological details). These
results have been presented in Tables 5.10 to 5.16. The empirical results
pertaining to contribution of inputs (L, K and N), productivity (captured
by the coefficient of time variable), inter-state differences in TFP levels
and TFPG have been provided in these tables. The estimates of TFPG
across states and the TFPG over the reform period are given in Table
5.17.
The main observations that emerge from the scrutiny of Table 5.10
to 5.17 are as follows:
-
Output growth in the Indian manufacturing sector has been
resource intensive, as can be seen from the coefficients of N,
across industries and also for the manufacturing sector as well.
-
The policy environment, per se, does not emerge as enabling
factor leading to a shift to a higher growth path for the organised
manufacuturing sector. Contribution of inputs seems to be the
main causal factor behind manufacturing exapansion. However,
for METAL and LEATH industries, the coefficient of policy
dummy is positive and statistically significant, whereas, for
CHEM, FBT and TEX, this coefficient is statistically not
significant and hence, its sign and magnitude do not matter.
Table 5.10 Production Function Estimates for MFG$ |
lnQ |
Coefficient |
t-statistic |
Significance level |
Constant |
1.0730 |
3.06 |
0.00 |
ln L |
0.0560 |
1.91 |
0.06 |
ln K |
0.0303 |
2.95 |
0.00 |
ln N |
0.8565 |
43.33 |
0.00 |
t |
0.0181 |
11.44 |
0.00 |
Dpolicy |
-0.0513 |
-6.04 |
0.00 |
DAP |
-0.1683 |
-5.39 |
0.00 |
DBIH* |
-0.1042 |
-2.95 |
0.00 |
DDEL |
-0.1964 |
-3.23 |
0.00 |
DGUJ |
-0.1216 |
-4.69 |
0.00 |
DHAR |
-0.1600 |
-3.01 |
0.00 |
DKAR |
-0.1000 |
-2.42 |
0.02 |
DKER |
-0.1555 |
-3.22 |
0.00 |
DMAH |
(dropped) |
DMP* |
-0.1157 |
-2.81 |
0.01 |
DORI |
-0.2124 |
-3.43 |
0.00 |
DPUN |
-0.1559 |
-3.25 |
0.00 |
DRAJ |
-0.1864 |
-3.39 |
0.00 |
DTN |
-0.0705 |
-2.82 |
0.01 |
DUP* |
-0.1558 |
-5.76 |
0.00 |
DWB |
-0.0823 |
-3.27 |
0.00 |
DAPT |
0.0008 |
0.53 |
0.60 |
DBIH*T |
0.0001 |
0.07 |
0.94 |
DDELT |
(dropped) |
DGUJT |
0.0011 |
0.77 |
0.44 |
DHART |
0.0007 |
0.43 |
0.67 |
DKART |
-0.0011 |
-0.70 |
0.48 |
DKERT |
-0.0028 |
-1.89 |
0.06 |
DMAHT |
-0.0016 |
-1.16 |
0.25 |
DMP*T |
-0.0015 |
-1.02 |
0.31 |
DORIT |
0.0000 |
0.01 |
0.99 |
DPUNT |
-0.0018 |
-1.13 |
0.26 |
DRAJT |
0.0006 |
0.42 |
0.68 |
DTNT |
-0.0026 |
-1.70 |
0.09 |
DUP*T |
0.0001 |
0.07 |
0.94 |
DWBT |
-0.0055 |
-3.67 |
0.00 |
Note: $ indicates that estimates are up to 2003-04. |
Table 5.11 Production Function Estimates for CHEM$ |
lnQ |
Coefficient |
t-statistic |
Significance level |
Constant |
0.5432 |
1.16 |
0.25 |
ln L |
0.0793 |
1.11 |
0.27 |
ln K |
-0.1543 |
-6.60 |
0.00 |
ln N |
1.0079 |
21.38 |
0.00 |
t |
0.0187 |
3.54 |
0.00 |
Dpolicy |
0.0156 |
0.52 |
0.60 |
DAP |
0.3152 |
2.65 |
0.01 |
DBIH* |
0.5570 |
4.77 |
0.00 |
DDEL |
(dropped) |
DGUJ |
0.5048 |
3.18 |
0.00 |
DHAR |
0.4476 |
4.64 |
0.00 |
DKAR |
0.2953 |
3.05 |
0.00 |
DKER |
0.2489 |
2.60 |
0.01 |
DMAH |
0.4305 |
2.36 |
0.02 |
DMP* |
0.2437 |
2.50 |
0.01 |
DORI |
0.0578 |
0.69 |
0.49 |
DRAJ |
0.1480 |
1.72 |
0.09 |
DTN |
0.2618 |
1.70 |
0.09 |
DUP* |
0.2580 |
2.22 |
0.03 |
DWB |
0.3453 |
2.49 |
0.01 |
DAPT |
(dropped) |
DBIH*T |
-0.0512 |
-7.43 |
0.00 |
DDELT |
-0.0076 |
-1.35 |
0.18 |
DGUJT |
-0.0013 |
-0.24 |
0.81 |
DHART |
-0.0200 |
-3.53 |
0.00 |
DKART |
-0.0050 |
-0.91 |
0.36 |
DKERT |
-0.0177 |
-3.15 |
0.00 |
DMAHT |
-0.0027 |
-0.47 |
0.64 |
DMP*T |
-0.0035 |
-0.64 |
0.52 |
DORIT |
-0.0110 |
-1.96 |
0.05 |
DRAJT |
0.0073 |
1.30 |
0.20 |
DTNT |
0.0004 |
0.06 |
0.95 |
DUP*T |
0.0054 |
0.97 |
0.33 |
DWBT |
-0.0113 |
-1.83 |
0.07 |
Note: $ indicates that estimates are up to 2003-04. |
Table 5.12 Production Function Estimates for FBT$ |
lnQ |
Coefficient |
t-statistic |
Significance level |
Constant |
2.6829 |
8.84 |
0.00 |
ln L |
0.0143 |
0.74 |
0.46 |
ln K |
-0.0321 |
-1.68 |
0.09 |
ln N |
0.7183 |
21.39 |
0.00 |
t |
0.0162 |
5.01 |
0.00 |
Dpolicy |
-0.0117 |
-0.59 |
0.56 |
DAP |
0.9315 |
7.46 |
0.00 |
DBIH* |
0.4188 |
6.03 |
0.00 |
DDEL |
0.4211 |
5.61 |
0.00 |
DGUJ |
0.8055 |
7.15 |
0.00 |
DHAR |
0.4071 |
5.78 |
0.00 |
DKAR |
0.6665 |
7.03 |
0.00 |
DKER |
0.5204 |
6.34 |
0.00 |
DMAH |
1.0896 |
8.38 |
0.00 |
DMP* |
0.4600 |
5.79 |
0.00 |
DORI |
(dropped) |
DPUN |
0.7389 |
7.11 |
0.00 |
DRAJ |
0.3999 |
4.38 |
0.00 |
DTN |
0.9130 |
6.93 |
0.00 |
DUP* |
0.9395 |
7.54 |
0.00 |
DWB |
0.6604 |
6.84 |
0.00 |
DAPT |
-0.0003 |
-0.07 |
0.95 |
DBIH*T |
0.0037 |
1.02 |
0.31 |
DDELT |
(dropped) |
DGUJT |
0.0045 |
1.19 |
0.23 |
DHART |
0.0108 |
2.75 |
0.01 |
DKART |
0.0117 |
3.08 |
0.00 |
DKERT |
0.0068 |
1.78 |
0.08 |
DMAHT |
0.0074 |
1.95 |
0.05 |
DMP*T |
0.0201 |
4.37 |
0.00 |
DORIT |
0.0143 |
3.33 |
0.00 |
DPUNT |
0.0045 |
1.17 |
0.24 |
DRAJT |
0.0108 |
2.52 |
0.01 |
DTNT |
0.0011 |
0.27 |
0.79 |
DUP*T |
0.0071 |
1.88 |
0.06 |
DWBT |
0.0004 |
0.10 |
0.92 |
Note: $ indicates that estimates are up to 2003-04. |
Table 5.13 Production Function Estimates for LEATH$ |
lnQ |
Coefficient |
t-statistic |
Significance level |
Constant |
0.2590 |
2.54 |
0.01 |
ln L |
0.1218 |
4.74 |
0.00 |
ln K |
0.0078 |
0.38 |
0.70 |
ln N |
0.8608 |
36.26 |
0.00 |
t |
0.0097 |
3.00 |
0.00 |
Dpolicy |
0.0648 |
2.63 |
0.01 |
DAP |
-0.1142 |
-1.32 |
0.19 |
DDEL |
(dropped) |
DHAR |
0.0116 |
0.20 |
0.84 |
DKAR |
-0.0319 |
-0.47 |
0.64 |
DMAH |
-0.0051 |
-0.06 |
0.95 |
DMP* |
-0.0484 |
-0.55 |
0.58 |
DTN |
-0.0409 |
-0.31 |
0.76 |
DUP* |
-0.1208 |
-1.13 |
0.26 |
DWB |
-0.0349 |
-0.32 |
0.75 |
DAPT |
-0.0017 |
-0.45 |
0.66 |
DDELT |
0.0021 |
0.34 |
0.73 |
DHART |
0.0010 |
0.19 |
0.85 |
DKART |
-0.0015 |
-0.32 |
0.75 |
DMAHT |
0.0010 |
0.28 |
0.78 |
DMP*T |
0.0039 |
1.08 |
0.28 |
DTNT |
-0.0008 |
-0.21 |
0.83 |
DUP*T |
0.0049 |
1.19 |
0.23 |
DWBT |
(dropped) |
Note: $ indicates that estimates are up to 2003-04. |
Table 5.14 Production Function Estimates for METAL$ |
lnQ |
Coefficient |
t-statistic |
Significance level |
Constant |
0.4757 |
1.67 |
0.10 |
ln L |
0.0670 |
2.04 |
0.04 |
ln K |
0.0634 |
5.52 |
0.00 |
ln N |
0.8455 |
33.86 |
0.00 |
t |
0.0167 |
6.98 |
0.00 |
Dpolicy |
0.0506 |
3.25 |
0.00 |
DBIH* |
0.0426 |
0.40 |
0.69 |
DDEL |
-0.0444 |
-0.95 |
0.34 |
DGUJ |
0.0194 |
0.28 |
0.78 |
DHAR |
0.0209 |
0.37 |
0.71 |
DKAR |
-0.0141 |
-0.23 |
0.82 |
DKER |
(dropped) |
DMAH |
0.1307 |
1.31 |
0.19 |
DMP* |
0.0937 |
1.08 |
0.28 |
DORI |
-0.0218 |
-0.30 |
0.77 |
DPUN |
0.0250 |
0.38 |
0.71 |
DRAJ |
-0.0257 |
-0.50 |
0.62 |
DTN |
-0.0081 |
-0.12 |
0.91 |
DUP* |
0.0310 |
0.43 |
0.67 |
DWB |
0.0021 |
0.02 |
0.98 |
DBIH*T |
(dropped) |
DDELT |
-0.0096 |
-3.52 |
0.00 |
DGUJT |
-0.0083 |
-2.53 |
0.01 |
DHART |
-0.0108 |
-3.71 |
0.00 |
DKART |
-0.0066 |
-2.15 |
0.03 |
DKERT |
-0.0090 |
-3.16 |
0.00 |
DMAHT |
-0.0131 |
-4.55 |
0.00 |
DMP*T |
-0.0090 |
-3.14 |
0.00 |
DORIT |
-0.0021 |
-0.73 |
0.47 |
DPUNT |
-0.0088 |
-3.15 |
0.00 |
DRAJT |
-0.0077 |
-2.67 |
0.01 |
DTNT |
-0.0087 |
-2.91 |
0.00 |
DUP*T |
-0.0080 |
-2.68 |
0.01 |
DWBT |
-0.0117 |
-4.28 |
0.00 |
Note: $ indicates that estimates are up to 2003-04. |
Table 5.15 Production Function Estimates for MTE$ |
lnQ |
Coefficient |
t-statistic |
Significance level |
Constant |
0.6942 |
2.3800 |
0.02 |
ln L |
0.1378 |
4.5000 |
0.00 |
ln K |
0.0584 |
2.6000 |
0.01 |
ln N |
0.7589 |
28.0100 |
0.00 |
t |
0.0189 |
7.0400 |
0.00 |
Dpolicy |
-0.0237 |
-1.5500 |
0.12 |
DAP |
0.1340 |
1.3100 |
0.19 |
DBIH* |
0.1596 |
1.4200 |
0.16 |
DDEL |
0.0451 |
0.6400 |
0.52 |
DGUJ |
0.0665 |
0.6800 |
0.50 |
DHAR |
0.0489 |
0.5700 |
0.57 |
DKAR |
0.1832 |
1.8300 |
0.07 |
DKER |
-0.1537 |
-2.1200 |
0.04 |
DMAH |
0.2533 |
1.7200 |
0.09 |
DMP* |
0.1427 |
1.8600 |
0.06 |
DORI |
(dropped) |
DPUN |
0.0299 |
0.36 |
0.72 |
DRAJ |
0.0534 |
0.76 |
0.45 |
DTN |
0.1390 |
1.11 |
0.27 |
DUP* |
0.0746 |
0.69 |
0.49 |
DWB |
0.1766 |
1.40 |
0.16 |
DAPT |
0.0005 |
0.18 |
0.86 |
DBIH*T |
-0.0040 |
-1.17 |
0.24 |
DDELT |
(dropped) |
DGUJT |
0.0020 |
0.68 |
0.50 |
DHART |
0.0065 |
1.81 |
0.07 |
DKART |
0.0004 |
0.11 |
0.91 |
DKERT |
0.0083 |
2.44 |
0.02 |
DMAHT |
-0.0010 |
-0.36 |
0.72 |
DMP*T |
-0.0011 |
-0.38 |
0.70 |
DORIT |
-0.0046 |
-1.62 |
0.11 |
DPUNT |
0.0024 |
0.83 |
0.41 |
DRAJT |
0.0022 |
0.75 |
0.46 |
DTNT |
0.0029 |
1.00 |
0.32 |
DUP*T |
0.0050 |
1.56 |
0.12 |
DWBT |
-0.0057 |
-1.80 |
0.07 |
Note: $ indicates that estimates are up to 2003-04. |
Table 5.16 Production Function Estimates for TEX$ |
lnQ |
Coefficient |
t-statistic |
Significance level |
Constant |
3.5742 |
9.18 |
0.000 |
ln L |
0.0111 |
0.26 |
0.796 |
ln K |
0.0342 |
1.70 |
0.089 |
ln N |
0.6866 |
25.67 |
0.000 |
t |
0.0305 |
8.77 |
0.000 |
Dpolicy |
-0.0199 |
-1.02 |
0.308 |
DAP |
-0.5512 |
-7.42 |
0.000 |
DBIH* |
-1.0788 |
-9.09 |
0.000 |
DDEL |
-0.6440 |
-6.07 |
0.000 |
DGUJ |
-0.0778 |
-1.56 |
0.120 |
DHAR |
-0.8195 |
-7.96 |
0.000 |
DKAR |
-0.6622 |
-7.92 |
0.000 |
DKER |
-0.7148 |
-7.48 |
0.000 |
DMAH |
(dropped) |
DMP* |
-0.5659 |
-7.48 |
0.000 |
DORI |
-1.2171 |
-11.19 |
0.000 |
DPUN |
-0.4581 |
-5.79 |
0.000 |
DRAJ |
-0.5241 |
-6.80 |
0.000 |
DTN |
-0.2836 |
-5.13 |
0.000 |
DUP* |
-0.4117 |
-6.41 |
0.000 |
DWB |
-0.2042 |
-3.57 |
0.000 |
DAPT |
-0.0116 |
-3.26 |
0.001 |
DBIH*T |
-0.0335 |
-7.76 |
0.000 |
DDELT |
(dropped) |
DGUJT |
-0.0175 |
-4.61 |
0.000 |
DHART |
-0.0026 |
-0.71 |
0.479 |
DKART |
0.0026 |
0.67 |
0.502 |
DKERT |
-0.0154 |
-4.03 |
0.000 |
DMAHT |
-0.0199 |
-5.15 |
0.000 |
DMP*T |
-0.0070 |
-1.85 |
0.066 |
DORIT |
0.0092 |
1.57 |
0.118 |
DPUNT |
-0.0069 |
-1.95 |
0.052 |
DRAJT |
-0.0039 |
-1.07 |
0.286 |
DTNT |
0.0002 |
0.05 |
0.961 |
DUP*T |
-0.0152 |
-4.14 |
0.000 |
DWBT |
-0.0187 |
-4.55 |
0.000 |
Note: $ indicates that estimates are up to 2003-04. |
Table 5.17: State wise TFPG across Policy Regime: 19801-81 to 2003-04 |
State |
TFPG (pcpa) obtained by PFA |
Policy Regime |
AP |
1.84 |
NIL |
BIH* |
-0.52 |
NIL |
DEL |
1.09 |
NIL |
GUJ |
1.45 |
NIL |
HAR |
0.86 |
NIL |
KAR |
1.98 |
NIL |
KER |
1.89 |
NIL |
MAH |
2.44 |
POSITIVE |
MP* |
1.65 |
NIL |
ORI |
1.20 |
NIL |
PUN |
1.41 |
NIL |
RAJ |
1.93 |
NIL |
TN |
2.11 |
NIL |
UP* |
1.44 |
NIL |
WB |
1.12 |
NIL |
For MTE industry, it is negative, but statistically significant at
12 per cent level. In other words, the TFPG performance of
various industries has varied across the policy regimes.
-
The inter-state differences in TFP levels are by and large,
statistically significant across industries, except for METAL,
MTE and LEATH industries. The inter-state differences in
productivity levels are rather stark in the case of TEX industry.
This observation is based on the statistical significance of
coefficients of state dummies.
-
As regards the inter-state differences in TFPG, these are more
pronounced in FBT and TEX, which are traditional and labour
intensive industries. This observation is based on the statistical
significance of coefficients of multiplicative dummies for states
and time.
-
TFPG across states though differs from the highest for
Maharashtra (2.44 per cent pcpa) to almost zero for Bihar* (the
negative coefficient of ‘t’ for Bihar* is not statistically significant),
it is only Maharashtra that seems to have witnessed a higher productivity during the post-reform period, as it is only in this
case the coefficient of Dpolicy dummy is positive and statistically
significant (though only at 10 per cent level). For all other states,
this coefficient turns out to be statistically insignifiant.
In Tables 5.18A and 5.18B, we provide the summary of empirical
results obtained for the organised manufacturing sector based on the ASI
industry-state level data. In the former table, we provide ranks of the
various states for both TFP and TFPG. TFPG ranks are derived by applying
the two alternative methodologies, viz., PFA and GAA. In the latter Table
(5.18B), we highlight the consistency or otherwise of the TFPG ranks of
three best/worst states. The states highlighted in blue color are the states in which an industry contributes significantly to the output of organised
manufacturing of the respective state. Thus, mainly it is the consistency/
inconsistency of TFPG ranks for these states that is of prime concern to
us. Gujarat emerges as the best performer according to the PFA and worst
performer according to GAA. As mentioned earlier, leather industry
constitutes a rather insignificant proportion of the total organised
manufacturing. The ranking of UP* (for leather industry) which is one of
the three major states that specializes in leather industry also gets
inconsistent ranking with the two alternative methodologies. However,
for most of the states, the ranking of best performing and worst performing
states in major states seems to be consistent, as can be seen from Table
5.18B.
Table 5.18A: Inter-State Comparison of TFP levels and TFPG$ TFP Level Ranking: Production Function Approach |
Industry |
CHEM |
FBT |
LEATH |
METAL |
MTE |
TEX |
MFG |
Best Performers |
BIH* |
MAH |
HAR |
MAH |
MAH |
MAH |
MAH |
GUJ |
UP* |
DEL |
MP* |
KAR |
GUJ |
TN |
HAR |
AP |
MAH |
BIH* |
WB |
WB |
WB |
Worst Performers |
DEL |
ORI |
UP* |
DEL |
KER |
ORI |
ORI |
ORI |
RAJ |
AP |
RAJ |
ORI |
BIH* |
DEL |
RAJ |
HAR |
MP* |
ORI |
PUN |
HAR |
RAJ |
TFPG Ranking: Production Function Approach |
Best Performers |
RAJ |
MP* |
UP* |
BIH* |
KER |
ORI |
GUJ |
UP* |
ORI |
MP* |
ORI |
HAR |
KAR |
AP |
TN |
KAR |
DEL |
KAR |
UP* |
TN |
HAR |
Worst Performers |
BIH* |
AP |
AP |
MAH |
WB |
BIH* |
WB |
HAR |
DEL |
KAR |
WB |
ORI |
MAH |
KER |
KER |
WB |
TN |
HAR |
BIH* |
WB |
TN |
TFPG Ranking: Growth Accounting Approach |
Best Performers |
BIH* |
TN |
WB |
ORI |
RAJ |
ORI |
BIH* |
WB |
PUN |
HAR |
AP |
AP |
WB |
RAJ |
ORI |
MAH |
MP* |
BIH* |
UP* |
KAR |
AP |
Worst Performers |
TN |
MP* |
DEL |
GUJ |
KER |
DEL |
TN |
UP* |
AP |
KAR |
MAH |
HAR |
AP |
GUJ |
KER |
HAR |
UP* |
UP* |
DEL |
MAH |
PUN |
Note: $ indicates that estimates are up to 2003-04. |
Table 5.18B: Consistency/Inconsistency of TFPG Ranking with GA and PFA$ |
Consistency/Inconsistency of Ranks with PFA & GAA |
CHEM |
FBT |
LEATH |
METAL |
MTE |
TEX |
MFG |
Consistent |
KER |
AP |
KAR |
BIH* |
UP* |
KAR |
AP |
|
|
MP* |
MAH |
|
ORI |
TN |
|
|
|
ORI |
|
MAH |
|
Inconsistent |
BIH* |
MP* |
UP* |
|
KER |
WB |
GUJ |
TN |
|
DEL |
|
|
|
|
UP* |
|
|
|
|
|
|
Note: $ indicates that estimates are up to 2003-04. |
5.2 ASI Unit Level Data: 1993-94 to 2003-04
As mentioned in section 4 of this study, ASI has also been providing
unit level data, since 1993-94 (barring 1995-96). Unfortunately, we are
unable to compile a panel data set from this database. This is partly due
to the fact that the ASI does not provide the identification codes for the
census sector. For the sample sector, in any case it cannot be done. In
view of this, we have estimated year-wise mean efficiency scores of the
units belonging to the six industry groups. These estimates have been
presented in Table 5.19A and 5.19B. We have used SFPF both CD and TL
specifications. The methodology has been outlined in Section 4.2.2 for estimating the mean efficiency levels for the ASI unit level dataset, for
each of the years spanning from 1993-94 to 2003-04 (barring 1995-96).
These estimates reveal that though in most of the years, a majority of
firms were operating close to the frontier (since the mean efficiency score
is very close to unity), in 1994-95 FBT and LEATH industries show
inefficiencies with CD specification, but only FBT shows inefficiency with
TL specifications. In 1996-97 all industry groups indicate efficiency levels
ranging between 0.44 and 0.59 with CD specification and between 0.49
and 0.88 with translog specification. In 2002-03 and 2003-04, FBT
industry displays inefficiency levels ranging from 10 to 16 percent. It can
also be seen that TFP indices obtained by growth accounting estimates at
industry levels (Figure 5.2) in these years are rather low as compared to
those in observed for other years. However, we are not very comfortable with these estimates for two reasons: (i) the data base is too large and the
detection of outliers is a difficult proposition; and, (ii) we believe that
there is problem in measurement of input, particularly that of capital. In
view of this, we can treat these results as rather tentative. However, these
results can be treated as indicators of problems associated with the database.
Table 5.19A: Mean Efficiency Obtained by Using SFPF (CD Specification) |
Year |
CHEM |
FBT |
LEATH |
METAL |
MTE |
TEX |
1993-94 |
0.9995 |
0.9993 |
0.9987 |
0.9998 |
0.9980 |
0.9994 |
1994-95 |
0.9967 |
0.7128 |
0.5787 |
0.9931 |
0.9929 |
0.5913 |
1996-97 |
0.5889 |
0.5074 |
0.4874 |
0.5206 |
0.5650 |
0.4437 |
1997-98 |
0.9959 |
0.9921 |
0.9976 |
0.9956 |
0.9966 |
0.9941 |
1998-99 |
0.9994 |
0.9988 |
0.9993 |
0.9994 |
0.9994 |
0.9993 |
1999-00 |
0.9993 |
0.9987 |
0.9992 |
0.9995 |
0.9995 |
0.9992 |
2000-01 |
0.9994 |
0.9992 |
0.9993 |
0.9996 |
0.9994 |
0.9994 |
2001-02 |
0.9993 |
0.9987 |
0.9993 |
0.9995 |
0.9993 |
0.9993 |
2002-03 |
0.9994 |
0.9989 |
0.9995 |
0.9996 |
0.9994 |
0.9989 |
2003-04 |
0.9994 |
0.9990 |
0.9997 |
0.9996 |
0.9995 |
0.9993 |
Table 5.19B: Mean Efficiency Obtained by Using SFPF (TL Specification) |
Year |
CHEM |
FBT |
LEATH |
METAL |
MTE |
TEX |
1993-94 |
1.000 |
0.999 |
0.947 |
0.999 |
0.999 |
0.955 |
1994-95 |
0.997 |
0.690 |
0.908 |
0.995 |
0.994 |
0.917 |
1996-97 |
0.593 |
0.508 |
0.489 |
0.517 |
0.566 |
0.884 |
1997-98 |
0.996 |
0.833 |
0.879 |
0.871 |
0.887 |
0.914 |
1998-99 |
0.999 |
0.999 |
0.966 |
0.999 |
0.955 |
0.948 |
1999-00 |
0.956 |
0.949 |
0.961 |
1.000 |
0.959 |
0.956 |
2000-01 |
0.960 |
0.999 |
0.887 |
0.966 |
0.962 |
0.963 |
2001-02 |
0.958 |
0.999 |
0.999 |
0.968 |
0.962 |
0.966 |
2002-03 |
0.963 |
0.897 |
0.999 |
0.969 |
0.964 |
0.958 |
2003-04 |
0.962 |
0.842 |
0.822 |
0.969 |
0.999 |
0.954 |
5.3 Technical Efficiency and Productivity for Public Limited (Non-
Government) Manufacturing Companies: 1993-94 to 2004-05
We have also used Public Limited (Non-Government non-financial)
Manufacturing Companies database that is compiled by the Reserve Bank
of India. This database spans over the period 1993-94 to 2004-05. For
this database, we could construct a balanced panel dataset and have
applied the Data Envelopment Approach (DEA) outlined in Section 4.2.3
in order to estimate the mean levels of technical efficiency and the scale
efficiency. Besides, we have estimated TFPG with the help of Malmquist
productivity index. These calculations have been done by using DEAP33.
The sample firms in leather and tobacco industries were too few in number
and hence, we could not include these industries in analysis conducted
in this section. The analysis is not carried out at state level due to the
small number of companies in the sample.
5.3.1 Industry-wise Technical Efficiency using DEA (Multi-Stage):
1993-94 to 2004-05
It can be seen from Figure 5.18 that scale efficiency (SE) is very
close to unity and hence, the assumption of constant returns to scale
(CRS) for the manufacturing sector as such seems to be justified. Technical
efficiency (TE) has ranged between 0.69 and 0.75 (under the assumption
of CRS). In Figure 5.19, we present the technical efficiency scores of the
five selected industries under both CRS (TECRS) and VRS (TEVRS)
assumptions. It can be seen that with the CRS assumption, the efficiency
of food and beverages (FB) industry is the lowest and that of metal industry
is the highest.
Yearly movements in technical efficiency for various industries are
given in Tables 5.20 to 5.25.
Table 5.20: Chemical Industry (78 firms) |
Year |
Technical Efficiency
from CRS DEA (CRSTE) |
Technical Efficiency
from VRS DEA (VRSTE) |
Scale Efficiency (scale) =
CRSTE/VRSTE |
1993-94 |
0.823 |
0.895 |
0.920 |
1994-95 |
0.843 |
0.890 |
0.946 |
1995-96 |
0.821 |
0.873 |
0.941 |
1996-97 |
0.845 |
0.877 |
0.964 |
1997-98 |
0.818 |
0.848 |
0.967 |
1998-99 |
0.826 |
0.866 |
0.955 |
1999-2000 |
0.813 |
0.865 |
0.941 |
2000-01 |
0.832 |
0.870 |
0.960 |
2001-02 |
0.835 |
0.866 |
0.965 |
2002-03 |
0.849 |
0.866 |
0.980 |
2003-04 |
0.832 |
0.852 |
0.976 |
2004-05 |
0.845 |
0.869 |
0.972 |
Table 5.21: Food & Beverages (60 firms) |
Year |
Technical Efficiency
from CRS DEA (CRSTE) |
Technical Efficiency
from VRS DEA (VRSTE) |
Scale Efficiency (scale) =
CRSTE/VRSTE |
1993-94 |
0.697 |
0.865 |
0.804 |
1994-95 |
0.622 |
0.756 |
0.821 |
1995-96 |
0.766 |
0.869 |
0.885 |
1996-97 |
0.761 |
0.860 |
0.889 |
1997-98 |
0.841 |
0.907 |
0.928 |
1998-99 |
0.859 |
0.907 |
0.948 |
1999-2000 |
0.856 |
0.897 |
0.956 |
2000-01 |
0.854 |
0.883 |
0.968 |
2001-02 |
0.865 |
0.905 |
0.957 |
2002-03 |
0.900 |
0.922 |
0.976 |
2003-04 |
0.807 |
0.871 |
0.927 |
2004-05 |
0.817 |
0.889 |
0.923 |
Table 5.22: Metals and Metal Products (METAL) (47 firms) |
Year |
Technical Efficiency
from CRS DEA (CRSTE) |
Technical Efficiency
from VRS DEA (VRSTE) |
Scale Efficiency (scale) =
CRSTE/VRSTE |
1993-94 |
0.895 |
0.938 |
0.954 |
1994-95 |
0.911 |
0.946 |
0.963 |
1995-96 |
0.902 |
0.936 |
0.965 |
1996-97 |
0.897 |
0.935 |
0.960 |
1997-98 |
0.885 |
0.909 |
0.974 |
1998-99 |
0.878 |
0.902 |
0.975 |
1999-2000 |
0.869 |
0.888 |
0.979 |
2000-01 |
0.896 |
0.914 |
0.980 |
2001-02 |
0.863 |
0.886 |
0.974 |
2002-03 |
0.884 |
0.906 |
0.976 |
2003-04 |
0.887 |
0.912 |
0.974 |
2004-05 |
0.873 |
0.908 |
0.964 |
Table 5.23: Machinery and Transport Equipment (MTE) (116 firms) |
Year |
Technical Efficiency
from CRS DEA (CRSTE) |
Technical Efficiency
from VRS DEA (VRSTE) |
Scale Efficiency (scale) =
CRSTE/VRSTE |
1993-94 |
0.869 |
0.905 |
0.961 |
1994-95 |
0.869 |
0.902 |
0.965 |
1995-96 |
0.860 |
0.901 |
0.955 |
1996-97 |
0.851 |
0.895 |
0.952 |
1997-98 |
0.853 |
0.887 |
0.962 |
1998-99 |
0.840 |
0.878 |
0.956 |
1999-2000 |
0.860 |
0.899 |
0.957 |
2000-01 |
0.806 |
0.867 |
0.931 |
2001-02 |
0.803 |
0.863 |
0.929 |
2002-03 |
0.848 |
0.874 |
0.970 |
2003-04 |
0.832 |
0.859 |
0.970 |
2004-05 |
0.865 |
0.890 |
0.972 |
Table 5.24: Textiles and Textile Products (TEX) (53 firms) |
Year |
Technical Efficiency
from CRS DEA (CRSTE) |
Technical Efficiency
from VRS DEA (VRSTE) |
Scale Efficiency (scale) =
CRSTE/VRSTE |
1993-94 |
0.868 |
0.915 |
0.950 |
1994-95 |
0.826 |
0.880 |
0.941 |
1995-96 |
0.834 |
0.878 |
0.952 |
1996-97 |
0.861 |
0.899 |
0.958 |
1997-98 |
0.888 |
0.912 |
0.975 |
1998-99 |
0.848 |
0.884 |
0.961 |
1999-2000 |
0.832 |
0.862 |
0.967 |
2000-01 |
0.836 |
0.859 |
0.975 |
2001-02 |
0.861 |
0.882 |
0.977 |
2002-03 |
0.848 |
0.880 |
0.967 |
2003-04 |
0.840 |
0.868 |
0.970 |
2004-05 |
0.845 |
0.863 |
0.980 |
Table 5.25: All Companies (449 firms) |
Year |
Technical Efficiency
from CRS DEA (CRSTE) |
Technical Efficiency
from VRS DEA (VRSTE) |
Scale Efficiency (scale) =
CRSTE/VRSTE |
1993-94 |
0.698 |
0.804 |
0.871 |
1994-95 |
0.690 |
0.774 |
0.891 |
1995-96 |
0.751 |
0.811 |
0.927 |
1996-97 |
0.738 |
0.793 |
0.931 |
1997-98 |
0.744 |
0.785 |
0.949 |
1998-99 |
0.748 |
0.782 |
0.956 |
1999-2000 |
0.748 |
0.775 |
0.966 |
2000-01 |
0.715 |
0.782 |
0.916 |
2001-02 |
0.712 |
0.774 |
0.921 |
2002-03 |
0.748 |
0.777 |
0.963 |
2003-04 |
0.725 |
0.761 |
0.954 |
2004-05 |
0.724 |
0.756 |
0.957 |
5.3.2 Total Factor Productivity Growth (Measured Using the Malmquist
Index)
In Table 5.26, we present the TFPG across industries using the
Malmquist Index.
It can be seen from Table 5.26 that the average TFPG of
manufacturing sector is about 1.50 pcpa for the entire period. TFPG of
Food and Beverages is lowest followed by the TEX industry. MTE and
METAL industries have recorded the highest TFPG which is almost double
of that for the CHEM industry.
The empirical results of estimates of efficiency obtained by
application of various methodologies which have been applied to the RBI
dataset on Public Ltd. Companies belonging to the manufacturing sector
have been compared in Table 5.27.
The empirical results of efficiency estimates derived from DEA, SFPF
(both Cobb-Douglas and Translog specifications) and productivity estimates
derived by using Malmquist Index indicate that the efficiency and
productivity of Food and Beverages industry is the lowest. The latter two
approaches also indicate that the next worst performer is the TEX industry.
According to SFPF estimates, CHEM industry is the best performer in terms of efficiency. As per the Malmquist index, MTE and METAL industries
are the best performers as regards TFPG.
Table 5.26: Total Factor Productivity Growth
(Measured Using the Malmquist Index) |
Year |
CHEM |
FB |
MTE |
METAL |
TEX |
MFG |
1994-95 |
0.50 |
0.90 |
1.40 |
3.70 |
0.20 |
2.90 |
1995-96 |
5.20 |
0.40 |
4.40 |
7.70 |
-5.30 |
4.00 |
1996-97 |
4.00 |
-7.50 |
1.90 |
-2.60 |
-2.50 |
-0.40 |
1997-98 |
-1.60 |
13.50 |
0.30 |
-2.90 |
2.00 |
0.70 |
1998-99 |
-1.50 |
-9.50 |
0.50 |
5.20 |
6.60 |
-0.60 |
1999-2000 |
-9.30 |
-2.40 |
7.20 |
4.30 |
4.80 |
1.60 |
2000-01 |
10.50 |
-5.90 |
-1.50 |
-1.00 |
0.80 |
1.50 |
2001-02 |
2.20 |
5.10 |
1.70 |
4.00 |
1.80 |
3.80 |
2002-03 |
0.70 |
2.60 |
2.60 |
4.90 |
0.40 |
2.70 |
2003-04 |
4.10 |
1.90 |
7.70 |
-2.30 |
4.70 |
3.70 |
2004-05 |
-1.70 |
-13.10 |
1.00 |
4.30 |
-6.90 |
-3.50 |
Average |
1.10 |
-1.50 |
2.40 |
2.20 |
0.50 |
1.50 |
Table 5.27: Efficiency and Productivity of Selected Public Ltd.
Companies: 1993-94 to 2004-05 |
Industry
(Number of firms included) |
Mean Efficiency Estimates |
Mean TFPG (pcpa) |
Data Envelopment Analysis (DEA) |
Stochastic Frontier Production Function (SFPF) |
Using Malmquist Index |
TECRS |
TEVRS |
Cobb-Douglas Function |
Translog Function |
CHEM (78) |
0.832 |
0.870 |
0.9245 |
0.9256 |
1.1 |
Food & Beverages (60) |
0.804 |
0.878 |
0.6462 |
0.6267 |
-1.5 |
MTE (116) |
0.846 |
0.885 |
0.8021 |
0.8030 |
2.4 |
METAL(47) |
0.887 |
0.915 |
0.8170 |
0.8121 |
2.2 |
TEX (53) |
0.849 |
0.882 |
0.7131 |
0.7268 |
0.5 |
All Companies (449) |
0.728 |
0.781 |
0.6586 |
0.6688 |
1.5 |
5.4 Labour Productivity: A Comparison of Organised and
Unorganised Sector
The NSSO has been conducting enterprises surveys relating to
various activities in its periodical rounds. The enterprises surveys on
unorganised manufacturing sector were introduced since 33rd round of
NSS (1978-79). Thereafter, the unorganised manufacturing sector has
been covered in the 40th, 45th, 51st, 56th and 62nd rounds and the
consolidated results were published by the NSSO accordingly. We have
obtained the unit level raw data for 45th, 51st and 56th rounds from the
NSSO, which were processed at our end. These unit level data were
compiled state-wise and industry-wise for the estimation of labour
productivity. The industrial classification used for unit level data in the
ASI dataset has been adopted for this analysis.
We now present the levels of labour productivity in organised and
unorganised manufacturing sectors in Table 5.28. From Table 5.28, we
can see that labour productivity in both organised and unorganised sector
has increased over time. Labour productivity in various organised
manufacturing industries ranged between 1.6 to 2.2 times in 2000-01 as compared to the respective figures in 1989-90. This range for the
unorganised sector was 1.7 to 2.8 times. However the disparity in the
levels of labour productivity between organised and unorganised sectors
are rather sharp and have perpetuated. Organised manufacturing sector
had labour productivity which was 13 times, 14 times and 15 times higher
than its unorganised counterpart in years 1989-90, 1994-95 and 2000-
01, respectively.
Table 5.28: Average Labour Productivity (in ` at constant 1981-82 prices) |
Industry |
Organised Manufacturing Sector |
Unorganised Manufacturing Sector |
1989-90 |
1994-95 |
2000-01 |
1989-90 |
1994-95 |
2000-01 |
CHEM |
370829 |
435405 |
608237 |
40214 |
39637 |
67136 |
FBT |
131511 |
146965 |
291494 |
11594 |
14959 |
24782 |
LEATH |
117840 |
162685 |
208403 |
9953 |
19850 |
27800 |
METAL |
201258 |
261629 |
360641 |
13774 |
23138 |
34954 |
MTE |
187704 |
245622 |
400788 |
27192 |
41890 |
54120 |
TEX |
109609 |
151414 |
201862 |
9645 |
9979 |
18072 |
MFG |
168007 |
208557 |
336726 |
12452 |
14949 |
22794 |
Table A5.1 : TFP Indices for Chemical Industry (ASI data, State level Data using GAA) |
Year |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
RAJ |
TN |
UP* |
WB |
INDIA |
1980-81 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
1981-82 |
105.2 |
112.7 |
99.1 |
105.5 |
109.6 |
108.2 |
107.4 |
101.0 |
105.7 |
100.7 |
111.5 |
107.7 |
94.0 |
100.4 |
104.5 |
1982-83 |
115.5 |
116.1 |
115.4 |
116.8 |
141.7 |
109.6 |
121.6 |
103.4 |
101.7 |
81.6 |
111.0 |
114.5 |
113.7 |
105.9 |
111.7 |
1983-84 |
140.7 |
123.5 |
124.8 |
115.9 |
159.0 |
121.9 |
128.5 |
117.7 |
106.3 |
90.7 |
123.1 |
116.7 |
122.9 |
112.7 |
120.3 |
1984-85 |
123.3 |
147.4 |
125.3 |
125.8 |
171.6 |
113.0 |
123.9 |
114.0 |
113.1 |
105.0 |
115.7 |
117.2 |
123.0 |
113.1 |
121.6 |
1985-86 |
124.2 |
153.5 |
141.7 |
126.2 |
185.4 |
119.0 |
106.2 |
114.3 |
116.9 |
103.9 |
115.9 |
113.4 |
122.5 |
114.1 |
122.2 |
1986-87 |
126.0 |
157.3 |
106.2 |
124.4 |
157.8 |
122.9 |
101.9 |
116.3 |
116.3 |
102.4 |
105.9 |
106.3 |
113.0 |
118.4 |
120.7 |
1987-88 |
127.9 |
159.6 |
114.8 |
131.4 |
186.6 |
115.8 |
104.0 |
119.0 |
121.7 |
100.3 |
124.8 |
111.3 |
136.1 |
115.9 |
126.2 |
1988-89 |
146.1 |
139.5 |
115.6 |
144.3 |
201.8 |
125.0 |
106.8 |
126.3 |
130.1 |
75.3 |
123.5 |
125.2 |
128.8 |
121.7 |
133.4 |
1989-90 |
147.1 |
161.7 |
131.6 |
135.2 |
181.2 |
141.7 |
111.0 |
143.7 |
129.7 |
100.4 |
132.9 |
121.6 |
147.3 |
118.5 |
140.7 |
1990-91 |
151.4 |
176.3 |
137.0 |
140.5 |
194.8 |
157.5 |
111.1 |
147.9 |
155.8 |
80.9 |
135.1 |
134.8 |
149.5 |
139.6 |
146.9 |
1991-92 |
175.7 |
221.5 |
153.3 |
138.6 |
197.4 |
152.2 |
118.4 |
136.3 |
171.5 |
96.7 |
130.1 |
126.6 |
164.5 |
140.4 |
146.2 |
1992-93 |
173.2 |
206.6 |
134.1 |
156.5 |
186.8 |
140.4 |
129.2 |
147.5 |
164.1 |
86.2 |
128.7 |
116.2 |
158.5 |
133.6 |
150.0 |
1993-94 |
168.7 |
192.6 |
123.5 |
147.1 |
185.1 |
140.6 |
118.0 |
151.8 |
155.2 |
95.9 |
121.0 |
114.6 |
155.1 |
138.2 |
148.9 |
1994-95 |
169.5 |
176.5 |
130.4 |
154.7 |
197.6 |
162.4 |
122.4 |
134.8 |
165.3 |
96.6 |
153.3 |
118.9 |
122.8 |
136.9 |
145.0 |
1995-96 |
189.0 |
182.8 |
134.2 |
145.3 |
191.8 |
137.3 |
120.6 |
153.4 |
151.5 |
99.2 |
164.1 |
119.7 |
123.9 |
145.8 |
148.7 |
1996-97 |
157.3 |
228.7 |
132.5 |
143.1 |
189.5 |
155.0 |
107.6 |
142.6 |
158.6 |
88.3 |
172.2 |
115.8 |
129.4 |
144.7 |
144.7 |
1997-98 |
139.6 |
153.7 |
131.3 |
131.1 |
182.2 |
135.9 |
104.8 |
142.8 |
165.1 |
92.9 |
146.2 |
109.3 |
118.3 |
143.4 |
136.7 |
1998-99 |
155.2 |
200.6 |
123.1 |
142.5 |
192.6 |
134.3 |
112.6 |
138.6 |
148.3 |
86.6 |
142.1 |
112.9 |
123.1 |
141.5 |
140.0 |
1999-2000 |
161.8 |
176.6 |
174.4 |
140.7 |
200.4 |
139.9 |
120.8 |
130.3 |
151.1 |
92.9 |
155.0 |
127.7 |
122.7 |
138.1 |
141.4 |
2000-01 |
146.1 |
152.4 |
121.6 |
139.4 |
204.0 |
145.3 |
113.9 |
124.8 |
160.2 |
95.6 |
174.6 |
118.3 |
118.5 |
155.0 |
138.4 |
2001-02 |
163.6 |
171.9 |
136.0 |
138.4 |
194.5 |
139.2 |
109.5 |
125.8 |
155.4 |
117.7 |
154.1 |
118.3 |
123.9 |
162.3 |
140.1 |
2002-03 |
160.1 |
171.7 |
144.0 |
143.5 |
199.6 |
161.5 |
110.1 |
127.1 |
157.4 |
92.3 |
155.5 |
113.2 |
130.0 |
186.3 |
144.8 |
2003-04 |
161.8 |
194.0 |
139.6 |
157.7 |
235.1 |
156.8 |
110.9 |
136.8 |
165.0 |
92.4 |
166.7 |
119.2 |
132.6 |
222.2 |
153.0 |
Table A5.2: TFP Indices for FBT Industry (ASI data, State level Data using GAA) |
Year |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
INDIA |
1980-81 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
1981-82 |
113.4 |
114.4 |
112.4 |
113.2 |
113.0 |
114.1 |
112.5 |
120.3 |
116.5 |
114.3 |
115.0 |
114.0 |
115.8 |
114.8 |
116.8 |
111.7 |
1982-83 |
127.7 |
120.0 |
126.7 |
125.8 |
128.6 |
129.1 |
128.2 |
149.9 |
132.0 |
128.6 |
128.0 |
125.3 |
132.2 |
130.7 |
128.9 |
123.4 |
1983-84 |
138.9 |
164.6 |
137.3 |
142.0 |
150.8 |
181.4 |
150.6 |
160.6 |
143.0 |
146.0 |
141.9 |
136.4 |
152.1 |
146.5 |
147.1 |
137.0 |
1984-85 |
142.6 |
136.4 |
153.5 |
139.6 |
149.6 |
155.0 |
164.5 |
162.7 |
146.8 |
151.6 |
149.3 |
146.1 |
158.1 |
151.7 |
155.4 |
138.2 |
1985-86 |
136.8 |
133.1 |
133.3 |
132.0 |
138.1 |
149.5 |
139.9 |
163.7 |
135.5 |
136.9 |
136.1 |
130.0 |
147.3 |
144.4 |
142.6 |
127.2 |
1986-87 |
140.3 |
145.9 |
135.6 |
135.9 |
144.9 |
147.3 |
152.1 |
170.0 |
145.8 |
138.7 |
137.2 |
137.6 |
155.7 |
150.1 |
149.1 |
129.9 |
1987-88 |
140.1 |
138.3 |
139.9 |
137.0 |
145.5 |
144.1 |
151.0 |
168.1 |
146.0 |
146.1 |
136.8 |
134.8 |
152.8 |
150.6 |
142.5 |
127.4 |
1988-89 |
138.0 |
176.7 |
128.7 |
135.4 |
149.1 |
142.0 |
149.2 |
176.1 |
148.5 |
139.2 |
135.7 |
139.4 |
159.0 |
147.1 |
142.9 |
126.2 |
1989-90 |
133.4 |
127.7 |
118.3 |
131.0 |
139.1 |
140.7 |
141.7 |
173.3 |
138.0 |
124.7 |
123.7 |
128.7 |
154.6 |
137.3 |
136.3 |
116.0 |
1990-91 |
133.6 |
130.6 |
120.5 |
127.0 |
136.7 |
133.4 |
140.2 |
160.3 |
130.0 |
125.7 |
124.6 |
127.7 |
146.6 |
131.1 |
132.9 |
112.4 |
1991-92 |
134.2 |
150.0 |
115.3 |
124.2 |
129.6 |
138.5 |
141.4 |
151.2 |
131.9 |
126.8 |
125.0 |
129.5 |
150.6 |
129.9 |
129.5 |
111.5 |
1992-93 |
132.8 |
134.4 |
121.8 |
124.2 |
128.7 |
138.0 |
131.6 |
161.7 |
131.9 |
128.1 |
130.7 |
129.8 |
149.6 |
133.5 |
132.0 |
111.2 |
1993-94 |
134.4 |
144.7 |
125.4 |
128.9 |
138.5 |
145.1 |
139.1 |
150.3 |
136.0 |
138.7 |
146.3 |
131.4 |
159.5 |
140.5 |
136.7 |
115.3 |
1994-95 |
139.0 |
131.8 |
126.3 |
128.2 |
131.2 |
143.4 |
134.4 |
174.1 |
133.3 |
126.4 |
148.3 |
136.0 |
161.9 |
138.0 |
130.6 |
114.8 |
1995-96 |
134.4 |
144.0 |
128.7 |
133.1 |
136.9 |
143.5 |
140.3 |
176.4 |
138.9 |
133.8 |
143.1 |
132.3 |
164.3 |
141.3 |
135.1 |
115.6 |
1996-97 |
135.3 |
135.5 |
131.0 |
132.7 |
127.0 |
148.9 |
147.5 |
169.7 |
116.8 |
138.6 |
136.4 |
134.3 |
170.0 |
144.9 |
137.8 |
116.0 |
1997-98 |
136.1 |
147.8 |
127.5 |
128.9 |
120.4 |
148.8 |
139.0 |
215.8 |
109.0 |
122.5 |
139.7 |
139.6 |
166.0 |
138.1 |
125.0 |
111.9 |
1998-99 |
109.1 |
137.9 |
129.9 |
127.5 |
126.2 |
150.8 |
134.6 |
177.8 |
118.5 |
118.0 |
145.2 |
131.6 |
164.0 |
140.4 |
136.8 |
112.3 |
1999-2000 |
118.4 |
132.0 |
118.3 |
127.1 |
132.5 |
144.8 |
128.5 |
151.7 |
116.2 |
120.9 |
138.0 |
127.9 |
155.6 |
135.0 |
130.9 |
109.2 |
2000-01 |
153.5 |
129.0 |
122.7 |
133.2 |
134.6 |
146.8 |
135.2 |
181.2 |
121.6 |
128.7 |
143.8 |
134.3 |
168.4 |
145.8 |
138.3 |
113.9 |
2001-02 |
140.1 |
132.2 |
128.2 |
134.4 |
131.3 |
150.1 |
134.6 |
141.2 |
126.4 |
129.1 |
151.9 |
136.1 |
167.6 |
146.2 |
140.2 |
115.5 |
2002-03 |
117.8 |
130.2 |
134.0 |
138.5 |
129.3 |
150.3 |
132.8 |
179.9 |
123.0 |
131.1 |
146.2 |
141.4 |
165.9 |
146.1 |
141.6 |
115.0 |
2003-04 |
125.7 |
124.2 |
125.0 |
134.8 |
112.6 |
146.6 |
132.3 |
125.4 |
117.0 |
127.7 |
146.1 |
138.6 |
163.7 |
142.8 |
139.6 |
111.9 |
Table A5.3: TFP Indices for Leather Industry
(ASI data, State level Data using GAA) |
Year |
AP |
DEL |
HAR |
KAR |
MAH |
MP* |
ORI |
TN |
UP* |
WB |
INDIA |
1980-81 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
1981-82 |
101.7 |
98.9 |
124.7 |
112.9 |
112.6 |
133.0 |
100.7 |
111.0 |
115.2 |
111.9 |
111.8 |
1982-83 |
106.4 |
109.8 |
135.8 |
120.5 |
114.6 |
130.3 |
81.6 |
117.9 |
122.2 |
117.4 |
118.7 |
1983-84 |
125.7 |
120.4 |
124.9 |
175.9 |
132.0 |
135.3 |
90.7 |
124.2 |
109.9 |
121.8 |
122.1 |
1984-85 |
119.0 |
107.5 |
121.4 |
168.4 |
132.0 |
129.7 |
105.0 |
123.6 |
122.4 |
133.1 |
125.2 |
1985-86 |
97.1 |
110.1 |
126.2 |
149.8 |
141.1 |
139.1 |
103.9 |
118.6 |
117.2 |
116.9 |
119.2 |
1986-87 |
116.0 |
99.7 |
123.9 |
140.1 |
107.8 |
139.1 |
102.4 |
119.5 |
115.7 |
125.1 |
119.4 |
1987-88 |
125.1 |
109.4 |
123.2 |
144.2 |
138.8 |
142.7 |
100.3 |
124.5 |
123.6 |
129.9 |
125.6 |
1988-89 |
113.8 |
89.6 |
122.9 |
146.3 |
133.9 |
138.4 |
75.3 |
118.1 |
123.7 |
101.4 |
117.8 |
1989-90 |
116.4 |
94.6 |
105.8 |
147.2 |
143.4 |
136.9 |
100.4 |
118.1 |
119.5 |
115.3 |
119.6 |
1990-91 |
111.1 |
94.0 |
125.2 |
136.7 |
128.6 |
131.5 |
80.9 |
111.8 |
116.2 |
99.3 |
112.7 |
1991-92 |
118.5 |
111.4 |
120.9 |
138.0 |
135.6 |
135.1 |
96.7 |
124.3 |
118.5 |
110.3 |
122.3 |
1992-93 |
130.7 |
106.8 |
139.8 |
146.2 |
135.2 |
159.8 |
86.2 |
131.0 |
122.8 |
119.8 |
129.6 |
1993-94 |
114.2 |
132.0 |
131.8 |
148.2 |
132.1 |
129.0 |
95.9 |
144.9 |
140.6 |
127.5 |
138.8 |
1994-95 |
122.2 |
105.9 |
118.9 |
145.8 |
122.4 |
134.0 |
96.6 |
129.2 |
124.5 |
130.3 |
127.7 |
1995-96 |
132.8 |
79.1 |
126.5 |
148.0 |
148.7 |
139.7 |
99.2 |
133.9 |
128.5 |
135.4 |
131.6 |
1996-97 |
141.6 |
83.8 |
146.8 |
160.9 |
131.2 |
186.6 |
88.3 |
137.9 |
128.5 |
129.1 |
134.8 |
1997-98 |
120.9 |
82.0 |
153.7 |
150.8 |
148.7 |
161.1 |
92.9 |
137.9 |
131.3 |
177.8 |
137.4 |
1998-99 |
148.6 |
86.2 |
152.7 |
131.0 |
155.9 |
186.8 |
86.6 |
134.3 |
119.0 |
166.0 |
138.8 |
1999-2000 |
114.3 |
90.3 |
138.7 |
124.8 |
133.5 |
162.1 |
92.9 |
135.6 |
109.4 |
137.2 |
130.4 |
2000-01 |
147.7 |
88.8 |
133.9 |
138.5 |
113.1 |
126.8 |
95.6 |
131.9 |
120.3 |
151.2 |
132.8 |
2001-02 |
140.6 |
91.9 |
174.7 |
126.5 |
135.7 |
163.8 |
117.7 |
142.4 |
134.3 |
163.9 |
143.6 |
Table A5.4: TFP Indices for Metal Industry (ASI data, State level Data using GAA) |
Year |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
INDIA |
1980-81 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
1981-82 |
114.8 |
95.8 |
97.4 |
96.6 |
93.9 |
100.3 |
95.3 |
105.0 |
89.1 |
97.5 |
94.5 |
89.0 |
96.6 |
97.8 |
98.5 |
1982-83 |
114.2 |
99.6 |
97.2 |
97.2 |
93.9 |
108.4 |
96.3 |
102.8 |
89.6 |
98.2 |
96.3 |
92.3 |
101.6 |
101.6 |
99.8 |
1983-84 |
120.2 |
102.0 |
100.9 |
98.7 |
86.7 |
95.2 |
99.7 |
104.2 |
96.4 |
98.3 |
105.6 |
95.3 |
110.2 |
97.4 |
102.4 |
1984-85 |
121.9 |
94.5 |
95.1 |
94.5 |
85.0 |
95.7 |
97.5 |
90.2 |
84.5 |
98.8 |
96.8 |
92.1 |
102.8 |
93.3 |
97.3 |
1985-86 |
117.9 |
93.2 |
93.0 |
90.9 |
85.8 |
95.9 |
93.4 |
87.1 |
84.6 |
90.0 |
88.0 |
89.7 |
98.5 |
94.3 |
94.3 |
1986-87 |
115.1 |
93.7 |
95.2 |
96.9 |
91.0 |
103.8 |
97.9 |
84.3 |
94.3 |
95.8 |
100.5 |
94.6 |
100.4 |
84.0 |
95.3 |
1987-88 |
119.8 |
96.4 |
102.7 |
94.8 |
94.3 |
100.3 |
100.5 |
92.7 |
89.7 |
93.1 |
96.5 |
94.8 |
102.3 |
89.0 |
98.2 |
1988-89 |
132.7 |
92.2 |
99.0 |
91.0 |
94.6 |
100.2 |
98.2 |
96.3 |
105.0 |
93.3 |
92.0 |
98.8 |
100.0 |
84.8 |
100.0 |
1989-90 |
129.3 |
92.9 |
100.1 |
93.0 |
105.2 |
104.1 |
94.4 |
96.1 |
108.6 |
93.8 |
98.2 |
94.9 |
105.8 |
82.9 |
100.5 |
1990-91 |
127.9 |
94.7 |
99.5 |
92.2 |
102.9 |
95.5 |
92.2 |
100.5 |
104.7 |
94.3 |
93.6 |
94.4 |
101.8 |
88.2 |
99.7 |
1991-92 |
130.1 |
101.1 |
117.6 |
96.3 |
105.6 |
113.4 |
87.3 |
84.9 |
105.4 |
97.6 |
97.7 |
104.1 |
109.9 |
92.2 |
100.2 |
1992-93 |
128.4 |
102.2 |
104.4 |
95.8 |
106.0 |
105.8 |
99.9 |
100.3 |
106.3 |
96.8 |
105.2 |
99.6 |
100.0 |
89.6 |
102.8 |
1993-94 |
138.8 |
101.3 |
96.2 |
99.0 |
107.6 |
100.4 |
100.5 |
104.8 |
98.8 |
94.8 |
92.5 |
102.4 |
95.0 |
94.7 |
104.9 |
1994-95 |
132.8 |
97.8 |
92.0 |
96.5 |
102.1 |
114.9 |
102.3 |
101.7 |
101.2 |
99.8 |
97.3 |
101.5 |
105.5 |
99.4 |
106.1 |
1995-96 |
139.4 |
105.7 |
94.4 |
101.6 |
114.5 |
119.1 |
105.3 |
124.3 |
105.5 |
101.3 |
104.9 |
104.0 |
107.3 |
106.0 |
114.3 |
1996-97 |
146.4 |
105.3 |
99.5 |
102.2 |
113.1 |
131.1 |
98.2 |
110.6 |
98.3 |
109.4 |
105.7 |
101.8 |
100.3 |
96.3 |
111.2 |
1997-98 |
168.3 |
119.1 |
93.1 |
100.4 |
93.9 |
118.1 |
97.3 |
128.2 |
96.2 |
127.6 |
101.3 |
109.4 |
100.7 |
97.1 |
117.4 |
1998-99 |
170.7 |
112.4 |
88.8 |
96.8 |
91.9 |
112.4 |
95.1 |
117.6 |
114.6 |
107.1 |
124.2 |
107.8 |
106.3 |
100.3 |
113.0 |
1999-2000 |
173.8 |
112.1 |
95.3 |
105.0 |
92.0 |
134.1 |
91.3 |
104.1 |
145.0 |
106.9 |
119.9 |
112.1 |
106.3 |
112.9 |
117.5 |
2000-01 |
144.0 |
116.2 |
99.0 |
109.7 |
93.1 |
140.6 |
101.7 |
105.1 |
161.2 |
112.2 |
131.0 |
112.2 |
109.0 |
111.8 |
114.2 |
2001-02 |
136.9 |
119.7 |
95.9 |
99.2 |
97.9 |
136.5 |
99.5 |
111.5 |
149.9 |
110.8 |
124.0 |
116.1 |
112.8 |
120.0 |
117.3 |
2002-03 |
173.8 |
120.2 |
105.6 |
110.3 |
108.6 |
132.4 |
104.6 |
120.1 |
164.3 |
116.8 |
123.3 |
112.9 |
115.0 |
120.4 |
124.8 |
2003-04 |
169.1 |
115.8 |
101.0 |
105.6 |
110.3 |
123.7 |
100.8 |
124.5 |
212.7 |
107.4 |
122.7 |
109.4 |
111.9 |
121.0 |
123.3 |
Table A5.5: TFP Indices for MTE Industry (ASI data, State level Data using GAA) |
Year |
AP |
BIH* |
DEL |
GUJ |
HAR |
KAR |
KER |
MAH |
MP* |
ORI |
PUN |
RAJ |
TN |
UP* |
WB |
INDIA |
1980-81 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0< | |