Transcript Productivity

ASIA KLEMS DATABASE MANAGEMENT WORKSHOP
17 October 2014
TOKYO
India KLEMS research team

To generate time series on output and inputs in various sectors of
the economy consistent with the National Accounts.

To create a productivity database for India that would be
comprehensive.

To undertake research on India’s productivity growth at the
economy and sectoral level through creation of a database on
indicators of economic performance.
2

Consistency with NAS.
 Large part of the database utilized for series construction comes from NAS and related data
series (input-output tables).

Interpolation done for constructing annual time series wherever required (labour
input, intermediate input, GVO of services sectors)

Annual Series is formed because It would help in checking consistency among different input/output series.
 It would be useful for other users who require data for specific years.

Comparability is maintained in the concept used for data construction.
 Objective is to get a comparable data set for India and other countries.

Choice of sector classification is such that it is by and large comparable to the
classification being used in similar studies or undertaken in other countries
 EU KLEMS countries
 ASIA KLEMS countries
3
List of KLEMS-industries
Sl. No.
1
2
3-15
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18-26
18
19
20
21
22
23
24
25
26
Description of Industries in KLEMS
Agriculture, Hunting, Forestry and Fishing
Mining and Quarrying
Manufacturing sector
Food Products, Beverages and Tobacco
Textiles, Textile Products, Leather and Footwear
Wood and Products of Wood
Pulp, Paper, Paper Products, Printing and Publishing
Coke, Refined Petroleum Products and Nuclear Fuel
Chemicals and Chemical Products
Rubber and Plastic Products
Other Non-Metallic Mineral Products
Basic Metals and Fabricated Metal Products
Machinery, nec
Electrical and Optical Equipment
Transport Equipment
Manufacturing, nec; recycling
Electricity, Gas and Water Supply
Construction
Service sector
Trade
Hotels and Restaurants
Transport and Storage
Post and Telecommunication
Financial Services
Public Administration and Defense; Compulsory Social Security
Education
Health and Social Work
Other Services
4
Agriculture -1
Mining-1
Manufacturing -13,
Economy
26 Sectors
Electricity, gas, water
supply-1
Construction-1
Services-9
5
Construction of Variables
6

Data: NAS (National Accounts Statistics), ASI (Annual Survey of Industries)
and NSSO surveys on unorganized manufacturing

•
Methodology:
Step 1: Concordance of classification between NAS and the 26-KLEMS
(NIC- 1970, 1987, 1998 and 2004)
•
Step 2: Adjustment of Financial Intermediation Services Indirectly
Measured (FISIM).
NAS: Output net of FISIM at the aggregate level
(Manufacturing, Trade and Other services)
Allocation of FISIM according to sectoral GDP shares.
•
Step 3: ASI and NSS surveys [ 40th round(1984-85), 45th round (1989-90), 51st
round
(1994-95), 56th round (1994-95) and 62nd round (2005-06)] to split
NAS industries
7
List of Manufacturing Sector for which GVA obtained by making adjustment with NAS industries
KLEMS Ind.
No.
KLEMS Industry List
NAS
Methodology
5
Wood and products of Wood(20)
7
Coke, refined Petroleum and Nuclear
Fuel(23)
Rubber, petroleum Products(23+25)
Used ASI/ interpolated NSSO proportions to split
NAS sector (23+25) into separate 23 and 25
9
Rubber and Plastic Products (25)
Rubber, petroleum Products(23+25)
same as above
11
Basic metals and Fabricated Metal
Products(27+28)
Basic Metals (271+272+2731+2732)
Metal Products and machinery
(28+29+30)
Use ASI/ interpolated NSSO proportions to split
28,29 and 30.
Add fraction of 28 to Basic metals obtained from
NAS
12
Machinary,nec(29)
Metal Products and machinery
(28+29+30)
Use ASI/NSSO data to split NAS sector to separate
29 from 28+29+30
14
Electrical and optical Equipment(30 to
33)
Electrical machinery(31+32)
Split Nas sector Metal Products and machinery
(28+29+30) using ASI /NSSO proportions.
Add fraction of 30 to Electrical machinery obtained
from NAS
Manufacturing nec; recycling(36+37)
Other manufacturing(33 +369)
recycling (371+372)
Split Nas sector other manufacturing using ASI
/Nsso proportions.
Add fraction of 36 and 361 to Recycling (371+372)
15
Wood and Wood Products ,Furniture,
fixtures etc(20+361)
From 1980 to 1999 NAS sector (20+361) is split
using ASI ratio/NSSO ratios.
For interim years NSSO ratios are interpolated
between two consecutive rounds
Since 1999-00 NAS provides separate series for 20
and 361
Note : The figures in parenthesis are 2 digits NIC 98
classification.
Source : NAS Differential Issues, India KLEMS database
phase II
8
DATA SOURCES

National Accounts Statistics- 2007,2009 ,Back Series and NAS 2011

ASI( Annual Survey of Industries) for Registered Manufacturing sectors.

NSSO survey reports for Unregistered Manufacturing sectors.
Major Rounds Used: 40th Round ( 1984-85)
45th Round (1989-90)
51st Round (1994-95)
56th Round (2000-01)
62ndRound (2005-06)

Input Output Transaction Tables for services sectors
Benchmark Year:1978-79
1983-84
1989-90
1993-94
1998-99
2003-04
9
KLEMS
INDUST
RY NO
1,2,17
INDUSTRY
DESCRIPTI
ON
Agriculture,
Mining and
Quarrying,
Construction
3-15
METHODOLOGY

Details
GVO obtained from NAS both at current and constant prices from 1980-08.
The NAS 1999-00 series is used up to 2004-05 and then the new NAS series is spliced.
GVO for Total Manufacturing obtained by Aggregating Registered and Unregistered segments of
Manufacturing.
For 7 out of 13 industries GVO directly picked up from NAS
Manufacturing
sectors
For other 6 industries ASI ratios( for registered industries) and interpolated NSSO ratios(for unregistered
industries) are used to split output of NAS industries to concord with KLEMS industries.
Prior to 1999 GVO is not available in NAS for unregistered industries .Thus,
 GVO to GVA ratios is obtained from NSSO survey reports, for 1984-85(40th Round),
1989- 90 (45th round ) and 1994-95 (51st round )
 GVO to GVA ratio from 1999, is directly picked up from NAS.
 Ratios are linearly interpolated between four data points 1984,1989, 1994 and 1999. and
applied to Time Series of GVA of NAS to obtain time series on GVO consistent with NAS
from 1984 -98.The ratio of 40th round is taken backward to estimate output of 1980 to 1983.
The current price estimates have been deflated using WPI deflators to obtain constant price estimates.
16,18-26
Electricity, Gas
and water supply,
Services Sectors
Concordance between KLEMS and IOTT checked.
GVO to GVA ratio is obtained from IOTT benchmark years of 1978, 1983,1989,1993,1998,2003.
Ratio is linearly interpolated for intervening years.
This ratio is applied to GVA of NAS to obtain Time Series on GVO consistent with NAS.
KLEMS sector 24 : Public Administration and Defense –A special case !
GVO to GVA ratios from SNA tables applied to GVA series to compute output series
10
Major tasks in creating a Data Base on Labour

Make a Time series of Employment [number of persons]
from 1980 to 2008[A]

Prepare a Labour Quality Index from 1980 to 2008 [B]

Make a Time series of Labour Input from 1980 to
2008[=A*B]
Data sources

Employment and Unemployment Surveys 38th (1983) to
66th (2009-10) Round by National Sample Survey Office
(NSSO), and

Population Census.
11
 Employment has been computed as follows:
 Work Participation Rates (WPRs) by UPSS from EUS are applied to the corresponding
period’s census population of Rural Male, Rural Female, Urban Male and Urban
Female to find out the number of workers in the four segments
 The 26-industry distribution of Employment from EUS is applied to the number of
workers in step I to obtain Lij for each industry where i=1 for rural and 2 for urban
sectors, and j=1 for male and 2 for female
 Total persons in a year were obtained for each industry as the sum of the Lij over
gender and sectors, ΣiΣjLij


The estimates of employment for all the sectors are first obtained
for the major rounds.
Then interpolation between the two nearest major rounds has
been done for the period 1980-81 to 2008-09.
 For extrapolation backward to 1980-81 to 1982-83, the interpolation of the broad
industrial classification of 32nd round and 38th round is used. So the estimates
from 32nd round are mainly used as control numbers.
12
The methodology given by Jorgenson, D.W. et. al. (1987),
Productivity and US Economic Growth, uses the Tornqvist
translog index and it incorporates all compositional changes in
the labour force; e.g. age, sex, education, etc and uses earnings*
as the weights.
 But due to data limitations only educational composition of
workers has been considered in the current study.

* The use of current or real earnings does not affect the index.
The labour composition index has been computed using 5 educational categories namely :
1) up to primary, 2 ) primary , 3) middle,4) secondary and higher secondary, and 5) above
higher secondary
13
NSSO’s EUS provides earnings data mainly for regular- salaried
workers and casual workers
 The issue was how to estimate earnings of self employed?


The present study has used two approaches:
 First, a Mincer Wage equation has been estimated and sample
selection bias has been corrected for by using Heckman's two step
procedure – the equation is then used to get an estimate of the
earning of each self-employed person in the sample.
 Secondly, earning of self employed has also been estimated from the
monthly consumption expenditure of these households.
The lower of the two alternate estimates of earnings is taken to be the
earnings of a self employed person.

Detailss
14

Detailss

Manufacturing Sector: computations based on ASI and NSSO

Labour Share = Emoluments/GVA, computed for organized and unorganized
manufacturing separately

Combined on the basis of weights of share of GVA in Registered and unregistered Manufacturing sector

Non-Manufacturing Sector: computations based on NAS, NSSO and
Estimated GVA

Labour Share = [Compensation of Employees (CE) + Labour income of Selfemployed ]/GVA

NDP at factor cost= CE+OS+MI [available from NAS]
Self-employed wages = (wage per day) * (employment in terms of number of days
employed)

Note: labour income of self employed is based on estimated wage
equations and data on consumption expenditure of such households
15

India KLEMS uses measures of capital services rather than capital stock

WHY capital services?


Accounts for the heterogeneity of various assets
o A computer is added to a truck, only after adjusting for their
differences in marginal productivities
Does it make a difference

Increasing share of fast depreciating assets, which are expected to
deliver more capital services in short time, will increase the capital
service growth rates faster than capital stock

Effect on measured TFPG: TFPG will be overestimated when measured
using capital stock, if share of fast-depreciating assets are increasing
16

National Accounts Statistics, CSO
 Asset wise Gross Fixed Capital Formation (GFCF) at current prices, 1950-2008
 9 broad sectors, separately for Public and Private sectors

Annual Survey of Industries for organized manufacturing
 Gross fixed capital in historic prices (Bt)
 Depreciation (Dt)
 Gross fixed capital formation, total (across all assets)
 Gross value of assets by asset types
o GFCF= Actual additions (newly purchased, purchased second hand and own construction) during the year; minus
deduction and adjustment during the year plus depreciation adjustment for discarded assets during the year by asset types
o Differs from past studies which used aggregate capital stock using perpetual inventory method, disregarding the asset
composition (e.g. Ahluwalia, 1986 and 1991; Goldar, 1986; Rao,1996; Balakrishnan and Pushpangadan, 1994; Das,
2004), where investment is defined as Bt-Bt-1 +Dt.
 Time-series 1964-2008, with some breaks

NSSO surveys on unorganized manufacturing
 Net additions to fixed capital stock
 Rounds 45th (1989-90), 51st (1994-95), 56th (2000-01) and 62nd (2005-06)
17

Detailss
18

DATA SOURCE :
 National Accounts Statistics 2011,2009, 2007 and Back Series
 WPI, Office of the Economic Advisor, Ministry of Commerce and Industry
 Supply and Use Tables (known as the Input Output Transaction Tables) for Benchmark Years: 1978,
1983, 1989, 1993, 1998 and 2003

METHODOLOGY :
 Step 1 : Concordance is done between Input Output Transactions Table and India KLEMS
industries
 Step 2 : Estimates of inputs from IOTT, for 26 KLEMS industries are aggregated into Material,
Energy and Service Inputs .

Step 3: Projecting a time series of intermediate input vector from 1980 to 2008, for 26 KLEMS
Industries
Obtaining E,M , S for benchmark years :
 Concordance matched between IOTT tables and study industries.
 Estimates of inputs from IOTT are aggregated into E,M,S
 (continued…..)
19
Obtaining E MS for intervening years :
 For the Benchmark I/0 years proportions of Material Inputs, Energy Inputs, and Service Inputs in Total
Intermediate Inputs are calculated.
 Similar proportions for intervening years are projected by linear interpolation of the benchmark proportions.
 This gives a time series of proportions of Material, Energy and Service Inputs from 1980 to 2008
 The projected input vector in has been proportionately adjusted to match this gap between value added and
gross output in NAS to maintain consistency with NAS
 Detailss

Step 5: Constructing Deflators of Materials, Energy and Service Inputs for 26 India KLEMS Industries
separately
o
Deflators are obtained for each of the 115 commodity inputs( WPI deflators for Energy, Material inputs, GDP deflator for
service inputs)
o
Deflators obtained for different IO sectors have been combined using weights(Two IOTT has been used for this purpose 1989 and 1998. )

Step 6 : The deflators for Material, Energy and Service Inputs for each KLEMS Industry have been used to
deflate the Current prices Intermediate Input series to Constant prices

Step 7 : The time series of Intermediate Input at constant prices for 26 KLEMS industries have been
aggregated
( Tornqvist)to form higher level estimates for the broad sectors.

Detailss
20
Interpolation of Data
21
Estimating employment series in our Study :



Employment is based on UPSS from 38th (1983) to 66th (2009-10) round, adjusted for population census, and
various NIC concordances with KLEMS industries.
The estimates of employment for all the sectors are first obtained for the major rounds.
For non-survey years, employment numbers are intra/extrapolated to create time series for the period 1980 to
2008
Methodology used in other KLEMS Study

Jorgenson ,Ho and Stiroh (2007)
Uses decanal Census population data, Current population survey annual demographic survey
data(sample size is about 1/10th of population Census)


First step is to scale employment data based on CPS so that economy totals equals to Bureau of labour statistics
(BLS) annual tabulation based on monthly CPS.
This scaled matrices are then taken as marginal totals to interpolate and extrapolate the Census benchmarks.
The inter censal years is derived from the nearest two censuses and made consistent with these marginal totals
The census based matrices are calibrated to the BLS in order to provide a smooth series.
Ren and Sun (2007) for China, due to data limitation starts with estimate of labour input on several benchmark
years and eventually by interpolation/extrapolation completes time series for labour input for the whole period.
Pyo et.al (2005) for Korea relies on employment tables which is published as a supporting table to IO ,
available at an interval of 5 years and uses interpolation/extrapolation to create the employment series.
22
Although annual series is being constructed, conclusion drawn are based on trend growth rates which are
not much sensitive to the assumption underlying interpolation of input-output coefficients.

Methodology used in other KLEMS studies
 Jorgenson, Ho and Stiroh (2007) for US uses official benchmark Inter Industry
transaction tables and generates a time series of inter industry transaction tables for two
sub periods : 1977 -82 and 1983-2000 . To make the two sets comparable 1977-82 tables
are adjusted to the new set by iterative proportional fitting (RAS) method.
 Timmer et.al ( 2010) for EU KlEMS countries uses shares in intermediate inputs from
NA. IO tables as available at regular intervals , which necessities interpolation and
assumption of constant shares to build time series of E,M.S
 Ren and Sun (2007) for China , uses the current price benchmark I/O tables
1981,1987,192,1997 and constructs a time series of Use tables based on the adjusted
comparable benchmark tables using interpolation .
 Pyo et .al (2005) for Korea, uses benchmark I/O tables published by Bank of Korea and
estimates time series of input output by interpolation of I/O coefficients
 Liang (2005) for Taiwan , have constructed the time series of value shares of each
intermediate inputs by interpolating between the IO tables available in benchmark years
23
Possible use of data series being created
24
1. Estimates and analysis of Total Factor Productivity growth
Post and Telecommunication
5.49
Public Administration and Defense
3.94
Hotels and Restaurants
2.59
Trade
2.02
Electricity, Gas and Water Supply
2.01
Agriculture, Hunting, Forestry and Fishing
1.49
Other services
1.23
Financial Services
0.95
Food Products, Beverages and Tobacco
0.86
Transport and Storage
0.73
Other Non-Metallic Mineral Products
0.72
Health and Social Work
0.53
Electrical and Optical Equipments
0.48
Mining and Quarrying
0.3
Education
0.25
Chemicals and Chemical Products
0.17
Manufacturing, nec
-0.18
Pulp, Paper, Paper products, printing and publishing
-0.34
Textiles & Leather Products
-0.47
Basic Metals and Fabricated Metal Products
-0.75
Construction
-0.76
Transport Equipment
-0.82
Machinery, nec.
-1.71
Rubber and Plastic Products
-2.42
Coke, Refined Petroleum products and Nuclear fuel
-2.89
Wood and Products of wood
-5.53
Source: Authors’ Calculation
-6
-4
-2
0
2
4
Back
6
25
Trend growth rates of TFP (1980 to 2008) % per annum[ Gross output Approach]
4.97
Post and Telecommunication
2.81
Public Administration and Defense
1.64
Trade
1.31
Manufacturing, nec
Electricity, Gas and Water Supply
1.22
Agriculture, Hunting, Forestry and Fishing
1.17
1.08
Electrical and Optical Equipments
0.97
Other services
Hotels and Restaurants
0.79
Financial Services
0.78
0.55
Basic Metals and Fabricated Metal Products
Transport Equipment
0.42
Other Non-Metallic Mineral Products
0.41
0.24
Textiles & Leather Products
Education
0.2
Chemicals and Chemical Products
0.06
Pulp, Paper, Paper products, printing and publishing
-0.02
Health and Social Work
-0.06
Transport and Storage
-0.13
Food Products, Beverages and Tobacco
-0.15
Mining and Quarrying
-0.2
Rubber and Plastic Products
-0.23
-0.5
Machinery, nec.
-1.18
Construction
-1.3
Coke, Refined Petroleum products and Nuclear fuel
-4.23
Wood and Products of wood
-6
-4
-2
0
2
Note : TFP based on Labour input,capital services, E, M, S
4
6
26
Sources of Gross Output growth -1980-2008 (% per annum)
Post and Telecommunication
Public Administration and Defense
Trade
Manufacturing, nec; recycling
Electricity, Gas and Water Supply
Agriculture, Hunting, Forestry and Fishing
Electrical and Optical Equipment
Other Services
Hotels and Restaurants
Contribution of TFP growth
Financial Services
Basic Metals and Fabricated Metal Products
Transport Equipment
Contribution of Capital input
Other Non-Metallic Mineral Products
Textiles, Textile Products, Leather and Footwear
Education
Contribution of labour input
Chemicals and Chemical Products
Pulp, Paper, Paper Products, Printing and Publishing
Health and Social Work
Contribution of intermediate
Transport and Storage
inputs
Food Products, Beverages and Tobacco
Mining and Quarrying
Rubber and Plastic Products
Machinery, nec
Construction
Coke, Refined Petroleum Products and Nuclear Fuel
Wood and Products of Wood
-10.00
-5.00
0.00
5.00
Source : Authors’ calculation
10.00
15.00
20.00
27
Industry Contributions to Aggegate TFP growth, 1980-2008
Agriculture, Hunting, Forestry and Fishing
Trade
Public Administration and Defence
Other Services
Post and Telecom
Electrical and Optical Eqp
Electricity, Gas and Water Supply
Basic Metals and Fabricated Metal Products
Financial Intermediation
Manufacturing, nec
Hotels and Restaurants
Textiles & Leather Products
Transport Equipment
Other Non-Metallic Mineral Products
Education
Chemicals and Products
Pulp, Paper
Health and Social Work
Rubber and Plastic Products
Mining and Quarrying
Transport and Storage
Machinery, nec.
Food Products
Wood and Products
Coke, Refined Petroleum
Construction
-0.3
0.409
0.271
0.227
0.129
0.076
0.070
0.058
0.055
•Trend rate of TFP growth in
0.046
Indian economy from 1980-2008
0.031
was 1.4 % p.a.
0.027
0.023
•TFP
estimate
based
on
0.015
aggregate production possibility
0.010
frontier approach.
0.007
0.005
•Using
Domar
aggregation
0.000
-0.002
-0.006
-0.007
-0.016
-0.018
-0.019
-0.036
-0.063
weights the estimate of TFP
growth is 1.11 % p.a
•Inference
drawn: Resource
reallocation has contributed
about 0.3 percentage point gain
in TFP per year.
-0.184
-0.2
Note : TFP calculated with labour input, capital services
Source : Authors’ calculation
-0.1
0
0.1
0.2
0.3
0.4
0.5
Detailed of TFP
Detailed of Agriculture TFP
28
2. Analysis of trends in energy intensity of different sectors of Indian economy and determinants
of energy intensity.
Trend growth rates of Energy Input Intensity 1980-2008 ; (% per annum)
Construction
Food Products,Beverages and Tobacco
Transport and Storage
Education
Electricity, Gas and Water Supply
Other services
Basic Metals and Fabricated Metal Products
Coke, Refined Petroleum products and Nuclear fuel
Machinery, nec.
Financial Services
Trade
Pulp, Paper,Paper products,printing and publishing
Transport Equipment
Other Non-Metallic Mineral Products
Electrical and Optical Equipments
Agriculture,Hunting,Forestry and Fishing
Rubber and Plastic Products
Mining and Quarrying
Post and Telecommunication
Chemicals and Chemical Products
Manufacturing, nec
Health and Social Work
Textiles & Leather Products
Hotels and Restaurants
Public Administration and Defence
4.57
3.08
1.62
1.59
0.90
0.89
0.49
0.11
-0.15
-0.44
-0.65
-1.15
-1.16
-1.35
-1.60
-1.80
-1.81
-1.94
-2.10
-2.23
-2.34
-2.35
-2.35
-3.52
-4.00
-5.00 -4.00 -3.00 -2.00 -1.00
0.00
1.00
2.00
3.00
4.00
5.00
29
3.
Estimation of substitution possibilities (elasticity of substitution) – capital vs
labour & energy vs non-energy input.
- No such research is being done due to non-availability of time series.
4. Estimation of production function parameters for different sectors of the
Indian economy including bias in technical changes
- This may help in understanding employment trends and changes in factor income
share.
30

Shifting all variables to NAS 2004-05 base series.

Updating input and output estimates by including IOTT 2007, NSSO 67th round data.

Extending TFP estimates till 2009-10.

Determinants of productivity growth differences across industries in India under
different policy regimes [1980s, 1990s. 2000s].

Productivity comparison with other countries, especially emerging ones at the
individual industry level.

Separate analysis of organized and unorganized segments in the manufacturing sector.

Employment implications of our productivity results.

Determinants Energy efficiency intensity of the individual industries.
31
Trend Growth Rate of Total Factor Productivity using a Gross Output framework: 26
Industries, 1980-2009
Industry Description
Agriculture, Hunting, Forestry and Fishing
Mining and Quarrying
Food Products, Beverages and Tobacco
Textiles, Textile Products, Leather and Footwear
Wood and Products of Wood
Pulp, Paper, Paper Products, Printing and Publishing
Coke, Refined Petroleum Products and Nuclear Fuel
Chemicals and Chemical Products
Rubber and Plastic Products
Other Non-Metallic Mineral Products
Basic Metals and Fabricated Metal Products
Machinery, nec
Electrical and Optical Equipment
Transport Equipment
Manufacturing, nec; recycling
Electricity, Gas and Water Supply
Construction
Trade
Hotels and Restaurants
Transport and Storage
Post and Telecommunication
Financial Intermediation
Public Administration and Defense; Compulsory Social Security
Education
Health and Social Work
Other Services
GVO Growth
2.70
5.07
6.49
6.47
-0.86
6.20
5.59
8.02
10.12
7.75
6.62
6.32
9.81
8.17
11.04
7.03
6.64
7.43
9.15
7.33
14.08
9.77
5.71
7.02
5.09
6.60
TFPG*
1.25
-0.21
-0.13
0.13
-4.96
0.27
-0.92
0.46
0.59
0.17
-0.10
0.34
1.25
0.42
0.54
1.66
-1.31
1.88
1.84
0.49
5.12
1.84
2.91
-0.89
-1.12
1.02
TFPG**
1.17
-0.19
-0.15
0.11
-4.97
0.13
-0.97
0.42
0.55
0.11
-0.16
0.31
1.14
0.31
0.69
1.58
-1.31
1.87
1.83
0.32
4.83
1.44
2.88
-1.44
-1.42
0.96
.
Note: In the TFP estimates of Agriculture, Land is taken as an input.
* TFP Growth computed using Labour input, K stock and intermediate input.
** TFP Growth computed using Labour input, K service and Intermediate inputs.
32
Thank You
33
Additional slides
34
Back
35
Construction of Time Series on Gross OutputMETHODOLOGY
METHODOLOGY :
STEP 1 Measuring Gross Output of Agricultural Sector: (KLEMS Industry No:
1)
NAS provides nominal and real GVO series for
• Crops and plantation
•Animal Husbandry
•Forestry and Lodging
•Fishing
Aggregating GVO of these 4 subsectors we derive GVO of Agricultural Sector
Step 2Measuring Gross Output of Mining & Quarrying and
Construction: (KLEMS Industry No: 2and 17)
• Gross output estimates in current and constant prices form 1980 to 2008
is
directly available from NAS
Back
36
STEP 3
MEASURING GROSS OUTPUT FOR MANUFACTURING SECTORS ( KLEMS INDUSTRY
NO :3 TO 15)
Time series of GVO on total manufacturing is obtained by summing registered
with unregistered manufacturing segments.
◦ Measuring Gross output of registered manufacturing segments
Table 1: List of Registered Manufacturing Sectors whose GVO is directly taken from NAS
KLEMS
INDUSTRY
NIC98
Industries
Industry Description
3
4
15 to 16
17 to 19
Food Products, Beverages and Tobacco
Textiles, Textile Products, Leather and Footwear
5
20
Wood and Products of Wood*
6
21 to 22
Pulp, Paper, Paper Products, Printing and Publishing
8
24
Chemicals and Chemical Products
10
26
Other Non-Metallic Mineral Products
14
34 to 35
Transport Equipment
Note : *Direct estimates are available in NAS from 1999 to
2008
Source : India KLEMS database-PHASE II
Back
37
Table 2: List of KLEMS Registered Manufacturing Sector for which Gross Output is obtained by making adjustment
with NAS industries
KLEMS Ind.
No.
KLEMS Industry List
NAS
Wood and Wood Products ,Furniture,
fixtures etc(20+361)
Methodology
From 1980 to 1999 NAS sector (20+361) is split
using ASI ratio.
Since 1999-00 NAS provides separate series for 20
and 361
5
Wood and products of Wood(20)
7
Coke, refined Petroleum and Nuclear
Fuel(23)
Rubber, petroleum Products(23+25)
Used ASI proportions to split NAS sector (23+25)
into separate 23 and 25
9
Rubber and Plastic Products (25)
Rubber, petroleum Products(23+25)
same as above
11
Basic metals and Fabricated Metal
Products(27+28)
Basic Metals (271+272+2731+2732)
Metal Products and machinery
(28+29+30)
Use ASI proportions to split 28,29 and 30.
Add fraction of 28 to Basic metals obtained from
NAS
12
Machinary,nec(29)
Metal Products and machinery
(28+29+30)
Use ASI data to split NAS sector to separate 29 from
28+29+30
14
Electrical and optical Equipment(30 to
33)
Electrical machinery(31+32)
Split Nas sector Metal Products and machinery
(28+29+30) using ASI proportions.
Add fraction of 30 to Electrical machinery obtained
from NAS
15
Manufacturing nec; recycling(36+37)
Other manufacturing(33 +369)
recycling (371+372)
Split Nas sector other manufacturing using ASI
proportions.
Add fraction of 36 and 361 to Recycling (371+372)
Note : The figures in parenthesis are 2 digits NIC 98
classification.
Source : NAS Differential Issues, India KLEMS database
phase II
Back
38
◦ Measuring Gross output of unregistered manufacturing sectorsa)Estimating GVO series from 1999 to 2008 :
GVO directly obtained from NAS for 7 KLEMS unregistered manufacturing
sectors
For other 6 sectors NSSO Ratio’s (56th Round ,62nd Round) are used to split
GVO of certain NAS sectors to concord with KLEMS sectors.
Table 3: List of KLEMS Unregistered Manufacturing Industry for which Gross output is obtained by making
adjustment with NAS
KLEMS
INDUSTRY NO
KLEMS Industry List
NAS
Methodology
7
Coke, refined Petroleum and
Nuclear Fuel(23)
Rubber, petroleum
Products(23+25)
Used NSSO proportions to split NAS sector (23+25) into separate
23 and 25.
Sectoral GVO ratio(23 upon 23 and 25) computed for NSSO 56th
Round (2000-01) and NSSO 62nd Round(2005-06)For the interim
years the ratio have been interpolated and then applied to split
NAS sector .
9
Rubber and Plastic Products (25)
Rubber, petroleum
Products(23+25)
same as above
11
Basic Metals
Used NSSO proportions to split 28,29 and 30. Major NSSO rounds
Basic metals and Fabricated Metal
(271+272+2731+2732)
used : 56th Round (2000-01) and 62nd Round (2005-06).For
Products(27+28)
Metal Products and machinery
Interim years ratio's have been interpolated.
(28+29+30)
Add fraction of 28 to Basic metals obtained from NAS
12
Machinary,nec(29)
Metal Products and machinery
(28+29+30)
Use NSSO data to split NAS sector to separate 29 from 28+29+30.
Major NSSO rounds used are : 56th round (2000-01) and 62nd
round(2005-06). Ratio for interim period has been interpolated.
14
Electrical and optical
Equipment(30 to 33)
Electrical machinery(31+32)
Split Nas sector Metal Products and machinery (28+29+30) using
NSSO proportions.
Add fraction of 30 to Electrical machinery obtained from NAS
15
Manufacturing nec;
recycling(36+37)
Other manufacturing(33 +369)
recycling (371+372)
Split Nas sector other manufacturing using NSSO proportions.
Add fraction of 36 and 361 to Recycling (371+372)
Back
39
Post and Telecommunication
Electrical and Optical Equipments
Financial Services
Manufacturing, nec
Rubber and Plastic Products
Chemicals and Chemical Products
Hotels and Restaurants
Transport Equipment
Other Non-Metallic Mineral Products
Basic Metals and Fabricated Metal Products
Electricity, Gas and Water Supply
Transport and Storage
Education
Construction
Other services
Textiles & Leather Products
Food Products,Beverages and Tobacco
Trade
Pulp, Paper,Paper products,printing and publishing
Public Administration and Defence
Coke, Refined Petroleum products and Nuclear fuel
Mining and Quarrying
Health and Social Work
Machinery, nec.
Agriculture,Hunting,Forestry and Fishing
Wood and Products of wood-1.09
-2.00
14.16
12.18
9.87
9.75
9.45
8.06
7.89
7.86
7.66
7.56
7.38
7.25
7.02
6.68
6.65
6.45
6.38
6.22
5.72
5.57
5.42
5.08
4.94
4.28
Back
2.82
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
40
Back
 Major (Quinquennial) Rounds of EUS: 32nd (197778), 38th(1983), 43rd(1987-88), 50th(1993-94),
55th(1999-00), 61st(2004-05), and 66th (200910)

For Quinquennial (or major) rounds EUS uses
 Usual Status [Usual Principal Status(UPS) and Usual
Principal & Subsidiary Status (UPSS)]
 Current Weekly Status(CWS)
 Current Daily Status (CDS)
Back
42
For India KLEMS, employment estimates are based on
UPSS from 38th (1983) to 66th (2009-10) round,
adjusted for population census.
 Since different rounds of EUS use different National
Industrial Classification (NIC), so a Concordance
between India KLEMS, NIC-1970, 1987, 1998 and
2004 was done for all the 26 sectors.
* Cotton ginning is included in cotton textiles and custom

tailoring in other business services
Back
43
Jorgenson’s Approach
-The methodology given by Jorgenson, et al (1987) uses the Tornqvist
translog index and it incorporates all compositional changes in the labour
force; e.g. age, sex, education, etc and uses earnings* as the weights.
- But due to data limitations only educational composition of workers has
been considered in the current study.
* The use of current or real earnings does not affect the index.
The labour composition index has been computed using 5 educational catagories namely :
1) up to primary, 2 ) primary , 3) middle,4) secondary and higher secondary, and 5) above
higher secondary
Back
44
Construction
5.96%
Post and Telecom
5.87%
Rubber and Plastic Products
5.30%
Financial Intermediation
4.65%
Machinery, nec.
4.49%
Transport and Storage
4.17%
Coke, Refined Petroleum
4.07%
Electrical and Optical Eqp
4.04%
Education
4.04%
Real Estate
4.00%
Hotels and Restaurants
3.77%
Sale, Maintenance and Repair of Motor
3.75%
Health and Social Work
3.54%
Pulp, Paper
3.25%
Manufacturing, nec
3.12%
Transport Equipment
2.91%
Chemicals and Products
2.60%
Basic Metals and Fabricated Metal Products
2.43%
Food Products
1.92%
Other Non-Metallic Mineral Products
1.86%
Mining and Quarrying
1.54%
Electricity, Gas and Water Supply
Agriculture
1.40%
0.88%
Wood and Products
0.65%
Textiles & Leather Products
0.64%
Public Administration and Defence
0.59%
0.00%
1.00%
2.00%
3.00%
Source : Authors’ calculations
4.00%
5.00%
6.00%
7.00%
Back
45
Machinery, nec.
Chemicals and Products
Other Services
Mining and Quarrying
Pulp, Paper
Rubber and Plastic Products
Electricity, Gas and Water Supply
Other Non-Metallic Mineral Products
Textiles & Leather Products
Health and Social Work
Coke, Refined Petroleum
Transport Equipment
Food Products
Manufacturing, nec.
Trade
Transport and Storage
Education
Public Administration and Defence
Post and Telecom
Electrical and Optical Equipment
Hotels and Restaurants
Financial Intermediation
Construction
Basic Metals and Fabricated Metal Products
Agriculture
Wood and Products
Source : Author’s calculations
1.92
1.22
1.15
1.13
1.05
1.04
0.9
0.87
0.87
0.86
0.83
0.81
0.81
0.8
0.77
0.73
0.62
0.62
0.57
0.57
0.52
0.46
0.42
0.4
0.26
0.17
0
0.5
1
1.5
2
2.5
Back
46
Growth Rate of Labour Input by Industry, 1980 to 2008
(% per annum)
Post and Telecom
Machinery, nec.
Construction
Rubber and Plastic Products
Other Services
Financial Services
Coke, Refined Petroleum
Transport and Storage
Education
Electrical and Optical Equipment
Trade
Health and Social Work
Pulp, Paper
Hotels and Restaurants
Manufacturing, nec.
Chemicals and Products
Transport Equipment
Basic Metals and Fabricated Metal Products
Other Non-Metallic Mineral Products
Food Products
Mining and Quarrying
Electricity, Gas and Water Supply
Textiles & Leather Products
Public Administration and Defence
Agriculture
Wood and Products
6.44
6.41
6.38
6.33
5.15
5.11
4.9
4.89
4.67
4.62
4.52
4.4
4.31
4.29
3.91
3.81
3.72
2.83
2.72
2.72
2.67
2.3
1.51
1.22
1.14
0.82
0
1
Source : Authors ‘ calculations
2
3
4
5
6
7
Back
47
48
Some Key Findings
Labor productivity growth has been computed by as the difference
of growth rate of real value of gross output and growth rate of
persons employed.
The median for the 26 industries is 4.1 percent per annum.
Labor productivity growth rates vary widely across industries for
the given periods. Post and Telecommunication, Electrical and
Optical equipment, Electricity Gas and Water, textile and Leather
products are some of the industries which record high rates of
growth in labor productivity.
As regards the sources of labor productivity growth, we find that
the median contribution of intermediate inputs is 1.8 percentage
points out of 4.1 percentage points followed by 1.1 percentage
points by capital deepening.
49
8.29
Post and Telecommunication
8.14
Electrical and Optical Equipments
6.63
Manufacturing, nec
5.98
Electricity, Gas and Water Supply
5.81
Textiles & Leather Products
5.8
Other Non-Metallic Mineral Products
5.47
Chemicals and Chemical Products
5.22
Financial Services
5.13
Basic Metals and Fabricated Metal Products
4.98
Public Administration and Defence
4.95
Transport Equipment
4.47
Food Products, Beverages and Tobacco
Rubber and Plastic Products
4.15
Hotels and Restaurants
4.12
3.54
Mining and Quarrying
3.08
Transport and Storage
2.98
Education
2.65
Other services
2.47
Trade
2.47
Pulp, Paper, Paper products, printing and publishing
1.94
Agriculture, Hunting, Forestry and Fishing
1.4
Health and Social Work
1.35
Coke, Refined Petroleum products and Nuclear fuel
0.72
Construction
Machinery,
nec.
-0.21
Wood -1.74
and Products of wood
-4
-2
0
2
4
6
8
10
Source : Authors’ calculations
50
Other Services
Health and Social Work
Education
Public Administration and Defense
Financial Services
Post and Telecommunication
Transport and Storage
Hotels and Restaurants
Trade
Construction
Electricity, Gas and Water Supply
Manufacturing, nec; recycling
Transport Equipment
Electrical and Optical Equipment
Machinery, nec
contribution of Labour
Basic Metals and Fabricated Metal Products
Composition
Other Non-Metallic Mineral Products
Rubber and Plastic Products
Contribution of capital service
Chemicals and Chemical Products
per person employed
Coke, Refined Petroleum Products and Nuclear Fuel
Pulp, Paper, Paper Products, Printing and Publishing
Contribution of Intermediate
Wood and Products of Wood
inputs per person employed
Textiles, Textile Products, Leather and Footwear
Contribution from TFPG
Food Products, Beverages and Tobacco
Mining and Quarrying
Agriculture, Hunting, Forestry and Fishing
-6.00
-4.00
-2.00
Source : Authors’ Calculations
0.00
2.00
4.00
6.00
8.00
10.00
51
Details of Capital Input
Series
Back
52

Objective
 Construction of capital input – A major input in the productivity analysis

Capital services series for the 26 India KLEMS industries and for the total
economy for the period 1980-2008
 This includes capital stock by different asset types
 In particular, distinguish between equipment and non-equipment
capital
Investment prices of different asset types
Rental prices (user costs) of capital services by different asset types
(equivalent to wages paid for labor services provided by workers)
 Depreciation rates by asset types


Back
53

Existing measures of capital in India
◦ NAS estimates of gross and net capital stock
 GFCSt = GFCFt + GFCSt-1 - GFCFt-L ; t > L; GFCSt = GFCFt + GFCSt-1 ; t<=L;
GFCS=Gross fixed capital stock; GFCF=Gross fixed capital formation; t=year; L =
average life time of asset (one-hoss-shay depreciation; light bulb depreciation)
 NFCSt=GFCSt - CCFCt ; NFCS=Net fixed capital stock; CCFC=cumulative
consumption of fixed capital, where consumption of fixed capital is measured as
GFCSt/L.

Past academic attempts
◦ TFPG studies, in particular in organized manufacturing sector used aggregate
capital stock using perpetual inventory method, disregarding the asset
composition (e.g. Ahluwalia, 1986 and 1991; Goldar, 1986; Rao,1996; Balakrishnan and
Pushpangadan, 1994; Das, 2004)
◦ Often no depreciation, or a common depreciation rate (e.g. Goldar, 1985, Timmer,
1999, but still capital stock) for all assets is used
◦ Timmer, 1999 shows an overestimation of capital stock of more than 50% in other
studies, when no allowance for depreciation is given
Back
54
Co-existence of multiple vintages
=Different vintages have different marginal productivities
=Each generation of capital assets will embody different levels of
technology, and are therefore not homogenous
And
Heterogeneity of Capital Assets
=Aggregating computers, machines, trucks and many more!
=Cambridge Controversy (aggregating money value vs.
impossibility of aggregation)
Back
55

Perpetual Inventory Method
◦ Aggregate money value of different assets (e.g. value of computers + value
of trucks) of different vintages (e.g pentium 3 + pentium 4)

Problems

Aggregation of vintages:
◦ Use efficiency weights (under the assumption that newer vintage embody
newer technology).
◦ Takes account of differences in vintages to some extent, given that
depreciation and asset prices are properly measured

Aggregation across assets:
◦ Aggregate money value of different assets. Takes no account of asset
heterogeneity
Back
56
Capital services using Jorgenson methodology:
Asset-wise capital stock growth rates are aggregated using compensation shares of each
asset as weights, so that differences in marginal productivities of these assets are
incorporated

Capital service growth rates are arrived at as the weighted growth rate of individual
capital stock, where the weights being the compensation share of each asset type, i.e.
 ln K j 
v
K
k, j
 ln K k , j
k

Where weights are
v
K
k, j

pkK, j K k , j
pK
j K j
 And individual asset wise capital stock is estimated using Perpetual Inventory
Method:
S k ,T  S k ,T 1 (1   k )  I k ,T
 And rental prices as
pkK,t  pkI ,t 1it*   k pkI ,t
Back
57

Capital stock by asset type (Perpetual Inventory Method)
 Time-series on gross investment by asset type
 Time-series on investment prices by asset type
 Depreciation profile by asset type
 An estimate of benchmark capital stock

Compensation share by asset type
 Rental prices
 Rate of return
 Depreciation rates by asset type
 Investment prices by asset type
 Capital stock by asset type
Back
58
Depreciation assumptions

Asset price deflators

Concordance between KLEMS and
Indian classifications
NDP

Services
Unavailability of data on capital
formation by assets and industries
100
90
80
70
60
50
40
30
20
10
0
Manufacturing

Share in Net Domestic Product, 2008
Mining &
Utilities
Presence of large unorganized
sector
Agriculture

Unorganized
Organized
Back
59
Asset in basic source
NAS
ASI
NSSO
KLEMS
Transport Equipment
X
X
X
Transport
Machinery & Equipment
X
X
X
Machinery
X
X
Excluded
X
X
Construction
Land
Construction
X
Non-residential structures
X
Construction
Residential structures
X
Construction
Other assets
X
Machinery
Census Adjustment to ASI data
Census/factory ratio in 1973 is used to covert investment series prior to 1973,
in order to ensure temporal comparison
NAS adjustment to both ASI and NSSO data
All data are redistributed across industries and assets, in such way that the
aggregates are consistent with published NAS totals for the relevant sectors.
Back
60

Current price GFCF (I) series by 26 industries and 3 asset types.

NAS deflators for machinery, transport and construction separately

Depreciation rates (), based on assumed lifetimes in NAS (double
declining balance rate)
Asset Type
Building and Construction
Transport Equipment
Non-ICT Machinery
Hardware and Software
Communication Equipment
Depreciation Rate (%)
2.5
10.00
8.00
31.5
11.5
Source: NAS and EU KLEMS

Initial Capital stock approximated by published net fixed capital stock
from NAS.
 If asset/industry wise initial stock is not available, industry/asset
distribution of GFCF is used.
Back
61

Time-series of capital stock in asset k (Sk), for any given industry is
computed using PIM
S k ,T  S k ,T 1 (1   k )  I k ,T

And rental prices are computed using investment prices, depreciation
rates & an external rate of return proxied by average of government
securities and prime lending rate as
pkK,t  pkI ,t 1it*   k pkI ,t
62
Back
Capital Services, Aggregate Economy and
Broad Sectors
Capital
services show a
faster
growth
rate
in
manufacturing
sector,
compared to all other
sectors of the economy.
Agricultural
sector has
witnessed
only
very
negligible growth in capital
services over years.
While capital services in
manufacturing sector grew by
almost 9 times over the period
of a quarter of a century,
capital
services
in
the
agricultural sector has only
increased by 3 times.
63
1400
1200
Agriculture
Manufacturing
Construction
Economy
Mining
Electricity
Services
1000
800
600
400
200
0
Source: Authors’ calculations
Back
14
12
10
8
6
4
2
0
Back
64
◦ The study provides new estimates of capital input for productivity analysis in Indian
economy and 26 sectors for the period 1980-2008.
◦ Compared to conventional measures of capital stock, measure of capital services are
theoretically pertinent, as it takes asset heterogeneity into account, while aggregating
capital services.
◦ This is of particular significance, given the importance of equipment investment for
economic growth, which is growing faster in the Indian economy.
◦ Measures of capital services are increasingly preferred in the international academic
sphere, over the conventional measures of capital stock.
◦ This study makes an important contribution by providing estimates of capital
services.
• Capital services grow faster than capital stock in almost all sectors, reflecting the
increasing share of equipment capital
• This will have serious implication for productivity measurement, as conventional
measure of capital stock will overestimate the contribution of TFPG to GDP growth.
65
The measure of capital service we use is constructed under somewhat
unrealistic neoclassical assumptions, and are subject to any criticisms to
standard neoclassical assumptions, that equate marginal cost with prices.
In addition, any limitations associated with the measurement of capital
stock using the perpetual inventory method, such as the assumption of
geometric depreciation rate and imputation of initial stock are also
applicable. This is because the estimation of the flow of capital services are
assumed to be proportional to capital stock at individual asset level.
At the individual asset level, the assumed depreciation rates are lower
than many cross-country studies and databases (e.g. Easterly and Levine,
2001; Penn World Tables, EU KLEMS). The assumed low depreciation might
overestimate our measure of capital input growth rate. This issue will be
addressed in the further revisions of the data.
66
THANK YOU
67
Details of Intermediate Input
Back
68
ENERGY INPUT
1.
2.
3.
4.
5.
COAL AND LIGNITE
PETROLEUM PRODUCTS
ELECTRICITY**
NATURAL GAS
GAS (LPG)
** For electricity used in the electricity sector, since there is a good
amount of inter-firm sale and purchase of electricity, it has been
treated as material rather than as energy
Back
69
SERVICE INPUT
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Water supply
Railway transport services
Other transport services
Storage and warehousing
Communication
Trade
Hotels and restaurants
Banking
Insurance
Ownership of dwellings
Education and research
Medical and health
Other services
Public administration

All other intermediate inputs barring the above mentioned inputs are classified as material
input
Back
70

DATA SOURCE :
1.
National Accounts Statistics 2011,2009, 2007 and Back Series
2.
WPI, Office of the Economic Advisor, Ministry of Commerce and Industry
3.
Supply and Use Tables (known as the Input Output Transaction Tables) for
Benchmark Years: 1978, 1983, 1989, 1993, 1998 and 2003

METHODOLOGY :
Step 1 : Concordance is done between Input Output Transactions Table and India
KLEMS industries
 A concordance table between the classification used in the India KLEMS
Industries and the Input Output Transaction Table (1978, 1998 and 2003) has been
prepared.
 1978: 60x60 matrix
 1998: 115x115 matrix
 2003: 130x130 matrix
 The IOTT industries are aggregated to form 26 India KLEMS Industries.
Back
71

METHODOLOGY :
Step 2: Obtaining estimates for Material, Energy and Service Inputs for 26
India KLEMS Industries, for benchmark years
 Estimates of inputs from IOTT, for 26 KLEMS industries are aggregated into
 Value of Energy Inputs
 Value of Material Inputs
 Value of Service Input
 Value of Total Intermediate Inputs (summation of the above three)
Thus, for each benchmark year, estimates are obtained for Material, Energy
and Service Inputs in 26 India KLEMS Industries.
Back
72
KLEMS Sector 24, Public Administration and Defense- A Special Case!
IOTT gives no information on Inputs for this Industry. The estimation of inputs for
Public Administration for years prior to 1999 has been done using Tables on ‘Final
consumption expenditure of Govt. administrative departments’ published in NAS and
for years post 1999, the ‘SNA’ tables have been used.
 Statement 27, Final Consumption Expenditure of Administrative Departments, in
NAS provides figures on net Purchase of Commodities, by Administrative department.
This figure is taken for benchmark IOTT years 1978, 1983, 1989, 1993 and 1998
 Statement 67, Cross Classification of Output/Value Added by Kind Of Economic
Activity, in SNA Table provides figures on Intermediate Consumption in Public
Administration and Defense, for the year 2003.
 For each benchmark year thus, the total intermediate consumption has been distributed
across all IO sectors, using the GFCE proportion to get Material, Energy and Service
Inputs.
Back
73
Benchmark Year: 1998
Output/Input
Source
NAS
GVO
GVA
Gap
Material
For Benchmark IOTT year: Absolute
Energy
value of Inputs from Input Output
Service
Transaction Table are available
Total Input
Values
(in Rs Crore)
241873
88784
153089
73132
4308
37399
114839
Ratio
Gap from NAS/ Total Input from IOTT = 1.33
Adjusted Input reported
Material
Energy
Service
Total Input
= 1.33
= 1.33
= 1.33
= 1.33
x
x
x
x
73132
4308
37399
114839
97490
5743
49855
153089
Back
74
Non Benchmark Year: 1999
Output/Input
Values
(in Rs Crore)
GVO
279464
GVA
102007
Gap
177457
Source
NAS
Material
For Non-Benchmark IOTT year:
Interpolated Proportions of Inputs Energy
from Input Output Transaction Service
Table have been used
Total Input
Adjusted Input reported
0.64
0.04
0.32
1
Material
= .64 x 177457
113081
Energy
= .04 x 177457
7300
Service
= .32 x 177457
57076
Total Input
= 1 x 177457
177457
Back
75
Step 6: Constructing Deflators of Materials, Energy and Service Inputs for 26 India
KLEMS Industries separately
 Deflators are obtained for each of the 115 commodity inputs(each row of the IO
Matrix)
 WPI for the period 1980 to 2008 is taken from Office of the Economic
Adviser, Ministry of Commerce and Industry.
 For Electricity as an input entering into the production process of an
industry, depending on the nature of economic activity, the right price of
electricity has been chosen.
 For Service Inputs, since WPI is not available hence implicit GDP deflators
from NAS are used.
 Deflators obtained for different IO sectors have been combined using weights.
 The weights are based on the column of the relevant KLEMS industry in the
IO table.
 Two IOTT has been used for this purpose - 1989 and 1998.
 The price series based on 1989 table has been used from 1980 to 1993 and
the 1998 table has been used for the price series from 1993 to 2008.
 Once the two series have been formed, these have been spliced.
Back
76
Step 7 Computing Time Series on Intermediate Input for 26 India KLEMS
Industries from 1980-2008, Constant prices
 The deflators for Material, Energy and Service Inputs for each KLEMS
Industry have been used to deflate the Current prices Intermediate Input
series to Constant prices.
Step 8: Computing Time Series on Intermediate Input for Broad Sectors of
Indian Economy from 1980-2008, Constant prices
 The time series of Intermediate Input at constant prices for 26 KLEMS
industries have been aggregated to form higher level estimates for the broad
sectors.
Thus a time series of Intermediate Input at constant price is constructed for
KLEMS 26 Sectors and Broad Sectors.
Back
77


For certain INDIA KLEMS Sectors, there are huge year to year fluctuations in
Intermediate Inputs, especially in Energy Input and Service Inputs. This is primarily
because, in certain IOTT years, there is an abrupt increase or decrease in the
proportion of an input going into a KLEMS industry. Therefore such fluctuations have
been smoothened by excluding the particular year in which there is an unusually
high/low IOTT Input Proportion going into an Industry’s Production process.
The KLEMS sectors where such adjustment has been done are as follows:










Textiles, Textile Products, Leather and Footwear
Wood and Products of Wood
Machinery, nec
Manufacturing, nec; recycling
Electricity, Gas and Water Supply
Trade
Health and Social Work
Public Administration and Defense; Compulsory Social Security
Education
Other Services
Back
78
 The Intermediate Input Series at constant prices is not consistent with the
Gross Output Series at Constant Prices i.e. the following equation does not
hold true at constant prices.
‘E + M + S’ at Constant Price = ‘GVO – GVA’ at Constant Price
However only for Agriculture since NAS gives double deflated value, hence the
Intermediate Input series is consistent with the Output series in constant
prices.
Back
79
Post and Telecommunication
Electrical and Optical Equipment
Rubber and Plastic Products
Manufacturing, nec; recycling
Other Non-Metallic Mineral Products
Financial Services
Chemicals and Chemical Products
Transport and Storage
Construction
Mining and Quarrying
Other Services
Hotels and Restaurants
Transport Equipment
Textiles, Textile Products, Leather and Footwear
Food Products, Beverages and Tobacco
Pulp, Paper, Paper Products, Printing and Publishing
Coke, Refined Petroleum Products and Nuclear Fuel
Basic Metals and Fabricated Metal Products
Electricity, Gas and Water Supply
Public Administration and Defense; Compulsory Social Security
Machinery, nec
Trade
Health and Social Work
Agriculture, Hunting, Forestry and Fishing
Wood and Products of Wood
Education
14.74
12.48
10.87
10.46
9.82
9.06
8.72
8.57
8.49
8.18
8.15
7.51
6.98
6.62
6.31
6.21
6.11
6.00
5.70
5.19
4.71
4.59
2.41
2.01
2.00
0.84
0
2
4
6
8
10
12
14
16
Back
80
12.06
11.25
10.58
Post and Telecommunication
Construction
Electrical and Optical Equipment
Food Products, Beverages and Tobacco
Financial Services
Transport and Storage
Education
Electricity, Gas and Water Supply
Basic Metals and Fabricated Metal Products
Rubber and Plastic Products
Other Services
Manufacturing, nec; recycling
Transport Equipment
Other Non-Metallic Mineral Products
Wood and Products of Wood
Chemicals and Chemical Products
Trade
Coke, Refined Petroleum Products and Nuclear Fuel
Pulp, Paper, Paper Products, Printing and Publishing
Hotels and Restaurants
Machinery, nec
Textiles, Textile Products, Leather and Footwear
Mining and Quarrying
Health and Social Work
Public Administration and Defense; Compulsory Social Security
Agriculture, Hunting, Forestry and Fishing
9.46
9.43
8.87
8.61
8.28
8.05
7.64
7.54
7.41
6.70
6.31
6.05
5.83
5.57
5.53
4.57
4.37
4.13
4.10
3.14
2.59
1.57
1.02
0
2
4
6
8
10
12
14
Back
81
10.87
10.65
10.64
Electrical and Optical Equipment
Post and Telecommunication
Financial Services
Education
Hotels and Restaurants
Transport and Storage
Food Products, Beverages and Tobacco
Transport Equipment
Construction
Electricity, Gas and Water Supply
Other Services
Basic Metals and Fabricated Metal Products
Chemicals and Chemical Products
Textiles, Textile Products, Leather and Footwear
Rubber and Plastic Products
Other Non-Metallic Mineral Products
Mining and Quarrying
Public Administration and Defense; Compulsory Social…
Coke, Refined Petroleum Products and Nuclear Fuel
Pulp, Paper, Paper Products, Printing and Publishing
Manufacturing, nec; recycling
Machinery, nec
Agriculture, Hunting, Forestry and Fishing
Wood and Products of Wood
Trade
Health and Social Work
9.66
9.35
8.79
8.55
8.27
8.27
7.94
7.80
7.70
6.69
6.56
6.43
5.90
5.86
5.84
5.81
5.76
4.11
3.94
3.87
3.87
2.78
2.62
0.00
2.00
4.00
6.00
8.00
10.00
12.00
Back
82
Additional slides on factor income share
Back
83
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1
0.9
0.8
agriculture
0.7
mining
0.6
manufactuirng
0.5
electricity
0.4
construction
0.3
services
0.2
economy
0.1
0
Back
84

The labour income and capital income out of gross value added, is further
distributed into income share of labour in gross output and income share of
capital in gross output.

The individual shares of intermediate inputs, that is, Material, Energy and
Service in Gross Output is first calculated.
Thus we get the share of labour, capital, material, energy and service in Output.
Back
85
Details of TFP results
Back
86
Contribution of Factor Inputs and TFP to GVA growth, by sub-periods (percent per annum)
BROAD SECTOR
Period
Real value
added
growth
3.1
Contribution of
Labour persons
Contribution of
Capital
services
0.67
TFP
growth
0.54
Contribution of
Labour
quality
0.11
2000-08
1980-99
2.61
5.69
-0.02
0.70
0.24
0.14
1.68
4.63
0.71
0.22
2000-08
1980-99
2000-08
1980-99
1.95
6.45
9.48
6.59
0.54
0.67
0.68
0.79
0.57
0.46
0.42
0.37
2.43
5.28
5.62
4.29
-1.59
0.04
2.76
1.14
2000-08
1980-99
2000-08
1980-99
2000-08
1980-99
2000-08
9.99
1.51
9.36
6.49
8.94
5.24
7.55
0.46
4.51
7.32
1.92
1.43
0.91
0.72
0.28
0.18
0.31
0.51
0.60
0.71
0.69
2.31
1.01
2.43
2.32
4.78
2.51
3.89
6.93
-4.19
-0.69
1.74
2.14
1.11
2.26
Agriculture, Hunting, 1980-99
Forestry, Fishing
Mining and
Quarrying
Manufacturing
Electricity, Gas and
Water Supply
Construction
Services
Total Economy
Source : Author’s calculations
1.78
87
Post and Telecommunication
Public Administration and Defense
Hotels and Restaurants
Trade
Electricity, Gas and Water Supply
Other Services
Financial Services
Food Products, Beverages and Tobacco
Transport and Storage
Other Non-Metallic Mineral Products
Agriculture, Hunting, Forestry and Fishing
Health and Social Work
Electrical and Optical Equipment
Contribution of TFP growth
Mining and Quarrying
Contribution of factor inputs
Education
Chemicals and Chemical Products
Manufacturing, nec; recycling
Pulp, Paper, Paper Products, Printing and Publishing
Textiles, Textile Products, Leather and Footwear
Basic Metals and Fabricated Metal Products
Construction
Transport Equipment
Machinery, nec
Rubber and Plastic Products
Coke, Refined Petroleum Products and Nuclear Fuel
Wood and Products of Wood
-6.00
Source: Authors’ Calculation
-1.00
4.00
9.00
14.00
19.00
Back
88
SLOWEST GROWING TFP SECTORS
FASTEST GROWING TFP SECTORS
Other Services
-1.46
Post and Telecommunication
18.75
-1.57Manufacturing, nec
Financial Services
5.45
Education
-2.55
Public Administration and
3.92
Defence
Wood and Products
-3.21
Basic Metals and Fabricated
3.77
Metal Products
Electricity, Gas and Water Supply
3.24
0.00
5.00
Coke, Refined Petroleum
-3.27
10.00
15.00
20.00
-4.00
-3.00
-2.00
-1.00
0.00
Source : Author’s calculations
89
Education
2.12
Post and Telecommunication
1.88
Construction
1.78
Financial Services
1.7
Other Services
1.54
Trade
1.1
Health and Social Work
1.04
Transport and Storage
0.93
Industry Mean
0.67
Machinery, nec
0.55
Manufacturing, nec; recycling
0.53
Pulp, Paper, Paper Products, Printing and Publishing
0.45
Hotels and Restaurants
0.44
Mining and Quarrying
0.4
Industry Median
0.39
Rubber and Plastic Products
0.38
Public Administration and Defense
0.35
Agriculture, Hunting, Forestry and Fishing
0.34
Electrical and Optical Equipment
0.3
Electricity, Gas and Water Supply
0.29
Transport Equipment
0.29
Other Non-Metallic Mineral Products
0.25
Basic Metals and Fabricated Metal Products
0.2
Chemicals and Chemical Products
0.17
Food Products, Beverages and Tobacco
0.17
Textiles, Textile Products, Leather and Footwear
0.1
Coke, Refined Petroleum Products and Nuclear Fuel
0.06
Wood and Products of Wood
-0.01
-0.5
0
0.5
1
1.5
2
Back
2.5
90
0.41
Other Services
0.39
Public Administration and Defense
0.34
Education
0.3
Mining and Quarrying
0.25
Health and Social Work
0.23
Trade
0.22
Machinery, nec
0.19
Post and Telecommunication
0.17
Financial Services
0.16
Transport and Storage
0.15
Industry Mean
0.15
Pulp, Paper, Paper Products, Printing and Publishing
0.14
Manufacturing, nec; recycling
Industry Median
0.13
Construction
0.13
0.12
Electricity, Gas and Water Supply
0.12
Textiles, Textile Products, Leather and Footwear
0.11
Other Non-Metallic Mineral Products
Agriculture, Hunting, Forestry and Fishing
0.1
Transport Equipment
0.09
Rubber and Plastic Products
0.07
Chemicals and Chemical Products
0.07
0.07
Food Products, Beverages and Tobacco
0.06
Hotels and Restaurants
Electrical and Optical Equipment
0.03
Basic Metals and Fabricated Metal Products
0.03
Coke, Refined Petroleum Products and Nuclear Fuel
0.01
Wood and Products of Wood
0.01
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Back
0.45
91
5.18
Financial Services
4.91
Post and Telecommunication
3.74
Education
3.03
Mining and Quarrying
2.59
Trade
2.39
Health and Social Work
2.17
Other Services
Manufacturing, nec; recycling
1.98
Rubber and Plastic Products
1.94
Industry Mean
1.87
Wood and Products of Wood
1.86
1.82
Chemicals and Chemical Products
1.72
Other Non-Metallic Mineral Products
Transport Equipment
1.55
Industry Median
1.52
Basic Metals and Fabricated Metal Products
1.49
Electrical and Optical Equipment
1.41
Hotels and Restaurants
1.37
Coke, Refined Petroleum Products and Nuclear Fuel
1.36
Transport and Storage
1.34
Textiles, Textile Products, Leather and Footwear
1.34
Electricity, Gas and Water Supply
1.33
Machinery, nec
0.91
Pulp, Paper, Paper Products, Printing and Publishing
0.91
0.69
Agriculture, Hunting, Forestry and Fishing
0.62
Food Products, Beverages and Tobacco
0.55
Construction
0.5
Public Administration and Defense
0
1
2
3
4
Back
5
6
92
6.78
Electrical and Optical Equipment
5.76
Rubber and Plastic Products
5.03
Manufacturing, nec; recycling
Coke, Refined Petroleum Products and Nuclear Fuel
4.18
Chemicals and Chemical Products
4.15
3.81
Food Products, Beverages and Tobacco
3.65
Hotels and Restaurants
Construction
3.55
Transport Equipment
3.51
3.01
Textiles, Textile Products, Leather and Footwear
2.89
Other Non-Metallic Mineral Products
2.84
Pulp, Paper, Paper Products, Printing and Publishing
2.76
Basic Metals and Fabricated Metal Products
2.53
Industry Median
2.44
Industry Mean
2.29
Machinery, nec
1.71
Electricity, Gas and Water Supply
1.48
Transport and Storage
1.25
Post and Telecommunication
1.04
Other Services
Mining and Quarrying
0.96
Health and Social Work
0.91
0.73
Wood and Products of Wood
0.34
Agriculture, Hunting, Forestry and Fishing
Financial Services
0.26
Public Administration and Defense
0.25
0.14
Trade
0.04
Education
Source: Authors’ Calculation
0
1
2
3
4
5
6
Back
7
93
Transport and Storage
1.61
Electricity, Gas and Water Supply
1.41
Basic Metals and Fabricated Metal Products
1.05
Other Non-Metallic Mineral Products
1.04
Chemicals and Chemical Products
0.6
Coke, Refined Petroleum Products and Nuclear Fuel
0.41
Electrical and Optical Equipment
0.38
Industry Mean
0.37
Rubber and Plastic Products
0.37
Pulp, Paper, Paper Products, Printing and Publishing
0.34
Transport Equipment
0.31
Post and Telecommunication
0.28
Construction
0.24
Manufacturing, nec; recycling
0.23
Industry Median
0.22
Textiles, Textile Products, Leather and Footwear
0.21
Food Products, Beverages and Tobacco
0.21
Hotels and Restaurants
0.18
Mining and Quarrying
0.18
Financial Services
0.14
Machinery, nec
0.13
Trade
0.1
Wood and Products of Wood
0.09
Other Services
0.05
Health and Social Work
0.03
Education
0.02
Public Administration and Defense
0.01
Agriculture, Hunting, Forestry and Fishing
0.01
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Back
1.6
1.8
94
Back

There have been a good deal of research on productivity growth of Agriculture
sector.

Such studies include: Sivasubramaniam (2004), Pratt, Yu and Fan (2009) , Fuglie
(2008) Evenson et al (1999) , Bosworth, Collins and Virmani (2007), and Bosworth
and Maertens (2010)

Variation in results for different periods but all support the following broad
conclusions :

TFP growth faster in 1980’s to 1970’s. (ranges between 1.5 % to 2% per annum in
1980’s)
In 1990’s TFP growth is lower ( ranges between .7% to 1.2% per annum)
Growth rates of TFP further diminishes in 2000’s


Back
96
S. SivaSubramaniam(2000) :

Study distinguishes between TFP of residential and non residential sector.

Non residential is further subdivided into Sector A :Agriculture, Forestry and Fishing and
Sector Non A : non agriculture excluding dwellings.

Followed Denison methodology and gave a detailed account of data source and methods:
Data sources for Agriculture sector :
 Variables: : Labour, GFCS, Inventories, Land
 Coverage: 50 years period from 1950 to 2000
 Land : Net cropped Area+ fallow land ( Ministry of Agriculture)
 Cost shares : .56 to.45 for labour, .17 to .34 for GFCF, .045 to .01 for inventories and .25 to
.19 for land.
Back
97
Evenson ,et al (1999)

TFP measured as ratio of index of Aggregate output to an index of aggregate inputs .

Study uses Tornqvist –Theil TFP index

Estimates of TFP only for production crops ( 5 major and 13 minor) across 13 states and do not
include livestock.
Data sources for Agriculture sector
 Variables : Unirrigated land, irrigated land, human labour, animal labour, tractors, fertilizer and
irrigation capital.
 Land : Data form Directorate of Economics and statistics; Agriculture Situation report.
 Farm harvest prices , Agricultural wages from Directorate of Economics and Statistics
 Labour: decennial population census
 Tractors : Interpolated data from census
 Fertilizer : fertilizer statistics ; fertilizer association of India.
 Factor shares from micro level studies
Back
98
Back
99
Apart from KLEMS Land is included as an input in Agriculture
Two alternative definition of land input for agriculture sector.
Definition 1: Land input= Net Cropped Area + fallow land
Definition 2: Land input= Gross Cropped Area + fallow land
As National Accounts Statistics (CSO) includes irrigation as part of capital stock, we have excluded land quality
as this will amount to double counting.
Table : Trend growth rates of land inputs ( % per annum)
Period
Gross cropped Area
+ fallow land
Agriculture, Hunting,
Forestry and .Fishing
Net cropped Area+
fallow land
1980-1999
0.38
0.01
2000-2008
0.29
0.01
1980 -2008
0.35
0.01
Source: India KLEMS database-Phase II
Back
10
0
Table : TFP for Agriculture including land input (% per annum)
Period
TFPG, alternate estimates ( with land as an additional input)
Growth in Gross output
A1
B1
1980-1999
2.90
1.45
1.37
2000-2008
2.59
0.83
0.58
1980 -2008
2.82
1.29
1.17
Note: Land input= Net Cropped Area+ fallow land
Source: India KLEMS database-Phase II
A1 : TFPG computed using Labour input capital stock
B1 : TFPG computed using Labour input capital services
Back
10
1
Suggestion : Use of alternate input cost structure where land=35%; labour=25%,
capital=20% and intermediate input = 20%
Period Wise Trend growth rate of TFP of Agriculture Sector (% per annum)
Period
TFPG, alternate estimates
Growth in Gross output
A1
B1
1980-1999
2.90
1.45 (1.54)
1.37(1.46)
2000-2008
2.59
0.83(0.88)
0.58(0.63)
1980 -2008
2.82
1.29(1.38)
1.17(1.25)
Note: Land input= Net Cropped Area+ fallow land
Source: India KLEMS database-Phase II
A1 : TFPG computed using Labour input capital stock
B1 : TFPG computed using Labour input capital services
Figures in parenthesis presents the TFP estimates obtained using alternative input cost structure as
suggested by Agriculture Cost and prices Commission
Back
10
2
Period wise Trend growth rate of TFP of Agriculture Sector (% per annum)
Period
TFPG, alternate estimates
A2
B2
1980-1999
Real value added
growth**
3.10
1.89
1.78
2000-2008
2.61
1.30
0.71
1980 -2008
3.03
1.68
1.52*
Note: Land input= Net Cropped Area+ fallow land
Source: India KLEMS database-Phase II
A1 : TFPG computed using Labour input capital stock
B1 : TFPG computed using Labour input capital services
*Trend growth rate in TFP in Agriculture using fixed weights as suggested by the Agriculture Cost and prices
Commission is about 1.7 percent per annum
** Trend growth rates of real gross value added is based on aggregate production frontier Approach which involves
application of Tornqvist index
Back
10
3
Authors
Commodity
Period
TFP
Evenson et al (1999)
Crops
1956-65
1966-76
1977-87
1.1
1.39
1.05
Avila and Evenson(2004)
Crops
1961-80
1981-01
1.54
2.33
Livestock
1961-80
1981-01
2.63
2.66
Crops and livestock 1980-00
0.90
Coelli and Rao (2003)



Above table shows that estimates of Evenson and his associates covers only crop
sector.
The coverage of Agriculture sector under the KLEMS project is much wider.
It includes plantations, animal husbandry, forestry and fishing.
Back
10
4
Author (s)
Period
Estimated TFP growth rate (% per annum)
Sivasubramonian (2004)
1950-1960
1960-1970
1970-1980
1980-1990
1990-1999
1.7
0.9
-0.4
1.9
1.7
Pratt, Yu and Fan (2009)
1961-1973
1974-1980
1981-1991
1991-2006
-1.7
0.5
1.0
0.5
Fugli (2011)
1981-1990
1991-2000
2001-2009
1.4
1.2
1.7
Bosworth, Collins and Virmani (2007)
1980-2004
1983-1993
1993-1999
1999-2004
1.1
1.2
1.3
-0.1
Bosworth and Maertens (2010)
1980-1990
1990-2000
2000-2006
1.9
0.7
0.9
1980-1999
2000-2008
1980-2008
1.8*
0.7*
1.5*
Our study: Estimates of productivity
growth for Indian Economy (2012)
Note *estimates based on Gross value added framework.
Back
10
5
Thank you
Back
10
6