India KLEMS
Labour Input- Quantity and
Quality by Industry
Suresh Aggarwal
First World KLEMS conference
Harvard University
19-20 August 2010
Research assistance by Gunajit Kalita in creating the India
KLEMS Labour Input dataset
1
Objectives-India KLEMS
 To create a comprehensive data base on
productivity growth using Growth
Accounting Approach.
 Construct a Time Series data on output,
capital, labour, labour quality and
intermediate inputs.
2
Major tasks for Data Base on Labour
Make a Time series of Employment from
1980 to 2004.
Prepare a Labour Quality Index from
1980 to 2004.
Make a Time series of Labour Input from
1980 to 2004.
3
Major Contributions of the Paper
Efforts have been made for the first time to
estimate employment in Hours.
Average number of Hours worked in a day have
been estimated for the first time.
Both the Quinquennial and the annual rounds
have been used, for the first time for
constructing the time series of employment.
A separate decomposition of Labour Quality
into indices of age, sex and education has been
attempted.
4
Broad classifications for all the series
 Gender: Males/Females
 Age : <29; 30-49; and 50+
 Education: Up to Primary; From
Primary to Higher Secondary; and
above Higher Secondary.
 Sectors : 31 sectors.
So it is 2*3*3*31 classification.
5
Major Sources of Data Used


For all sectors of the economy Employment
and Unemployment Surveys (EUS) by
National Sample Survey Organization
(NSSO) and Population Census. The two are
Household/Individual specific.
Manufacturing Sector:
 Organized Manufacturing
industriesAnnual Survey of Industries(ASI) by
Central Statistical Organization (CSO).
 Unorganized Manufacturing industriesResidual.
6
Methodology for Constructing the Time Series of
Employment
Time Series of employment requires estimation of:
a) Number of persons, and
b) Total days and hours worked by each person.
Time Series of Labour Input- Number of
persons employed
 In India, the number of employed may be
estimated from Census and/or from EUS.
 While Census has been held every ten years,
NSSO has conducted both major (or
Quinquennial) and thin (or annual) rounds of
EUS.
7
Employment Unemployment Survey (EUS)
 Major (Quinquennial) Rounds of EUS since
1980: 38th (1983), 43rd(1987-88), 50th(1993-94),
55th (1999-00) and 61st(2004-05).
 Thin (Annual) Rounds: 45th to 60th .
 EUS uses Usual Status [Usual Principal
Status(UPS) and Usual Principal & Subsidiary
Status (UPSS)], Current Weekly Status(CWS)
and Current Daily Status (CDS) measures for
Quinquennial (or major) rounds and Usual
Status & CWS for annual (thin) rounds.
 While UPS, UPSS and CWS measure number of
persons, the CDS gives number of jobs.
8
EUS- contd….
For India KLEMS we have used UPSS to estimate
employment.
Both the Quinquennial and the annual rounds
have been used, for constructing the time series.
Since different rounds of EUS use different
National Industrial Classification (NIC), so a
Concordance between India KLEMS, NIC-1970,
1987 and 1998 required for all the 31 sectors has
been done.
‘Total hours worked’ have been estimated by also
using the CDS schedule of the EUS.
12
Estimation of Employment
Employment has been computed as follows:
I.
Used; like all the previous studies, the Work
Participation Rates (WPRs) by UPSS from EUS
and applied them to the corresponding
period’s population of Rural Male, Rural
Female, Urban Male and Urban Female to find
out the number of workers in the four
segments .
II. Use
the
31-industry
distribution
of
Employment from EUS and used these to the
number of workers in step I and obtained Lij
for each industry where i=1 for rural and 2 for
urban sectors, and j=1 for male and 2 for female.
17
Contd….
III. Find out the average number of days worked
per week ‘dij’ for each industry from the
intensity of employment as given in the CDS
schedule.
IV. Assuming average 48 hours work week for
regular workers and 8 hours per day for self
employed and casual workers, find out the
expected number of hours ‘hij’ worked per day
from the status-wise distribution, in each
industry for rural male, rural female, urban
male and urban female.
18
Contd….
V. From the major rounds separate interpolation
of Lij ; dij; and hij was done for rural male, rural
female, urban male and urban female to obtain
the respective time series.
VI. Broad Industrial distribution from annual
rounds was used as a control total on the
corresponding interpolated Lij and revised
numbers were obtained.
VII. Total person hours in a year were obtained for
each industry as the sum of the products of
revised Lij; dij; and hij over gender and sectors.
ΣiΣjLij*dij*hij*52
19
Time Series of Labour Quality Index
 Quality Index has been constructed using the standard
methodology given by Jorgenson, et al (1987), which uses the
Tornqvist translog index.
 Analogously, other first order contributions by gender, age and
education, Qs , Qa, and Qe , have also been computed.
Data required for Quality Index is:
a) Employment by sex by age by education by industry;
b) Earnings for each of these cells.
 Since the required labour composition data is available only from
major rounds of EUS, so
 Only Major rounds have been used for estimating the indices
and the indices have been interpolated to get the time series
for the entire period.
 Only for aggregate 31 sectors- not for organized and
unorganized separately.
20
Earnings Data
NSSO’s EUS relates earnings to only regularsalaried workers and casual workers.
The issue was how to estimate earnings of self
employed.
Earnings of Self Employed is required for quality index and
labour compensation.
The present study has used the Mincer Wage
equation for the same and sample selection bias
has been corrected for by using Heckman's two
step procedure.
24
Results
Results are presented as follows:
Firstly, for the Total economy.
Secondly, by the broad industrial
classification.
Lastly, by the 31 KLEMS industrial
classification.
27
Workforce Participation rate in different NSSO
rounds (% of Total Population)
60
50
53.87
53.15
54.49
42.05
41.21
41.97
29.60
28.51
28.56
38th(1983)
43rd(1987-88)
50th(1993-94)
52.73
39.67
40
30
25.89
54.68
42.01
28.67
20
10
0
Male
Female
55th(1999-00)
61st(2004-05)
Total
29
Labour Input and Quality Change
for the Total Economy
30
Growth Rates of Labour Input, Hours and Labour
Quality (% per annum)
1980
to
1985
1986
to
1990
1992
to
1996
1997
to
2004
1980
to
2004
1980
to
1989
1990
to
1999
2001
to
2004
5.28
5.89
6.54
5.93
5.71
5.58
6.16
6.41
Labour Input
1.82
2.93
2.49
2.64
2.64
2.01
2.46
3.42
Labour Persons
1.20
1.55
1.66
2.15
1.85
1.15
1.64
2.83
Labour Hours
1.46
2.55
2.10
2.14
2.22
1.65
2.06
2.85
Labour Quality
0.35
0.37
0.39
0.50
0.41
0.36
0.39
0.56
Qs (Gender)
0.01
-0.01
0.00
-0.01
-0.01
0.00
0.00
-0.02
Qa (Age)
0.07
0.07
0.06
0.04
0.06
0.07
0.06
0.03
Qe (Education)
0.28
0.33
0.36
0.48
0.38
0.30
0.35
0.56
GDP
Variable Labour
First order
Quality Indices
31
Total Employment (persons and million hours) and hours
per day
Hours Per Day
1.790
1.001
1.690
1.000
1.590
0.999
1.490
1.390
0.998
1.290
0.997
1.190
0.996
1.090
0.990
1980
0.995
1983
1986
Employment (Persons)
1989
1992
1995
1998
Employment(million Hours)
2001
2004
Hours per day
32
Aggregate Quality and its first order Approximation
Qs
0.94
1.020
0.92
1.000
0.90
QL
Qs*Qa*Qe
Qa
Qe
2004-05
1.040
2001-02
0.96
1998-99
1.060
1995-96
0.98
1992-93
1.080
1989-90
1.00
1986-87
1.100
1983-84
1.02
1980-81
1.120
Qs
35
Comparison with two other major studies
Author
Bosworth; Collins & Virmani
Period
Growth rate in
Employment
Index
Growth in
Education
Index
Growth in
Labour Input
Index
1980-2004
2.00
0.40
-
1980 to 1999
1.74
0.34
2.22
1980 to 1990
2.02
0.31
2.47
1990 to 1999
1.43
0.37
1.93
1980 to 2004
1.85
0.38
2.64
1980 to 1989
1.15
0.30
2.01
1990 to 1999*
1.64
0.35
2.46
(2007)
Sivasubramonian (2004)
Current study (2010)
*Year 1991 has been excluded from the current study because of it being an abnormal year
The results for employment growth are different from Sivasubramonian’s
study, but are close with Bosworth; Collins & Virmani (BCV).
The results for education growth rates are however, very close.
36
Composition of Labour
Education
The proportion of more educated workers has increased, and of literate up to
primary has reduced
Cumulative Distribution of educational attainment of workers
110
Above Higher Secondary
100
97.56
97.01
90
80
95.91
94.99
92.88
Primary to Higher Secondary
82.22
80.01
Upto Primary
74.31
70
68.47
64.72
60
38th(1983)
43rd(1987-88)
50th(1993-94)
55th(1999-00)
61st(2004-05)
37
Gender: Female’s share of workforce, relative
wages, days and hours
1.20
Hours Per day Ratio
1.00
1.02
0.80
1.02
0.74
1.01
1.01
Days Per Week Ratio
0.76
0.75
Wage Ratio
0.69
0.68
0.60
0.62
0.65
0.40
Share of Workforce
0.26
0.27
0.20
38th(1983)
50th(1993-94)
females share of workforce
females/males days per week
0.66
0.26
0.27
55th(1999-00)
61st(2004-05)
females/males wages per day
females/males hours per day
43
Labour Input by the broad industrial
classification.
1980 to
1985
1986 to
1990
1992 to
1996
1997 to
2004
1980 to
2004
1980 to
1989
1990 to
1999*
2001 to
2004
Agriculture
0.63
1.19
1.46
0.84
1.37
0.79
1.08
1.25
Industry
4.29
3.52
2.44
4.84
3.88
3.76
2.45
6.80
Services
3.00
6.70
5.35
4.27
4.45
3.68
5.76
4.88
Total
Economy
1.82
2.93
2.49
2.64
2.64
2.01
2.46
3.42
Industry
* Excludes 1991
46
Growth in Labour Hours and Labour Input by Industries
Labour Hours
Labour Input
53
Growth and Acceleration in Labour Quality
Growth in Quality
Acceleration in Quality
54
Labour Quality in Industries
The growth in labour quality was fastest in real
estate activities; machinery; electricity, gas & water
supply; and financial intermediation and very
slow in wood & products of wood; construction;
non-metallic minerals, agriculture and wholesale
trade & commission.
The growth in labour quality was only 0.19 per
cent in the pre reform period and it increased to
0.29 in the post reform decade indicating change in
the composition of the workforce.
55
Contd….
 The inter industry differences in the pattern of change in
growth rate shows that the variation in growth rates has
reduced over the period
 The industries with either negative or very low growth
rate in the first sub period (Sale, maintenance of motor
vehicles etc., Construction, mining & quarrying, etc.)
have generally been able to pick up the growth rate in
the last period.
 The reverse has also happened where the growth rate in
labour quality for these industries has slowed down over
the period (real estate, chemicals & chemical products,
financial intermediation, etc.).
56
Manufacturing Employment- Organized & Unorganized Sector
Employment share of organized-2004
Growth of Employment
Unorganized
Total
Organized
14.99
3.1
Total
Rubber and Plastic Products
1.63
34.78
8.6
Rubber and Plastic Products
Coke, Refined Petroleum
Products and Nuclear Fuel
67.11
Manufacturing, nec; recycling
4.54
Coke, Refined Petroleum Products and
Nuclear Fuel
14.3
4.38
2.47
4.5
Manufacturing, nec; recycling
Food Products, Beverages and
Tobacco
4.35
18.09
2.3
Food Products, Beverages and Tobacco
Chemicals and Chemical
Products
3.24
39.81
4.8
Chemicals and Chemical Products
Electrical and Optical Equipment
35.07
Other Non-Metallic Mineral
Products
29.43
4.6
1.02
Pulp, Paper, Paper Products, Printing
and Publishing
7.1
0.91
Textiles, Textile Products, Leather and
Footwear
11.18
0.86
0
11.7
1.90
Basic Metals and Fabricated Metal
Products
17.54
Wood and Products of Wood
(20)
2.9
2.24
Machinery, nec.
22.25
Textiles, Textile Products,
Leather and Footwear
2.26
Other Non-Metallic Mineral Products
Machinery, nec.
Pulp, Paper, Paper Products,
Printing and Publishing
10.0
Electrical and Optical Equipment
9.47
Basic Metals and Fabricated
Metal Products
2.94
3.1
0.60
1.5
Wood and Products of Wood
-0.57
20
40
60
80
-2
0
2
4
6
8
10 12 14 16
60
Conclusion
 The WFPR remained almost unchanged over the period.
 The share of 30-49 age-group is highest.
 The share of educated workforce has gradually increased
during the period.
 There is a tendency for the share of female workers to increase,
though the share is still less than half to that of males.
 Nominal Wages are generally higher for more educated and
experienced workers.
 Along with increase in employment of labour hours there has
also been increase in labour quality, leading to a faster growth
of labour input.
 The share of unorganized employment has increased in the
Indian manufacturing sector.
61
62