Journal of Research in Peace, Gender and Development (ISSN: 2251-0036) Vol. 2(4) pp. 81-88 April 2012
Available online@ http://www.interesjournals.org/JRPGD
Copyright ©2012 International Research Journals
Review
Factors affecting female labour force participation in
Punjab: An inter-district analysis
Pardeep Kaur1 and Gian Kaur2
1
Senior Research Fellow, Punjab School of Economics, Guru Nanak Dev University, Amritsar
2
Professor, Punjab School of Economics, Guru Nanak Dev University, Amritsar
Accepted 24 April, 2012
The study of labour market participation is essential for implementing and formulating employment and
human resource development polices. Throughout this paper, an attempt is made to examine the trends
and patterns of female workforce participation across Punjab, during 1991 and 2001. At the same time,
this paper also examined the various determinants of women’s work in Punjab. The OLS regression
technique is employed to estimate the various factors, which affect the woman, work participation rate
in Punjab. According to 2001 census, the total working force in Punjab was estimated to be 78,35,732
out of which 64, 26,028 were males and 14,09,704 were females which indicates that huge difference is
found between the male and female population in Punjab. The comparative estimates of male work
participation rate and female work participation for India and Punjab indicates that although female
work participation rate in Punjab has increased from 2.79 percent in 1991 to 12.39 in 2001, still this rate
is lower than all India figure (25.7 percent). It has been found that at the district level, a high interdistrict variation has been observed for labor force participation for both males and females. The
highest female work participation rate is found in Nawanshaher district and the lowest in Gurdaspur
district.
Keywords: Female labor force, Inter-district, determinants, work participation.
INTRODUCTION
The economic analysis of female labor participation
attracted considerable attention since the pioneering
works of Mincer (1962) and Cain (1966). The female
labor force participation rates increased considerably in
the developed countries in recent years. Moreover, in the
third world economies, the issue of woman’s contribution
in economic activities is recent one and this has led to
policy and academic research on the subject as well as
much social
activism
through
Non-government
organizations (NGOs). The labor force participation rate
plays a significant role in determining socio economic
development and growth. The increasing trend toward
women’s participation in the labor market in both
developed and developing countries has drawn both
social and academic interest resulting in many insightful
studies on gender aspects of labor market issues(Ackah,
Charles, 2009).
The main research of particularly the women in
*Corresponding Author E-mail: pari_84sek@yahoo.com
economic activities are that in this modern world
everywhere the cost of living has increased. It becomes
necessary for women to undertake economic activities
and support their families. Female workers play very
important role in agriculture based Indian economy. They
participate in farm and non-farm activities besides
domestic work. Now the attitude of the society has also
changed and working woman is not seen with suspicious
eyes like earlier. She is more liberated now and modern
woman leads a very happy and peaceful life with her
economic activity. The second important reason is the
marital status, which determines female labor force
participation, but even more important is the working
conditions, particularly availability of flexible work for both
men and women. Among the states of India, Punjab is
one of the smaller states. It covers nearly one and half
percent of the area and constitutes about 2.4 percent of
India’s population. Even though in all walks of life the
females now entered into high positions, the majority of
females are still working in the unorganized sector.
The main objectives of the study are to:
82 J. Res. Peace Gend. Dev.
(i) Examine the pattern of female labor force at the
district level in Punjab between 1991 and 2001 and to
(ii) Indentify the determinants of female labor force
participation in Punjab.
It has been well demonstrated in the literature that
labor force participation rate of women is the result of
many factors. Therefore, this study is an effort to indentify
the socio-economic and demographic factors, which
determine the women workforce participation rate. This
study is organized into six sections. Introduction is
presented in Section I. In Section II, review of existing
literature on female workforce is discussed. In Section
III, we have presented the database and methodology.
Section IV provides the nature and pattern of
employment in Punjab as well as in its various districts.
Section V presents the findings of regression analysis
while in last section presents some concluding remarks
and suggestions.
Review of literature
In this section, an attempt is made to review literature on
studies relating to working women at national and
international level.
Sahoo and Mohanty (1978) tried to analyze the interdistrict variation in female participation in Orissa on the
basis of data from 1971 census. They worked out the
correlation coefficient of female participation rate with
various variables such as male population, percentage of
Scheduled caste / Tribes, education. They found positive
but insignificant correlation sex ratio and concluded that
in those districts where population of SC and ST was
less, female participation rate (FPR) was also high. The
findings of the study suggest that no single variable
satisfactory explains the differences. The difference may
be due to a host of causes such as social, economic,
geographical, and cultural. B.S Panghal and Mange Ram
1985) conducted a study to examine the nature and
extent of employment pattern of woman labor on farms in
different agro climatic zones of Haryana state. The study
has revealed that as the size of the farm increased, the
participation of women labor also increased. Naqvi and
Shahnaz (2002) have analyzed the effects of various
demographic, socio-economic, and human capital related
factors on women participation in economic activities.
They applied probit and multinomial logit model to
estimate the parameters. The findings indicate that
marital status, primary education, number of children and
female head of households are inversely related with
women’s participation in economic activities. Zareen and
Lubna (2002) Women’s economic participation is
significantly influenced by factors like their age,
education, marital status. Employment status of the head
of the household (generally a male), presence of male
member, and children of ages 0-5 are also important
variables that significantly affect women’s participation in
economic activities. Giavazzi and others (2009), used
panel data collected from the World Value Survey,
studied whether cultural attitudes towards work, gender,
and the young are significant determinants of the
evolution over time of the employment rate of women.
They found that attitudes towards a women’s role in the
family are statistically and economically important
determinants of the female rate of employment. Aslam
and others (2008) found a similar strong association
between higher education and female labor force
participation for Pakistan. Shaheen, Safana (2011)
investigated the patterns of female labor force
participation in case of Pakistan. The study utilized
Multiple Indicator Cluster Survey 2007-08 data of Punjab.
The variables used in the analysis are female labour
force participation, age, age square, marital status, area,
female monthly income, family monthly income, family
size, household head education, different classes of
female education and employment status. Results of
Logit model depicts that household head education,
primary, middle, matric & mudrassa education level is
negatively related with the decision of female labor force
participation while, decision towards participation is
strong if female belonged to urban area, if she is married,
if she has higher education, and if she has large family
size. Faridi et al., (2011) by used the data of Bahawalpur
(district of Punjab, Pakistan) and found that women’s selfemployment is positively related with age and
experience. Analysis of various education level shows
that women who have low-level education highly tends
towards self-employment than women who have high
level of education. Maglad (1998) used demographic
survey of Sudan for the period of 1990-91and found that
education is positively and significantly related with
female decision to enter for work in market. Moreover,
female labor force participation is positively related with
own wage and negatively related with spouse’s wage,
assets, and having small children.
DATA BASE AND METHODOLOGY
The present study is based on the secondary data and
the required data of the study has been taken from the
various issues of Statistical Abstract of Punjab, Human
Development Report of Punjab (2004) and Ministry of
women and Child Development. Theoretically, there are
number of demographic and economic factors which can
be included in the study as a possible determinant of
FLFP but due to availability of district wise data we have
included male work participation rate (MWPR), Sex-ratio
(SR) Female literacy rate(FLR). Following Mazumdari
and Guruswamy (2006) data for 1991 and 2001 is used
(to increase sample size) by taking time as a continuous
dummy variable.
FLFP=α+ βiXis+Dt+U
Where Dt =0 for 1991
Kaur and Kaur 83
Dt = 1 for 2001
Where Female labor force participation rate (FLFP) is
the dependent variable and X’s are the independent
variables, explaining variations in female’s labor force
participation rate. α shows the intercept and βi provides
the estimated co-efficient of the respective repressor and
U is the constant variance disturbance term.
Women and work in punjab
Since the post independence period, Punjab has
witnessed a high level of economic prosperity, which has
resulted in high per capita income. However, the higher
level of economic development did not improve economic
and social status of women in Punjab. The poor status of
women in Punjab is reflected through the gender
development index (GDI), which estimates the unequal
achievements of men and women using the same three
dimensions that are captured in the HDI (Human
Development Index) . HDI is a simple average of three
dimensions indices each of which measures average
achievements in a country with regard to‘ A long and
healthy life’ Knowledge’ and‘ decent standard of living”
(Ministry of women and child development , 2009) while
UNDP’s Human Development Index using three indices:
Life Expectancy at birth, educational attainment and per
capita GDP. GDI adjusts the average achievement in the
same three dimensions that are captured in the HDI to
account for inequalities between men and women.
Table 1 reveals the HDI and GDI of Punjab and
different states of India, which has been measured by the
Ministry of Women and Child Development, Government
of India in 2009. According to this report, gender
disparities exist in the state if GDI score is less than the
HDI score and equal to HDI if there is no gender
disparity. The table reveals that although gender disparity
was lower in Punjab than national average (0.015) but
still there exists gender disparity. Punjab recorded the
lowest participation rates over the decades from 1981 to
2001.
Female labor force participation rate (FLFP), a useful
measure of economic activity is generally computed as
the ratio of the female labor force (employed and
unemployed but seeking work) to the female Population.
This rate refers to the probability that a female works.
The number of females employed includes those who are
in paid employment and those who are unpaid family
laborers. Women working on the family farm or business
are considered economically active and thus counted in
the labor force (Tansel, Aysıt, 2002). In Punjab women
are relatively, contribute insignificant role in workforce.
Notwithstanding the high level of development and
education in Punjab, Punjab has the lowest female
workforce participation in the country (Human
Development Report Punjab, 2004). According to 2001
census, the total working force in Punjab was estimated
to be 78, 35,732 out of which 64, 26028 were males and
14, 09704 were females. Table 2 provides the census
wise workforce in rural as well as in urban areas in
Punjab during 1991 and 2001. It has been clear from the
table that in rural areas the female labor force grown at
faster rate than the urban female labor population rate.
The table also shows that huge difference is found
between the male and female participation rate in Punjab
during 1991 and 2001.
Table 3 depicts the comparative estimates of MWPR
and FWPR for India and Punjab in 1991 and 2001. From
table 2, it is clear that WPRs have increased for both
India and Punjab between the period 1991 and 2001. In
the same period, female work participation rate in Punjab
no doubt, have increased but this rate is lower than all
India figure.
Generally, it has been recognized that nature of
disparity among the districts over time is a pre condition
to analyze the work-participation rates at the district level.
Therefore, we have calculated the co-efficient of variation
for the period 1991 and 2001. The co-efficient of variation
indicates the ratio of standard deviation to its mean
expressed in percentage terms. The higher ratio
represents an increasing disparity among the districts. In
Table 3 we presented the district-level FWPR and MWPR
for 1991 and 2001. In 1991, the co-efficient of variation
among districts for female workers have turned out to be
37% but it has declined to 27 percent in 2001. For male
workers it has declined from 3.53 percent to 3.43 percent.
Table 4 clearly shows that the total work participation
rate has increased in all the districts of the state during
1991-2001. According to 2001 census, the highest FWPR
is found in Nawanshaher district and the lowest in
Gurdaspur district. FWPR for the state as a whole has
increased from 4.4 percent in 1991 to 18.7 percent in
2001. Table 3 shows that female WPR has increased in
all the districts of the state, whereas male WPR has
increased in some districts, decreased in some others
and remained the same in the rest. This can partly be
attributed to the level of changes in the growth of different
sectors in various districts. However, there has been a
significant increase in the proportion of female marginal
workers during the decade. Between Indian States and
Union Territories, Punjab ranked 24th, 14th, and 26th
respectively during 2001 in terms of the total, male and
female workers.
Table 5 provides the figures at the district level for the
main and marginal workers in Punjab. From the table it is
clear that there has been decline in the marginal workers
for women from 91 percent to 59 percent during the last
decade. The decrease is comparatively more found in the
districts of Ludhiana, Kapurthala and Jallandhar.
Table 5 further reveals that of the total marginal
workers 59 percent marginal workers are females. In the
category of marginal workers, Mansa ranks at the top
with 74 percent female marginal workers, while
Jallandhar has the lowest percentage of female marginal
84 J. Res. Peace Gend. Dev.
Table 1. HDI and GDI score differences and ranks for states/UTs, 2006
State
HDI
Rank
GDI
Rank
HDI and GDI differences
Andhra Pradesh
0.585
28
0.574
27
0.011
Arunchal Pradesh
0.647
20
0.642
18
0.005
Assam
0.595
26
0.585
26
0.010
Bihar
0.507
35
0.479
35
0.028
Goa
0.764
2
0.747
2
0.017
Gujarat
0.634
23
0.624
22
0.010
Haryana
0.643
21
0.632
20
0.011
Himachal Pradesh
0.667
15
0.664
13
0.004
Jammu & Kashmir
0.590
27
0.568
28
0.021
Karnataka
0.622
25
0.611
25
0.011
Kerla
0.764
2
0.745
3
0.018
Madhya Pradesh
0.529
33
0.516
33
0.013
Mahrashtra
0.689
11
0.677
10
0.102
Manipur
0.702
7
0.699
6
0.003
Meghyla
0.629
24
0.624
23
0.005
Mizoram
0.688
12
0.687
9
0.002
Nagaland
0.700
8
0.697
7
0.003
Orissa
0.537
32
0.524
32
0.013
Punjab
0.668
14
0.663
14
0.005
Rajasthan
0.541
31
0.526
31
0.015
Sikkim
0.665
17
0.659
15
0.006
Tamil Nadu
0.666
16
0.655
16
0.011
Tripura
0.663
18
0.626
21
0.037
Uttar Pradesh
0.528
34
0.509
34
0.019
West Bengal
0.642
22
0.622
24
0.020
Chhattisgarh
0.549
30
0.542
30
0.008
Jharkhand
0.574
29
0.558
29
0.017
Utthrakhnd
0.652
19
0.647
17
0.005
Adman & Nikobar
0.708
6
0.692
8
0.016
Chandigarh
0.784
1
0.763
1
0.020
Dade
0.677
13
0.673
12
0.004
Daman
0.700
9
0.677
11
0.024
NCT Delhi
0.740
4
0.701
5
0.039
Lakshadweep
0.690
10
0.635
19
0.062
Pondicherry
0.725
5
0.706
4
0.029
All India
0.657
0.632
0.015
Source: Ministry of Women and Child Development, Government of India, 2009
Kaur and Kaur 85
Total UrbanRural Area
Table 2. Female Workforce participation rate in Punjab: 1991-2001
Year
No. of Workers
Male
Female
Total population
Male
Female
1991
2001
1991
2001
1991
2001
4157285
4161003
1675567
2265025
5832852
6426028
7569423
8516596
3208611
4468449
10778034
12985045
145465
1087222
120057
322482
265522
1409704
6719321
7579892
2784614
3794062
9503935
11373954
MWPR
%of
Male
workers
54.92208587
48.85758348
52.22094545
50.68928839
54.1179588
49.48791475
FWPR
%of Female
Workers
2.164876481
14.34350252
4.31144137
8.499650243
2.793811195
12.39414191
Source: Statistical Abstract of Punjab, Various Issues
Table 3. Work Participation Rate in India and Punjab in 1991 and 2001
Table, 2: Work Participation Rate in 1991 and
2001
MWPR FWPR
State/ Country
1991
2001
1991
2001
India
51.6
49.48
22.3
25.7
Punjab
54.22
2.79
4.40
12.39
Source: Census of India, 2001
Table 4. Female and Male Workforce participation rate
in Punjab: 1991-2001
FWPR
MWPR
District
1991
2001
1991
2001
Gurdaspur
2.4
12.7
51.3
51.9
Amritsar
2.7
16.3
55.0
53.2
Kapurthala
5.8
14.1
54.0
53.4
Jalandhar
4.6
12.3
53.0
54.1
Nawanshehar
4.0
33.0
53.0
55.6
Hoshiarpur
4.7
17.3
50.6
51.0
Rupnagar
4.6
23.8
52.2
52.8
Ludhina
2.6
15.7
55.5
55.9
Firozpur
7.4
18.5
54.5
53.6
Faridkot
6.8
23.0
55.7
59.5
Muktsar
7.1
22.3
56.8
55.2
Moga
4.5
24.2
55.1
54.3
Bathinda
7.1
17.0
55.5
55.4
Mansa
7.5
25.1
57.6
54.4
Sangrur
4.7
24.1
56.3
54.9
Patiala
4.1
17.6
53.2
54.1
Fathegarh
Sahib
Punjab
2.1
18.3
54.7
55.1
4.4
18.7
54.2
54.1
C.V
37.5
28.0
3.53
3.43
Source: Statistical Abstract of Punjab
86 J. Res. Peace Gend. Dev.
Table 5. Female main and marginal workers in Punjab (percentage)
Main workers
Marginal workers
District
Gurdaspur
Amritsar
199
1
3
4
200
1
12
15
Cha
nge
9
11
199
1
83
79
2001
47
55
Chang
e
-36
-24
Kapurthala
5
16
10
94
48
-46
Jalandhar
Nawanshehar
5
3
14
33
8
30
91
96
46
59
-45
-37
Hoshiarpur
5
18
13
86
53
-33
Rupnagar
5
27
22
93
57
-36
Ludhina
4
17
13
97
50
-47
Firozpur
5
15
10
95
66
-29
Faridkot
5
20
15
96
59
-37
Muktsar
5
20
15
96
70
-25
Moga
5
23
18
96
61
-35
Bathinda
4
21
16
96
73
-23
Mansa
4
19
15
95
74
-21
Sangrur
3
20
17
96
67
-29
Patiala
5
17
12
86
60
-26
Fathegarh Sahib
2
18
16
80
54
-26
Punjab
4
19
15
91
59
-32
Source: Statistical Abstract of Punjab, various issues
Table 6 . District wise female literacy rate in Punjab, 2001
Total Literacy Rate
Female Literacy Rate
District
Gurdaspur
Amritsar
1991
61.84
58.08
2001
73.8
70.4
1991
53.33
50.1
2001
67.1
65.2
Kapurthala
Jalandhar
Nawanshehar
63.31
68.93
64.42
59.9
77.91
76.86
55.83
62.05
54.55
73.9
78.0
76.4
Hoshiarpur
72.08
81.40
63.34
81.0
Rupnagar
68.15
78.49
55.52
71.4
Ludhina
67.34
76.54
61.25
72.1
48.02
60.7
37.88
52.33
Firozpur
Faridkot
49.97
62.0
41.88
57.09
Muktsar
46.18
58.2
37.05
50.3
Moga
Bathinda
Mansa
51.52
46.48
37.21
63.5
61.2
52.50
44.92
38.04
28.5
63.5
61.2
45.2
Sangrur
45.99
59.9
37.67
59.9
Patiala
Fathegarh Sahib
57.27
63.25
69.3
73.6
48.94
56.13
62.6
68.3
Punjab
58.51
69.7
50.41
63.4
Source: Statistical Abstract of Punjab, various issues
Kaur and Kaur 87
Table 7. Results of OLS regression model
Variables
Constant
MWPR
DUM
SR
FLR
Adjusted R-squared
F-Statistics
P-value(f)
Co-efficient
-159.436
1.65050
13.9618
0.08224
0.03796
0.72490
22.7395
1.37e-08
t-ratio
-2.662
3.016
7.011
1.921
0.4266
P-value
0.0125**
0.0053***
1.04e-07**
0.0646
0.6728
Note: ** signifies at 5% level of significance ***signifies at 1% level of
significance
workers. It is interesting to note that in all districts of
Punjab, percentage of main workers among female has
increased. Further table reveals that female main workers
percentage has increased from 4 percent to 19 percent at
the state level. It is noticed that Nawanshehar has the
highest percentage of female main workers, while
Gurdaspur has the lowest.
Table 6 reveals that according to 2001 census female
literacy rate in Punjab is 63.4 per cent. It is the highest in
Hoshiarpur (75.56%), the lowest in Mansa (45.07%) and
just above the midway mark in Muktsar (50.5%) and
Ferozepur (52.33%). Further, there are nine districts in
Punjab with a lower female literacy rate than the state
average.
As per 2001 census in terms of total literacy rate,
Hoshiarpur district is the most literate in the state, with a
literacy rate of 81.40 per cent, followed by Rupnagar
(78.49%), Jalandhar (77.91%), Nawanshahr (76.86%),
and Ludhiana (76.54%). All these districts have atleast
three-fourths of their population literate. On the other
hand Mansa is the least literate district in the state with a
literacy rate of (52.50 %) followed by Mukstar (58.2 %). It
is interesting to notice here that the Doaba region has
recorded high literacy rate than other regions of Punjab.
The major factor contributed for high literacy in the Doaba
region is that educational facilities started early in this
area. The per square availability of primary schools is the
highest in Hoshiarpur district. The high literacy rate is
also the outcome of the culture and nature of work of the
people in the Doaba region. The economy in this area is
largely dependent on the service sector rather than
primary sector.
Factors affecting the women work participation in
punjab
Note: ** signifies at 5% level of significance ***signifies at
1% level of significance.
Estimated results of the model are presented in Table 7
The expected relationship between female literacy rate
and female labor force participation rate is positive. It has
been clear from the table that the variable FLR is affected
FLFP positively and it has not found to be significant at
any conventional level of significance. The findings of the
study suggest that education plays an important role in
women’s decisions of economic participation. Education
qualifications enhance the job prospects of all individuals,
and for women. Generally, for women as the education
level increases the economic participation increases. Few
studies have demonstrated that in practice the effect of
education on the female labor force participation is not so
straightforward which implies that higher level of female
literacy rate do not necessarily lead to higher female
labor force participation rate.
Another variable, which is considered as the
determinant of FLFP rate is the sex ratio. The expected
relationship between female literacy rate and female
labor force participation rate is positive in the sense that
districts with higher sex ratio have more women available
to join labor force and high female participation in
economic activities. It has been observed from the table
that sex ratio emerged as significant predictor for inter
district variations in female work participation rate which
indicates that FLFP rate is positively affected by the FLR
and it has not found to be significant at any conventional
level of significance.
MWR is the important variable included in the model to
capture the inter district variations in female labor force
participation in Punjab. The expected sign of co-efficient
of variable MWPR is positive which implies that higher
workforce participation of males is more likely to bring
about high level of female workforce. We can be
observed from the table that the sign of the MWPR
variable is according to priori expectation. However, in
practice in Punjab, other socio-economic and household
conditions like poverty also affect the female’s
contribution in economic activities. The time dummy also
has shown significant and positive impact on the female
labor force participation.
CONCLUSION AND SUGGESTIONS
Throughout this paper, an attempt is made to examine
the trends and patterns of female workforce participation
88 J. Res. Peace Gend. Dev.
across Punjab, during 1991 and 2001. At the same time,
this paper also examined the various determinants of
women’s work in Punjab. It has been found that at the
district level, a high inter-district variation has been
observed for labor force participation for both males and
females. However, the inter-district variations in female
work participation rates have declined in Punjab due to
sharp rise in female work participation rates in 2001. As
we have found male workforce participation, sex ratio,
and female literacy rate to certain extent emerged as the
variables having significant correlation with female
workforce participation rate. Therefore, it may be
concluded that the basic level of education is not
sufficient to enter the labor market and the matric level of
education can be considered as the minimum criterion for
female labor market participation. Female labor market
participation increases with the rising levels of education.
It is suggested that government should provide higher
education to the females especially in rural areas. Quality
of education should also be improved and female’s
training opportunities should be provided. It is concluded
that females are more likely to participate in rural market
activities. It is also suggested that discrimination of
wages should be removed so that the female workers are
not discouraged if less wages are given to the female
workers to the equal work to men, the female workers
would be disappointed. Women labor’s union should be
organized so that the exploitation of female workers is
ended. It will also help in improving their working
conditions and in reducing the discrimination done with
them on the basis of sex. Various employment schemes
should be encouraged that can increase the participation
of women from rural areas. Programmes should be
started to make them aware about new technologies.
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