O

Discussion Paper No.206

Region and Gender Specific Labour Market Participation inIndia: A Study on Inter-State Variations and Determinants

Arup Mitra and Aya Okada
June 2017

Graduate SchoolofInternational Development

NAGOYA UNIVERSITYNAGOYA 464-8601, JAPAN
〒464-8601

名古屋市千種区不老町

名古屋大学大学院国際開発研究科

Region and Gender Specific Labour Market Participation in India:
A Study on Inter-State Variations and Determinants1
Arup Mitra2 and Aya Okada3
Abstract
The female participation rate is significantly lower than that among the males. The rural-urban
differentials are more pronounced and the inter-state variations are sizable in the case of females,
reflecting the influence of social, cultural and economic factors as well. Even in the large cities the female
labour market participation is lower than that in the rural areas despite higher levels of education. The
impact of infrastructure, education and health and urbanization on labour force participation of both thegender is quite distinct. While industrialization and growth in services both show a positive effect onparticipation, though very mildly especially in the case of urban women, economic growth shows anincreasing impact for urban males only. Also, there is evidence on poverty induced participation inagricultural activities, suggesting clearly the importance of rural diversification for participation to pickup in the rural context. Women’s participation improves child health significantly as noted from thebeneficial effect of female labour force participation rate (from the panel data analysis) on infantmortality of girls as well as boys. Access of mothers to resources through labour market participation
improves the health status of the children as their nutritional status and access to curative health care getbetter. On the whole, women’s participation in productive activities has a double effect: first, it raises thehousehold income and second, it contributes to wellbeing of the household. These findings are importantfrom policy point of view because different infrastructure variables are seen to improve both participationand labour productivity. With improved infrastructure the quantum of investment is expected to shoot up
and the accessibility to growth centres offering better livelihood opportunities can perk up. Infrastructure(social, physical and financial) can to certain extent break the social and cultural barriers and help womenparticipate in the labour market and make productive contribution to the growth process. Though the1This study was carried out during Arup Mitra’s visit (November 2016 - March 2017) at the Graduate School of International

Development, Nagoya University, Nagoya, Japan
2

Institute of Economic Growth, Delhi University Enclave, Delhi-110007, India; arup@iegindia.org

3

Graduate School of International Development, Nagoya University, Nagoya, Japan; aokada@gsid.nagoya-u.ac.jp

1

level of urbanization raises the urban participation rate in an inter-spatial sense, a similar pattern is notevident in the context of rural females (at least at the state level). How urbanization can be made moregenerative with positive spillover effects in the rural neighbourhood is, therefore, an important policyquestion. Increased urbanization ushering in greater concentration of non-farm activities can expand
employment prospects and at the same time result in productivity gains.

1.

Analytical Frame

Labour force participation rate is an important indicator of development. With increased human capitalformation people are able to participate in productive activities which result in higher levels of value
addition. Without human capital formation a high level of labour market participation is also evident, butthat is associated with low levels of labour productivity. How labour market participation can improvewith rising labour productivity is, therefore, an important research and policy question. Particularly insocieties experiencing rise in life expectancy population must be engaged in productive activities so thatfor future years (for old age) they are able to save enough. India has witnessed a considerable decline infertility rate with an increase in the percentage of population in the working age brackets, which is seen
to be the source of demographic dividend. But such benefits can materialize and enhance economic
growth only when from supply point of view there is skilled and highly employable labour force andfrom demand side there are enough opportunities to absorb them productively. The new technology isskill intensive and the study by Okada (2004) reveals that dynamic industrial transformations in the 1990s
significantly changed the nature, content and extent of skills development as far as the domestic suppliers
are concerned. However, in India a large majority of young people still continue to have limited accessto education and training as the dropout rates are high despite an expansion in the capacity of educational
institutions and enrollments (Okada, 2012). The skill mismatch index is huge, indicating poor
employability of a large percentage of the available labour force (Mitra, 2013a). Dreze and GandhiKingdon (1999) present evidence to suggest that school participation, especially among girls, responds
to a wide range of variables, including parental education and motivation, social background, dependency
ratios, work opportunities, village development, teacher postings, teacher regularity, midday meals andalso school quality.
One of the important drivers of socio-economic transformations is urbanization. There is an economic
reason to justify this view. Large human settlements emerge from concentration of activities. Large2

volume of investments, indivisibilities in infrastructure and other assets and large expenditure incurredby the state naturally give rise to several external economies of scale attracting firms and business toconcentrate. The agglomeration economies in other words motivate growth centres to expand which inturn attract labour to these areas as job search cost tends to decline sizably. Thus population growth takes
place and cities grow in size. The external economies of scale result in higher total factor productivity
growth which gets translated in terms of higher real wages. Many development indicators such as literacy
and social capital formation are likely to improve significantly, as urbanization draws larger government
resources for human capital formation. On the whole, it envisages upward mobility for all, both the
migrants and the natives. The received theory suggests that urbanization follows as a concomitant ofindustrialization and at a later stage it is related to growth in services. In other words, with urbanization
the decline in the share of agriculture or any other primary activity is an obvious phenomenon. Since
productivity levels in the non-primary sectors, particularly in the industrial sector due to theagglomeration benefits, are higher, wage differentials also rise rapidly, which in turn raise theparticipation rate, particularly in the context of the developing countries. Higher levels of income perperson may result in a higher dependency ratio but the counter argument can also be put forth to suggestthat at higher levels of development each individual is conscious of her own identity; hence urbanization,
productivity growth and wage growth may all coincide with increased participation rate. From a different
angle, if urbanization occurs in response to rapid outflow of rural population who are in economically
active age brackets, then naturally the participation rate is expected to pick up.
Ideally speaking urbanization is unlikely to pursue the traditional caste-based occupations which are
more prevalent in the rural areas. It also envisages upward mobility for the socially backward classes
particularly if we are talking about the possibility of inclusive growth and development. Quite opposite
results may be expected if the modern growth process involves labour who is highly skilled. Since thelow caste population did not have access to higher levels of education their absorption in modern sector
does not carry a high probability. Thus, dualism in development is not an unexpected outcome – theurban areas remain populated by the advantaged classes, while their disadvantaged counterparts remain
engaged in petty and low productivity jobs even when they migrate to the urban areas in the face of atight rural labour market. However, jobs in the informal sector also attract migrants and the rural job
seekers respond positively (Banerjee, 1986) instead of searching jobs exclusively in the large scaleindustrial sector, as many of the theoretical models made us believe (Harris and Todaro, 1970). The
reservation system followed in the formal sector (particularly in the government sector) for the scheduled
caste and scheduled tribe population encourages many of them to migrate to the urban areas, expected to3result in an enhanced participation rate. The private job placement cells have been recruiting many tribal
women from the rural areas and engaging them in home care and other related services (Mitra, 2013b).
Among various supply and demand side factors which impinge on female labour force participation rateeconomic growth is seen to have a non-linear relationship (see Mathur, 1994): initially growth is foundto have a negative impact on labour force participation rate but at higher levels of growth it tends toincrease, thus, giving rise to a U-shaped relationship. Cagatay and Ozler (1995) also suggest thepossibility of a U-shaped relationship between long-term development and women’s share of the labour
force. Even the historical record of the developed countries indicates such a relationship between
economic development and women’s labour force participation rate (Goldin, 1994)4. With urbanizationand industrialization female-dominated home-based production is expected to decline, as it would belargely replaced by male-dominated factory production (Boserup, 1970). This falling part of the U-shape
curve corroborates Boserup’s analysis of women’s contribution. However, with further economic
development women’s labour force participation rate is expected to increase as enhanced urbanizationand industrialization, more education for women, commodification of domestic labour, and falling
fertility rates help women workers participate in the labour market more explicitly (Oppenheimer, 1970
and Boserup, 1970). Also, as per the neoclassical approach with economic growth gender inequalities interms of access to employment opportunities, work conditions, nature of work, and earnings tend to
decline (Forsythe, Korzeniewicz and Durrant, 2000). This implies an increase in female labour
participation rate since discouraged dropouts tend to decline: with improved and equal status in the job
market women get encouraged to participate in the labour market (Mitra, 2005).
Some of the recent evidence also supports that higher human development index (HDI), let alone growth,does not necessarily ensure gender equality measured in terms of gender development index (GDI): in
the Asia-Pacific context Japan and Korea have the highest HDI-GDI gap, while Thailand and China
whose HDI and GDI are both lower in absolute terms than Japan and Korea, demonstrate lower gender
gaps (Murayama, 2005). Gender norms and systems vary widely across cultures but they shape people’s
lives and interactions in all societies (Hayase, 2005). In general as women’s educational level improves,gender inequality declines (UN, 2001). In other words, with improved levels of education labour market
participation of women in high income jobs is expected to rise (Murayama, 2005; Pradhan, Singh and
Mitra, 2015), though in India many educated women remain outside the labour market, implying that theGoldin (1994) found this association for women aged 45 to 59 years for cross-section of countries

4

using GDP per capita as an index of development.
4education level of women non-workers is more than that of women workers. Keeping in view a long term
perspective the “Gender Kuznets Curve” and the U-shaped relationship between female labour
participation rate and development are mutually consistent.
A variety of other factors have been considered as determinants of female labour force participation rate.
These include opportunities for informal employment which tend to decline with development(Bharadwaj, 1989), technological and structural change (Sen, 1981)5, the conflict between housework(including child care) and earning opportunities in the labour market, and spouse’s income resulting inwithdrawal from the labour market (rationalizing the backward sloping supply curve of female labour).
While education of various types may become instrumental to labour market participation, income levels
also matter. For example, activities with very low wages with a low elasticity with respect to labour
productivity attract less labour. Many of the women workers are subjected to this phenomenon of‘discouraged dropouts’ from the labour market. The neoclassical principle of marginal productivity based
pricing of the factor of production is not necessarily realistic as many women workers are seen to beengaged in intense work with meagre earnings because of their docility and poor bargaining power,
inability to commute and limited access to diversified social network (Mitra, 2005).
Among various socio-economic factors, fertility, cross-regional cultural norms, attitude towards manual
work, the relative incidence of low caste and tribal population, the size of the agricultural sector,
cultivation techniques, crop patterns, poverty and technology are some of the determinants of female
labour participation rate (see Agarwal 1988). Fertility and age and marriage are inter-woven and theyboth influence the decision to participate in the labour market. Usually a higher fertility rate and a lower
age at marriage reduce women’s labour market participation. However, in the rural context some of thesocially backward classes such as scheduled tribes exhibit a higher labour participation rate amongfemales in spite of a higher fertility rate, as the matrilineal traits are more prevalent among them. Also,
there can be a positive association between labour participation rate and the percentage of workers
engaged in the tertiary sector as activities in this sector provide greater employment opportunities for
women and teen-age workers. However, low productivity activities are mostly concentrated in the tertiary
sector, and hence, as the share of the tertiary sector in total employment increases, dropouts from thelabour market are also expected to be high, thus reducing the labour participation rate.

5

Sen (1981) in the case of Indian agriculture showed that women withdraw from the labour market as male income

increases.

5

Rapid economic growth is definitely a determinant of employment, though it is not a sufficient condition.
Mechanization of agriculture, for example, may raise production without any impact on employment.
The composition of agricultural production also impinges on participation, particularly in the case of
women. In rice cultivation, for example, a number of activities are to be carried out by labour for which
the domestic supplies involving women are tapped. Many of the folklores, particularly in rice cultivatingareas in India, are therefore pro-women, indicating clearly that even in a prevailing patriarchal milieu
women’s participation in the agricultural process has contributed to enhancing their status because in ricecultivating areas women have a major role in performing at every stage of the production process in somuch so that without cooperation of women there can be a steady decline in agricultural production(Mohanty, 2008).
Similarly in non-farm sector rapid industrialization and faster growth in the services are likely to generate
employment opportunities on a large scale which would in turn raise the labor force participation rate byaugmenting labour demand. However, the adoption of capital and skill intensive technology is seen to
reduce the pace of the industrialization of workforce. With adoption of capital intensive technology
economic growth can take place but with no dent on employment, rather it may prompt a withdrawal
from the labour market and/or lead to a residual absorption of labour in low productivity informal sector
activities. Similarly high productivity services sector which are again capital and skill intensive can raise
growth but cannot generate large scale employment for unskilled and semi-skilled variety of theworkforce except nominally through secondary effects. From the point of view of workers’ preference it
is noted that women, particularly the urban based educated ones, have a natural urge to work in theservices sector (Nord, 1989). This means that female labour force participation rate and growth in tertiary
sector are expected to go hand in hand.
Relating to labour regulations it is believed that they tend to reduce the pace of employment generation.
Besley and Burgess (2004) showed that Indian states which amended the Industrial Disputes Act in a
pro-worker direction experienced lowered output, employment, investment and productivity in organizedmanufacturing. Bhattacharjea (2006) on the other hand criticized the widely-used index of state-levellabour regulation devised by Besley and Burgess (2004), and the econometric methodology they used toestablish that excessively pro-worker regulation led to poor performance in Indian manufacturing. On
the whole, while there may be a case for removing labour market rigidities by discouraging the political
patronization of the unions and relaxing the strict labour laws that prohibit employment growth, attention
also needs to be given to the labour welfare issues.

6

Notwithstanding these concerns expressed by ILO the Indian labour market has been experiencing rapid
contractualization. In order to keep the labour cost low it is being followed both in the rural and urban
labour markets. Both agriculture and other activities in the rural areas such as construction involve agreat deal of contractual labour who migrated from other areas with the help of the labour contracting
firms. Similarly in the urban context manufacturing and services both in the formal sector are hiringcontract labour on a large scale in order to avoid the labour market regulations. Often it is seen that theemployers prefer female workers as they have a lesser bargaining strength and also as their wages are
fixed at a level substantially lower than the male wages on a false belief that female workers are lessproductive. This has resulted in feminization in the labour market. In a number of activities the female
to male worker ratio has steadily gone up over time (Banerjee, 1997). In fact, like contractualization,
feminization is an arrangement pursued to reduce labour cost sizably. The other new change which isperceived in the recent years is the practice of business subcontracting from the formal to the informal
sector. Though it creates work opportunities in the informal sector, the business contractors follow thepayment practice based on piece rate, reducing the remuneration of the workers. As many of the homebased workers are women, they are the ones who are worst hit (Patrick, (2001).

Poor health conditions hamper labour force participation rate, particularly in developing countries where
many prime age adults are under-nourished and are in poor health (Currie and Madrian, 1999). In ageing
societies more individuals keep reaching age brackets where health has the greatest impact on labour
market participation (Currie and Madrian, 1999). In general poor health reduces the capacity to work andproductivity levels which in turn reduce wages. The cost of accommodating a worker in poor health ispassed on in the form of lower wages. Low wages dampen participation. Both from supply and demandsides poor health can reduce participation. Particularly in the case of women, those burdened with highfertility rate and poor maternal and child health are unable to participate in the labour market. Even manyeducated women in India let alone their uneducated counterparts, are outside the labour market partly
because of the social norms reinforcing the division of labour along the lines of gender and partly becauseof poor health conditions. A mere increase in literacy is not able to counter the cultural practices and thesocial outlook which perceives women participation in the labour market as a low status phenomenon.
Even among the educated lot this mindset is widely prevalent, forcing many educated and married
women to withdraw from the job market (Jalan, 2000; Schultz, 1990). Household activities and domestic
responsibilities involving children and the elderly are given higher priority over participation (Hirway,2010). As the IFC (2013) report observed globally, while women’s education levels have increased and7educated women earn more than their uneducated peers, gender gaps in labor-market participation andwage levels persist. In the rural areas even among the poor households the labour force participation rate
remains modest possibly because of poor health conditions and health care facilities. Long and frequent
absence from work can discourage employers to engage workers while poor health reduces the supply oflabour hours per individual. Workers in poor health are constantly discriminated against, as the efficiency
wage hypothesis would posit.
Relating to physical and financial infrastructure it has been noted extensively that the potential entrants
to the labour market respond to these facilities positively. Especially women are severely constrained byphysical, financial and poor sanitation facilities. With slightest support they are eager to explore earning
opportunities which augment family income (Cook et. al. 2005). The micro credit provision for example
is seen to have provided earning possibilities across regions despite the serious limitations associatedwith this option (ICDF, 2002). Many rural children with better connectivity and support for acquiring
reading materials have shown greater enrolment ratio. The accessibility to road and sanitation has
encouraged educational enrolment, particularly in the case of girl children. As Dreze and Khera (2015)
write, the child development index fared better in the states of Kerala, Tamil Nadu and Himachal Pradesh:part of this improvement was expansion in elementary education while part of it is attributed to theconstructive role of the state in providing a wide range of facilities ranging from healthcare and cleanwater to social security and basic infrastructure. All these have a future bearing on the labour market
participation, as childhood conditions (heath and education) have profound effects on future outcomes(Grossman, 1975, Wadsworth, 1986).
The rural employment guarantee act (MGNREGA) in order to enhance the livelihood security of the rural
population offers hundred days of employment in a year to every household whose adult members
volunteer to do unskilled jobs (Dreze and Khera, 2009). In response to this programme it has been
observed that women participation has gone up extensively. Males being the primary bread earners
possibly look for jobs with longer duration and hence, they may not be seeking work under MGNREGA,
while women, who are mainly homemakers and/or seasonal workers in the agriculture sector or pursue
economic activities as subsidiary status workers, may take the opportunity of participating in
MGNREGA as it enables them to access cash income. Also, as the male members migrate out from therural areas in search of better jobs women from the rural households remain as the primary job seekers
under MGNREGA. However, the number of days of work per household being only one hundred it isunlikely to raise the female labour force participation rate as per the usual principal status which considers
one hundred and eighty days as the benchmark.
8

Keeping in view this perspective the present study proposes to examine the labour force participation
rates across regions (rural and urban) and sexes in India. The unit of analysis is both state and district.
Further, we consider the participation rates at the level of cities in order to capture the impact of urbanism
which can possibly be felt in large cities. The database of the study is drawn from population census(2011) as well as the annual surveys of labour force conducted by the Ministry of Labour and
Employment (Labour Bureau) for the years 2009-10, 2011-12, 2012-13, 2013-14 and 2014-15. Different
correlates are taken from population census as well as other sources mentioned in the text. Since thepopulation census does not report figures on labour force (working persons and unemployed), rather on
working persons only, we consider the work participation rates. Further, we focus only on the main
workers leaving out those who are working on subsidiary basis called as marginal workers. However,the Labour Bureau’s estimates are wide which consider the main or principal status workers, the marginalor subsidiary status workers and also those who are not working but seeking or available for work andthey are termed as labour force participation rates. The rest of the paper is organized as follows. The
following section examines the labour (and work) participation rates across regions and sexes,
highlighting the regional variations and the gender differentials. It is argued that the gender differentials
in participation are instrumental to gender subjugation; empowerment can be attained throughimprovement in women participation rates. Section 3 makes an attempt to identify the determinants ofparticipation. It is attempted for regions and sexes separately: rural male, rural female, urban male andurban female as the variables are expected to generate differential impact across regions and sexes.
Section 4 turns to issues related to productivity and women participation rate. Finally section 5 concludes
with policy directives.

2.

Broad Patterns Relating to Participation

The male work participation rates pertaining to all age groups are relatively high and are nearly 50 per
cent both in the rural and in the urban areas if we consider the main or usual principal status workers(Table 1 for all-India and for states Figures 1 and 2). Secondly, the inter-state variations measured interms of coefficient of variation are limited (17.5 and 15.5 per cent in the rural and urban areas
respectively). On the other hand, the female participation rates are significantly lower than their malecounterparts (Table 1 for all-India and for states Figures 1 and 2), and more so in the urban areas,
implying that the rural-urban differentials in the case of women are more pronounced than in the case ofmales (Table 1 and Figures 3 and 4). Besides, the inter-state variations are sizable in the case of females,9reflecting the influence of economic, social and cultural factors (coefficient of variation being 53.2 and36.0 per cent in the rural and urban areas respectively). The participation rates in the north-eastern andthe southern regions for example are considerably higher than the northern states. A relatively lower
magnitude of variation in the urban areas may be taken to signify the possibility of convergence (to alimited extent though), while the dominance of the social factors in the rural areas can be said to be more
prominent. But the interpretation can be quite erroneous: the female work participation rates being by
and large lower in the urban areas than in the rural areas indicate the limited impact of education on
participation in the face of social factors. In fact, why the participation rate of Indian urban women isstill so low, given that the per capita income and the educational attainment levels are higher than theirrural counterparts, is an important research question. The plot of participation rates against per capita
income (Figure 5) is not suggestive of any significant positive relationship in the case of females though
among males such a pattern can be somewhat deciphered (Figure 6). In relation to females only a subsetof the observations at the most may conform to this pattern.
Table 1: Work Force Participation Rate (WFPR) and Labour Force Participation Rate (LFPR)
Category

Rural Male
Rural Female
Urban MaleUrban Female
Persons Rural
Persons Urban
All Males
All Females
All Persons All Areas

WFPR (%)
LFPR (%)
Population
Labour BureauCensus-2011
2011-1241.6 (17.5)
79.4 (6.4)
16.7 (53.2)
33.9 (48.0)
48.7 (15.5)
73.7 (7.6)
11.9 (36.0)
19.1 (46.9)
29.5
57.9
30.9
48.0
43.8
77.9
15.2
30.0
29.9
55.4

Note: (1) Though the Labour Bureau (LB) estimates refer to the year 2011-12 – close to the population census year, 2011 they are not comparable with each other because the population census estimates are work participation rates for all age groups
covering only the main (equivalent to the usual principal status) workers whereas the LB estimates are for age groups 15 andabove and they cover all workers (usual principal and subsidiary status) and those who are unemployed.
(2) Figures in parentheses are coefficient of variation based on the state level data.
Source: Population Census, 2011 and Labour Bureau, 2011-12.

10

80
60
40
20
0

UworkPJAMMU & KASHMIRHIMACHAL PRADESHPUNJAB
CHANDIGARHUTTARAKHANDHARYANA
NCT OF DELHI
RAJASTHANUTTAR PRADESH
BIHAR
SIKKIM
ARUNACHAL…
NAGALAND
MANIPURMIZORAMTRIPURAMEGHALAYAASSAMWEST BENGALJHARKHANDODISHACHHATTISGARH
MADHYA PRADESHGUJARATDAMAN & DIU
DADRA & NAGAR…
MAHARASHTRA
ANDHRA PRADESH
KARNATAKAGOALAKSHADWEEPKERALATAMIL NADU
PUDUCHERRYANDAMAN &…
RworkPJAMMU &…
HIMACHAL…
PUNJAB
CHANDIGARHUTTARAKHANDHARYANA
NCT OF DELHI
RAJASTHANUTTAR PRADESH
BIHAR
SIKKIM
ARUNACHAL…
NAGALAND
MANIPURMIZORAMTRIPURAMEGHALAYAASSAMWEST BENGALJHARKHANDODISHACHHATTISGARH
MADHYA PRADESHGUJARATDAMAN & DIU
DADRA & NAGAR…
MAHARASHTRA
ANDHRA PRADESH
KARNATAKAGOALAKSHADWEEPKERALATAMIL NADU
PUDUCHERRYANDAMAN &…

70
60
50
40
30
20
10
0

Figure 1: Work Force Participation Rate in Rural Areas of States
and Union Territories: 2011

RworkM

UworkM

Note: See notes to Figure 1.

Source: Same as Figure 1.

11

RworkF

Notes: (1) F for female, M for male and P for person. (2) Only main workers are being considered.

Source: Population Census, 2011

Figure 2: Work Force Participation Rate in Urban Areas of States
and Union Territories: 2011Chart Title

UworkF

40
35
30
25
20
15
10
5
0

JAMMU &…
HIMACHAL…
PUNJAB
CHANDIGARHUTTARAKHANDHARYANA
NCT OF DELHI
RAJASTHANUTTAR PRADESH
BIHAR
SIKKIM
ARUNACHAL…
NAGALAND
MANIPURMIZORAMTRIPURAMEGHALAYAASSAMWEST BENGALJHARKHANDODISHACHHATTISGARH
MADHYA…
GUJARATDAMAN & DIU
DADRA &…
MAHARASHTRA
ANDHRA…
KARNATAKAGOALAKSHADWEEPKERALATAMIL NADU
PUDUCHERRYANDAMAN &…
80
70
60
50
40
30
20
10
0

Figure 3: Difference in Rural and Urban Work
Participation Rate of Males: 2011

RworkM
UworkM

Source: Same as Figure 1.

Figure 4: Difference in Rural and Urban Work
Participation Rate of Females: 2011

RworkF

UworkF

Source: Same as Figure 1.

12

Figure 5: Per Capita Income (2011-12) and Rural
Work Participation Rate (2011)
70
60
50
40
30
20
10
0
0

20000

40000

60000

80000

RworkM

100000120000140000

RworkF

Source: Data book for Planning Commission, Govt. of India and Population Census, 2011.

Figure 6: Per Capita Income (2011-12) and
Urban Work Participation Rate (2011)
80
70
60
50
40
30
20
10
0
0

20000

40000

60000
UworkM

80000
UworkF

Source: Same as Figure 5.

13

100000120000140000Labour Bureau has been collecting information since 2009-10 on yearly basis (except 2010-11). Basedon the estimates of labour force participation rate for the year 2011-12, corresponding to the age groups
15 and above, again similar differences across gender are evident (Figures 7 and 8). The coefficient ofvariation is much higher among the rural and urban females than among the males, indicating wide interspatial differences (Table 1).

Figure 7: Labour Force Participation Rate in Rural Areas 2011

JAMMU & KASHMIRHIMACHAL…
PUNJAB
CHANDIGARHUTTARAKHANDHARYANA
NCT OF DELHI
RAJASTHANUTTAR PRADESH
BIHAR
SIKKIM
ARUNACHAL…
NAGALAND
MANIPURMIZORAMTRIPURAMEGHALAYAASSAMWEST BENGALJHARKHANDODISHACHHATTISGARH
MADHYA PRADESHGUJARATDAMAN & DIU
DADRA & NAGAR…
MAHARASHTRA
ANDHRA PRADESH
KARNATAKAGOALAKSHADWEEPKERALATAMIL NADU
PUDUCHERRYANDAMAN &…

100
80
60
40
20
0

RlfprM

RlfprF

Note: All workers based on usual principal-cum-subsidiary status.
Source: Labour Bureau, 2011-12, Ministry of Labour and Employment, Govt. of India.

90
80
70
60
50
40
30
20
10
0

JAMMU & KASHMIRHIMACHAL PRADESHPUNJAB
CHANDIGARHUTTARAKHANDHARYANA
NCT OF DELHI
RAJASTHANUTTAR PRADESH
BIHAR
SIKKIM
ARUNACHAL PRADESH
NAGALAND
MANIPURMIZORAMTRIPURAMEGHALAYAASSAMWEST BENGALJHARKHANDODISHACHHATTISGARH
MADHYA PRADESHGUJARATDAMAN & DIU
DADRA & NAGAR HAVELIMAHARASHTRA
ANDHRA PRADESH
KARNATAKAGOALAKSHADWEEPKERALATAMIL NADU
PUDUCHERRYANDAMAN & NICOBAR…

Figure 8: Labour Force Participation Rate in Urban Areas 2011-12

UlfprM

UlfprF

Note and Source: Same as Figure 7.
The figures below (Figures 9 and 10) provide Labour Bureau’s estimates of gender specific labour
participation rate based on panel data, i.e., for each of the years since 2009-10 (except 2010-11) in the14rural and the urban areas of the states and union territories. The estimates for a given category seem tohave been consistent over time and the gender differences in the estimates are pertinent for each of theyears. The inter-state variations in the rate of a given category and the rural-urban differences in thefemale rate particularly (Figure 11), also seem to have remained stable over time, thus confirming thetime invariance in the female participation rate which not only hovers around a low magnitude but also
varies widely across space. Though the time frame is too short to expect any significant change in theparticipation rate, the complete invariance in the female participation rate suggests that the genderconcerns need to be addressed more seriously.

Figure 9: Rural Labour Force Participation Rate: Panel Data
100
80
60
40
20
0

RlfprM0910
RlfprF0910
RlfprM1112
RlfprF1112
RlfprM1213
RlfprF1213
RlfprM1314

Note and Source: Same as Figure 7.

Figure 10: Urban Labour Force Participation Rate: Panel Data
100
80
60
40
20
0

UlfprM0910
UlfprF0910
UlfprM1112
UlfprF1112
UlfprM1213
UlfprF1213
UlfprM1314

15

Note and Source: Same as Figure 7.
Figure 11: Rural-Urban Differences in the Female Participation Rate: Panel Data

80
70
60
RlfprF0910

40

RlfprF1112

30

RlfprF1213

20

RlfprF1314

10

RlfprF1415

0

JAMMU & KASHMIRHIMACHAL PRADESHPUNJAB
CHANDIGARHUTTARAKHANDHARYANA
NCT OF DELHI
RAJASTHANUTTAR PRADESH
BIHAR
SIKKIM
ARUNACHAL PRADESH
NAGALAND
MANIPURMIZORAMTRIPURAMEGHALAYAASSAMWEST BENGALJHARKHANDODISHACHHATTISGARH
MADHYA PRADESHGUJARATDAMAN & DIU
DADRA & NAGAR HAVELIMAHARASHTRA
ANDHRA PRADESH
KARNATAKAGOALAKSHADWEEPKERALATAMIL NADU
PUDUCHERRYANDAMAN & NICOBAR…

50

UlfprF0910
UlfprF1112
UlfprF1213
UlfprF1314
UlfprF1415

Note and Source: Same as Figure 7.
The Platform for Action adopted at the United Nations Fourth World Conference on Women (Beijing,1995) highlighted the concept of bringing gender issues into the mainstream of society. It is a necessity
to ensure gender equality in all areas of social and economic development. Suggestions were made to
make the concerns and experiences of women as well as of men an integral part of the design,
implementation, monitoring and evaluation of policies and programmes in all political, economic andsocietal spheres, so that women and men benefit equally, and inequality is not perpetuated.6
One of the major suggestions in the gender studies literature is that women can be engaged directly inthe development process and the benefits of growth can be distributed equitably by generating

6

International Labour Organization (ILO). Gender Equality Tool. Definition of Gender Mainstreaming.
http://www.ilo.org/public/english/bureau/gender/newsite2002/about/defin.htmAccessed Dec 2, 2016.

16

increasingly productive employment opportunities for women (Behrman and Zhang 1995). This can raisethe degree of women’s empowerment, as with increased resources they will be able to participate in thedecision making process, both within the household and outside the household. In other words, labour
market participation would have positive spillover effects on various other aspects of wellbeing such as
health, education and the overall attitude towards female sexuality. Employment aspect plays a pivotal
role in relation to other capabilities of women such as health care and nutrition, self-respect and autonomy
and full political functioning (Nussbaum and Glover 1995). Biases against girls/women lead to theirinaccessibility to education and health which results in poor human capital formation and a low labour
force participation rate. This in turn adversely affects the future human capital formation, female labour
productivity, individual welfare, health and participation in the decision-making process and also theoverall economic growth (United Nations 2007). However, other than skill differences between females
and males, biases of employers against women employees also restrict their accessibility to high-income
jobs. Patriarchy’s gendered division of labour often does not allow women to participate in the labour
market on a full-time basis, which in turn adversely affects their job market experience and bargaining
power (Mitra 2005). As an outcome of a wide range of constraints impacting both from demand andsupply sides on female labour market participation, the rates are indeed low and the gender differences
are glaring irrespective of which source of data we use.
There are other alternative ways of empowering women instead of taking the labour market participationroute. But the model of human capital formation and productive employment enhances the individual
capabilities and, more importantly, empowers women to take part in the decision-making process. In thecontext of women employment, extensive citation has been made of the fact that arise in work
accessibility of women improves the accessibility of children, particularly that of the girl children, to
education and nutritious diet. Critics may find fault with the employment approach being a basic meansof human development and generating pro-poor growth if women are largely employed in low
productivity activities in the informal sector. Therefore, while pursuing the labour market participation
approach we explicitly highlight the concept of productive employment, which can contribute to humandevelopment and curb inequalities not only in economic terms but also in other respects. The
participation rates which we are analyzing are, however, too aggregative in nature and cannot throwmuch light on the quality of employment. Only as a rough measure of engagement in the growth processthese rates are examined in order to initiate the work.

17

3.

Quantitative Analysis

Cross-sectional Results
In this section we turn to the determinants of work and labour force participation (WFPR and LFPR) in
the backdrop of the analytical frame presented in Section 1. The variables which are chosen are as
follows: household size (HHSZ), literacy rate (LIT), child-woman ratio (CHILD), female-male ratio in
the population (F/M), urbanization level (URBN), percentage of scheduled caste (SC) and scheduled
tribe population (ST), per capita net state domestic product (PCNSDP), percentage of agriculture,industry and services in total gross state domestic product (AG, IND and SER), state wise road length(in km.) in relation to 100,000 population (ROAD), per capita consumption of electricity (ELEC), creditdeposit ratio of scheduled commercial banks (CRE-DEP), percentage of households with access to safe
drinking water (WATER), infant mortality rate (IMR), fertility rate (FR) and gross enrolment rate inclasses 1 to 8 (ENROL). As these variables have been gathered from various sources they do not refer to
one specific year. The appendix table gives the years for which the variables are available along with
their sources. Secondly, while some of the variables are available for rural and urban areas and males
and females separately some other like infrastructure, growth and composition of growth andurbanization are aggregative in nature. IMR and enrolment are reported among girls and boys separately
without any reference to area while accessibility to safe drinking water is given for rural and urban areas.
Since the analysis is carried out at the state, district and city levels we first discuss the results in detailwith a view to highlighting the commonality at a later stage.
Some of these variables are self-explanatory. But some others need elaboration. For example, the femalemale ratio is taken to represent visibility of women: with larger number of women in a region theparticipation rate is likely to increase as solidarity and bargaining power of women may rise. A balanced
sex ratio may mean less violence against women, which may motivate and provide a conductive
environment to participate in the labour market. IMR is taken to represent the overall health condition of
the population and so is the case with the access to safe drinking water. TFR or child-woman ratio (abroad indicator of fertility), covers the demographic pressure and the dependency on women. The overallhousehold size is another indicator of dependency keeping in view that in India the joint family systemis still prevalent in many regions. ROAD and ELECT represent physical infrastructure while CRE-DEP
is an indicator of financial infrastructure.

18

The factor analysis has been carried out for males and females and for rural and urban areas separately.
In relation to work participation rate among rural females (Table 2), four factors are seen to be statisticallysignificant. Corresponding to the most important factor i.e., Factor 1, women work participation rate does
not have a significant factor loading. In Factor 2, however, it enters with a factor loading of 0.74. It has
a strong positive relationship with the incidence of scheduled tribe women population confirming theview that among the scheduled tribe population women are earners in addition to their household
responsibilities. On the other hand, the incidence of scheduled caste population reduces the women work
participation rate. In the rural situation where caste system is more prevalent it is possibly more difficult
for scheduled caste women to find jobs. Poverty-induced participation is evident and women being
engaged largely in agriculture sector is also noticeable. What is most striking is the effect of physicalinfrastructure on women work participation. Other studies confirmed increased female enrolment inschools in response to improved infrastructure (Dreze and Gandhi-Kingdon, 1999), and here we notice
the positive influence of road network on female work participation. Literacy is also seen to have a mild
positive association with work participation while infant mortality rate shows a negative one. The
positive relationship between female-male ratio and participation is distinct, more so in Factor 3.
Urbanization level is, however, not seen to raise women work participation rate in the rural areas. In
other words, states with higher levels of urbanization are though expected to have positive spillover
effects in the rural areas are somehow not seen to have experienced so, at least in terms of women work
participation rate. Large household size and child women ratio affect women work participation
adversely, as observed in Factor 3.
Table 2: Factor Analysis: Rural Female Work Participation Rate
Variables
HHSZ(R)CHILD®
WPR(F,R)
LIT(F,R)
SC(F,R)
ST(F,R)
F/M®
URBAN
MPCE®
POV®
ELEC
ROAD
CR-DP
WATER®

Factor 1

Factor 2
0.29390.11800.0471-0.49450.1186
0.09440.2869-0.8406-0.5091
0.2633-0.32810.1431-0.2545-0.0572

Factor 3-0.0283
0.10250.74190.1145-0.5926
0.8396
0.1144-0.0632-0.00180.2332-0.2369
0.7807-0.2080-0.3597

19

Factor 4
0.69490.8878-0.2058-0.5993-0.15410.2463-0.5349-0.0043-0.1649
0.1563-0.1902-0.1776-0.0626-0.05180.01210.17900.1516-0.4374-0.0253
0.11340.2783-0.2637-0.73390.7146-0.1206-0.0959-0.0743-0.1071

IMR(F)%AGDP
Eigen Value% Explained
N=320.59070.78185.39
0.42-0.1382
0.27173.04
0.24

0.44470.26011.62
0.13

0.31980.17151.12
0.09

Instead of work if we consider the labour force participation rate, in Factor 1 itself some of these findings
are discernable (Table 3). Scheduled caste incidence reduces the labour participation while among thetribes women join the labour market extensively. Poverty induced participation, requirement of female
labour more in the agriculture sector, the positive effect of health and education and a strong impact ofphysical infrastructure on labour market participation and the lack of urbanization’s spillover effect onrural women labour participation are also evident. That the demographic pressure reduces labour market
participation is brought out by Factor 3. The positive association between the increased presence of
women, measured in terms of female-male population, and the labour market participation is also
verifiable across all the three statistically significant factors (Table 3).

Table 3: Factor Analysis: Rural Female Labour Participation Rate
Variables
HHSZ(R)CHILD®
LFPR(F,R)
LIT(F,R)
SC(F,R)
ST(F,R)
F/M®
URBAN
MPCE®
POV®
ELEC
ROAD
CR-DP
WATER®
IMR(F)%AGDP
Eigen Value% Explained

Factor 1

Factor 2-0.0174
0.16500.69550.1521-0.66910.90210.1208-0.1004-0.03770.2463-0.2930
0.7616-0.3097-0.4586-0.1171
0.28245.49
0.43

Factor 3
0.28980.11330.1650-0.4966
0.11620.10010.2895-0.8603-0.5262
0.2733-0.32370.1448-0.2702-0.0466
0.58730.7781
3.09
0.25

N=32

20

0.7006
0.8865-0.2695-0.6012-0.14280.2282-0.5208-0.0109-0.17400.1571-0.1929-0.1999-0.0690-0.0550
0.46150.25831.65
0.13

Turning to rural male work participation, it has the highest factor loading in Factor 4 compared to theother three (Table 4). The overall growth in the rural areas measured in terms of rural consumption
expenditure per capita, the financial infrastructure (credit deposit ratio) and the overall urbanization in
the state are seen to raise the male participation. Rural diversification has a positive effect as thepercentage of agriculture in gross state domestic product is negatively associated with participation. From
Factor 2 in which the male work participation takes a moderate factor loading, the positive impact ofliteracy and the adverse effect of demographic variables are noticed evidently. Many of these findings
are, however, not confirmed as we shift to labour force participation (Table 5). There is rather evidence
on poverty induced participation in agricultural activities, i.e., Factor 3. Besides, large household size
tends to raise the male labour market participation which could be because of economic compulsions.
Urbanization reduces the rural male participation possibly because the economically active ones migrateout from the rural areas.
Table 4: Factor Analysis: Rural Male Work Participation Rate
Variables
HHSZ(R)CHILD®
WPR(M,R)
LIT(M,R)
SC(M,R)
ST(M,R)
URBAN
MPCE®
POV®
ELEC
ROAD
CR-DP
WATER®
IMR(M)%AGDP
Eigen Value% Explained

Factor 1

Factor 2

0.03300.16670.1223-0.0167-0.7406
0.8717-0.0779
0.01570.2492-0.3349
0.8194-0.4352-0.5049-0.1916
0.27655.86358
0.4983

Factor 3
0.66080.8944-0.3631-0.7136-0.11540.2447-0.0618-0.2332
0.1958-0.2067-0.1957-0.0159-0.0691
0.52840.31002.65340.2255

Factor 4-0.1955-0.02100.24360.4474-0.1616-0.11390.74920.3917-0.18640.3194-0.2352
0.16610.0348-0.4633-0.7619
1.14647
0.0974

N=32
Table 5: Factor Analysis: Rural Male Labour Participation Rate
Variables
HHSZ(R)CHILD®
LFPR(M,R)

Factor 1

Factor 2-0.0090-0.19400.0708

Factor 3
0.6996
0.8925-0.047121

0.27450.03360.2205-0.4429-0.1266
0.77150.04530.1574-0.0359
0.41750.1548-0.13030.1085-0.06410.77970.1278-0.0723-0.16291.03490.0880LIT(M,R)
SC(M,R)
ST(M,R)
URBAN
MPCE®
POV®
ELEC
ROAD
CR-DP
WATER®
IMR(M)%AGDP
Eigen Value% Explained

0.03430.7560-0.8766
0.0793-0.0136-0.2530
0.3627-0.7887
0.41840.56680.1827-0.26445.52720
0.4885-0.7322-0.1205
0.2387-0.1316-0.29230.2236-0.2141-0.2147-0.0781-0.0701
0.57300.32102.625140.2320-0.4432
0.13400.1291-0.8239-0.45910.2695-0.3339
0.2669-0.3324-0.05930.47180.80051.38563
0.1225

N=32

In the urban context the female work participation with the highest factor loading in Factor 2 among all
the three factors, unravels the positive effect of literacy and road infrastructure (Table 6). The femalemale ratio is also positively associated with participation. Again, the positive relationship with theincidence of scheduled tribe population and a negative one with scheduled caste population come out
sharply. High demographic pressure and poor health reduce participation. Urbanization, industrializationand growth in services all three show positive effect on participation, very mildly though (Factors 1 and
4 in Table 7).
In relation to the labour force participation rate by and large similar findings are obtained if we consider
the most important factor and the one in which the female labour force participation takes the highest
factor loading: Factor 1 and Factor 3 respectively (Tables 8a and 8b). In Factor 1 (Table 8b) in fact, all
the infrastructure variables are positively associated with urban female labour force participation.
Financial infrastructure possibly allows women to set up small businesses which enable them to earn.

Table 6: Factor Analysis: Urban Female Work Participation Rate
Variables
HHSZ(U)CHILD(U)WPR(F,U)
LIT(F,U)
SC(F,U)

Factor 1

Factor 2
0.90530.9209-0.4124-0.7754-0.0728

Factor 3-0.06110.15700.75700.3486-0.6424

22-0.0909-0.0126
0.06280.21760.0407

0.1053-0.4064-0.1199-0.1466-0.2336-0.0858-0.0058
0.4215-0.0491
3.716070.3547ST(F,U)
F/M(U)
URBAN
ELEC
ROAD
CR-DP
WATER(U)IMR(F)%SERDP
Eigen Value% Explained
N=320.92810.20270.0129-0.2226
0.5771-0.2531-0.2040-0.0980-0.0869
3.37977
0.3226-0.0677-0.01160.87170.1497-0.16070.42200.0362-0.49840.90081.50656
0.1438

Table 7: Factor Analysis: Urban Female Work Participation Rate with Some Different Variables

Variables
HHSZ(U)CHILD(U)WPR(F,U)
LIT(F,U)
SC(F,U)ST(F,U)
F/M(U)
URBAN
ELEC
ROAD
CR-DP
WATER(U)IMR(F)%SERDP%INDP
Eigen Value% Explained

Factor 1

Factor 2-0.9030-0.9166
0.41430.79270.0701-0.09920.41070.15080.13490.22880.09840.0003-0.4346
0.10700.14643.728650.3186

Factor 3-0.0450
0.16410.75080.3489-0.65930.93090.20900.0068-0.2506
0.5634-0.2541-0.2113-0.0866-0.0676-0.0255
3.42559
0.2927

Factor 4
0.03960.0786-0.06470.13670.05250.0122-0.0233
0.4923-0.2774-0.01080.2756-0.0891-0.20230.9061-0.9301
2.173730.1858-0.1403-0.05100.11260.10340.0218-0.1092-0.04310.78840.6585-0.20430.31930.1901-0.5051
0.34270.11941.07259
0.0917

N=32
Table 8a: Factor Analysis: Urban Female Labour Participation Rate
Variables
HHSZ(U)CHILD(U)LFP(F,U)
LIT(F,U)
SC(F,U)ST(F,U)
F/M(U)

Factor 1

Factor 2
0.2164-0.6172
0.23680.24530.17080.07540.8898

Factor 3
0.19440.20570.37430.3402-0.66240.91080.096023

Factor 4-0.8739-0.5705
0.53800.74020.0005-0.02940.1297-0.1184-0.1398-0.0800
0.2769-0.0894
0.0369-0.0209

URBAN
ELEC
ROAD
CR-DP
WATER(U)IMR(F)
Eigen Value% Explained-0.2073-0.89080.20920.1267-0.3161|-0.0475
3.32991
0.35440.0443-0.0374
0.4625-0.4581-0.4819-0.0135
2.37299
0.2526

0.07320.1646
0.29890.03490.0653-0.3521
2.152650.22910.83830.1847-0.16190.2096-0.0968-0.7285
1.10843
0.1180N=35
Table 8b: Factor Analysis: Urban Female Labour Participation Rate with Some Different
Variables
Variables
HHSZ(U)CHILD(U)LFPR(F,U)
LIT(F,U)
SC(F,U)ST(F,U)
F/M(U)
URBAN
ELEC
ROAD
CR-DP
WATER(U)IMR(F)%SERDP%INDP
Eigen Value% Explained

Factor 1

Factor 2-0.9059-0.9050
0.51100.8061
0.0511-0.0565
0.42370.14620.12140.24010.0893-0.0079-0.4234
0.09340.15793.832210.3238

Factor 3-0.00100.0547-0.19420.16020.0606-0.0116-0.0304
0.6537-0.1157-0.0697
0.3629-0.0430-0.28880.9616-0.87853.449930.2915

Factor 4
0.01390.21640.55260.3405-0.6668
0.94130.1849-0.0173-0.26120.5684-0.3486-0.2161-0.0458-0.0833-0.0244
2.18838
0.1849-0.1253-0.0524-0.34220.02300.0751-0.1072-0.1366
0.56010.8142-0.21880.28010.2637-0.2299
0.10450.28661.002530.0847

N=32
Among males in the urban areas the demographic pressure is seen to reduce participation quite contrary
to the belief that large family size or a large number of children forces men to participate in the labour
market (Table 9). In low income households large family size and participation in petty activities coexist.
But the male respondents often do not consider those activities as proper jobs and hence, they claim tobe outside the workforce. Growth shows a positive impact on participation. At very high levels of income,participation is expected to decline but India being one of the low income countries it is unrealistic to
expect a negative association between them. Similarly literacy and better health conditions tend to
improve participation, irrespective of whether we use work or labour force participation rate (Table 10).
Usually literacy or enrolment is expected to cause withdrawal from the labour market but our findings
24based on the cross sectional data suggests that though in certain age groups it occurs in the short run, in
the long run regions with better human capital formation reveal higher participation rates.
Table 9: Factor Analysis: Urban Male Work Participation Rate
Variables
HHSZ(U)CHILD(U)WPR(M,U)
LIT(M,U)
SC(M,U)
ST(M,U)
URBAN
ELEC
ROAD
CR-DP
WATER(U)IMR(M)PCNSDP%INDP%SERDP
Eigen Value% Explained

Factor 1

Factor 2-0.9019-0.89100.71190.8691
0.0658-0.0958
0.14220.10550.26290.1232-0.0181-0.4138
0.33780.14340.08384.70756
0.3892

Factor 3-0.1639-0.07290.29450.2175-0.0781-0.08440.77680.6646-0.14460.20190.2178-0.5361
0.87700.08780.38892.82700
0.2337

Factor 4
0.0154-0.1879
0.2085-0.24880.6973-0.8909
0.05590.3121-0.6956
0.37580.2496
0.09990.0116
0.06410.10232.298850.19000.04080.0751
0.02270.02010.08140.01740.4533-0.31740.00190.2758-0.0934-0.1980
0.0968-0.94680.88011.232760.1019

N=32
Table 10: Factor Analysis: Urban Male Labour Participation Rate
Variables
HHSZ(U)CHILD(U)LFPR(M,U)
LIT(M,U)
SC(M,U)
ST(M,U)
URBAN
ELEC
ROAD
CR-DP
WATER(U)IMR(M)PCNSDP%INDP%SERDP
Eigen Value% Explained

Factor 1

Factor 2
0.90590.8851-0.2125-0.8651-0.0456
0.0636-0.1305-0.0627-0.3030-0.0825
0.06340.4398-0.3209-0.1388-0.0744
4.188160.3653

Factor 3-0.1819-0.0891
0.0616
0.2163-0.0695-0.09640.77380.6900-0.15340.22080.2759-0.53220.88670.10070.38512.935190.256025

Factor 4-0.0097-0.21570.5107-0.22800.6811-0.8884
0.05790.3074-0.6958
0.37460.28900.08890.02220.07270.09972.310190.20150.03720.0744-0.04540.02110.08270.01630.4674-0.3173-0.0015
0.2822-0.1088-0.2089
0.1019-0.9432
0.88401.22377
0.1067

N=32

District level Analysis
At the district level a couple of new variables have been introduced such as sex ratio among children(CHILDSR), shares of agricultural labour (AGLAB), cultivators (CUL), household manufacturing(MFGHH) and other activities (OTHER) in the total workforce. The child sex ratio may represent theextent of gender discrimination at young age brackets. Districts with low levels of child sex ratio would
mean a high degree of gender discrimination. The employment composition is taken to assess how
dynamic an area is.
The negative effect of fertility and household size on rural female work participation comes out sharply
from Factor 1 (Table 11). Greater domestic burdens in large households do not allow women toparticipate in the workforce. Literacy raises participation (Factor 1) though there are other districts
forming a separate group in which literacy reduces participation (Factor 2) possibly because enrolment
of girls leads to labour market withdrawal. The positive association between tribal population andparticipation and the negative effect of low caste population on participation are evident.
The visibility of women and urbanization level seems to be enhancing women participation in the rural
areas. With increased level of urbanization the rural-urban dis-continuum tends to decline which brings
in work opportunities even for rural women who do not migrate out. There is a cluster of districts whichis indicative of women workers being engaged primarily as agricultural labour or cultivators (Factor 2)
though there are some other districts which tend to show increased participation with improvement in
activities other than agriculture and household manufacturing. The non-household manufacturing andthe services sector offer possibilities of better earnings, encouraging women, particularly the literate ones,
to join the workforce. Also, some of the jobs in the health sector, for example, are meant specifically forliterate women. Unfortunately rural diversification has been very sluggish in the Indian context with its
limited impact on women work participation rate which can be seen from the low magnitude of the factor
loading for the share of workers in non-household manufacturing and services in Factor 1. Gender
discrimination at early ages is reflective of its continuation even in the later years: an improvement in
the gender ratio among children improves the work participation to a very limited extent.
Table 11: Factor Analysis: Rural Female Work Participation Rate at the District Level
Variables

Factor 1

Factor 2
26

Factor 3

Factor 4

-0.6820-0.8783
0.46260.70670.1048-0.0695
0.41010.35920.0185-0.1301-0.0726
0.1467
0.09672.91318
0.3283

HHSZ(R)CHILD(R)WPR(F,R)
LIT(F,R)
SC(F,R)
ST(F,R)
F/M(R)URBAN
AGLAB(F,R)
CUL(F,R)
MFGHH(F,R)
OTHER(F,R)
CHILDSR(R)
Eigen Value% Explained-0.04780.10080.5607-0.3227-0.0205
0.24420.1304-0.2852
0.26790.6514-0.1470-0.95310.11672.49798
0.28150.23150.0717-0.00510.1442-0.20990.2478-0.0642-0.0290-0.9526
0.67900.00370.2402-0.1530
1.782440.2009-0.1173
0.16590.2973-0.0066-0.70080.70810.16310.0236-0.10240.1662-0.0821-0.0518
0.47891.05884
0.1193

N=631
Among the rural males the positive effect of urbanization on participation is evident (Table 12). Literacy
raises the work participation rate while demographic pressure reduces it. Large families with a large
number of children are not able to generate better human capital formation which in turn reduces thepossibility of being absorbed in high productivity activities. Activities other than household
manufacturing and agriculture have a positive effect on participation, suggesting the importance of rural
diversification.
Table 12: Factor Analysis: Rural Male Work Participation Rate at the District Level
Variables
HHSZ(R)CHILD(R)WPR(M,R)
LIT(M,R)
SC(M,R)
ST(M,R)URBANAGLAB(M,R)
CUL(M,R)
MFGHH(M,R)
OTHER(M,R)
CHILDSR(R)
Eigen Value% Explained

Factor 1

Factor 2-0.7141-0.87420.66120.59560.1588-0.0818
0.25650.0228-0.1644-0.0853
0.13380.11893.19995
0.3785

Factor 3-0.1482-0.2242-0.0179
0.2986-0.0013-0.2575
0.38950.0286-0.9483
0.13310.8054-0.0092
1.973250.2334N=631

27

0.1723-0.0524-0.06790.2413-0.2127
0.19380.1347-0.99280.2152-0.02800.5778-0.1499
1.739380.2057

Among the urban females the findings are again quite similar to those for rural females except in relation
to gender ratio which does not have a positive effect on participation in the urban areas (Table 13). Thisis mainly because many educated women living in the urban areas remain confined to the domestic
activities. Even when the gender ratio rises due to migration of women after marriage – the biggest reasonof female migration in India (Mitra and Murayama, 2008) – women do not necessarily participate in thelabour market. Besides, women who accompany their husbands while migrating from the rural to theurban areas are not able to find employment in the urban labour market. At times there is skill mismatchand at times the labour demand for women is limited and transient. For example, in the construction
sector demand for women labour is highly sporadic and when there is a decline in labour demand, thewomen workers are the first ones to be retrenched. So the discrimination issue is much wide and deeprooted, it can exist even when at the time of birth it is not prevalent in the form of gender selective feticide.
As Table 13 shows the child sex ratio has a positive association with work participation but the overall
gender ratio has a much stronger negative relationship with female work participation rate. With
accessibility of women to job opportunities female feticide seems to have declined but rise in the women
to men ratio has not resulted in enhanced participation due to cultural barriers.
Table 13: Factor Analysis: Urban Female Work Participation Rate at the District Level
Variables
HHSZ(U)CHILD(U)WPR(F,U)
LIT(F,U)
SC(F,U)ST(F,U)
F/M(U)URBAN
AGLAB(F,U)
CUL(F,U)
MFGHH(F,U)
OTHER(F,U)
CHILDSR(U)Eigen Value% Explained

Factor 1

Factor 2-0.7893-0.8262
0.61370.76000.12540.0176-0.82620.3041-0.0445-0.0879-0.1243
0.14700.2846
3.14689
0.3584

Factor 3-0.0272
0.11740.1048-0.23290.0876-0.0951
0.1174-0.1769
0.99780.09140.0239-0.67150.12682.37446
0.2705

Factor 4
0.06570.23220.2320-0.0343-0.1199
0.41840.2322-0.1508
0.01380.9793-0.0724-0.48190.04241.73720
0.19790.03180.1146
0.41840.1495-0.62220.72400.1146-0.0241-0.0404
0.1661-0.0562-0.0296
0.52881.01386
0.1155

N=637
Among the males the findings are again not different from what we noted in the rural context. However,
the association between the share of activities comprising non-household manufacturing and services
and the work participation rate is highly significant. Higher levels of urbanization are seen to raise the28participation confirming the agglomeration effects. With greater concentration of infrastructure andactivities labour demand seems to have raised the participation rate.
Table 14: Factor Analysis: Urban Male Work Participation Rate at the District Level
Variables
Factor 1
Factor 2
Factor 3-0.7569
0.05130.1070
HHSZ(U)-0.8366
0.16710.2077
CHILD(U)0.6008-0.1819-0.2105
WPR(M,U)0.7096-0.3451-0.0913
LIT(M,U)
0.16050.0831-0.0248
SC(M,U)-0.0642-0.14140.2994
ST(M,U)
0.2488-0.1746-0.1904
URBAN-0.15200.97310.1671AGLAB(M,U)-0.1462
0.1956
0.9631
CUL(M,U)-0.15390.08510.0186
MFGHH(M,U)
0.2178-0.6645-0.6270
OTHER(M,U)
0.26000.1206
0.0630
CHILDSR(M,U)
3.94216
1.67724
1.27130
Eigen Value
0.49610.21110.1600% Explained

At the Level of Cities
Since cities are considered to be the dynamic centers of growth compared to other medium sized andsmall towns we have undertaken the analysis separately for the class 1 cities each with a population of
100,000 and above. As mentioned above these cities comprise more than 60 per cent of the urban
population. The association between the overall gender ratio and the female work participation is positiveand quite strong unlike at the level of all urban areas which means that women in relatively large cities
are better off as far as the labour market outcome is concerned (Table 15). With their greater presence
women are able to participate in the labour market as the social restrictions are relatively less in large
cities whereas in smaller urban locations the effect of larger presence gets neutralized by the culturalbarriers. The demonstration effect of relatively advanced households on the rest is a strong possibility inlarge cities which comprise more heterogeneous households compared to the homogeneous habitation of
the small towns. Also, large cities because of scale effects are able to conduct greater economic activities,
generating larger demand for labour, while many of the activities in the services sector prefer women
workers, specifically. Besides, women from low income households in large cities are often forced toparticipate because of higher cost of living.

29

The findings for males in cities (Table 16) are not different from what was observed at the state or districtlevel. The effect of city size on both male and female participation is noteworthy, suggesting that even
within the set of large cities the participation of males and females both tend to improve as the city size
increases. Very large cities naturally offer better prospects for employment; and the social restrictions
which are more prevalent in the case of women tend to get dissipated.
Table 15: Factor Analysis: Urban Female Work Participation Rate at the City Level
Variables
HHSZ(C)CHILD(C)WPR(F,C)
LIT(F,C)
SC(F,C)
ST(F,C)
F/M(C)CTYSZ
MFGHH(F,C)
OTHER(F,C)
CHILDSR(C)
Eigen Value% Explained

Factor 1

Factor 2-0.8032-0.80850.63770.68400.08490.0781
0.46870.1482-0.05070.18280.33613.24645
0.56280.15920.17520.3242-0.36850.0099-0.12760.1003-0.11810.8753-0.8652
0.19002.01018
0.3485

N=467
Table 16: Factor Analysis: Urban Male Work Participation Rate at the City Level
Variables
Factor 1
Factor 2-0.8671-0.2156
HHSZ(C)-0.6653-0.1376
CHILD(C)0.79590.1823
WPR(M,C)
0.47240.3683
LIT(M,C)
0.02220.0062SC(M,C)
0.04380.1237
ST(M,C)
0.14040.1180CTYSZ-0.1258-0.8155
MFGHH(M,C)
0.25580.8420OTHER(M,C)
0.4167-0.1573
CHILDSR(M,C)
3.28815
1.08844
Eigen Value
0.70080.2320% Explained
N=467

Panel Data Analysis
30

Since Labour Bureau has supplied data for five time points corresponding to each of the states and union
territories we have tried to carry out the panel data analysis as well. But in this exercise only a limited
number of variables such as per capita income, the sectoral shares in gross state domestic product andthe infant mortality rate could be included.
The results from Table 17 indicate that the female participation rate in the rural areas is influenced byindustrialization. The rural diversification possibly creates more opportunities in which women workers
can step into. The service sector on the other hand reduces participation refuting some of the popular
views which suggest about women’s preference to work in this sector. The residual activities carried out
in this sector with meager earnings can discourage women from participating in the labour market(‘discouraged dropouts’). However, the female health condition measured in terms of infant mortality
rate is a strong determinant of their participation both in the rural and in the urban areas (Table 17).
For rural males none of the variables turns out to be significant though in the fixed effect model (FE) the
overall growth index is statistically significant (Table 18). The positive effect of industry and services
are evident in the case of urban males, across all the three models (classical OLS, Fixed Effect andRandom Effect).
Keeping in view the literature, suggesting the beneficial effect of women’s access to income and its
positive effect on nutrition, education and health of the children, we have turned the causality fromparticipation to IMR after controlling for growth. Both female participation and economic growth areseen to reduce infant mortality rate among girls as well as boys. And this beneficial effect of female
participation is evident across both rural and urban areas as can be seen from all the three models(classical OLS, FE and RE; Tables 19 and 20). However, the participation definitely has a beneficial
effect on children’s health, particularly that of a girl child: with an increase in female labour force
participation the infant mortality rate among girls both in the rural and the urban areas tends to decline.

Table 17: Regression Results for Labour Force Participation Rate: Panel Data Analysis (OLS
Results)
Variables

Dep var:
LFPR(F, R)(OLS)

Dep var:
LFPR(M,R)(OLS)

Dep var:
LFPR(F,U)(OLS)

PCNSDP-0.00002(-0.28)-0.0001(-1.84)0.0001(2.22)*
31

Dep var:Dep
var: Depvar:
LFPR(M,U) LFPR(F,R) LFPR(F,U)
(OLS)
(OLS)
(OLS)
-0.00005(-1.70)-0.0002(-1.81)-0.00002(-0.39)

%INDP

0.5(3.54)*

0.069(1.29)

0.25(0.80)-0.073(-0.61)-0.29(-1.39)0.40(-1.97)*%SERDP%AGDP
IMR amonggirls
Constant
Adj R2N

17.93(1.69)
0.09
154

78.78
(19.56)
0.02
154

0.24
(2.62)*
0.19(2.17)*

45.67(3.13)*
0.19
154

56.81
(8.95)*
0.04
154

0.69
(4.73)*-0.22(-1.09)-0.41(-2.07)*0.35(1.08)-0.43(-3.61)*34.40(2.70)*
0.23
122

-0.42
(-5.96)*68.25(4.66)*
0.24
122

Table 18: Regression Results for Labour Force Participation Rate: Panel Data Analysis (Fixed
Effect and Random Effect Models)
Variables

Depvar:
LFPR(F,R)
(FE)

Dep
var:
LFPR(F,R)
(RE)

Dep
var:
LFPR(M,R)
(FE)

Dep
var:
LFPR(M,R)
(RE)

Dep
var:
LFPR(F,U)(FE)

PCNSDP-0.0001(-0.95)-0.0002(-1.7)

0.0002(3.32)*-0.00001(-0.22)0.0001(0.80)

0.37(1.28)

0.57(2.83)*-0.47(-1.15)0.06(0.81)-0.24(-0.35)-0.69(-4.51)*61.35(3.47)*
0.06

0.34(0.83)-0.67(-5.43)*48.21(3.39)*
0.19-0.11(-0.63)0.03(0.20)

74.69(6.58)*
0.03

122

122

154

%INDP

IMR
among
girls
Constant

R2
N

Dep var:
LFPR(M,U) (FE)Dep var:
LFPR(M,U) (RE)-00006(-1.04)-0.00005(-1.54)0.25(0.49)
0.49(0.92)

5.98e-06(0.07)-0.26(-0.93)-0.32(-1.18)

1.26(4.16)*
1.11(3.72)*

0.34(2.63)*
0.28(2.23)*

74.74(13.86)*
0.03

-0.18
(-1.51)-9.18(-0.21)0.01-0.35(-3.98)*60.56(2.90)*
0.07-22.80(-0.96)0.02

49.70(5.48)*
0.04

154

122

122

154

154

%SERDP%AGDP

Dep var:
LFPR(F,U)

Table 19: Impact of Female LFPR in Rural Areas on IMR (Panel Data Results)
IMR(Girls) IMR(Girls) IMR(Girls)
OLS
FE
RE
PCNSDPLFPR(F,R)
Constant
Adj. R2N

IMR(Boys) IMR(Boys)
OLS
FE

IMR(Boys)RE-0.0004(-9.36)*-0.18(-2.86)*64.37(19.47)*0.42-0.0005(-5.18)*-0.31(-4.81)*
70.16(17.31)*0.42-0.0004(-7.16)*-0.28(-5.07)*68.5(17.89)*0.41-0.0004(-9.55)*-0.18(-2.87)*61.43(18.36)*0.44-0.0004(-4.43)*-0.27(-4.28)*61.96(15.59)*0.42-0.0004(-6.62)*(-0.25(-4.50)*62.11(16.23)*
0.43

122

122

122

122

122

122

32

Table 20: Impact of Female LFPR in Urban Areas on IMR (Panel Data Results)
IMR(Girls) IMR(Girls)
OLS
FE
PCNSDPLFPR(F,U)
Constant
Adj. R2N

4.

IMR(Girls) IMR(Boys) IMR(Boys) IMR(Boys)
RE
OLS
FE
RE

-0.0004(-9.27)*-0.514(-5.64)*67.60(24.75)*0.51-0.0005(-5.07)*-0.20(-2.10)*65.6(15.24)*0.46-0.0004(-6.72)*-0.30(-3.54)64.03(18.04)*0.49-0.0004(-9.49)*-0.53(-5.70)*64.76(23.47)*0.52-0.0004(-4.49)*-0.13(-1.44)*
57.48(13.74)*0.46-0.0004(-6.44)*-0.24(-2.88)*58.27(16.39)*
0.49

122

122

122

122

122

122

Productivity and Female Participation

In this section we assess if female labour participation raises labour productivity as well. However, there
are certain practical difficulties in doing this. A highly capital intensive technology which reduces thepace of labour absorption without reducing value added would mean higher labour productivity. Hence,
there is difficulty in assessing the impact of labour force participation rate on labour productivity as lower
employment levels would always mean higher labour productivity. So this needs to be interpreted very
carefully. Secondly, labour productivity is defined as value added per labour. But no information isavailable on how much of value added is contributed by the male and the female workers separately.
Hence, it becomes difficult to assess the association between female labour force participation and female
labour productivity. Again, the information on rural and urban labour productivity is missing because
value added figures are given sector wise. So what is doable is as follows. We can only investigate
whether there is a positive relationship between female labour force participation and overall labour
productivity.
On a priori basis the relationship between the variables can go in either direction - positive or negative.
In aging societies as labour supply shrinks, the shortages can be mitigated by raising the female labour
force participation. Also, given the fact that female labour is docile and sincere, labour productivity can
actually rise with increased participation of women in the labour market. Besides, female wages being
lower than the male wages the substitution of female labour for male labour can reduce the wage share(or labour cost) in value added. In other words, wage to labour productivity ratio can decline which means
33

increased efficiency of the unit from the point of view of the employer. This can then contribute to overall
growth.
However, from another angle, particularly in a typical neoclassical frame, female wages are lower
because female productivity is believed to be lower than the male productivity: the rationalisation isderived from the proposition that each of the factors of production gets paid according to its marginal
productivity under perfect competition. This postulation has led to the concern that increased
participation of females in the labour market can actually reduce labour productivity and growth.
Further, as we say low productivity means low wages which in turn raise labour demand, there is thepossibility of a causal relationship running from productivity to participation as well. Or to put in plainly
lower productivity would mean more manpower to complete the job. So the bi-directionality issue isquite important. In addition, productivity cannot be measured as a function of participation only. Other
relevant variables representing technology, skill, production structure, to cite a few, need to be controlledfor. However, given the paucity of information and the lack of possibility of carrying out a rigorous
quantitative exercise we simply take overall labour productivity as a function of certain variables on
which information are readily available. This is pursued basically to address the importance of certain
policy options. For example, economic necessity may compel women to participate in the labour market
and without adequate education and skill they may pursue petty activities. This sort of a situation willlead to increased participation but with low levels of productivity. On the other hand, with higher levels
education and skill when women participate in the labour market their productivity can be much higher.
But with higher levels of education women necessarily cannot participate in the labour market as thesocial factors may pose major hindrances. Hence, based on the observed association between productivity
and participation certain broad inferences can only be drawn regarding the quality of jobs that women
get absorbed into and accordingly suggestions can be made in favour of strategies which can help attainthe twin objectives of productivity growth and increased participation.

Table 21: Effect of Female Participation on Labour Productivity

Variables

Dep var:Lab Prod(OLS)

Dep var:Lab Prod
(OLS)

Dep
var:
WORK(F,R) (OLS)Dep var:
WORK(F,U) (OLS)34

Dep var:
LFPR
(F,R)
(OLS)

Dep var:
LFPR(F,U)(OSLS)

Dep
var:
Lab
Prod
(2SLS)

Dep var:Lab
Prod(2SLS)

WORK(F.R)
WORK(F,U)

474.98(-0.21)-185178.5(-1.84)-9LFPR(F,R)-185115.6(0-.57)

LFPR(F,U)

0.36783.7(-0.76)

SERDP
URBAN

75500.8(1.82)
78042.7(3.43)*

80466.8(1.71)
62162.9(2.52)*
150.1(0.16)

ROAD
CR-DP
ENROL
of girls-26709.8(-0.26)-385561.6(-1.35)44694.7(2.57)*

40649.1(2.07)*

0.013(3.99)*
0.180(3.95)*
0.136(2.30)*IMR(F)

0.005(2.96)*
0.05(2.26)*
0.107(3.61)*

0.017(2.71)*
0.19(2.13)*
0.389(3.46)*-0.12(-2.62)*

F/M
Constant-8386365(-2.89)*-8751124(-2.65)*32.33(1.78)-43.35(-2.42)*

Adj R2
N

0.64
32

0.66
32

0.55
35

0.009
(2.05)-0.02(-0.35)0.26(3.52)*

68489.8(1.54)
100857(3.14)*
1817.1(1.23)

103884.
6(-2.0)
190538.
2(-1.84)64334.9(1.55)
92146.9(3.59)*
3335.0(2.05)*

59537(2.31)*

109144.
9(3.21)*

1.10+e07(-3.56)*
0.64
32

0.28
(-2.47)*0.51(0.13)

77.59(2.24)*
96.22(-2.81)*

2.58(0.27)-8347673(-2.69)*

0.48
35

0.47
35

0.50
35

0.59
32

When we take labour productivity as a function of participation rate, the endogeneity problem arises
because participation itself is an endogenous outcome which is determined in terms of certain variables.
We have, therefore, presented two sets of results below (Table 21). One set gives the OLS results while
the other set corrects for endogeneity by considering an appropriate instrument for participation. The
instrument is generated by estimating the participation equation first and then considering the predictedvalue. The results tend to indicate that the female participation rate is statistically insignificant in mostof the equations with labour productivity as the dependent variable. It has a negative sign but it can be
primarily because many women workers are engaged in petty activities in the informal sector, both in
the rural and the urban areas.

What is striking from Table 21 is that given participation, women’s education (captured throughenrolment), urbanization and infrastructure availability are some of the most important determinants of35

productivity. Also, the social infrastructure covering health and education specific variables, and physicaland financial infrastructure impact on women participation in the labour market significantly. Poor healthconditions measured in terms of higher infant mortality rate reduce the participation. The panel data
analysis also confirms that poor health reduces women participation. These findings are important frompolicy point of view because different infrastructure variables are seen to improve both womenparticipation and labour productivity. Infrastructure (social, physical and financial) can to certain extent
help break the social and cultural barriers and help women participate in the labour market and makeproductive contribution. For voicing the women’s need, overcoming the social constraints and enhancing
their bargaining power their physical presence is essential as indicated by a positive association between
female-male ratio and women participation, particularly in the rural areas where the social barriers arestrong.
We also noted in the previous section the beneficial effect of female labour force participation rate (fromthe panel data analysis) on infant mortality of girls as well as boys after controlling for growth indicatorwhich also has a very strong effect on infant mortality rate. Access of mothers to resources through labour
market participation improves the health status of the children as their nutritional status also improves.
Also, greater volume of resources is required for enhancing the provision of heath care facilities which
can be met through higher levels of growth.
Elsewhere it has been shown based on data from a number of Asia Pacific countries that decline in genderinequality in labour market through improvement in female labour force participation reduces inequality
in many other spheres, and eventually it leads to greater participation of women in the decision making
process both within the households and at the macro level (Mitra, 2009). Particularly when it comes tofertility decisions the working women are able to voice their preference better compared to thehousewives. Similarly the working women could vouch for gender budgeting and participate in thepolitical process as well. Refuting the view that increased participation of females in the labour market
would reduce growth the study also casted evidence in favour of economic growth responding positively
to a rise in female participation. The poor levels of skill resulting from gender discrimination, limited
scope to undergo on the job training, information asymmetry aggravated by the inadequate access of
women to job market information, inability to pursue job search on a full time basis due to domestic
responsibilities, unavailability of productive jobs with flexible hours and weak bargaining power resultin lower wages for women workers than their male counterparts. And this wage discrepancy has sent awrong message about women being less productive.
36

Table 22: Dependent Variable: Regression Equation for Weekly Wage (in Rs per person)

Variables

Coefficient.

t ratio

Age

-1.98

-0.58

Agesq

0.39

8.98*

hhhold_size-10.28-4.57*

Gedu2

18.63

0.24

Gedu3

201.0613.44*

Gedu4

831.2846.45*

Gedu52028.2995.56*

Tedu22756.5247.92*

Tedu3

690.7820.12*

Tedu4

903.4519.08*

vtrn1-766.49-24.02*vtrn2-124.35-3.89*

vtrn3-77.98-3.59*

mst1

-8.88

-0.29

mst2

193.45

7.68*

gender1

371.4927.14*

reldum1-184.97-10.26*

reldum2-155.69-6.42*

Socdum1

26.93

1.56

Socdum2-101.55

-7.4*

occ1

-700.31-16.03*occ3-412.47

-9.3*

occ4

-586.32-13.42*37

occ5-750.70-16.42*

occ6-323.05-7.09*

occ7

101.45

2.34*

Secdum1-258.73-22.27*

Note: Number of obs = 75518, F( 27, 75490) = 1914.54, Adj R-square = 0.41; Age: age of the earner,
Agesq: age square, hhhold_size: household size, Gedu2: literate without formal schooling, Gedu3:
primary and middle level, Gedu4: secondary and higher secondary, Gedu5: graduation and post
graduation, Tedu2: technical degree in agriculture/ engineering/ technology/ medicine, etc. , Tedu3:
diploma or certificate (below graduate level) in: agriculture, engineering/ technology, medicine, crafts,
other subjects, Tedu4: diploma or certificate (graduate and above level) in: agriculture, engineering/
technology, medicine, crafts, other subjects, vtrn1: receiving formal vocational training, vtrn2: received
vocational training: formal, vtrn3: received non-formal: hereditary, self-learning, learning on the job,others; mst1: never married, mst2: currently married, gender1: male, reldum1: Hindu, reldum2: Islam,
Socdum1: schedule tribe, Socdum2: schedule caste, occ1: agriculture, hunting, forestry, fishing, occ3:
manufacturing, electricity, gas and water supply, occ4: construction, occ5: wholesale and retail trade,hotel and restaurant, occ6: transport, storage and communication, occ7: other services including
financing etc., secdum1: rural dummy (1 for rural and 0 for urban areas).
Source: Based on NSS unit level data (66th Round, 2009-10).

An important way of showing the relevance of skill in the job market is to calculate the returns to skill
and the differences in the returns across various levels of skills. One of the convenient ways ofconceptualizing the returns is to estimate an earning function with dummies representing different
educational levels. The weekly wage function estimated on the basis of the NSS unit level data from the66th round (2009-10) shows that the male wages are substantially higher than the female earnings (Table
22). Education dummies (the general and technical both) tend to enhance earnings. Those who havegeneral education receive higher earnings in comparison to the illiterates. In fact, most of the education
dummies have positive coefficients and are significant except Gedu2 which represents literates without
formal schooling. Similarly technical education dummies are statistically significant. But those who haveacquired vocational training are not better off in comparison to those who do not have it. Scheduled38

castes receive a lower wage compared to the general-cum-OBCs. Large households are also worse off interms of earnings, suggesting lower productivity levels and poor quality of labour. Though age, taken to
be a proxy for job market experience, is not significant, age-square is and it has a positive coefficient.
All this tends to suggest that skill formation and experience do contribute to higher levels of earnings.
The sector specific dummies are mostly negative suggesting lower earnings in most of the sectors in
relation to the comparison group which is mining and quarrying. However, the occ7 representing ‘otherservices’ including financing etc. has a positive coefficient indicating higher earnings in this sector.
5.

Conclusion and Policy Issues

This study on labour (and work) force participation rate has been pursued gender-wise, at three spatial
levels: state district and city. Further, the state and district specific rates have been studied at the ruraland urban areas separately. Among the cities and towns the relatively large ones, i.e., each with apopulation of 100,000 and above, have been considered in order to assess the effects of agglomeration
economies and the cultural factors which are supposedly more advanced in the large cities.
Based on the population census data the study noted that the female participation rates are significantly
lower than those among the males. The inter-state variations in the male specific rates are rather limited.
On the other hand, the rural-urban differentials are more pronounced and the inter-state variations are
sizable in the case of females, reflecting the influence of social, cultural and economic factors. There
does not seem to be any significant positive relationship between per capita income and participation ratein the case of females (except in a subset of sates) though among males such a pattern is somewhat
distinguishable. This finding brings out the inadequacy of growth to usher in gender equality.
The Labour Bureau’s panel estimates of gender specific labour participation rate have been consistentover time and the gender differences in the estimates are again pertinent for each of the years. Though
the time frame (2009-10 through 2014-15) is too short to expect any significant change in theparticipation rate, the rigidity in the female participation rate does raise concerns to be addressed in alarger ambit covering social, economic, political and cultural arena. On the whole, the female
participation is considerably low. Even in the urban areas and the relatively large cities the social factors
and the practice of gender-based division of labour are so prevalent that the female labour market
participation is lower than its rural counterpart despite higher levels of education.
In terms of determinants the study clearly brings out the importance of infrastructure, education andhealth and urbanization on labour force participation of both the gender. This in turn points to three types
39

of factors which are instrumental in resulting enhanced participation rate. The first set refers to creationof greater volume of jobs motivating participation, the second enhances the ability of the individuals toparticipate and the third, the most important one, facilitates the accessibility of the capable ones to thelocations where jobs are available. Hence, strategies for creating clusters with greater employment
potentials (which in turn is cost-effective in making pro-poor growth happen), consecrated efforts forhuman capital formation and investments to remove barriers between jobs and their seekers need to be
pursued aggressively.
The negative impact of fertility and household size on rural female work participation comes out sharply:
greater domestic burdens in large households do not allow women to participate explicitly in the labour
market. Though the urbanization level does not show a positive effect on rural women participation at
the state level the beneficial effects are evident at the district level for both the gender. On the other hand,in the urban context, the participation rates of both females and males vary positively with the level ofurbanization. In other words, the urban areas in states and districts with higher levels of urbanization
unravel higher levels of participation both for females and males in comparison to the states or districts
with lower levels of urbanization, revealing the agglomeration effects. With greater concentration of
infrastructure and activities labour demand tends to increase. Further evidence in this regard is brought
out from the city level data which confirm the positive effect of city size on both male and female
participation. The urban specific rates, particularly for females, may be lower than the rural rates,
showing the persistence of cultural backwardness but within the urban context there is a positive impactof urban settlement, i.e., the relatively large settlements are associated with better outcomes.
While industrialization and growth in services both show a positive effect on participation, though very
mildly especially in the case of urban women, growth shows a rising impact only in the case of urban
males. Also, there is evidence on poverty induced participation in agricultural activities, suggesting
clearly the importance of rural diversification for participation to pick up in the rural context.
The positive relationship between the female-male ratio and the female participation is striking in both
the rural and urban areas, at least at the state level. Greater presence of women in the society is aprecondition to their participation in the labour market though there is evidence, particularly from theurban district level data that it is not a sufficient condition. In fact, many educated women remain
confined to domestic activities not being able to participate in the labour market. However, theassociation between the gender ratio and the female work participation rate is positive and quite strongin large cities compared to all urban areas which comprise medium sized and small towns. The social40

restrictions are relatively less in large cities enabling more women to participate in the labour market.
The demonstration effect of relatively advanced households on the rest is also a possibility. Besides,
large cities because of scale effects are able to conduct greater economic activities, generating larger
demand for labour. Many of the activities in the services sector specifically prefer to have women
workers, motivating the potential entrants to join the job market. This, however, does not rule out thepossibility of women from low incomes households being residually absorbed in low productivity
services in the face of a high cost of living.
Women’s participation does not reduce productivity. On the contrary, it improves child healthsignificantly as noted from the beneficial effect of female labour force participation rate (from the panel
data analysis) on infant mortality of girls as well as boys after controlling for growth, which too has astrong effect on infant mortality. Access of mothers to resources through labour market participation
improves the health status of the children as their nutritional status and access to curative health care getbetter.
Regarding the negative effect of female labour force participation on labour productivity and economic
growth the study clears up the misgivings, suggesting that the negative sign is statistically not significant
and secondly it arises because of women’s residual absorption in petty activities in the informal sector,
both in the rural and the urban areas. Further, the female-male wage differences are due to skill
differences, and not because males are more productive than females in relation to a given task. The
results suggest that given participation, women’s education (captured through enrolment), urbanizationand infrastructure availability are some of the most important determinants of productivity. Further, as
mentioned above, the social infrastructure (covering health and education specific variables) and physicaland financial infrastructure impact on women participation as well.
On the whole, based on these results, it may be inferred that women participation in productive activities
has a double effect: first, it raises the household income and second, it contributes to wellbeing of thehousehold as the health status of the children improves. A mere increase in participation without ensuring
productive absorption may not be beneficial as it would only raise the burden on the women members.
Also, institutional support to discourage the practice of gender based discriminatory wages is
instrumental to the beneficial effects of women participation in the labour market.
These findings are important from policy point of view because different infrastructure variables are seen
to improve both participation and labour productivity. With improved infrastructure the quantum ofinvestment is expected to shoot up and the accessibility to growth centres offering better livelihood
41

opportunities can get better. Infrastructure (social, physical and financial) can to certain extent break thesocial and cultural barriers and help women participate in the labour market and make productive
contribution to the growth process. Though the level of urbanization raises the urban participation rate
in an inter-spatial sense, a similar pattern is not evident in the context of rural females (at least at the statelevel). How urbanization can be made more generative with positive spillover effects in the ruralneighbourhood is, therefore, an important policy question. Increased urbanization ushering in greater
concentration of non-farm activities can expand employment prospects and at the same time result in
productivity gains.
Higher fertility rate harms maternal health, reducing women’s ability to work in the labour market. For
voicing the women’s need, overcoming the social constraints and enhancing their bargaining power their
physical presence is essential as indicated by a positive association between the female-male ratio andthe women participation, particularly in the rural areas where the social barriers are strong. Ban on sex
selective termination of pregnancy requires greater enforceability. That despite higher levels of education
women average participation rate in the urban areas is lower in comparison to the rural areas calls for
introducing more effective strategies for encouraging women to participate in the labour market. Rural
women workers are mostly engaged in agriculture, and rural poverty forces many women to pick up
marginal activities outside agriculture. How productivity of these workers can be enhanced is, therefore,
another key policy concern.
The age structure shift in the Indian context, i.e., a relatively large percentage of population in working
age brackets, can prove to be beneficial only when their accessibility to productive jobs develops. The
improvement in the job market participation of both females and males and their absorption in productive
activities would be possible when economic growth, human capital formation and social progress takeplace simultaneously. For all this to happen, strategic action is required. With enhanced equality between
males and females in terms of work opportunities and labour market attributes, several positive outcomesin terms of economic, social, political, educational and health related equality may take place in asignificant way. All this would subsequently enhance the overall social status of women and their
participation in the decision-making process. Going a step ahead, one may argue that with specificinterventions enhanced labour market participation of women can result in higher productivity andgrowth as well, which will indeed be sustainable in the long-run. Since women labour has remained
unutilised in remunerative and economic activities to a large extent, higher female labour force
participation, particularly in ageing societies, can mitigate the shortage of labour and contribute to propoor growth.
42

The government’s effort to introduce the new manufacturing policy, urban policy with a thrust to create
smart cities, ‘skill India’ programme to improve the employability of the labour force and ‘make in India’
programme to raise labour demand and production simultaneously are expected to have positive impact
on both female and male labour force participation rates. Specifically for women, a series of measures
which include maternity benefits, provision of child care leave, equal remuneration for both male andfemale workers, prevention of domestic violence and workplace harassment, are there which may
encourage participation. A scheme on gender budgeting was introduced in 2007 to build capacity so thata gender perspective is retained at all levels of planning, budget formulation and implementation stages.7
However, as we have noted based on our analysis, more effective strategies need to be evolved to improve
the labour force participation rate, especially among women. Shifting workers of both the gender from
low to high productivity activities has been a major challenge. On top of that if the participation rate has
to improve, both demand and supply side factors will have to be considered.

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48

Appendix
Variables, year of availability and source:

WPR: Workforce
e participation rate among females and males in rural and urban areas: Population Census, 2011
LFPR: Labour force participation rate among females and males in rural and urban areas, 2011-12 incross-sectional study and from 2009-10 through 2014-15 except 2010-11 in the panel data analysis,
Labour Bureau, Ministry of Labour and Employment, Government of India.
AGDP: Share of agriculture in gross state domestic product, 2011-12
INDP: Share of Industry in gross state domestic product, 2011-12
SERDP: Share of services in total gross state domestic product, 2011-12
URBAN: % of population residing in urban areas, 2011
ROAD: State-wise road length in relation to population, 2011, Ministry of Road Transport and
Highways
ELEC: State-wise annual per capita consumption of electricity in kWh, 2012-13, Ministry of Power.
CR-DP: Credit-deposit ratio, 2010, Data Book for Planning Commission, 2014
ENROL: Gross enrolment in classes I-VIII, 2010-11; Data Book for Planning Commission, 2014.
LIT: Literacy rate among females and males in rural and urban areas, Population Census, 2011
IMR: Infant mortality rate, boys and girls: for cross-section study 2011 and for panel 2006, 2010, 2011
and 2012 Sample Registration System 46, 47 and 48, Office of Registrar General, Ministry of Home
Affairs.
WATER: % OF Households with access to safe drinking water, rural and urban, 2011;Data Book for
Planning Commission, 2014
F/M: Female per thousand male, Population Census, 2011
PCNSDP: Per capita net state domestic product in Rs, 2011-12; for panel data analysis 2008-09, 200910, 2010-11, 2011-12 and 2012-13 are taken in 2004-05 prices, Data Book for Planning Commission,
2014. .
AGDP: Share of agriculture in gross state domestic product, 2011-12; for panel data analysis 200809, 2009-10, 2010-11, 2011-12 and 2012-13 are taken in 2004-05 prices, Data Book for Planning
Commission, 2014.
INDP: Share of Industry in gross state domestic product, 2011-12; for panel data analysis 2008-09,
2009-10, 2010-11, 2011-12 and 2012-13 are taken in 2004-05 prices; Data Book for Planning
Commission, 2014. .
SERDP: Share of services in total gross state domestic product, 2011-12; for panel data analysis 200809, 2009-10, 2010-11, 2011-12 and 2012-13 are taken in 2004-05 prices; Data Book for Planning
Commission, 2014.
Lab Prod: Labour productivity in Rs. 2011-12 calculated by the author from state domestic productand workers reported by population census.
SC: Percentage of scheduled castes among females and males in rural and urban areas, Population
Census, 201149

ST: Percentage of scheduled tribes among females and males in rural and urban areas, Population
Census, 2011
CTYSZ: City Size in log terms, Population Census, 2011
AGLAB: Share of agriculture labour in total workforce, male and female, Population Census, 2011
CUL: Share of cultivators in total workforce, male and female, Population Census, 2011
MFGHH: Share of household manufacturing in total workforce, females and males in rural and urban
areas: Population Census, 2011
OTHER: Share of non-household manufacturing and services in total workforce, female and maleand rural and urban areas, Population Census, 2011
CHILDSR: Child sex ratio (age bracket: up to 4 years): Population Census, 2011
CHILD: Children (up to 4 years) to women ratio, Population Census, 2011.
HHSZ: Average household size, Population Census, 2011.
TFR: Total fertility rate for 2009 (since 2010 figures are not available for a number of states); Data
Book for Planning Commission, 2014.
MPCE: Monthly mean per capita consumption expenditure in Rs, in rural and urban areas,2011-12,
Data Book for Planning Commission, 2014
POV: Incidence of poverty in rural and urban areas, 2011-12, Data Book for Planning Commission,
201450