ERIN K.

FLETCHER*
Harvard Kennedy School
ROHINI PANDE†
Harvard Kennedy School
CHARITY TROYER MOORE‡
Harvard Kennedy School

Women and Work in India:

Descriptive Evidence and
a Review of Potential Policies

ABSTRACT Sustained high economic growth since the early 1990s has brought
significant change to the lives of Indian women. Yet female labor force participa-
tion has stagnated at under 30 percent, and recent labor surveys even suggest some
decline since 2005. Using the 2011–12 National Sample Survey, we lay out five
facts about female labor force participation in India. First, there is significant demand
for jobs by women currently not in the labor force. Second, female non-workers
have difficulty matching to jobs. Third, women are more likely to be working in
sectors where the gender wage gap and unexplained wage gap, commonly attributed
to discrimination, is higher. Fourth, vocational training is correlated with a higher
likelihood of working among women. Finally, female-friendly employment policies,
including job quotas, are correlated with higher female participation in some key
sectors. Combining these facts with a review of the literature, we map out impor-
tant areas for future investigation and highlight how policies such as employment
quotas and government initiatives focused on skilling and manufacturing could be
leveraged to increase women’s economic activity.

Keywords: Female Labor Force Participation, Jobs, India

JEL Classification: J16, J20, J48, O14, O15

∗ erinkfletcher@gmail.com
† rohini_pande@hks.harvard.edu
‡ charity_troyer_moore@hks.harvard.edu
§ The authors are particularly grateful for comments from Farzana Afridi, Pranab Bardhan,

and Karthik Muralidharan, and for helpful input from many participants at the NCAER 2018
India Policy Forum.

149
150 I N D I A P O L I C Y F O R U M , 2018

1. Introduction

O ver the past four decades, India has experienced rapid population
and economic growth, urbanization, and demographic change.
Between 1990 and 2013, GDP growth averaged 6.4 percent (Figure 1); the
share of agriculture in GDP roughly halved (from 33 to 18 percent), while
that of services increased from 24 to 31 percent. Alongside, urbanization
increased from 26 to 32 percent (World Bank 2018). At the same time,
women’s education and childbearing patterns have changed: over the same
period, total fertility fell from 4.0 to 2.5 children per woman (World Bank
2014a). Girls’ primary school enrollment has reached parity with that of
boys, and universal enrollment1 was achieved in 2015 (Neff et al. 2012;
UNESCO 2015). Between 1994 and 2010, the fraction of women aged
15–24 attending any educational institution more than doubled from 16.1
to 36 percent (Kapsos et al. 2014).
However, despite this rapid economic growth, educational gains, and
fertility decline, India’s women remain conspicuously absent from the

FIGURE 1. GDP per capita and Female Labor Force Participation in India
over Time
Income and Female LFP: India 2500
40

1000 1500 2000

30

GDP per capita India

FLFP India
20
10

500
0

1990 1995 2000 2005 2010 2015

Year
Female LFP GDP per capita, constant US$

Source: World Bank World Development Indicators.

1. As a fraction of the school-age population.

 Erin K. Fletcher et al. 151

labor force. Female labor force participation (FLFP)2 rates remain low and
have even fallen in the recent years.3 This perceived decline persists even
when we account for increased schooling, which delays entry into the labor
force (Klasen and Pieters 2015). Figure 2 shows that FLFP in India is well
below its economic peers, and the mismatch between economic growth and
FLFP rates in India presents a puzzle. In this paper, we examine possible
constraints on participation and potential policy interventions that could
increase it, highlighting five descriptive facts relating to patterns of FLFP in
India and incorporating a literature review of policy evaluations to identify
promising policies worth further investigation.

F I G U R E 2 . The Cross-Country Relationship between Income and Female

Labor Force Participation is U-Shaped, but India is a Major Outlier
1

Lao PDR
Nepal
Female: Male Labor Force Participation Ratio

Kazakhstan
China Australia
.8

Singapore
Bangladesh Philippines Japan
Uzbekistan
Indonesia
.4 .6

India
Pakistan
.2

7 8 9 10 11

Log GNI Per Capita (2013)

Source: World Bank (GNI) and International Labor Organization (LFPR), 2013.
Notes: Labor data for Ages 15+. Excludes the Middle East.

2. We calculate the LFP rate by dividing the number of individuals in the working-age
population (ages 15–70) employed in wage labor, own-account work, casual labor, unpaid
labor, self-employment, or as an employer, plus those unemployed and seeking work, by the
entire working-age population (15–70) not currently enrolled in school.
3. Although estimates based on household surveys vary, from a low of 24 percent using
the National Sample Survey (NSS; for 2011–12) to a high of 31 percent using the Indian
Human Development Survey (for 2004), it is widely acknowledged that FLFP growth has
been stagnant, and that some earlier gains have been reversed.
152 I N D I A P O L I C Y F O R U M , 2018

Implementing effective, evidence-based policy to increase FLFP and

increase women’s economic activity could have a large impact on economic
growth. Recent evidence from the USA suggests that misallocation of talent
in the labor market, whereby high-ability women are in low-skilled, low-
return occupations, presents a significant hindrance to growth (Hsieh et al.
2013).4 Specifically, in the Indian context, Esteve-Volart (2004) shows that
a 10 percent increase in the female-to-male ratio of workers, a proxy for
discrimination-based differential access to labor markets, would increase
per capita net domestic product by 8 percent.
From an individual woman’s perspective, participation in wage work
delays age of marriage and age at first childbirth (Sivasankaran 2014),
increases her decision-making power in the household, and increases child
schooling (Qian, 2008).5 Figure 3, on the basis of India’s most recent

F I G U R E 3 . Empowerment/Decision-Making Index by Education Levels using

Women’s Report of Autonomy in Decision-Making on Various Expenditures
.4
Decision-Making Index
0 .1 .2
–.1 .3

Primary or Less Secondary Tertiary

Not Working Family Worker

Self-Employed Wage Work

Source: 2015–2016 NFHS. See footnote 6.

Notes: Includes ever-married women aged 15–49.

4. Hsieh et al. (2013) find that alleviating gender- and race-based talent misallocation
accounted for 16 to 20 percent of US growth over the years 1960–2008.
5. Several observational studies find that women with more control over resources such as
land report greater mobility, have children with better nutritional outcomes (Swaminathan et
al. 2012), and are less likely to experience violence (Panda and Agarwal 2005). In addition,
access to and information regarding female-specific labor market opportunities improves
female educational attainment and delays age of marriage and childbearing (Heath and
Mobarak 2015; Jensen 2012).
 Erin K. Fletcher et al. 153

National Family Health Survey (NFHS), shows that women who work,
regardless of education level, have more say in household decisions. 6
Women’s work also has positive spillovers: Sivasankaran (2014) shows
that sisters of women with longer work tenures marry later, and villages
that are exposed to more female leaders show lower rates of sex selection
(Kalsi 2017).
The recent trends in India’s FLFP, combined with their already low levels
of participation, are increasingly seen as a challenge that requires policy
intervention to ensure that these changes do not result in deterioration in
women’s well-being and already low levels of empowerment. Although
the justification for a policy focus on FLFP is clear, the fact that observed
FLFP levels reflect both supply and demand factors makes determining
causation, and thus the range of appropriate policy responses, difficult.
To better understand these potential factors, we use household surveys
to document key descriptive facts highlighting both the role of social and
economic factors that affect labor supply, demand, and outcomes. Given
our use of one cross-sectional survey, we primarily focus on the low level
of FLFP, rather than the recent decline in rural FLFP. Then we discuss the
implications for further investigation tied to existing high-profile policies
and government programs.
On the supply side, Indian households often require that women prior-
itize housework and may even explicitly constrain work by married women
(Bose and Das 2018; Sudarshan 2014; Sudarshan and Bhattacharya 2009).
Societal expectations of a woman’s role as caregiver and caretaker of the
household often mean that women who seek work encounter opposition
from their peers and families, leading to lower participation. Women
frequently internalize these views and may therefore suppress labor sup-
ply even in the absence of explicit constraints. Rustagi (2010) provides
evidence that these norms per se have not significantly changed over the
last two decades. There is also evidence that these norms are more bind-
ing among wealthier, upper-caste households, suggesting that economic
growth alone may not alter their influence.7 Low urban FLFP is consistent
with this possibility.

6. Figure 3 was made using questions from the 2015–16 NFHS data on women’s roles in
household decision-making and women’s views on whether beating is not justified in each
of a given set of situations. Using these questions, we create an “empowerment” or decision-
making index through principal components analysis, standardized to be equal to zero with
a standard deviation of one.
7. Here and elsewhere, we define social norms to be a set of beliefs or perceptions of what
one’s community holds to be true or acceptable (Ball Cooper, Paluck, and Fletcher 2012).
154 I N D I A P O L I C Y F O R U M , 2018

On the demand side, women face legal, normative, and economic con-
straints to work. Indian women are still subject to laws governing when
(i.e., which shifts) and in which industries they can work. These rules may
disproportionately affect women even as the economy grows: for example,
female participation in export-oriented manufacturing jobs fell despite
increased trade and reduced trade barriers during the 1990s, likely due to
legal constraints on women’s working hours through factory laws (Gupta
2014). Though these laws may change soon, employers still may be less
apt to hire a woman over an equally qualified man. As long as there exist
norms against women’s market engagement, we expect to see gender-based
discrimination in hiring, legal or otherwise, and gender wage gaps persist
that cannot be explained by common sources of observable market variation
in wages. Demand for labor of rural Indian women engaged in agriculture is
also particularly vulnerable to seasonal and local labor market fluctuations,
leading women who count themselves as workers to withdraw into domestic
activities when other work is not available (Bardhan 1984).
Overall, high, sustained economic growth in India has not necessar-
ily brought more jobs (Bhalotra 1998; Chowdhury 2011; Kannan and
Raveendran 2009; Papola and Sahu 2012). Jobless growth in sectors
that employ more women or seem friendlier to women necessarily limits
growth in FLFP. In the 1980s, jobless growth was evident in manufacturing
(Bhalotra 1998), and there is some reason to believe women may have suf-
fered from this relatively more acutely than males. Recent work highlights
the lack of jobs to absorb women transitioning out of agriculture, which
may further depress demand for potential female labor (Chatterjee, Murgai,
and Rama 2015).
Norms around women and work clearly affect both supply of, and demand
for, female labor. Data from the World Values Survey (WVS) give insight
into how norms in India may constrain women’s labor force outcomes, while
also highlighting that norms alone can only partially explain India’s low
FLFP. Figure 4 shows responses that highlight the prominence of gender-
biased views on women’s roles in the economic and political landscape in
countries comparable to India. These statistics suggest that views against
women holding an equal footing in the classroom and market still persist in
India and elsewhere, even among women (albeit to a more limited extent
than in males). Interestingly, although India’s FLFP looks most similar to
Pakistan, its norms-related responses look more in line with countries that
have a significantly higher FLFP, suggesting variation in these views on
women and work cannot fully explain India’s lagging FLFP.
FIGURE 4. FLFP and World Values Survey Attitudes on Women and Work
Views of Women in the Workplace and FLFP

Working Women Do Not Have as Good Relations with Children Ratio of Female-to-Male Labor Force Participation Rate
vs. Stay-at-home Mothers

% who agree
0 .2 .4 .6 .8
30 40 50 60 70 80
n ia sh ia

Ratio Female: Male LFP

ista Ind glade o nes
Pak Ba n Ind n ia h ia na
ista Ind gla des o nes Chi
Pak Ban Ind
Female Male Country

Men Should Have Preference for Jobs Men Make Better Business Executives

% who agree
% who agree

0 .2 .4 .6 .8
0 .2 .4 .6 .8
n ia sh sia na n
ista Ind lade one Chi ia sia na
Pak ng Ind ista Ind one Chi
Ba Pak Ind

Female Male Female Male

Source: Attitudes from most recent World Values Survey for each country. Female-to-Male LFP ratios are 2016 ILO estimates.
156 I N D I A P O L I C Y F O R U M , 2018

Our descriptive analysis, focused on the 68th Round of NSS data, high-
lights five features of Indian women’s market engagement important for
understanding the constraints to higher FLFP and potential policy solutions.
First, a large proportion of Indian women express willingness to take on
work despite being counted outside of the labor force. There is a strong
rural–urban divide in this statistic, as others have noted (Kapsos et al. 2014).
Second, women have more trouble matching to jobs than men. They report
seeking or being available for jobs longer than men when unemployed, and
women who did work reported spending more time unemployed than males.
Third, wage gaps and unexplained wage gaps—typically interpreted as at
least partially reflecting gender-based discrimination in the labor market—
are relatively higher in fields with greater female representation. Fourth,
at all levels of education, women with vocational training are more likely
to work than those without training. Finally, women are doing relatively
well in terms of representation in specific jobs, namely, education and
work provided by the government’s job guarantee program, the Mahatma
Gandhi National Rural Employment Guarantee Act (MGNREGA); factors
potentially driving this success should be investigated further.
Alongside these descriptive features, we examine evidence from recent
high-quality academic research that seeks to provide causal estimates of poli-
cies and other factors affecting FLFP in India. The review of this evidence
again underscores the importance of access to jobs, networks, social norms,
and the potential importance of policy interventions in women’s labor force
decisions. Taken together, the descriptive analysis and evidence review sug-
gest several key areas on which to focus research inquiry, some of which
converge with the Government of India’s policy priorities.
The government has already put in place programs and policies to
increase women’s access to labor market opportunities, namely, increased
funding to skills and vocational training programs and gender-based employ-
ment quotas. There is some diagnostic evidence and literature that support
the implementation of these policies, but the immediate pressing need is for
more rigorous research to better understand the causal mechanisms for how
these policies might affect female employment. Rigorous testing would also
allow for better targeting of policies, both in who is most affected and how
they are applied to different groups.
An area requiring urgent attention is that of improving data and evidence
to better understand the constraints and solutions to India’s low FLFP. We
outline specific steps related to data collection that can raise women’s visibil-
ity in the labor force and serve as a potential impetus for important dialogue
and initiatives aimed at engaging them more effectively in the economy.
 Erin K. Fletcher et al. 157

2. Data and Diagnostics Methodology

2.1. Data
Our primary data source is the employment module of the Indian NSS for
2011–12 (68th Round). Our analysis sample consists of 136,465 women
and 131,542 men aged 15–70 who are not currently enrolled in school.8 We
define and examine labor force participation (LFP) using the survey question
on usual principal activity of each household member who meets our inclu-
sion criteria, unless otherwise noted.9 The LFP rate is calculated using the
sum of all individuals employed in wage labor, own-account work, casual
labor, unpaid labor, self-employment,10 or as an employer, plus those who
are unemployed and seeking work, divided by the working-age population
(15–70) not currently enrolled in school.11

2.2. Descriptive Summary of FLFP in India

The variation in FLFP across India is striking—at the state level, FLFP rates
vary from below 20 percent of the male LFP rate to nearly 80 percent—and
its cross-sectional relationship with income does not align with the standard
economic development story. Figure 5 examines the relationship between
the natural log of net state per capita domestic production, a proxy for per
capita income, and the ratio of female-to-male LFP, for comparison to the
cross-country estimates presented in Figure 2. Although Indian FLFP is low
from a cross-country perspective (below the U-shape), Indian states do not
follow any sort of U-shape themselves in the cross-section; instead, FLFP
is generally flat, with outliers on the higher end where some states at middle
and higher relative incomes are associated with higher FLFP.
What explains the differences in FLFP across India’s states? Figure
6 shows the level of FLFP by state, on the left, and the unexplained

8. This nationwide survey includes 459,784 individuals from 100,957 households. We

drop individuals who do not report marital status or employment type and weight the survey
as instructed, unless otherwise indicated.
9. We use the question on “principal usual activity status” from Block 5.2 of NSS Schedule 10.
10. Own-account workers are self-employed individuals operating their own enterprises,
largely without hiring labor. Self-employment generally refers to persons who work in their
own enterprises, often with the help of hired labor or employees. Unpaid refers to unpaid
family workers. Regular employees receive salary or wages on a regular basis. Casual workers
receive a wage according to the terms of a daily or periodic work contract.
11. Though some analyses of LFP in India include secondary activity statuses (e.g., Kapsos
et al. 2014), we limit the definition of LFP to usual principal activity.
FIGURE 5. Indian States’ FLFP Is Relatively Flat across Income Levels
State Per Capita Income and Relative Female Labor Force Participation

.8
SK
HP
ML

.6
CG
MZ
AP AR

.4 NL TN
AN KA MH
RJ RJ
UK GA

Participation Ratio
MN MP TR PY
GJ

Female: Male Labor Force

OR CH
.2 WB DL
UP JH
AS
JK PB HR
BR
0

10 10.5 11 11.5 12 12.5

Natural Log State Net Domestic Product Per Capita

Source: State Net Domestic Product per Capita from MoSPI for 2011–12; Ratio of female-to-male labor force participation rates computed using NSS Round 68 from 2011–12.
FIGURE 6. Some States Have Higher FLFP than Others, after Controlling for Income and Education

Source: NSS 68th Round.

160 I N D I A P O L I C Y F O R U M , 2018

component of each state’s FLFP after controlling for the state’s mean
income and (dummied) education levels in cross-state regressions on NSS
data. Strikingly, some states have both high FLFP and a large component
that is not explained by their income or education levels. A key question
for policy, then, is: What are the features of these states, such as Himachal
Pradesh or Chhattisgarh, from which other states can learn? One potential
explanation here is the more progressive gender norms typically thought
to characterize these two states.
Beyond the state-level differences, descriptive statistics on FLFP show a
significant difference in how men and women interact with the labor market,
as well as regional and inter-caste differences among women. Male LFP
averages 96 percent, while FLFP averages only 27 percent, and, as docu-
mented elsewhere (Klasen and Pieters 2015), FLFP is lower in urban areas
relative to rural areas. Further, 76 percent of women in urban areas report
their primary activity as domestic duties compared to 67 percent in rural
areas. Women in rural areas are more likely than their urban counterparts
to work in unpaid family labor. Rates of wage work and self-employment
for women are similar, but low, in rural and urban areas. Table 1 provides
basic summary statistics related to FLFP in India, and Figure 7 highlights
the diversity in district-level FLFP patterns.
These urban–rural differences in FLFP are important, given the much
higher education levels among urban women: over 60 percent of women in
rural areas have at best a primary education, while this is only true for 30
percent of urban women. Yet higher education does not predict higher FLFP
rates linearly. Instead, we observe a U-shaped relationship between educa-
tion and FLFP (Figure 8), much like the cross-country relationship between
income and FLFP (Figure 2). Women at very low levels of education are
more likely to be in the labor force, with 20 percent of low-educated women
in the labor force in urban areas and 28 percent in rural areas. Women with
some secondary education have the lowest levels of participation (around
22 percent) and highly educated women again post higher levels of FLFP.
The U-shaped relationship is the clearest for urban women and likely reflects
an income effect, whereby women opt out of the workforce and into greater
household production and leisure as household incomes rise, and then opt
back into market work as the opportunity cost of remaining out of the labor
force increases. This U-shaped relationship between education and work
for women stands in contrast to male LFP, which increases with education
and is nearly universal, excluding those currently enrolled.
Figure 9 shows that the age profile for FLFP differs across rural and
urban areas. Young urban and rural women are similarly likely to enter the
TABLE 1. Summary Statistics
Out of Labor Force but Out of Labor Force and
In Labor Force Willing to Work Not Willing to Work
Rural Urban Rural Urban Rural Urban Rural Urban
Variable Males Females Females Females Females Females Females Females Females Females
Age 38.591 38.249 38.057 38.558 37.838 36.720 31.324 32.170 39.894 40.715
(13.170) (13.535) (13.723) (13.224) (12.499) (11.889) (10.129) (9.834) (13.817) (13.155)
Married 0.774 0.805 0.815 0.789 0.744 0.594 0.860 0.853 0.877 0.875
(0.418) (0.396) (0.388) (0.408) (0.436) (0.491) (0.347) (0.354) (0.328) (0.331)
In labor force 0.960 0.263 0.289 0.223 1.000 1.000 – – – –
(0.196) (0.441) (0.453) (0.416) 0.000 0.000 – – – –
Less than primary education 0.259 0.435 0.509 0.317 0.521 0.308 0.403 0.262 0.526 0.318
(0.438) (0.496) (0.500) (0.465) (0.500) (0.462) (0.491) (0.440) (0.499) (0.466)
Primary education 0.318 0.278 0.283 0.271 0.260 0.220 0.346 0.330 0.281 0.276
(0.466) (0.448) (0.450) (0.445) (0.439) (0.414) (0.476) (0.470) (0.450) (0.447)
Secondary education 0.157 0.118 0.101 0.146 0.088 0.096 0.130 0.166 0.102 0.165
(0.363) (0.323) (0.302) (0.353) (0.284) (0.294) (0.336) (0.372) (0.303) (0.371)
Certificate/Sr. secondary education 0.123 0.084 0.066 0.113 0.070 0.113 0.081 0.124 0.060 0.113
(0.328) (0.278) (0.248) (0.316) (0.256) (0.317) (0.273) (0.330) (0.238) (0.317)
Tertiary education 0.143 0.084 0.041 0.153 0.060 0.263 0.040 0.118 0.031 0.128
(0.350) (0.277) (0.198) (0.360) (0.238) (0.440) (0.197) (0.323) (0.174) (0.334)
(Table 1 Continued)
(Table 1 Continued)

Out of Labor Force but Out of Labor Force and

In Labor Force Willing to Work Not Willing to Work
Rural Urban Rural Urban Rural Urban Rural Urban
Variable Males Females Females Females Females Females Females Females Females Females
Self-employed 0.394 0.061 0.069 0.049 0.237 0.220 – – – –
(0.489) (0.239) (0.253) (0.216) (0.425) (0.415) – – – –
Unpaid family worker 0.098 0.075 0.103 0.031 0.357 0.137 – – – –
(0.297) (0.264) (0.304) (0.172) (0.479) (0.344) – – – –
Wage worker 0.439 0.111 0.104 0.123 0.359 0.551 – – – –
(0.496) (0.314) (0.305) (0.328) (0.480) (0.497)
Domestic duties/Collection of goods 0.006 0.703 0.677 0.745 – – 1.000 1.000 1.000 1.000
(0.079) (0.457) (0.468) (0.436) – – 0.000 0.000 0.000 0.000
Unemployed/Other 0.064 0.050 0.049 0.053 0.047 0.092 – – – –
(0.244) (0.218) (0.215) (0.223) (0.213) (0.288) – – – –
N 131,542 136,465 83,936 52,529 24,238 11,705 18,462 11,088 38,319 28,049
Source: NSS, 2011–12.
Notes: Standard errors in parentheses. Sample restricted to individuals aged 15 to 70 years not currently enrolled in school.
 Erin K. Fletcher et al. 163

FIGURE 7. FLFP by District

Female Labor Force Participation Rate by District

Female Labor
Force Participation

Source: NSS, 2011–12.

labor market, but FLFP rates across rural and urban areas for women in their
mid-20s and older diverge; the higher rural FLFP primarily reflects these
women’s participation in agricultural activities. The cross-section does not
allow us to separate cohort and secular trends, limiting the conclusions
that can be drawn, but the relatively low FLFP among both rural and
urban young women is particularly disturbing since these young women
are not enrolled in school. It is also suggestive of a lack of opportunities
(or acceptable opportunities) for young women in rural areas, in comparison
to less educated older rural women, in general.
164 I N D I A P O L I C Y F O R U M , 2018

FIGURE 8. Educational Profile of Labor Force Participation for Men and Women
Male LFP by Education Female LFP by Education
1

1
.8

.8
mean of ilolf

mean of ilolf
.6

.6
.4

.4
.2

.2
0

0
Rural Urban Rural Urban

<Primary Primary
Secondary Certificate/Sr. Secondary
Graduate/Post-graduate

Source: NSS, 2011–12.

Note: Includes individuals aged 15–70 not enrolled in school.

F I G U R E 9 . Age Profile of Labor Force Participation among Women by

Geographic Location
Relationship Between LFP and Age by Rural/Urban
.4 .3
Share in Labor Force
.2 .1
0

15 25 35 45 55 65
Age
Urban Female Rural Female

Source: NSS, 2011–12.

Note: Includes women aged 15–70 not enrolled in school.
 Erin K. Fletcher et al. 165

Social norms surrounding female work are an important constraint on

FLFP in India, as they may dictate that women are primarily caregivers and
thus belong in the home. Although we do not observe a sharp M-shaped
relationship between age and FLFP—exit at childbearing and re-entry as
children get older—as in Japan or Korea (Kawata and Naganuma 2010;
Lee et al. 2013), FLFP does show a drop-off among women in their early
to mid-20s in urban areas, suggesting that marriage and family-related
responsibilities may specifically limit women’s LFP. Household surveys
show that 13 percent and 50 percent of women are not allowed to visit vil-
lage markets or stores alone, respectively, so imagining that women face
constraints on working outside the home is not a large jump. These social
norms are linked to the caste system; upper-caste women are more likely to
face restrictive norms (Field et al. 2013).12
Figure 10, using the NSS, shows FLFP age profiles by whether the house-
hold is identified as Scheduled Caste (SC), Scheduled Tribe (ST), Other
Backward Class (OBC), or other Hindus and Muslims. Those identified as
SCs are the most likely to be working at all ages. All other social groups are
much less likely to be working, but particularly for the youngest cohorts.
High-caste Hindus and Muslims post the lowest rates of FLFP at all ages,
consistent with other research.
Household responsibilities and childrearing duties are often cited as key
constraints to women’s participation in the labor force. Figure 11 illustrates
how FLFP varies for married and unmarried women with and without
children in the household over the cross-sectional age profile. The biggest
takeaway from this figure is that women who marry have low LFP across all

12. Social norms may also affect whether survey questions can adequately measure the
full extent of female participation in the labor market. If women identify strongly with a
non-labor market role, such as caregiver or mother, or feel they are expected to identify
with that role, they may designate that as their primary activity, even if they spend time
in remunerated activities. Other nationally representative datasets from India also show
slightly different levels of overall FLFP. The first round of the IHDS, a survey undertaken
in 2004–05, estimated overall FLFP in India at 31 percent (14.6 percent in urban areas and
39 percent in rural areas), compared to 35 percent as reported by the ILO for 2004 (World
Bank 2014b). The difference in overall levels of participation may reflect that women do
not necessarily identify with work as their primary activity, and the use of more probing
questions and time-use data would result in more available information on the productive
and even income-generating activities of women.
Further analysis of the IHDS shows similar patterns to the NSS in the relationships between
key variables such as age, urban/rural location, and social group, even while the levels of
participation for these sub-groups tend to be higher in the IHDS. Trends over time shown in
the NSS data and statistics collected by the ILO and World Bank are likely real, even if we
are concerned that the actual level of participation is obscured by reporting biases.
166 I N D I A P O L I C Y F O R U M , 2018

FIGURE 10. Labor Force Participation by Age, Disaggregated by Social Group

.5
.4 Relationship Between FLFP and Age by Social Group and Religion
Share in Labor Force
.3
.2
.1
0

15 25 35 45 55 65
Age
SC ST
OBC Other Hindus and Muslims

Source: NSS, 2011–12.

Note: Includes individuals aged 15–70 not enrolled in school.

FIGURE 11. FLFP by Marital Status and Presence of Children in the Household
Proportion of Women in Labor Force by Marital Status
and Presence of Children in Household
.8
Proportion in Labor Force
.6
.4
.2
0

15 25 35 45 55
Age

Not married, children in hh Not married, no children in hh

Married, children in hh Married, no children in hh

Source: NSS 68th Round, 2011−12.

Note: hh = Household.
 Erin K. Fletcher et al. 167

ages, suggesting that older cohorts have not entered the labor force even as
children grow up. A second insight is that the largest differences in LFP are
reflected in marital status rather than the presence of children in the house-
hold, particularly during prime working ages. As approximately 95 percent of
Indian women aged 25 and older are married (or formerly married), lower
FLFP dominates.
Below we highlight additional key descriptive facts about India’s FLFP
to build on some of these more well-established features.
1. A significant portion of out-of-labor-force women express willingness
to work: although socially constrained labor supply may explain part of low
FLFP, women do express willingness or desire to work. Among both rural
and urban women, particularly of certain demographic groups, a significant
portion would be willing to take on work if it were offered. More than 30
percent of the group of women engaged primarily in domestic activities—
and counted outside the labor force—would like to work and thus constitute
a potential addition to the labor force or latent labor supply.13 If all these
women who stated they would take work actually did, we would see a
21-percentage point (78 percent) rise in the FLFP rate, substantial given
the low rates of participation overall.
Women currently out of the labor force who are willing to take a job
tend to be more educated, slightly more likely to live in rural areas, and not
belonging to the SCs or STs. Figure 12 summarizes how education, geog-
raphy, and social group (SC, ST, OBCs, and general categories) correlate
with willingness to work. The percentage willing to work is slightly higher
in rural areas (32 percent of respondents) than in urban areas (28 percent).
Among rural women, latent labor supply is generally higher among those
with more education. Almost 45 percent of rural, highly educated women
who report their primary activity as domestic duties also report that they
would accept work.
Inter-caste differences in reported willingness to take on work point
to the importance of norms in latent labor supply, particularly in urban
areas, as suggested by Klasen and Pieters (2015). Figure 12 shows that
women from “Other” and “OBC” categories consistently express lower
willingness to work than SC and ST women of the same education levels
and geographic sector. Among urban women in the OBC/Other categories,
willingness to work does not increase with education. In contrast, urban

13. While only 815 males in the entire NSS were categorized as belonging to the domestic
worker category and were asked this same question, a similar percentage (35 percent) report
being willing to take on work.
168 I N D I A P O L I C Y F O R U M , 2018

F I G U R E 1 2 . Women’s Willingness to Take Work by Education Level and

Social Group (Those Occupied with Domestic Duties Only)
Willingness to Accept Work by Housewives
Proportion of Housewives Willing to Work
0 2 4 6
ary

ary

ary

ry

ate

ary

ary

ary

ry

ate
da

da
im

im

nd

du

du
im

im

nd
on

on
Pr

Pr

Pr

Pr
co

gra

gra
co
ec

ec
<

<
Se

Se
st-

st-
.S

.S
Po

Po
/Sr

/Sr
te/

te/
ate

ate
ua

ua
fic

fic
ad

ad
rti

rti
Gr

Gr
Ce

Ce
Rural Urban
ST SC
OBC Other

Source: NSS, 2011–12.

Notes: Includes women aged 15–70 not enrolled in school.

SC and ST women have a relatively U-shaped expressed willingness to

work, reflecting the typical income and substitution effects. Rural women’s
willingness to work, in contrast, generally increases within caste as edu-
cation increases, pointing again to the lack of jobs for women at higher
education levels in rural areas.
Unsurprisingly, among women who did not work, over 90 percent were
primarily occupied with domestic duties in the previous year; 92 percent of
these women said domestic duties were their principal activity in the previ-
ous year because they were required (needed) to perform these activities,
with 60 percent of these women reporting that there was no other household
member available to carry out these tasks. Only 15 percent reported social or
religious constraints as the predominant reason they were required to spend
their time focused on domestic duties.
2. Job matching is more difficult for females than males: Analysis of
available data on job-seeking suggests that women experience greater dif-
ficulty matching to jobs that suit them than men. If women have preferences
 Erin K. Fletcher et al. 169

for non-agricultural jobs in rural and peri-urban areas, the lack of non-
agricultural jobs for women may explain low FLFP, in general, and the
decline in rural women’s LFP specifically (Chatterjee et al. 2015).
The types of jobs women report wanting vary by age, but are primarily of
a part-time nature, reflecting the demands of other household responsibilities,
particularly in the context of marriage and childbearing. While 73 percent of
women willing to take a job prefer regular, part-time work, 22 percent want
regular, full-time work; the remaining 5 percent want a mixture of only
occasional full or part-time work. The youngest women are most likely to
report wanting a full-time job, while those in the middle age ranges are most
likely to prefer regular part-time work (Figure 13).
Yet preferences of those outside the labor force do not align with jobs
women have. Figure 14 compares the type of work undertaken by female
workers to the type of work preferred by women out of the labor force who
report being willing to take on a job. Of women who work, just under 17
percent report working part-time, over six times the rate that males report
but less than a quarter the rate expressed as preferred by willing women
workers—again pointing to a potential lack of jobs that may suit women’s

FIGURE 13. Type of Work Women Counted Out of the Labor Force Would
Accept by Age
Type of Work Women Would Accept, By Age
1
.8
.6
.4

Full−time Regular part−time

Occasional full−time Occasional part−time

Source: NSS, 2011−12.

Note: Includes individuals aged 15−55 not enrolled in school. Excludes those in the labor force.
170 I N D I A P O L I C Y F O R U M , 2018

F I G U R E 1 4 . Current Female Employment Distribution and Type of Work

Preferred by Female Domestic Workers Who Say They Want Jobs
Type of Employment of Female Workers and Preferred Work
by Women out of Labor Force
Actual Employment, Preferred Type of Employment,
Women in Labor Force Women out of Labor Force
Who Report they Would Take on Work
1

1
.8

.8
.6

.6
.4

.4
.2

.2
0

Regular Full–time Regular part–time

Occasional full–time Occasional part–time

Source: NSS, 2011–12.

Note: Includes women aged 15–45 not enrolled in school. Women asked question for the graph on the right
are those occupied with domestic duties and counted out of the labor force but say they would take on work
made available to their household.

preferences or obligations. Although only 5 percent of women out of the

labor force who report being willing to take on work say they would prefer
occasional work, 16 percent of women who did work were not working
regularly, nearly twice the rate reported by males. Although women who
work may prefer different types of work than those that remain at home
occupied with domestic duties, the fact that employed women are over-
whelmingly situated in full-time work while those who would like to enter
the labor force prefer part-time work points to important supply–demand
mismatches relevant to low FLFP rates.
Finally, the process of job search itself is gendered: among those
counted in the labor force, women who did not work the entire previous
year spent more time seeking a job or being available for a job than men.
Women who did work report being without work slightly longer than men
as well. And even a sub-set of women reporting they were solely occupied
 Erin K. Fletcher et al. 171

with domestic duties report this was because there was no work available
for them.14 Consistent with the possibility that labor market conditions
constrain women’s market activities, those women counted in the labor
force in the NSS Round we use also report significant differences in time
spent in work and domestic activities in the previous week based on the
month in which they were surveyed.15 Taken together, these statistics
point to a market less closely aligned with female job-seekers than males.
However, despite their stated willingness to work, women reported
searching for jobs with less intensity than men. One-third of women report
not seeking a job when they were unemployed, compared to 18 percent of
men. It is difficult to disentangle the reasons for this differential search.
Social desirability bias, whereby respondents are unable or unwilling to
report true answers on sensitive subjects due to their perception of what
is right or acceptable for women’s work, may lead to under-reporting of
women’s willingness to take a job or—probably more consequentially—
actual activities undertaken in a job search (Fisher 1993). Lower expected
success in job searches may also result in women searching for jobs with
less intensity than men, and, again, norms may constrain labor supply even
when women prefer to work.
3. Wage gaps and unexplained wage gaps are higher in fields with greater
female representation: How do women tend to fare in sectors in which they
are most likely to work? We examine this question looking at the first
(primary) activity women report undertaking in the previous week and
the daily wages they report for this activity. Activities are classified using
India’s National Industrial Classification (NIC) codes from 2008.16,17 The
graph on the left-hand side of Figure 15 highlights how economic activities

14. The NSS question covering latent labor supply reads, “In spite of your preoccupation in
domestic duties, are you willing to accept work if work is made available at your household?”
It is asked of individuals who say they are primarily occupied with domestic duties only or
domestic duties and the free collection of goods.
15. We utilize the NSS current weekly activity status to regress time spent on work, and
time spent on domestic duties on the month of the survey for women counted in the labor
force, similar to the approach used by Bardhan (1984) for rural West Bengal.
16. Of the 8 percent of women primarily occupied with domestic duties who said they were
not required to be occupied with these tasks, just under 20 percent reported they continued
working on domestic activities because there was no other work available to them.
17. NIC codes, produced by the Central Statistical Organisation in India, classify economic
activities at the group, class, and sub-class level. We collapse the two-digit numeric codes,
known as divisions, further among similar types of activities without fully condensing to the
much broader section categorization. A detailed mapping of the NIC codes to the collapsed
codes is available in Table A.1.
FIGURE 15. Gender Wage Gaps, and Unexplained Wage Gaps across Types of Work
Wage Gap and Proportion of Employees Unexplained Wage Gap and Female
in Sectors that are Female Representation in Sectors

2
1.5
.5

1
Unexplained Wage Gap
0

.5

Female Wage as Proportion of Male Wage

0
–.5
0 .1 .2 .3 .4 .5 .6 0 .1 .2 .3 .4 .5 .6

Proportion of Workers in Sectors that are Female Proportion of Workers in Sectors that are Female

Manufacturing/Construction Services
Ag/Forestry/Fishing

Source: NSS, 2011−12.

Note: Daily wages have been calculated on the basis of pay for main activity reported in the previous week. The Y−axis on the right-hand graph shows the unexplained component
of the male−female wage gap after controlling for worker marital status age, social group, education (secondary, tertiary), and state using Oaxaca−Blinder decomposition for
each NIC sector of work.
 Erin K. Fletcher et al. 173

in which women represent a larger proportion of the workforce are also those
in which gender wage gaps are larger, as measured by the female wage as
the proportion of male wages.
Overall, women tend to be less represented in the service sector, and
manufacturing industry is an important employer of women. In other work,
we have shown how the gender gap in LFP in the services sector is 19 per-
cent in favor of men, but 1 percent in favor of women in manufacturing,
and women’s relative representation in manufacturing grew from 15 percent
to 25 percent between 2010 and 2012 (Prillaman and Moore 2016). These
facts alone raise important questions about the future of female employment,
given the often-cited narrative on the role of service sector jobs in women’s
increased employment, particularly as countries continue to develop eco-
nomically (Goldin 1995).
Wage gaps alone, however, may simply reflect differences in the labor
force composition across genders on the basis of easily observable charac-
teristics, such as education. Oaxaca–Blinder decompositions can highlight
the extent to which the gender wage gap is driven by these observable dif-
ferences across genders (Blinder 1973; Oaxaca 1973). The right-hand side
graph in Figure 15 plots the unexplained wage gap that remains within each
NIC category after netting out observable differences in marital status, age,
social group (SC, ST, OBC, Other), education (secondary and tertiary edu-
cation), and state-fixed effects across workers by gender on the natural log
of wages by gender. Importantly, the unexplained component of the wage
gap also tends to be larger for sectors in which females represent a larger
proportion of all employed in that sector.
Stated differently, the sectors in which females tend to fare relatively
better in terms of wage gaps are often those in which they are least rep-
resented. Sectors with the lowest unexplained wage gap tend to be in the
service sector, although a good number of service sector jobs also perform
relatively poorly on this measure.
4. Women with vocational training are more likely to work at all levels
of education: Conditional on reporting they were willing to accept a job,
the NSS asked a sample of women whether they have the requisite skills
to take on the type of work they preferred. More than half of these out-of-
labor-force women who were primarily occupied with domestic duties and
stated they were willing to take on work said they did not have the skills
required to undertake work in their desired fields (Figure 16).
Interestingly, women who have attended skills or vocational training,
whether formal or informal, are more likely to be working. Women who
 174 I N D I A P O L I C Y F O R U M , 2018

FIGURE 16. Women’s Stated Skill Deficits

Women in Domestic Duties Lacking Skills by Desired Type of Work
.8
Proportion of Willing with Requisite Skills
.2 .4
0 .6

dry ng ng f. he
r
an ssi ilori man Ot
b e
Hus roc s /Ta o od
al odp tile er/W
nim Fo x
A Te ath
Le
Rural Urban

Source: NSS, 2011–12.

Note: Includes women aged 15–70 not enrolled in school.

have participated in skills (vocational) training have higher levels of FLFP,

regardless of educational levels (Figure 17), though the U-shaped relation-
ship between education and FLFP persists. Although noteworthy, skills
trainees are likely positively selected on a variety of dimensions and this
relationship should, therefore, simply draw attention to the need for addi-
tional investigation and testing.
5. Fields with female-friendly policies have higher female representation:
Despite their overall low LFP, certain fields and occupations employ many
women and, in some cases, more women than men. Figure 18 highlights
fields with high numbers of women employed by rural/urban status. As
expected, agriculture is the most common employer of working women,
with approximately 55.6 million women working in agriculture in rural
areas alone. The next most common is manufacturing of textiles, food, and
other products, which is a significant employer of women in both rural and
urban areas. Women are also frequently employed in construction across
both geographies. Other common fields employing women across urban
and rural areas in the service sector include education, retail trade, and
home-based services.
 Erin K. Fletcher et al. 175

F I G U R E 1 7 . Labor Force Participation by Educational Attainment of

Respondents on the Basis of Participation in Skills Training
Labor Force Participation by Skills Training Recipients
.6
Proportion in the Labor Force
.2 0 .4

<Primary Primary Secondary Certificate/ Graduate/

Sr. Secondary Post-graduate

No Training Formal or Informal Vocational Training

Source: NSS, 2011–12.

Notes: Includes women aged 15–70 not enrolled in school.

Fields with the highest proportion of female workers are not necessarily
those with the highest numbers of female workers, and only a few fields
exceed 50 percent representation. These fields include domestic workers in
both rural and urban areas and some limited manufacturing in rural areas.
Notably, female representation and overall employment numbers are rela-
tively high in education, some manufacturing, and limited services across
both rural and urban areas.
The Government of India has worked to implement gender-sensitive
policies in certain industries and occupations to increase gender parity.
Primarily, these have worked through quotas, which we discuss further in
the policy section, but here highlight the sectors in which there are quotas
and women have relatively high participation.
The Mahatma Gandhi National Rural Employment Guarantee Scheme
(MGNREGS) provides up to 100 days of paid unskilled work per rural house-
hold annually. In contrast to the national labor market, which is comprised
of only 22 percent women overall, 54 percent of MGNREGS person-days
FIGURE 18. Number of Females Employed by Type of Work
Fields with Highest Number of Female Employees

Rural Urban

673
55,

00 00
3,0 3,0

00 00
2,5 2,5

00 00

Thousands
Thousands
2,0 2,0

00 00
1,5 1,5

00 00
1,0 1,0

500 500

0 re nu nu re on de on nu es nu 0 u n e e e u s h n u
cu ltu Ma Ma ectu cati l tra ucti l ma rvic Ma M an atio ltur trad h us Man vice ealt ctio man
c c u i l h e r h r u l
ri od ile it du ai tr ta /se od le Edu gri ta el/ od /s an st ta
Ag Fo Text arch E Ret Cons /Me pair Wo xti A
. Te Re sonn Fo epair Hum Con o/Me
re r i
ng Bio re B
v i le e m/ ome cp me m/
Ci Ch er h e sti r ho Che
h m he
Ot Do Ot

Source: NSS 68th Round, 2011−12.

Note: The type of employment is that listed as first activity in the weekly time-use module for the sector.
 Erin K. Fletcher et al. 177

were completed by women in fiscal year 2018–19.18 MGNREGS uses a

gender quota, requiring that at least one-third of person-days are worked by
females, but the 33 percent requirement is clearly exceeded, and, therefore,
cannot fully explain such high levels of female participation. Other potential
reasons why MGNREGS attracts women include its wage parity policy,
which may be particularly appealing for unskilled rural women accustomed
to large gender wage gaps, and because it provides work for women near
their households.
The education sector is also a large employer of women in both rural and
urban areas, as mentioned earlier, and the share of female teachers has risen
over the past four decades (Chin 2005). One possible explanation for this
rise is the implementation of Operation Blackboard in 1990, a government
initiative to increase educational attainments, which included a de jure quota
for the proportion of female teachers at 50 percent. This quota has not been
rigorously analyzed, and female representation continues to fall short of the
50 percent mark. However, the fact that education is an important sector
for female employment suggests that gender-sensitive policies directed at
the education sector may be features relevant to women’s relatively high
participation.

3. Evidence Review

Against the background of descriptive facts, we review recent academic lit-

erature to identify potential policy levers for increasing FLFP. India has been
host to a number of rigorous academic studies that seek to tackle causality
concerns; several of these exploit the varied conditions and policies in India’s
states. We perform a selective review of rigorous papers with a strong causal
identification strategy (i.e., quasi-experimental, Randomized Controlled Trial
(RCT), experimental) from a list of top academic journals and working paper
series over the years 2004 to 2017 from India, with select papers of particu-
larly high relevance included from other countries in the region. The review
methodology and included papers are summarized in Appendix (Table A.2).
The literature confirms findings from the descriptive evidence above
that women have limited access to the labor force. Norms, declining FLFP
in rural areas due to a lack of access to part-time work and work outside
of agriculture, job mismatch, and more are important constraints that we

18. According to the MGNREGA Report Dashboard, available at http://mnregaweb4.nic.

in/netnrega/all_lvl_details_dashboard_new.aspx (accessed on April 10, 2019).
FIGURE 19. Fields of Work with Highest Representation of Females
Fields with Highest Proportion of Female Employees
(Excludes Agriculture)
Urban
Rural
10000 32%
10000

8000 40% 8000

44%
51% 38%
6000 47%
6000

Thousands
Thousands
4000 4000 39% 31%
70% 40%
2000
53% 34% 2000
49% 31% 22%
44% 39% 37%
0 26% 25%
0
el g k g g d t n ia g
o nn turin Wor turin ultin elate emen catio Med turin el n s g g s k s h rt
r s c l c s R / onn atio ivitie turin turin rvice Wor rarie earc ppo
Pe ufa ocia ufa Con nd ag Edu ing ufac ers uc t c c e a l s u
a M an sh n P Lib Re e S
c
s tic Man re/S Man e , e bli Ma tic
Ed h Ac nufa nufa air/S oci
e s e a lt Ma Ma e p re/S ffi
o me ood . Ca tile
x
tc ur ast Pu her
m H d l e e R Ca ts /O
D F s ite W Ot n o i
Re Te Do ma Ac
A rch Fo Text Hom es.
r R e nt
Hu h e
g., Ot ym
i l En plo
Civ Em

Males Females

Source: NSS, 2011–12.

Note: Numbers above bars show percentage employees in the sector that are female. Type of employment is that listed as first activity in time use module for sector. Excludes agriculture.
 Erin K. Fletcher et al. 179

examine in more detail in this section. Randomized and quasi-experimental

evaluations show that there are proven methods to alleviate these constraints
and encourage more women to join the labor force, also described further.

3.1. Information
Women often lack information about returns to work and access to adequate
job opportunities. When coupled with restrictive social norms, lack of infor-
mation may depress how and when a woman may work, but research shows
that these norms are not immutable. Information, obtained via active recruit-
ment or through family ties, can affect women’s work and family outcomes.
Active recruitment of women by the business processing outsourcing sector
increased FLFP in that sector and by 2.4 percentage points overall (Jensen
2012) and sisters of factory workers were more likely to delay marriage
and childbearing (Sivasankaran 2014). In the Philippines, women who were
encouraged to attend a job fair were more likely to be in formal and informal
employment, though less likely to be self-employed (Beam 2016).

3.2. Job Location

Where travel is difficult, costly, or constrained due to norms linked to
mobility, proximity to jobs is an important constraint. Although evidence on
importance of job proximity in India is low, in nearby Bangladesh, factory
placement is predictive of who works. Women living in close proximity
to garment factories were 6.5 to 15.4 percentage points more likely to be
employed than women far away from them (Heath and Mobarak 2015). In
Pakistan, the presence of a government school was associated with more
private schools, which increased female employment as women primarily
staff such schools (Andrabi et al. 2013).

3.3. Peer Effects

Like information, role model or peer effects can have an impact on women’s
participation. In areas where jobs that women prefer are not available, self-
employment may provide opportunity and flexibility for women to enter
the labor market, and having contacts and role models can lead women to
take steps to grow their businesses. Business training on its own increases
the likelihood that women will take out loans for self-employment (Field
et al. 2013; 2016), but inviting a friend to business training has a positive
differential impact in encouraging women to take out loans over and above
business training itself, particularly for women most constrained by norms
(Field et al. 2016).
180 I N D I A P O L I C Y F O R U M , 2018

3.4. Economic Returns and Norms Formation

Environmental and institutional features can shape FLFP and have lasting
effects. Comparing districts with soils in need of significant hard labor to
areas with soil that is more easily worked, Carranza (2014) shows that high
FLFP is persistent across time; a 10-percentage point higher fraction of loamy
to clayey soils (proxies for areas in which females would be less likely to
provide agricultural labor) is associated with a 5.1 percent decrease in FLFP
in India. Similarly, plough use, which is associated with soil type, is con-
nected to historical FLFP in agriculture, which contributed to the formation
of norms around women’s work (Alesina et al. 2013).

3.5. Discriminatory Laws

Legal barriers to female employment—restrictions on working hours or
differential skill levels—are key to understanding how a discriminatory
policy may affect overall participation. These restrictions interact with other
policies. Notably, Gupta (2014) shows that reductions in trade barriers in
India actually reduced female employment. Though the author cannot show
that these effects are directly linked to discriminatory policies, the factory
laws, which prohibit women from working certain shifts, are a likely culprit.

3.6. Targeted Policies

Equality-enhancing laws may also exert effects on FLFP. Note that tradi-
tional economic levers, such as tax policies and incentives, which have been
shown to be important contributors to women’s labor supply decisions in
developed countries, are likely not a major determinant of FLFP in India,
where 10 percent of the population19 is part of the formal labor force. The
Hindu Succession Act, which granted women in parts of India equal inherit-
ance rights, differentially affected geographic, religious, and ethnic groups.
Heath and Tan (2014) exploit this natural experiment to show that women
in the affected groups were 9.7 percentage points more likely to be working
and 5 percentage points more likely to be working outside the home.
Cash and asset transfers to female-headed households where recipients
often survive on less than two dollars per day have also been shown to
increase welfare for women. Banerjee et al. (2011) show that productive

19. As estimated from NSS 68th Round data for the population ages 15–70 not in school.
A respondent is considered to be part of the formal labor force if they have a written contract
for a job they hold, thus providing a lower bound on population participation in a job where
income taxes would be relevant to the household.
 Erin K. Fletcher et al. 181

asset transfers (namely, livestock) to very poor women in West Bengal, when
paired with training and savings, resulted in increased consumption, at least
in part through increases in small business activity as well as an increase in
labor supply on the intensive margin. Other findings from Bandiera et al.
(2009) show that such asset transfers led to increased business skills and
increased time spent working. These intensive margin effects on LFP could
improve outcomes for self-employed women by increasing self-employment
income or profits. In nearby Sri Lanka, business training plus cash grants
were more effective at increasing profitability of female-owned businesses
(De Mel et al. 2014).
Finally, research also shows how transfers of MGNREGS wages into
a woman’s own bank account, rather than that of the household head, in
an RCT in Madhya Pradesh, increased women’s work under MGNREGS.
Beyond this expected impact, the intervention also highlighted the poten-
tial importance of gender-specific norms related to women’s work in the
household: women who were granted access to their workfare wages also
worked more in the private sector and undertook more economic activities
overall. The authors attribute these changes to increases in women’s intra-
household bargaining power that induced them to work despite the social
costs incurred to men whose wives worked. Survey data collected three
years after the intervention began point to the role of this policy in chang-
ing views on women and work: women viewed women’s work outside the
home more favorably, and husbands thought the social cost paid when their
wives were working was lower (Field et al. 2019). The study points both
to the role that social norms can play in restricting women’s work and the
potential of targeted policies to help overcome these constraints.

3.7. Quotas
India has a long history of implementing quotas. Since 1982, a certain per-
centage of public sector jobs has been reserved for SCs and STs. Starting in
1987, Operation Blackboard required that 50 percent of teachers be women.
Further quotas have been proposed; the Women’s Reservation Bill would
reserve 33 percent of seats in India’s lower house of Parliament for women,
but has been awaiting passage in the Lok Sabha since 2010. Few of these
gender-based quotas have been rigorously evaluated, but perhaps the greatest
wealth of knowledge we have on causal evidence to increase FLFP comes
from the Indian Government’s experiment with quotas for female leader-
ship at the local level.
A 1993 law mandated that one-third of seats on village councils (gram
panchayats) be reserved for women. In many Indian states, the choice of
182 I N D I A P O L I C Y F O R U M , 2018

which councils would be reserved was in effect random, which allowed for
a rigorous examination of the effects of quotas on various outcomes. Quotas
were implemented on a village-by-village basis and a village reserved for a
female head in one election was not reserved in the next.
Several papers exploit the as-good-as-random variation in the rotating
system of implementation to show the effects of gender-based electoral quo-
tas on female participation in politics. Bhavnani (2009) shows that wards in
Maharashtra that had been reserved for female heads once saw a 120 percent
increase in the average number of female candidates in the subsequent elec-
tion. In West Bengal, women living in villages that were twice reserved were
2.8 to 3.2 percentage points more likely to stand for office and 4.5 to 5.5
percentage points more likely to win (Beaman et al. 2009).
The electoral program quotas exerted effects on FLFP, female time use,
and entrepreneurship, in addition to their direct participation in politics.
Women in areas with female leaders were 39 to 52 percent more likely
to start businesses than those in areas without leaders (Ghani et al. 2014).
Beaman et al. (2009) showed that the gender gap in career aspirations of
adolescents closed by 32 percent in villages that had been reserved for two
election cycles. The gender gap in adolescent educational attainment was
completely erased in villages with a reserved female head, while girls spent
less time on household chores. Female participation in the MGNREGS
national workfare program increased following the election of female lead-
ers. Female person-days worked in the program were higher by 6 percent in
areas that were exposed to quotas (Bose and Das 2018).

4. High-Potential Research Areas

Given the descriptive evidence and existing research, and in light of India’s
current policy priorities, what are the most important avenues for investi-
gation and testing to increase FLFP? We highlight several important areas
that merit additional investigation, building on our core characterizations
of FLFP in India, further.

4.1. Access to Suitable Jobs

As shown earlier, there is a significant mismatch in the composition of
female jobs and the job preferences of out-of-labor force women who are
willing to work. In addition, out-of-labor force women express a willingness
to participate in market work, but women spend a longer time searching
for jobs. The types of jobs women are willing to take are likely correlated
 Erin K. Fletcher et al. 183

with their life stage (married or not), geographic location, and education,
but the general need to identify ways for them to access jobs they will
take prevails. Overall, women (especially married women) prefer regular
work—particularly regular part-time work—but few women working are
in part-time jobs. Several areas of research could shed light on how to help
women access jobs they are willing to undertake.
First, job search costs are likely higher for women than for men, but
more research is needed to understand the dimensions of that search. The
literature suggests that access to information about jobs is a constraint and
social norms often dictate that women spend much of their time engaged
in domestic duties rather than looking for work. Norms may also restrict
network size for women. More efficient search could be achieved through
increased information about job opportunities. Further research should focus
on understanding how to ensure women have information about jobs that
helps them more efficiently match to jobs.
Second, women out of the labor force who want work overwhelmingly
say they would prefer regular part-time work. More research is needed to
understand how policies or market forces that increase the availability of
part-time or flexible work arrangements could incentivize greater female
participation. More work is needed to connect the desire for part-time work
to women’s time use, and subsequently how to promote socially acceptable,
flexible childcare arrangements for working women to allow for labor mar-
ket participation. Support for women’s self-employment, whether through
more appropriate financing or training, would also likely suit many women,
given the demands on their time in the household. An obvious policy link-
age here is to the government’s National Rural Livelihoods Mission, which
supports self-help groups (SHGs) and aims to eventually connect women’s
groups to flexible work opportunities convenient to the groups. Other major
initiatives, such as the Self Employed Women’s Association (SEWA),
already support similar initiatives, with success.
Again, women’s demographic characteristics matter: age and marital
status are important predictors of labor force attachment. Our analysis sug-
gests that marriage is a more significant correlate of women’s lower LFP
than childbearing, and younger, out-of-labor force women with expressed
willingness to work are more likely to prefer full-time work. Work oppor-
tunities have been shown to delay marriage, but there is little evidence
on how to incentivize labor market attachment to persist post-marriage.
Incentivizing full-time opportunities for younger, unmarried women is one
testable solution; further research should explore how pre-marriage career
experience affects post-marriage labor market decisions.
184 I N D I A P O L I C Y F O R U M , 2018

While women may prefer part-time work in an unconstrained environ-

ment, it is also possible that particular technologies or costs restrict the
choice set upon which they optimize. For example, women may state a
preference for part-time work because their household duties require they
spend hours cooking each day, searching for firewood, or even retrieving
water. Technology relevant to household production has been relevant
to increasing women’s employment in other settings (e.g., Dinkelman
[2011] for electrification in South Africa). Additional research on how
technologies can reduce time burdens on women in India may be useful.
The extent to which environmental degradation may contribute to time
poverty relevant to women’s labor force decisions is also an important
area for study.
A similarly important example relates to women’s actual and perceived
safety: women may report preferring jobs close to home not simply because
they enjoy short commutes but also because they and family members are
concerned about their safety if they venture far from home. Recent work
has highlighted that young women in India are willing to incur higher costs
(and lower education gains) for higher safety (Borker 2018). Rigorous
studies diving further into these issues are all likely going to be important
in the coming years.

4.2. Government Priorities: Quotas, Investments in Skills and

Manufacturing, and Income Transfers
The Government of India has recently committed to increased investments
in skills training, to promoting manufacturing employment, and to additional
gender-based quotas in areas from police forces to corporate boards. These
commitments, combined with our diagnostics and literature review, suggest
they are fruitful areas for rigorous pilots and evaluations to better understand
how they can support women’s economic activities.
The scope for improving skills and vocational training is significant.
Many skills and vocational programs have been shown to be relatively inef-
fective (Blattman and Ralston 2015; McKenzie 2017); in India, some of us
found that only one-fifth of trainees are employed one year after training in a
major skills scheme in India (Prillaman et al. 2017). That said, the potential
for such programs to support women, in particular, is high: many govern-
ment-funded programs have gender quotas, and some programs incentivize
placement and retention in a first job after training, which could serve as a
crucial linkage connecting women to jobs. Our diagnostics show that women
with skills training are more likely to be employed. Given concerns over
 Erin K. Fletcher et al. 185

selection into training, research that examines the causal impact of training
on labor market outcomes, as well as studies focused on how programs can
help women overcome search frictions may be useful. A desire for more
training by out-of-labor force women also suggests that supporting training
for women seeking non-traditional (part-time, and potentially home-based)
work is an important area for further study.
In addition, manufacturing employment for women has grown over
the past ten years despite its generally slow overall employment growth
(Nayyar 2009; Prillaman and Moore 2016), with women occupying 25
percent of manufacturing positions by 2012. An expansion of manufactur-
ing employment may be particularly important in rural areas. As employ-
ment in agriculture is declining and an increasingly educated workforce
lacks access to jobs, sector-specific investments to improve job quality and
availability could benefit women. Here, research to better understand the
factors driving wage gaps, and potential ways to level the playing field,
are warranted.
Although the literature on quotas provides solid evidence on how
increasing women’s political representation can benefit women and girls,
questions remain on whether and how employment quotas can help women.
For instance, should they be applied universally or only to certain fields,
are there associated negative externalities, and are quotas strictly better
than other policies aimed to increase FLFP? We suggest better evaluation
of gender-based employment quotas that are already in place, such as those
associated with the national welfare scheme, MGNREGS, and Operation
Blackboard20 as well as more rigorous comparisons to alternate policies.
Finally, since discrimination may also play a significant role in FLFP—both
in discouraging women from applying for jobs, and from obtaining jobs they
apply to—quotas have the potential to put more women in visible positions
and possibly change social norms around women and work.
There may also be important opportunities for the government itself
to provide more women, particularly those with relatively higher levels of
education, with access to suitable jobs in their own communities while con-
ferring the additional benefit of improved service delivery (Muralidharan

20. To our knowledge, there has only been one evaluation of Operation Blackboard’s
policies, but it did not specifically address the quota. Chin (2005) shows that primary school
completion rates improved for girls under Operation Blackboard, despite no significant changes
in class size or number of teachers. Although we cannot attribute the effect on schooling
directly to the quota and Chin offers no estimation of effects on female employment, we can
take this as prima facie evidence that the program—including the quota—was important and
should be evaluated in more depth.
186 I N D I A P O L I C Y F O R U M , 2018

2016). Frontline public sector workers in health and nutrition, for example,
are overwhelmingly women, and yet evidence suggests these workers are
overburdened and generally understaffed (Kapur et al. 2017; Muralidharan
2016). Hiring more frontline workers in health, nutrition, education, and
other important community services may be an important way to legitimize
women’s work and increase FLFP. Beyond this, expanding public childcare
seems an important avenue to increase women’s employment while provid-
ing other women with greater flexibility to participate in income-generating
opportunities.
A final area that has seen increasing attention is that of income transfers
from the government to citizens, most recently in the form of a Minimum
Income Guarantee or Universal Basic Income. The impacts of such a benefit
directed to women are theoretically ambiguous. For example, although a
transfer directed to women could compensate them for unpaid work in the
household, it could also lead working women to decrease their labor supply
(due to the income effect) or drop out of the labor force entirely. On the
other hand, if women want to work outside the home, directly paying them
in ways that allow them to access and control these funds may increase their
intra-household bargaining power and help them negotiate within house-
holds to enter the labor force. The income could also be useful to investing
in training or capital that likely deter women from self-employment or other
economic activities. Making the transfers conditional on earning less than a
certain amount of income, however, would likely suppress their labor supply.
All this suggests that any direct transfers, whether directly for women or
to their households, should be carefully designed and tested to understand
their impact on women’s labor supply (on this, also see Field et al. 2019).

4.3. Data Collection and Transparency

A major limiting factor to better understand the reasons for India’s low
FLFP is lack of up-to-date data. Additional data collection through more
regular employment surveys would be particularly valuable. More regular
surveys, as are now undertaken in the Periodic Labour Force Survey, will
help policymakers adjust programs and policies quickly in response to
economic shocks. They can also help increase understanding of anomalies
in the data, such as the uptick in India’s FLFP in 2004 and its subsequent
decline, the cause for which remains unresolved in the literature.
In addition, time-use surveys would identify how India’s 200 million
women engaged primarily in domestic activities spend their days and clarify
the extent to which they may already be involved in labor market activities.
 Erin K. Fletcher et al. 187

They would also help reconcile large discrepancies in FLFP as measured

by different household surveys and would prove constructive to analysis of
gender dynamics in household activities, if collected for several members of
the same household. India is positioned to collect quality time-use data due
to the lessons from a 1998 pilot of six Indian states and recent announce-
ments by the government to implement such exercises.
States and the Central Government can also play a role in coordinating
data collection by trainers and employers involved in major employment-
oriented initiatives mentioned earlier. Ensuring both requisite technological
infrastructure, as well as appropriate incentives, are in place to collect high-
quality data is an important step toward better understanding FLFP and how
women can fit into the “Skill India” and “Make in India” programs.
The government can also do more to systematically collect and track both
short-term economic migration and contract labor, both of which involve
women (and possibly increasingly so), but around which data collection is
extremely limited, particularly in terms of gender disaggregation. Finally,
in cases when data are collected—through both surveys and administrative
data systems—promoting and incentivizing data sharing and transparency
will facilitate a study of these important topics.

5. Conclusion

Despite increases in education, declines in fertility, and strong economic

growth, India’s FLFP has declined over the recent years and overall is
quite low for India’s income levels, suggesting that action is necessary to
increase women’s labor market participation and attachment. The micro and
macroeconomic implications of India’s low and declining FLFP are at once
adverse and consequential, and must be better understood and addressed.
Our simple descriptive analysis of NSS data points to significant con-
straints on FLFP driven by both social and economic factors on the supply
and demand side. Many women counted out of the labor force and primarily
occupied with domestic duties say they want not simply to work, but to work
in a regular job. Further evidence suggests women search less, or less effi-
ciently, for jobs even as they face greater discrimination in the marketplace.
Many women additionally lack the skills required to undertake work they
would like. Although skills training may be able to address this constraint,
more research is needed to better understand how women can best benefit
from the government’s current investments in skilling.
188 I N D I A P O L I C Y F O R U M , 2018

Indian women also tend to opt out of the labor market at marriage, losing
high-potential early career earnings and experience that may be important
for their socioeconomic trajectories. Once in jobs, women are also often at
a disadvantage: in fields where women enjoy higher relative representation,
pay is less equitable across men and women. Yet some fields with important
female-friendly measures, including quotas, equal pay, and work close to
women’s homes, have successfully attracted female workers. The specific
features driving this relative success in FLFP need to be better understood.
In addition to undertaking research focused on the challenges outlined
here, a key step to improve our understanding of how to increase women’s
economic engagement is to increase the frequency of data collected about
Indian women’s economic activities and time use, to improve data collected
relevant to government initiatives that can influence FLFP, and to ensure that
data are released regularly and transparently. Over the past several years,
a growing set of researchers have turned their attention to India’s low, and
apparently declining, FLFP. This trend is promising, but much more needs
to be done to spur rigorous innovations in both the public and private sectors
to increase women’s economic engagement.
Finally, although this paper focuses on constraints and potential strategies
to increase FLFP in India, it goes without saying that the goal of increasing
this outcome is to improve women’s welfare overall. Women’s perceived
welfare reflects a variety of factors, of which economic engagement is one
factor among many. Any policies that aim to increase women’s economic
engagement should aim to measure changes beyond simply LFP, to better
understand their implications for welfare of women and their household
members.

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Appendix
T A B L E A . 1 . This table maps the original NIC codes to the condensed codes
used in this paper.
Condensed version Original NIC code
Accommodation Accommodation
Advertising, market research Advertising & market research
Agriculture Crop & animal prod., hunting & related service activities
Arts/Entertainment/Sports Sports Act. & Amusement & Recreation Act.
Creative arts & entertainment activities
Chemical/biological/metal Manufacture of other non-metallic mineral products
manufacturing Manufacture of coke & refined petroleum products
Manufacture of pharmaceuticals, medicinal, chemical, &
botanical products
Manufacture of rubber & plastic products
Manufacture of chemical & chemical products
Manufacture of basic metals
Manufacture of paper & paper products
Manufacture of metal products, except machinery & equipment
Civil engineering, architecture, Architecture & engineering act., tech. testing & analysis
tech testing, analysis Civil engineering
Computer programming Computer prog., consultancy & related act.
Construction Specialized const. activities
Construction of buildings
Consulting Act. of head offices mgt. Consultancy act.
Domestic personnel/household use Act. of households as employers of domestic personnel
Education education
Electricity, gas, AC supply Electricity, gas, steam, & air condition supply
Electronic manufacturing Manufacture of computers, electronic & optical products
manufacture of electrical equipment
Employment acts/office support Employment activities
Office administrative, office support & other business
support act.
Equipment repair Repair & installation of machinery equipment
Equipment/vehicle manufacturing Manufacture of motor vehicles, trailers, & semi-trailers
Manufacturing of other transport equipment
Manufacture of machinery & equipment N.E.C.
Financial/info services Other financial activities
Information service activities
Financial service act. except insurance & pension funding
Food manufacturing Manufacture of food products
Manufacture of beverages
Manufacture of tobacco products
Food service Food & beverage service activities
Forestry/fishing Fishing & aquaculture
Forestry & logging
Gambling Gambling & betting act.
Human health activities Human health act.
Insurance, pensions Insurance, reinsurance, & pension funding except
compulsory social security
Legal, accounting Legal & accounting activities
(Table A.1. Continued)
194 I N D I A P O L I C Y F O R U M , 2018

(Table A.1. Continued)

Condensed version Original NIC code

Libraries Libraries, archives museums, & other cultural act.
Media production Printing & reproduction of recorded media
Mining Mining of coal & lignite
Mining of metal ores
Extraction of crude petrol. & natural gas
Other mining & quarrying
Mining support service activities
Other Act. of extra territorial org. & bodies
Activities of membership org.
Other home repair/services Other personal service act.
Repair of computers & personal & household goods
Other manufacturing Other manufacturing
Other science/tech Other prof. scientific & tech. activities
Postal/courier Postal & courier activities
Public administration/defense Public admin. & defense, compulsory social security
Publishing/media Program & broadcasting activities
Publishing activities
Motion picture/video & TV prog. prod and related activities
Real estate Rental & leasing act.
Real estate act.
Research Scientific research development
Residential care, social work Residential care activities
Social work act. without accommodation
Retail trade Retail trade, except of motor vehicles & motorcycles
Security/building services Services to buildings & landscape act.
Security & investigation activities
Telecoms Telecommunications
Textile manufacturing Tanning & dressing of leather and manufacturing of related
stuffs
Manufacture of wearing apparel
Manufacture of textiles
Trade/repair vehicles Wholesale & retail trade, repair of motor vehicles &
motorcycles
Transport Air transport
Land transport & transport via pipelines
Warehousing & support activities for transportation
Water transport
Travel/tours Travel agency, tour operator, & other reservation service act.
Veterinary Veterinary act.
Waste management Remediation act. & other waste management services
Waste collection, treatment & disposal act. material recovery
Sewerage
Water collection/supply/ Water collection, treatment, and supply
treatment
Wholesale trade Wholesale trade, except of motor vehicles & motorcycles
Wood manufacturing Manufacturing and prod. of wood except furniture and other
related items
Manufacturing of furniture
Source: NIC Codes based on the National Industrial Classification (2008), Central Statistical Organisation,
Ministry of Statistics and Programme Implementation, Government of India.
T A B L E A . 2 . This table presents the results of a review of top-tier journals in economics, including both general interest and
field journals, and academic working papers over the years 2004–17. We include only papers with strong causal identification
strategies such as a natural experiment caused by a policy change or a randomized control trial.
Paper Area of Study Context Strategy for Assessing Impact LFP Estimate
A. Information and Job Location
Jensen (2012) North India Information provision on RCT: Compare FLFP in villages exposed to Women in villages visited by recruiters were 4.6
(Haryana) job opportunities recruiters for business process outsourcing percentage points more likely to be employed in
jobs. the BPO sector and 2.4 percentage points higher
overall.
Heath and Bangladesh Location of textile Natural experiment: Compare women on Women in close proximity to garment factories
Mobarak manufacturing firms the basis of proximity to garment factories. were 6.5 to 15.4 percentage points more likely to
(2015) be employed.
Sivasankaran South India The role of longer Natural experiment: Compare outcomes on An additional month of contract length increased
(2014) (Tamil Nadu) duration-work contracts the basis of exposure to wage and contract length of employment by 0.5 months.
policies.
Andrabi et al. Pakistan The role of primary and Natural experiment: Compare teacher Areas with government schools were 20 to 27
(2013) secondary education in jobs in areas where schools were built to, percentage points more likely to have a private
determining skill profiles where they were not built to see effects on school, which employs, on average, four women.
job opportunities for women.
Afridi et al. India Increasing education Parametric and nonparametric Changes in women’s education over time explain
(2018) level in rural areas decomposition using Blinder (1973) and about 21.8% of the total decline in FLFP.
Oaxaca’s (1973) technique to decompose Women’s own education and that of the men in
the change in employment rates of women their household accounts for between 87% and
over time on the basis of the data from 95% of the overall decline in FLFP in 1987–99.
employment and unemployment rounds of In the 1999–2009 decade, they explain 25–37%
India’s of the total decline in women’s LFPR. In both
NSS in 1987–88, 1999–2000 and decades, education is the largest contributor to
2009–10. the decline in women’s LFPR.
(Table A.2. Continued)
(Table A.2. Continued)

Paper Area of Study Context Strategy for Assessing Impact LFP Estimate
Beam (2016) Philippines Job fair Randomized encouragement design: Attending the job fair causes a 10.6-percentage
(Sorsogon Measure the impact of attending a job fair point increase in being employed in the formal
Province) on employment outcomes. sector (pooled men and women). Attending the job
fair increases likelihood of female being employed
in informal sector by 11.4 percentage points and
decreases likelihood of female being self-employed
by 16.0 percentage points.
B. Information via Quotas
Beaman et al. East India Gender electoral quotas Natural experiment: Compare number of Women in villages that were twice reserved were
(2009) (West Bengal) women in elected positions in villages 2.8–3.2 percentage points more more likely to
exposed to female leader quotas. stand for office and 4.5–5.5 percentage points
more likely to win.
Bhavnani West India Gender electoral quotas Natural experiment: Compare number of Number of women standing for election was
(2009) (Mumbai) women in elected positions in villages 120% (0.5 candidates to 1.1 candidates) higher
exposed to female leader quotas. inward that were once reserved compared to
never reserved.
Ghani et al. India Gender electoral quotas Natural experiment: Compare number Women in exposed states were 39–52% more
(2014) of women-owned small enterprises in likely to start own businesses.
states exposed to female leader quotas at
different times.
Bose and Das Northern Workfare program Natural experiment: Compare women’s Number of female person-days worked under
(2018) Indian (Uttar gender quotas employment in areas with political NGREGA was 6% higher in administrative units
Pradesh) positions reserved for female leaders. with female leaders.
Deininger et India Workfare program Panel data analysis: 4,000 panel Program increases wages both for male and
al. (2016) gender quotas households in 232 villages from 17 Indian female participants and also brings a shift from
states. farm to non-farm and salaried employment in
female labor supply.
C. Control of Resources and the Ultra-Poor
Heath and India Property and lifetime Natural experiment: Rollout of Hindu Women in treated group (Hindu and affected by
Tan (2014) unearned income Succession Act varied exposure to female HSA) were 9.7-percentage points more likely to be
control of assets by state and time. working, 5 percentage points more likely to work
outside the home.
Banerjee et al. East India Asset transfers and RCT: Compare small enterprise activity Recipient households increased work by one hour
(2011) (West Bengal) small enterprise activity in households given productive asset per day.
transfers-to those not receiving transfers.
Bandiera et Bangladesh Asset transfers to RCT: Compare labor force activity by Increase in self-employment and quality of jobs
al. (2009) ultra-poor women given asset transfers to those not among those women receiving transfers; 1%
receiving transfers. increase in hours worked.
D. Peer Effects
Field et al. Western India Business training and RCT: Evaluate interaction between Women who received business training were
(2013) (Ahmedabad) microcredit randomized business training and social 13-percentage points more likely to take out
norms. loans.
Field et al. Western India Business training, RCT: Evaluate effectiveness of business Women who received business training with a
(2016) (Ahmedabad) microcredit, peer training when combined with existing friend increased working hours by 17% and were
networks social networks. 5.3 percentage points more likely to take out a
loan from SEWA.
Carranza India Soil type Natural experiment: soil types vary by Women in areas with a 10-percentage point higher
(2014) district. fraction of loamy to clayey soils is associated
with a 5.1% decrease in FLFP as agricultural
workers (1.5 percentage points of rural FLFP
average).
(Table A.2. Continued)
(Table A.2. Continued)

Paper Area of Study Context Strategy for Assessing Impact LFP Estimate
De Mel et al. Sri Lanka Business training versus RCT: Evaluate the impact of (a) Existing business owners: Management
(2014) business training + cash business training solely and practices improved in both interventions but
grant business training coupled slightly higher in training + cash. Training
with cash grant on existing only doesn’t improve business outcomes but
business female owners and training + cash increases capital stock by
potential start-ups. `10,000 and profits temporarily; (b) Potential
start-ups: Training only increases business
ownership rate by 12 percentage points and
training + cash increases it by 29 percentage
points points in the short run, both have no long-
term impact. Training only increases work income
by `1,494 (significant) and training + cash
increases it by `697 (not significant).
Note: Refer to Carranza (2014). The FLFP percentage estimate is determined by taking the percentage change in FLFP and dividing by the total FLFP in rural areas from the NSS.
 Comments and Discussion*

Pranab Bardhan
University of California, Berkeley

In general, I agree with most of the points made in this paper about the
characteristics and trends described and policy issues raised.
My comments and suggestions below therefore are mainly supplementary
and rather piecemeal.

• The paper simultaneously discusses both low and declining female

labor force participation (FLFP)—the two aspects should be separated
more clearly for analysis. The factors explaining them can be different.
For example, gender norms or social expectations which may explain
low FLFP may not be used as easily in explaining declining FLFP,
even when those norms and expectations are not immutable.
• Across countries, it is still not clear to me why India and Pakistan
have such low FLFP, even as compared to their poorer South Asian
neighbors such as Nepal and Bangladesh. Hindu or Muslim cultural
norms in general are not enough to explain why India and Pakistan
are so much of an outlier.
• For the participation rate, NSS usual status data are used in the
paper, but given the fragmentary and part-time nature of a great deal
of women’s work, one should also make full use of the NSS current
status data, where the reference period is the previous week. Detailed
time disposition data are available for such current activities.
• A statistical analysis of the number of days in work in the reference
week (not just the number of women in work) can yield some valu-
able insights. For example, in my old work on a statistical analysis
of the NSS household level data for rural West Bengal—reported
in my book Land, Labor and Rural Poverty (1984)—I found the
following demand side factors significant in explaining variations
in the number of days in work:

* To preserve the sense of the discussions at the India Policy Forum, these discussants’
comments reflect the views expressed at the IPF and do not necessarily take into account
revisions to the conference version of the paper in response to these and other comments in
preparing the final, revised version published in this volume. The original conference version
of the paper is available at www.ncaer.org.
200 I N D I A P O L I C Y F O R U M , 2018

a. Rainfall pattern in the area, with better rainfall areas having

higher FLFP;
b. Lean or busy season (even though imperfectly captured in the
NSS sub-round variations), with women entering the current labor
force in the busy season and withdrawing in the lean season; and
c. A “discouraged, dropout” effect in seeking work, controlling
for other factors, seen in households with more male members
unemployed, where the number of days of female work partici-
pation was lower.
• For examining the puzzle of the declining FLFP in NSS data in the
face of education gains and fertility reduction, one should try to cross-
check with the (scanty) panel data available, for example, IHDS data
for 2004–05 and 2011–12.
• Some additional explanations for the decline in FLFP worth examin-
ing are as follows:
• With environmental degradation, collection activities mainly done by
women, for example, of water and firewood, may take up increasing
amounts of time in the day, leaving less time for “gainful” work.
• With income and education improving, the same oppressive,
dead-end, low-status jobs which women have been working on
for generations are now less acceptable (this is an example of how
declining FLFP can be welfare-improving).
• In many lines of activity, with possibly worsening job prospects for
the men in the family, the discouraged dropout effect on women
may get stronger.
• Mechanization of agricultural operations, particularly in female
labor-intensive tasks such as harvesting, threshing, and food pro-
cessing, among others, may be impacting the FLFP.
• Perception of the increasing lack of safety for women in public
places may be reducing FLFP.
• On ecological factors like soil quality discussed in Section 3.4 in the
paper, a related issue may be the particular crop grown. For example,
cultivation and post-harvest operations for rice are more female labor-
intensive than for, say, wheat.
• On the adverse impact of trade liberalization on female employment
discussed in Section 3.5, it may be less due to factory laws, and more
due to the wiping out of low-productivity informal enterprises—
which have more women workers—as a result of foreign competition
(Nataraj 2011).
• Regarding the explanation of why the gender wage gap is distinctly
higher in sectors where more women are represented (see Figure 15 in
 Erin K. Fletcher et al. 201

the conference paper), could it be that in industries such as garments or

bidi-making, where the majority of workers are women, men mostly
do the supervisory–managerial work, and the gender wage gap partly
reflects the wage gap between production and managerial work?
• Here are a few brief suggestions on some additional policy issues:
• The paper points to the latent female labor supply: large numbers
of women currently in domestic work express willingness to work,
but mostly for part-time work. As the NSS question on this suggests
(see Footnote 14 in the conference paper), such part-time work has
to be dovetailed with domestic work. Often the work, such as sew-
ing, tailoring, animal husbandry, food processing, basket-making,
and other handicrafts, may have to be brought home. There are
special policy issues here involving credit, provision of supplies,
marketing and transportation, organization of cooperatives and
self-help groups, among others.
• The idea of community kitchens (such as “amma canteens” in Tamil
Nadu and “Indira canteens” in Karnataka) and community day-
care centers needs to be tried on an all-India scale. An important
special effect of this is not just on an adult woman’s outside work
participation but also on the schooling of her elder daughter.
• Extension services, specially oriented to women, located in nearby
community centers or panchayat offices, is imperative, not just
with respect to new production technology but also on information
relating to job search.

References
Bardhan, P. 1984. Land, Labor and Rural Poverty. New Delhi: Oxford University
Press.
Nataraj, S. 2011. “The Impact of Trade Liberalization on Productivity: Evidence from
India’s Formal and Informal Manufacturing Sectors,” Journal of International
Economics, 85(2): 292–301, November.

Farzana Afridi
Indian Statistical Institute

There has been a dramatic increase in women’s labor supply in the US and
Europe since the beginning of the 20th century (Goldin 2006). Research has
underlined the importance of increasing levels of education of women and
falling fertility accompanied by more favorable gender wage ratios in raising
202 I N D I A P O L I C Y F O R U M , 2018

women’s workforce participation. In contrast to the Western experience,

female labor force participation (FLFP) in India has been low and either
falling or stagnant for the last few decades, despite a decline in gender gaps
in education, falling fertility, and high economic growth (Afridi et al. 2018).
By some estimates, raising women’s participation in the economy to the
same levels as men’s can raise GDP by as much as 27 percent (Lagarde
and Solberg 2018). The paper by Fletcher, Pande, and Moore in this volume
of the India Policy Forum is, therefore, not only timely but also imperative
for finding policy solutions to address this issue.
The authors provide an excellent and thorough summary of the issues
surrounding FLFP in India. My comments focus primarily on the exposi-
tion of the observed levels and trends in FLFP in India with the objective of
highlighting the policy measures that would be effective in addressing this
issue. I, therefore, classify my comments and suggestions into two broad
groups: (a) describing FLFP in India, and (b) policy recommendations
emerging from the data analysis in the paper.

1. Describing FLFP in India

1.1 Distinction between Levels and Trends in FLFP

The authors have used NSS survey data to describe the status of women’s
work in India quite exhaustively. However, the paper’s data description tends
to oscillate between the discussion of levels and trends in FLFP. I suggest
that the authors distinguish between the two up front and clearly, since the
policy prescriptions for addressing low levels and declining trends in FLFP
may be quite different. To elaborate, FLFP in India has been historically low
(Figure 1), lower in urban areas as opposed to rural areas. However, it is well
documented now that while FLFP has declined in the recent decades in rural
areas, in the urban areas, it has been mostly stagnant (Figure 1). While low
levels of FLFP may be due to patriarchy and cultural factors that prevent
women from working, the trends that suggest a decline may be related to
the structural changes, or lack thereof, in the Indian economy. I elaborate on
these points further, but the essential point is that the authors should clarify
how their policy suggestions aim to address levels or trends, or both, while
clearly distinguishing between the two.

1.2. Spatial and Sectoral Variation in FLFP

The reasons for the significant difference in FLFP in rural and urban India
are likely to vary spatially and so then would the policy prescriptions.
 Erin K. Fletcher et al. 203

FIGURE 1. LFP by Gender and Location (NSS)

1
0 .1 2 .3 .4 .5 .6 7 .8 .9

Male Female
1987 1999 2011 Conf. interval
(a) Rural
1
0 .1 2 .3 .4 .5 .6 7 .8 .9

Male Female
1987 1999 2011 Conf. interval
(b) Urban

Source: Afridi, Dinkelman, and Mahajan (2018).

In rural areas, on average, FLFP is higher perhaps due to relatively greater

destitution, which leads to higher willingness or need for women to work.
In urban areas, there is less destitution, as a result of which social norms
or stigmas against women’s work are likely to become more binding (a la
Goldin). Moreover, the majority of the women in rural areas classify them-
selves as self-employed in agriculture (Figure 2), followed by those engaged
204 I N D I A P O L I C Y F O R U M , 2018

FIGURE 2. Rural, Married FLFP by Sector (NSS)

LFPR (UPSS): Rural, Age 25–65

50.0%

40.0%

30.0%

20.0%

10.0%

0.0%
1987 1999 2009
Year

Agriculture Manufacturing Construction Service

Source: Author’s calculations.

Note: UPSS = Usual Principal and Subsidiary Status

in casual work. The predominant role of agriculture in employing women

in rural areas, hence, cannot be ignored.
Furthermore, we see that the decline in FLFP is almost completely due
to a reduction in self-employment (Figure 3). Thus, any policy prescription
for addressing FLFP should be conducted in the backdrop of the spatial
and sectoral variations in both the levels and trends in women’s workforce
participation in the country.

1.3. Women’s Demographic Characteristics

In 2011, only 20 percent of rural, married women in the age group of 15–60
years were in the labor force, 30 percentage points lower than for unmar-
ried women. While workforce participation rates among urban unmarried
women went up by 11 percentage points between 1999 and 2011, the rate
has remained stagnant for married women at 20 percent for the past 30
years (NSS, various years). I suggest that the authors distinguish between
workforce participation by marital status as there is a large marriage pen-
alty on women’s work in India (discussed further). I would also urge the
authors to update the analysis to the 2014–15 NFHS to allow access to
the latest data available on this issue, given that the last NSS survey data
are available only until 2011.
 Erin K. Fletcher et al. 205

FIGURE 3. FLFP by Type of Employment (NSS)

LFPR (UPSS): Age 25–65
40%
35%
30%
25%
20%
15%
10%
5%
0%
1987 1999 2009

Year

Self-Employed Casual Salaried

Source: Author’s calculations.

UPSS = Usual Principal and Subsidiary Status

To summarize, I suggest that the authors clearly distinguish the spatial

and demographic variation in the observed levels and trends in women’s
workforce participation over the last few decades in India. While their
existing exposition is exhaustive, a more organized structure of the data
descriptions would be easier for the reader to follow, and, more impor-
tantly, make it possible to distinguish between policy measures that target
these constraints.

2. Policy Recommendations

I suggest that the authors classify the policy recommendations into two
broad categories—those that address supply side or household factors
and those that could loosen demand constraints to address economic and
structural factors.

2.1. Supply Side Constraints

• Emphasizing the role of cultural and social norms: Cultural norms
underlying the traditional role of men and women in Indian households
manifest themselves in the significantly greater time spent by women
206 I N D I A P O L I C Y F O R U M , 2018

in home production than men, irrespective of their level of education

and thereby potential wage earnings. This leads to a higher elasticity
of labor supply for women relative to men, and low substitutability of
female labor with male labor in home production.
  Using the only detailed time-use data available for India (NSS,
Time Use Survey, 1998), Afridi, Bishnu, and Mahajan (2018) find that
across education levels, women spend significantly less time at work
than men (Figure 4). On average, 15–60 year-old married women in
urban India spend a mere 9.36 hours at work per week, while their
male counterparts spend 58.71 hours. As women go from being illit-
erate to completing higher secondary schooling, work hours show a
declining trend and then jump up at the “graduate and above” level.
Despite the rise in the time spent at work by women at the highest
education level, the average weekly hours only reach 13.32.
  In contrast, across education levels, women spend significantly
more time on domestic work than men (Figure 5). On average, 15–60
years old married women in urban India spend 51.85 hours on domes-
tic work per week, while their male counterparts spend 4.18 hours.
The average weekly hours of domestic work increase for women—
although at a declining rate—up to the higher secondary schooling
level and then fall by 14.25 percent at the “graduate and above” level
but still remain at 47 hours per week. The domestic work hours of
men do not vary significantly by education.
  While men and women spend comparable time on leisure, across
education levels women spend significantly more time on childcare
than men. When there are children below five years of age in the
household, married women in urban India spend an average of 12.68
hours per week on childcare. The corresponding figure for men is
2 hours (Figure 6). When there are children below 14 years of age
in the household, the figures are 9.91 and 1.73 for women and men,
respectively (Figure 7). The gender gap in childcare hours does not
vary significantly across levels of education in both cases. While the
figures here are for urban areas, these conclusions hold for rural India
as well.
  To summarize, women disproportionately bear the burden of domes-
tic work in the household and hence face time scarcity. It appears
that childcare, which is a large component of domestic work, is a
key constraint on the FLFP, even for educated women. Therefore,
to enhance the FLFP, increasing women’s education is perhaps not
 Erin K. Fletcher et al. 207

FIGURE 4. Time Spent on Work by Urban, Married Women and Men Aged
15–60 Years
Urban Women 15–60, married
10 15 20 25 30 35 40 45 50 55 60
0 5

Illiterate <Primary Primary Middle Higher Secondary >=Graduate

Education level

Mean 95% Conf. interval

Urban Men 15–60, married

10 15 20 25 30 35 40 45 50 55 60
0 5

Illiterate <Primary Primary Middle Higher Secondary >=Graduate

Work
Mean 95% Conf. interval

Source: Afridi, Bishnu, and Mahajan (2018).

208 I N D I A P O L I C Y F O R U M , 2018

F I G U R E 5 . Time Spent on Domestic Work by Urban, Married Women and

Men Aged 15–60 Years
Urban Women 15–60, married
10 15 20 25 30 35 40 45 50 55 60
0 5

Illiterate <Primary Primary Middle Higher Secondary >=Graduate

Domestic work

Mean 95% Conf. interval

Urban Men 15–60, married

10 15 20 25 30 35 40 45 50 55 60
0 5

Illiterate <Primary Primary Middle Higher Secondary >=Graduate

Domestic work

Mean 95% Conf. interval

Source: Afridi, Bishnu, and Mahajan (2018).

 Erin K. Fletcher et al. 209

F I G U R E 6 . Time Spent on Childcare in Households with Under-5 Children,

by Urban, Married Women and Men Aged 15–60 Years
Urban Women 15–60, married
10 15 20 25 30 35 40 45 50 55 60
0 5

Illiterate <Primary Primary Middle Higher Secondary >=Graduate

Education level

Mean 95% Conf. interval

Urban Men 15–60, married

10 15 20 25 30 35 40 45 50 55 60
0 5

Illiterate <Primary Primary Middle Higher Secondary >=Graduate

Education level

Mean 95% Conf. interval

Source: Afridi, Bishnu, and Mahajan (2018).

210 I N D I A P O L I C Y F O R U M , 2018

F I G U R E 7 . Time Spent on Childcare in Households with Under-14 Children,

by Urban, Married Women and Men Aged 15–60 Years
Urban Women 15–60, married
10 15 20 25 30 35 40 45 50 55 60
0 5

Illiterate <Primary Primary Middle Higher Secondary >=Graduate

Education level

Mean 95% Conf. interval

Urban Men 15–60, married

10 15 20 25 30 35 40 45 50 55 60
0 5

Illiterate <Primary Primary Middle Higher Secondary >=Graduate

Education level

Mean 95% Conf. interval

Source: Afridi, Bishnu, and Mahajan (2018).

 Erin K. Fletcher et al. 211

sufficient. Policy should focus on the provision of reliable and acces-

sible childcare arrangements for working women. Further, flexible
working conditions for women can enable them to balance work
and home better while we simultaneously chip away at social norms
that are very sticky and less responsive to policy. In addition, there
are several policy initiatives that are more broadly aimed at poverty
reduction, but which can also free women’s time from home produc-
tion. For instance, technological changes that reduce women’s time in
household chores (e.g., subsidized LPG, and improved and universal
access to electricity) have been shown to release women’s time from
home production (Dinkelman 2011). Such policies should be encour-
aged and emphasized from the perspective of women’s time scarcity.
• Restrictions on women’s mobility: While the authors acknowledge the
concerns regarding women’s safety, I would suggest they also empha-
size the role of providing basic infrastructure to improve women’s
access to and safety in public spaces. For instance, improvements in
the frequency and quality of public transportation, street lighting and
regular safety audits could significantly improve women’s mobility
and thereby their workforce participation.

2.2. Demand Side Constraints

In the current version of the paper, the authors have not elaborated on the
possible demand side constraints that may have been a factor in the low level
and declining trend in FLFP in India. I would encourage the authors to do so
in their revised draft and consider the policy recommendations as follows.

• Create more good jobs: India needs policies that create good (read
formal sector) jobs which women, even with relatively low levels of
education, can engage in (e.g., in agriculture and manufacturing). This
is linked to the issue of lack of jobs in general in India, and also to
a special focus on the greater disadvantage women face in accessing
formal sector jobs. Policies that aim at improving farmers’ access to
new technology, credit and markets in agriculture, and fostering the
growth of manufacturing are measures that are on the policy radar,
but lack a gender lens.
• Encourage flexible work hours and piece rate work for women:
Several surveys suggest that women prefer work that allows them
to balance household and work for pay. Encouraging employers to
provide flexible work hours and/or piece rate work to women in the
212 I N D I A P O L I C Y F O R U M , 2018

manufacturing sector would be one such measure that could increase

women’s workforce participation.
• Provide home-based work: Policies that bring work closer to women’s
homes would address both their time poverty as well as safety concerns.
Programs such as the NREGA in rural areas have been shown to
increase FLFP wherever it has functioned well. In the manufacturing
sector, contractors often provide factory-based work to women in
residential units around the industrial towns. However, women are
typically exploited in these transactions in terms of extremely poor
remuneration for the work they do.
• Reduce gender gap in wages and earnings: Gender gaps in wage
earnings in India are well documented and typically higher than in
developed countries. Along with lack of decent jobs, a prime factor in
the perceived low returns to FLFP in India is the lower wages women
receive for the same kind of work they do as men. Legal provisions
that make gender discrimination untenable, and their effective enforce-
ment, would be essential for long-run improvements in the perceived
returns to women’s work.

Finally, I would suggest the authors exercise caution on the following:

• The authors interpret the response to the question on “willingness to

work if made available at household” in the NSS as an unconditional
statement of women’s willingness to work. In my opinion, since this
question is conditional on availability of work close to home, it high-
lights the point I made previously about the constraints women face
due to the gendered division of labor within the home.
• The authors tend to interpret the observed relationship between voca-
tional training and FLFP as causal. I would caution against this inter-
pretation because women who chose to take up vocational training may
already be predisposed to working. It is not obvious that vocational
training per se improves their LFP.
• The authors fleetingly suggest extending political quotas for women
to jobs in the public sector. While political quotas at the local govern-
ment level have had, unarguably, a benign effect on women’s political
participation, it is neither clear that there are enough public sector
jobs to go around for gender quotas nor is it certain that it would lead
to a significant increase in FLFP given the supply side constraints
discussed earlier.
 Erin K. Fletcher et al. 213

References

Afridi, Farzana, Taryn Dinkelman, and Kanika Mahajan. 2018. “Why Are Fewer
Married Women Joining the Workforce in Rural India: A Decomposition Analysis
over Two Decades,” Journal of Population Economics, 31(3): 783–818.
Afridi, Farzana, Monisankar Bishnu, and Kanika Mahajan. 2018. “Home Production,
Social Norms and Women’s Labor Supply in India.” Paper presented at the 13th
Annual Conference on Economic Growth and Development at Indian Statistical
Institute, Delhi.
Dinkelman, Taryn. 2011. “The Effects of Rural Electrification on Employment: New
Evidence from South Africa,” American Economic Review, 101(7): 3078–3108.
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2018, held at Davos-Klosters, Switzerland. January 23–26.

General Discussion

In response to Pranab Bardhan’s comment that it is hard to understand

the trends in labor force participation (LFP) on the basis of norms, Dilip
Mookherjee pointed to the stigma underlying males or in-laws in the family
preferring the women not to work, but allowing them to do so if the household
was very poor. But as the household’s earning or wealth improves, there is
an income effect with the women pulling out of the labor force, which can
lead to a declining labor force participation rate (LFPR). He also referred
to his experiment in West Bengal giving loans to low-income households,
which had led to women withdrawing from the labor market. But the time
allocation data showed that the women were not spending more time in
leisure or on household chores, but were running self-employed businesses.
Was this income effect empowering women? The women said they preferred
to be self-employed at home and to socialize with other women during
work. However, it is possible that the norm effect was also kicking in, with
the family dissuading the women from working outside the home, but not
minding their running a business from home. It is hard to separate the two
effects, and difficult to make welfare judgments about female empowerment
by looking just at female labor force participation (FLFP).
Devesh Kapur agreed with Pranab Bardhan and Farzana Afridi that
the role of technology was under-emphasized in the paper, especially
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technological change that is displacing women from agriculture or construc-

tion and easing workloads through more readily available cooking fuels and
kitchen implements. This should be empowering for women.
Surjit Bhalla suggested that India had undergone a large expansion in
female education, catching up with men in education, and the implication
of that should be an increase in the female labor force participation rate
(FLFPR). In his earlier work on the emerging middle class, he had also found
a backward bending supply curve for women, possibly because of status or
cultural reasons, since India and Pakistan are the outliers also in this IPF
paper. He also suggested that over the next 20 years, the picture in the world
would be about declining male labor force participation rates (MLFPRs).
Abhijit Banerjee highlighted the need to be careful in using LFP as a wel-
fare outcome for women: it is important to also consider who is making the
choice to participate or not, and how that choice is being affected by policy
interventions, for which there is usually no clear theory. Microcredit may
often directly contribute to women being made to start a business to serve
some particular goal of their husbands. He then referred to a RCT done in
Mirzapur, Uttar Pradesh, which looked at self-improvement interventions
for women. When women say that they want to work at home, are they also
speaking about particular things they feel they can do, pointing to a lack
of self-belief that they cannot go out and work, say, in a factory? Once the
RCT self-efficacy treatments were done, women could be convinced that
they could go out and participate in the labor force.
Mihir Desai noted that the paper had missed the opportunity to use the
district-level variation in the FLFP, which cannot be fully explained by the
urban–rural gap, as done in the paper. He thought that MLFP was not nearly
as variable at the district level. This presented an opportunity.
Rohini Somanathan cautioned against getting hung up on LFPRs and
jumping to issues of efficiency and welfare without thinking them through.
She contended that if women’s wages were doubled, we could certainly
get to much higher FLFPRs, but it is not clear at all if that would improve
efficiency. Thinking this through requires consideration of which markets
don’t work, why we may be in a bad equilibrium, and what the source of
inefficiency is. For example, corresponding to the figure of 30 percent of
women saying that they wanted to work as cited by the authors, she noted
that it would be useful to know that number for men as well. Finally, she
said that policy interventions may work for women at certain ages to increase
participation and welfare, but women might be better off out of the labor
force at other ages.
 Erin K. Fletcher et al. 215

Ratna Sudarshan praised the paper for its emphasis on women’s part-time
work, noting that it was perhaps the first time she was seeing this emphasis.
But she cautioned against this leading to gender-conflictive situations
where existing work is merely being re-allocated. Instead, the imperative
is to expand work and job opportunities for women, and upgrading work
that is already being done. Among younger, more educated women, there
is actually a search for newer, different types of part-time work. Second,
she stressed the importance of developing a more realistic narrative in the
paper about women’s work in India, one that brings family and marriage
also into the picture and helps develop a sense of identity and purpose in
women’s work. Part-time work is very central to that narrative, so the paper
could develop that further.
Renuka Sané maintained that safety is a very big issue when it comes to
FLFP, but is not discussed enough. She related this issue with the comment
on how and why women prefer to work closer to home. She advised caution
in interpreting a policy recommendation out of that statement, remarking
that instead of recommending bringing work closer to home for women,
we should perhaps focus on making it safer for women to travel to work.
Anushree Sinha noted that government policies like UJALA that promote
electrification and infrastructure, in part to save women time for care work,
may actually be reinforcing traditional gender roles. The argument is made
that women now have facilities to save time for care work, which is their
role anyway, and they can do market work in whatever residual time is
saved. It is important to devise ways to facilitate sharing of both care work,
outside work, and unpaid work between men and women, and to take into
account more the choices that women actually want to make.
Premila Nazareth shared work she had done for the International Finance
Corporation on women’s participation in the Indian mining sector. Mining
firms were keen to use women more at all levels, but for mining engineers
there was a law that women were not permitted to work in underground
mines. Furthermore, the website of the Ministry of Labour and Employment
mentioned that women were not allowed to work at night on the shop floor
in factories. So these two laws that were supposed to protect women actually
were holding them back from participating in a fuller way in a key growth
sector. The long-term result was that women who had trained in mining were
instead going into IT, so that the top CEO-type jobs were going to men in
mining and manufacturing. On a more individual level, there is a need to
change the narrative of how we think about women’s work, which in India
involves not only childbearing and child-rearing but also looking after the
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elderly. She appealed to economists to usher a change in the way we think

about this and to bring women into the workforce.
Rajnish Mehra was surprised that India’s and Pakistan’s FLFPR were so
different from others, including neighboring countries. He asked if similar
studies have been done on Bangladesh and Nepal, particularly studies com-
paring FLFP on two sides of the border. He also noted that India exhibited
a lot of variation across states in fertility, agricultural productivity, educa-
tion levels, and health outcomes. He wondered if this could be exploited to
examine any systematic relationships about LFP.