The document analyzes trends in female labor force participation (FLFP) in India from 1981-2011 across 15 major states. It finds that in some states, FLFP follows a U-shaped curve as predicted by the "Feminization-U" theory where rates initially decrease with economic development before rising again. Panel regression analysis indicates that rising literacy rates, declining fertility rates, increasing urbanization, and rising NSDP explain the downward portion of the U, while further increases in these variables along with economic factors explain the upward turn. The U-shape is observed for certain types of labor in states like Kerala, Maharashtra, Madhya Pradesh, and Orissa. Other states show only increasing or decreasing FLFP trends

2. TRENDS IN
FEMALE LABOUR
FORCE
PARTICIPATION
IN INDIA

3. The basic story
1
Feminisation-U: Across the process of economic development, the
Female Labour Force Participation (FLFP) rate initially decreases and then
rises after crossing a minimum threshold and thus forms a U-shaped curve.
Women’s economic progression is one of the biggest path-breaking
development of recent times.
There is an observed trend of Feminisation-U across the world.
What is Feminisation-U?

4. Multidimensional Roles of Women
Agricultural
Activities
Non-Agricultural
Activities
E.g.- Sowing,
Irrigation, etc.
Domestic Activities Allied Activities White-collar jobs
E.g.- child
rearing, etc.
E.g.- IT services, etc.E.g.- construction
works, etc.
2

5. 1.
• To verify the proposition of Feminisation-U
in the context of 15 major states of India in
the decadal timeline of 1981-2011 .
2.
• To determine the influence of certain
demographic and economic variables on
the rate of FLFP.
MOTIVATION
3

6. LITERATURE REVIEW
 In “Feminization of the Labor Force: The Effects of Long-term Development
and Structural Adjustment” by Cagatay and Ozler (1995) studied the –
- Relationship between women’s share in the labour force and long-
term economic development. It is found to be U-shaped.
• Some reasons being:
- Industrialization
- more women education
- declining fertility rates
SAP
Worsening of income
distribution
Increased openness
Increase in
Feminisation
4

7. Men’s access to
education &
technology
Growing
productivity
differences
in women’s LFPR
Emergence of the
factory system
Female dominated home based
production is replaced by male
dominated factory system
Associated
with
downward
portion of U
Further Economic
Development
in women education and
in fertility rate
in women’s LFPR
5

8. - in feminisation in investment
- in intensity of female household labour in savings
 Relation between gender composition of labour & business cycle is
described in terms of 3 theses:
Buffer Hypothesis: It views women as flexible reserve army of
labour whose LFPR increases during upswings and decreases during
downturns.
Segmentation Hypothesis: Gender segmentation of labour
market protects women from being last-hired & first-fired.
Substitution Hypothesis: Women become substituted for
men workers during downswings as a cost saving measure.
6
 In “Macroeconomic Consequences of Cyclical and Secular Changes in Feminisation:
An experiment at Gendered Macromodeling” by Erturk and Cagatay (1995) observed,

9. 7

10. • SAP Switch from non-tradable sector to tradable sector.
• Following a period of SAP for a gender based recovery from
economic crisis to succeed,
Impact of feminisation of
labour force on investment
Impact of rising intensity
of female household
labour on savings
 Rich and higher middle income countries can benefit from ‘feminisation’
process but might not last.
 Export led growth in its higher stages may reverse the process.
8

11.  In “Global Feminization Through Flexible Labor: A Theme Revisited” by
Guy Standing (1991) observed,
Labour market
flexibility
in informal employment Feminisation
Labour cost
becomes important
 Reasons behind such feminisation:
- in global integration and competition
- Technological Revolution
- SAP
 Women are opted as cost saving measure.
 Women are not in good position since they become more informal than men.
 In rapidly industrialising economies, we observe high female LFPR along with
high wage differential between male & female.
9

12. Possible Solutions
Reform of Social Protection
System
Combine flexibility with steadily
improving economic security
10

13.  In “The U-shaped Female Labour Force Function In Economic Development And
Economic History” by Claudia Goldin (1994) stated,
Falling portion of the U:
A strong income effect dominates over a weak substitution effect due to
change in the locus of production from the home to the factory.
Rising portion of the U:
Substitution effect dominates over income effect due to increased women
education, more prestigious job opportunities, etc.
11

14. TWO CASES
Stigma:
Households lose utility from
having the wife work in
manufacturing sector. Higher
income implies higher the
chance that stigma will be
binding. Stigma is not
attached to women working
in white-collar sector.
Non-Stigma:
Households do not lose
utility from having the wife
work in manufacturing
sector.
12

15. 0
g
g
d
c
b
a
Time
Goods (G)
A BC
U1
U2
VNS
VS
T
OT: Total time endowment
c: Non-Stigma Equilibrium
d: Stigma Equilibrium
13

16. Period 1-
AT: Time used in Production
OA: Time used in child care
Increased income from other family
members leads to shift in PPF
Period 2- Income effect operates
BT: Time used in Production
OB: Time used in child care
Female education and productivity increases
Period 3- Non-Stigma Equilibrium (c)
CT: Time used in Production
OC: Time used in child care
VNS: Wage Rate
Period 3- Stigma Equilibrium (d)
Stigma is removed when white collar
job is offered with higher wage (VS)
14

17. Reasons Behind u-shape
Explanations for the falling portion of U:
 Men’s Availability and
Accessibility to new
technologies and education
Growing productivity
differences between male
and female
 Demand for skilled labour Defeminisation

Agricultural Sector Higher demand for men workers
Urbanisation and
Industrialisation
Female dominated home based
production is replaced by male
dominated factory system
Non-Agricultural
Sector

Combination of production and reproduction
activities becomes difficult for women

Strong income effect
15

18. Explanations for the rising portion of U:
 Use of unskilled labour in
export oriented sector
Feminisation
 in inflow of women workers
in the labour force
Demand for
cheap labour
Global
Competition
 SAP Worsening of
income distribution
Access to education by
women

Feminisaton
 Strong substitution effect
lower income groups
succumb to paid
employment, pushing
women into the
labour market.
16

19. technique
17

20. INSPECTION
PANEL
REGRESSION
GRAPHICAL
EXPLORATION
COURSE OF ACTION
18

21. 19

22. PRIMARY
CENSUS
ABSTRACT
AGRICULTURE
NON
AGRICULTURE
MAIN
WORKER
MARGINAL
WORKER
MAIN
WORKER
MARGINAL
WORKER
MALE
MALE
MALE
MALE
FEMALE
FEMALE
FEMALE
FEMALE
20
Main Worker: Workers engaged in economically productive activities for six months or
even more than that in a year.
Marginal Worker: Workers who work less than six months in a year.

23. 15 major states are considered to analyze the data, which are-
We plot the FLFP rates against NSDP over the decades for all the 15
states.
Female Proportion = No. of Female Workers / Total Workers
For each type of labour considered, we calculate the corresponding
female proportions (FLFP Rates).
NSDP: Index of economic development
Andhra Pradesh, Bihar, Gujarat,
Haryana, Himachal Pradesh, Karnataka, Kerala,
Madhya Pradesh, Maharashtra, Orissa, Punjab, Rajasthan,
Tamil Nadu, Uttar Pradesh and West Bengal.
21

24. The demographic and economic variables play a vital role in
explaining the changes in FLFP Rate.
22

25. 23

26. DEPENDENT
VARIABLES
FLFP Rate
Case1. Main Agricultural Workers
Case2. Marginal Agricultural Workers
Case3. Main Non-Agricultural Workers
Case4. Marginal Non-Agricultural Workers
EXPLANATORY
VARIABLES
Female Literacy Rate
Fertility Rate
Urbanisation
NSDP
NSDP-Squared
24

27. Panel Regression
Fixed Effect Model
Unobserved
heterogeneous character
is fixed
Random Effect
Model
Unobserved
heterogeneous
character is stochastic
To choose between the two model Hausman
Test
Null: Random Effect
Alternative: Fixed Effect
 In our model, we have accepted the Null hypothesis in all the four cases because
the p-value was found to be insignificant. 25

28. THE FORMAL MODEL
C: Constant Term
ln_ltr: Log of Female Literacy Rate
ln_ ftr: Log of Fertility Rate
ln_urban: Log of Urbanisation
ln_nsdp: Log of NSDP
ln_nsdp2: Log of squared NSDP
U: Random Error Term
FLFP = C + ln_ltr + ln_ftr + ln_urban + ln_nsdp + ln_nsdp2 + U
26

29. 1 ln_ltr(Literacy Rate) 0.11871336 -.15440627*** .22831882*** -.22516276***
2 ln_ftr(Fertility Rate) 0.14410393 0.23093358 -.39334343* 0.11674867
3 ln_urban(Urbanisation) 0.08005492 0.16273383 0.01560998 .23814506**
4 ln_nsdp(NSDP) 0.03698251 -0.07290441 0.04270475 -0.06898093
5 ln_NSDP2 (NSDP^2) (omitted) (omitted) (omitted) (omitted)
6 _cons (Constant) 2.1360803 4.3712729*** 2.0499077*** 4.2660535***
N 60 60 60 60
Non-Agri_Marginal
Female Labour Force Participation
Rate (Dependent Variable)
SL. No. Agri_Main Agri_Marginal Non-Agri_Main
Note: Legends represent * p<0.05; ** p<0.01; *** p<0.001
Source: Panel Regression Results, using STATA 12
The following table illustrates the influence of the explanatory variables on
the dependent variable.
27

30. FEMALE AGRICULTURAL MAIN WORKERS – The coefficients of all the
explanatory variables are positive but insignificant.
FEMALE AGRICULTURAL MARGINAL WORKERS –
• Increase in Literacy rate Increase in Female education Decrease
in marginalization and FLFP rate
FEMALE NON-AGRICULTURAL MAIN WORKERS-
• Increase in Literacy rate increase in opportunities for women &
Increase in FLFP rate
• Increase in Fertility rate trade off between production and
reproduction activities and decrease in FLFP rate.
FEMALE NON-AGRICULTURAL MARGINAL WORKERS –
• Increase in Literacy rate High reservation wage Decline in
FLFP rate
• Increase in Urbanisation More job opportunities Increase in
FLFP rate
28

31. - Cases wherewe observedthe U- shaped curves
29

32. 30

33. SL. NO STATES INCREASING DECREASING U - SHAPED MISCELLANEOUS
2 Bihar
Agriculture – Main,
Non Agriculture
Main
Agriculture –
Marginal, Non
Agriculture -
Marginal
- -
4 Haryana
Agriculture - Main,
Non Agriculture -
Main
Agriculture –
Marginal, Non
Agriculture -
Marginal
- -
5 Himachal Pradesh
Agriculture - Main,
Non Agriculture -
Main
Agriculture –
Marginal, Non
Agriculture -
Marginal
- -
6 Karnataka
Agriculture - Main,
Non Agriculture -
Main
Agriculture –
Marginal, Non
Agriculture -
Marginal
- -
7 Kerala
Non Agriculture -
Main
Agriculture -
Marginal
Agriculture – Main,
Non Agriculture -
Marginal,
-
8 Madhya Pradesh
Non Agriculture -
Main
Agriculture –
Marginal, Non
Agriculture -
Marginal
Agriculture - Main -
1
Agriculture – Main,
Non Agriculture -
Main
3 -
Andhra Pradesh
Agriculture –
Marginal, Non
Agriculture -
Marginal
- -
Gujarat
Non Agriculture -
Main,Agriculture-
Main
Agriculture –
Marginal, Non
Agriculture -
Marginal
-
31

34. SL. NO STATES INCREASING DECREASING U - SHAPED MISCELLANEOUS
9 Maharashtra
Non
Agriculture -
Main
Agriculture -
Marginal
Non
Agriculture -
Marginal
Agriculture - Main
10 Orissa
Non
Agriculture -
Main
-
Agriculture –
Marginal,
Non
Agriculture
Marginal
Agriculture - Main
11 Punjab
Non
Agriculture -
Main
Agriculture –
Marginal, Non
Agriculture -
Marginal
- Agriculture - Main
12 Rajasthan
Agriculture –
Main, Non
Agriculture -
Main
Agriculture –
Marginal, Non
Agriculture -
Marginal
- -
13 Tamil Nadu
Agriculture –
Main, Non
Agriculture -
Main
Agriculture –
Marginal, Non
Agriculture -
Marginal
- -
14 Uttar Pradesh
Non
Agriculture -
Main
Agriculture –
Marginal, Non
Agriculture -
Marginal
- Agriculture - Main
15 West Bengal
Agriculture –
Main, Non
Agriculture -
Main
Agriculture –
Marginal , Non
Agriculture -
Marginal
- -
32

35. MAIN AGRICULTURAL WORKERS
0
10
20
30
40
50
70.12
111.07
160.43
292.75
FLFP(%)
NSDP (IN BILLIONS)
MADHYA PRADESH
MAIN AGRI
Figure 1
0
10
20
30
40
38.23
52.62
89.45
189.2
FLFP(%)
NSDP (IN BILLIONS)
KERALA
MAIN AGRI
Figure 2
 Declining fertility rate explains
the falling portion.
 Rise in other explanatory
variables explains the rising
portion.
 Declining fertility rate explains
the falling portion.
 Rise in other explanatory
variables explains the rising
portion.
33

36. MARGINAL AGRICULTURAL WORKERS
0
20
40
60
80
100
34.43
43.45
63.95
128.86
FLFP(%)
NSDP (IN BILLIONS)
ORISSA
MARGINAL
AGRI
Figure 3
MARGINAL NON AGRICULTURAL WORKERS
0
20
40
60
80
151.63
272.24
475.84
1125.22
FLFP(%)
NSDP (IN BILLIONS)
MAHARASHTRA
MARGINAL
NON AGRI
Figure 4
 Declining fertility rate, rising
literacy rate, increase in NSDP
explain the falling portion.
 Rise in urbanisation explains the
rising portion.
 Declining fertility rate, rising
literacy rate, increase in NSDP
explain the falling portion.
 Rise in urbanisation explains the
rising portion.
34

37. 0
10
20
30
40
50
60
38.23
52.62
89.45
189.2
FLFP(%)
NSDP (IN BILLIONS)
KERALA
MARGINAL
NON AGRI
Figure 5
0
20
40
60
80
100
34.43
43.45
63.95
128.86
FLFP(%)
NSDP (IN BILLIONS)
ORISSA
MARGINAL
NON AGRI
Figure 6
 Declining fertility rate, rising
literacy rate, increase in NSDP
explain the falling portion.
 Rise in urbanisation explains the
rising portion.
 Declining fertility rate, rising
literacy rate, increase in NSDP
explain the falling portion.
 Rise in urbanisation explains the
rising portion.
35

38. inference
Panel regression results depict the following reasons behind the changes
in FLFP rate:
36
The process of Feminisation takes the U shape for certain types of labour in
the states of:
• Kerala(main agricultural workers and marginal non-agricultural workers)
• Maharashtra (marginal non-agricultural workers)
• Madhya Pradesh (main agricultural workers)
•Orissa(marginal agricultural workers and marginal non agricultural workers)
For other states, we observe either increasing trend or decreasing trend.

39. *All the coefficients
are Positive &
Insignificant
*Literacy Rate:
Negative & Significant
*Literacy Rate:
Positive & Significant
*Fertility Rate:
Negative & Significant
*Literacy Rate:
Negative & Significant
*Urbanisation:
Positive & Significant
Non-
Agriculture
Agriculture
Marginal
Worker
Main
Workers
37

40. thank you
38