Ashwini Deshpande
Delhi School of Economics
University of Delhi
INDIA.
RURAL LFPRs by gender (age>=15)
100
90
80
70
60
50
40
30
20
10
0
1983
1993-94
1999-00
Male
Female
2004-05
All
2009-10
Urban LFPRs by gender (age>=15)
100
80
60
40
20
0
1983
1993-94
1999-00
Male
Female
2004-05
All
2009-10
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Demand-based explanations: Low prody
agriculture + excess supply of lab. Women’s paid
lab needed only when men’s lab exhausted.
Supply-side explanations: Socially ordained
division of lab: women in reproductive activities
within the household
Discrimination-based explanation: employers
discriminate against female workers, both in
terms of hiring and wages.
Measurement issues: lot of women’s work not
counted as “productive” plus women often deny
involvement in productive work.
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
Share of women in regular wage/salaried
employment lower than that of men (rural: 4%
female WF in RWS (vs. 9) & urban: 39 vs. 42)
(2009-10)
Correspondingly, share of women in casual
workers & self-employment higher than men.
Rural: 79% women in agri; Urban: 53% women
in tertiary
2009-2010: NSS 66th round emp. survey
 Urban: RWS average daily wage: Rs. 364.95.
Rural: Rs. 231.59
 Rural Male: Rs. 249.15; Rural Female: 155.87
=> ratio of 0.63.
Urban Male: 377.16; Urban female: 308.79
 Ratio of 0.82
* Casual labour: 0.67 (Rural) and 0.58 (Urban)
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Blinder-Oaxaca Decomposition (Khanna
2012): Strong evidence of labour market
discrimination
Gender Wage Gap (log)
QR Coef.
OLS Coef.
1
0.9
Log Wage Gap
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
10
20
30
40
50
60
Percentiles
70
80
90
100
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
This plots the log of the gender wage gap at
each of the 99 percentiles using NSS 20092010 wage data for those reporting regular
wage/salaried employment (Khanna 2012).
The “Sticky Floor” effect is evident: wage gaps
are much larger at the bottom of the
distribution and decline almost monotonically
till the top of the wage distribution.
The average gap (given by the OLS
coefficient) is instructive, but misses out on
this nuanced picture.
0.9
0.8
0.7
0.6
0.5
total_differential
0.4
characteristics
0.3
coefficients
0.2
0.1
0
1
2
3
4
5
6
7
8
9
1
0.8
0.6
Total
0.4
Charecteristics
Coefficients
0.2
0
-0.2
0
0.5
1
Registered SSI sector: 3rd MSME Census (2001-2)
Women- owned enterprises
10.01%
Women -managed enterprises
8.36%
States with higher than all-India proportions
States with higher than all-India proportions
Arunachal Pradesh
Arunachal Pradesh
20.08
24.8
Assam
14.11
Assam
13.46
Karnataka
14.49
Karnataka
12.87
Manipur
16.69
Kerala
19.76
Meghalaya
33.59
Manipur
16.01
Mizoram
25.35
Meghalaya
33.02
Sikkim
25.17
Mizoram
26.71
Tamil Nadu
14.83
Nagaland
14.04
Sikkim
17.01
Tamil Nadu
13.33
Overlap of ownership and management
65% of women-owned enterprises are managed by women
2% of male-owned enterprises are managed by women
Percent women employed by gender of ownership and management
In women-owned enterprises
In men-owned enterprises
57.56
6.17
In women-managed enterprises
In men-managed enterprises
68.48
6.1
owned
Manuf of wearing apparel
managed
40.3
48.76
13.06
12.09
7.3
7.25
Manuf of fabricated metal prodts, except machinery
4.51
3
Manuf of chemicals and chemical products
3.73
3.25
Manuf of other non-metallic min products
3.69
2.64
Manuf of furniture
3.47
2.97
Retail trade, repair of household goods
2.92
2.51
Manuf of wood and straw products
2.68
2.3
Mauf of food prodt and bev
Manuf of textiles
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Women are not a homogenous category.
For example: overlap of caste and gender.
Earlier evidence: greater taboos on upper
caste women, who were materially more
prosperous – trade-off between prosperity
and immurement.
LFPRs among SC-ST women higher than UC.
Now: trade-off vanishing. Dalit women worst
off: triple burden of gender, caste and class.
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With greater legal differentiation, fewer
women work, own or run businesses (WBL,
2012)
South Asia (except SL) one of the 3 regions
where explicit legal gender differentiation in
accessing institutions and in using property is
most common.
Moreover, benefits such as paternity leave
absent.
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Lack of autonomy in interacting with
government institutions
Access to judicial system
Getting a job: differences in work hours,
restrictions by industry, poor antidiscriminatory laws, with even poorer
implementation
Benefits (e.g. maternity leave): India:
employer pays (rather than the government),
raising the cost of hiring women.
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India: gender disc in lab mkt => wage gaps
and differential access to wage employment,
sometimes exclusion of women.
Discrimination  inefficiencies and lower
growth (Esteve-Volart, 2004).
Individuals belonging to a group which is
discriminated against face higher interest
rates in credit markets.
=> lab mkt disc  credit market
discrimination
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Esteve Volart (2004):
An increase of 10% in the F/M ratio of
managers would increase PC NDP by 2%
An increase of 10% in F/M ratio of total
workers would increase PC NDP by 8%.
=> Gender inequality in the access to
working positions is a bigger break on
growth than gender inequality in the access
to managerial positions.