1. TIME-SPACE GEOMETRIES OFTHE
GENDERED LANDS OF EASTERN
GANGETIC BASIN
Sucharita Sen
Drawing from a Study Supported by
Australian Centre for International Agricultural Research
2. Connect with the agenda
• Focus not on migration
• Nor on rice systems- happens to be the area with predominantly rice based
agriculture
• Entry point women’s work- both with respect to time and space
• Feminizing agriculture in developing countries taken as a rule.
• While explaining the trajectory- migration comes in, more to review the
relationship between feminization of agriculture and male-selective out-migration
(Rahman 2000; Joshi 2000; Hossain et al 2004; Paudel et al 2009; Gartaula et al
2010; Jaim et al 2011; Neff et al 2012; Kannan and Raveendran 2012;Tamang et al
2014; Cunningham et al 2015; Pattnaik et al 2017; Mehrotra and Sinha 2017).
3. The case of Eastern Gangetic Basin
• High incidence of poverty
• Rice based cropping pattern
• High male outmigration
• However, plural gender space
4.
5. Genesis and trajectory of the study
• Striking difference in trends and levels of women’s participation in the three
countries, in spite of sub-regional commonalities. No earlier attempt to compare
the sub-basin in its entirety.
• Macro and micro studies do not talk to each other, particularly in India; the larger
study is an attempt to connect them.
• What does feminization indicate? U shaped hypothesis- implications and
limitations (Goldin 1994, Fatima and Sultana 2009,Tam 2011, Verick 2018).
6. What is feminization in agriculture?
Four indicators are proposed to capture this shift:
1. Whether more women work in agriculture over time
2. Women’s participation relative to men
3. Whether they spend longer hours in agriculture, and
4. Whether they are engaged in high-skilled work: as managers of their own
farms at the one end of the spectrum or unpaid family work on the other
Slavchevska et al. (2016)
)
7. Primary Questions
• How do we interpret feminization or defeminization in agriculture, and in rural
work in the context of the plurality embedded in EGB? (Homogeneity in incidence
of poverty, rice based cropping systems, small holding sizes etc.)
• What are the processes that explain these trajectories (gender and cultural space,
changes in economic paradigm and the interaction between the two)?
8. The secondary data
Country/ Data Source India Nepal Bangladesh
Labour or Employment
Surveys by Government
agencies
Based on sample surveys, but more detailed.
Main advantage: available at the unit level (household/ individuals)-
analytical strength.
1983, 1993, 2000, 2005,
2008, 2010, 2012
2014, 2015
1998, 2008, 2017 (?) (to
be published)
12 annual rounds
available but digital
formats available only
from 1999 (flood year).
2003 and 2013 (last
annual round)
National Sample
Survey, Employment
unemployment round,
Labour Bureau
Nepal Labour Force
survey
Bangladesh Labour
force survey
9. Sample size: Rural Population
India
NSSO (All age groups) Rural
Sample
1983 414278
1993 356351
2000 441548
2005 398025
2008 374294
2010 281327
2012 280763
Labour Bureau (Age
15years &above)
2014 302481
2015 323595
Nepal Rural
Sample
Labour Force Survey
(All age groups)
1998 36927
2008 39813
Bangladesh Rural
Sample
Labour Force Survey
(All age groups)
2003 84443
2013 125414
10. Concept of work and how they are represented in data
Important
Indicators
Factors impacting data
quality
Labour force Criteria (majority time/ at
least one hour)
Depth of work
(principal/subsidiary)
Reference period
(yearly/monthly/weekly)
Sequence of questions or
filters
Survey time (for weekly
status)
Age criteria
Work force
Unemployment
Underemployment
Wages
Major problems:
1. Bangladesh data not entirely
comparable with Nepal and India
both in terms of criteria and
reference period
2. Unemployment and
underemployment underestimated
for India
3. Nepal does not have subsidiary
status, particularly important for
women.
General undercounting of women’s work, fuzzy areas between home-space and work-space, non-
monetized; CPR related work crucial for livelihood, but not counted.
11. Exploratory qualitative field based data
Two purposes:
1. To triangulate the macro trends
2. To complement the macro trends- to add on.
3. To explore new processes
4 clusters of villages in three countries
Choice depended on trends of feminization and defeminization
23. Processes of Defeminisation
1. Education related withdrawal (Neff et al 2012, Abraham 2013,
Mehrotra and Sinha 2017,)
2. Prosperity induced withdrawal (Neff et al 2012, Mehrotra and Sinha
2017)
3. Sectoral shifts (Lahoti and Swaminathan 2013)
Distress Indications (Kannan and Raveendran 2012, Chandrasekhar and
Ghosh 2013, Abraham 2013)
1. Mechanization and displacement
2. Unemployment in urban areas and men coming back to agriculture
often seasonally, replacing women or increasing under-employment
of women
3. CPR degradation – water, fodder fuel collection.
24. 0
10
20
30
40
50
60
70
80
90
100
Age 5-
14
15-20 21-30 31-40 41-50 51-60 61-70 71-80 81 &
above
%Worker
Age Groups
Age wise WPR for male and female in the EGP India (2005 and 2012)
India EGP Female 2005 India EGP Female 2012
India EGP Male 2005 India EGP Male 2012
0
10
20
30
40
50
60
70
80
90
100
Age 5-14 15-20 21-30 31-40 41-50 51-60 61-70 71-80 81 &
above
Age wiseWPR of male and female in the EGP regions of
Bangladesh (2003 and 2013)
Bangladesh EGP Female 2002 Bangladesh EGP Female 2013
Bangladesh EGP Male 2002 Bangladesh EGP Male 2013
0
20
40
60
80
100
Age 5-14 15-20 21-30 31-40 41-50 51-60 61-70 71-80 81 &
above
Age wiseWPR for male and female in the EGP regions of
Nepal (1998 and 2008)
Nepal EGP Nepal Female 1998 Nepal EGP Nepal Female 2008
Nepal EGP Nepal Male 1998 Nepal EGP Nepal Male 2008
Fall in relative
WPR rates not
explained by
education-related
withdrawals
25. Prosperity InducedWithdrawal?
Class-specificity of defeminisation processes..
0
5
10
15
20
25
30
35
1993 2005 2008 2010 2012
Fig 4: Reduction of WPR by Consumption (MPCE) Categories
Poorest quartile 2nd quartile
3rd quatile Richest quartile
MPCE categories
% reduction in
RWPR from 1993
to 2012
Poorest quartile 39
2nd quartile 35
3rd quartile 4
Richest quartile 29
Defeminization coupled with increased
relative unemployment levels
26. 0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
1983 2005 2010 2012
Female WPR-EGP India-Rural
WPR without CPR WPR with CPR
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
1983 2005 2010 2012
Male WPR-EGP India-Rural
WPR without CPR WPR with CPR
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
1983 2005 2010 2012
Female WPR-Non-EGP-Rural
WPR without CPR WPR with CPR
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
1983 2005 2010 2012
Male WPR-Non-EGP-Rural
WPR without CPR WPR with CPR
Inclusion of
collection of
water
outside
premises,
collection of
fuel, fodder,
small game,
NTFPs lifts
theWPR
levels of
women
almost to
that of men
27. 0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
1983 1993 2000 2005 2008 2010 2012 2015
Quality ofWork of Men inAgriculture
Self Employed Unpaid family Work Casual Work
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
1983 1993 2000 2005 2008 2010 2012 2015
Quality ofWork for Women in Agriculture
Self Employed Unpaid family Work Casual Work
Unpaid family work reducing from
2005 (NREGA commenced in the same
year), replaced by paid casual work
More men coming back to rural areas
and agriculture from 2000, some in
part-time capacity- signs of lack of
full-time jobs in urban centres
29. Loss of Access to CPR?
Year
Poorest
quartile
2nd
quartile 3rd quartile
Richest
quartile
2005 36.2 30.6 26.3 20.0
2010 38.7 31.7 25.4 18.7
2012 41.2 34.8 28.5 22.8
% point
reduction
5.0 (-10) 4.2 (-8) 2.1 (-5) 2.7 (-7)
Essential activities- cooking, drinking water-
care/emotional/identity work
30. Migration- the pointers from the study
• Can work both ways and it has.
• Reverse migration happening in India.
• Even when it leads to feminization, not always favourable for gender relations.
• When it results in defeminization- not all positive.
• Why migration happened was mostly distress induces
