Second PIM webinar in the series on Feminization of Agriculture, recorded on July 7, 2021.

1. Feminization of agriculture:
Building evidence to debunk
myths on current challenges
and opportunities
P I M W E B I N AR S E R I E S
Webinar #2: July 7, 2021, 9:00-10:30 AM EDT

2. Feminization of agriculture grants 2019---
• 9 PIM grants to challenge the prevailing assumptions re: the feminization of agriculture
• Term “feminization” of agriculture used widely but inconsistently
• Two main narratives (Doss et al 2021, forthcoming):
Negative
- increased workload on women without resources needed for success: extension services, credit, hired
labor and so on
- women are “left behind” and “left out” as men move into more lucrative off-farm activities
Opportunity
- for women’s empowerment and gender equality as women’s visibility and voice increase
- Implications for agri-food systems could be positive
• Projects analyze the dynamics of feminization but also use proactive approaches to ensure that women also
benefit from agricultural labor as they do more of it
• Agri-food systems are changing – what are the implications?
• What do we understand about the feminization of agriculture?
2

3. Feminization of Agriculture grants (2)
• Gender and feminization processes in wheat agriculture in South Asia, presented by Cathy Farnworth (freelance consultant)
• Gender and Youth Employment in Agricultural Value Chains, presented by Kate Ambler, IFPRI
• Exploring feminization of agriculture through gender dynamics across scales, presented by Alessandra Galiè, ILRI
• The use of ICTs to challenge gender stereotypes and empower women farmers in the age of feminization of agriculture,
presented by Bjorn van Campenhout (IFPRI) and Els Lecoutere (CGIAR GENDER Platform)
Discussant: Ruth Meinzen-Dick, senior research fellow, International Food Policy Research Institute; co-leader of Governance of
Natural Resources flagship in the CGIAR Research Program on Policies, Institutions, and Markets (PIM)
NOTE: these projects were all initiated pre-COVID

4. GENDER AND FEMINIZATION PROCESSES
IN WHEAT AGRICULTURE IN SOUTH ASIA
Cathy Rozel Farnworth, Lone
Badstue (PI), Preeti Bharati, Lara
Roeven
Photo: Dr Vijesh Krishna

5. RESEARCH QUESTIONS
(1) Is decision-making in wheat becoming feminized?
(2) Is labour in wheat becoming feminized?
(3) In what ways do interactions between caste and gender determine
and limit the spaces within which women can exert their agency?
(4) In what ways are women challenging gender and caste norms to
enhance their livelihoods?

6. METHODOLOGY and OUTPUTS
Qualitative (Quantitative)
❖ Literature Review: feminiziation processes in
Bangladesh, India, Nepal and Pakistan.
❖ Deep dive: revisited GENNOVATE village (1st
visit 2016) in Madhya Pradesh. Analysis trend
data from 2006. Our follow up research, 2019.
❖ Participatory: semi-structured questionnaires/
PRA activities (seasonal calendars)
❖ Tools adjusted daily – followed respondent
thinking/ tracked new learning/ awareness
raising.
Outputs
• “Are wheat-based farming systems in South Asia
feminizing?” Submitted.
• “Supporting labor and managerial feminization
processes in wheat in the Indo-Gangetic Plains
(IGP): a guidance note.” Published.
❖ Cooperation with (non-fem grant) quantitative
research led by Dr. Vijesh Krishna in Jamari and
17 other villages in MP.
• Co-written paper: “Caste and gender in wheat
growing communities in Madhya Pradesh, India”.
Submitted.
• Insights from feminization of agriculture
meetings etc. – incorporated into further work
by Dr. Krishna and team.

7. FINDINGS (Jamari, Madhya Pradesh)
Research Question Findings
Decision-Making -
wheat
Women report highly variable participation
Men across caste deny women participate – because women are not
„farmers“
Women‘s SHG key source of wheat finance /GBV
Labour - wheat Women: paid and family labour (men report lower participation than women)
Almost all field tasks (many normative surveys don‘t capture this)
Scheduled caste/scheduled tribes = additional field tasks on own low-quality
land
Mechanisation = destroying women‘s income opportunities
Caste-gender
interactions x agency
All women regardless of caste = paid fieldwork since mid-2000s: outcome of
women‘s agency/norm-breaking esp. general caste. Many young women: no
choice but agriculture/ young men exit esp. GC/OBC
Challenging caste-
gender norms
Methodology: participatory discussion processes, including exploring the trend
analyses = some women (esp general caste/ other backcward caste) began
to claim identity as ‚farmers‘

8. CONTRIBUTION TO BODY OF KNOWLEDGE
Knowledge
Women as
farmers
Is women‘s self-awareness/ men‘s recognition/ recognition by the rural advisory services
(RAS) that women are farmers necessary for (i) how we understand feminization of
agriculture, (ii) providing effective support?
Women as
hired
labourers
Participation in hired labour = outcome of women‘s aspírations and agency (not just
agrarian distress).
Women as
financiers
Women = financially supporting wheat. More research SHG-agricultural financing needed.
Caste-
gender
interactions
Inter-caste relations limit women-women interactions, but women‘s challenges and
aspirations similar across caste. Women share agricultural knowledge with each other
because RAS/ men rarely share. Knowledge networks intra-caste.

9. Gender and Youth Employment in Agricultural Value
Chains
Presented by:
Kate Ambler, IFPRI
With:
Sylvan Herskowitz: IFPRI
Mywish Maredia: MSU

10. BACKGROUND
Case studies have suggested that the role of women and youth in high-value, off-farm
employment in the agri-food system is growing
These types of jobs present real opportunities for employment outside of on-farm work
But little evidence exists fully characterizing this trend

11. METHODS
We employ three methods of analysis:
Review of existing literature
Analysis of existing household surveys: Uganda, Tanzania, Niger, Ghana
Examination of new methods in primary data collection: Survey in rural Ghana
 Randomized ordering of labor module
 Comparing methods of collecting information

12. FINDINGS
Systematic analysis of productive employment in agri-food systems is not well
covered in existing literature
Analysis of household surveys suggests
 Off-farm employment in agri-food system is low in rural areas
 Higher for women in urban areas
 May not be high-value
Primary data collection suggests
 Existing methods may miss work done by women and youth
 Direct questions may simplify data collection

13. CONTRIBUTION
Employment in high-value activities in the agri-food system is a promising growth
area
Methodological issues may lead to undercounting of women and youth
Current data is not otherwise well suited to track high-value employment
 On-going work designed to speak to this

14. Better lives through livestock
Gender at scale: exploring ‘feminization’ of
agriculture through gender dynamics across scales
in Sub-Saharan Africa
Team:
Alessandra Galiè; Catherine Pfeifer; Stephen Oloo; Dolapo Enahoro

15. 15
Background
FoA often estimated in numerical terms: how many women vs men in ag over years
Projections on future composition of the ag. labour force uses same data
Yet, FoA is affected by gender dynamics and norms in the household that change
over time in complex ways…making projections data inaccurate
Research Question: What methodology to identify the gender-based drivers of FoA?
Goal: methodology to identify key drivers of FoA/MoA for
more accurate modelling based on processes of change (not their outcomes)

16. 16
Methods: inductive + deductive
Identify:
1. Possible key drivers of FoA: desk work (inductive)
2. Proxy indicators for each driver and related datasets
(DHS; World Bank Indicators; OECD-SIGI)
3. African countries characterized by FoA or MoA using existing datasets
(Demographic Health Survey (DHS))
4. Patterns of drivers within FoA countries/MoA countries (deductive):
statistical analysis and machine learning
Then:
5. Validation of drivers at community level. X
6. Adjustment of our model of interacting drivers.

17. 17
Findings
Key umbrella drivers: 1. individual capabilities, 2. market opportunities, 3. formal and
informal institutions – at household and national levels
Feminizing countries, SSA: Burundi, Ghana, Kenya, Liberia, Rwanda, Sierra Leone, Uganda
Masculinizing countries: Niger, Namibia, Benin…
Emerging drivers:
Statistical analysis: Laws restricting women’s access to financial services (both for FoA/MoA)
Machine learning: women facing discrimination (FoA; no discrimination for MoA); GDP per
capita (low for FoA; high for MoA); HH wealth index (medium for FoA; poor and rich for MoA)
Across 2 approaches: HH exposure to media; medium/high HH wealth index (FoA); women
facing legal discrimination (FoA); high GDP per capita (MoA)

18. 18
Contribution to the body of knowledge on FoA
Only 4 common drivers emerged across 2 approaches: ‘exposure to media’,
‘hh wealth index’, ‘laws discriminating women’, ‘GDP per capita’
Machine learning approach: showed key drivers and complementary for
FoA and MoA…shows nuances…need qual to explain ‘how’
Tension: umbrella drivers…granularity of drivers within each country
Our driver indicators shaped by available data…
More countries Masculinize…FoA and MoA fluid and complementary
phenomena affected by context- and time- specific drivers

19. Empowering women through targeting
information or role models: Evidence from an
experiment in agricultural extension in Uganda
Els Lecoutere CGIAR Gender Platform
David J. Spielman IFPRI
Bjorn Van Campenhout IFPRI/LICOS KU Leuven
Reduce intra-household constraints to women’s
effective participation, agency, and benefits from
agriculture to enable women turning
Feminization of Agriculture to their advantage?
https://academic.oup.com/ofid/article-abstract/6/3/ofz001/5272497
www.mapsopensource.com
Key constraints to women’s empowerment in
agriculture
including in maize farming in Uganda
▪ Women’s information disadvantage wrt
productivity enhancing technologies and
practices for agriculture, and information
asymmetry within the household
▪ Women’s role as agricultural producers
rarely recognized

20. 20
Messenger
M F Cpl
Recipient
M
F
Cpl
Impact of women as messenger/role
model in extension videos (vs men)
In couple Woman alone
Methods
Messenger
M F Cpl
Recipient
M
F
Cpl
Impact of providing women with direct
access to extension information (vs men)
In couple Woman alone
Messenger
M F Cpl
Recipient M
F
Cpl
Messenger
M F Cpl
Recipient
M
F
Cpl
Messenger
M F Cpl
Recipient
M 385 385 369
F 385 385 369
Cpl 342 342 369

21. Results
women
joint
men

22. Contributions to body of knowledge
IFPRI Discussion Paper 1889 - Women’s empowerment, agricultural extension, and digitalization: Disentangling information and role model effects in rural Uganda
VoxDev - Providing information to empower women in agriculture: Evidence from Uganda
E.Lecoutere@cgiar.org D.Spielman@cgiar.org B.VanCampenhout@cgiar.org
Reducing intra-household constraints to women’s empowerment in agriculture can aid women to
maximally benefit from processes of feminization of agriculture
▪ If the aim is to empower women
then give the necessary information directly to women
▪ If the aim is to empower women in collaboration with male co-heads in the household
then provide extension information to the female and male co-heads together
▪ Extension information presented with inclusion of women, whereby they can function as role
models, can reduce men’s dominant decision making in agriculture and create space for greater
involvement of women