Alessandra Galiè 1 Stephen Oloo 1 Catherine Pfeifer 2 1 International Livestock Research Institute ILRI 2 Research Institute of Organic Agriculture FiBL Writeshop at KIT November 2019 ID: 793184
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Slide1
Exploring feminization of agriculture through gender dynamics across scales
Alessandra Galiè
1
, Stephen Oloo1, Catherine Pfeifer21International Livestock Research Institute (ILRI)2 Research Institute of Organic Agriculture (FiBL)
Writeshop at KIT, November 2019
Slide2This study in a nutshell
Methodological inter-disciplinary study
to understand how gender dynamics
at household and community level
influence Feminization of Agriculture (
FoA)
Research question:How do intra-household gender dynamics scale to shape the regional/global trend ‘FoA’?
Slide3What we do in this study
Develop a methodology to study how gender dynamics at hh and community level scale to influence FoA.Pilot methodology with existing datasets on FoA in selected countries of sub-Saharan Africa (focus on livestock) and qual findings.
Validate/challenge persistent
assumptions about FoA.
Slide4How we do it: 6 main steps
Develop a conceptual framework on drivers affecting FoA
Identify key drivers and dynamics behind FoA
Use existing DHS national-level data to identify countries with FoA or MoA Use DHS, WB and SIGI to analyse how 3 drivers interact to form clusters of countriesUse existing data sets at hh
level (i.e. DHS) to identify interaction among drivers in the countries identifiedIdentify patterns of interactions
Conducts qualitative fieldwork to explore how gender dynamics have played out in realityAdjust the drivers and modelPossibly: adapt ‘model’ for exploring future emerging social landscapes in agriculture
Slide5Key findings: 3 drivers identified
Individual capabilitiesMarket opportunities
Institutions
(policies and gender norms)
Individual and household level
Local, national, global levels
Hh
, community, national levels
Slide6Key Findings
cont…Identified 6 clusters of countries with similar patterns: Cluster 1 : Uganda
Cluster 2 : Congo-BrazavilleCluster 3 : Kenya, Liberia, Rwanda, Tanzania
Cluster 4 : Burundi, Benin, Lesotho, Nigeria Cluster 5 : Democratic Congo, Namibia, Zimbabwe, ZambiaCluster 6 : Egypt, Ghana, Sierra LeoneOverall: Only in Ug: FoA; Proportion of women in ag is decreasing (Y2-Y1) in 3 clusters and increasing in 3 clusters. The proportion of men in ag (Y2-Y1) is increasing in all clusters. First 5 clusters show a move from agriculture to non-agriculture, these are also the countries with economic growth.There is a return to agriculture (mostly by men but also some women) in cluster 6 - countries which show economic recession.
Slide7Questions and concernsOur aim is to scale gender dynamics (not gender-disaggregated data). Are we getting there? Is qual the best approach? Or how can we scale unpredictable, gendered, human behaviour?
Slide8