Collective Agency
cg.authorship.types | CGIAR and advanced research institute | en |
cg.contributor.affiliation | International Food Policy Research Institute | en |
cg.contributor.donor | Umbrella Facility for Gender Equality | en |
cg.contributor.donor | Bill & Melinda Gates Foundation | en |
cg.coverage.region | Africa | |
cg.coverage.region | Sub-Saharan Africa | |
cg.howPublished | Grey Literature | en |
cg.identifier.dataurl | https://doi.org/10.7910/DVN/FSKWIY | en |
cg.identifier.dataurl | https://doi.org/10.7910/DVN/IXQ7KW | en |
cg.identifier.dataurl | https://doi.org/10.7910/DVN/MFE7GR | en |
cg.identifier.dataurl | https://doi.org/10.7910/DVN/HJFRRC | en |
cg.identifier.dataurl | https://doi.org/10.7910/DVN/XYEYKK | en |
cg.identifier.doi | https://doi.org/10.7910/dvn/nhwjsj | en |
cg.identifier.project | IFPRI - Natural Resources and Resilience Unit | |
cg.identifier.project | IFPRI - Poverty, Gender, and Inclusion Unit | |
cg.identifier.publicationRank | Not ranked | |
cg.place | Washington, DC | en |
cg.reviewStatus | Internal Review | en |
cg.subject.actionArea | Systems Transformation | |
cg.subject.impactArea | Gender equality, youth and social inclusion | |
cg.subject.sdg | SDG 5 - Gender equality | en |
dc.contributor.author | Measures for Advancing Gender Equality (MAGNET) Initiative | en |
dc.date.accessioned | 2024-12-19T18:02:28Z | en |
dc.date.available | 2024-12-19T18:02:28Z | en |
dc.identifier.uri | https://hdl.handle.net/10568/168083 | |
dc.title | Collective Agency | en |
dcterms.abstract | The Measures for Advancing Gender Equality (MAGNET) initiative aims to broaden and deepen the measurement of women’s agency, based on the development of new tools and rigorous testing and comparison of both new and existing methods for measuring agency, and promoting the adoption of these measures at scale. By increasing the availability of innovative meaningful measures of agency for a broad range of contexts, we hope our work will lead to an improved understanding of what women’s agency is, how it manifests and how it can best be measured across contexts given the research question at hand. Collective agency, or “power with”, is a critical component of women’s empowerment beyond the household. At the individual level, women often rely on groups to identify and achieve goals that advance them professionally, socially, economically, and politically. Within their homes, participation in groups can empower women by improving self-efficacy and self-esteem. Groups—whether entirely comprised of women, or mixed gender—can provide the skills and experience necessary for women, individually and collectively, to build agency, increase productivity, incomes, reduce workloads, and improve resilience. We recommend the use of this tool, Collective Agency, in the design, monitoring, and evaluation of group-based development programs aimed at improving women’s empowerment. This data study includes following files. 1. A survey document (including implementation guidelines) | en |
dcterms.accessRights | Open Access | |
dcterms.audience | Academics | en |
dcterms.audience | General Public | en |
dcterms.audience | Policy Makers | en |
dcterms.bibliographicCitation | Measures for Advancing Gender Equality (MAGNET) Initiative. 2024. Collective Agency. Washington, DC: International Food Policy Research Institute (IFPRI) [dataset]. https://doi.org/10.7910/DVN/NHWJSJ. Harvard Dataverse. Version 2. | en |
dcterms.isPartOf | Household- and Community-level Surveys | en |
dcterms.issued | 2024-04-18 | |
dcterms.language | en | |
dcterms.license | CC-BY-4.0 | |
dcterms.publisher | International Food Policy Research Institute | en |
dcterms.relation | https://doi.org/10.7910/DVN/NHWJSJ | en |
dcterms.subject | gender equality | en |
dcterms.subject | women | en |
dcterms.subject | women's empowerment | en |
dcterms.subject | collective action | en |
dcterms.subject | efficiency | en |
dcterms.subject | data collection | en |
dcterms.subject | research | en |
dcterms.subject | survey methods | en |
dcterms.subject | households | en |
dcterms.type | Dataset |
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