Lentz Erin, Jensen Nathan, Lepariyo Watson, Narayanan Sudha, Bageant Elizabeth
Lyndon B Johnson School of Public Affairs, University of Texas at Austin, United States.
Global Academcy of Agriculture and Food Systems, University of Edinburgh, United Kingdom.
World Dev. 2025 Apr;188:106887. doi: 10.1016/j.worlddev.2024.106887.
Women face a disproportionate burden of malnutrition and food insecurity. Research has shown that women's empowerment can buffer women against nutritional problems. This paper contributes to ongoing efforts to measure women's empowerment that are both context-sensitive and universal, focusing on the recently developed Women's Empowerment in Nutrition Index (WENI). Earlier research has shown it is both a valid construct and positively related to dietary and nutritional outcomes of women in South Asia. We establish that WENI is generalizable to agropastoral and pastoral Kenya, an area with substantially different livelihoods, food system, norms, and institutions than South Asia. We find that a locally contextualized WENI is strongly associated with women's body mass index and dietary diversity as well as household level food insecurity. We also present findings for two shorter variations of WENI: an abbreviated WENI (A-WENI) and a cross context WENI (CC-WENI). A-WENI contains a small subset of WENI indicators identified using machine learning with South Asian data and therefore is context-specific. CC-WENI does not contain indicators specific to the validation context. We find that they perform comparably well with caveats. Thus, as use of WENI expands we recommend adapting WENI for in-depth analyses of women's nutritional empowerment; using CC-WENI for cross-context comparisons; and using A-WENI for rapid appraisals of community level progress in a given context.
女性面临着不成比例的营养不良和粮食不安全负担。研究表明，增强妇女权能可以使妇女免受营养问题的影响。本文有助于持续开展对妇女权能的衡量工作，这些衡量工作既考虑具体情况又具有普遍性，重点关注最近制定的营养领域妇女赋权指数（WENI）。早期研究表明，该指数不仅是一个有效的指标，而且与南亚妇女的饮食和营养状况呈正相关。我们证实，WENI可推广至肯尼亚农牧区和牧区，该地区的生计、粮食系统、规范和制度与南亚有很大不同。我们发现，根据当地情况调整的WENI与女性的体重指数、饮食多样性以及家庭层面的粮食不安全状况密切相关。我们还展示了WENI的两个较短版本的研究结果：一个简化版WENI（A-WENI）和一个跨背景WENI（CC-WENI）。A-WENI包含使用南亚数据通过机器学习确定的一小部分WENI指标，因此是特定于具体情况的。CC-WENI不包含特定于验证背景的指标。我们发现它们在有一些注意事项的情况下表现相当不错。因此，随着WENI的应用范围扩大，我们建议对WENI进行调整，以深入分析妇女的营养赋权情况；使用CC-WENI进行跨背景比较；使用A-WENI对特定背景下社区层面的进展进行快速评估。
