UNDP’s 2030 agenda of Sustainable Development Goals (SDGs) emphasized gender equality in augmenting human capital and alleviating poverty. For eradication of extreme poverty and building resilience for persons who are vulnerable to poverty, SDGs calls for a pro-poor and gender-sensitive policy framework. In this context, a gender-based study on multi-dimensional aspects of poverty is highly significant. Extant literature reveals that females are more deprived in different dimensions of poverty such as education, health, living standard, empowerment, environment, autonomy and social relationship. The present study is conducted with the basic objective of examining feminization of poverty in rural areas of Jagatsinghapur district of Odisha. Seven socio-economic dimensions comprising sixteen indicators have been taken into consideration to construct the Multidimensional Poverty Index (MPI) using the Alkire-Foster (AF) Method at the individual level. The novelty of the study lies in analyzing MPI at the individual level for rural Odisha. Higher female deprivation is observed across social groups and all occupation categories except services. Dummy variable regression analysis also supports the major findings of the study. Complementary Cumulative Distribution Function satisfies strict first-order stochastic dominance condition and substantiates the feminisation of poverty at each level of poverty cut-off across all social groups and occupational categories except for services. The findings of the study have significant implications for developing suitable policies for gender equalization and poverty alleviation.

中文翻译：

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奥里萨邦农村多维贫困的女性化

联合国开发计划署的 2030 年可持续发展目标 (SDG) 议程强调在增加人力资本和减轻贫困方面的性别平等。为了消除极端贫困并为易受贫困影响的人建立复原力，可持续发展目标要求建立一个有利于穷人和对性别敏感的政策框架。在此背景下，对贫困的多维方面进行基于性别的研究非常重要。现有文献表明，女性在教育、健康、生活水平、赋权、环境、自主和社会关系等贫困的不同维度上被剥夺更多。本研究的基本目的是检验奥里萨邦 Jagatsinghapur 地区农村地区的贫困女性化。在个人层面使用 Alkire-Foster (AF) 方法构建多维贫困指数 (MPI) 时，考虑了包括 16 个指标的七个社会经济维度。该研究的新颖之处在于在个体层面分析奥里萨邦农村的 MPI。在除服务之外的所有社会群体和所有职业类别中都观察到更高的女性剥夺。虚拟变量回归分析也支持了研究的主要发现。互补累积分布函数满足严格的一阶随机优势条件，并证实了除服务外的所有社会群体和职业类别的每个贫困线水平的贫困女性化。