This study presents the first in-depth analysis of spatial differences and factors influencing wealth distribution among households in India. It uses data from the latest National Family Health Survey, covering 707 districts. Techniques like the Lorenz curve, Gini coefficient, Location Quotient, Morans statistics, and Univariate and Bivariate LISA methods explore inequalities, concentration, and clustering patterns of rich-poor households at the district level. Additionally, spatial regression models such as OLS, GWR, and MGWR help to uncover spatial disparities and variability. Our findings demonstrate significant regional disparities, with the affluent household concentration being notably higher in north-western and southern India, while central, eastern, and northeastern regions exhibit greater inequality. Key factors impacting wealth inequality include rurality, low female literacy rates, educational level of household heads and prevalence of Scheduled Castes/Tribes. This study highlights the spatial dimensions of wealth inequality and provides a nuanced understanding of the factors contributing to these patterns. The GWR and MGWR models prove most effective, explaining more than 90% of the variation in wealth distribution factors. This study sheds light on the spatial dynamics and factors behind wealth disparities in India, offering strategic insights for equitable growth initiatives targeting diverse socio-economic sectors.

中文翻译：

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贫富差距：揭示印度财富不平等的空间复杂性和决定因素

本研究首次对印度家庭财富分配的空间差异和影响因素进行了深入分析。它使用最新的全国家庭健康调查数据，覆盖 707 个地区。洛伦兹曲线、基尼系数、区位商、莫兰斯统计以及单变量和双变量 LISA 方法等技术探讨了地区层面贫富家庭的不平等、集中和聚集模式。此外，OLS、GWR 和 MGWR 等空间回归模型有助于揭示空间差异和变异性。我们的研究结果表明存在显着的地区差异，印度西北部和南部的富裕家庭集中度明显较高，而中部、东部和东北部地区则表现出更大的不平等。影响财富不平等的关键因素包括农村地区、女性识字率低、户主的教育水平以及在册种姓/部落的普遍存在。这项研究强调了财富不平等的空间维度，并对造成这些模式的因素提供了细致入微的理解。 GWR 和 MGWR 模型被证明是最有效的，可以解释 90% 以上的财富分配因素的变化。这项研究揭示了印度财富差距背后的空间动态和因素，为针对不同社会经济部门的公平增长举措提供了战略见解。