The rich-poor divide: Unravelling the spatial complexities and determinants of wealth inequality in India.

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. • Widening wealth gap has far-reaching consequences for society. • Sharp Rich-Poor contrast: Northwest & south vs. central, eastern & northeastern India. • Education, rurality, female literacy are key factors influencing rich-poor divide. • Bi-LISA reveals localized clustering pattern for both Rich vs. Poor. • GWR, MGWR models explain over 90% of wealth distribution variation effectively. [ABSTRACT FROM AUTHOR]