Income and Multidimensional Poverty across Indian States: A Comparative Cluster Analysis

Income-based poverty measures remain widely used in India but often overlook deprivation in health, education, and living standards. This paper compares income poverty with multidimensional poverty (NITI Aayog MPI) across 33 Indian states and union territories, and traces how structural variables—per capita SDP, urbanisation, social sector expenditure, non-agricultural GVA, population density, and governance (Public Affairs Index)—are systematically related to both measures. Monthly Per Capita Consumption Expenditure (MPCE) data from 2022–23 and the NITI Aayog Multidimensional Poverty Index (MPI) were used in this cross-sectional study. Using Pearson correlation and cluster analyses, the study finds: (1) a moderate correlation between income and multidimensional poverty; (2) strong, statistically significant negative correlations between multidimensional poverty and per capita SDP, urbanisation, and social expenditure, and non-agricultural GVA; (3) income poverty was significantly associated with per capita income, per capita social expenditure and urbanisation, but not significantly associated with non-agricultural GVA; (4) population density was not significantly associated with either form of poverty; (5) eight observed poverty-profile groups showing geographic differences in poverty profiles; and (6) Bihar and Jharkhand had particularly high levels of multidimensional and dual poverty, respectively. Structural indicators, particularly per capita income, urbanisation, social expenditure and economic composition, were more strongly associated with multidimensional poverty than with income poverty. The findings suggest that region-specific poverty-reduction strategies should consider structural conditions and access to public services alongside monetary measures.