Multidimensional poverty in selected Indian cities

Devarupa Gupta, National Institute Of Urban Affairs (NIUA)

Intra-urban variations across housing, health, and infrastructure services are evident in many cities. This study used data on eight selected Indian cities from the NFHS 4 survey (2015-16) and the MPI methodology developed by OPHI and Niti Aayog. India’s MPI has three equally weighted dimensions: health, education, and standard of living, represented by twelve indicators. Binary logistic regression analysis was applied to examine the association of factors affecting multidimensional poverty. Meerut had the highest poor population (19.7%; MPI: 0.086), followed by Indore, Kolkata, Hyderabad, Mumbai, Delhi, Nagpur, and Chennai. Poor education contributed more than 40% to the MPI followed by poor health in Indian cities and of the 11 indicators, less than 6 years of schooling and undernutrition contributed the maximum. In Kolkata, poor standard of living subsidised to one-third of the index. Unimproved sanitation was a major concern (13.8%) except in Meerut where solid cooking-fuel usage was maximum. Regression results showed slum households, bigger household sizes, Muslim population, and SC/ST population were more likely to be poor. Variability implemented that poverty is inevitably different across Indian cities and emphasis on city-specific situations is required. Targeted policy interventions should be formulated according to the pattern of deprivation in these cities.

Keywords: Internal Migration and Urbanization, Human Capital, Education, and Work, Inequality, Disadvantage and Discrimination, Economic Demography

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