Contextualizing multidimensional poverty in urban India

Abstract

Urban poverty is complex and conventional money-metric poverty fails to measure the multiple deprivations of the urban population. Though recent estimates of multidimensional poverty do capture multiple deprivations, they do not capture the extent of multidimensional poverty in urban India. Using the urban sample from the National Family Health Survey, 2015-16, this paper estimates and decomposes multidimensional poverty in urban India. Urban poverty is measured in four key domains: Education, health, standard of living, and housing. A multilevel logistic model is used to decompose the variations in multidimensional poverty across geographical regions. Results suggest that about one-third of the urban Indian population is multidimensionally poor and one-sixth is vulnerable to multidimensional poverty. The state patterns of multidimensional poverty were diverse, with more than half of the urban population in Manipur and Bihar being multidimensionally poor, followed by Nagaland and Uttar Pradesh. On controlling for household characteristics, 17.5% of the total variation in multidimensional poverty was attributable to census enumeration blocks, 6.6% to districts, 1.8% to regions, and 9.9% to states. The odds of multidimensional poverty were higher among large households, female-headed households, widowed, and scheduled tribes. Contextualizing multidimensional poverty and prioritizing vulnerable groups and regions are essential for reducing multidimensional poverty in urban India.

Uneven burden of multidimensional poverty in India: A caste based analysis

“…This finding is consistent with the findings of the studies conducted by Bagli & Tewari [44] in Purulia district of West Bengal, Alkire et al [33] in India, and in a recent study in urban India by Kaibarta et al [45]. Our result that SCs and OBCs have nearly equal rates of multidimensional poverty is disproved by findings from another study [46], which show that OBCs have a far lower rate than SCs. When it comes to decomposing MPI across social groups, OBCs contribute the most, which can be linked to their population share being the highest of all social groups.…”

“…Both standard of living and health are key contributors to multidimensional poverty at the national level, with almost equal contributions. In their studies of 82 natural regions in India and urban India, Dehury & Mohanty [31] and Mohanty & Vasishtha [46], respectively, revealed that the health dimension provides the largest share to MPI, which is consistent with our findings. In contrast to our findings, Mohanty and Vasishtha [46] claim that standard of living has the smallest impact on MPI.…”

“…In their studies of 82 natural regions in India and urban India, Dehury & Mohanty [31] and Mohanty & Vasishtha [46], respectively, revealed that the health dimension provides the largest share to MPI, which is consistent with our findings. In contrast to our findings, Mohanty and Vasishtha [46] claim that standard of living has the smallest impact on MPI. The dimensional contribution varied noticeably between social groups.…”

“…Whereas, for STs, standard of living is the greatest contributor, followed by health, which supported by the findings of Kaibarta et al [45]. This finding backs up by Megbowon [46] and Espinoza-Delgado & Klasen [18], who argued that the standard of living dimension contributes the most to multidimensional poverty, particularly in poorer countries and rural areas. Because STs in India are usually located in hard-to-reach places and have historically been economically poorer than others, which restricts them from availing improved facilities in the standard of living dimension.…”

Prevalence and correlates of multidimensional child poverty in India during 2015–2021: A multilevel analysis

“…A study based in Burkina Faso identified seven domains for child poverty for children aged 5-18. Countries analyse multidimensional poverty using various indicators and unit of analysis depending on how poverty is perceived [31][32][33][34][35][36][37].…”