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.

中文翻译：

---

印度城市多维贫困的背景

城市贫困是复杂的，传统的货币贫困无法衡量城市人口的多重剥夺。尽管最近对多维贫困的估计确实反映了多重剥夺，但它们并未反映印度城市多维贫困的程度。本文使用 2015-16 年全国家庭健康调查的城市样本，估计并分解了印度城市的多维贫困。城市贫困在四个关键领域进行衡量：教育、健康、生活水平和住房。多层次逻辑模型用于分解跨地理区域的多维贫困变化。结果表明，大约三分之一的印度城市人口多维贫困，六分之一容易受到多维贫困的影响。多维贫困的状态模式多种多样，曼尼普尔邦和比哈尔邦超过一半的城市人口是多维贫困，其次是那加兰邦和北方邦。在控制住户特征后，多维贫困总变化的 17.5% 归因于人口普查点查区块，6.6% 归因于地区，1.8% 归因于地区，9.9% 归因于州。在大家庭、女户主家庭、寡妇和在册部落中，多维贫困的几率更高。将多维贫困背景化并优先考虑弱势群体和地区对于减少印度城市的多维贫困至关重要。在控制住户特征后，多维贫困总变异的 17.5% 归因于人口普查点查区块，6.6% 归因于地区，1.8% 归因于地区，9.9% 归因于州。在大家庭、女户主家庭、寡妇和在册部落中，多维贫困的几率更高。将多维贫困背景化并优先考虑弱势群体和地区对于减少印度城市的多维贫困至关重要。在控制住户特征后，多维贫困总变异的 17.5% 归因于人口普查点查区块，6.6% 归因于地区，1.8% 归因于地区，9.9% 归因于州。在大家庭、女户主家庭、寡妇和在册部落中，多维贫困的几率更高。将多维贫困背景化并优先考虑弱势群体和地区对于减少印度城市的多维贫困至关重要。