Urban population in India has been rising rapidly as millions of migrants are moving to urban areas aspiring for higher earning and better living. The number of urban poor is also growing and a significant number of these poor find spaces in slums and continue to struggle for better living standards. Improving their conditions call for significant efforts from the governments for better policy designs. However, better policy design requires understanding the commonalities and differences across slums within and between cities. In this paper, we conduct a comparative study of representative slums across three largest metro cities in India through primary surveys. We find certain characteristics, such as large average household size, poor housing quality, low female labour market participation and high school enrolment rates among children, to be common across and within three cities. Our study however reveals crucial differences between the cities in the demographic pattern of migration and its temporal element. And that in turn brings out considerable heterogeneity among different groups within slums of each cities with respect to living standards, access to civic amenities like sanitation facilities and drinking water. Moreover, there exists major cross-city differences in adult literacy rates across gender, consumption pattern, and subjective wellbeing. Overall, we find that slums in Mumbai on average perform much better in various living condition and social indicators than slums in Delhi and Kolkata.
As part of Sustainable Development Goals, the United Nations have set targets of upgrading slums and reducing poverty in all its dimensions by 2030. Policies towards improving the living conditions of slum-dwellers require proper assessment of their standard of living as well as understanding the associated characteristics. In this paper, using slum-level primary household survey data from three largest Indian cities, we, first, assess the standard of living of slum dwellers using both monetary and non-monetary approaches and then explore how various household and spatial characteristics are consistently or differently associated with both forms of assessments. We use standard monetary indicators, but to assess non-monetary standard of living, use a counting approach framework and justify the selection of specific indicators in the context of slums. Our analysis yields some interesting observations as some characteristics are differently associated with monetary and non-monetary living standards, which should affect policy designs in slums.
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