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Multidimensional poverty in India: patterns of reduction across population subgroups and geographical locations during 2005–06 and 2019–21

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Abstract

To meet the national development goals in India on poverty alleviation and to trail the Sustainable Development Goals (SDG), policy makers need to have adequate information on poverty in the country. To this end the aim of this paper is to analyse the trends in multidimensional poverty at the all-India level, across geographic locations, castes and religious groups. We have used Alkire–Foster methodology to measure multidimensional poverty index (MPI) and utilized National Family Health Survey unit level data of the latest three rounds 2005–06, 2015–16 and 2019–21. To the best of our understanding this is the first paper that utilizes the latest data sets to analyse the trends of MPI in India. We have also made a comparative assessment of trends of poverty in India measured by Global MPI approach and India’s MPI approach. Over the period 2005–2006 to 2019–2021 the absolute decline in MPI was larger in rural areas of India. However, the contribution to MPI in rural areas is relatively high and that increased over time. There was no evidence of reduction of inter-caste and inter-religious based disparities. There are larger doses of decline in MPI in the sub period 2015–2016 to 2019–2021 but the decline across the states of India continues to be non-regressive. The states which were worst performing in 2005–2006 continue to be the same in 2019–2021. The paper concludes that by exploring the MPI over time one can decipher the urgencies in policy development to move towards the SDGs.

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Availability of data and materials

Data used for this study are available at International Institute for Population Sciences (IIPS) and ICF. (2017). National Family Health Survey of India: 2005–06 (NFHS-3), 2015–16 (NFHS-4) and 2019–21 (NFHS-5). URL: https://www.dhsprogram.com/Countries/Country-Main.cfm?ctry_id=57

Notes

  1. The multidimensional poverty measurement is robust also if we alter different weighting schemes with specific cut-off, k = 1/3 (Alkire and Seth 2015; Das et al. 2020).

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Correspondence to Sudeshna Ghosh.

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Appendix

Appendix

See Figs. 4, 5 and Tables

Table 5 Dimensions, indicators and deprivation cut-offs for the measurement multidimensional poverty by global approach

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Table 6 Percentage share of deprived people by the indicators of MPI of India

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Das, P., Ghosh, S. & Paria, B. Multidimensional poverty in India: patterns of reduction across population subgroups and geographical locations during 2005–06 and 2019–21. GeoJournal 88, 3851–3870 (2023). https://doi.org/10.1007/s10708-023-10833-6

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  • DOI: https://doi.org/10.1007/s10708-023-10833-6

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