Paper short abstract:

Challenges of global poverty measures-data limitations and diverse human experiences. Countries develop their own multidimensional poverty indices to address this. The revised MPI will consider various indicators reflecting India's poverty landscape, aiding policymakers in poverty alleviation.

Paper long abstract:

Introduction: Poverty measures compare people in a society in order to assess the extent of unacceptable disadvantages that exist. Yet any poverty measure is itself imperfect. Imperfections stem primarily from-data limitations and the diversity of human lives. Internationally comparable measures face a greater challenge on both counts: the pool of comparable data is narrower, and the diversity of lives and contexts being compared is greater (Alkire and Jahan, 2018). Many countries national multidimensional poverty index using context specific dimensions and indicators (Mexico, Colombia, Bhutan, Chile, Costa Rica, El Salvador, Pakistan, among others). In the development of National MPIs, countries have revised dimensions and indicators. Following the G-MPI revision in 2018, this study is an exploration to further revise the indicators based on indian national development agenda and data availability.

Methodology: This paper utilized data from the fifth National Family and Health Survey (NFHS) round conducted in 2019-2021. The NFHS is a comprehensive cross-sectional demographic health survey conducted throughout India under the authority of the Ministry of Health and Family Welfare. The survey provides information on demographic and health indicators at the national and subnational levels. The Alkire and Foster (AF) methodology was employed to calculate multidimensional poverty indices. Three estimates of multidimensional poverty—headcount ratio, Intensity of poverty, and Multidimensional Poverty Index (MPI). We have used three dimensions (Health, Education, and Standard of Living) like the Global-MPI specifications. The multidimensional poverty threshold was determined at 33% of the weighted deprivation score. The revised framework incorporated modification in child mortality estimation, increase in education threshold, and composition of asset components.

Results: The OPHI estimates have identified 16.4% multidimensional poverty in 2019-21. While only revising the child mortality indicator, the headcount ratio was increased to 16.5%, with the revision in education and asset indicator the poverty incidence was 18.8% and 18.3%. The multidimensional poverty level increased from 16.4% to 21% with the revision of all the indicators.

Conclusion

This paper is a significant robust estimate of multidimensional poverty. This study is a primary contribution to construct a relevant multidimensional poverty index of India to reflect the multidimensional poverty of the country. Identification of the poverty level is crucial to achieve the SDG 1.2. This exercise adds to the literature of multidimensional poverty to track the progress and monitor policies and programs in reducing deprivation in development indicators.