Tracking Trends of Multidimensional Poverty in Different Mainstream Agro-climatic Regions in India: A Geo-spatial Analysis

Abstract

The present study uses secondary data collected from National Family Health Survey (NFHS) rounds 4 and 5 to track the progress in the multidimensional poverty status of the Indian population. We use differential methodology to identify poor individuals in both rural and urban areas. Further, the multidimensional poverty index (MPI) and agriculture are closely associated, and this is also reflected in the study. Among the agro-climatic regions, it was found that MPI is relatively higher in the Middle Gangetic Plains Region, while MPI in the West Coast Plains and Hills Region is relatively lower compared with other agro-climatic regions. As we develop a new MPI for different agro-climatic zones (ACZ), it has significant policy relevance. We believe that this new index provides a substantial contribution to the literature on poverty measurement and could be an important tool to track progress towards the achievement of SDG 1.

Keywords

Multidimensional poverty index agro-climatic regions rural poverty urban poverty developing countries no poverty SDGs

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References

Alkire

, & Foster

(2011). Understandings and misunderstandings of multidimensional poverty measurement. The Journal of Economic Inequality, 9(2), 289–314. https://doi.org/10.1007/s10888-011-9181-4

Alkire

, Foster

J. E.

, Seth

, Santos

M. E.

, Roche

J. M.

, & Ballon

(2015). Multidimensional poverty measurement and analysis (Leeds University Business School Working Paper No. 15-01). Oxford University Press.

Alkire

, & Jahan

(2018). The new global MPI 2018: Aligning with the Sustainable Development Goals (HDRO Occasional Paper). UNDP.

Bao

, & Tingxuan

(2024). Multidimensional poverty and growth: Evidence from India 1998–2021. Economics Modelling, 130, Article 106586.

Datt

(2019). Distribution-sensitive multidimensional poverty measures. The World Bank Economic Review, 33(3), 551–572. https://doi.org/10.1093/wber/lhx017

Laderchi

C. R.

(1997). Poverty and its many dimensions: The role of income as an indicator. Oxford Development Studies, 25(3), 345–360.

Pogge

, & Wisor

(2016). Measuring poverty: A proposal. In Adler

& Fleurbaey

(Eds), The Oxford handbook of well-being and public policy (pp. 645–676). Oxford University Press.

Pradhan

, Jalandhar

, & Binayak

(2023). Caste-based analysis of multidimensional early childhood poverty in India: Patterns and determinants. Children and Youth Services Review, 155, Article 107220.

Sen

A. K.

(1992). Inequality re-examined. Harvard University Press.

10.

Shukla

(2023). Multidimensional poverty and per capita income in Uttar Pradesh: A district-level analysis. Indian Journal of Human Development, 17(2), 210–219.

11.

Singh

N. P.

, Bhawna

, & Surendra

(2020). Impact of climate change on agriculture in India: Assessment for Agro-Climatic zones [Policy Paper 35]. ICAR-National Institute of Agricultural Economics and Policy, Research (NIAP).

12.

Stewart

, Saith

, & Harriss-White

(2007). Defining poverty in developing countries. Palgrave Macmillan.

13.

UNDP. (2010). Human development report 2010: The real wealth of nations: Pathways to human development.

14.

UNICEF & End Child Poverty Global Coalition. (2017). A world free from child poverty: A guide to the tasks to achieve the vision. UNICEF.

15.

Varghese

, & Roy

(2023). Empirical analysis of poverty in India using multidimensional poverty index. Journal of Public Health and Primary Care, 4, 63–68.

16.

World Bank. (2018). Poverty and shared prosperity 2018: Piecing together the poverty puzzle.