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Source: The post issues with Multidimensional Poverty Index (MPI) has been created on the article “MP Index reduction under the NDA is flawed” published in “The Hindu” on 7th December 2023.
UPSC Syllabus Topic: GS paper2- Governance-mechanisms, laws, institutions and bodies constituted for the protection and betterment of these vulnerable sections
News: This article discusses the flaws in the Multidimensional Poverty Index (MPI) used by the United Nations and India. It argues that the MPI’s method of measuring poverty is misleading and not detailed enough. The article also highlights the impact of COVID-19 and political factors on poverty levels.
What is Multidimensional Poverty Index (MPI)?
Historically, poverty estimation was done by largely focusing on income as the sole indicator.
However, there was criticism that poverty is multi-dimensional, going beyond monetary poverty. Income- and consumption-based poverty measures fail to capture the impact of deprivations in other non-economic factors on standard of living.
To capture this multidimensional poverty, Niti Aayog, along with UNDP, has come out with the National Multidimensional Poverty Index. Modeled on the Global Multidimensional Poverty Index, the index captures overlapping deprivations in health, education and living standards.
For more information on MPI read here
For information on key findings of the national MPI 2023 read here
What are the issues with MPI?
Simplified Approach to Poverty Measurement: The MPI uses uniform weights for its three components – health, education, and standard of living – which simplifies the complex issue of poverty. For example, Amartya Sen presents a broader perspective on well-being, emphasizing on both capabilities (potential actions in a fair environment) and functioning (actual achievements).
Questionable Data Sources: The MPI’s reliance on National Family Health Survey (NFHS) 4 and 5 is highlighted as inadequate. For instance, NFHS 5’s data on open defecation contradicted official claims of its elimination, leading to its blockage and questioning its reliability. Yet, this same data was used by NITI Aayog and the UNDP for MPI calculations.
State-Level Variations Ignored: The MPI’s general claims of poverty reduction in India are contradicted by specific state-level data. For example, in Uttar Pradesh, poverty actually rose, showcasing a discrepancy in the MPI’s assessment. This rise in poverty at the state level indicates that the MPI’s national figures may be overlooking significant regional variations in poverty.
Discrepancy with Pandemic Impact: Despite the economic turmoil caused by COVID-19, the MPI suggests a reduction in poverty from 24.85% to 14.96% between 2015-16 and 2019-21. However, this seems contradictory, considering the significant job losses and healthcare challenges the pandemic brought.
Inconsistency of MPI against Important Covariates: As per the author, covariates like per capita state income, urban population share, share of criminals among State MPs, and health and education expenditure significantly influence poverty levels. Their analysis of the trends in these covariates and its impact on poverty suggest the findings of MPI on poverty reduction are highly exaggerated.
Way forward
To improve poverty measurement, the MPI should integrate detailed surveys like the 75th Round of the NSS with NFHS data and consider broader economic indicators. It should also account for political influences on resource allocation and adopt a nuanced approach to weighting, reflecting the complex nature of poverty.
Question for practice:
Examine how the Multidimensional Poverty Index (MPI) might be failing to accurately reflect the true extent of poverty in India?
