Introduction

The recent paper by Niti Aayog has highlighted a significant reduction in ‘multidimensional poverty’ among Indians between 2013-14 and 2022-23, an achievement acknowledged by PM Modi.
To comprehend this data accurately, it is essential to grasp the concept of multidimensional poverty and evaluate the methodology used.

Understanding Multidimensional Poverty

Traditional Poverty Metrics: Poverty is commonly measured monetarily, based on income or expenditure thresholds.
Multidimensional Poverty Index (MPI): India employs a global MPI that assesses poverty by considering 12 life aspects beyond income. These aspects fall under categories like education, health, and living standards.
Deprivation Assessment: Households are evaluated for deprivation across each of the 12 indicators. If they are deprived in several areas, they are labelled ‘multidimensionally poor’ (MDP).

National Family Health Surveys (NFHS): Household-level data from NFHS serves as the raw material. Niti Aayog further processes this data to calculate MDP figures.
NFHS Rounds: NFHS data is available for three rounds: 2005-06 (NFHS-3), 2015-16 (NFHS-4), and 2019-21 (NFHS-5).
Share of MDP Indians: In 2005-06, it was 55%, which decreased to 25% in 2015-16. Assuming a consistent pace, the paper suggests it may have been 29% in 2013-14. Further extrapolation estimates it to be 11% by 2022-23.

Vague Starting Point: The choice of 2013-14 as a starting point may be open to interpretation and serves as a defining factor for evaluating nine years of Modi’s leadership.
Uniform Pace Assumption: Assuming a uniform pace over such a long period can be challenging, as it may not account for variations in progress over different years.
Neglecting Pandemic Impact: Extrapolating progress without considering the pandemic’s effects on data collection and welfare reversals may lead to inaccuracies.

Value of Indices: While indices like MPI offer a combined view of multiple indicators, they should not overshadow the importance of monetary poverty data.
Not Equivalent to Poverty: Multidimensional poverty should not be equated with poverty itself, as they represent different aspects. It is essential to differentiate between the two.
Selective Maths: The exercise of interpolation and extrapolation to align with a government’s tenure should be viewed critically and with consideration of potential limitations.

The reduction in multidimensional poverty in India is a noteworthy achievement, as evidenced by NFHS data.
However, it is crucial to approach such data with a nuanced understanding of the methodology, assumptions, and its implications.
While multidimensional poverty indices provide valuable insights, they should complement, not replace, comprehensive poverty assessment methods.