250 million Indians leave poverty!

  • Niti Aayog’s latest research found a considerable reduction in ‘multidimensional poverty’ among Indians between 2013-14 and 2022-23, which PM Modi welcomed.
  • To fully interpret this data, it is necessary to understand the idea of multidimensional poverty and assess the technique utilised.

Understanding Multidimensional Poverty

  • Traditional Poverty Metrics: Poverty is often assessed monetarily, using income or spending levels.
  • The multidimensional poverty index (MPI): India uses a global MPI to evaluate poverty, which takes into account 12 living characteristics other than income. These characteristics are divided into categories such as education, health, and living standards.
  • Deprivation Assessment: Households are assessed for deprivation using each of the 12 indicators. If they are deficient in many areas, they are classified as’multidimensionally poor’ (MDP).

Data Sources:

  • National Family Health Survey (NFHS): The input material is household-level data from the NFHS. Niti Aayog further analyses this information to create MDP values.
  • NFHS Rounds: Data are provided for three rounds: 2005-06 (NFHS-3), 2015-16 (NFHS-4), and 2019-21 (NFHS-5).
  • Share of MDP Indians: It was 55% in 2005-06, but had dropped to 25% by 2015-16. Assuming a continuous pace, the report says it might have reached 29% in 2013-14. Extrapolation predicts 11% by 2022-23.

Assessing the Assumptions

  • Vague Starting Point: The use of 2013-14 as a starting point is subject to interpretation and acts as a defining aspect in measuring Modi’s nine-year administration.
  • Uniform Pace Assumption: Assuming a consistent pace over such a long period might be difficult since it does not account for differences in progress across years.
  • Neglecting Pandemic Impact: Extrapolating progress without taking into account the pandemic’s effects on data collecting and welfare reversals may result in inaccurate results.

Interpreting the Data

  • When interpreting data, consider the value of indices such as the MPI, which provide a comprehensive perspective of numerous variables. However, it’s important to also include monetary poverty statistics.
  • Multidimensional poverty should not be confused with poverty itself, since they reflect distinct characteristics. It is crucial to distinguish between the two.
  • Selective Maths: The use of interpolation and extrapolation to fit with a government’s term should be approached with caution and awareness of potential constraints.


  • According to NFHS data, India has made significant progress in reducing multidimensional poverty.
  • However, it is critical to examine such data with a sophisticated knowledge of the methodology, assumptions, and outcomes.
  • While multidimensional poverty indices are useful, they should be used in conjunction with comprehensive poverty evaluation approaches rather than as a replacement.
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