MULTIDIMENSIONAL POVERTY 

2. Basis for the Assessment 

• The basis for this assessment is outlined in a discussion paper titled "Multidimensional Poverty in India Since 2005-06," published by NITI Aayog on January 15.
• The paper incorporates technical inputs from the United Nations Development Programme (UNDP) and the Oxford Policy and Human Development Initiative (OPHI). 
• The study reveals that multidimensional poverty in India witnessed a decline from 29.17% in 2013-14 to 11.28% in 2022-23, resulting in approximately 24.82 crore people escaping poverty during this period.
• At the state level, Uttar Pradesh led the way with 5.94 crore people emerging from poverty, followed by Bihar with 3.77 crore and Madhya Pradesh with 2.30 crore.

While traditional poverty measures rely solely on income or expenditure levels, the Multidimensional Poverty Index (MPI) offers a more nuanced picture. Developed by the Oxford Poverty and Human Development Initiative (OPHI) and the United Nations Development Programme (UNDP), the MPI takes into account not just income, but also various deprivations across three key dimensions:

• Health: This dimension incorporates indicators like nutrition and child mortality, reflecting access to basic healthcare and well-being.
• Education: Years of schooling and school attendance are used to gauge educational attainment and opportunities for future development.
• Standard of living: A set of six indicators, including housing, household assets, cooking fuel types, sanitation access, water availability, and electricity, capture essential living conditions and resource ownership.

In the Indian context, the MPI goes a step further by including two additional indicators:

• Maternal health: Recognizing the importance of mothers' well-being for family health, this indicator reflects access to proper care during pregnancy and childbirth.
• Bank accounts: This indicator signifies financial inclusion and the potential for accessing financial services, which can be crucial for escaping poverty.

4. Calculation of the Multidimensional Poverty Index (MPI)

To determine the Multidimensional Poverty Index (MPI), the process involves three distinct calculations according to the MPI methodology:

1. Incidence of Multidimensional Poverty (H): This calculation determines the proportion of multidimensionally poor individuals in the population. It is achieved by dividing the number of multi-dimensionally poor individuals by the total population. In simpler terms, it answers the question: How many people are considered poor?
2. Intensity of Poverty (A): The intensity of poverty assesses the average proportion of deprivation experienced by multidimensionally poor individuals. To compute intensity, the weighted deprivation scores of all poor individuals are summed and then divided by the total number of poor people. More technically, it answers the question: How poor are the individuals who are considered multidimensionally poor?
3. Multidimensional Poverty Index (MPI): The MPI is derived by multiplying the incidence of multidimensional poverty (H) and the intensity of poverty (A). The MPI value for a given population is, therefore, the share of weighted deprivations faced by multidimensionally poor individuals divided by the total population.

6. Calculation Methodology for MPI in 2012-13 and 2022-23

The determination of the Multidimensional Poverty Index (MPI) for the years 2012-13 and 2022-23 involved a process of interpolation for the former and extrapolation for the latter, as outlined in the paper.

• Interpolation for 2012-13: The estimation for the year 2013-14 served as a reference point. To obtain MPI values for the preceding year, interpolation techniques were applied, allowing for a comprehensive understanding of poverty and deprivation in 2012-13.
• Extrapolation for 2022-23: For the year 2022-23, extrapolation methods were employed to project MPI values based on the available data points. This forward projection allowed for an assessment of poverty and deprivation in the specified year.

7. The Way Forward

The NITI Aayog paper provides valuable insights, understanding the basis for the assessment requires considering the limitations of interpolation and extrapolation used for crucial years and the lack of detailed information about the methods employed. Transparency in data sources and methodologies is crucial for a more comprehensive evaluation of the claim.