Measuring poverty and inequality in India is a multi-layered exercise, as both are influenced by economic, social, and demographic factors. To address this, various committees and statistical tools have been used to capture their extent and depth.

Poverty Measurement Methods

1. Consumption Expenditure Method
   
   
   • Traditionally, poverty in India was measured by determining a poverty line based on minimum consumption needs.
      
   • Alagh Committee (1979): defined poverty in terms of calorie intake.
      
   • Lakdawala Committee (1993): refined methodology using state-specific price indices.
      
   • Tendulkar Committee (2009): shifted focus from calories to broader needs such as health and education.
      
   • Rangarajan Committee (2014): suggested a higher poverty line with expanded food and non-food items.
      
   • Data source: NSSO Household Consumption Expenditure Survey.
      
   • Limitation: Critics argue it underestimates deprivation, ignores access to services, and sets the poverty line too low.
      
2. Multidimensional Poverty Index (MPI)
   
   
   • Developed by OPHI and UNDP, adapted by NITI Aayog for India.
      
   • Based on the Alkire-Foster method, it measures poverty across health, education, and living standards.
      
   • Indicators include nutrition, mortality, years of schooling, sanitation, drinking water, housing, electricity, and assets.
      
   • It provides a holistic picture, recognizing that income poverty ≠ human deprivation.
      

Inequality Measurement Methods

1. Gini Coefficient (0 = equality, 1 = inequality).
2. Lorenz Curve – graphical representation of distribution.
3. Income/Wealth Shares – analysis of quintiles (e.g., richest 10% vs poorest 20%).
4. Palma Ratio – compares income share of top 10% to bottom 40%.
5. Supplementary data: tax records, wealth surveys, NSSO data.

Analysis

Together, these methods provide a comprehensive understanding of poverty and inequality in India. While expenditure-based measures are useful for tracking trends, the MPI highlights hidden deprivations, and inequality metrics reveal structural imbalances. Hence, using them in combination is essential for framing inclusive growth policies.

Overall, combining monetary and multidimensional poverty measures with inequality indices gives a comprehensive understanding, crucial for inclusive policy-making.