1. Evolution of Poverty Measurement in India

• 1970s to 2005: Poverty was defined based on the expenditure required to sustain a minimum calorie intake, with updates every five years using National Sample Survey Office (NSSO) data.
• Post-2005: Divergence between NSSO’s private expenditure estimates and National Accounts led to questions about data accuracy. The government appointed the Tendulkar Committee to address these concerns.

2. Methodological Shifts in Data Collection

• Recall Periods: NSSO experimented with varying recall periods to improve consumption data accuracy:
  • Uniform Reference Period (URP): 30-day recall for all items.
  • Mixed Reference Period (MRP): 30-day recall for food and 365-day recall for other goods.
  • Modified Mixed Reference Period (MMRP): 7-day recall for food items, with 30-day and 365-day periods for other items.
• Recent Changes: The latest methodology involves visiting households in three sittings instead of one, allowing for better data reporting through improved recall.

3. Recent Findings from the Household Consumption Expenditure Survey (HCES) 2023-24

• Average Monthly Per Capita Expenditure (MPCE):
  • Rural Areas: ₹4,122
  • Urban Areas: ₹6,996
• Consumption Patterns:
  • Non-Food Expenditure: Accounts for 53% in rural areas and 60% in urban areas, indicating a shift towards non-food items.
  • Food Expenditure: Significant spending on beverages, refreshments, and processed foods.
• Urban-Rural Gap: The consumption gap has narrowed to 70% in 2023-24 from 84% in 2011-12, reflecting improved rural consumption.
• Consumption Inequality: The Gini coefficient declined from 0.266 in 2022-23 to 0.237 in 2023-24 for rural areas, and from 0.314 to 0.284 for urban areas, indicating reduced inequality.

4. Debates on Poverty Estimation

• Divergent Estimates: Some studies claim a drastic reduction in poverty, while others, using different methodologies, estimate higher poverty rates. For instance, a study by the Foundation for Agrarian Studies estimated around 25% poverty using the Rangarajan methodology on 2022-23 data.
• Need for Consensus: There’s a call for an agreed-upon methodology for poverty estimation, with government endorsement to ensure consistency and reliability.

5. Multidimensional Poverty Index (MPI)

• Broader Definition: The MPI includes indicators beyond income, such as education, health, and living standards.
• India’s Customization: India’s MPI includes additional indicators like bank accounts and maternal health, expanding upon the United Nations Development Programme’s framework.
• Criticisms: Some argue that certain indicators may not be applicable to all households, and once a household attains certain assets or services, they are no longer considered deprived in those areas, potentially leading to perpetually low poverty estimates.

6. Policy Implications

• Data-Driven Policies: Accurate poverty measurement is crucial for effective policy formulation and resource allocation.

Targeted Interventions: Understanding the nuances of poverty through comprehensive data allows for more targeted and effective poverty alleviation programs.