4. (a) What are the methods used in measuring poverty and inequality in India? Analyse.

Methods Used in Measuring Poverty in India

Poverty estimation in India has historically been a contentious issue, evolving through various expert committees appointed by the Planning Commission (now NITI Aayog). The methodologies have shifted from purely calorie-based norms to a more comprehensive consideration of consumption expenditure and, more recently, to a multidimensional approach.

1. Income/Consumption-Based Poverty Line (Absolute Poverty)

This is the most common method, where a "poverty line" is defined as the minimum monetary expenditure required per capita to meet basic needs, including food and non-food items. Those whose income or consumption falls below this line are considered poor. India primarily uses consumption expenditure surveys conducted by the National Sample Survey Office (NSSO) due to income fluctuations and challenges in tracing income patterns for many informal sector workers.

• Pre-1970s: Early estimations, like the 1962 Planning Commission Working Group, set separate rural and urban poverty lines based on minimum expenditure without regional variations, excluding health and education.
• Alagh Committee (1979): This task force established poverty lines based on calorie requirements: 2400 kcal/person/day for rural areas and 2100 kcal/person/day for urban areas, converted into monetary terms.
• Lakdawala Committee (1993): It recommended continuing the calorie-based approach but suggested state-specific poverty lines updated using the Consumer Price Index for Industrial Workers (CPI-IW) in urban areas and Consumer Price Index for Agricultural Labourers (CPI-AL) in rural areas. It emphasized reliance on NSS data over National Accounts Statistics.
• Tendulkar Committee (2009): A significant shift occurred with this committee, moving away from purely calorie-based estimation. Its recommendations included:
  • Incorporation of private expenditure on health and education in the poverty line basket.
  • Uniform Poverty Line Basket (PLB) across rural and urban India (though with different monetary values).
  • Use of Mixed Reference Period (MRP) instead of Uniform Reference Period (URP) for consumption data collection.
  • For 2011-12, the poverty line was set at ₹816 per capita per month for rural areas and ₹1,000 per capita per month for urban areas. It estimated India's poverty rate at 21.9% in 2011-12.
• Rangarajan Committee (2014): This committee was constituted to review the Tendulkar methodology. Its key recommendations included:
  • Higher poverty lines based on a broader consumption basket including food, health, education, clothing, and housing.
  • Monthly Per Capita Expenditure (MPCE) of ₹972 for rural areas (~₹32/day) and ₹1,407 for urban areas (~₹47/day) for 2011-12.
  • Use of Modified Mixed Recall Period (MMRP) for consumption expenditure data.
  • It estimated the poverty ratio at 29.5% for 2011-12, significantly higher than the Tendulkar Committee.

Despite these committees, the Tendulkar Committee's estimates for 2011-12 remained the official poverty figures for a long time, as the Rangarajan Committee's recommendations were not officially adopted.

2. Multidimensional Poverty Index (MPI)

Recognizing the limitations of income/consumption-based measures, India, through NITI Aayog, has adopted the Multidimensional Poverty Index (MPI) in consonance with the global MPI developed by UNDP and OPHI. This approach captures poverty across multiple deprivations a person faces.

• Dimensions and Indicators: India's National MPI uses three equally weighted dimensions:
  • Health: Nutrition, Child-Adolescent Mortality, Maternal Health
  • Education: Years of Schooling, School Attendance
  • Standard of Living: Cooking Fuel, Sanitation, Drinking Water, Electricity, Housing, Assets, Bank Accounts
• Data Source: The MPI estimates for India are based on data from the National Family Health Surveys (NFHS). The latest National Multidimensional Poverty Index: A Progress Review 2023, published by NITI Aayog, uses NFHS-5 (2019-21) data.
• Threshold: A person is identified as multidimensionally poor if they are deprived in at least one-third of the weighted indicators.

3. World Bank's International Poverty Lines

The World Bank provides global benchmarks for poverty measurement, which are often used for international comparisons and to understand extreme poverty. These lines are based on Purchasing Power Parity (PPP) to account for differences in cost of living.

• Extreme Poverty Line: Historically $2.15 per day (2017 PPP). Recently revised to $3.00 per day (2021 PPP).
• Lower-Middle-Income Poverty Line: Revised to $4.20 per day (2021 PPP) for countries like India.

Methods Used in Measuring Inequality in India

Measuring inequality goes beyond identifying the poor and focuses on the distribution of income, wealth, and opportunities among the entire population.

1. Gini Coefficient (or Gini Index)

The Gini coefficient is the most widely used measure of income or wealth inequality. It is a statistical measure of distribution intended to represent the income or wealth distribution of a nation's residents.

• Calculation: It ranges from 0 to 1 (or 0% to 100%). A Gini coefficient of 0 represents perfect equality (everyone has the same income), while a value of 1 (or 100%) implies perfect inequality (one person has all the income, and everyone else has none).
• Interpretation: A lower Gini coefficient indicates a more equal distribution, while a higher value indicates greater inequality.
• Data for India: According to recent World Bank data, India's Gini Index stands at 25.5 in 2022 (based on consumption expenditure), an improvement from 28.8 in 2011. This places India among the more equal countries globally in relative terms when using consumption-based data. However, other reports based on income and wealth suggest higher inequality.

2. Palma Ratio

The Palma ratio is another measure of inequality that focuses on the extremes of the income distribution. It is often considered a better measure than the Gini coefficient for capturing the importance of top incomes.

• Calculation: It is the ratio of the income share of the richest 10% of the population to the income share of the poorest 40% of the population.
• Interpretation: A higher Palma ratio indicates greater inequality. For instance, a ratio of 1.5 means the richest 10% earn 1.5 times more than the poorest 40%.
• Data for India: For India, this ratio is approximately 1.5 (Source: Civilspedia). However, some reports like the World Inequality Report indicate a Palma Ratio of 4.75-5.7 in 2021, suggesting a significant increase in income concentration at the top. This discrepancy highlights the impact of data sources (consumption vs. income) on inequality estimates.

3. Quintile Ratio/Decile Distribution

These methods involve dividing the population into equal groups (e.g., quintiles of 20% each or deciles of 10% each) based on their income or consumption and then comparing the share of income/consumption held by different groups.

• Quintile Ratio: Compares the income/consumption of the richest 20% to the poorest 20%. For India, the income of the richest 20% is approximately 45% of total income, while the poorest 20% is 8%, giving a Quintile Ratio of 5.6.
• Decile Distribution: Often used to highlight the share of the top 1% or 10% versus the bottom 50%. For example, the Oxfam International report "Survival of the Richest: The India Story" (2023) highlighted that the richest 1% own more than 40% of India's total wealth, while the bottom 50% hold just 3%.

Analysis of Methodologies

The evolution of poverty and inequality measurement in India reflects a continuous effort to capture these complex socio-economic realities more accurately. Each method has its strengths and limitations:

Methodology Aspect	Strengths	Limitations
Consumption Expenditure based Poverty Line	Easier to collect data in informal economies. Reflects actual living standards more stably than fluctuating incomes. Provides a clear threshold for identifying the poor.	Fails to capture non-income deprivations (health, education). Does not account for qualitative aspects of poverty. Debates over the appropriate poverty line (e.g., Tendulkar vs. Rangarajan). Data collection issues (e.g., 2017-18 HCES data withheld).
Multidimensional Poverty Index (MPI)	Holistic view, capturing deprivations beyond income. Better for policy targeting as it identifies specific deprivations. Reflects the interconnectedness of various aspects of poverty.	Requires robust data across multiple indicators. Weighting of indicators can be subjective and debated. Does not directly capture income/consumption shortfalls.
Gini Coefficient	Widely recognized and comparable internationally. Provides a single, aggregated measure of inequality.	Less sensitive to changes at the extreme ends of income distribution. Different Gini values can represent different distribution curves. Often relies on consumption expenditure data in India, which tends to show lower inequality than income data due to smoothing of consumption.
Palma Ratio/Decile Distribution	Highlights disparities between the rich and the poor more directly. More sensitive to changes in income shares of the top and bottom groups.	Can be less intuitive for general understanding compared to Gini. May still struggle with accurate income data for the very wealthy.

The choice between consumption-based and income-based data significantly impacts inequality measures. Consumption-based Gini coefficients often indicate lower inequality than income-based measures because consumption tends to be more stable than income, particularly for the poor. The World Bank's recent reports, using consumption expenditure, indicate India's Gini Index at 25.5 in 2022, placing it relatively high in global equality rankings. However, reports using income and wealth data, like those from Oxfam and Piketty, suggest much higher levels of inequality, with the top 1% holding substantial shares of national income and wealth. This "equality paradox" underscores the need for careful interpretation of these metrics.

Recent developments, such as the NITI Aayog's National Multidimensional Poverty Index: A Progress Review 2023 (based on NFHS-5, 2019-21), highlight a significant reduction in multidimensional poverty in India, with 135 million people escaping poverty between 2015-16 and 2019-21. This demonstrates the utility of MPI in tracking progress beyond monetary measures.