Why in the News?

The Indian government recently claimed that India is among the world’s most equal societies, citing a Gini Index of 25.5 from the World Bank’s Poverty and Equity Brief, which would place India as the fourth most equal country globally. However, this claim has sparked debate and criticism from economists and inequality researchers.

What are the flaws in using consumption-based Gini to measure inequality?

• Underestimates Real Inequality: Consumption is usually smoother than income because high earners tend to save more rather than spend proportionately. This leads to an underestimation of inequality. Eg: A billionaire may consume modestly while saving most income, appearing similar to a middle-class consumer in surveys, but with vastly different wealth.
• Poor Cross-Country Comparability: India uses consumption-based data while most other countries use income-based Gini, making international comparisons misleading. Eg: India’s Gini of 25.5 (consumption-based) appears more equal than OECD countries, but income-based Gini (62) shows much higher inequality.
• Low survey participation: Surveys often miss the richest due to non-response or sampling issues, failing to reflect the real inequality they contribute to. Eg: The richest 1% earn disproportionately more, but their low survey participation leads to underreported inequality.

Why is the World Inequality Database seen as more reliable?

• Uses Income and Wealth Tax Data: Unlike consumption surveys, WID incorporates income tax and wealth tax data, which captures the top 1% of earners often missed in surveys. Eg: WID shows India’s income Gini Index rose from 52 in 2004 to 62 in 2023, revealing growing inequality missed by consumption-based metrics.
• Captures Extreme Disparities: WID focuses on distributional national accounts, helping identify disparities between the top 10% and bottom 50%, which Gini often misses. Eg: In 2023-24, the top 10% in India earned 13 times more than the bottom 10%, a gap accurately captured by WID.
• Global Comparability and Peer Review: WID data is transparent, methodologically standardised, and peer-reviewed by global economists, making it a trusted source for cross-country comparison. Eg: Countries like France and the US use WID for policy framing on progressive taxation and redistribution.

What are the alternatives to the Gini Index that better reflect extreme disparities?

• Palma Ratio: The Palma Ratio compares the income share of the top 10% to that of the bottom 40%, focusing directly on income inequality between the rich and poor. Eg: In countries like South Africa, the Palma Ratio highlights stark disparities that are often missed by the Gini Index.
• Theil Index (Generalized Entropy Measures): The Theil Index allows for decomposition of inequality within and between population groups like rural vs urban. Eg: In Brazil, it has been used to analyze racial and regional disparities more precisely than the Gini Index.

What are the policy risks of underestimating inequality?

• Misguided Policy Design: When inequality is underestimated, governments may prioritize growth-focused policies without ensuring inclusive development. This can lead to insufficient investment in social protection, health, and education for marginalized groups.
• Widening Socioeconomic Gaps: Underestimating inequality allows elite capture of resources and opportunities, worsening wealth concentration. This can deepen inter-generational poverty, especially for rural, low-caste, and female-led households.
• Social and Political Instability: Failure to address real inequality can fuel public discontent, protests, and even extremism. It undermines trust in institutions and weakens democratic legitimacy over time.

What are the policy risks of underestimating inequality?

• Misguided Policy Priorities: Underestimating inequality leads to policies focused only on aggregate growth, neglecting equity. Eg: India’s high GDP growth often overshadowed poor social investment in rural health and education, worsening human development gaps.
• Weak Targeting of Welfare Schemes: If inequality is not accurately measured, social protection may miss the truly needy. Eg: Exclusion errors in schemes like PDS or PM-KISAN arise because top income groups are not properly excluded due to lack of granular data.
• Rising Social Unrest and Distrust: Ignoring inequality can result in resentment, protests, and political instability. Eg: Farmer protests in India reflected deeper rural-urban income divides and perceived neglect of smallholder concerns.

Way forward: 

• Improve Data Collection Methods: Strengthen surveys by combining consumption data with income tax records, and ensure better representation of top income groups to capture true inequality.
• Adopt Comprehensive Inequality Metrics: Use alternative indicators like the Palma Ratio or income shares of top 10% vs bottom 50%, alongside the Gini Index, for a more accurate assessment.
• Design Inclusive Policy Frameworks: Align fiscal policies, welfare schemes, and tax reforms with accurate inequality data to target marginalized groups effectively and reduce social and regional disparities.

Mains PYQ:

[UPSC 2024] Despite comprehensive policies for equity and social justice, underprivileged sections are not yet getting the full benefits of affirmative action envisaged by the Constitution. Comment.

Linkage: This question critically examines the effectiveness of current policies intended to reduce inequality and promote social justice. It suggests that, despite official claims or stated objectives, the intended benefits are not effectively reaching the marginalised groups, thereby raising doubts about the actual progress in reducing inequality. It reflects the broader issue of implementation challenges in governance.

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