Inequality in India: Contrasting Narratives from Income vs Consumption-Based Measurements

Inequality in India—the unequal distribution of income, wealth, and consumption—is a deeply entrenched structural challenge in democracies. In India, where socio-economic stratification intersects with caste, region, and gender, measuring inequality becomes a contentious exercise.

The World Bank's April 2025 “India Poverty and Equity Brief”, which reported a decline in consumption inequality and near-eradication of extreme poverty, sparked sharp debates due to its perceived dissonance from widely held beliefs regarding India’s growing income inequality.

These different lenses produce contrasting narratives—one of progress and one of deepening inequality—raising critical questions about data credibility, inclusivity, and policy relevance.

Contrasting Nature of Consumption and Income-Based Measures

Methodological Differences in Data Collection

• HCES 2022–23 and MMRP: The latest consumption data is collected using the Modified Mixed Reference Period (MMRP), aligned with international best practices.
• WIL’s Income Estimates: Based on Income Tax returns and national accounts data, it uses pre-tax income and ignores government welfare and taxes.
• Survey Coverage Bias: HCES misses elite households, while tax data underrepresents informal earners—creating skewed distribution images.

Variations in Reported Inequality Trends

• Consumption Gini Decline: Fell from 28.8 (2011–12) to 25.5 (2022–23), making India one of the least unequal countries in terms of consumption.
• Income Inequality Claims: WIL reports India’s top 1% income share rose from 21.7% (2017) to 22% (2022), but critics suggest this is due to better disclosures.
• Contrasting Public Perception: Conflicting data leads to polarisation in public discourse, with consumption growth masking income inequality.

Impact on Perception of Poverty and Inequality

• Poverty Reduction Success: 27 crore Indians exited extreme poverty (2011–2023); 19.3 crore out of multidimensional poverty (2015–21).
• Income Disparities Persist: The top 1% pays over 72% of total tax, indicating persistent wealth dominance.
• Mismatch of Lived Realities: Despite increased consumption, inequality in access to health, education, and mobility remains high.

Role of Welfare and Redistributive Policies

• Welfare Schemes: Ayushman Bharat, PM Awas Yojana, Ujjwala Yojana improved living conditions for the bottom 40%.
• DBTs: ₹3 lakh crore in FY2023–24 reached over 50 crore people, cushioning income shocks.
• Redistribution Impact: NIPFP (2024) showed India’s post-transfer Gini drops by 3–4 points due to effective redistribution.

Structural and Systemic Limitations in Current Data Frameworks

Underreporting and Informality

• Income Data Gaps: India’s informal sector (~50% GDP) leads to underreporting among rural and self-employed groups.
• Consumption vs Income: Consumption is observable; income is hard to measure in cash-based economies.
• Elite Non-Participation: Rich households often avoid surveys, skewing results in both data sets.

Challenges in Capturing Capital and Wealth Inequality

• Wealth Ownership: Data excludes land, stocks, gold, and other capital assets, misrepresenting true inequality.
• Capital Gains Ignored: Most indices miss capital and passive income, hiding actual disparities.
• Inheritance & Caste: Surveys fail to reflect long-term mobility barriers due to inherited wealth and caste structures.

Urban-Rural and Regional Disparities

• Regional Divide: Southern states perform better socially than BIMARU states, skewing national data.
• Urban-Rural Inequality: Urban areas have diverse consumption but unstable incomes due to informal work.
• Access Gap: Public services help, but private alternatives remain costly and unequal.

Misinterpretation and Media Narratives

• Headline Bias: Media often focuses on pre-tax income inequality while ignoring redistributive impacts.
• Conceptual Confusion: Inability to differentiate income vs consumption leads to flawed conclusions.
• Lack of Transparency: No regular income surveys or public microdata limits scholarly analysis.

Policy Implications and the Road Ahead

Better and Regular Data Collection

• National Income Survey: India needs periodic income surveys following international standards.
• Capture Elite Data: Use GST, PAN, and property databases to assess top-tier inequality.
• Tech Integration: Nightlight and satellite data can estimate rural or hidden inequality patterns.

Focus on Growth Quality and Mobility

• Welfare-Based Indicators: Prioritise HDI, MPI, and mobility metrics over GDP alone.
• Equity in Services: Expand affordable, quality education and healthcare access.
• Rural-Urban Linkages: Enhance programs like PMGSY and skill missions to boost income.

Strengthening Taxation and Redistribution

• Progressive Taxation: Tax capital, inheritance, and property to reduce wealth gaps.
• Institutionalise DBTs: Universalise transfers for the poor, linked to inflation.
• Transparency: Publish annual tax incidence reports like OECD nations.

Bridging Measurement with Perception

• Public Literacy: Educate on consumption vs income inequality.
• Institutional Oversight: Establish an independent commission on inequality data.
• Media Ethics: Promote evidence-based reporting and data literacy.

Conclusion:

India stands at a crucial juncture where inequality is evolving, not vanishing. Consumption-based data shows undeniable progress—poverty has declined, basic needs are better met, and social welfare has uplifted millions. However, income and wealth inequalities remain entrenched, and data gaps fuel conflicting narratives.

The key lies in building a transparent, comprehensive, and multidimensional inequality measurement system, combining consumption, income, wealth, and opportunity. As we approach Amrit Kaal, India must not only lift people out of poverty but ensure they stay out—through equitable growth, better data, and inclusive opportunity.

RECAP