India’s inequality claims ignore data gaps and reality

Source: The post India’s inequality claims ignore data gaps and reality has been created, based on the article “Measuring inequality” published in “Indian Express” on 10th July 2025

UPSC Syllabus Topic: GS Paper 3- Inclusive growth and issues arising from it.

Context: A recent government release cited the World Bank’s Poverty and Equity Brief to claim India is among the most equal countries globally based on its low Gini Index. However, this interpretation has been widely questioned due to methodological issues, data limitations, and alternate measures showing rising inequality in India.

For detailed information on India’s Rising Inequality read this article here

Questioning the Government’s Equality Claim

1. Selective Use of Data: The government highlighted India’s 2022–23 consumption-based Gini Index of 25.5 to claim it is the world’s fourth most equal society. But the same World Bank brief mentioned data limitations and potential underestimation of inequality.
2. Contradictions from Other Sources: The World Inequality Database presents a different picture. It shows India’s income Gini Index rose from 52 in 2004 to 62 in 2023. Also, in 2023–24, the top 10% earned 13 times more than the bottom 10%, indicating sharp wage disparity.
3. Omission of Important Qualifiers: The government release ignored disclaimers from the World Bank about data gaps and did not mention the alternate income-based Gini Index, which shows growing inequality.

Flaws in Consumption-Based Gini Index

1. Mismatch in Measuring Income and Consumption: India measures inequality using consumption data, not income. Since richer individuals tend to save more, consumption varies less than income, leading to underestimation of inequality.
2. Global Inconsistency: Most other countries use income-based Gini measures. Comparing India’s consumption-based figure to these is misleading and undermines credibility.
3. False Equivalence with Other Countries: Economists stress that cross-country comparisons based on different data types are inappropriate and distort the real inequality picture.

Limitations of Inequality Survey Data

1. Surveys Miss the Rich: The richest often decline survey participation – a pattern known as “differential non-response”. This skews the data by excluding the highest incomes.
2. Low Sampling Probability of the Richest: Even with full cooperation, surveys rarely include the ultra-rich due to their small numbers. If the top 1% drive most inequality, missing them distorts findings.
3. Correcting Survey Bias: Combining survey data with income tax records offers more accurate results. Studies in the US, UK, and India show surveys alone underreport inequality.

Shortcomings of the Gini Index

1. Insensitive to Extremes: The Gini Index mainly reflects middle-income changes, not shifts at the top or bottom. Thus, it misses extremes in inequality.
2. Need for Alternative Metrics: Experts advocate the Palma Ratio, which compares top 10% and bottom 50% income shares, offering a clearer view of disparities.
3. Colonial-Era Comparison: When using income tax data, some studies find Indian inequality today is worse than during the colonial period, with the top 1% earning far more than the bottom 50%.

Policy Risks of Misreading Inequality

1. Risk of Misguided Policies: Underestimating inequality can lead to ineffective policies or even worsen inequality, rather than reduce it.
2. Socioeconomic Consequences: Unchecked inequality may trigger social unrest and obstruct sustained economic growth.
3. Need for Broader Measurement Tools: Relying solely on a limited Gini Index, especially with severe data issues, hides reality. Policymakers need better, multidimensional tools for accurate assessment.

Question for practice:

Examine how the use of consumption-based data affects the measurement and interpretation of inequality in India.