Inequality in India

17.07.2025

Inequality in India

Context

A recent World Bank report suggests India’s consumption inequality has declined, but other studies highlight rising income and wealth inequality, creating a policy contradiction.

About the News

• World Bank reported a fall in Gini coefficient from 0.288 (2011–12) to 0.255 (2022–23).
   
• It suggests India has one of the lowest inequality levels globally in consumption terms.
   
• World Inequality Database (WID) contradicts this with much higher income and wealth inequality data.
   
• Multiple data limitations cast doubt on the accuracy of official inequality estimates.
   

Other Findings of the Report

• Consumption inequality measures the variation in household spending, not income or wealth. While it appears low, this can be misleading as families often maintain consumption by borrowing or using savings, even when their income is falling. Hence, consumption-based data often underestimates real economic inequality.
   
• According to the World Inequality Database (WID), India’s income Gini coefficient in 2023 stands at 0.61. This indicates a very high level of income inequality, where a small portion of the population earns a large share of the national income. Only a few countries in the world are more unequal by this measure.
   
• The wealth Gini coefficient for India is even more alarming at 0.75, suggesting that assets like land, property, and stocks are highly concentrated in the hands of the top 1% of the population. This reveals a deep structural imbalance in asset ownership.
   
• Household surveys, such as the Household Consumption Expenditure Survey (HCES), often miss the richest households, who do not respond or under-report their earnings. Moreover, informal sources of income—common in India—are difficult to capture accurately in such surveys.
   
• Tax data is also limited in scope. As per the Central Board of Direct Taxes (CBDT), only about 6 crore individuals file income tax, in a country of over 140 crore people. This leaves out a huge part of the workforce, especially those in the informal and unorganised sectors.
   

Challenges

• Underreporting by the rich skews data. E.g., Billionaires rarely show true net worth in surveys.
   
• HCES surveys miss top earners. E.g., Informal sector income remains undocumented.
   
• No asset-level tracking. E.g., Property and shareholdings not fully captured in national data.
   
• Gini hides intensity of wealth gaps. E.g., Doesn’t reflect top 0.1% ownership.
   

Way Forward

• Progressive taxation on wealth and inheritance. E.g., Tax ultra-rich to reduce asset concentration.
   
• Universal public services in health, education. E.g., Free schooling can equalize life chances.
   
• Boost skilling for jobs in low-income groups. E.g., Train for MSMEs and green sectors.
   
• Improve data transparency using tax and asset records. E.g., Link PAN, GST, and survey data.
   

Conclusion

To build a fair and sustainable economy, India must go beyond consumption data and tackle structural inequality. Only inclusive growth and redistributive policies can ensure long-term social and economic stability.