India’s income inequality: Why income surveys matter more than global data

Rajesh Shukla    December 29, 2024

OPINION I Financial Express

Income inequality remains a critical challenge in India, a nation characterized by a multifaceted economy where formal and informal sectors operate side by side. Accurate measurement of income inequality is vital for formulating policies that promote inclusive and equitable growth.
Over the years, two primary approaches have emerged to study income distribution in India: the Indian Household Income Surveys conducted by private research organizations such as the National Council of Applied Economic Research (NCAER) and People Research on India’s Consumer Economy (PRICE) from 1953-54 to 2022-23, and estimates provided by the World Inequality Lab (WIL).
While both approaches contribute valuable insights, the divergence in their results underscores the complexity of assessing inequality accurately. By examining data from 11 household income surveys conducted over seven decades, this article highlights the strengths of these surveys in capturing the nuanced realities of income distribution and explores the evolution of inequality trends shaped by economic policies, demographic shifts, and political events.
Strengths and Weaknesses of Two Approaches: Household Income Surveys vs. WIL
• Inclusive Coverage of the Informal Sector: Household surveys—a cornerstone of inequality measurement in India—excel at capturing income data from the informal sector, which accounts for a significant portion of India’s workforce. Unlike WIL, which primarily relies on tax data and national accounts, household surveys include earnings from informal sources such as daily wage labor, small enterprises, and subsistence agriculture. This inclusivity is reflected in higher shares of income attributed to the bottom 50% and middle 40% in survey data. In contrast, WIL’s reliance on formal data sources skews income distribution estimates, underestimating the economic contributions of these groups.
Representativeness and Granularity: Household surveys are designed to represent India’s diverse demographics, encompassing rural and urban populations and various social strata. This granularity allows for a nuanced understanding of income distribution, revealing regional and temporal variations. For example, the surveys have consistently highlighted trends in income recovery for lower-income groups. In comparison, WIL estimates, aggregated at a macro level, often fail to capture such details, limiting their ability to portray the complexities of India’s income landscape.
• Temporal Trends and Policy Relevance: One of the key strengths of household surveys lies in their ability to track changes in income distribution over time. Recent survey data, for instance, show an encouraging recovery in the income share of the bottom 50%, underscoring the impact of targeted economic policies. WIL estimates, on the other hand, often present static snapshots that fail to reflect these dynamic shifts. This makes them less useful for policymakers seeking timely insights to address inequality.
• Balanced Representation of Income Groups: While household surveys may underrepresent the ultra-wealthy due to limitations in self-reported data, they provide a more balanced picture of income distribution. The disparity between the top 10% and top 1% is less pronounced in survey data compared to WIL estimates. This suggests that household surveys capture a broader spectrum of incomes, offering a more equitable perspective on distribution across various population segments.
Limitations of World Inequality Lab Estimates
Exclusion of Informal Sector Earnings: WIL’s reliance on tax data and national accounts inherently excludes the informal sector, which plays a pivotal role in India’s economy. This omission results in significant underrepresentation of income shares for the bottom 50% and middle 40%. The stark differences between WIL and household survey estimates highlight this gap, with WIL data consistently underreporting the economic contributions of lower-income groups.
Overemphasis on Wealth Concentration: WIL’s focus on the top 1% and top 10% income brackets amplifies the perception of wealth concentration. While understanding top-end income distribution is important, this emphasis often overshadows the broader realities faced by lower-income groups. For example, WIL’s estimates frequently show disproportionately high-income shares for the top 1%, a finding not corroborated by household surveys. This overemphasis distorts the overall picture of inequality in India.
Incompatibility with Mixed Economies: In mixed economies like India, where informal earnings constitute a large portion of GDP, WIL’s aggregated datasets fail to provide a complete picture. The heavy reliance on formal tax data renders their estimates less relevant for understanding the economic conditions of a majority of the population. This limitation diminishes their utility for crafting policies tailored to India’s unique economic structure.
Policy Implications and Recommendations
Integrating Data Sources: To achieve a more accurate and holistic view of income inequality, it is essential to integrate the strengths of household surveys with WIL’s macro-level data. By combining granular survey data with broader national estimates, researchers can bridge methodological gaps and develop a comprehensive understanding of income distribution.
Improving Representation of the Informal Sector: Given the prominence of informal employment in India, WIL must refine its methodologies to account for informal sector incomes. Incorporating this data would not only enhance the accuracy of inequality estimates but also increase their relevance for economies with significant informal contributions.
Strengthening Household Survey Systems: To keep pace with evolving economic realities, household income surveys in India should be conducted more frequently and with expanded coverage. Regular updates would ensure timely data availability, particularly in the wake of economic shocks like the COVID-19 pandemic. This would enable policymakers to design and implement targeted interventions to reduce inequality effectively.
The Way Forward
The comparison between Indian household income surveys and WIL estimates underscores the strengths and limitations of both approaches in measuring income inequality. Household surveys excel in capturing the complexities of a diverse economy, particularly the contributions of the informal sector. Their ability to provide granular, dynamic, and representative data makes them indispensable for understanding income distribution among the bottom 50% and middle 40%.
On the other hand, WIL estimates highlight wealth concentration at the top, offering valuable insights into income shares of the top 1% and 10%. However, their reliance on formal data sources and exclusion of informal sector earnings significantly limits their applicability in the Indian context.
The divergence between these approaches underscores the need for integration. Policymakers and researchers must harmonize methodologies to ensure that informal sector contributions are adequately represented in macro-level analyses. By combining the inclusivity of household surveys with the macroscopic insights of WIL estimates, India can develop a more balanced and nuanced understanding of income inequality.
As India continues to evolve economically, improving the frequency, coverage, and methodological alignment of income data will be critical. Such efforts will pave the way for more effective policies and interventions, fostering a more equitable economic landscape for all citizens.