OPINION I Times of India

Income inequality in India remains a critical challenge, despite decades of robust economic growth, particularly following the economic liberalization of the 1990s. While millions have risen out of poverty, wealth continues to concentrate in the hands of a small elite, widening the gap between the rich and the poor. The wealthiest individuals now hold a disproportionate share of the nation's wealth, while vast segments of the population—especially in rural and marginalized communities—still struggle with limited access to essential services like education, healthcare, and economic opportunities. To effectively tackle these deep-seated inequalities, accurate and comprehensive measurements of income distribution are essential for shaping policies that promote inclusive growth and social equity.

Historically, India's approach to measuring inequality has relied more on expenditure-based surveys, primarily due to a lack of detailed income data. The National Sample Survey Office (NSSO), for example, used consumption patterns as a stand-in for income distribution, providing valuable but incomplete insights. This method, however, has limitations—especially in an economy with a significant informal sector where much of the income goes unreported or is difficult to quantify.

In recent years, scientifically conducted household income surveys, such as those led by institutions like the National Council of Applied Economic Research (NCAER) and the People Research on India’s Consumer Economy (PRICE), have become indispensable tools for understanding income inequality in India. These surveys, including the ICE 360 studies, provide a more direct and nuanced picture of household income across various regions, economic sectors, and income groups. Unlike expenditure-based proxies, these surveys offer a more granular view of income distribution, particularly in India's complex and largely informal economy.

Economists like Sir Angus Deaton, Amartya Sen, Jean Drèze, and Pranab Bardhan have long stressed the importance of such household surveys. In The Analysis of Household Surveys (1997), Deaton emphasized that direct income data is crucial in developing economies with large informal sectors. By contrast, model-based estimates, such as those from the World Inequality Database (WID), depend heavily on national accounts, tax records, and capital income data. While valuable, these estimates tend to overstate inequality by focusing predominantly on top earners, neglecting the vast informal economy.

Empirical Evidence: Discrepancies Between Household Surveys and WID Estimates

The discrepancies between household surveys and synthetic estimates become evident when we look at the specific data on income distribution from both sources. According to the latest household income surveys (2022-23), the bottom 50% of earners in India held 23% of the national income, while WID estimates this share to be much lower at 15%. Similarly, for the middle 40%, household surveys report a 47% share of income, compared to 27% in WID estimates.

In contrast, the top 1% of earners, according to household surveys, control 7% of national income, while WID places this figure at a staggering 23%. The top 10% also show similar discrepancies: household surveys estimate their income share at 31%, while WID places it significantly higher at 58%. This trend of discrepancy is consistent across earlier years as well: in 2020-21, household surveys estimated the top 1% held 9%, while WID reported 22%; in 2015-16, household surveys showed the top 1% held 6%, compared to 22% according to WID; in 2013-14, household surveys suggested the top 1% controlled 6% of income, while WID reported 21%; and going further back to 2004-05, household surveys placed the top 1% share at 8%, whereas WID estimated it at 19%.

Even in the early 1950s, household surveys indicated that the bottom 50% held 22% of the national income, and this share increased to 25% during the 1960s before declining in recent decades, dropping to 16% in 2020-21 and recovering slightly to 23% in 2022-23. Meanwhile, WID data consistently shows lower figures for the bottom half of income earners, suggesting a more pessimistic view of income inequality in the country.

These stark discrepancies highlight the limitations of WID’s synthetic estimates, which tend to overstate income concentration at the top by focusing on capital income and overlooking significant portions of earnings from the informal economy. India’s informal sector, which employs nearly 90% of the workforce, generates income that is often unreported in tax data and national accounts, leading to an underestimation of the earnings of lower- and middle-income groups in WID’s estimates. In contrast, household surveys provide a more accurate and comprehensive view by capturing income directly from households, including those in the informal sector. The detailed data from these surveys, especially regarding the lower income groups, present a more balanced picture of income distribution, showing that income inequality may not be as extreme as WID’s synthetic estimates suggest.

Policy Implications: Targeting Inequality More Effectively

The differences between household survey data and WID estimates have profound implications for policy-making. Household surveys provide data that more accurately reflects the situation of lower- and middle-income groups, offering insights that can shape more effective, targeted policies. For example, initiatives aimed at improving access to education, healthcare, and employment opportunities for the bottom 50% of the population would benefit from the granular data provided by household surveys.

In contrast, policy strategies informed by WID’s estimates—such as those focused on redistributing wealth through taxation of capital and property—might overlook deeper structural challenges that prevent inclusive growth, particularly in the informal sector. Economists like Pranab Bardhan and Amartya Sen have long advocated for using localized, survey-based data to develop policies that expand opportunities for the poor, rather than relying solely on wealth redistribution at the top.

In summary, while synthetic estimates like those provided by WID offer valuable macro-level insights, they fall short in capturing the full scope of income distribution in developing economies like India, where informal sector earnings play a crucial role. Scientifically conducted household income surveys, such as those by household surveys, offer a more reliable and detailed view of income distribution. These surveys account for both formal and informal incomes, ensuring that the economic realities of all segments of society are represented. By drawing on household survey data, policymakers can craft more effective interventions that promote inclusive growth and ensure that the benefits of economic development are shared equitably.

As India continues on its path of economic growth, household income surveys will remain indispensable for crafting policies that effectively address income inequality and leave no one behind.