A recent global report sparked controversy by stating that India has the lowest consumption inequality yet the highest income inequality, raising concerns over data interpretation and methodological inconsistencies.

Misinterpretation of Inequality Metrics

• Contradictory Indices – Consumption vs. Income:India recorded a Gini coefficient of 0.25 for consumption in 2022–23, the lowest globally. In contrast, income inequality estimates—derived from modeled data—placed India among the most unequal, despite lacking household-level income data.
• Miscommunication in Policy Discourse:Official communications equated India’s low consumption inequality with overall socioeconomic equality, without distinguishing it from income-based metrics, resulting in misleading policy perceptions.
• Synthetic Estimations – Methodological Weaknesses:Income inequality estimates rely on synthetic distributions from databases like WID, based on assumptions rather than direct surveys, introducing bias and reducing empirical validity.

Data Credibility and Institutional Contradictions

• Absence of Harmonized Income Data Across Economies:Developing nations, including India, often lack standardized income datasets. Global inequality estimates fill these gaps using imputed data, which may diverge sharply from ground realities.
• Divergence in Domestic vs. International Data Sources:India’s household-level consumption data, from NSSO and PLFS surveys, provide granular insight. Meanwhile, global models that bypass such empirical sources introduce substantial inaccuracies.
• Comparative Evidence – India and South Africa:South Africa, with an officially reported Gini coefficient of 63.0 for income, is considered the most unequal. India’s real consumption Gini of 25.5 discredits any suggestion of it leading income inequality rankings.

Broader Implications and Recommendations

• Risk of Flawed Policy Formulation:Erroneous inequality interpretations can distort social policy frameworks, misguide welfare interventions, and misallocate resources at both national and global levels.
• Importance of Transparent Data Collection:Developing countries require investments in real-time, standardized income and consumption surveys to strengthen evidence-based policymaking and avoid dependency on external modeled estimates.
• Responsibility of Global Institutions:Global agencies must recognize data limitations, avoid overreliance on synthetic estimations, and adopt stringent methodological standards to maintain credibility in international statistics.

Practice Question:

“Discuss the challenges associated with measuring income and consumption inequality in developing economies. Critically analyse the implications of relying on synthetic datasets for international inequality comparisons, with reference to India.”   (250 words)