IEWG study favours ‘Social Justice 2.0’, a paradigm of targeted social policy

The concept of Social Justice 2.0 represents a paradigm shift in Indian social policy. Traditional reservation systems have often relied on the 'share of population' metric, which sometimes benefits dominant backward castes disproportionately. The expert group's proposal argues for a nuanced approach focusing on the 'share of proportional backwardness'. This requires identifying castes based on their actual socio-economic deprivation rather than their numerical strength. By neutralizing the 'bad birth lottery'—the systemic disadvantages inherited at birth—this targeted approach aims to reach the most marginalized groups, often referred to as the 'Ati-Pichhda' (Most Backward). From a UPSC perspective, this touches upon the debates surrounding the sub-categorization of OBCs, a task previously assigned to the Justice G. Rohini Commission, which aimed to ensure equitable distribution of reservation benefits among all OBC communities.

This report reignites the debate on the constitutional framework for reservations under Article 15(4) and Article 16(4), which allow the State to make special provisions for the advancement of socially and educationally backward classes (SEBCs). The challenge has always been defining 'backwardness' accurately. While the Mandal Commission used multiple indicators, the new proposal emphasizes targeted social policy based on rigorous data from a caste census. The Supreme Court's landmark judgment in the Indra Sawhney Case (1992) upheld the 50% ceiling on reservations but emphasized that reservations should not lead to reverse discrimination and should genuinely benefit the backward classes. The shift toward 'Social Justice 2.0' requires robust empirical data, highlighting the importance of state-led caste censuses (like those in Bihar and Telangana) in shaping future reservation policies and potentially challenging existing legal ceilings.

Implementing Social Justice 2.0 requires a highly sophisticated governance framework capable of accurate targeting and delivery. Moving away from broad-brush categorization demands meticulous data collection, analysis, and continuous monitoring to identify castes in 'order of backwardness.' This shift aligns with the broader governance goal of evidence-based policymaking. However, it also presents significant administrative challenges, including preventing data manipulation, addressing potential social friction resulting from re-categorization, and ensuring that targeted policies do not become tools for political appeasement. For UPSC Mains, candidates must analyze how state capacity—the ability of the government to implement complex policies effectively—is crucial for transitioning from a quota-based system to a nuanced, targeted welfare model.