The Independent Expert Working Group (IEWG) has put forward a new approach titled “Social Justice 2.0”, highlighting how data-based policymaking can improve the delivery of welfare schemes to the most disadvantaged sections of society.
Speaking in Hyderabad, members of the group explained that government benefits should move beyond broad distribution models and instead focus on a more targeted system that prioritises the poorest and most deprived households. According to the experts, such an approach would ensure that public resources are used more efficiently and reach those who need them the most.
The framework is based on an analysis of caste survey data using a tool known as the Caste Backwardness Index (CBI), which measures levels of socio-economic disadvantage among different communities. The group clarified that while it does not directly make policy decisions, its findings can help governments create better-informed and evidence-based welfare strategies.
IEWG convenor Pravin Chakravarty stated that the group’s role was limited to studying the data, but the insights clearly show the need for a more refined system of identifying beneficiaries. He noted that such data can support policymakers in making more accurate decisions on allocation of welfare benefits.
One of the key ideas highlighted in the report is “proportional backwardness”, which focuses on identifying communities based on the depth of their deprivation rather than just their population numbers. This method aims to ensure that those facing the greatest hardship receive a larger share of support.
The report also refers to the concept of a “birth-based disadvantage”, pointing to how individuals often face unequal opportunities due to their social and economic background. Addressing this issue, the group stressed the importance of adopting data-driven governance models that can directly target inequality.
Under the proposed Social Justice 2.0 model, emphasis is placed on assessing real-life indicators such as income levels, education access, housing conditions, and availability of basic services. This would allow governments to design more precise, transparent, and effective welfare programmes.
Experts believe that implementing such a framework could significantly improve how welfare schemes are delivered by reducing leakages, improving targeting, and enhancing accountability within the system.
The recommendations come at a time when discussions around caste data and welfare distribution are becoming increasingly important in policy circles. The IEWG’s findings suggest that a data-led approach to social justice could reshape how governments tackle inequality and deliver public benefits.
Overall, the proposed framework presents Social Justice 2.0 as a modern, evidence-based model aimed at creating a fairer and more inclusive system of governance, where support is directed to those who need it most.
