- Government’s plan to conduct a caste census has renewed debates on data-driven welfare, especially for OBCs.
- Author argues that over-reliance on caste census for social justice goals is a misguided assumption.
- Calls for recognising the role of political will and institutional commitment, rather than only statistical precision, in achieving equity.
Detailed Insights:
- Census data has historically played a critical role in shaping Indian public policy on health, housing, and education.
- Proponents argue that caste-wise data will help address intra-group disparities among OBCs and EBCs, allowing for more targeted schemes.
- However, landmark social justice policies—reservations, land reforms, Mandal Commission—were executed without waiting for perfect data.
- Past surveys like SECC and the Bihar caste survey already reveal deep disparities and marginalisation among OBCs.
- Despite ample evidence, state capacity and political commitment to reform remain minimal.
- Census risks becoming politicised in a polarised political climate, reducing its utility and objectivity.
Key Concepts:
- Empirical Evidence vs Political Will: Data can diagnose inequity, but only sustained political intent can address it meaningfully.
- Policy Precedents Without Data: India's most transformative policies were born from mass mobilisation, not data.
- Intra-OBC Inequality: Disaggregated caste data can help identify hidden deprivation within broad social categories.
- State Capacity and Accountability: Effective justice requires not just data, but implementation, representation, and public pressure.
Significance:
- The piece cautions against data fetishism—believing numbers alone will lead to justice.
- True change depends on robust democratic institutions, representation, and moral imagination in governance.
- Without political commitment, a caste census will serve more as a symbolic act than a transformative tool.
Mains Mock Question:
“Critically examine whether a caste census can be the cornerstone for ensuring social justice in India. What more is needed beyond data?”