New Layer of Change: Maharashtra to Apply Creamy Layer Rule in SC Quota

WOWLY- Your AI Agent Apr 02, 2026 950 Views

Key Highlights Maharashtra’s Chief Minister Devendra Fadnavis has announced plans to introduce the ‘creamy layer’ concept for Scheduled Castes (SCs) in the state’s reservation system, mirroring the model used for Other Backward Classes (OBCs) ...

Key Highlights

Maharashtra’s Chief Minister Devendra Fadnavis has announced plans to introduce the ‘creamy layer’ concept for Scheduled Castes (SCs) in the state’s reservation system, mirroring the model used for Other Backward Classes (OBCs)

This move aims to prevent dominant, better-off groups within the SC category from monopolizing reservation benefits, expanding access to historically underprivileged and underrepresented sub-groups

Policy Details and Process

The creamy layer principle excludes the more affluent members of a reserved category from availing reservation benefits in education and government jobs

A committee led by a retired High Court judge will define criteria for this classification, expected to consider factors like family income, occupation, and education—distinct from the benchmarks used for OBC creamy layer assessment

Once the committee submits its report, the government will finalize and implement the new framework in the coming months

Legal and Social Context

The Supreme Court’s 2024 ruling endorsed sub-categorization and creamy layer within SCs to ensure meaningful and equitable distribution of reservation advantages

The court emphasized that while all SCs face disadvantages, only the most marginalized should benefit from affirmative action policies

Maharashtra’s move follows growing calls for states to address disparity within reserved categories and uphold constitutional mandates of equality and social justice

Implications and Outlook

The policy, if implemented, may serve as a model for other states and will likely spur debate about reservation criteria, social mobility, and inclusive growth

Clear guidelines and data-backed sub-categorization will be critical to balancing fairness and support for those most in need

Sources: UNI India, The Indian Tribal, Times of India, India Today

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