State of Punjab & Others v. Davinder Singh & Others (2024)

Quick Summary

The Supreme Court held that States can sub-classify SCs/STs within the reservation quota to reach the most backward groups. The bench (6:1) rejected the old idea from E.V. Chinnaiah (2005) that treated SCs as one single block. Any preference must be based on credible data, stay within constitutional limits, and avoid blanket exclusion of other SC groups.

Issues

• Can the State make sub-groups inside SC/ST reservations under Articles 14, 15(4), 15(5), and 16(4)?
• Are Scheduled Castes one homogeneous unit or a heterogeneous set of communities?
• Does Article 341 create a legal fiction that SCs are a single class?
• What limits and safeguards must guide sub-classification?
• Are State laws or policies enabling such preferences constitutionally valid?

Rules

• Art. 14: Allows reasonable sub-classification when the larger class is not truly uniform.
• Arts. 15(4), 15(5), 16(4): Permit special provisions and reservation for socially and educationally backward groups, including SC/ST.
• Art. 335: SC/ST claims should be considered in public services consistently with efficiency.
• Art. 341: Presidential List identifies SCs, but does not freeze them into a homogeneous unit for all purposes.

Arguments

Judgment

Majority (6:1): Sub-classification within SC/ST reservations is constitutionally permissible. E.V. Chinnaiah is overruled. States must rely on empirical evidence of inadequate representation and use rational lines of preference. Two workable models were noted:

• Preference model — give first preference to the most backward SC sub-groups within the SC quota.
• Exclusive sub-quota model — carve out a measured sub-quota within the SC reservation for a specific, most backward sub-group.

Any policy must not corner the entire SC reservation for one sub-group and should apply creamy layer-type filters so that benefits reach those who still need support.

Dissent (Justice Bela M. Trivedi): Article 341’s Presidential List has finality. Only Parliament may change the list. State-level sub-classification of listed castes is beyond competence and violates the Constitution; Chinnaiah should stand.

Ratio

1. SCs/STs are not a uniform block for all constitutional purposes; reasonable sub-classification is allowed under Articles 14, 15(4), and 16(4).
2. Article 341 lists beneficiaries but does not bar internal prioritisation to remedy unequal backwardness.
3. Data-driven justification and proportional design are mandatory; no 100% sub-group capture.
4. Creamy layer filter is a valid tool to keep benefits targeted.

Why It Matters

This ruling gives States a lawful toolkit to fix imbalances inside SC/ST reservations. It helps benefits reach communities that stayed under-represented even after decades of quotas, while building guardrails against over-broad or unfair preferences.

Key Takeaways

• Sub-classification is allowed, but must be backed by solid data.
• Use measured preference or sub-quota models — not total capture.
• Creamy layer approach can apply to SC/ST to keep focus on the truly disadvantaged.
• Chinnaiah (2005) is no longer good law on this point.
• Periodic reviews can remove families who have moved ahead.

Mnemonic + 3-Step Hook

Mnemonic: “DATA-FAIR-FOCUS”

• DATA — collect credible evidence.
• FAIR — design balanced preference, no total capture.
• FOCUS — use creamy layer-type filter.

IRAC Outline

Issue: Can States sub-classify SC/ST groups within reservations?

Rule: Articles 14, 15(4), 16(4) allow reasonable sub-classification with data; Article 341 lists beneficiaries but does not bar prioritisation.

Application: The Court found real differences in representation inside SC groups. With proof, States may prefer the most backward through calibrated models and creamy layer filters.

Conclusion: Yes, sub-classification is valid when it is data-driven, proportionate, and does not exclude other listed groups.

Glossary

Sub-classification

Creating smaller groups inside a reserved category to target the most disadvantaged.

Creamy Layer

A filter that keeps out relatively advanced members so benefits reach poorer sections.

Empirical Data

Reliable, measurable facts (e.g., representation statistics) used to justify policy.

FAQs

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