Context

• India is one of the most disaster-prone countries in the world, with varying degrees of vulnerability across its States; Odisha stands out due to its long coastline and repeated exposure to severe cyclones.
• Over the past two decades, Odisha has significantly improved its disaster preparedness, reducing cyclone-related deaths to near zero through investments in early warning systems, evacuation mechanisms, and infrastructure.
• Despite this progress and high exposure to natural hazards, the 16th Finance Commission has reduced Odisha’s share in disaster funding.
• This paradox highlights deeper structural issues in the Commission’s allocation formula, raising concerns about the effectiveness and fairness of disaster risk assessment in India.

The Revised Disaster Risk Framework

• Shift from Additive to Multiplicative Model
  • The 16th Finance Commission introduced a Disaster Risk Index (DRI) based on a multiplicative formula
• DRI = Hazard × Exposure × Vulnerability
  • This marks a departure from the additive approach used by the 15th Finance Commission.
  • The new model is theoretically sound, as it reflects the idea that disasters occur only when hazards intersect with exposed and vulnerable populations.
  • This new model is consistent with frameworks proposed by the Intergovernmental Panel on Climate Change.
• Increase in Overall Allocation
  • The Commission allocated ₹2,04,401 crore to State Disaster Response Funds, representing a 59.5% increase compared to the previous Commission.
  • While this increase is significant, the distribution methodology has produced uneven and controversial outcomes.

Key Flaws in the Allocation Formula

• Misrepresentation of Exposure
  • The Commission measures exposure using total State population, scaled linearly. This approach is flawed because:
    • Exposure, as defined by the IPCC, refers to populations in hazard-prone areas, not total population.
    • It ignores geographical distribution and concentration of risk.
  • As a result, populous States such as Uttar Pradesh and Bihar receive disproportionately high exposure scores, even if large portions of their populations are relatively safe.
  • Impact on Smaller but High-Risk States
    • Despite having the highest hazard score, Odisha receives a lower Disaster Risk Index due to its smaller population.
    • This demonstrates that the formula prioritizes demographic size over actual risk exposure.
• Oversimplified Measurement of Vulnerability
  • Vulnerability is calculated using per capita Net State Domestic Product (NSDP), inverted so that poorer States rank higher.
  • While this captures fiscal capacity, it fails to account for:
    • Housing quality
    • Healthcare infrastructure
    • Early warning systems
    • Livelihood dependence on climate-sensitive sectors
  • Case Example: Kerala
    • Kerala, despite experiencing devastating floods in 2018, receives a low vulnerability score due to its relatively high per capita income.
    • This highlights how economic averages mask real disaster vulnerability.
  • Case Example: Jharkhand
    • Jharkhand, though highly vulnerable due to poverty and structural fragility, loses funding share because its population size does not sufficiently boost its overall risk score.
• Bias Toward Population Size
  • The multiplicative nature of the formula amplifies the influence of population:
  • Larger States gain disproportionately higher DRI scores
  • Smaller or moderately populated States are penalized
  • Twenty States have lost funding share despite facing real risks
  • This outcome contradicts the objective of a risk-based allocation system.

Consequences of the Current Framework

• The flaws in the formula lead to several critical issues:
  • Misallocation of disaster funds
  • Underserving high-risk but less populous States
  • Ignoring intra-state inequalities
  • Weak alignment with real-world disaster patterns
• Ultimately, the current model reduces disaster risk assessment to a population-based calculation rather than a scientifically grounded evaluation.

Proposed Reforms

• Redefining Exposure
  • Exposure should be measured as the population residing in hazard-prone areas, such as:
  • Coastal cyclone zones
  • Floodplains
  • Earthquake-prone regions
  • Data from the Building Materials and Technology Promotion Council Vulnerability Atlas and Census records can enable precise mapping.
• Developing a Composite Vulnerability Index
  • Vulnerability should include multiple indicators, such as:
    • Housing conditions
    • Health infrastructure
    • Agricultural dependence
    • Insurance coverage
    • Effectiveness of early warning systems
  • These can be derived from national datasets and surveys.
• Institutionalising Risk Assessment
  • The National Disaster Management Authority should be mandated to develop and publish a standardized Disaster Vulnerability Index.
  • This would ensure consistency, transparency, and scientific accuracy in future allocations.

Conclusion

• As climate change intensifies the frequency and severity of natural disasters, the need for an accurate and equitable disaster funding framework becomes increasingly urgent.
• States like Odisha, which face high hazard exposure and have invested heavily in preparedness, must not be penalized by flawed methodologies.
• The current allocation model of the 16th Finance Commission, while theoretically sound, fails in its execution.
• A meaningful reform must prioritise real exposure and multidimensional vulnerability over simplistic metrics.
• Only then can disaster finance in India move beyond a mere headcount to become a true reflection of risk and resilience.