[1st April 2026] The Hindu Oped: Counting people is not counting disaster risk

Mentor’s Comment

There is a critical flaw in India’s disaster financing architecture, the shift from risk-based assessment to population-based allocation. The issue is in the news due to concerns over the 16th Finance Commission’s disaster risk funding formula, which paradoxically allocates higher funds to States with larger populations rather than those with greater disaster exposure. This marks a sharp departure from earlier approaches and undermines decades of progress in disaster preparedness. The scale of the problem is significant, States like Odisha, with the highest hazard score (12), receive less effective consideration than States like Bihar (224.2) and Uttar Pradesh (413.2) due to population weighting.

What structural flaw exists in the disaster funding formula?

1. Multiplicative Risk Formula: Uses Disaster Risk Index (DRI = Hazard × Exposure × Vulnerability), but distorts outcomes due to flawed exposure metrics.
2. Population-Based Exposure: Defines exposure as total population (scaled 1-25), ignoring actual hazard-prone zones.
3. Bias Toward Larger States: Ensures States like Uttar Pradesh receive higher weight despite lower hazard intensity.
4. Departure from Previous Approach: Replaces additive model of 15th Finance Commission, which treated hazard and vulnerability separately.
5. Outcome Distortion: Rewards demographic size rather than disaster risk, contradicting risk-based allocation principles.

Why is ‘exposure’ measurement scientifically flawed?

1. Incorrect Definition: Uses total population instead of hazard-zone population.
2. IPCC Standard Ignored: Defines exposure as people in hazard-prone areas, not administrative boundaries.
3. Misleading Comparisons: Inland plateau populations treated equal to cyclone-prone coastal populations.
4. Example: Odisha’s high-risk coastline equated with safer inland regions in other States.
5. Result: Artificial inflation of exposure scores for populous but less vulnerable States.

How does vulnerability measurement misrepresent actual risk?

1. Income-Based Proxy: Uses per capita NSDP, which measures fiscal capacity, not vulnerability.
2. Multidimensional Nature Ignored: Overlooks housing quality, health infrastructure, and early warning access.
3. Kerala Case Study: Despite ₹31,000 crore flood damages (2018), receives low vulnerability score (1.073).
4. Hidden Inequality: Average income masks intra-state disparities and disaster susceptibility.
5. Outcome: Underestimates real vulnerability in disaster-prone but relatively richer States.

Why does the formula penalize disaster-prone States?

1. Population Bias: Prioritizes demographic size over risk intensity.
2. Funding Paradox: Odisha (highest hazard score) loses out due to lower population score.
3. Disproportionate Allocation: Bihar (224.2) and UP (413.2) overshadow Odisha despite lower hazard exposure.
4. Kerala’s Loss: Loses 0.78 percentage points despite high vulnerability ranking.
5. Systemic Inequity: Smaller, disaster-prone States receive inadequate fiscal support.

What are the implications for disaster governance in India?

1. Misallocation of Resources: Funds diverted away from high-risk zones.
2. Reduced Preparedness: States with higher hazard exposure face fiscal constraints.
3. Climate Risk Escalation: Cyclones, floods, and droughts increasing in intensity and frequency.
4. Regional Inequality: Coastal and northeastern States disproportionately affected.
5. Policy Credibility Issue: Undermines objective of risk-based disaster financing.

What reforms are required in disaster risk assessment?

1. Hazard-Zone Mapping: Measures exposure based on population in disaster-prone areas.
2. Composite Vulnerability Index: Includes housing, health, agriculture, and infrastructure indicators.
3. Use of Data Systems: Integrates Building Materials and Technology Promotion Council (BMTPC) Vulnerability Atlas, National Family Health Survey-5 (NFHS-5), Pradhan Mantri Fasal Bima Yojana (PMFBY) database, National Health Mission (NHM) facility surveys, and India Meteorological Department (IMD) monitoring records. 
4. Institutional Mechanism: Mandates NDMA to publish annual State Disaster Vulnerability Index.
5. Policy Continuity: Institutionalizes methodology across Finance Commissions. 

Conclusion

A population-based approach to disaster funding undermines the principle of risk-sensitive governance. A shift toward hazard-specific exposure mapping and multidimensional vulnerability assessment is essential to ensure equitable and effective disaster resilience in India.