Disaster Funding Allocation Challenges in Indian States

The recent 16th Finance Commission report on disaster funding has sparked concern. Odisha, India’s most disaster-prone State, has seen the largest reduction in disaster fund share. This is despite its high hazard score and extensive disaster preparedness. The problem lies in the formula used to allocate funds to States. About this issue is crucial for grasping disaster management funding in India.

Disaster Risk Index and Allocation Formula

The 16th Finance Commission increased total disaster funding by 59.5% to ₹2,04,401 crore. It used a multiplicative Disaster Risk Index (DRI) calculated as Hazard × Exposure × Vulnerability. This method replaces the earlier additive approach. Theoretically, risk arises only when hazards meet exposed and vulnerable populations. The formula aims to target funds where disaster risk is highest.

Issues with Exposure Measurement

Exposure is measured by total State population scaled between 1 and 25. For example, Uttar Pradesh scores 25, Sikkim 1. This is simple but flawed. Exposure should reflect people living in hazard-prone zones, not total population. Odisha’s large coastline and hazard score of 12 are overshadowed by populous States with lower hazard. Hence, Odisha’s risk score appears low, reducing its funding share unfairly.

Problems with Vulnerability Assessment

Vulnerability is measured by inverted per capita Net State Domestic Product (NSDP). Poorer States score higher. This assumes poorer States have less capacity to absorb shocks. However, NSDP does not capture true vulnerability factors like housing quality or health infrastructure. Kerala, despite severe floods, scores low vulnerability due to high per capita income. This misrepresents actual disaster risk.

Recommendations for Improved Funding Formula

Exposure should be based on populations in defined hazard zones using detailed data like the Building Materials and Technology Promotion Council’s Vulnerability Atlas and Census block data. Vulnerability should be a composite index including housing, health infrastructure, early warning systems, and agricultural factors. National data sources such as NFHS-5 and PMFBY should inform this. The National Disaster Management Authority should publish an annual State Disaster Vulnerability Index for consistent funding decisions.

Topics for Prelims:

Disaster Risk Index (DRI)

1. DRI = Hazard × Exposure × Vulnerability
2. Multiplicative model replaces additive model
3. Focus on intersection of hazard and vulnerable population
4. Used by 16th Finance Commission for fund allocation
5. Designed to improve targeting of disaster funds

Exposure Measurement Flaws

1. Measured by total State population scaled 1-25
2. Ignores population in hazard-prone zones
3. Leads to overfunding populous but safer States
4. Undermines States with high hazard but smaller population
5. Contradicts IPCC definition of exposure

Vulnerability Index Limitations

1. Based on inverted per capita NSDP
2. Measures fiscal capacity, not multidimensional vulnerability
3. Ignores intra-state inequalities and infrastructure gaps
4. Fails to capture disaster preparedness and housing quality
5. Results in underestimation of vulnerability in richer but hazard-prone States

Questions for Mains:

1. Discuss in the light of disaster management policies how the allocation of disaster funds based on population size rather than hazard exposure affects disaster resilience in Indian States. [GS-III-Environment & DM]
2. Critically examine the limitations of using economic indicators such as per capita Net State Domestic Product to measure disaster vulnerability. How can vulnerability indices be improved for better policy outcomes? [GS-III-Economic Development]
3. Explain the role of early warning systems and infrastructure in reducing disaster mortality. With suitable examples, discuss the challenges faced by Indian States in integrating these factors into disaster risk assessments. [GS-II-Governance]
4. Comment on the impact of climate change on disaster frequency and intensity in India. How should disaster funding formulas evolve to address these emerging challenges? [GS-III-Science & Technology]

Answer Hints:

1. Discuss in the light of disaster management policies how the allocation of disaster funds based on population size rather than hazard exposure affects disaster resilience in Indian States. [GS-III-Environment & DM]

1. Disaster funds allocated by population size ignore actual hazard exposure zones, leading to misallocation.
2. States with high hazard but smaller populations (e.g., Odisha) receive less funding, weakening preparedness and resilience.
3. Population-based allocation rewards demographic size, not risk, undermining risk-based disaster management principles.
4. Exposure should be measured by people living in hazard-prone areas, not total state population.
5. Misallocation reduces incentives for hazard-prone states to invest in disaster mitigation infrastructure.
6. Effective disaster resilience requires targeted funding aligned with hazard and vulnerability, not just population figures.

2. Critically examine the limitations of using economic indicators such as per capita Net State Domestic Product to measure disaster vulnerability. How can vulnerability indices be improved for better policy outcomes? [GS-III-Economic Development]

1. Per capita NSDP measures fiscal capacity, not multidimensional vulnerability to disasters.
2. It overlooks intra-state inequalities, housing quality, health infrastructure, and early warning reach.
3. High-income states (e.g., Kerala) may appear less vulnerable despite disaster impacts.
4. Vulnerability indices should include composite factors – housing type, health facilities, agricultural dependence, crop insurance, and early warning effectiveness.
5. Use data from NFHS-5, PMFBY, NHM, IMD, and BMTPC for comprehensive vulnerability assessment.
6. Improved indices enable more accurate targeting of funds and better disaster risk reduction policies.

3. Explain the role of early warning systems and infrastructure in reducing disaster mortality. With suitable examples, discuss the challenges faced by Indian States in integrating these factors into disaster risk assessments. [GS-II-Governance]

1. Early warning systems enable timely evacuation and preparedness, drastically reducing mortality (e.g., Odisha’s cyclone shelters and alerts).
2. Robust infrastructure like cyclone shelters, flood embankments, and resilient housing mitigates disaster impact.
3. Challenges include inadequate data integration, lack of real-time monitoring, and uneven infrastructure distribution.
4. Disaster risk assessments often omit detailed infrastructure and early warning effectiveness metrics.
5. States vary in capacity to implement and maintain early warning systems due to fiscal and technical constraints.
6. Institutional coordination and data sharing between agencies remain weak, limiting comprehensive risk assessment.

4. Comment on the impact of climate change on disaster frequency and intensity in India. How should disaster funding formulas evolve to address these emerging challenges? [GS-III-Science & Technology]

1. Climate change is increasing cyclone frequency/intensity, droughts, and extreme rainfall in India’s vulnerable regions.
2. States like Odisha, Andhra Pradesh, Kerala, and Assam face rising disaster risks due to changing climate patterns.
3. Current funding formulas based on total population fail to capture shifting hazard landscapes and exposure changes.
4. Funding formulas should incorporate dynamic climate risk projections and localized hazard exposure data.
5. Annual State Disaster Vulnerability Index publication by NDMA can institutionalize updated risk metrics.
6. Integrating climate science with socioeconomic vulnerability enables adaptive, forward-looking disaster finance allocation.