2011年-世界发展银行全球_Enhancing_Crop_Insurance_in_India_132页_2mb

Summary of "Enhancing Crop Insurance in India"

Core Content

This report, published in April 2011, provides a detailed analysis of the National Agricultural Insurance Scheme (NAIS) and the Weather Based Crop Insurance Scheme (WBCIS) in India. It outlines technical and operational improvements to enhance the effectiveness and efficiency of crop insurance programs, with a focus on transitioning the NAIS to a modified NAIS (mNAIS) under an actuarial regime. The report is co-funded by the Global Facility for Disaster Reduction and Recovery (GFDRR) and is part of a larger initiative to support the Agriculture Insurance Company of India (AICI) in improving its risk management and insurance product design capabilities.

Main Objectives

The primary goals of the report are:

• To improve the technical soundness of the NAIS by introducing actuarial methodologies.
• To enhance the design and pricing of crop insurance products.
• To propose modifications to the risk financing structure.
• To evaluate the fiscal implications of transitioning to an actuarial regime.
• To suggest a comprehensive action plan for implementing the mNAIS.

Key Findings and Recommendations

1. Current NAIS Structure

• The NAIS uses an "area yield" index based on Crop Cutting Experiments (CCEs) to determine indemnity payments.
• It is mandatory for farmers who borrow from financial institutions, but voluntary for others.
• The scheme covers food crops, oilseeds, and selected commercial crops.
• The loss ratio (claim/premium) has historically been above 100%, indicating that the total indemnities paid exceed the premiums collected.
• The loss ratio disparity between non-borrowing and borrowing farmers highlights adverse selection.

2. Challenges with the Current System

• Ex-post funding leads to systemic delays in claim settlements (up to 9-12 months).
• Poor risk classification results in inequity between farmers in different insurance units.
• Basis risk arises from inaccurate yield estimates and lack of standardization in CCE data collection.
• The current design process for WBCIS is vulnerable to data mining, leading to systematic under-pricing.

3. Proposed Modifications to mNAIS

• Standardized product design with a double index approach: combining weather-based early payments and area-yield-based final payments.
• Improved yield estimation: through de-trending and randomized audits of CCE data.
• Experience-based ratemaking: to allow for uniform premium rates across states while enabling risk classification through product differentiation.
• Transition from ex-post to ex-ante subsidies: with state subsidies moving towards ex-ante as CCE data quality improves.

4. Risk Financing Strategies

• The report suggests multi-year reserves, reinsurance, and contingent debt facilities as viable risk financing tools.
• A contingent debt facility could help reduce reinsurance costs and increase risk retention within a sound financial framework.

5. Fiscal Implications

• The modified NAIS could reduce government contingent liability.
• A crop-blind subsidy structure is proposed, which could increase efficiency and target subsidies more effectively.
• The fiscal impact of universalisation and new subsidy structures is analyzed, with a focus on budgetary sustainability.

6. Action Plan

• A detailed action plan is provided, including short and medium-term measures.
• The plan includes improvements in product design, refinement of risk differentiation, and integration of the private sector.
• Pilot testing is recommended before full-scale implementation to ensure effectiveness and scalability.

Key Concepts and Definitions

• Crop Cutting Experiments (CCEs): Method used to estimate crop yields for the NAIS.
• Threshold Yield (TY): The yield level below which indemnity payments are triggered.
• Double Index Product Design: A hybrid approach combining weather index and area yield index for indemnity payments.
• De-trending: A statistical method used to adjust historical yield data to remove long-term trends.
• Actuarial regime: A system where insurance products are priced based on statistical analysis and risk assessment.

Conclusion

The report emphasizes the technical and operational improvements necessary to enhance the NAIS and WBCIS. It highlights the importance of moving to an actuarial regime for better risk management, timely claim settlements, and equitable subsidies. The proposed mNAIS is seen as a critical step towards a more efficient and sustainable agricultural insurance system in India. The transition to ex-ante subsidies and the introduction of double index products are highlighted as key strategies to improve risk classification and reduce basis risk. The report also underscores the need for collaboration between the government, AICI, and the private sector to implement these changes effectively.

Appendices and Supporting Materials

• Annex A: Graphical overview of the history of the NAIS portfolio.
• Annex B: Summary of main proposed modifications to the NAIS.
• Annex C: Details of the CCE process.
• Annex D: Methods for calculating threshold yields.
• Annex E: Double index insurance policy worked example.
• Annex F: Yield de-trending examples.
• Annex G: Ratemaking methodology for weather-based crop insurance.
• Annex H: Fiscal impact analysis of the modified NAIS.

Tables and Figures

• Table 1.1: Summary comparison between GOI and World Bank suggestions for mNAIS.
• Table 2.1: Yield radii at 95% confidence for selected states, crops, and years.
• Table 3.1: Ability of AICI to classify risks under alternative schemes.
• Table 3.2: Comparison of five methods for calculating threshold yields.
• Table 4.1: Statistical investigation of aggregate linear trends for six products.
• Table 5.1: Loss cost for the NAIS portfolio by season (1985–2007).
• Table 6.1: Risk profile of existing subsidy structure by state.
• Table 7.1: Summary of all short and medium-term suggested actions under mNAIS.
• Figure 1.1: Farmers covered under NAIS.
• Figure 1.2: NAIS premium volume, 2000–2008.
• Figure 1.3: NAIS loss ratio (indemnities/premiums).
• Figure 1.4: NAIS premium income and insurance claims by state.
• Figure 3.1: Threshold yield calculations for the district of Khargone, Madhya Pradesh.
• Figure 4.1: Indicative commercial premium after de-trending for cotton in Gujarat.
• Figure 4.3: Flow chart of the experience-based ratemaking methodology.
• Figure 5.1: Annual historic loss costs (all crops).
• Figure 5.2: Loss exceedance curve for the NAIS portfolio.
• Figure 5.3: Hypothetical NAIS risk financing strategy.

Acknowledgements

The report was jointly led by Olivier Mahul and Niraj Verma from the World Bank, with contributions from Daniel Clarke, Saket Saurabh, Raghvendra Singh, and Ligia Vado. It also acknowledges the support from Agriculture Insurance Company of India (AICI) and various Ministries in India. The peer reviewers and funding support from GFDRR are also noted.

Disclaimer

The report has been discussed with the Government of India but does not necessarily reflect its approval for all content. The World Bank has expressed its judgment and policy recommendations.