A Safety Net or a Structural Flaw? A Policy Analysis of PMFBY Implementation and Outcomes

For decades, Indian farmers have faced unpredictable crop failures caused by droughts, floods, pests and market volatility- often without reliable financial protection. To address this vulnerability, the Ministry of Agriculture & Farmers’ Welfare (MoAFW) launched the Prime Minister’s Fasal Bima Yojana (PMFBY) in February 2016, envisioning a robust crop insurance mechanism that could safeguard incomes and encourage confidence in agricultural investment.

Nearly ten years on, the scheme’s journey is riddled with complexity. The key question remains: is PMFBY effectively functioning as the safety net it was designed to be, or is its structure laden with inherent flaws that undermine its impact? This article analyses how the policy works, who it affects, what the major gaps are, and what reforms are needed.

How PMFBY Works – Design & Mechanism

PMFBY uses a risk-sharing model between farmers, insurers and governments. Key features:

• All farmers growing notified crops in notified areas (loanee and non-loanee) are eligible; enrolment is mandatory in many states for loanee farmers.
• Premiums: For food crops, the farmer pays up to 2 % of the sum insured in kharif, up to 1.5 % in rabi; for horticulture, up to 5 %. The remainder is subsidised by the Central and State governments.
• Two loss-assessment approaches:
  • Area-based yield loss: yields in a revenue circle or notified area fall below threshold → all enrolled farmers receive payout.
  • Individual farm approach (in limited cases): individual claims triggered if farm-specific loss is certified (less common).
• Workflow: Notification of insured crops/areas → enrolment → yield assessment via crop-cutting experiments (CCEs), remote sensing pilots → claim settlement. Guidelines aim for settlement within two months of harvest.

According to official dashboard data (June 2025): the total claims paid under PMFBY and Restructured Weather Based Crop Insurance Scheme (RWBCIS) show large volumes.
For example, state/UT-wise claims paid between 2019-20 and 2023-24 are published.

Stakeholders: Who Is Affected & How

Farmers

Small- and marginal-farmers (who constitute roughly 86 % of cultivators) are the intended primary beneficiaries. The scheme targets broad coverage. But in practice, benefit incidence varies by state, crop, and enrolment status.

State Governments

States subsidise part of the premium, specify crops/areas, and supervise yield estimation. They also carry fiscal risks during high-loss years and bear overheads for implementation.

Insurance Companies

Private and public insurers administer the scheme, handle data/claims, collect premiums and pay out compensation. Their profitability is impacted by claims-to-premium (CP) ratios, administrative expenses, and re-insurance terms.

Banks & Financial Institutions

For loanee farmers, banks facilitate enrolment and premium deduction; failure to enrol/verify can exclude farmers from credit-linked benefits.

Agriculture Extension & District Administrations

They carry the field-level burden: notifying areas, doing CCEs, capturing data, building awareness, and handling grievances.

Given this stakeholder network, performance depends not just on scheme design but on institutional capacity, incentive structures and data governance across multiple levels.

Evidence-Rich Gaps in Implementation & Design

1. Enrolment, Coverage & Premiums

According to data.gov.in:

• Year-wise enrolments (farmer applications insured) under PMFBY & RWBCIS: e.g., in 2022-23: 11,19,50,452 farmer applications insured with a farmers’ share of premium Rs 3,957.3 crore; 2023-24: 14,34,46,756 enrolled with Rs 3,203.3 crore premium.
• Aggregate sum insured and insured area show growth, but the claims paid relative to premium vary widely. The IIMA evaluation report (2018) showed that in 2016-17, gross premium was Rs 22,165 crore, claims were Rs 13,858 crore → CP ratio ~62.5 %.

2. Claims Paid, Pending & Settlement Ratios

• As on 30 June 2025, state-wise claims pending (2020-21 to 2024-25) show large unresolved liabilities: e.g., Andhra Pradesh had Rs 2,565.8 crore pending in that period.
• In states such as Chhattisgarh, for 2024-25 (up to kharif), reported claims were Rs 248.4 crore; paid: Rs 111.45 crore → settlement ratio ~45%.

3. Geographic/State Variation & Exits

• Punjab exited the scheme in March 2023; as a result, farmers in flood-hit years remained without cover.
• Maharashtra data: Between 2016-17 and 2023-24, premiums collected Rs 52,969 crore, whereas payouts were only Rs 36,350 crore → insurer premium higher than payout by ~45%.

4. Yield Estimation & Data Delays

• The Parliamentary report (via PRS) notes that only 15 states had notified grievance redressal committees as mandated; yield-estimation delays (via CCEs) were a major cause of claim delays.
• The scheme’s Operational Guidelines require tech-enabled CCE (smart sampling, satellite, drone) but many states have not scaled this.

5. Inclusion, Awareness & Equity Concerns

• Many studies reveal that tenant farmers, sharecroppers and women land-holders are inadequately covered because the scheme is linked to land records and formal documentation. The IIMA report flagged non-loanee farmers’ lower uptake.
• In Latur (Maharashtra), over 11,200 farmers who applied were declared ineligible despite experiencing losses-raising serious questions about rejection criteria and transparency.

Interpretation: What the Data Suggests

Fragmentation & weak data systems

Wide variation across states, delayed clearing of pending claims, and low settlement ratios in some regions reveal that not all parts of the system are functioning in sync. Where state agencies, insurers and district machinery are weak, the scheme falters.

Risk-sharing skewed

When insurers collect significantly more premiums than they pay out (as in Maharashtra: 52,969 crore vs 36,350 crore), the risk model looks skewed-farmers pay (through premiums or state subvention), states subsidise heavily, yet farmers may not timely receive benefits.

Coverage versus relevance

High enrolment numbers don’t always translate to timely and equitable payouts. For example, although 11.2 crore farmers reportedly enrolled in one state over eight years, only 6.2 crore got compensated. This gap points to structural mismatches: area-based assessments may fail individual losses; data delays hamper access; administrative bottlenecks exclude eligible farmers.

Institutional inertia and exit risk

States opting out (e.g., Punjab) or limiting participation demonstrate that the scheme’s sustainability depends on state fiscal comfort and institutional willingness. If a state perceives the scheme as fiscally burdensome or administratively opaque, farmers are left vulnerable.

Equity and exclusion issues

The linking of insurance to formal land records, mandatory enrolment deadlines and poor awareness campaigns means that marginalised groups remain under-covered. Inequity undermines the scheme’s core purpose.

Implications: Who Pays & How

Farmers

Delayed payouts or non-coverage translate into distress sales, credit burdens and increased vulnerability in adverse seasons. The safety-net fails when farmers cannot rely on timely compensation.

State Governments

Unresolved claims – for instance, Andhra Pradesh’s Rs 2,565.8 crore pending as on June 2025 – tie up state fiscal bandwidth and credibility. States may face pressure to bail-out insurers or substitute schemes.

Insurance Companies

High premiums, low payouts in good years and heavy losses in bad years can distort incentives. A scheme where insurer profits matter more than farmer cover undermines trust.

Agricultural Governance & Risk Resilience

In a climate-volatile agrarian context, crop insurance should be a pillar of resilience (crop distress → compensation → recovery). When the architecture is weak, the capacity to respond to shocks is eroded.

Inequality and Exclusion

If tenant farmers, women land-holders, small plots or fragmented holdings are excluded, the scheme deepens inequality rather than alleviating it.

Recommendations: Re-engineering PMFBY for Impact

1. Modernise yield estimation & data infrastructure

• Scale satellite-based and drone-based yield assessment nationally.
• Deploy mobile apps for real-time CCE reporting and farmer grievance tracking.
• Publicly display state/district level dashboards: enrolment, premium collected, claims paid, average time to settlement. Transparency builds accountability.

2. Introduce hybrid assessment (area + individual)

• Retain area-based model for large-scale calamities but add individual-farm loss triggers for cases where farmers lose but zone averages are unaffected.
• This will reduce exclusion due to heterogeneity of loss.

3. Strengthen inclusion of vulnerable groups

• Extend cover to tenant/share-croppers through geo-tagged land plots, updated registries and digital proof of cultivation.
• Conduct targeted awareness campaigns in local languages through panchayats, Krishi Vigyan Kendra (KVK) networks and farmer organisations.

4. Reform financial risk-sharing & premium design

• The financial structure of PMFBY needs greater transparency and fairer risk distribution. Publishing insurer-level claims-to-premium (CP) ratios annually by state would help address this imbalance. For instance, Maharashtra’s approximate 45% payout-to-premium ratio over eight years is not merely a statistic – it reflects a systemic opacity in how insurance companies share risk versus how much they retain. Making CP ratios publicly available at both state and district levels would allow policymakers, state governments, and farmers to assess whether insurers are functioning as genuine risk partners or merely accumulating premiums.
• Such transparency would also place performance pressure on insurers: those with consistently poor claim settlement behaviour would face public scrutiny and contractual consequences.
• Alongside transparency, subsidy design must become more equitable. A sliding-scale subsidy model-in which marginal farmers (below two hectares) pay a significantly reduced premium or none at all, while larger farmers contribute proportionally more-aligns public investment with vulnerability rather than acreage alone.
• Finally, the fiscal load on states needs recalibration. In years of widespread losses, delayed state share payments have led to paused enrolments or state withdrawal. Establishing a central risk-stabilisation fund to offset extreme-loss years would protect continuity of coverage and ensure that farmers are not penalised due to budget cycles or intergovernmental delays.

5. Institutionalise grievance redressal & accountability

• Ensure every state sets up the mandated stakeholder grievance committee; currently, only 15 states have done so.
• Mandatory toll-free helplines, mobile grievance apps, and published resolution statistics.
• Link a small component of scheme funding to settlement-timeliness and claim-acceptance metrics.

6. Link insurance to the wider agrarian ecosystem

• Integrate PMFBY with soil health cards, irrigation coverage, crop-rotation advisory, and market intelligence. Insurance should be part of a resilience ecosystem, not stand-alone.
• Use insurance data analytics to identify hotspot geographies for repeat losses; direct extension and infrastructure investment there.

Conclusion

PMFBY remains an ambitious instrument, potentially transformative in an agrarian economy increasingly shaped by climate volatility, unpredictable monsoons, rising input prices, and market instability. Yet ambition alone is not enough. The gaps in data systems, implementation machinery, and institutional incentives continue to dilute the promise of the scheme. The numbers do not reflect a failed idea; they reflect a policy still struggling to match design with delivery.

If India begins treating crop insurance not merely as another subsidy but as a structural governance reform, rooted in transparency, real-time loss assessment, inclusion of tenant and women farmers, and accountability across insurers and states, PMFBY can evolve from a transactional scheme into a foundational pillar of rural risk management. Without such reform, the risk is clear: the scheme may become a costly architecture with limited farmer protection, more symbolic than stabilising.

For millions of farmers, this is not a bureaucratic debate; it is survival. Crop failure is not just a statistical category; it is a lost season, a delayed loan repayment, a postponed education fee, a ration cutback, or a migration decision. Insurance delayed is resilience denied.

The coming decade will determine whether PMFBY matures into a reliable safeguard that farmers can trust or remains a well-intentioned policy constrained by structural contradictions. The stakes are high. Indian agriculture does not need another promise. It needs protection that arrives on time, reaches those who need it most, and restores dignity, not just compensation, to farming livelihoods.

References

Department of Agriculture & Farmers’ Welfare. (2018). Performance evaluation of Pradhan Mantri Fasal Bima Yojana (PMFBY): Part I – Governance analysis. Ministry of Agriculture & Farmers’ Welfare, Government of India. https://desagri.gov.in/

Department of Agriculture & Farmers’ Welfare. (n.d.). Pradhan Mantri Fasal Bima Yojana (PMFBY) operational guidelines and dashboard. Government of India. https://pmfby.gov.in/

Government of India. (2024). State/UT-wise claims paid under PMFBY and RWBCIS: 2019–2024 (Dataset). Open Government Data (OGD) Platform India. https://data.gov.in/

Indian Institute of Management Ahmedabad (IIMA). (2018). Evaluation study of PMFBY and weather-based crop insurance scheme. Government of India. https://iima.ac.in/

Ministry of Agriculture & Farmers’ Welfare. (2025, June 30). Pending claim status under PMFBY: District-wise summary. Press Information Bureau. https://pib.gov.in/