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Helping smallholder farmers manage risks: Innovations to improve agricultural insurance

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PIM Webinar held on 19 September 2018.
Presenters: Berber Kramer (IFPRI), Patrick Ward (Duke Kunshan University). More information at http://bit.ly/AgInsuranceWebinar

Published in: Science
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Helping smallholder farmers manage risks: Innovations to improve agricultural insurance

  1. 1. Helping smallholder farmers manage risks: Innovations to improve agricultural insurance Berber Kramer1 and Patrick S. Ward2 PIM Webinar September 19, 2018 1 Research Fellow in the Markets, Trade and Institutions Division of the International Food Policy Research Institute 2 Assistant Professor of Environmental Economics and Policy in the iMEP program at Duke Kunshan University
  2. 2. Impacts of weather shocks on livelihoods
  3. 3. Impacts of weather shocks on livelihoods
  4. 4. Impacts of weather shocks on livelihoods
  5. 5. Global warming: Worsening vulnerability to food insecurity Change in Hunger and Climate Vulnerability Index relative to baseline calculated for simulated climate states at 2°C global warming Source: Betts et al. (Phil. Trans. R. Soc. A, 2018).
  6. 6. Challenges in providing agricultural insurance Traditional indemnity-based insurance: ▪ High administrative and transaction costs. ▪ Asymmetric information between insured and insurer, which in turn gives rise to adverse selection and moral hazard. Index-based insurance was designed to overcome these challenges. Problem: ▪ Basis risk: The index used to determine payouts does not capture farmers’ conditions on the ground ▪ Lack of trust and understanding
  7. 7. This webinar: Outline 1. Using a stream of smartphone camera data over time to monitor crop conditions, management and claims of crop losses and damage ▪ Formative evaluations on the feasibility and economic viability of picture-based insurance and advisory services in India 2. Bundling insurance with climate-smart, risk-reducing, agricultural practices and technologies ▪ Studies on insurance design and evaluating impacts in Bangladesh and India
  8. 8. Innovation I: Picture-Based Insurance (PBI)
  9. 9. Picture-based loss assessment
  10. 10. Tamper-proof smartphone app Initial picture of a site: Saved within the app
  11. 11. Features to ensure standardization Follow-up pictures: Taken of same site as initial picture Goal: More accurate loss assessment and limit moral hazard
  12. 12. Punjab Haryana Formative evaluation of PBI ▪ Study in 6 districts, 50 villages, 750 wheat producers ▪ In return for sending in pictures of their crops, they received insurance: ▪ Weather index-based for extreme heat, excess rainfall ▪ Wth picture-based loss monitoring, covering any visible damage from natural disasters e.g. lodging and pest/disease
  13. 13. Four questions 1. Does picture-based loss monitoring help reduce basis risk? 2. Can pictures be used to monitor crop growth stages? 3. Does this approach improve demand for insurance? 4. Should we worry about tampering and moral hazard?
  14. 14. Lower yields among farmers with insurance payouts? 19.84 19.82 No payout Payout CCEsYield (Quintalsperacre) Weather index-based insurance
  15. 15. Picture-based loss assessment performs better
  16. 16. Normalized greenness is predictive of growth stage Application for weather index-based insurance: cover a specific growth stage instead of a fixed calendar period, which can help shorten the coverage period and reduce costs Tillering Stem extension Heading + ripening Source: Hufkens et al., 2018
  17. 17. Four questions 1. Does picture-based loss monitoring help reduce basis risk? √ 2. Can pictures be used to monitor crop growth stages? √ 3. Does this approach improve demand for insurance? 4. Should we worry about tampering and moral hazard?
  18. 18. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.5% 3.1% 4.6% 6.2% 7.7% 9.2% 10.8% 12.3% 13.8% 15.4% 16.9% 18.5% INSURANCE PREMIUM (% OF SUM INSURED) WBI only PBI only WBI + PBI Demand is higher for PBI than for WBI
  19. 19. No evidence of tampering or moral hazard
  20. 20. Results 1. Does picture-based loss monitoring help reduce basis risk? √ 2. Can pictures be used to monitor crop growth stages? √ 3. Does this approach improve demand for insurance? √ 4. Without inducing tampering and moral hazard? √
  21. 21. ▪ Working with HDFC ERGO General Insurance to use picture-based loss assessment in existing insurance products o Insurance actors in Kenya have also expressed interest ▪ Automate image processing and damage detection to the extent possible o Large training sets required ▪ Impact evaluation, with focus on higher value/risk crops (e.g. tomatoes) o Emphasis on technology acceptance and inclusiveness ▪ Enabling environment: Complementarities with agro-advisory services and other financial services (credit, price insurance) ▪ Vision: Existing index-based products (covariate risk) with picture-based loss monitoring (idiosyncratic risk / second fail-safe trigger). Picture-Based Insurance: What’s Next?
  22. 22. Innovation II: Bundling index- based insurance with CSA
  23. 23. Payoff/Profits Intensity of disaster Conventional varieties Drought-tolerant variety Traditional index insurance Optimized index insurance None Moderate ExtremeSevere Why bundle climate-smart agriculture with insurance?
  24. 24. Drought-tolerant rice ▪Several varieties developed under the Stress-Tolerant Rice for Africa and South Asia (STRASA) program ▪Sahbhagi dhan released in 2010 and approved for cultivation in upland and rainfed lowland rice-growing regions oShort duration: allows the crop to escape early/late season drought (late monsoon onset, early monsoon cessation) oCan withstand longer dry spells than most widely-used local varieties
  25. 25. Optimized weather index insurance ▪Structured to begin paying out to farmers during “severe” droughts ▪Payouts structured to be roughly equivalent to the value of lost production during “severe” droughts oCoverage area per unit of insurance 0.1 ac
  26. 26. Product marketing ▪Product was marketed through information sessions at the village level ▪Year 1: Randomized subsidies (up to 30%) ▪Year 2: Flat effective price, but some villages were offered a subsidy (on top of an inflated price)
  27. 27. Patterns of uptake across years Purchased in Year 1 Did not purchase in Year 1 Purchased in Year 2 25.5% 11.8% Did not purchase in Year 2 33.1% 29.6%
  28. 28. Determinants of DT-WII demand ▪Sequential decisionmaking process 1. Should I purchase? (participation) 2. How much should I purchase? (intensity)
  29. 29. What might we expect to influence uptake? ▪ Price (subsidy level) ▪ Drought expectations ▪ Behavioral characteristics: o Time preferences o Risk preferences o Ambiguity preferences ▪ Trust in the insurance provider ▪ Neighbors’ purchases (peer effects) ▪ Year 1 ▪ Year 2 ▪ Receipt of a “subsidy” ▪ Drought expectations ▪ Behavioral characteristics ▪ Trust in the insurance provider ▪ Neighbors’ purchases ▪ Experiences from Y1 o Did they purchase in Y1? o Did they experience a drought in Y1? ▪ Neighbors’ experiences in Y1 o Did neighbors purchase in Y1? o Did neighbors experience a drought in Y1?
  30. 30. What actually influences uptake in Year 1 ▪Price affects participation in the market (-) ▪Peer effects crowd in participation (+) ▪Risk aversion does not affect participation, but limits the intensity (-) ▪Trust in the insurer increases intensity of participation (+)
  31. 31. What actually influences uptake in Year 2 ▪ Own experience in Y1 matters a lot oIf farmer purchased DT-WII in Y1 and experienced a drought (but also rec’d insurance payout), more likely to participate (+) and purchase more units (+) in Y2 ▪ Experience of neighbors not important oHard to observe? ▪ Peer effects crowd in participation and intensity in Y2 (+)
  32. 32. No effect of price reduction framed as subsidy ▪Year 1 results indicate that farmers are sensitive to price ▪Year 2 results indicate that this price-sensitivity is not sensitive to how it is framed ▪Policy implications: More sustainable/less distortionary investments to reduce the cost of the product could be successful at stimulating demand
  33. 33. Why does this matter? ▪ Can DT-WII offset the ex ante and ex post impacts of droughts? ▪ Evidence from a similar WII product in Bogra district, Bangladesh suggests it can: o Ex ante o Increased investments in agricultural production during the monsoon season (coverage period) as a result of having risk management (risk management effect) o Ex post o Increased investments in agricultural production during the dry season (not covered by insurance) as a result of increased income from the insurance payout (income effect)
  34. 34. Concluding remarks ▪ Exciting time to work on agricultural insurance ▪ There have been many exciting innovations in recent years that can contribute to the development of improved agricultural insurance products that can meet the needs of farmers ▪ But we must not rest on our laurels – there is more work to be done: o Lots of excitement around the use of remote sensing technologies, but current generation of open-access or affordable remote sensing too coarse to detect plot-level losses o Picture-based insurance project demonstrates possibility of engaging with farmers to document crop losses o Potential for positioning insurance as part of a portfolio of risk management products, including CSA
  35. 35. Thank you! For more information: (Project notes available at https://www.ifpri.org/project/PBInsurance) Contact Berber Kramer (B.Kramer[at]cgiar.org)

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