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The design and implementation of index insurance initiatives: Three challenges for policy

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Presented by Michael R. Carter (University of California) at the IBLI policy workshop, Nairobi, 9 June 2015

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The design and implementation of index insurance initiatives: Three challenges for policy

  1. 1. The Design and Implementation of Index Insurance Initiatives: 3 Challenges for Policy Michael R Carter NBER & University of California, Davis BASIS I4 Index Insurance Innovation Initiative http://basis.ucdavis.edu Workshop on Developing Policy Innovations for the Pastoralist Rangelands International Livestock Research Institute, Nairobi June 8, 2015 M.R. Carter Index Insurance Design & Implementation
  2. 2. What Can Index Insurance Do? Know that risk is directly costly Makes Households Poor when it leads them to adopt less risky activities, but lower returning activities Keeps Households Poor when it leads them accumulate unproductive ’buffer’ assets Deepens capital constraints also as Cuts off self-finance by holding of ’unproductive’ savings Discourages external finance (credit), especially when risk is correlated Reducing risk via insurance should address all these problems Note also that when twined with technology adoption, insurance becomes relatively inexpensive But can insurance really work for low wealth agricultural & pastoralist households? M.R. Carter Index Insurance Design & Implementation
  3. 3. What Can Index Insurance Do? Maize producers in Ghana invest more when insured Randomly offered some farmers insurance at variable prices Other farmers offered a capital grant for purchasing inputs Found that farmers offered insurance: Expand area cultivated by some 15% Increase input use by 40% Capital grants by themselves have little impact Source: Karlan et al. (2014). “Agricultural Decisions after Relaxing Risk & Credit Constraints,” Quarterly J of Econ. M.R. Carter Index Insurance Design & Implementation
  4. 4. What Can Index Insurance Do? Cotton producers in Mali invest more when insured Designed a dual-scale index contract with radically lower basis risk Insurance offered to a random subset of villages–find that in insured villages: Area planted to cotton & input use increased by 45% Output & income increased as expected (result not statistically significant) Currently scaling up in Burkina Faso Source: Elabed & Carter (2014) “Ex-ante Impacts of Agricultural Insurance: Evidence from a Field Experiment in Mali,” Working Paper M.R. Carter Index Insurance Design & Implementation
  5. 5. What Can Index Insurance Do? Kenyan pastoralists reduce dependence on costly coping strategies IBLI Insurance had payout in October 2011 after a prolonged drought that sparked 30-40% livestock mortality October 2011 survey asked insured and uninsured households how they had been coping with the drought prior to the payout/survey & how they anticipated coping after the payout/survey M.R. Carter Index Insurance Design & Implementation
  6. 6. What Can Index Insurance Do? Kenyan pastoralists reduce dependence on costly coping strategies Initially better off households: Before Payout: No impact on consumption reduction nor on asset sales prior to payout After Payout: 65 %-point reduction in asset sales after payout Initially worse off households: Before Payout: 30 %-point reduction in “meals reduced” prior to payout; No impact on asset sales After Payout: 43 %-point reduction in “meals reduced” after payout; No impact on asset sales Source: Janzen & Carter (2014) “After the Drought: The Impact of Microinsurance on Consumption Smoothing and Asset Protection,” NBER Working Paper No. 19702.M.R. Carter Index Insurance Design & Implementation
  7. 7. Policy Challenges for Index Insurance Achieving these social protection and growth impacts faces key policy challenges: 1 Cost-effective, integrated social protection system Response: Public provision of minimum insurance protection for vulnerable families 2 Quality contracts that do not fail households when losses occur Response: Public certification of index insurance quality 3 Competitive insurance (risk) pricing Response: Backstop public reinsurance facility Let’s look at each of these M.R. Carter Index Insurance Design & Implementation
  8. 8. Challenge 1 Cost-effective, integrated social protection system Cash transfers, food aid & other forms of social protection target those who are poor and have perhaps become economically unviable/stuck Clear financial logic to protecting the vulnerable so that they do not fall into destitution and become transfer eligible And yet public funds devoted to the vulnerable diverts resources from the already poor While the ability of the vulnerable to pay for their own insurance is limited, analysis suggests that some public funding of insurance for the vulnerable can be cost effective M.R. Carter Index Insurance Design & Implementation
  9. 9. Challenge 1 Cost-effective, integrated social protection system Constant budget policy experiments Cash transfers for indigent 50% insurance subsidy for poor and vulnerable, with residual budget spent on cash transfers M.R. Carter Index Insurance Design & Implementation
  10. 10. Challenge 1 Cost-effective, integrated social protection system In the policy simulation, insurance subsidy works well because it harnesses two forces: Restores assets so families do not collapse Enhances investment incentives so families can prudentially invest & reduce vulnerability Together these forces bring the dramatic drops in chronic poverty & increases in self-reliance Key features of this approach Public purchase (or partial purchase) helps make the market (& instills confidence) Unlike ad hoc schemes in some South American countries, individuals can buy insurance beyond the publicly-funded margin, enhancing investment incentives & growth M.R. Carter Index Insurance Design & Implementation
  11. 11. Challenge 2 Quality contracts that do not fail households Disappointed (angry) farmers & what are sometimes called “Basis Risk Events” have punctuated the importance of designing contracts that protect farmers The problem is far from trivial as the following analysis of the relationship between average losses and indemnity payments under rainfall insurance in India shows: Clarke, D. et al. (2012). “Weather Based Crop Insurance in India.” Such contract failures can also hurt lenders & destroy trust & confidence in insurance companies (and government) M.R. Carter Index Insurance Design & Implementation
  12. 12. Challenge 2 Quality contracts that do not fail households At its simplest, insurance should stabilize income flows (and, or protect assets), underwriting: More stable consumption and stable investment in human capital (nutrition & education) that are subject to ’irreversiblilities’ Improved investment incentives by reducing the risk of capital loss A simple picture can help frame our thinking: M.R. Carter Index Insurance Design & Implementation
  13. 13. Challenge 2 Quality contracts that do not fail households Perfect insurance will put a level floor under families Index insurance will never reach that level of protection However, can use a localized (say village level) area yield contract as a benchmark Keep in mind that index insurance will not be the answer if risk is not the constraint or if most risk is individual (not common or covariate) M.R. Carter Index Insurance Design & Implementation
  14. 14. Challenge 2 Quality contracts that do not fail households Safe minimum quality standard: Expected utility allows calculation of a ’reservation’ price defined as the maximum amount an individual could pay for a contract without making herself worse off A safe minimum quality standard might be: Reservation Price > Market Price Let’s look at a real example from Tanzanian rice cultivation M.R. Carter Index Insurance Design & Implementation
  15. 15. Challenge 2 Measuring insurance quality for rice farmers in Northern Tanzania M.R. Carter Index Insurance Design & Implementation
  16. 16. Challenge 2 Village-level Area Yield vs. Optimized Satellite-based Contract For each small area (“village”), we collected 10 years of retrospective data on yields Best satellite predictor of village yields proved to be based on ’Gross Primary Production’ (based on EVI, FPAR & LAI) Let’s compare this (cheap to administer) satellite based index with an (expensive) village-level area yield contract: 50010001500200025003000 Predictedyield(kg/acre) 500 1000 1500 2000 2500 Actual yield (kg/acre) Predicted vs. actual area yields in Makindube M.R. Carter Index Insurance Design & Implementation
  17. 17. Challenge 2 Will I get paid probability measure Consider a contract that pays anytime either measured or satellite predicted village yields fall below average: 0.1.2.3.4.5.6.7.8.91 Probabilityofpayout 0 .2 .4 .6 .8 1 1.2 1.4 Plot-level yield (% of historic mean) 95% Confidence Interval Satellite Index Contract Area-Yield Contract Probability of receiving a payout by zone-level yields M.R. Carter Index Insurance Design & Implementation
  18. 18. Challenge 2 How much will I get paid measure M.R. Carter Index Insurance Design & Implementation
  19. 19. Challenge 2 Reservation Price Quality Measure Actuarially fair prices for these contracts are 130 kg of rice per-hectare insured Unrealistically, assuming no local risk sharing Minimalist Quality Standard: Reservation Price > Market Price of Contract M.R. Carter Index Insurance Design & Implementation
  20. 20. Challenge 2 Can We Do Better with an Audit Rule? Can see that the satellite does well separating good from bad, but has trouble distinguishing quite bad from slightly bad What if we augmented satellite index with an audit on demand scheme: Agreed upon crop cut methodology at village level Incentive compatible penalties to prevent unnecessary audits M.R. Carter Index Insurance Design & Implementation
  21. 21. Challenge 2 Can We Do Better with an Audit Rule? Assume that audits only requested when predicted yields are 5% below actual village area yields 17% of the time audits will take place Shadow price will be close to pure area yield insurance Data collection costs will only be 17% of those under area yield! M.R. Carter Index Insurance Design & Implementation
  22. 22. Challenge 2 Work here suggests a safe minimum quality standard: Reservation Price > Market Price Note that even if insurance is subsidized, beneficiaries would be better if simply given the subsidy rather than given cheap insurance that approximates a lottery ticket Issues of quality assurance are very important: Clarke & Wren-Lewis (2014) make a very compelling argument that without quality certification standards, markets will supply low quality contracts predominate Much to learn about how to implement those standards, but also how to design contracts that meet those standards M.R. Carter Index Insurance Design & Implementation
  23. 23. Challenge 3 Competitive Pricing Some jargon: The actuarially fair price (or pure premium) of an insurance contract is equal to the expected payouts under the contract To cover administration costs (which should be low for index insurance) & cost of capital, insurance companies have to mark-up or load the cost of insurance (in US crop insurance, the mark-up results in a price that is between 125% and 130% of the ’AFP’) A number of index insurance pilots have faced prices of 175-200% of AFP–why? Thin markets, small project size & lack of competition/interest Sparse data and uncertainty-penalized pricing It is clear that prices at this level are non-starters for low wealth households What is to be done? M.R. Carter Index Insurance Design & Implementation
  24. 24. Challenge 3 Competitive Pricing With generous support from various donors, the IFC’s Global Index Insurance Facility (GIIF) has to date provided an across the board subsidy on prices set by private industry to various projects, including IBLI The USAID-funded Global Action Network on Index Insurance, collaboration with the ILO & I4 is now in conversation with IFC & the World Bank about alternative ways to lower price Let a public sector entity (not allergic to uncertainty) carry the layers of risk that are most worrisome to the private sector Let a public sector entity serve a broker role, transparently pricing and putting out to bid contracts with certified risk estimates (perhaps bundling together multiple projects in the spirit of the African Risk Capacity) This same entity could also certify quality Note that creating a minimum demand based on public purchase of basic insurance coverage could further help bring down prices M.R. Carter Index Insurance Design & Implementation
  25. 25. In Conclusion While we are beginning to see that index insurance can realize its promise to fundamentally alter income growth & poverty dynamics, it is no magic bullet Substantial challenges remain to be resolved if index insurance is to fully reach its promise The KLIP program is promising, not only because it is well-designed, but also because it will likely help us answer many of the key challenges about index insurance that we have discussed today Forward! M.R. Carter Index Insurance Design & Implementation
  26. 26. Thank you! M.R. Carter Index Insurance Design & Implementation

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