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The Insurability Framework Applied to Agricultural Microinsurance: What Do We Know, What Can We Learn?

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Abstract

Agricultural microinsurance has gained increasing attention over the last years. In light of climate change, changing weather trends and more repeatedly occurring natural disasters, a wave of innovative approaches to insuring agricultural production risks, particularly index-based insurance products, have been proposed and implemented. However, the results of many of these projects were disappointing, raising the question whether microinsurance could provide viable coverage for agricultural production risks at all. This paper reviews existing theoretical and empirical research on the feasibility of agricultural microinsurance and discusses different approaches to cover agricultural productions risks. It finds that, contrary to common beliefs, many agricultural production risks, including drought, hail, floods and livestock diseases, could be effectively covered by microinsurance, although not all by index-based approaches. It makes the case for a flexible combination of different approaches to microinsurance, such as community-based and group insurance, with technological advancements such as remote-sensing techniques and index insurance.

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Notes

  1. Höppe (2007).

  2. Churchill (2006).

  3. For an overview about existing agricultural microinsurance schemes, refer to Roth and McCord (2008).

  4. ClimateWise (2011).

  5. See, for example, Loewe (2009) and Brown and Churchill (2000).

  6. Loewe (2006).

  7. Coheur et al. (2007).

  8. Giné et al. (2010).

  9. Vaté and Dror (2002).

  10. See, for example, Berliner (1982) and Rejda (2008). Additionnal evidence has been provided i.a. by Gollier (2001) and Nguyen (2007).

  11. Index-based insurance bases payouts on the realisation of exogenous, objectively assessable indices, such as rainfall levels, that are correlated with actual losses instead of assessing losses on a case by case basis.

  12. Nguyen (2007).

  13. A crop insurance pilot in Bolivia used yield realisations of a representative farmer’s fields as trigger for payouts. Since realised yield not only depends on weather shocks or pests and diseases, but also on a farmer’s ability and performance, such an index clearly violates the first insurability condition. Area/yield based triggers are only appropriate if they cover a random sample of all farmers in one region.

  14. Arrow (1983).

  15. Gollier (2001).

  16. Hess et al. (2005).

  17. It remains to be noticed, however, that indemnity-based insurance contracts also create basis risk. Payouts are usually calculated as the difference between current yield and a moving average of observed yield in the last 4–10 years. Such an average can be more or less representative of a farmer’s expected yield. The lower this representativity, the higher is a farmer’s basis risk. See Barnett et al. (2005).

  18. Zweifel and Eisen (2003).

  19. Loewe (2009).

  20. Roberts (2005).

  21. Chantarat et al. (2013).

  22. Hess (2007).

  23. No indemnity-based agricultural insurance products were found that cover drought, do not depend on public subsidies and still reach high crop insurance penetration rates.

  24. Payouts are made on the basis of area livestock mortality rate indices. As these data have been collected since the 1920s, sufficient information for risk calculation and classification as well as for the purchase of reinsurance was available. For more information, refer to www.iblip.mn and Hellmuth et al. (2009).

  25. Although much of Kilimo Salama is loan default insurance, a significant number of clients purchase unsubsidised, unbundled individual contracts.

  26. Munich Re (2009).

  27. In Uruguay, non-subsidised agricultural insurance covers hail risk in an indemnity-based scheme. Penetration rates are quite high: about 80 per cent of the total area sown is covered by this scheme. However, it is not clear if this insurance scheme is accessible to smallholder farmers or only to large-scale farms. (Mahul and Stutley, 2010).

  28. Wyser Vizcarra (2013).

  29. Roberts (2005); Kang (2007).

  30. Mahul and Stutley (2010).

  31. Gurenko and Mahul (2004).

  32. In a group insurance, the transaction costs of loss assessment could be divided among all claimants. This is only possible if group members live in the same community or region and if all policyholders are affected by the same hazard.

  33. Banerjee and Berg (2011).

  34. CRED (2011).

  35. Although its name suggests otherwise, this scheme covers households directly, see Oxfam (2013).

  36. Citlak and Wagner (2001).

  37. Hausmann (1998).

  38. Lotsch et al. (2010).

  39. In the Indonesian scheme, payouts are linked to water gauge levels in Jakarta. But, basis risk was so high that it seriously hindered insurance take-up. For a description of this index-based flood insurance, see Rohregger and Rompel (2010).

  40. Oxfam (2013).

  41. Skees and Collier (2010); Lotsch et al. (2010).

  42. Roth and McCord (2008).

  43. De facto, IFFCO-TOKIO cross-subsidises its livestock insurance with other products; see Dalal et al. (2012).

  44. See Dalal et al. (2012).

  45. Hung (2010).

  46. Lamballe and Rosner (2005).

  47. Roberts (2007).

  48. Coble et al. (2006).

  49. Dufhues et al. (2004).

  50. See, for example, Binswanger-Mkhize (2012) and Miranda and Farrin (2012).

  51. This approach has been implicitly used for quite some time in many credit schemes, inter alia in Malawi, where group members are jointly liable for each other’s loan payments. See, for example, Simtowe and Zeller (2006).

  52. Clarke (2011) and Dercon et al. (2014).

  53. Ibarra (2004).

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Acknowledgements

I would like to thank Gerald Leppert, Markus Loewe, and Hans-Jürgen Rösner for their continued support and advice. Special thanks are also due to Tilman Altenburg, Imme Scholz and two anonymous reviewers for their many helpful comments.

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  1. Competitiveness and Social Development, German Development Institute and University of Passau, Tulpenfeld 6, Bonn, 53113, Germany

    Esther Gehrke

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Gehrke, E. The Insurability Framework Applied to Agricultural Microinsurance: What Do We Know, What Can We Learn?. Geneva Pap Risk Insur Issues Pract 39, 264–279 (2014). https://doi.org/10.1057/gpp.2014.2

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