Index Insurance, Probabilistic Climate Forecasts, and Production

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ABSTRACT

Index insurance and probabilistic seasonal forecasts are becoming available in developing countries to help farmers manage climate risks in production. Although these tools are intimately related, work has not been done to formalize the connections between them. We investigate the relationship between the tools through a model of input choice under uncertainty, forecasts, and insurance. While it is possible for forecasts to undermine insurance, we find that when contracts are appropriately designed, there are important synergies between forecasts, insurance, and effective input use. Used together, these tools overcome barriers preventing the use of imperfect information in production decision making.

INTRODUCTION

Climate-related risks have profound impacts on agricultural producers around the world. (1) These effects are particularly important in developing countries, where agriculture makes a significant contribution to gross domestic product (World Bank, 2001) and insurance markets are underdeveloped or nonexistent. Recently, microinsurance and probabilistic seasonal forecasts have become available to help farmers manage climate risks in production. Although these tools are intimately related, work has not been done to formalize the fundamental connections between them. (2)

It is well known that climate risk can keep low-income households in poverty traps (see a review by Barnett, Barrett, and Skees, 2007). Lack of assets and risk exposure may lead households to forego activities with high returns, perpetuating their poverty. In response to this challenge, innovative insurance pilots to help farmers in developing countries have been gaining increasing interest (Hellmuth et al., 2009). (3) Recent studies analyze the potential of insurance in helping to escape poverty traps (Kovacevic and Pflug, Forthcoming).

Work in the agricultural economics literature has examined the relationship between insurance and input usage for both farm-level insurance (e.g., Ramaswami, 1993; Babcock and Hennessy, 1996) and index insurance (Chambers and Quiggin, 2000; Mahul, 2001). Focusing on the relationship between insurance and input use, this literature does not address interactions between insurance, climate forecast, and input decisions. Trade-offs between basis risk and moral hazard afforded by index-based products have been analyzed from an insurance company standpoint (Doherty and Richter, 2002).

Seasonal climate is predictable in many regions of the world (Goddard et al., 2001), with the El Nino Southern Oscillation (ENSO) linked to variations of seasonal precipitation (Ropelewski and Halpert, 1987). Work in agricultural and climate science has modeled the impact of probabilistic climate forecasts on production decisions (e.g., Hansen, 2002) but has, with few exceptions (Mjelde, Thompson, and Nixon, 1996; Cabrera, Letson, and Podesta, 2005), ignored the impact of insurance. (4)

Although yields and production practices are impacted by ENSO phases, this relationship is not built into existing insurance products. Insurance implementers acknowledge that climate forecasts may undermine the financial soundness of a product by providing opportunities for intertemporal adverse selection. The advocated strategies are to finalize insurance transactions contracts months ahead of time, before the forecast is informative (see, e.g., Hess and Syroka, 2005; World Bank, 2005), or allow insurance premiums to reflect forecast information (Skees, Hazell, and Miranda, 1999). Implementation of these strategies is undermined by the lack of a clear conceptual understanding about the interaction between seasonal forecast, insurance, and production decisions. Potential opportunities to exploit synergies may be being missed. Since the reinsurance industry uses probabilistic seasonal climate forecasts in pricing (Hellmuth et al., 2009), (5) it is important to understand forecasts and pricing from the perspective of the insurance company that must develop a product that: (1) results in products that provide value to farmers, unlocking consumer demand; (2) reflects fluctuating reinsurance costs; and (3) cannot be undermined through strategic use of forecast information. …