Improving the design of climate insurance: combining empirical approaches and modelling

Extreme weather conditions in the face of due to climate change often disproportionately affects the weakest members of society. Agricultural insurance programs that are specifically designed specifically for smallholders in developing countries are valuable tools that can help farmers to cope with the resulting risks. A broad range of methods including household surveys, experimental games, and agent-based models have been used to assess and improve the effectiveness of such climate insurance products. In addition Furthermore, process-based crop models have been used to derive suitable insurance indices. However, climate change raises specific socioeconomic andas well as environmental challenges that need to be considered when designing insurance schemes. We argue that, in light of these pressing challenges, some of the methodological approaches currently applied to study climate insurance reach their limits when applied independently. This has fundamental implications. On the one hand, not all undesired side effects of insurance can be detected and, on the other hand, insurance indices cannot be derived sufficiently well. We therefore advocate a sound combination of different methods, especially by linking empirical analyses and modelling, and underline the resulting potential with the help of stylized examples. Our study highlights how methodological synergies can make climate insurance products more effective in supporting the most vulnerable households, especially under changing climatic conditions.

Automated summary

Extreme weather conditions caused by climate change disproportionately affect the most vulnerable members of society, and agricultural insurance programs designed for smallholders in developing countries are valuable tools. Combining different methods, especially by linking empirical analysis and modeling, can enhance the effectiveness of climate insurance products, especially under changing climatic conditions.