Weather insurance is a financial instrument proposed to increase coverage of unprotected weather shocks in developing countries. Structuring sales as group-based products has been argued as a strategy to increase the attractiveness of index insurance, raising the question as to what impacts farmer demand for group insurance choices. We test if farmers prefer to purchase real-world insurance products as groups, and if groups of more similar individuals are more likely to demand group over individual index insurance for the upcoming season. We exogenously assign farmers into groups of similar versus dissimilar perceived farm size. We find that farmers, when offered, prefer group over individual insurance contracts, and that groups of farmers who perceive each other to be more similar in farm size are more likely to purchase in a group, but purchase less insurance on average.

A new data set was commissioned over Latin America with the goal of supporting decision‐making in various socioeconomic activities and, in particular, for climate insurance products. The Historical Database for Gridded Daily Precipitation Dataset over Latin America ( LatAmPrec ), based on the combined scheme approach developed at the Centro de Previsão de Tempo e Estudos Climáticos, Instituto Nacional de Pesquisas Espaciais (CPTEC/INPE), provides a new high‐resolution, low‐latency, gauge–satellite‐based analysis of daily precipitation over Latin America for the period March 2000–July 2017. In order to understand the strengths and limitations of the new data set for use in weather index insurance, the present study applies two different validation methodologies. The first focuses on capturing, through a cross‐correlation process, the accuracy and improved characteristics of the new gauge‐merged data set. Second, to gauge the skill of the data set in the context of insurance losses, the study uses a statistical approach, previously applied at a village level and here applied at regional levels, to assess how well the new data set predicts evidence of loss events. This is performed for both farmer interview data and national‐level disaster data sets. The results from both validation methodologies show that LatAmPrec performs well when compared with other data sources and can satisfactorily capture the insurance‐relevant losses on the ground. One main advantage of the new product is its high spatial resolution and low latency compared with other existing products used in the weather index insurance industry.