While index insurance o ffers a compelling solution to the problem of covariant risk among smallholder farmers in developing countries, most weather-based contracts have su ffered from poor quality. This paper demonstrates that a contract based on satellite data combined with a second-stage conditional audit has the potential to improve index insurance quality. We develop a welfare-based metric for quantifying index insurance quality and use this to identify an optimal audit trigger. Using plot-level panel data of 323 smallholder rice farmers in Tanzania, we estimate a hypothetical willingness to pay for a set of contracts. Our simulation results indicate that demand for the conditional audit contract is 36% under reasonable assumptions, while demand for a satellite-only contract is 22%. Overall, these results indicate that in data-scarce environments where area-yield contracts are infeasible, a satellite-based conditional audit contract may be superior to standard index insurance contracts.

Work joint with Jon Einar Flatnes and Ryan Mercovich.