We present results of experimental games with smallholder farmers in Tigray, Ethiopia, in 2010, in which participants in the games allocated money across risk management options. One of the options was index insurance that was the same as commercial products sold locally. Participants exhibited clear preferences for insurance contracts with higher frequency payouts and for insurance over other risk management options, including high interest savings. The preference for higher frequency payouts is mirrored in commercial sales of the product, with commercial purchasers paying substantially higher premiums than the minimal, low frequency option available. This combined evidence challenges claims that the very poor universally choose minimal index insurance coverage and supports concerns that demand may outpace supply of responsible insurance products.

A challenge in addressing climate risk in developing countries is that many regions have extremely limited formal data sets, so for these regions, people must rely on technologies like remote sensing for solutions. However, this means the necessary formal weather data to design and validate remote sensing solutions do not exist. Therefore, many projects use farmers’ reported perceptions and recollections of climate risk events, such as drought. However, if these are used to design risk management interventions such as insurance, there may be biases and limitations which could potentially lead to a problematic product. To better understand the value and validity of farmer perceptions, this paper explores two related questions: (1) Is there evidence that farmers reporting data have any information about actual drought events, and (2) is there evidence that it is valuable to address recollection and perception issues when using farmer-reported data? We investigated these questions by analyzing index insurance, in which remote sensing products trigger payments to farmers during loss years. Our case study is perhaps the largest participatory farmer remote sensing insurance project in Ethiopia. We tested the cross-consistency of farmer-reported seasonal vulnerabilities against the years reported as droughts by independent satellite data sources. We found evidence that farmer-reported events are independently reflected in multiple remote sensing datasets, suggesting that there is legitimate information in farmer reporting. Repeated community-based meetings over time and aggregating independent village reports over space lead to improved predictions, suggesting that it may be important to utilize methods to address potential biases.

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