This can be because information is simply missing, or because the search costs for this information and insurance coverage are too high. The second is a tendency to underestimate the true probability of disaster as it applies to oneself. There are well known behavioral inconsistencies associated with hyperbolic discounting, Monte Carlo fallacy with recency bias (reinforcement learning with negative updating as time passes), and denial of self-exposure to particular risks. The third is narrow bracketing, in the sense that consistency is not achieved in insurance coverage across categories of risk (Rabin, 1998). Some risks may be over-insured while most (background risks) are under-insured. The fourth is strategic behavior toward public shock-coping interventions. This is the typical Samaritan’s dilemma due to moral hazard behavior. The fact that the state offers aid affects the recipient’s behavior, inducing him to take more risks and under-insure, and consequently increase the “need” for future aid.

If the state considers designing a DRFI program, it needs to assess two behavioral responses to make an optimum decision. The first is agent responses to changes in exposure to residual risk after a DRFI program has been introduced; the second is the degree of political support the state will get if it engages in fiscally costly risk protection. These two behavioral responses need to be known to appraise the expected impact of risk of DRFI.

We have experience in assessing willingness-to-pay in insurance games (de Janvry, McIntosh, Povell, and Sadoulet, 2013). In these games-in-the-field, agents such as farmers are presented with randomized risk scenarios combined with access to different financial instruments. Decisions are made and rewards collected according to outcomes. Games can be managed to approximate stakes in real-life situations. Results show that the demand for index-based insurance varies in relation to background and basis risk (as predicted by Clarke, 2011). Specifically, we find that demand for insurance against a particular risk declines with the level of background risk. Demand for hybrid insurance can also be assessed. In the particular case of coffee cooperatives in Guatemala, there is a great deal of interest in institutional level index-based insurance, but rules for the intragroup distribution of payouts on an observed damage basis need to be clearly codified to have ex-ante credibility and induce demand for the hybrid product. Since most catastrophic insurance is index-based and contracted at the level of an institution, credible commitments on how payouts will be allocated intra-institution is equally key to mobilize willingness to pay (or political support) for this type of insurance.

Results from insurance games also show that willingness-to-pay is typically well below fair price. Hence the need for subsidies to initiate take-up. This has raised the important question of how best to use one-time subsidies to induce repeated purchase of insurance coverage. Using an RCT to study weather insurance take-up in China, Cai et al. (2014) find that people learn about insurance not by holding an insurance policy (habit formation from use) but by witnessing insurance payouts either to oneself (learning-bydoing) or to others in your social network (learning-from-others). Subsidies may have the disadvantage that they undermine attention given to learning from observed payouts, but the advantage of broadening immediate adoption and hence the occurrence of payouts that can be witnessed and on the basis of which learning takes place. Since most 6