Published online by Cambridge University Press: 11 October 2019
This paper compares the effects of two types of behaviorally informed policy – nudges and boosts – that are designed to increase consumer demand for insurance against low-probability, high-consequence events. Using previous findings in the behavioral sciences literature, this paper constructs and implements two nudges (an ‘informational’ and an ‘affective’ nudge) and a statistical numeracy boost and then elicits individual risk beliefs and demand for flood insurance using a contingent valuation survey of 331 participants recruited from an online labor pool. Using a two-limit Tobit model to estimate willingness to pay (WTP) for flood insurance, this paper finds that the affective and informational nudges result in increases in WTP for flood insurance of roughly $21/month and $11/month relative to the boost, respectively. Taken together, the findings of this paper suggest that nudges are the more effective behaviorally informed policy in this setting, particularly when the nudge design targets the affect and availability heuristics; however, additional research is necessary to establish sufficient conditions for this conclusion.