Comparing the effects of behaviorally informed interventions on flood insurance demand: an experimental analysis of ‘boosts’ and ‘nudges’

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 (LPHC) 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.