Leveraging Behavioral Science to Close the Flood Insurance Gap
March 30, 2020
Despite the fact that flood insurance is critical to weathering potentially high-cost disaster events, many households fail to purchase coverage. In the United States, the take-up rate (i.e., the proportion of households purchasing insurance) for flood insurance available through the National Flood Insurance Program (NFIP) remains around 49% in high risk areas. This shortfall in the take-up of insurance among those at risk is often referred to as the “flood insurance gap.”
Given the importance of flood insurance for financial recovery, why is there still a flood insurance gap in the US?
While there are a number of non-behavioral factors that affect households’ decision to purchase flood insurance, such as ability-to-pay and lack of information about risk or insurance, behavioral biases likely play a large role. Indeed, experiments and empirical studies confirm that many individuals neglect low-probability risks such as those posed by flooding and do not purchase insurance, while others reveal a willingness to pay (WTP) for flood insurance that exceeds the loss’s expected value. This is consistent with biased decision-making at the household level.
What does the presence of biased decision-making in the market for residential flood insurance mean for policymakers seeking to close the flood insurance gap?
In a recent study, I leverage findings from the behavioral sciences literature to design and test behaviorally-informed policy interventions with the goal of increasing flood insurance take-up. I find that that these policy levers potentially have a significant role to play in closing the flood insurance gap.
Interest in behavioral policy interventions has increased dramatically in recent years thanks in part to the incorporation of key insights from psychology into economic models and the popularization of the field through well-known books. Behavioral policies assume many different forms, but a helpful taxonomy distinguishes between ‘nudges’ and ‘boosts.’ Generally, these two policy types are defined as follows:
- Nudges are changes to a decision frame (i.e., the manner in which a decision is presented) that alter individuals’ behavior in predictable ways without excluding options or altering the incentive structure. For example, a relatively widespread nudge seeks to increase retirement savings by having employees commit in advance to allocating a portion of their future salary increases toward retirement savings, thereby reducing the impact of myopia and self-control on lifetime savings decisions.
- Boosts are interventions that seek to expand decision-makers’ abilities to accomplish the objective of the boost. While less widely employed than nudges, boosts are common in the behavioral psychology literature. For example, a boost employed in an experimental setting trains people in the use of simple rules of thumb for making financial decisions, such as trading off current and future consumption.
I test three different behaviorally-informed interventions, one boost and two nudges, each designed to increase demand for flood insurance in a hypothetical scenario among a pool of 331 participants recruited from an online labor pool.
In the control condition, the risk of flooding in the hypothetical scenario is presented as an annual probability. The first of the two nudges, which I call the informational nudge, alters the presentation of the flood risk by translating this annual probability into the probability of experiencing a flood over a 30-year period. The second nudge, which I call the affective nudge, presents a 30-year flooding probability while also providing information about coastal flooding in the US, including images from recent major US flooding events intended to evoke an emotional response. Lastly, based on a large literature on statistical training, the boost condition provides individuals with information intended to better equip them to understand and internalize the annual probability of flooding in the hypothetical scenario.
Overall, I find that the two nudges are more effective than the boost in increasing take-up of and WTP for flood insurance in my experimental setting. In fact, I find that the boost as designed and implemented in the study reduces individuals’ WTP for flood insurance relative to the control condition of no behaviorally-informed intervention.
While the boost appears to have no—or worse, a negative—effect on WTP for flood insurance, the informational and affective nudges result in greater WTP for flood insurance relative to the control condition. Moreover, the informational and affective nudges result in increases in WTP of roughly $11/month and $21/month relative to the boost, respectively. This, along with participants’ responses to several questions eliciting the motivation for their insurance take-up decision implies that the effectiveness of a nudge in increasing WTP for flood insurance appears to depend on the inclusion of recent, salient examples of flooding events.
What can we learn from this study? I offer two main takeaways for policy-makers and practitioners alike.
- Behavioral policy matters: the differences between individuals’ WTP for flood insurance in two of the three behaviorally-informed interventions used in this study and the control condition in which no behaviorally-informed intervention is used are significant—both statistically and economically. Thus, any effort to close the flood insurance gap should consider including behaviorally-informed interventions.
- Policy-makers should pay particular attention to the framing of risk when providing information about flooding. Extending the time horizon over which risk probabilities are provided and presenting risk information alongside salient examples increases the attention individuals pay to the risk in question. In the case of flood insurance, this finding suggests that the common practice of presenting flood risk in terms of annual probability thresholds is less effective in drawing attention to this risk.
Read the full study here.