Insights from behavioral economics for policymakers of choice‐based health insurance markets: A scoping review

Abstract

This article uses the systematic scoping review method to summarize literature at the interface of behavioral economics and health insurance. We aim to offer policymakers of choice‐based health insurance markets an understanding of (a) behavioral factors that affect consumer decision‐making in health insurance markets and (b) behavioral interventions that can be used to help consumers make better health insurance decisions. In the process, we reviewed 80 studies from extant literature and found that 18 behavioral factors negatively impact three major aspects of consumer decision‐making: that is, purchasing, quality of plan choice, and switching. We also identify 17 behavioral interventions that policymakers could use to help consumers make better health insurance decisions. However, while using the findings of the field, policymakers must keep in mind that there are limitations to the generalizability of the findings coupled with limitations that behavioral economics comes along with. Based on this study, future research could focus on identifying the segments of the populous that are more likely to be affected by the behavioral factors, and what behavioral interventions can be used to help them. Future research is also needed in settings other than WEIRD (Western, Educated, Industrialized, Rich, and Democratic) nations for the benefit of policymakers of non‐WEIRD nations.

Bridging the gap: Experimental evidence on information provision and health insurance choices

“…At the same time, this population is younger and often has better health status, is more likely to be risk‐loving, has lower incomes, and has less experience with health insurance compared to older adults. While such characteristics must be considered when generalizing this study's results to a broader population (Sears, 1986), it is worth noting that our findings empirically support established theories (Krishnan S. et al., 2022; Lucas, 2003) and align with previous evidence (Kaufmann et al., 2018; Krishnan S. et al., 2022; Samek & Sydnor, 2020).…”

“…Decision support tools such as the provision of personalized information have been shown to improve decision‐making in the context of health insurance (Krishnan S. et al., 2022). However, there is a lack of evidence to date regarding what type or form of information provision would benefit individuals the most by reducing information frictions and issues due to mental gaps.…”

Assessing health insurance literacy in Switzerland: first results from a measurement tool

“…Finally, the models’ low R-squared values suggest that selected factors only explain a fraction of the variations in HIL, indicating the need to consider additional factors, such as behavioural factors (or biases, including hassle costs, inattention or optimism bias) influencing health insurance decision-making, 17 which population groups are subject to these biases, and what interventions can help them. Future research could explore these factors to enhance the understanding of HIL in Switzerland and abroad.…”

“… 5 Preference variables could be critical in selecting and utilizing health insurance plans, as risk-averse individuals are informed about health insurance to avoid risk. 17 , 21 , 22 Similarly, more future-oriented individuals may have greater confidence in their knowledge of health insurance decisions, making them more likely to be aware of health insurance details. 23 …”

“…Lastly, the study advances the HIL international literature by exploring factors associated with HIL in a novel way using quantile regressions, 4 , 6 , 7 extending examined factors with preference factors for deeper insights into HIL variations. 7 , 17 …”