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Thinking about default enrollment lowers vaccination intentions and public support in G7 countries.

Sanchayan BanerjeePeter JohnBrendan NyhanAndrew HunterRichard KoenigBlake Lee-WhitingPeter John LoewenJohn McAndrewsManu M Savani
Published in: PNAS nexus (2024)
Policymakers often face a conundrum between being transparent about policies and ensuring that those policies are effective. This challenge is particularly relevant for behavioral nudges, which are not usually disclosed. Rather than avoiding transparency, we suggest that policymakers encourage citizens to reflect on nudges to help them understand their own views and align those views with their behaviors. Using data from an online survey experiment with 24,303 respondents in G7 countries, we examine the impact of reflection on a hypothetical default nudge policy for COVID-19 booster appointments. Contrary to expectations, participants say they would be less likely to get the booster when automatically enrolled compared with a control condition. Similarly, encouraging citizens to think about the status quo (baseline) policy also reduces intentions for boosters. These interventions have no effect on approval of the policy. Further, encouraging people to think about automatic enrollment decreases approval of the policy and further decreases their intentions to get vaccinated. These findings suggest that reflection on a nudge can increase backlash from a nudge and also elicit policy disapproval, thereby aligning policy support with behavioral intentions.
Keyphrases
  • public health
  • healthcare
  • mental health
  • coronavirus disease
  • functional connectivity
  • physical activity
  • deep learning
  • health insurance
  • machine learning
  • affordable care act
  • data analysis