This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Private retirement savings are an increasingly important topic in public debate. One popular idea is to phrase savings reminders in one of two ways: either you emphasize what someone stands to lose if they don't save, or you emphasize what they stand to gain if they do.
To date, research into which of the two reminder messages is more effective in terms of influencing people's efforts to save has produced conflicting results. A team of researchers from the University of Cologne, LMU Munich and Paderborn University has now demonstrated that people react very differently to the same message. Messages pointing to potential losses tend to have a greater impact on people whose jobs involve processing large amounts of information.
Messages about potential gains, on the other hand, tend to resonate more with older people (ages 50 and older) and those who have already made numerous additional contributions. The paper titled "Causal machine learning reveals heterogeneous effects of loss versus gain framing on retirement savings," published in the journal Scientific Reports, demonstrates the extent to which targeted behavioral interventions can increase private pension savings. Markus Weinmann from the University of Cologne, the project also involves Kevin Bösch and Dr.
Oliver Müller from the University of Paderborn, and Dr. Stefan Feuerriegel from LMU Munich. In 2021 and 2022, the researchers carried out two field experiments in collaboration with a European private pension provider, involving a total of around 41,000 customers, most of whom were based in Switzerland.
They received a reminder via an app about the amount they could still save and claim as a tax deduction. Customers were randomly assigned a reminder framed either as a loss or a gain. The team used causal machine learning, a method that learns from experimental data for whom an intervention works and for whom it does not.
Using data from the first experiment, the researchers estimated who responds to which message. They then tested this prediction on the customers in the second experiment. On average, both messages have almost the same effect.
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