2024 Berkeley Haas Year in Review
How messaging can be framed to influence acceptance
Imagine you are an organ donor in need of an organ yourself. Should you get preferential treatment because you had volunteered to be a donor? A new study shows that most people would support moving you up on the waiting list. At the same time, they would vehemently oppose moving non-donors needing an organ down on the list.
Why do people accept some policies and reject others when the outcomes are the same? Getting the desired results depends on the policy’s messaging and whether people’s behavior is voluntary or obligatory. Study participants favored outcomes that reward positive and voluntary behavior. Likewise, people tend to favor punishing people’s behavior when it runs afoul of an obligation or rule but oppose preferential treatment for those who did not break the rules.
The study, “When Do People Prefer Carrots to Sticks? A Robust ‘Matching Effect’ in Policy Evaluation,” forthcoming in Management Science, suggests that by understanding how people evaluate policies, marketers and policymakers can better frame and improve acceptance rates. The paper is co-authored by Ellen Evers, an assistant professor at UC Berkeley’s Haas School of Business, Yoel Inbar of the University of Toronto, and Irene Blanken and Linda Oosterwijk of Tilburg University, Netherlands.
“For a policy to succeed, it must not only be effective in changing behavior, it must also be accepted by stakeholders,” says Evers. “Therefore it is crucial to understand how different descriptions of the exact same policy can lead to dramatically different rates of acceptance.”
When a policy addresses voluntary behaviors, study participants favored outcomes that help those who participated more than outcomes that punish non-volunteers, as in the organ donor scenario. The same results occurred in 13 similar scenarios. For example, people supported a plan to move community service volunteers up on a waiting list for a desirable apartment, while moving non-volunteers down on the list was seen as completely unacceptable.
The pattern flips when the policy addresses obligations. For example, study participants preferred a policy that cut test scores by 50% for students who cheated on an exam. Those students failed to fulfill their obligation not to cheat. At the same time, the study participants were less favorable toward a policy that doubled test scores for students who had not cheated because the honest students’ behavior is deemed voluntary.
Evers calls these differences in judgment a “matching effect.” Policies that provide a disadvantage—such as being moved down on the organ donor list—are considered punishment. At the same time, policies that create an advantage (moving up on the organ list) are favored because they are seen as rewarding a desired voluntary behavior. By understanding this matching effect when framing a message, policymakers are more likely to increase acceptance of a policy.
Think of the Netherlands’ so-called ‘fat tax’ proposal in 2012. Left-leaning parties wanted to increase taxes on unhealthy, fattening foods and use the proceeds to make healthy foods more affordable. Evers says the proposal failed because of its campaign message— introduce a fat-tax and use the proceeds to make healthy food cheaper—because it was perceived as punishing citizens for eating bad foods.
“If they had framed the campaign positively such as, ‘We should make healthy foods cheaper and fund this by increasing the cost of bad foods,’ it is likely many more people would have seen this as an acceptable intervention,” says Evers. “The same policy can get a lot of support, or be hated by most of the population, purely by the way it is described.”
Imagine you are an organ donor in need of an organ yourself. Should you get preferential treatment because you had volunteered to be a donor? A new study by Ellen Evers shows that most people would support moving you up on the waiting list. At the same time, they would vehemently oppose moving non-donors needing an organ down on the list.
Posted in: