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Field
Abstract
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Before
"Nudges" have increasingly shown to be cost-effective tools for promoting a wide range of behaviors, from medication adherence to saving to energy efficiency. But most research evaluates one intervention in isolation on target outcomes. As such, we have little understanding of how campaigns might interact with one another, or whether they generate spillovers in unanticipated domains. In particular, existing literature suggests that there are general mechanisms that might cause programs to interfere with one another: people might infer benefits to actions based on whether or not they get promoted, or they might find it difficult to change several behaviors at once, or they might be unable to pay attention to all the information generated by campaigns. This project proposes an experiment in which individuals randomly receive reminders or incentives to engage in one healthy behavior, two healthy behaviors, or nothing at all. By comparing success rates across treatments, the study will uncover whether these general mechanisms do indeed cause interference between programs, and if so, which mechanisms are most important. Any evidence of spillovers or interactions would imply that we can make our campaigns even more effective by coordinating them.
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After
"Nudges" have increasingly shown to be cost-effective tools for promoting a wide range of behaviors, from medication adherence to saving to energy efficiency. But most research evaluates one intervention in isolation on target outcomes. As such, we have little understanding of how campaigns might interact with one another, or whether they generate spillovers in unanticipated domains. This paper explores the hypothesis that such campaigns might interfere with one another due to limited attention. I propose a simple framework, motivated by a taxonomy of attention from the psychology literature that distinguishes between “internal” and “external” attention. I test the predictions of the framework using an experiment in which individuals receive combinations of messages and incentives for two healthy behaviors. I then estimate the model in order to run two important sets of counterfactuals. First, I vary characteristics of the behaviors—the returns they generate and their difficulty (which can potentially be measured with simple survey questions)—to explore interference between interventions that target different behaviors from the ones in the experiment. Second, I look at how interference changes when we move from interventions that involve lots of external stimuli to interventions that involve none.
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