We theorize and demonstrate how natural mood triggers influence volunteer prosocial behavior, analyzing quasi-experimental field evidence from volunteer crisis counselors at a nonprofit organization who help individuals facing crises and conflicts like suicidal ideation, abusive relationships, and depression. Deciding to engage in prosocial behavior can transform lives, but surprisingly little is known about the drivers of prosocial decisions. For 50 years, there has been a controversy over the true causal relationship between mood and prosocial behavior. Some studies assert that more positive moods boost prosocial behavior, while other studies assert the opposite—that more positive moods reduce prosocial behavior. We propose a possible theoretical resolution of this controversy by synthesizing both prior views together using the law of diminishing marginal utility, which predicts an inverted-U-shaped continuous causal relationship between more positive mood triggers and prosocial behavior. To test this inverted-U hypothesis, we introduce a new approach called revelation-curve: a massive field quasi-experiment in which all naturally occurring values of a plausibly exogenous continuous treatment variable are simultaneously analyzed. A revelation-curve analysis can provide plausibly causal evidence regarding the full natural continuous relationship between a treatment variable and an outcome variable without imposing functional-form assumptions (in contrast to the few prior studies of continuous causal relationships, which impose strong functional-form assumptions like linearity). We use revelation-curve analyses to investigate relationships between three plausibly exogenous mood triggers (daily sunlight amount, daily news valence, and daily stock-market returns) and prosocial behavior—specifically, millions of decisions by volunteer crisis counselors to engage in potentially lifesaving prosocial behavior. All revelation-curve analyses reveal inverted-U relationships, suggesting that more positive mood triggers first boost, then reduce, prosocial behavior. Forecasters failed to predict these inverted-U relationships. Our findings uncover important insights that leaders and policymakers can use to boost prosocial behavior and potentially save lives.
