Hundreds of studies have examined the “sadder-but-wiser” hypothesis—that sad people make wiser decisions—and most find support for it. However, such studies typically examined judgments and decisions in domains without precisely quantifiable, normative standards of “wisdom.” Moreover, virtually no tests of the hypothesis examined financial decisions, arguably the most frequent and consequential decisions people make. To address these gaps, the present experiments examined the effects of sadness on intertemporal financial choices of the form $X now versus $(X+Y) later—typical of the choices people make when considering whether to spend now or save to spend more later. Studies of intertemporal choices typically reveal extreme impatience. That is, people typically choose earlier rewards over significantly larger, later rewards, leading to regret. Would sadness reverse the typical pattern by increasing wisdom and decreasing impatience—per the sadder-but-wiser hypothesis? Three experiments show the opposite and quantify the exact financial disadvantage of sadness: Whereas the median neutral-mood participant was indifferent between receiving $19 today and $100 in a year, the median sad-mood participant became indifferent at only $4 today. Moreover, sadness increased impatience even though the emotion was normatively irrelevant to the choice. In sum, sadder is not wiser when it comes to making tradeoffs between time and money, calling into question the otherwise long-supported view that “sadder is wiser.” Explanations and implications are discussed.
The present paper introduces new sign tests for testing equality of conditional distributions of two (arbitrary) adapted processes as well as for testing conditionally symmetric martingale-difference assumptions. Our analysis is based on results that demonstrate randomization over ties in sign tests for equality of conditional distributions of two adapted sequences produces a stream of i.i.d. symmetric Bernoulli random variables. This reduces the problem of estimating the critical values of the tests to computing the quantiles or moments of Binomial or normal distributions. A similar proposition holds for randomization over zero values of three-valued random variables in a conditionally symmetric martyingale-difference sequence.