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Across nine preregistered studies of 5,548 participants, we show that the way in which budgeting decisions are elicited can have large effects on amounts allocated to saving vs. spending. First, we show that financial budgeting decisions are biased in the direction of equal allocation to each account into which a budget is partitioned, so that savings increases as the number of accounts earmarked for saving (versus spending) goes up. We observe these effects above and beyond the impact of specifying multiple specific saving goals. Additionally, we identify specific features of choice architecture that moderate the magnitude of this bias. We show that partitioning has maximal effects on saving rates when the elicitation procedure best resembles an allocation of total resources across all possible spending and saving options (i.e., the decision is broadly bracketed). In particular, saving is maximized when: (1) an explicitly articulated fixed sum of money is allocated (vs. leaving that amount implicit); (2) money is allocated to all accounts simultaneously (vs. sequentially); and (3) accounts comprise an exhaustive set of spending and saving options (vs. leaving residual amounts for spending implicit). We conclude by discussing implications for personal finance and other resource allocation decisions.

People are routinely judged based on the outcomes of their predictions. In this paper we argue that the perceived nature of uncertainty critically influences ascription of credit, blame, and luck to forecasters. In one archival study and five lab studies, we demonstrate that epistemic (knowable) uncertainty is associated with attributions of credit for correct predictions and blame for incorrect predictions, whereas aleatory (random) uncertainty is associated with attributions of good luck for correct predictions and bad luck for incorrect predictions.

We provide evidence that investor behavior is sensitive to two dimensions of subjective uncertainty concerning future asset values. Investors vary in the extent to which they attribute market uncertainty to: (1) missing knowledge, skill, or information (epistemic uncertainty), and (2) chance or stochastic processes (aleatory uncertainty). Investors who view stock market uncertainty as higher in epistemicness (knowability) are more likely to reduce uncertainty by seeking guidance from experts and are more responsive to available information when choosing whether or not to invest. In contrast, investors who view stock market uncertainty as higher in aleatoriness (randomness) are more likely to reduce uncertainty through diversification and their risk preferences better predict whether or not they choose to invest. We show, further, that attributions of uncertainty can be perturbed by the format in which historical information is presented: charts displaying absolute stock prices promote perceptions of epistemicness and greater willingness to pay for financial advice, whereas charts displaying the change in stock prices from one period to the next promote perceptions of aleatoriness and a greater tendency to diversify.

People differ in their lay theories about how and why the financial well-being of individuals changes over time or varies between individuals. We introduce a measure of Causal Attributions of Financial Uncertainty—the CAFU scale—and find that such attributions can be reliably described along three distinct dimensions, respectively capturing the extent to which changes in financial well-being are perceived to be: (1) knowable and within individuals’ control due to individual factors such as effort (“Rewarding”); (2) knowable and outside of individuals’ control due to systemic factors such as favoritism and discrimination (“Rigged”); and (3) inherently unpredictable and determined by chance events (“Random”). In a sample representative of the U.S. population on various demographic characteristics (N = 1,102), we find that differences in these beliefs are associated with political ideology, revealing a predicted pattern: conservatives scored higher on the Rewarding subscale and liberals scored higher on the Rigged and Random subscales, even when controlling for key demographics. Moreover, we find that these three dimensions predict responses to different policy messages when controlling for political ideology. In three preregistered experiments (combined N = 2,560), we observe increased support for various social welfare policies when we highlighted aspects of these policies that are compatible with people’s beliefs about financial well-being. Likewise, we observe increased support for political candidates when they expressed their positions in a way that is compatible with people’s beliefs. Thus, this work can help better understand drivers of political attitudes and guide in crafting more persuasive policy messages.