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People experience financial scarcity when they have insufficient financial resources to meet demands, and this experience can affect decision-making in various ways. One proposed consequence of financial scarcity is increased temporal discounting, which is a tendency to value immediate outcomes more strongly than delayed outcomes. To test whether financial scarcity indeed increases temporal discounting, we developed the Household Task—an experimental paradigm during which participants have to manage the finances of a household. In a pilot, we found that manipulating participants’ financial situation in the Household Task (debts vs. savings and control) induced an experience of financial scarcity. Next, Experiments 1 and 2 confirmed that this experience increased temporal discounting of gains and losses. In Experiments 3 and 4, we tested whether experienced scarcity also increases discounting when financial resources were equal between conditions. However, in these experiments, there was no evidence of such an effect. In Experiment 5, we found that discounting increased when available financial resources were constant while expectations about future financial problems might have differed between conditions. In sum, the current research suggests that when experiencing scarcity, discounting increases as a response to a current or anticipated future shortcoming of available financial resources. Yet, there was no evidence suggesting that discounting increases when a scarcity mindset is induced in isolation.

Sometimes interesting statistical findings are produced by a small number of “lucky” data points within the tested sample. To address this issue, researchers and reviewers are encouraged to investigate outliers and influential data points. Here, we present StatBreak, an easy-to-apply method, based on a genetic algorithm, that identifies the observations that most strongly contributed to a finding (e.g., effect size, model fit, p value, Bayes factor). Within a given sample, StatBreak searches for the largest subsample in which a previously observed pattern is not present or is reduced below a specifiable threshold. Thus, it answers the following question: “Which (and how few) ‘lucky’ cases would need to be excluded from the sample for the data-based conclusion to change?” StatBreak consists of a simple R function and flags the luckiest data points for any form of statistical analysis. Here, we demonstrate the effectiveness of the method with simulated and real data across a range of study designs and analyses. Additionally, we describe StatBreak’s R function and explain how researchers and reviewers can apply the method to the data they are working with.

The current study investigated the prospective associations between financial scarcity and financial avoidance. We hypothesized that over time, financial scarcity––the experience of lacking needed financial resources––is associated with an increase of financial avoidance––the tendency to avoid dealing with one’s finances––, and vice versa. In a longitudinal panel study, including a large and representative adult sample of Dutch citizens (initial N = 1,122, final N = 837), we measured experienced financial scarcity and financial avoidance twice over a period of 22 months. Data were analyzed using a cross-lagged panel model, which allows to test for prospective effects of one variable on the other, while controlling for autoregressive effects. Confirming our preregistered hypotheses, results showed that experienced financial scarcity was positively related with an increase in subsequent financial avoidance, whereas financial avoidance was positively related with an increase in subsequent experienced financial scarcity. While these longitudinal findings are not causal, they are in line with the concept of a poverty trap, where financial scarcity and financial avoidance form a temporally dynamic and increasing relationship.