LAUSR

The automatic assessment of psychological traits from digital footprints allows researchers to study psychological traits at unprecedented scale and in settings of high ecological validity. In this research, we investigated whether spending records—a ubiquitous and universal form of digital footprint—can be used to infer psychological traits. We applied an ensemble machine-learning technique (random-forest modeling) to a data set combining two million spending records from bank accounts with survey responses from the account holders (N = 2,193). Our predictive accuracies were modest for the Big Five personality traits (r = .15, corrected ρ = .21) but provided higher precision for specific traits, including materialism (r = .33, corrected ρ = .42). We compared the predictive accuracy of these models with the predictive accuracy of alternative digital behaviors used in past research, including those observed on social media platforms, and we found that the predictive accuracies were relatively stable across socioeconomic groups and over time.