Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We investigate the feasibility of inferring economic choices from simple biometric non-choice data. We employ a machine learning approach to assess whether biometric data acquired during sleep, naturally occurring daily chores and participation in an experiment can reveal preferences for competitive and team-based compensation schemes. We find that biometric data acquired using wearable devices enable equally accurate out-of-sample prediction for compensation-scheme choice as gender and performance. Our results demonstrate the feasibility of inferring economic choices from simple biometric data without observing past decisions. However, we find that biometric data recorded in naturally occurring environments during daily chores and sleep add little value to out-of-sample predictions.