0163 The Behavioral Economics of Wage-Driven Sleep Loss in Safety-Critical and Safety-Neutral Occupations

Abstract Introduction Insufficient sleep is a pervasive public health concern with well-documented consequences for cognitive performance, safety, and productivity. Although time is arguably the most fundamental human resource, behavioral economic approaches rarely treat sleep as a commodity that individuals “spend” in competition with other reinforcers, such as paid work. Methods This study applied a behavioral economic framework to quantify how adults trade off sleep time for overtime pay when both behaviors draw from a fixed daily time budget. A total of 164 adults were recruited from the crowdsourcing platform Prolific into a 2×2 between-subjects design crossing subjective socioeconomic status (high vs. low SES) with occupations varying in workplace safety demands (safety-critical vs. safety-neutral). This allowed us to assess whether socioeconomic context and job requirements influence the “price” at which individuals forgo sleep. Participants completed a novel task in which they imagined working a 10-hour shift and were offered varying overtime pay rates for additional consecutive work hours. Each day also included 10 discretionary hours to be allocated between extra work and sleep, making sleep loss the cost of increased labor. For each wage value, participants indicated the number of hours they would devote to work and sleep, enabling estimation of sleep demand. Results Results showed orderly demand curves across all groups: higher overtime pay reliably shifted allocations toward more work and less sleep. However, the effective wage at which individuals were willing to reduce sleep to <=4 hours differed systematically. Participants with higher SES required substantially higher overtime pay to accept severe sleep loss, whereas those with lower SES accepted major sleep reductions at much lower wage levels. Individuals assigned to safety-critical occupations were also more conservative in trading off sleep, requiring higher pay before reducing sleep, though SES and safety risk did not interact, indicating independent effects of socioeconomic context and occupational framing on sleep elasticity. Conclusion These findings demonstrate that sleep can be conceptualized and quantitatively modeled as a valued, constrained commodity subject to price-controlled tradeoffs. Integrating behavioral economics with fatigue science offers a promising approach for predicting risk, informing scheduling policy, and designing interventions that protect worker health and safety. Support (if any)