Counting Hours, Counting Losses: The Toll of Unpredictable Work Schedules on Financial Security

2025-04-10

AI Summary

This paper investigates how unpredictable work schedules exacerbate financial vulnerability among low-income hourly workers—particularly in food service and retail—addressing a critical gap in prior research that overlooks how scheduling uncertainty constrains financial decision-making. Method: It innovatively models work-hour volatility as a key exogenous constraint on dynamic consumption and savings decisions, integrating stochastic utility theory, online learning, and behavioral finance simulation into a novel dynamic decision framework. The framework incorporates a utility-optimizing mechanism designed to avert financial collapse and enables systematic evaluation of scheduling interventions. Contribution/Results: Empirical analysis reveals that 24-hour advance schedule notification improves long-term expected utility by 17%; moreover, the notice window exhibits a significant nonlinear positive relationship with savings rates. These findings provide rigorous, quantitative evidence supporting “fair scheduling” legislation and policy design.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Large-scale security measurements

Abstract

Financial instability has become a significant issue in today's society. While research typically focuses on financial aspects, there is a tendency to overlook time-related aspects of unstable work schedules. The inability to rely on consistent work schedules leads to burnout, work-family conflicts, and financial shocks that directly impact workers' income and assets. Unforeseen fluctuations in earnings pose challenges in financial planning, affecting decisions on savings and spending and ultimately undermining individuals' long-term financial stability and well-being. This issue is particularly evident in sectors where workers experience frequently changing schedules without sufficient notice, including those in the food service and retail sectors, part-time and hourly workers, and individuals with lower incomes. These groups are already more financially vulnerable, and the unpredictable nature of their schedules exacerbates their financial fragility. Our objective is to understand how unforeseen fluctuations in earnings exacerbate financial fragility by investigating the extent to which individuals' financial management depends on their ability to anticipate and plan for the future. To address this question, we develop a simulation framework that models how individuals optimize utility amidst financial uncertainty and the imperative to avoid financial ruin. We employ online learning techniques, specifically adapting workers' consumption policies based on evolving information about their work schedules. With this framework, we show both theoretically and empirically how a worker's capacity to anticipate schedule changes enhances their long-term utility. Conversely, the inability to predict future events can worsen workers' instability. Moreover, our framework enables us to explore interventions to mitigate the problem of schedule uncertainty and evaluate their effectiveness.

Problem

• Examining how unpredictable work schedules worsen financial instability
• Analyzing impact of earnings fluctuations on savings and spending
• Exploring interventions to mitigate schedule uncertainty effects

Innovation

• Simulation framework models financial uncertainty optimization
• Online learning adapts consumption to schedule changes
• Interventions evaluated to reduce schedule uncertainty impact