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

TL;DR

The paper investigates how unpredictable work schedules undermine financial security by modeling consumption decisions under temporal uncertainty using an online learning framework. It introduces an adaptive online algorithm with lookahead that updates consumption policies as future schedule information becomes available, and analyzes the value of lookahead both theoretically and empirically. Theoretical results show that lookahead yields a provable, growing advantage in long-horizon utility, with linear scaling in the amount of lookahead under a simple setting, and simulations demonstrate that even partial advance information substantially improves financial outcomes, while interventions like advance notice policies outperform monetary compensation alone. The work offers a framework for evaluating policy interventions in schedule-rich economies and highlights the potential to mitigate financial fragility through information provisioning and fair scheduling practices.

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.

Figures (3)

• Figure 1: The final utility gained for different levels of lookahead is illustrated for four distinct income classes, each comprising 27 individuals. Workers are of similar features, with variations solely based on the temporal aspect, i.e., the amount of lookahead in their work schedules. The $x$-axis depicts the lookahead value and the $y$-axis represents the total utility at the end of $T$ steps. The two orange triangles are in place to highlight the difference in the level of lookahead needed for approaching the near-maximum utility (near-maximum utility is a utility close to that of a worker with full information at L26) as the income levels change.
• Figure 2: Individuals with similar features but varying levels of lookahead under negative return rates ranging from 0.75 to 0.95 on their assets, as well as positive return rates between 1.05 and 1.25 on their assets.
• Figure 3: The box plot and the error bar plot of the statistical distribution of additional gained utilities for various interventions. The interventions considered are L0 + Money, which involves compensation fees for individuals with no future information; L2, assigning at least two weeks of lookahead to all; and L5, assigning at least five weeks of lookahead to everyone. In the box plot, the green triangle represents the mean, and the green line represents the median. In the error bar plot, the blue circle represents the mean and the error bars are 2 standard deviations from the mean (95% confidence interval).

Theorems & Definitions (6)

• Theorem 1
• proof
• Lemma 1
• proof
• Lemma 2
• proof