Workers clustered around a time clock with a diverging path in the background.

Decoding the Overtime Puzzle: What Bunching Designs Reveal About Work Hours

Samir D’Costa in Business & Economy December 2025 • 4 min read.

"A Fresh Look at Mandatory Overtime and Its Real-World Impact on Labor Markets"

Microeconometrics constantly grapples with separating causal relationships from the tangled web of individual differences. This is particularly true when examining choices individuals make, like how many hours to work. It's tough to isolate how much of a work decision is due to genuine economic factors versus personal preferences. Economists frequently use methods like instrumental variables or studying policy changes over time. However, in many real-world scenarios, these tools aren't applicable, making it crucial to develop alternative methods for understanding how people respond to incentives.

One increasingly popular approach is the "bunching design" method. This technique focuses on situations where a large number of decision-makers face a sudden change in incentives at a specific threshold, such as overtime pay kicking in after 40 hours. By analyzing the distribution of choices around this threshold—specifically, how many people "bunch" up right before or at it—researchers infer how sensitive these individuals are to those incentives. This approach hinges on the idea that rational people will adjust their behavior in response to changes in costs or benefits, leading to observable patterns in their decisions.

However, the bunching design isn't without its critics. The method relies on combining a specific model of how people make choices with assumptions about how different individuals' preferences are distributed. Recent studies highlight that identifying causal effects using bunching hinges on assumptions about the distribution of preferences that can't be directly checked with real-world data. This creates a challenge: Can we really trust the conclusions drawn from bunching designs if the underlying assumptions are unverifiable?

Beyond Standard Models: A New Approach to Bunching

Workers clustered around a time clock with a diverging path in the background.

This study introduces a novel framework to tackle the identification challenges within bunching designs. Instead of relying on specific models of choice, it uses a more general approach that requires less restrictive assumptions. The key innovation involves reframing the problem as one of extrapolation: What would individuals' choices look like in counterfactual scenarios that we can't directly observe? In the context of overtime pay, this means estimating how many hours people would work if overtime regulations didn't exist.

The core idea is that identifying the average response to a policy change involves grappling with two extrapolation problems: understanding the distribution of choices when those choices are only observed in a limited, censored manner. This framework allows us to use real-world data on work hours, combined with weak, non-parametric assumptions about the shape of the distribution, to estimate the impact of overtime regulations.

Shape Constraints: The research uses "bi-log-concavity" which imposes a weak shape constraint on each potential income distribution.
Testable regions: Bi-log-concavity encompasses previous distributional assumptions and can be tested in observed outcome regions.
Narrowed results: The resulting bounds provide significantly narrow average treatment effect (ATE) than conventional techniques by addressing each counterfactual individually.

To evaluate this approach, the study investigates the impact of mandatory overtime pay on the hours worked by U.S. employees. Using administrative payroll data, the model assumes a weak non-parametric shape constraint on the choice distributions. The resulting bounds are quite informative, suggesting a relatively small elasticity of demand for weekly hours. This sheds light on a long-standing question regarding the true effects of overtime mandates and their impact on the labor market.

The Broader Implications

This research offers a new perspective on the bunching design method, demonstrating its adaptability across diverse economic models. By emphasizing the importance of reduced-form average treatment effects and non-parametric assumptions, the study provides a robust framework for program evaluation. These insights are particularly valuable in contexts where traditional methods struggle, equipping researchers with the tools to analyze a wide range of policy questions and understand the intricate dynamics of economic decision-making.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2205.1031,

Title: Treatment Effects In Bunching Designs: The Impact Of Mandatory Overtime Pay On Hours

Subject: econ.em

Authors: Leonard Goff

Published: 20-05-2022

Everything You Need To Know

What is the 'bunching design' method and how is it used in economic analysis?

The 'bunching design' method is an economic analysis technique used to understand how individuals respond to incentives, particularly around a threshold like the start of overtime pay. It examines the distribution of choices around a specific point, such as the 40-hour work week, to see how many people 'bunch' their work hours just before or at the threshold. This helps researchers infer how sensitive people are to changes in costs or benefits. The method relies on the idea that rational people will adjust their behavior in response to those changes, leading to observable patterns in their decisions.

What are the main criticisms of the 'bunching design' method?

The primary criticism of the 'bunching design' method is its reliance on assumptions about the distribution of individual preferences. Specifically, the method hinges on combining a specific model of how people make choices with assumptions about how different individuals' preferences are distributed. These assumptions can't be directly checked with real-world data, which raises questions about the reliability of the conclusions drawn from bunching designs. This is because if the underlying assumptions are incorrect, the results could be misleading.

How does the study improve upon existing 'bunching design' approaches?

The study introduces a novel framework to address the identification challenges within bunching designs. Instead of relying on specific models of choice, the framework uses a more general approach that requires less restrictive assumptions. The key innovation involves reframing the problem as one of extrapolation. The core idea is that identifying the average response to a policy change involves grappling with two extrapolation problems: understanding the distribution of choices when those choices are only observed in a limited, censored manner. The research uses 'bi-log-concavity' which imposes a weak shape constraint on each potential income distribution. The resulting bounds provide significantly narrow average treatment effect (ATE) than conventional techniques.

How was the impact of mandatory overtime pay evaluated in the context of this research?

To evaluate the approach, the study investigates the impact of mandatory overtime pay on the hours worked by U.S. employees. Using administrative payroll data, the model assumes a weak non-parametric shape constraint on the choice distributions. The resulting bounds are quite informative, suggesting a relatively small elasticity of demand for weekly hours. This sheds light on a long-standing question regarding the true effects of overtime mandates and their impact on the labor market. The researchers estimated how many hours people would work if overtime regulations didn't exist.

What are the broader implications of this research for economic analysis and policy evaluation?

This research offers a new perspective on the 'bunching design' method, demonstrating its adaptability across diverse economic models. By emphasizing the importance of reduced-form average treatment effects and non-parametric assumptions, the study provides a robust framework for program evaluation. These insights are particularly valuable in contexts where traditional methods struggle, equipping researchers with the tools to analyze a wide range of policy questions and understand the intricate dynamics of economic decision-making. It allows economists to analyze policy impacts more effectively, especially in scenarios where traditional methods are not applicable, offering a valuable tool for understanding how people respond to incentives.