Dominoes falling in a ripple effect, representing policy spillovers.

Regression Discontinuity Design: How Spillovers Can Impact Policy Evaluations

Nico Varela in Politics & Policy September 2025 • 4 min read.

"Uncover the hidden ripple effects in RDD and how to accurately assess policy outcomes in interconnected environments."

In an interconnected world, policy evaluations often face a complex challenge: spillovers. Regression Discontinuity Design (RDD) is a popular method for estimating the causal effects of policies or interventions. It leverages a cutoff point to create a quasi-experimental setting, comparing outcomes for those just above and below the threshold. However, the classic RDD framework typically assumes that there are no spillovers—meaning that the treatment only affects those directly receiving it.

This assumption can be unrealistic. Policies rarely operate in a vacuum. Their effects can ripple outwards, influencing individuals or groups not directly targeted. For example, a job training program in one region might indirectly benefit neighboring areas through increased economic activity. Neglecting these spillovers can lead to biased and misleading conclusions about a policy's true impact.

Recent research has begun to address the challenges that spillovers pose to RDD. By acknowledging and accounting for these spillover effects, policy evaluators can gain a more accurate understanding of policy outcomes and make better-informed decisions. This article explores how spillovers can influence RDD estimates and introduces methods to mitigate these biases, offering practical insights for researchers and policymakers alike.

Understanding RDD and the Spillover Problem

Dominoes falling in a ripple effect, representing policy spillovers.

Regression Discontinuity Design relies on a clear threshold to create a comparison group. Units just above the threshold receive the treatment, while those just below do not. By comparing outcomes near this cutoff, researchers can estimate the treatment effect, assuming that potential outcomes are continuous around the cutoff (Hahn et al. 2001). This approach is valuable because it mimics random assignment near the threshold, reducing concerns about selection bias (Lee and Lemieux 2010).

However, the standard RDD framework hinges on the Stable Unit of Treatment Values Assumption (SUTVA). SUTVA dictates that the treatment status of one unit should not affect the outcomes of other units (Hahn et al. 2001). Spillovers violate this assumption, creating dependencies between units and potentially distorting RDD estimates. These spillovers can be either exogenous, where the treatment directly affects neighbors, or endogenous, where the outcome of one unit influences the outcomes of others (Manski 1993).

Exogenous Spillovers: Imagine a media campaign designed to increase voter turnout. Households directly exposed to the campaign might discuss it with friends and family in other areas, influencing their voting behavior as well.
Endogenous Spillovers: Consider a policy that incentivizes renewable energy adoption. If one household installs solar panels, their neighbors might feel social pressure to do the same, leading to a ripple effect of adoption.

Failing to account for these spillovers can lead to several problems. RDD estimates might overstate or understate the true treatment effect, or even produce estimates with the wrong sign. This is because the comparison group is no longer a pure control, as they are indirectly affected by the treatment. Therefore, understanding and addressing spillovers is crucial for accurate policy evaluation.

Moving Forward: Towards More Accurate Policy Evaluations

Accounting for spillovers in RDD is essential for robust policy evaluation. By acknowledging the interconnectedness of modern environments and adopting appropriate methodologies, researchers can obtain more accurate estimates of policy effects, leading to better-informed decisions and more effective interventions. As the complexity of policy challenges grows, so too must the sophistication of our evaluation techniques. Continued research and methodological development in this area are crucial for ensuring that policy decisions are based on sound evidence and a comprehensive understanding of real-world impacts.

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2404.06471,

Title: Regression Discontinuity Design With Spillovers

Subject: econ.em

Authors: Eric Auerbach, Yong Cai, Ahnaf Rafi

Published: 09-04-2024

Everything You Need To Know

What is Regression Discontinuity Design (RDD) and why is it used?

Regression Discontinuity Design (RDD) is a causal inference method. It's employed to assess the impact of a policy or intervention by comparing outcomes of units just above and below a specific cutoff point. This approach leverages a quasi-experimental setup, allowing researchers to estimate the treatment effect, assuming that potential outcomes are continuous around the cutoff and reducing concerns about selection bias. The goal of RDD is to mimic the conditions of a randomized controlled trial in situations where true random assignment is not feasible.

What are spillovers in the context of RDD and why do they matter?

Spillovers refer to the ripple effects of a policy or intervention that extend beyond the directly targeted individuals or groups. In RDD, these spillovers can invalidate the Stable Unit of Treatment Values Assumption (SUTVA), which is a cornerstone of the method. This assumption states that the treatment status of one unit should not affect the outcomes of other units. However, when spillovers are present, they create dependencies between units, potentially distorting RDD estimates. Ignoring spillovers can lead to either overestimation or underestimation of the true treatment effect, resulting in misleading conclusions about the policy's effectiveness.

Can you explain the difference between exogenous and endogenous spillovers, and provide examples?

Exogenous spillovers occur when the treatment itself directly affects the neighbors or surrounding units. A practical example is a media campaign to increase voter turnout, where households directly exposed to the campaign might discuss it with others in different areas, influencing their voting behavior. Endogenous spillovers, on the other hand, happen when the outcome of one unit influences the outcomes of others. For instance, a policy encouraging renewable energy adoption could prompt some households to install solar panels, which, in turn, might create social pressure on their neighbors, thus resulting in a ripple effect of adoption. Both types of spillovers violate the SUTVA and can skew the results of an RDD analysis.

Why is it important to account for spillovers when using Regression Discontinuity Design?

Accounting for spillovers in RDD is crucial for ensuring the accuracy and reliability of policy evaluations. If spillovers are not considered, the RDD estimates might overstate, understate, or even misrepresent the true effect of a policy. Because the comparison group is indirectly affected by the treatment due to spillovers, the standard RDD framework's assumption of a pure control group is compromised. This can lead to biased conclusions, hindering the ability to make sound, evidence-based decisions. Recognizing and mitigating these effects allows for a more comprehensive and precise assessment of policy outcomes and facilitates better-informed decision-making by policymakers.

What are the implications of neglecting spillovers in Regression Discontinuity Design for policy decisions?

Neglecting spillovers in Regression Discontinuity Design (RDD) can lead to significant misinterpretations of policy effectiveness, potentially resulting in misguided policy decisions. For example, if a job training program's positive impact is overestimated due to overlooking spillover effects on neighboring areas, policymakers might allocate resources inefficiently, believing the program is more effective than it is. Conversely, underestimating the true impact, could lead to a premature termination or scaling down of a program that, in reality, is beneficial when spillover effects are considered. Therefore, to ensure that policy decisions are based on accurate assessments of real-world impacts, it is essential to acknowledge and address spillovers within the RDD framework.