Scientist examining a web of interconnected data streams representing policy and economics.

Policy Evaluation Under Pressure? How to Navigate Aggregate Data When the Instruments are Shaky

Soraya Malik in Business & Economy December 2025 • 4 min read.

"Discover new methods for policy analysis using aggregate time-series instruments. Learn to overcome challenges like unobserved confounding and improve your economic evaluations."

Evaluating the impact of policies is a cornerstone of effective governance and economic planning. Aggregate data, which summarizes information across broad groups or regions, often serves as the foundation for these evaluations. However, accurately assessing policy outcomes using such data can be fraught with challenges. Traditional methods frequently falter when confronted with issues like unobserved confounding—hidden factors that skew results—or when dealing with imperfect instruments—variables used to isolate the effect of a policy.

A recent study by Arkhangelsky and Korovkin sheds light on innovative techniques to enhance policy evaluation when using aggregate time-series instruments. Their work addresses inherent limitations in conventional approaches, offering a robust estimator designed to eliminate unobserved confounders. This estimator is particularly valuable in scenarios where aggregate events occur frequently and influence multiple units simultaneously, a common yet complex situation in empirical economics.

This article delves into the methodologies proposed by Arkhangelsky and Korovkin, translating their complex findings into an accessible format for both seasoned economists and those new to the field. We’ll explore how these methods can be applied, why they are essential, and what advantages they offer over more traditional approaches to policy evaluation.

The Problem with Traditional Methods: Unobserved Confounding Explained

Scientist examining a web of interconnected data streams representing policy and economics.

Traditional policy evaluation methods often rely on strategies like difference-in-differences (DiD), which compares outcomes in a treated group to a control group before and after a policy change. While DiD is useful, it assumes that any differences between the groups are solely due to the policy. This assumption breaks down when unobserved confounders are present.

Unobserved confounders are factors that influence both the policy variable and the outcome of interest but are not accounted for in the analysis. For instance, consider evaluating the impact of military spending on state economic growth using national military spending as an instrument. A naive analysis might overlook that national military spending is often correlated with other fiscal and monetary policies that directly affect local economies. This correlation introduces bias, making it difficult to isolate the true effect of military spending.

Omitted Variable Bias: Results from factors not included in the model but correlated with both the policy and the outcome.
Endogeneity: Occurs when the policy variable is determined jointly with the outcome, making it hard to discern causation.
Aggregation Issues: Arise when aggregate data masks variations or heterogeneities at the unit level.

To address these challenges, Arkhangelsky and Korovkin introduce a new estimator that leverages a data-driven aggregation scheme. This method effectively eliminates the impact of unobserved confounders by constructing weights that balance the characteristics of different units, thus creating a more accurate comparison.

Why This New Approach Matters: Implications for Future Policy Evaluations

The methodologies introduced by Arkhangelsky and Korovkin offer a significant step forward in policy evaluation, particularly when dealing with the complexities of aggregate data and potential unobserved confounders. By providing a more robust and reliable estimator, their work enables policymakers and economists to make better-informed decisions. This leads to more effective policy interventions and a more accurate understanding of economic dynamics.

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Everything You Need To Know

What is the primary challenge when using aggregate data for policy evaluation, and how does the method proposed by Arkhangelsky and Korovkin address it?

The main challenge in policy evaluation using aggregate data is unobserved confounding, where hidden factors skew the results. Traditional methods, like difference-in-differences (DiD), struggle with this because they assume the groups are only different due to the policy. Arkhangelsky and Korovkin's estimator tackles this by using a data-driven aggregation scheme, effectively eliminating the impact of unobserved confounders by constructing weights that balance the characteristics of different units, thereby creating a more accurate comparison. This approach enables a more robust and reliable policy evaluation.

What are unobserved confounders, and how do they impact the accuracy of policy evaluations, with examples?

Unobserved confounders are factors that influence both the policy variable and the outcome of interest but are not accounted for in the analysis. They introduce bias, making it difficult to isolate the true effect of a policy. An example is evaluating the impact of military spending on state economic growth using national military spending as an instrument. If national military spending is correlated with other fiscal and monetary policies, which also affect local economies, a naive analysis would be inaccurate, leading to misleading conclusions about the true impact of military spending on economic growth. Other forms of this are omitted variable bias, endogeneity, and aggregation issues.

How does the estimator developed by Arkhangelsky and Korovkin improve upon traditional policy evaluation methods like difference-in-differences (DiD)?

Traditional methods such as difference-in-differences (DiD) assume that any differences between the treated and control groups after a policy change are solely due to the policy itself. However, DiD often fails when unobserved confounders are present. Arkhangelsky and Korovkin's estimator offers a significant improvement by addressing unobserved confounding directly. Their method uses a data-driven aggregation scheme to eliminate the impact of these hidden factors. This means the estimator constructs weights to balance the characteristics of different units, providing a more accurate and reliable comparison compared to DiD, particularly in complex scenarios where aggregate events occur frequently.

What are the implications of using Arkhangelsky and Korovkin's methodologies for future policy evaluations, and what benefits do they offer?

The methodologies introduced by Arkhangelsky and Korovkin enable policymakers and economists to make better-informed decisions, leading to more effective policy interventions and a more accurate understanding of economic dynamics. The primary benefit is a more robust and reliable estimator when dealing with aggregate data and potential unobserved confounders. Their approach allows for a more accurate assessment of policy impacts, ultimately leading to more effective economic planning and governance.

In the context of policy evaluation, what are the key issues related to aggregate data, and how does Arkhangelsky and Korovkin's work address these concerns?

Aggregate data, which summarizes information across broad groups or regions, can mask variations or heterogeneities at the unit level. Traditional methods can falter when confronted with issues like unobserved confounding or imperfect instruments. Arkhangelsky and Korovkin introduce an innovative estimator designed to eliminate unobserved confounders. This method utilizes a data-driven aggregation scheme that constructs weights to balance the characteristics of different units. This enhances policy evaluation, particularly where aggregate events occur frequently and influence multiple units simultaneously, which is a common and complex situation in empirical economics.