Data streams flowing between silos being unlocked by a magnifying glass.

Unlocking Insights: How 'Unpoolable' Data is Revolutionizing Research

Soraya Malik in Tech & Innovation December 2025 • 4 min read.

"Discover the innovative methods researchers are using to analyze sensitive data across jurisdictions, opening new doors to policy evaluation and social impact studies."

In an era defined by data-driven decisions, the ability to analyze information across different regions and sectors is crucial. However, stringent privacy laws and data-sharing restrictions often make it impossible to combine datasets, particularly when they cross jurisdictional boundaries. This creates 'unpoolable' data silos, hindering research and limiting our understanding of complex social and economic phenomena. Difference-in-Differences (DID) is a statistical technique commonly used to estimate the impact of policies and interventions. But what happens when the data needed for DID analysis is locked away in separate, unpoolable databases?

A new approach, UN-DID, or Difference-in-Differences with Unpoolable Data, is changing the game. This innovative method allows researchers to analyze data without physically combining it, respecting privacy and legal constraints while still producing robust and reliable results. UN-DID addresses a fundamental challenge: how to leverage the power of data analysis when traditional methods are off-limits.

Imagine trying to evaluate the impact of a national healthcare policy when each state's data is stored separately due to privacy regulations. Or consider the challenge of studying the effects of a new education program when student records cannot be shared across school districts. UN-DID provides a way forward, enabling researchers to answer critical questions and inform policy decisions even in these complex scenarios.

What is 'Unpoolable' Data and Why Does It Matter?

Data streams flowing between silos being unlocked by a magnifying glass.

'Unpoolable' data refers to datasets that cannot be physically combined due to privacy concerns, legal restrictions, or simply a lack of harmonization. This is particularly common in areas like healthcare, finance, and education, where sensitive personal information is involved. The inability to pool data creates significant challenges for researchers, preventing them from conducting comprehensive analyses and potentially leading to biased or incomplete findings.

Traditional Difference-in-Differences (DID) analysis, a staple in policy evaluation, relies on comparing outcomes between a treatment group and a control group before and after an intervention. This requires pooling data from both groups into a single dataset, a process that becomes impossible when data is unpoolable.

Here are some real-world examples of scenarios where UN-DID can make a difference:

Evaluating healthcare reforms across different provinces or states with varying data privacy laws.
Analyzing the impact of economic policies on businesses when financial data is held in separate, secure databases.
Studying educational outcomes across school districts with different data collection and reporting practices.
Assessing the effectiveness of social programs when participant data is protected by strict confidentiality rules.

UN-DID offers a solution by allowing researchers to analyze data within each silo separately and then combine the results in a way that preserves privacy and complies with legal requirements. This opens up new possibilities for conducting rigorous and ethical research in a wide range of fields.

The Future of Research with UN-DID

The development of UN-DID represents a significant step forward in our ability to conduct meaningful research in a data-rich but privacy-conscious world. By providing a robust and reliable method for analyzing unpoolable data, UN-DID empowers researchers to tackle complex questions, inform policy decisions, and ultimately improve outcomes for individuals and communities. As data privacy regulations become increasingly stringent, methods like UN-DID will be essential for unlocking the full potential of data while upholding ethical standards.

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.2403.1591,

Title: Difference-In-Differences With Unpoolable Data

Subject: econ.em

Authors: Sunny Karim, Matthew D. Webb, Nichole Austin, Erin Strumpf

Published: 23-03-2024

Everything You Need To Know

What is 'Unpoolable' Data, and why is it a problem for researchers?

'Unpoolable' data refers to datasets that cannot be combined due to privacy concerns, legal restrictions, or a lack of standardization. This poses a significant challenge because it prevents researchers from conducting comprehensive analyses, potentially leading to biased or incomplete findings. Traditional methods, such as Difference-in-Differences (DID) analysis, require pooled data, making them impossible to use when dealing with 'unpoolable' datasets. This limitation restricts the scope and depth of research, particularly in areas like healthcare, finance, and education, where data privacy is paramount.

How does UN-DID work, and what problem does it solve?

UN-DID, or Difference-in-Differences with Unpoolable Data, is an innovative method that allows researchers to analyze data without physically combining it. It addresses the challenge of conducting robust research when data is 'unpoolable' due to privacy or legal constraints. UN-DID enables the analysis of data within separate silos and then combines the results while preserving privacy. This approach allows researchers to leverage the power of data analysis even when traditional methods are off-limits, opening new possibilities for policy evaluation and social impact studies.

Can you provide an example of a real-world scenario where UN-DID would be beneficial?

Consider evaluating a national healthcare policy across different states with varying data privacy laws. Each state's data is stored separately, making it impossible to use traditional Difference-in-Differences (DID) analysis. UN-DID provides a solution by allowing researchers to analyze the healthcare data within each state separately and then combine the results to evaluate the impact of the policy effectively, while respecting privacy regulations and legal restrictions. Similar scenarios exist in education and finance, where 'unpoolable' data limits research.

What are the key differences between traditional Difference-in-Differences (DID) and UN-DID?

The main difference lies in how they handle data pooling. Traditional Difference-in-Differences (DID) analysis requires researchers to pool data from both treatment and control groups into a single dataset, which is impossible with 'unpoolable' data. UN-DID, on the other hand, is specifically designed for situations where data cannot be combined. It analyzes data within each silo separately and then combines the results in a privacy-preserving manner. This makes UN-DID suitable for scenarios where data privacy laws or other restrictions prevent the physical merging of datasets, while still enabling rigorous policy evaluation and research.

What is the future of research with UN-DID, and why is it important?

The development of UN-DID represents a significant step forward in our ability to conduct meaningful research in a data-rich, but privacy-conscious world. As data privacy regulations become more stringent, UN-DID and similar methods will be essential for unlocking the full potential of data while upholding ethical standards. By providing a robust method for analyzing 'unpoolable' data, UN-DID empowers researchers to tackle complex questions, inform policy decisions, and ultimately improve outcomes for individuals and communities across a variety of fields, including healthcare, finance, and education.