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From education to health to criminal justice, government regulation and policy decisions have important effects on social and individual experiences. New data science tools applied to data created by government agencies have the potential to enhance these meaningful decisions. However, certain institutional barriers limit the realization of this potential. First, we need to provide systematic training of government employees in data analytics. Second we need a careful rethinking of the rules and technical systems that protect data in order to expand access to linked individual-level data across agencies and jurisdictions, while maintaining privacy. Here, we describe a program that has been run for the last three years by the University of Maryland, New York University, and the University of Chicago, with partners such as Ohio State University, Indiana University/Purdue University, Indianapolis, and the University of Missouri. The program—which trains government employees on how to perform applied data analysis with confidential individual-level data generated through administrative processes, and extensive project-focused work—provides both online and onsite training components. Training takes place in a secure environment. The aim is to help agencies tackle important policy problems by using modern computational and data analysis methods and tools. We have found that this program accelerates the technical and analytical development of public sector employees. As such, it demonstrates the potential value of working with individual-level data across agency and jurisdictional lines. We plan to build on this initial success by creating a larger community of academic institutions, government agencies, and foundations that can work together to increase the capacity of governments to make more efficient and effective decisions.

Numerous surveys link interview data to administrative records, conditional on respondent consent, in order to explore new and innovative research questions. Optimizing the linkage consent rate is a critical step toward realizing the scientific advantages of record linkage and minimizing the risk of linkage consent bias. Linkage consent rates have been shown to be particularly sensitive to certain design features, such as where the consent question is placed in the questionnaire and how the question is framed. However, the interaction of these design features and their relative contributions to the linkage consent rate have never been jointly studied, raising the practical question of which design feature (or combination of features) should be prioritized from a consent rate perspective. We address this knowledge gap by reporting the results of a placement and framing experiment embedded within separate telephone and Web surveys. We find a significant interaction between placement and framing of the linkage consent question on the consent rate. The effect of placement was larger than the effect of framing in both surveys, and the effect of framing was only evident in the Web survey when the consent question was placed at the end of the questionnaire. Both design features had negligible impact on linkage consent bias for a series of administrative variables available for consenters and non-consenters. We conclude this research note with guidance on the optimal administration of the linkage consent question.