A framework for evaluating innovation in federal economic statistics

Introduction

Federal economic statistics are essential to evidence-based policymaking in the United States, informing decisions on issues such as job losses or sudden shifts in demand for goods. Yet their production faces significant challenges, including declining survey response rates, constrained budgets, and growing expectations for timely, granular data. This paper examines how these data are produced across the full data life cycle, which consists of six phases: (1) collection and acquisition, (2) storage, (3) sharing and transfer, (4) analysis, (5) dissemination, and (6) destruction or archival.

Understanding these phases is critical because challenges—and opportunities for innovation—arise at every stage. To frame this discussion, I define the core values of data governance as ensuring that data are accurate, accessible, private, and usable. Innovation should then introduce new ideas or changes that strengthen the federal statistical system’s ability to produce economic data that meet these core values for supporting better evidence-based decision-making, ultimately improving the well-being of our communities and nation.

• Accuracy ensures that society has high-quality data and statistics that meaningfully represent the data subjects. Inaccurate data can lead to flawed analyses, poor decisions, and misrepresentation.
• Accessibility addresses what data and statistics are available, to whom, and under what conditions. Data should be accessible to those who need it for legitimate purposes.
• Usability means data must be understandable, actionable, and fit for purpose. Even high-quality data can be ineffective if users cannot interpret or apply it.
• Privacy safeguards sensitive information from illegitimate access or misuse. Numerous laws and regulations—from local to federal—require data curators to protect privacy rights and maintain public trust.

These values are not discussed in detail later in the paper but serve as a lens for evaluating the persistent challenges and innovations highlighted in the following sections. Specifically, the paper identifies fifteen persistent challenges in producing high-quality economic data across the six life cycle phases. These include integrating administrative and private sector datasets, ensuring representation of hard-to-reach populations, balancing privacy protection with data utility, and modernizing metadata. To illustrate potential solutions, the paper highlights four recent innovations: 

1. Re-Engineering Statistics using Economic Transactions (RESET), which applies transaction-level data to improve GDP and price measurement;
2. National Experimental Well-being Statistics (NEWS), which blends survey and administrative data to refine income and poverty estimates;
3. Comprehensive Income Dataset (CID), which links multiple sources to fill gaps in measures of economic well‑being; and 
4. Safe Data Technologies, which applies privacy-enhancing technologies for secure access to confidential administrative tax data. 

Together, these examples show how technical and policy innovations can strengthen the federal statistical system’s ability to deliver cost-efficient, high-quality economic statistics that meet evolving societal needs. Not all proposed innovations will meet the core data governance values. The recent innovations must also address gaps in workforce skills, infrastructure, AI integration, and data archiving to uphold these core values. The paper concludes by calling for sustained modernization, cross disciplinary training, and adaptive policies to ensure federal economic statistics remain robust, trusted, and fit for purpose in a rapidly changing technological and economic landscape.

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