Statistics To Economic Security

Introduction

Statistics are not a technical afterthought. They are the bedrock of U.S. economic security. Timely, accurate, and credible statistics are the operating system of modern economies. Official data provides the shared factual baseline that enables markets to function well, businesses to invest wisely, policymakers to govern judiciously, and the public to hold institutions accountable. Federal statistics—about metrics such as interest rates, wage negotiations, infrastructure planning, and public health—shape countless decisions every day. The stakes are concrete: Banks set credit terms with labor and inflation data, mortgage rates move with Federal Reserve policies rooted in official figures, and hospitals direct research and development (R&D) with population and health statistics. Get the numbers right, and investment flows. Get them wrong, and markets misfire, supply chains snarl, and policymakers govern blind.

In this way, federal statistics are central to U.S. economic security, including the ability to promote growth, secure markets and assets, and expand global influence. Federal data underpin private sector investments in new technologies, guide state and local workforce development strategies, and inform federal decisions on issues ranging from sanctions and export controls to industrial policy and supply chain resilience.

Yet the foundations of the statistical system—long regarded as the global gold standard—are under growing strain. The credibility, neutrality, and trust that distinguish official statistics from advocacy or opinion are increasingly tested by politicization, as routine revisions to employment or inflation data are too often treated as evidence of manipulation rather than a feature of rigorous measurement. Statistical agencies continue to operate with professionalism and integrity, but they remain constrained by flat budgets, aging information technology (IT) systems, declining survey response rates, and limited authority to integrate new data sources at scale. The United States is entering a new era of economic security, yet the system remains focused on macroeconomic management, excluding increasingly essential domains such as technological competitiveness, supply chain vulnerabilities, and economic statecraft. Absent a clearer and expanded mission, along with the resources to match, these pressures threaten to weaken the system’s relevance precisely when it is needed most.

This misalignment is not inevitable. The United States has expanded and adapted its statistical system before when national priorities demanded it. James Madison pushed the census to capture the nation’s industrial base. Simon Kuznets built national accounts to mobilize for World War II. In the Cold War, new science and technology measures let the United States benchmark itself against the Soviet Union. In every era, statistics were part of the economic security arsenal. They must be again.

Against this backdrop, in November 2025, the CSIS Economic Security and Technology (EST) Department convened a workshop that brought together voices not often found at the same table: senior officials from federal statistical agencies, private sector data users, state and local governments, academic researchers, and national security practitioners. The goal was to examine how federal statistics are used today, where the system is under pressure, and which gaps matter most for U.S. economic security.

The findings were clear and consistent. The federal statistical system remains the gold standard and an essential piece of infrastructure for public and private sector decisionmaking, but it is under growing strain and increasingly misaligned with the needs of business leaders and policymakers. Participants highlighted the system’s essential contributions to private sector decisionmaking, investment, regional development, and national resilience, while also underscoring mounting pressures related to public trust, resources, privacy, and governance. Most importantly, they pointed to a structural gap between a system designed primarily for macroeconomic management and one that is needed for contemporary commercial challenges such as supply chain disruptions and technological breakthroughs. The takeaways that follow reflect this tension and collectively support a central conclusion: Sustaining U.S. competitiveness and resilience in the twenty-first century will require a federal statistical system that is explicitly designed to support economic security objectives alongside its traditional macroeconomic mission.

Upgrading the statistical system for today’s geopolitical and economic environment will require sustained research, experimentation, and capacity building across several priority areas: improving data sharing and interoperability across federal agencies, states, and trusted partners; developing privacy approaches that manage reidentification risks while preserving data usability; strengthening the statistical workforce to meet demands shaped by AI, data integration, and economic security; understanding how institutional design, transparency practices, and oversight mechanisms impact credibility and public trust; and sharpening the system’s focus on user-centered design, regional industrialization, technological competitiveness, and economic intelligence.

The federal statistical system is one of the United States’ greatest, albeit underappreciated, strategic assets. Adapting it to the realities of modern economic security is not optional—it is a prerequisite for governing wisely, investing confidently, and competing effectively amid rising global threats.

Methodology

In preparation for the workshop, the EST team conducted background research and held a series of informal consultations with stakeholders across the public and private sectors. These conversations included private sector data users, researchers and economists, former and current government officials with experience in the federal statistical system (FSS), and practitioners working at the intersection of economic policy and national security. This preparatory phase identified priority use cases, recurring challenges, and areas of consensus and disagreement, as well as informed the structure of the workshop agenda.

CSIS convened the workshop on November 6, 2025, bringing together senior data users and producers from across government, industry, academia, and the national security community. The event was structured in two parts. A public session featured keynote remarks, a fireside conversation with the U.S. chief statistician, and panel discussions with leaders from the federal statistical agencies, private sector data users, state government officials, economic security professionals, and experts on statistical innovation.

The second portion of the workshop consisted of private, off-the-record roundtable discussions designed to encourage candid exchange. Participants were organized into four roundtables focused on: (1) statistical system modernization, led by the Bipartisan Policy Center (BPC) and the American Statistical Association (ASA); (2) the role of federal statistics in supporting regional economic development, led by the Economic Innovation Group (EIG); (3) supply chains and economic resilience, led by MITRE; and (4) measuring technological competitiveness, led by CSIS. These sessions surfaced detailed insights about how federal statistics are used, where existing data falls short, and what trade-offs agencies and users face in balancing timeliness, granularity, privacy, and trust.

The Federal Statistical System

The FSS is a decentralized enterprise spanning the federal government. It encompasses 13 principal federal statistical agencies, along with over 100 other federal offices and programs that collect, analyze, and disseminate official statistics. Its most visible components, such as the Bureau of Labor Statistics (BLS), the Census Bureau, and the Bureau of Economic Analysis (BEA), produce national economic data that is widely regarded as the global gold standard. Yet the system’s remit extends far beyond headline economic indicators. Federal statistical agencies compile vital information on the nation’s demographics, health, education, housing, energy, transportation, natural resources, science and technology, and other domains that underpin economic performance and public well-being.

Decentralization is a defining feature of the U.S. statistical system. Unlike countries with centralized national statistical offices, the United States developed its system incrementally as new policy challenges faced the federal agenda. Over time, Congress and the executive branch created cabinet departments to address these challenges, many of which established their own statistical units to support policymaking and program administration. Coordination functions emerged beginning in the 1930s, with the chief statistician in the Office of Management and Budget (OMB) ultimately assuming responsibility for statistical policy, standards, and interagency coordination.

This structure produced a system that is widely used across the economy and society. Federal statistics inform decisions by policymakers across the executive branch and Congress, guide state and local governments in allocating resources and designing programs, and underpin private sector investment, risk management, and long-term planning. Researchers and academics rely on federal data to advance economic and social science, businesses and investors use it as a trusted baseline to benchmark performance and assess market conditions, and households rely on it to navigate everyday economic decisions.

The United States receives these benefits in return for a relatively modest sum. In FY 2022, Congress collectively appropriated $7.2 billion for the FSS (approximately 0.1 percent of that year’s $6.3 trillion federal budget), though, notably, funding levels spike once every 10 years for the decennial census. In FY 2020, for example, the FSS collectively requested $12 billion.

Workshop Takeaways

The findings were clear and consistent. The federal statistical system remains the gold standard and an essential piece of infrastructure for public and private sector decisionmaking, but it is under growing strain and is increasingly misaligned with the needs of business leaders and policymakers. Most critically, a system designed for macroeconomic management is not equipped to answer the questions now central to economic security policy.

The Current System Is Not Designed for Modern Economic Security Needs

The modern federal statistical system is not designed with economic security as an explicit organizing principle. Its core architecture reflects the priorities of the postwar era, when the primary objectives of federal economic policy were managing the business cycle, supporting fiscal and monetary policy, and ensuring public accountability through trusted macroeconomic indicators. Those functions remain essential and the system continues to perform them well, but they do not fully align with the data demands of today’s global economy.

Federal statistics provide substantial indirect benefits to U.S. competitiveness by supporting private sector decisionmaking, regional development, and investment. The system also informs economic security policy in specific domains. For example, the BEA has long tracked U.S. trade flows, helping policymakers and firms understand the structure of international commerce and its implications for growth, sectoral exposure, and global integration. Similarly, the Energy Information Administration (EIA) captured the rise in domestic energy production enabled by technological advances, as well as the resulting shift in U.S. import-export dynamics, informing assessments of energy security and geopolitical leverage.

Yet these contributions are largely byproducts of a system oriented toward macroeconomic measurement, not outputs of a framework designed to generate strategic economic intelligence. Federal statistical agencies are not tasked with measuring competitiveness, innovation capacity, supply chain resilience, or exposure to economic coercion. The Employment Act of 1946, which established much of the United States’ modern statistical infrastructure, focused on employment, growth, and price stability and made no mention of competitiveness. In the decades that followed, the United States has not developed a coherent, integrated approach to measuring its competitive position in key industries and technologies, even as global competition has intensified.

As a result, critical questions for modern economic security often fall outside the system’s core outputs. This is particularly evident in areas such as sanctions and economic statecraft. Workshop participants noted that while federal statistics can provide valuable context, they are often too slow or insufficiently granular to support time-sensitive decisions. Instead, economic security analysts often rely on academic research, ad hoc datasets, or bespoke analysis to fill gaps, which can be uneven across sectors, inconsistent over time, and difficult to keep up to date.

Federal Statistics Have Historically Supported U.S. Economic Security

Historically, the federal statistical system has served as a pillar of economic governance, providing the information needed to guide industrial development, inform investments, and respond to national crises. The system’s origins in the colonial period and its constitutional grounding in the decennial census reflect an early recognition that measurement was essential to governance, growth, and national power.

Throughout its history, leaders deliberately expanded what the FSS measured and why. The Department of the Treasury began the first regular federal economic statistical series, focused on foreign trade, in 1820 to provide essential information on tariffs, then a principal revenue source for the federal government. During World War II, policymakers directly oriented the FSS toward economic security and rapidly expanded its capacity to support industrial mobilization and wartime planning. Measurement was treated as a strategic function, essential to aligning economic capacity with national objectives.

In the postwar period, as U.S. economic dominance solidified, the system gradually shifted away from an explicit focus on economic security. Statistical priorities narrowed toward employment, growth, and price stability, forming the foundation of the system in use today. While these functions remain essential, they no longer encompass the full range of information required in a world marked by renewed great power competition and economic statecraft. Importantly, participants stressed that the foundations for adaptation remain intact: methodological rigor, institutional independence, and a demonstrated capacity to evolve. They also emphasized that, as history has shown, when national priorities change, the mission and capabilities of the FSS can expand to meet them.

U.S. Economic Security Requires a Statistical System Fit for Purpose

Workshop participants emphasized that the United States can no longer rely on a federal statistical system designed primarily for the needs of a past era to meet the demands of modern economic security. Today’s policy and business challenges require data that is more timely, granular, integrated, and decision-relevant than the system has been built to provide.

This mismatch has tangible consequences. Policymakers increasingly need insight into the structure and performance of industries, innovation inputs such as R&D and workforce skills, regional industrial capacity, and points of vulnerability where foreign dependencies create strategic risk. Business leaders increasingly need data to navigate supply chain risk, technological competition, and regional industrial development, which are areas where the current system often provides insufficient visibility. The current system is unequipped to generate such information at the speed and scale required. As a result, decisionmakers often rely on ad hoc analysis, academic research, or private data sources that lack the comprehensiveness, continuity, and transparency of official statistics.

The workshop underscored that addressing these gaps is not simply a matter of improving individual datasets, but of clarifying and updating the system’s underlying purpose and capabilities. Participants argued that sustaining U.S. competitiveness and resilience throughout the rest of the twenty-first century requires a federal statistical system intentionally designed to support economic security amid modern geopolitical competition. Such a system would complement its traditional macroeconomic role with the capacity to inform economic statecraft, technological competitiveness, and long-term national strategy.

Federal Statistics Underpin Private Sector Decisionmaking

Workshop participants consistently emphasized that federal statistics are essential for private sector decisionmaking across the U.S. economy. Their role as a shared baseline for investment, pricing, workforce planning, and risk management is widely understood among businesses and investors. Firms rely on federal statistics to inform capital spending decisions, cost-of-living adjustments for workers, asset allocation, borrowing and lending decisions, and assessments of market conditions at both the national and regional level. In this way, the statistical system supports economic security by informing the everyday decisions of the firms and investors that determine the strength, resilience, and technological leadership of the U.S. economy.

The economic footprint of federal data is substantial. Industries that depend heavily on government data, referred to as government data–intensive sectors, have grown rapidly over the past decade. Between 2012 and 2022, revenues in these sectors nearly doubled, rising from roughly $408 billion to $778 billion, and their share of U.S. GDP increased by approximately 50 percent. This growth occurred alongside relatively modest federal investment in statistical agencies, underscoring the high return that businesses derive from a comparatively small public expenditure on data infrastructure.

At a more granular level, firms utilize federal statistics to make location and workforce decisions that directly affect productivity and competitiveness. Data from the American Community Survey (ACS), for example, allows companies to estimate the local availability of specific types of workers, such as engineers or skilled technicians, when deciding where to open new facilities or expand operations. Locating in regions without the necessary workforce can undermine a firm’s long-term prospects, making this data a critical input into site selection and regional investment strategies.

Sector-specific examples are also illustrative. In the automotive industry, both supply chains and product markets are global, and business decisions depend heavily on data and forecasts related to GDP growth, inflation, interest rates, commodity prices, and exchange rates. Federal statistics are used to model short-term demand conditions, informing decisions about production rates, inventory management, and pricing at assembly plants, as well as to inform longer-term decisions about plant expansion and site location. Beyond traditional manufacturing, government data on transportation and mobility also supports the development of advanced technologies, such as AI and autonomous vehicles, where public datasets provide critical inputs for research and testing.

The energy sector offers a similar illustration. Energy-intensive industries rely on federal data to assess price trends, supply and demand conditions, and trade flows. Businesses use data from the EIA to inform import and export decisions, investments in storage and infrastructure, and operational planning across energy markets. Concrete applications range from diesel fuel price data used in setting interstate trucking rates to energy consumption surveys that guide the design and operation of commercial buildings, hospitals, schools, and industrial facilities. And as the AI data center build-out continues, the value of energy data is only increasing.

Federal Statistics Reduce Uncertainty and Attract Investment

Workshop participants underscored that federal statistics play a critical role in reducing uncertainty and supporting capital formation in the United States. For investors, official data provides a shared, trusted baseline that underpins assessments of macroeconomic conditions, sectoral performance, and regional opportunity. By anchoring expectations around growth, inflation, employment, and productivity, federal statistics allow capital to be allocated more efficiently across firms, regions, and industries.

When high-quality public data is absent or unreliable, markets become less transparent and less competitive. In such environments, access to information increasingly depends on personal networks, proprietary relationships, and expensive private datasets—advantages that accrue disproportionately to large, well-connected firms. This dynamic raises barriers to entry for smaller investors and new market participants, narrowing the pool of capital and reducing competition. Over time, these information gaps translate into greater uncertainty, prompting investors to demand higher expected returns to compensate for risk.

The investment consequences of uncertainty are material. As one private sector investor explained, federal statistics are difficult to replace because of their breadth, historical depth, and role as reference points for policymaking and market expectations. Long-time series allow investors to model performance across business cycles, including downturns that are not captured in recent private datasets. Official statistics also shape the policy environment itself. Regardless of alternative estimates, central banks, regulators, and governments make decisions based on federal data, making them unavoidable inputs for investment analysis.

In countries with limited, inconsistent, or unreliable statistics, investors rely more heavily on informal information and qualitative judgment, which increases volatility and narrows participation. The result is typically higher credit spreads, higher required returns, and lower overall investment. In effect, weak data infrastructure raises the cost of capital, making it more expensive to finance factories, infrastructure, and business expansion.

Federal Statistics Are Essential for Regional Economic Development

Federal statistics are also foundational to state and local governance and to the functioning of regional economies. Data from the decennial census, the ACS, and other federal programs is routinely used by states and localities for disaster preparedness, workforce and economic development planning, public health surveillance, transportation planning, and infrastructure investment. This same data underpins the allocation of hundreds of billions of dollars in federal funding for Medicaid, education, housing, food assistance, veterans’ programs, and transportation, making its accuracy and granularity directly consequential for regional outcomes.

Federal statistics provide economies of scale that cannot be replicated at the subnational level. Participants noted that designing and administering statistically valid surveys is time-consuming, expensive, and technically demanding, placing it well beyond the capacity of most states or cities to do independently. Federal data collection allows local governments to draw on nationally consistent, professionally produced statistics rather than attempting to recreate comparable datasets across 50 states or thousands of municipalities. Federal statistics also provide a shared language and infrastructure that allow regions to compare conditions across jurisdictions and coordinate across state lines, particularly in areas such as transportation, labor markets, and climate resilience.

Statistical Agencies Are Innovative and Responsible Stewards

Statistical agencies are long-standing centers of methodological innovation and responsible data stewardship. Across agencies, participants reported a commitment to improving data quality, timeliness, relevance, and cost-effectiveness, often under increasing fiscal and workforce constraints. This capacity was particularly evident during the Covid-19 pandemic, when agencies rapidly adapted to unprecedented disruptions. For instance, without the methodological and technological innovations tested and implemented throughout the 2010s, it would likely not have been feasible to conduct the 2020 census under pandemic conditions.

There was broad recognition at the workshop that innovation must go hand in hand with a commitment to credibility, transparency, and public trust. Representatives from the BLS, Census Bureau, and BEA repeatedly highlighted methodological rigor, transparency, and clear communication as core professional values. As one leader noted, some data issues, such as declining response rates, are not just technical problems but trust challenges that require demonstrating tangible value to households and communities.

The Federal Statistical System Faces Growing Strain

Despite its strengths and long history of adaptation, participants maintained that the federal statistical system faces mounting challenges that threaten its ability to deliver timely, trusted, and policy-relevant information. These pressures risk undermining key functions of the system such as supporting private sector decisionmaking, guiding regional development strategies, and informing investment across the U.S. economy. While many of these pressures are not new, their cumulative effect and their interaction with today’s economic and geopolitical environment have made them more acute. These challenges include the following:

1. Producing timely, high-quality data has become increasingly difficult. Declining survey response rates make it harder for agencies to maintain representative samples and produce accurate estimates, particularly at higher frequencies or finer levels of detail. Indicators such as the monthly employment report, for example, are based on partial samples and require extensive modeling to account for missing data. Constrained budgets, outdated IT systems, and procedural barriers to data access and sharing continue to hamper progress toward overcoming these technical obstacles.

2. Declining trust in government compounds technical challenges. Public confidence in federal institutions generally is near historic lows, according to recent national polling, while trust in federal statistics specifically is also falling. Participants connected low trust to declining participation by households and businesses in federal surveys, which reduces data quality and increases reliance on weighting adjustments, imputation, and alternative sources such as administrative data. While these tools are essential and increasingly sophisticated, they are not substitutes for robust direct responses and can introduce operational complexity, transparency concerns, and reputational risk.

3. Resource and workforce constraints limit adaptability. Since 2009, the real purchasing power of 8 of the 13 primary statistical agencies has fallen at least 16 percent, including 18.7 percent for the BLS, even as demands on the system have grown. Annual appropriations cycles and limited access to multiyear funding make long-term planning difficult and discourage experimentation. Staffing shortages exacerbate these pressures: Most agencies have lost 20 to 30 percent of their staff in the past year.

4. Organizational constraints in the FSS amplify existing challenges. Many statistical agencies now sit several layers below cabinet leadership within their departments, limiting their ability to advocate for priorities and coordinate across agencies. Department-specific rules—for example, around IT systems or web platforms—can further impede effectiveness. Congressional oversight is similarly fragmented, with responsibility for statistical agencies spread across multiple authorizing and appropriations committees. Although the OMB promotes a cross-government perspective on statistical priorities, there is no funding targeted for strengthening the agencies overall and insufficient funding for the chief statistician’s office to perform its current duties. The classification system presents another challenge: Participants emphasized that data is often over-classified, creating further barriers to data access and sharing, even when an individual has the appropriate classification. Together, these structural features make system-wide modernization difficult, even when problems and potential solutions are well understood.

5. Privacy is increasingly central to the FSS yet is growing harder to manage. Historically, the federal government has collected a substantial amount of socially valuable data that, due to confidentiality obligations, has been either inaccessible to researchers or available only under restrictive conditions. However, as survey response rates decline, pressure is mounting to make greater use of these data assets. At the same time, agencies that release public datasets face rising risks of reidentification as computing power and data-linkage techniques improve, even when explicit personal identifiers are removed. Meanwhile, public confidence in privacy protection is eroding. A recent survey found that the share of Americans who agree or strongly agree that federal statistical agencies generally respect people’s privacy fell from 35 percent in June 2025 to 27 percent in September 2025. These dynamics underscore an unavoidable trade-off: Stronger privacy protections can limit data detail and usability, while greater precision increases privacy risk, making careful confidentiality management central to the system’s credibility and long-term relevance.

6. Professional autonomy and accountability can be in tension. The credibility of federal statistics depends on agencies’ ability to apply rigorous methodologies and professional standards without inappropriate interference. At the same time, statistical agencies operate within democratic institutions and must remain responsive to oversight from Congress, the executive branch, and the public. Participants noted that maintaining the appropriate balance between these principles can become more difficult during periods of institutional change, such as when agencies adopt new data sources, modeling techniques, or AI tools that may be less familiar to policymakers and stakeholders. Investments in modernizing the statistical system must therefore navigate a delicate equilibrium: They must seek to preserve the professional autonomy needed to produce trusted statistics while ensuring transparency and accountability to sustain public confidence in the system.

Private Data Complements, But Cannot Replace, Federal Statistics

Much of the data needed for effective economic governance, market functioning, and national security cannot be reliably produced by the private sector alone. Private data sources can be valuable complements to official statistics, offering speed and granularity, but they often come with methodological differences in coverage, revision timing, and data collection. This makes them an insufficient substitute for government-produced data, particularly when decisions require comprehensiveness, consistency, transparency, and public trust.

Official statistical information is a public good. Its use by one individual does not reduce its availability to others, and it is difficult or inefficient to prevent people from using it once it exists. Private entities lack sufficient incentives to collect comprehensive, nationally representative data and make it broadly available because they will not capture much of the benefit. Further, the value of federal statistics comes from shared and open use. Markets coordinate around a single inflation rate, wage negotiations reference the same employment data, and policymakers, investors, and households anchor expectations to a common baseline. This logic has long justified public investment in federal statistics to ensure that accurate, timely, and credible information is available to all.

Beyond their breadth, government statistics also provide continuity, transparency, and a trusted historical record. Private data sources are shaped by business models that can change abruptly: Firms can alter methodologies, end data-sharing arrangements, restrict access, or exit markets entirely. Official statistics, by contrast, are produced with a long-term public mandate. They are free, consistent, and comparable over time—qualities that are essential for macroeconomic analysis, policymaking, and investing. Government data is also subject to rigorous quality controls, confidentiality protections, and public scrutiny. Errors are documented and corrected, and methodologies are transparent. For these reasons, even the most sophisticated private data providers rely on official statistics as benchmarks for their own products. In this sense, private data is dependent on the public data infrastructure that underlies it.

A Statistical System for the Age of Economic Security

In past eras, the United States built statistical systems to deliver on national goals such as building the industrial base or fighting wars. Today, the pressing national goal is to ensure U.S. economic security by promoting economic dynamism and protecting technological advantages. The United States needs a statistical reform agenda that fits this purpose. That means fixing the pipes by modernizing IT and data platforms; rebuilding the statistical workforce by recruiting and retaining top talent in statistics, economics, and AI; protecting credibility so accuracy supersedes politics; and innovating responsibly with high-frequency indicators, AI-enabled analysis, and new measures of emerging technologies.

Meaningful change is both possible and already underway. Statistical agencies are modernizing methods where they can, and a growing ecosystem of academic, policy, and private sector actors is developing tools and approaches that point toward what a more capable system could look like. Examples highlighted in the workshop included work at the Georgetown University Massive Data Institute on integrating administrative and alternative data, innovations such as JEDx that improve access to and usability of labor market information, and private sector platforms like StateBook that translate federal statistics into actionable insights for local decisionmakers. Complementary research by organizations such as ASA, BPC, Brookings, EIG, and the National Bureau of Economic Research is helping clarify both the opportunities and constraints facing the system.

At the same time, participants emphasized that the federal statistical system must evolve to support a new generation of economic security questions, particularly those related to technological competition, supply chains, and economic statecraft. Recent work by the CSIS EST Department, including the Tech Edge report, highlights the importance of rigorous measurement and comparative analysis in assessing the strengths and vulnerabilities of national technology ecosystems. The Tech Edge framework shows that technological leadership depends not only on frontier innovation but also on the broader ecosystems that translate research into production and scale. Effective policymaking in this domain requires better statistical tools to measure ecosystem dynamics, industrial capabilities, and strategic dependencies across countries and industries.

Past reform efforts have often faltered because change was not sufficiently tied to demonstrated value or emerging policy needs. Structural reorganization alone is unlikely to rebuild trust or deliver new capabilities. Instead, strengthening the FSS for the age of economic security will require sustained learning and targeted investment in areas where questions remain unresolved and demands are growing. The sections below outline several priority areas where further research, experimentation, and capacity building could help align the United States’ statistical system more closely with the economic security challenges it faces in the twenty-first century.

Economic Intelligence

Economic intelligence—the ability to collect and synthesize timely, granular information on strategic dependencies, capital and technology flows, and the real-world effects of tools like export controls, investment screening, and sanctions—is essential for economic security. Yet these capabilities remain underdeveloped. The United States lacks a systematic framework for translating fragmented public and private data into actionable insight about the competitiveness of traded sectors and the effectiveness of economic policy instruments.

Addressing this gap will require new approaches to integrating federal statistics with administrative data, private sector datasets, and emerging analytical tools. Future work could focus on identifying priority indicators for economic security analysis, developing frameworks for tracking strategic economic dependencies, and exploring institutional models for producing economic intelligence within government. Building these capabilities would help policymakers move from reactive analysis toward a more systematic understanding of how economic relationships shape security outcomes.

Measuring Technological Competitiveness

Assessing technological competition requires more sophisticated measurement of the ecosystems that support innovation, production, and industrial scale. As CSIS’s Tech Edge report emphasizes, technological leadership depends not only on individual breakthroughs but on the broader ecosystems of firms, researchers, institutions, supply chains, and policies that enable technologies to move from discovery to widespread deployment.

Improving the measurement of technological competitiveness will therefore require new analytical frameworks and data products that capture the full range of ecosystem drivers and capabilities. This includes better indicators of industrial capacity, supply chain linkages, workforce capabilities, capital formation, and the diffusion of emerging technologies across sectors and regions. Such measurement tools would allow policymakers to more systematically compare technology ecosystems across countries, identify structural vulnerabilities, and evaluate the effectiveness of industrial and innovation policies.

Future research could focus on developing practical approaches to measuring technology ecosystems, integrating multiple datasets, and producing decision-relevant indicators for policymakers. Strengthening these measurement capabilities would improve the United States’ ability to understand and respond to technological competition.

Measuring Regional Industrialization

As U.S. industrial and technology policy increasingly focuses on regional clusters, corridors, and place-based investment, federal statistics should better illuminate how regional economies evolve over time and which investments generate durable growth. Workshop participants emphasized that current data often lacks the granularity, interoperability, and longitudinal structure needed to assess cluster formation, spillovers, and the sustainability of regional technology and industrial activity. Further research is needed to improve measurement of regional industrial dynamics, align federal statistics with the geography of modern economic development, and support evidence-based design of federal and state place-based policies central to economic security.

Workforce Development

A statistical system fit for modern economic security depends on sustained investment in government capacity—specifically, a workforce with the skills to design, implement, and adapt data systems in an environment shaped by technological complexity and geopolitical competition. Workshop participants noted that while some agencies have effectively employed fellowships and internships in the past, evolving demands increasingly require personnel who combine technical expertise with domain knowledge and the ability to engage users across government and industry. Further research is needed to better define the competencies required for economic security–related statistical work, assess how civil service structures and labor markets support—or constrain—those roles, and identify ways to strengthen talent pipelines over time.

Data Sharing and Interoperability

Expanding secure data sharing across the federal statistical system is essential to improving timeliness, granularity, and analytical power, yet remains constrained by legal, institutional, and incentive-based barriers. Workshop participants emphasized that progress depends not only on common data-sharing agreements and technical standards but also on aligning incentives and ensuring that agencies, state partners, and private data holders see clear benefits from participation. Further research is needed to identify practical models for interoperability involving critical data stewards, such as the Internal Revenue Service and Social Security Administration, and for improving standardization so shared data is available and easily usable by both public and private users.

Use Cases to Inform User-Centered Design

Strengthening the FSS requires a clearer understanding of how different users across government, industry, and civil society actually use federal data in practice. Workshop participants emphasized the need for more systematic research on use cases, decision contexts, and points of friction, both to improve data design and to better communicate the public value of official statistics. Developing this user-centered evidence base would help data producers prioritize improvements, tailor dissemination, and align statistical outputs more closely with real-world economic security needs.

Privacy Practices

Advances in computing power, data linkage, and analytics have made protecting confidentiality more complex, increasing the risk of reidentification even in datasets without explicit personal identifiers. Federal statistical agencies have responded with stronger disclosure avoidance measures such as limits on data granularity, secure access environments, and synthetic data, but these approaches highlight an inherent trade-off between privacy protection and data usability. Workshop participants emphasized the need for further research on how to standardize privacy practices across agencies, leverage existing programs such as the National Secure Data Service, and sustain public trust in an environment of declining institutional confidence.

Building and Maintaining Credibility

Credibility is a foundational requirement for an effective statistical system. It stems from several reinforcing sources, including disciplined professional standards and methodological rigor, transparency and appropriate oversight, and the clear public value generated by statistical products. Building these characteristics and navigating tensions that emerge between them will become more complex as statistical agencies adopt new data sources, AI tools, and privacy-preserving techniques that may be less familiar to policymakers and the public. Further research is needed to better understand how institutional design, transparency practices, and oversight mechanisms can reinforce credibility in modern statistical systems. This includes examining how agencies communicate methodological changes, how they demonstrate the value of statistical products to users, and how modernization efforts can strengthen public confidence in official statistics.

Conclusion

The federal statistical system is one of the United States’ greatest strategic assets, and it is not being treated as one. Without a deliberate expansion of mission—supported by sustained investments in the relevant workforce, infrastructure, data sharing, and analytic capacity—the system risks falling behind national needs at precisely the moment when trusted statistics matter most.

History shows that this outcome is not inevitable. In past eras, the United States expanded and adapted its statistical system to meet defining national priorities, from industrialization to wartime mobilization. Today’s economic security challenges call for a similar recalibration. Doing so will require leadership from Congress and the executive branch, a clearer articulation of purpose, and a commitment to learning from innovation already underway across agencies, academia, and the private sector.

Treating the federal statistical system as core economic security infrastructure is no longer optional. The question is not whether the system should evolve, but whether it will do so intentionally and in time. Investing now in a purpose-built statistical system will strengthen U.S. competitiveness, resilience, and governance for decades to come, while helping to ensure that the nation continues to make decisions grounded in fact rather than fiction.

Chris Borges is a senior program manager and associate fellow for the Economic Security and Technology Department at the Center for Strategic and International Studies (CSIS). His research focuses on intellectual property, technology standards, antitrust, and innovation policy.

Navin Girishankar is president of the Economic Security and Technology Department at CSIS. He leads a bipartisan team of over 40 resident staff and an extensive network of non-resident affiliates dedicated to providing independent research and strategic insights on economic and technology policies and their critical role in competitiveness as well as national security.

Andrew Reamer is a senior associate (non-resident) for the Economic Security and Technology Department at CSIS. His research efforts aim to encourage U.S. national economic development and competitiveness. Areas of focus include strategic economic analysis and policy, innovation and entrepreneurship, workforce development, and the federal economic statistics system. Intended to inform public decisionmakers, Reamer’s work resides on the boundary between research and practice.