Published on November 19, 2025
Strong and independent national statistical agencies safeguard data integrity and underpin sound policy
BERT KROESE is the IMF’s chief statistician and director of the Statistics Department.
Opinions expressed in articles and other materials are those of the authors; they do not necessarily reflect IMF policy.
In the mid-2000s, Argentina’s once-reliable inflation statistics became a flashpoint for controversy. Official figures started to diverge from independent estimates. At first, the discrepancies were small. Then they grew. By 2007, private analysts pegged inflation as much as triple the officially reported rate. The credibility of Argentina’s national statistics office collapsed. Investors lost confidence and pulled out funds. Policymakers struggled to make decisions without accurate statistics to guide them, compounding the challenges confronting the economy.
Beneath the surface lay a deeper problem: a statistical institution weakened by chronic underfunding and political interference. Without independence and adequate resources, the integrity of economic data—and the decisions built upon it—is compromised.
In today’s data-saturated world, the role of national statistical offices (NSOs) has never mattered more. As trusted providers of official statistics, NSOs are the foundation of evidence-based policymaking. Yet their ability to fulfill this function is under pressure—from greater economic and social complexity and competition from unverified data sources.
Apart from manipulation, neglect of statistical quality can have serious consequences. GDP and inflation series must be rebased frequently to remain useful. Nigeria waited 20 years to rebase its national accounts and then, in 2010, announced its economy was almost 60 percent bigger than previously estimated. Such revisions completely change the picture of the economy.
NSOs need independence, access to data, and adequate funding to be effective. They should be able to innovate, adapt, and publish high-quality statistics.
Data is everywhere. From social media platforms to smart devices, information is generated at an unprecedented scale. Enabled by surging computing power and artificial intelligence, data can be transformed into insight, but these tools can also produce misleading or entirely fabricated results.
Large language models still hallucinate. For example, leading models consistently fail to produce accurate figures when prompted to produce a table of economic growth rates using the latest IMF World Economic Outlook—even when given the source. Most of the numbers are close but incorrect, which is arguably more dangerous than being wildly wrong: Plausible errors are harder to detect and more likely to mislead.
NSOs, by contrast, ground data in internationally harmonized concepts and methodologies. Their commitment to transparency builds trust. NSOs provide a benchmark against which other data sources can be measured. In a world where misinformation spreads rapidly and data manipulation is easier than ever, the integrity of official statistics is indispensable.
Moreover, the complexity and interconnectedness of today’s global economy demand integrated and well-defined data. Whether it’s tracking inflation, measuring unemployment, or assessing economic growth, policymakers need statistics that are accurate and comparable across countries. NSOs and global standards ensure that data serve as a reliable foundation for policy discussions—so that debates focus on the policies themselves, not the validity of the underlying numbers.
Despite their importance, NSOs face mounting challenges. One of the most pressing is declining response rates to traditional surveys. The response rate to the UK’s Labour Force Survey dropped below 15 percent in 2023, leading to a temporary suspension of the official releases that underpin employment estimates. As people become more wary of sharing personal information—or simply too busy to participate—data collection becomes more difficult and expensive. At the same time, the economy is evolving rapidly, with new sectors like the gig economy and digital services requiring fresh approaches to measurement.
To meet these challenges, NSOs must innovate. This means integrating alternative data sources—such as administrative records, satellite imagery, and private sector data—into statistical systems. NSOs can use big data and AI techniques to accomplish this. It also means that their data must be AI-ready, with well-structured metadata and access for application programming interfaces, so that the information can be easily found and used by modern tools and platforms. Cooperation with AI developers could ensure that official statistical data are more discoverable by those seeking statistics.
Support for NSOs starts with ensuring their independence—one of the fundamental principles behind their effectiveness. Statistics should reflect reality—not political agendas. Legal frameworks must protect NSOs from external interference, allowing them to choose methodologies and publish findings based on professional judgment. Leaders should be empowered to make decisions grounded in statistical expertise, and their staff should adhere to the highest ethical standards, including safeguarding confidential data and using it solely for statistical purposes.
Funding is another cornerstone of support. Unfortunately, the field of official statistics is not glamorous and is seldom a priority, particularly during trying fiscal times. By one estimate, the budget of the US Bureau of Labor Statistics—the agency responsible for producing employment and inflation data, key inputs for monetary policy—has declined by an inflation-adjusted 22 percent since 2010.
To stay relevant, NSOs must compete for talent, invest in technology, and conduct research into emerging data challenges. This includes analyzing and addressing response rates, developing new survey techniques, and exploring innovative data sources—as well as continuing the day-to-day work of producing the core statistics needed for policymaking and safeguarding their quality. Increased investment in statistical capacity is essential to maintaining data quality and relevance.
Access to public and private data is crucial. Governments should facilitate the sharing of administrative data with NSOs, and legal frameworks should enable secure and confidential data exchange between NSOs and other official statistics producers, such as central banks. Best practice includes establishing national coordination committees to oversee statistical governance and promote collaboration.
Stronger NSOs are a strategic imperative. Reliable statistics are essential for effective governance, economic planning, and public accountability. NSOs should be equipped with strong, innovative, and independent leadership capable of navigating the complexities of the modern data landscape.
NSOs should collaborate with other official statistics producers, academic institutions, and international organizations, such as the IMF, to share knowledge, align methodologies, and build capacity. Partnerships with technology companies can help, both as data providers and as channels for disseminating statistics.
Communication is another area for improvement. Producing high-quality data is only half the battle; making it accessible and understandable is equally important. NSOs should invest in data visualization, interactive dashboards, and plain-language summaries to reach diverse audiences. They should embrace open data initiatives and use modern communication channels—such as social media and data portals—to engage with the public and counter misinformation.
Doing so will enhance visibility, build public trust, and improve survey response rates. When people understand the role of NSOs and trust data handling practices, they are more likely to participate in data collection.
National statistical offices are the guardians of data integrity and the backbone of informed decision-making. Ensuring their independence, adequate resources, and capacity to innovate is fundamental to good governance and effective policymaking. Without strong statistical institutions, trust in economic data collapses—and with it, the foundations of sound policy.
