The incalculable costs of corrupt statistics

CAMBRIDGE – With GDP and employment figures dominating political debates, it's easy to forget that these are not timeless truths. In fact, the way we measure progress has changed dramatically over time. The Physiocrats—18th-century French economists who considered agriculture the source of all wealth—considered agricultural production the most important economic indicator. The Soviet Union, on the other hand, focused exclusively on the production of goods and completely ignored services.

What has remained constant, however, is that statistics, as their name suggests, have always been tools of the state. The Domesday Book of 1086, commissioned by William the Conqueror, served as an early economic survey cataloging the lands, properties, and resources of his newly acquired English kingdom. Centuries later, William Petty's book, Political Arithmetic (1690), attempted to demonstrate that Britain's tax base was solid enough to sustain its war against France.

The modern concept of GDP was developed in the 1930s and consolidated during World War II, as it served a national function. While Germany developed its own methods for measuring economic capacity, the United States and the United Kingdom gained a decisive strategic advantage by being the first to define total output and compile reliable statistics. This allowed the Allies to maximize production and more effectively manage the sacrifices demanded of their citizens.

Greece's 2012 debt crisis highlights the dangers of unreliable economic data. For years, the country relied on inflated GDP figures and understated debt levels to obtain cheap loans on international markets. Eurostat, the European Union's statistical agency, and others warned that Greek statistics were misleading, but their warnings were largely ignored, especially because banks were eager to profit from loan fees.

The result was inevitable: an emergency bailout from the International Monetary Fund, severe austerity measures, a deep recession, and political turmoil. A decade later, Greece's GDP—now accurately measured—was barely higher than it was in 2012.

One lesson from this episode—and others, such as Argentina's manipulation of inflation data in the mid-2000s—is that international investors should consider any attempt to undermine the integrity of official statistics as a red flag. History shows that while governments gain short-term political benefits by manipulating economic figures, the long-term costs can be enormous.

That's why economists were alarmed by US President Donald Trump's dismissal of Erika McEntarfer, commissioner of the Bureau of Labor Statistics. Trump's decision to replace her with EJ Antoni, an inexperienced loyalist, only fueled these concerns. The threat these measures pose to investor confidence is particularly acute in the United States, which relies heavily on foreign capital and the reliability of its domestic statistics as a key selling point.

But an equally serious, if more subtle, threat was that undermining the credibility of economic data weakens government effectiveness. Even an administration focused on reducing public spending and taxes must understand the country's productive capacity and tax base, especially in a context of rising geopolitical tensions and increasing security demands.

Trump's partisan campaign against nonpartisan statistics, marked by drastic cuts to data collection programs, thus limited his administration's ability to craft effective policies and demonstrate their success. While claims of "evidence-based policies" are sometimes exaggerated and often contradict political priorities, knowing whether government actions are working remains invaluable.

Furthermore, when governments begin to believe their own distorted figures, the consequences can be disastrous. In 1987, a CIA study concluded that, contrary to what many Western observers believed, the growth figures reported by the Soviet Union were generally accurate. However, after the sudden collapse of the USSR, it became clear that these figures had been severely inflated. Corrupted by political considerations, Soviet statistics overlooked critical indicators, such as shortages and poor quality of consumer goods, masking the deep vulnerabilities of the communist regime.

While we should not be naive about the political pressures surrounding sensitive figures such as inflation and employment, independent and competent statistical agencies keep governments grounded in reality and allow businesses and investors to make informed decisions.

Unfortunately, the OECD's official statistics are in poor condition. Faced with shrinking budgets, agencies are struggling to adapt to rapid technological and structural changes. Since no government will provide them with more resources, statisticians have no choice but to modernize their data collection and processing procedures.

In that sense, Trump's attack on America's statistical infrastructure has a silver lining: it could prompt officials to rethink how they measure economic performance and adopt new technologies that make it easier to sift through massive amounts of data. This change could be disruptive, but it's about time.

The author

Diane Coyle, Professor of Public Policy at the University of Cambridge, author of Cogs and Monsters: What Economics Is, and What It Should Be (Princeton University Press, 2021) and The Measure of Progress: Counting What Really Matters (Princeton University Press, 2025).

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