Introduction
In the realm of political discourse, where GDP and employment figures dominate, it’s easy to overlook that these are not timeless truths. Historically, the way we measure economic progress has undergone significant transformations. The physiocrats, French economists of the 18th century, regarded agricultural production as the source of all wealth. The Soviet Union, on the other hand, focused solely on goods production, disregarding services entirely.
The Evolution of Economic Measurement
What has remained constant, however, is that statistics have always been tools of the state. The Domesday Book of 1086, commissioned by William the Conqueror, served as an early economic study cataloging lands, properties, and resources in his newly acquired English kingdom. Centuries later, William Petty’s “Aritmética Política” (1690) attempted to demonstrate that Britain’s tax base was robust enough to sustain its war against France.
The modern concept of GDP emerged in the 1930s and solidified during World War II, serving a national function. While Germany developed its own methods for measuring economic capacity, the U.S. and the U.K. gained a decisive strategic advantage by being the first to define total production and compile reliable statistics. This allowed the Allies to maximize production and manage citizen sacrifices more effectively.
The Perils of Unreliable Economic Data
The Greek debt crisis of 2012 underscores the dangers of unreliable economic data. For years, Greece relied on inflated GDP figures and underestimated debt levels to secure cheap loans in international markets. Eurostat, the EU’s statistical body, and other organizations warned that Greek statistics were misleading, but their warnings were largely ignored, especially by banks eager to profit from loan commissions.
The outcome was inevitable: an emergency bailout by the IMF, harsh austerity measures, deep recession, and political unrest. A decade later, Greece’s GDP, now accurately measured, barely surpassed 2012 levels.
Lessons from Economic Data Manipulation
A lesson from this episode—and others, like Argentina’s manipulation of inflation data in the mid-2000s—is that international investors should view any attempt to undermine official statistics’ integrity as a red flag. History demonstrates that while governments may gain short-term political benefits from manipulating economic figures, long-term costs can be substantial.
Alarm Over Trump’s Attack on U.S. Statistical Integrity
Economists were alarmed by Donald Trump’s dismissal of Erika McEntarfer, the Commissioner of the Bureau of Labor Statistics (BLS). Trump’s decision to replace her with E.J. Antoni, an inexperienced loyalist, only exacerbated these concerns. The threat to investor confidence is particularly serious in the U.S., which heavily relies on foreign capital and the reliability of its national statistics as a key selling point.
However, a similarly grave threat, though more subtle, is the undermining of economic data credibility, which weakens government effectiveness. Even a spend-cutting administration needs to understand the country’s productive capacity and tax base, especially amidst growing geopolitical tensions and increasing security demands.
Trump’s partisan campaign against non-partisan statistics, marked by drastic cuts to data collection programs, has limited his administration’s ability to formulate effective policies and demonstrate success. While “evidence-based policies” claims are sometimes exaggerated and often contradict political priorities, knowing if government actions work remains invaluable.
Moreover, when governments start believing their distorted figures, the consequences can be disastrous. In 1987, a CIA study concluded that Soviet growth figures, contrary to many Western observers’ beliefs, were generally accurate. However, following the USSR’s sudden collapse, it became clear that these figures had been grossly exaggerated. Politically motivated, Soviet statistics overlooked critical indicators like consumer goods shortages and poor quality, masking the communist regime’s deep vulnerabilities.
The Importance of Independent, Competent Statistical Agencies
While we should be aware of the political pressures surrounding sensitive figures like inflation and employment, independent and competent statistical agencies keep governments grounded in reality and enable businesses and investors to make informed decisions.
Regrettably, the OECD’s official statistics are in disarray. Facing budget cuts, agencies struggle to adapt to rapid technological and structural changes. Without additional resources from any government, statisticians must modernize their data collection and processing procedures.
In this context, Trump’s attack on U.S. statistical infrastructure has a silver lining: it might prompt officials to reconsider how they measure economic performance and adopt new technologies facilitating massive information selection. This shift, though disruptive, is long overdue.
About 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).
Copyright: Project Syndicate, 1995 – 2025
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