• Economics
• United States

September 22, 2025, 12:01 AM View Comments ()

Welcome, America, to the age of data theater.

If U.S. President Donald Trump’s disdain for truth once played out on Truth Social or via his press secretary, the past month’s actions show that his administration is no longer content just to dispute official numbers. Instead, it is now engaging in what we term data theater. Data theater does more than present false information. Its goal is to hollow out the impartial production of knowledge by gutting the machinery that policymakers, markets, and the public rely on to know what’s real.

History is replete with examples that demonstrate the catastrophic ramifications of this strategy. Statistical manipulation has fueled debt crises, endangered citizens’ health, and threatened the global economy.

The administration’s recent actions show how reality is being rewritten on demand. When the U.S. attorney for D.C. claimed there was an (actually nonexistent) crime wave, she subsequently asserted that crime data challenging that reality was faked by the city’s police department. After the administration claimed that left-leaning domestic violence was deadlier than right-wing violence, a National Institute of Justice report showing the opposite conveniently disappeared from the site.

Data theater is not just a domestic problem; it undermines American credibility globally and exports uncertainty into the international system. What’s at stake are the independent facts that are the currency of global trust and coordinated action. When the president undermines the independent jobs report released by the Bureau of Labor Statistics, it signals to allies and adversaries alike that U.S. numbers cannot be trusted. Likewise, when the Environmental Protection Agency moved to no longer collect thousands of data points on emissions, it hobbles more than the U.S. ability to prevent climate change; it prevents the United Nations from evaluating global progress toward the goals of the 2015 Paris Agreement. Actions like these deprive the international community—researchers, corporations, and governments—of the reliable information it depends on to act.

Domestic data flows into OECD dashboards, informs International Monetary Fund (IMF) forecasts, underpins climate modeling, and shapes the planning assumptions of multilateral alliances. When they are corrupted, the knowledge infrastructure that sustains cooperation begins to erode, not only because U.S. data itself becomes unreliable, but because it shifts the very premise of why independent facts matter in the first place.

When statistics are cooked or suppressed in one country, the consequences rarely stop at its borders. Greece’s manipulated economic data helped trigger a debt crisis and cast doubt on whether the country should have qualified to join the European Union or adopt the euro. In 2007, Former Chinese Premier Li Keqiang admitted that provincial GDP figures that his office oversaw when he was head of the Communist Party in northeastern Liaoning province, which were long doubted by international bodies, were “man-made.” While his own office relied on alternative indicators, later dubbed the “Li Keqiang Index,” to gauge the real economy, outsiders had no such baseline. International institutions were forced to depend on satellite imagery, electricity consumption, and other workarounds to estimate the true changes in GDP growth, distorting IMF forecasts and introducing uncertainty in the global economic system.

Reliable economic indicators underpin everything from forecasting global growth to setting NATO contributions. Climate data that fails to anticipate extreme weather translates into global economic losses and makes it harder to slow climate change. Even incomplete or inaccessible National Oceanic and Atmospheric Administration (NOAA) datasets undermine the ability of shippers to plan efficient routes or insurers to predict rates.

It is a stark reminder that when facts collapse at home, the fallout has global ripples.

To combat data theater, it is essential to understand how it operates. Data theater’s deception is twofold. First, numbers are fabricated—data is cooked. Second, numbers are withheld—data is suppressed. In tandem, these moves inflate the favorable, bury the inconvenient, and cast these distortions as impartial reality.

Data theater is more insidious than data deletion or false assertions. The processes look legitimate. The numbers look official. This ritualized, bureaucratic lying fabricates a factual architecture, saturates the public sphere with nonsensical data, and corrupts our underlying knowledge infrastructure. Data theater is the opposite of evidence-based policymaking; instead, predetermined policies dictate the evidence.

This is data theater’s defining feature—the performance of objectivity through meaningless data staged to simulate truth. As Hannah Arendt warned, the point is not to replace truth with lies but to destroy the very compass by which we navigate reality. A kangaroo court is convened in a courtroom because it is the performance of authority, not its substance, that sustains autocratic legitimacy. Data theater does the same with numbers, staging objectivity to disguise control and erode faith not in facts, but the very idea of impartial facts. If successful, the administration won’t need to refute claims from independent agencies; it needs only to flood the field with its own baseless stories.

The recent lies about crime in Democratic cities, political threats against independent agencies, and repudiation of science are escalating the American retreat from reality. This administration is adept at equating opinion with statistical fact: Department of Government Efficiency (DOGE) “receipts” that don’t add up, improbable claims that 258 million Americans have been saved from fentanyl overdose, and the deletion of datasets that denies some people’s very existence.

Rep. Mike Johnson said in reference to DOGE’s activities that “the data doesn’t lie.” But what happens when it does? Other countries’ experiences offer some telling examples.

Under Mao Zedong, China’s Great Leap Forward became one of history’s deadliest case studies in data distortion. Local officials, desperate to avoid punishment, inflated grain production numbers to meet impossible quotas. The central government claimed the country was overflowing with food, while more than 30 million starved to death. Data stopped describing reality and started performing it, enabling preventable tragedy.

In more recent history, Argentina under President Cristina Fernández de Kirchner manipulated inflation statistics to hide the fallout of protectionist tariffs, pressuring the national statistics agency to underreport consumer price increases. While citizens watched their wages erode, official figures painted a picture of stability. Those who challenged this creative accounting were fired or discredited.

India, too, has rewritten its economic narrative by subtly overhauling how it measures its gross domestic product. There is credible evidence that by changing its data sources and estimation techniques, India inflated its growth rate from 4.5 percent to 7 percent. Arvind Subramanian, former chief economic advisor to the government of India, called recent numbers “absolutely mystifying.”

These examples, and many more like them, show that data can be made to appear authoritative even when it misleads. This is why understanding how numbers are produced—and the judgments behind them—is essential. Data is never truly objective. What gets measured, what doesn’t, and which outliers are discarded reflect human judgment. Without context, numbers become tools for projection—useful for justifying decisions already made, but useless for revealing what should be done. Recognizing that all data requires context doesn’t mean that all data is created equal, which is why there are internationally accepted processes for maximizing data accuracy and utility.

As the American era of data theater dawns, we need not dutifully play the role of captive audience. There is an air of inevitability to the administration’s takeover of trusted statistical agencies, but the final act is not written. It is precisely because the administration still needs the trappings of legitimacy that there is cause for hope. We can call out the lies, demand independent audits, and insist on a commitment to data that reflects reality.

Encouragingly, some Republicans have refused to cross the ethical Rubicon, sensing that in data theater the game has grown too dangerous. While the U.S. Senate must safeguard the independence of federal agencies, the fight is not only domestic.

Maintaining impartial facts is also a global imperative. Allies have pushed back before, as when French President Emmanuel Macron publicly corrected Trump about the nature of European defense spending to protect Ukraine.

Powerful storytelling can illustrate what is lost when government agencies no longer provide vital data. The modern state has always relied on numbers. Governments that don’t know how many people live in a flood zone can’t deploy enough aid. Not knowing how many women are dying in childbirth means no one can effectively intervene. Without knowing that U.S. hiring dropped in June and stalled in the last two months, economic forecasters can’t make accurate predictions.

The use of reliable data doesn’t just inform policy; it sustains the social contract between citizens and state. Data theater erodes that trust, hollowing out the foundation of democratic governance. And much like data itself, data theater flows across borders. Trust in government institutions and data are painstakingly built but take only moments to destroy.

When citizens sense that data is being cooked, they start to assume all statistics are fake. That’s when authoritarians rise—by offering not better data, but compelling lies.