Professional economists have been wrong more often than right for the past three decades. Not wrong in a scattered, sometimes-you-win-sometimes-you-lose kind of way. Wrong in a way that suggests their confidence intervals—the ranges they're supposedly 95 percent certain about—are mathematically useless.
Here's what we tell ourselves about economists: they're experts. They've got models, data, PhDs, and access to real-time market information. When the Federal Reserve chair speaks, markets move. When major institutions publish forecasts, governments and corporations make trillion-dollar decisions based on them. We treat economic prediction like a science, which is to say we treat it as something that works better than guessing.
Except it doesn't. According to analysis from the Federal Reserve Bank of St. Louis, professional economic forecasts from 1993 through 2024 landed within their own predicted confidence ranges less than half the time. Let that sink in. These aren't wide ranges built for the cautious. A 95 percent confidence interval is supposed to contain the actual outcome in 95 out of 100 forecasts. Instead, the data shows economists hit their marks roughly 40 to 50 percent of the time. You'd do nearly as well flipping a coin.
The pattern holds across multiple economic indicators. Whether forecasters are predicting GDP growth, unemployment, or inflation, their miss rate hovers stubbornly above 50 percent. It's not that they're wrong by a little—it's that they're wrong in a way that suggests their stated confidence levels are fiction. A forecast that claims 95 percent certainty but actually proves correct only 45 percent of the time isn't slightly off. It's unmoored from reality.
What makes this worse is that economists weren't blindsided by surprise market crashes or once-in-a-century events. Many of these misses happened during normal economic periods. The models simply can't account for the variables that matter, or they weight the variables they do account for incorrectly, or—most likely—both. Human behavior, policy shifts, supply chain disruptions, and geopolitical surprises don't fit neatly into equations built from historical data. By the time economists update their models to include new realities, those realities have already changed.
The deeper issue is structural. Economic forecasting exists in a fog of genuine uncertainty. Unlike physics, where you can predict where a ball will land given initial conditions, economics involves millions of autonomous agents making unpredictable choices. Central banks change policy. Consumers shift spending habits. Companies alter investment plans. Pandemics arrive. Wars start. The future isn't hidden; it's fundamentally open. Economists try to predict it anyway, and they've built elaborate confidence intervals around their guesses to make those guesses sound scientific.
This wouldn't matter much if we treated economic forecasts like horoscopes—entertaining but not actionable. Instead, they're the foundation of policy. The Federal Reserve raises interest rates or cuts them based on economic forecasts. Governments pass stimulus or austerity measures based on forecasted growth. Pension funds allocate assets, corporations plan hiring, and millions of people make financial decisions influenced by what they think economists know. All of this hinges on predictions that miss nearly half the time.
The implication isn't that economists are incompetent or that we should ignore them entirely. It's that we should treat economic forecasts with radical humility. They're useful as frameworks for thinking, not as reliable maps of the future. And policymakers betting trillions on them should probably know they're betting on something barely better than a guess.