Gross domestic product (GDP) is rising. Inflation is stabilizing. The unemployment rate is low.
On the face of it, the economy is in good shape. And yet something feels off. People experience instability, erosion, a sense of not belonging in the labor market.
The younger generation cannot afford to buy property, the middle class is shrinking, and financial crises erupt with surprising speed, as if from a place the models never saw. Perhaps this feeling is no accident. Perhaps the problem lies not in reality, but in the tools designed to describe it.
Since the first half of the 20th century, modern macroeconomics has relied on a set of clear indicators such as GDP, inflation, unemployment and the debt-to-GDP ratio. Each of these indicators was born of a particular worldview: industrial, centralized, national. This was a physical economy of goods, labor, factories and states. The indicators, then, were designed to measure a nation's growth rate, household consumption and changes in the price level.
But in recent decades, the economic infrastructure has changed fundamentally. The world has moved from physical production to value structures based on code, data, attention, networks and reputation. Companies are no longer measured by the quantity of goods they produce, but by their ability to collect information, predict behavior and build a digital relationship with the user. In other words, value has moved from matter to relationships.
What drives the economy is no longer only production and sales, but engagement, connectivity, screen time and network influence. The problem is that existing economic indicators do not know how to deal with this.
GDP, for example, records every economic activity as "positive," even if it wears people down or degrades the climate. It does not distinguish between profit generated by selling a medicine and profit derived from selling a biometric surveillance system. Moreover, it cannot measure the value of human time, the quality of information, or the weight of an algorithm in a financial decision.
The labor market is another example. Unemployment indicators assume a clear distinction between "employed" and "not employed." But what about the millions of people in a new in-between status: half self-employed, half gig workers, half unemployed? Is a digital artist working through a global platform "employed"? And what about a content creator who earns more from followers than from a salary?
Inflation, too, a cornerstone of every economic discussion, is measured through a basket of goods, yet it often fails to include real spending on digital services, private education, personalized health care or psychosocial needs. And when consumption moves to the cloud while the data stays on the ground, the indicators describe a world that no longer exists.
On the practical level, one can say that policymakers rely on indicators that mislead them. On the social level, it is important to note that the public's sense of alienation is growing, because it is experiencing a reality the language of economics does not recognize. And on the philosophical level, we continue to believe in the numbers, even when they have stopped telling the story. What is needed, then, is not merely a correction of formulas, but a reinvention of the language of economic measurement.
Indicators that understand distributed value, that value human time, that include impact data and not only production: tools capable of reading the new economy, even when it does not look like yesterday's. Because only if we understand the economy as it truly is can we also shape it into what it could be.