Žyma: ekonominė konvergencija

In late March 2026, a new seminar series opened at the Institute of Lithuanian Scientific Society — named after the economist Jonas Pranas Aleksa. The inaugural talk was delivered by Prof. Vygintas Gontis, and its subject was deceptively modest: how Lithuania managed to become one of the fastest-growing economies in Central and Eastern Europe. The answer, it turns out, involves birds.

The research behind the lecture, published in two recent papers on arXiv, proposes a fresh lens for understanding how economies grow. Forget the usual policy prescriptions and microeconomic tinkering. What if the most important dynamics of economic development happen at the macro level — visible only when you step back far enough to see the whole flock?

The Murmuration of Markets

Have you ever watched a murmuration of starlings — that fluid, shape-shifting cloud of thousands of birds moving as one? No single bird is in charge. No one issues commands. Each bird simply watches its neighbours and adjusts. The result is breathtaking collective intelligence.

Prof. Gontis and his collaborators argue that technology adoption among nations works the same way. They call it the herd model. When a wealthier country adopts a technology and visibly prospers, neighbouring countries notice — and imitate. Two forces drive this imitation:

Together, these two forces create a powerful engine of catch-up growth — especially for countries that are far behind the technological frontier. The further behind you are, the faster you can theoretically catch up, because the gap between your current state and what’s possible is enormous.

The Mathematics of Catching Up

To turn this intuition into something testable, the researchers built a mathematical model. It is elegant in its simplicity: each country begins at some level of technological development, the most advanced countries set a moving frontier, and lagging nations close the gap over time — but not at the same rate.

As a country nears the frontier, the pace of catch-up slows. This makes intuitive sense: there are fewer obvious imitation opportunities left, and the remaining innovations are genuinely hard to copy. The model produces a characteristic S-shaped curve of productivity growth.

The researchers tested this model against decades of real data from Central and Eastern European countries, comparing them against benchmarks like Germany and the United States. The fit was striking. Countries genuinely do follow the predicted trajectory — accelerating when far behind, slowing as they approach the leaders.

The Secret Variable: Debt

But not all countries catch up at the same speed. Some sprint. Others jog. A few stumble. What explains the difference?

The researchers found a surprisingly powerful predictor: private sector debt. Specifically, the level of household and corporate borrowing from banks.

Countries that over-borrowed before 2008 spent the following decade deleveraging — paying down debt rather than investing in new technologies. This depressed their catch-up speed precisely when they should have been accelerating.

Lithuania’s story is the inverse. It entered the 2008 crisis with relatively modest private debt levels. The crisis was painful — Lithuania suffered one of the sharpest GDP contractions in the EU — but the country emerged leaner, with its balance sheets clean and its capacity to invest intact. The low-debt position, the model suggests, is the structural reason Lithuania has since been able to grow so fast.

Contrasts: Romania Rockets, Slovenia Stalls

The elegance of the model lies in what it reveals about individual countries’ trajectories. Two contrasting cases stand out.

Romania started from a very low productivity base — which actually works in its favour. With such a large gap to close, even modest technology adoption generates dramatic percentage gains. The model projects rapid growth continuing well into the 2030s.

Slovenia, meanwhile, presents a cautionary tale. It began from a higher base than most CEE countries — closer to the Western European frontier — but its private debt dynamics have weighed on its catch-up speed. The model predicts slower convergence, confirming what the data has shown for the past decade.

A Different Way of Seeing Economics

Perhaps the most intellectually interesting aspect of this research is its methodological stance. The authors explicitly reject the conventional microeconomic approach of building up from individual agents, firms, and market interactions.

Instead, they adopt the perspective of an outside observer watching the whole system. Like a biologist studying a murmuration rather than tracking individual birds, they look for macro-level patterns — the collective dynamics that emerge from millions of individual decisions but cannot be reduced to any of them.

This approach, borrowed partly from statistical physics, allows them to sidestep the „noise” of micro-level fluctuations and identify the underlying signal: the smooth, predictable curve of technological catch-up. The model is explicitly approximate. It doesn’t account for political upheaval, institutional quality, or individual crises. But its authors argue that this simplicity is a virtue — it reveals the structural forces that operate beneath the policy noise.

Why This Matters Beyond Lithuania

The implications reach well beyond one small Baltic nation. If the herd model is correct, then economic policy advice that ignores private debt dynamics and focuses only on innovation, education, or institutional reform may be missing the central variable.

It also offers a more optimistic reading of development economics. Growth, in this view, is not primarily about rare genius or fortunate geography. It is a social process — systematic, learnable, and to a significant degree predictable. Countries that watch, imitate, and invest wisely will catch up. The mathematics says so.

For Lithuania, the message is one of hard-won vindication. The brutal austerity of 2009–2010, widely criticised at the time, left the country with clean books precisely when it needed them most. The sprint that followed wasn’t luck. It was structure.