Learn economics through real data
Not a textbook, not lectures. The Academy answers real questions with real official data across 47 countries — and every lesson ends the same way: go prove it yourself, on the live map, with the same numbers we used.
Real questions
Why do cities get richer?
Look at Germany, 2023: all ten districts with the highest GDP per capita are cities or city-districts — Wolfsburg (€185,100), Ingolstadt (€164,200), Landkreis München (€149,200)… not one rural Kreis among them. Economists call the mechanism agglomeration: firms cluster near suppliers, workers and customers; specialists find better-matching jobs; ideas spread faster face-to-face. Output concentrates where people concentrate. The data shows the pattern; the theory explains why it keeps reproducing.
Go prove it yourself →Why do populations age?
Three slow forces: fewer births per family than a generation ago, longer lives, and young people moving out of some regions into others. The result inside one country is dramatic: in Germany the median resident of Spree-Neiße is 55.2 years old, while in Heidelberg it is 35.8 — a 19-year gap. The three oldest districts are all in the former East; the youngest are university cities. Ageing is not uniform: it is the sum of decades of births, deaths and moves, and each region carries its own history.
Go prove it yourself →Why are neighboring regions so different?
Wolfsburg's GDP per capita is €185,100. Gifhorn — the Kreis literally next door — records €28,000. A 6.6× gap across one administrative border. The main reason is an accounting one: GDP is booked where value is produced, and Wolfsburg hosts a giant car plant whose workers commute in from Gifhorn and Helmstedt. Add industry mix, one dominant employer, and decades of separate history, and "next door" stops meaning "similar". Administrative borders cut through economic reality — which is exactly why comparing regions on one metric is only the start of a question, never the end.
Go prove it yourself →Why does density affect almost everything?
People per km² is the quietest number with the loudest consequences. Density decides whether a metro line pays for itself, how far the nearest hospital is, what housing costs, and how many customers a shop sees per day. Flip the map between density, GDP per capita and median age and watch the patterns track each other — dense city-districts and sparse rural Kreise differ on almost every other number too. Density does not cause everything, but it sets the conditions almost everything else operates under.
Go prove it yourself →Foundations
What "GDP per capita" actually measures
It's the economic output of a place divided by its people — a rough proxy for how much value is created per head. It is not what people earn, and a high number can hide inequality. See how it ranges from rural districts to industrial powerhouses.
Go prove it yourself →Why a region's median age matters
The median age splits the population in half. A rising one signals ageing — fewer workers per retiree, more pressure on services. It's one of the most reliable, slow-moving signals a region produces.
Go prove it yourself →What net migration tells you about a place
Net migration is arrivals minus departures. People move toward opportunity, so it's a vote-with-your-feet indicator — often leading other signals. Positive where a region pulls people in, negative where it loses them.
Go prove it yourself →How do you compare a tech hub to a farm region fairly?
You normalize. Each metric is rescaled 0–100 within the country so a tech hub and a farm region sit on the same scale. Then you pick the metric that matters to your question — income, education, density — and rank by it. Different metric, different ranking.
Go prove it yourself →What 20 years of one place reveals
A single year is a snapshot; a timeline is the story. Population growth, the COVID dip, recovery — change over time tells you more than any single snapshot. Every district has its own.
Go prove it yourself →Principles we operate by
The convictions behind every lesson — promoted from our internal thinking once we're sure they hold up in public.
Trust is the moat
Anyone can collect data. Only verifiability defends it — every number traces back to its official source.
Trace any number →The null test
If shuffling the geography gives the same signal, it's decoration, not diagnosis. That's why we describe — we don't predict events.
Open methodology →Every number is traceable
Country → metric → district → dataset → original value → normalization. Nothing is a black box.
See the lineage →We sell verifiable trust
Not data, not predictions — comparable regional data with an open method you can check yourself. Honesty you can touch.
Why you can defend it →We claim only what we can show
No "crisis detection," no "real-time risk," no magic. If a number can't be traced and reproduced, we don't say it — one overclaim would break the whole thing.
What we do and don't do →Checkable beats magical
A descriptive index you can verify is worth more than a prediction you can't. We'd rather hand you the method and the sources than ask you to trust a black box.
Check a number →The video series
Each lesson also becomes a short, data-first video — the same question → data → answer, narrated over the real numbers. New episodes roll out on the channel.
Watch on YouTube → Read the written studiesNeed the raw data behind these lessons?
Every figure in these lessons comes from the downloadable dataset — official indicators for 2,619 regions across 47 countries, normalized into one comparable schema.
Get the dataset →