How to map a digital economy using an economic system, and to do it in a way that looks intuitively familiar?
That’s what the authors of the Economic Map project wanted to find out.
They spent months working through data to create the maps they wanted.
“The goal was to get the best possible economic maps for a modern economic system,” says lead author Paul Taylor, a former economics professor at the University of Maryland and current research associate at Stanford University’s Hoover Institution.
And the maps look pretty great: “A digital economy is built on the assumption that people will have the best tools available to them, and that those tools will be efficient.”
For example, they showed the map of the world economy with its economic data coming from the United Nations.
It’s clear that the United States is a leader in the digital economy, but the map was designed to make it easy to compare that with other countries.
It shows that the U.S. has the highest GDP per capita of all OECD countries, which is just under $20,000.
The map also shows that Canada is the second-highest GDP per person, just over $14,000, and Japan is the third-highest.
(It’s worth noting that the OECD doesn’t have a national GDP number, so there’s no comparison between countries.)
China has the lowest GDP per-capita, just under 3,000 dollars per capita.
The chart below, from the map, shows that China and Japan have a lot in common: The United States, Canada, and Germany have very high GDP per people, while the European Union, Mexico, and Australia have very low GDP per populations.
The data is sourced from the Organization for Economic Co-operation and Development (OECD), and the maps are a direct reflection of that.
They use a complex algorithm to map out how each country’s economy would work in a digital world.
“You can’t look at a map and say, ‘Well, that’s where the U’s going to be, and you’re going to need to pay to have that infrastructure,’ ” Taylor says.
“It has to look like that.”
The data source for the maps is the Organization of Economic Co to nders for the data.
It came from the OECD’s Digital Data and Information Framework, a database of all data in the world.
It covers nearly 2,400 countries, from every major industrialized country, and covers all of their economic activity from a variety of sources.
The OECD collects data on a wide range of economic data, including consumer prices, GDP, employment, and the like.
The goal of the project was to build a data-driven, business-friendly map that could be used by companies and consumers alike to better understand the economy.
The maps show that, as you move through the economy, you will see more and more similarities.
There’s an economic cycle in which businesses and consumers spend more money in the economy as the economy grows, and there’s an economy-wide cycle where businesses and people spend less.
Taylor and co. also looked at the economic cycles of each country to determine which economies have more of the cycle in their data, and which economies had the most of the economy-specific cycles.
The results show that the economies with the most cycle-specific data are those that have the most similarities in the economic structure of their economies.
For example: “In the U, for example, the U is a rich country with a strong middle class, with a large percentage of the population living in low-income households, which means that the middle class has a lot of disposable income,” Taylor says, referring to the OECD data.
“In contrast, the middle-class of the U of A are living in a low-wage economy, which has been devastated by globalization.”
The map below shows the economic cycle-by-cycle structure of the United Kingdom and Ireland, and also the cycle-wide structure of Denmark and the Netherlands.
The blue dots are countries that have high cycle-based data and have high data-related similarity between them.
The red dots are the countries that don’t have cycle-related data, but have some data-specific similarity.
“What you can see is that the countries with the lowest cycle-like data and the highest cycle-similar data tend to be low- and middle-income countries,” Taylor notes.
“These countries are the most vulnerable to the effects of globalization and have been the most devastated by the economic crisis.”
In countries like Ireland, which suffers from the economic downturn, the data-based cycle-dependent cycle-linked data structure can be a challenge.
“So the solution to the Ireland problem is to look at the data and see what it’s really like,” Taylor said.
The researchers also took the opportunity to look beyond the data to identify where data-intensive businesses are concentrated in a country, which can also have a significant impact on its economic system.
For instance, a country like the Netherlands that’s