|ICT|Proxy for ICT flows. Estimated by combining|2003‐|
|---|---|---|
||bilateral duration of phone conversations and|2011|
||the bandwidth capacity between countries.<br>Source: Derived from TeleGeography data.||
|Airlines|Estimated bilateral number of flights (end|2002‐|
||destination) Source: ICAO|2012|
|Portfolio Flows|Total bilateral portfolio flows. Source: BIS,|2000‐|
||Consolidated Banking Statistics (CBS)|2014|

As an initial overview of ECA connectivity in the global context, we show graphically how countries in ECA and the rest of the world are connected in each layer of connectivity. We show data for the initial year and the last period available to see how connectivity in a particular dimension has changed over time. In Figures 2 to 7, ECA countries are highlighted in shades of blue, and the rest of the world is shown in shades of orange. The size of each country‐node is proportional to the size of its total connectivity in each layer of connectivity described. Outward arrows point to the two strongest bilateral partners. The methodology for plotting the countries attempts to show clearly the connections between countries in the global network of countries.<sup>3</sup> The largest country‐nodes are pulled to the outer boundaries of the graph, but the pull is counterbalanced by the number and strength of connections with partner countries. Consequently, country‐nodes will tend to be grouped together if they share common connections with well‐connected countries.

# _Trade_

Figure 2 is based on trade of manufactured goods between all the countries in the world in 2000 and 2014. The size of each country‐node reflects the total volume of trade (exports and imports) for each country, and the two outward arrows are pointed to each country’s two top export destinations. One of the most dramatic developments in global trade has been the emergence of China (CHN) since 2000, which has not only grown to one of the three largest traders, along with the United States (USA) and Germany (DEU), but also is pulled toward the center of the graph, with numerous connections to regional hubs in Europe, the Americas and Asia. ECA’s relative dominance in trade has declined, along with other regions, as China and other Asian countries’ share of global trade has increased. Interestingly, however, ECA country‐nodes are much closer to each other in recent years than in the past, reflecting the higher degree of regional integration and value chain development in Europe. Germany is the primary hub for ECA’s manufacturing

> 3 Country node placement utilizes the Barnes‐Hut algorithm <u>(http://arborjs.org/docs/barnes‐hut). The algorithm</u> attempts to place large country nodes closer to the edges of the graph as a means to more clearly show their numerous connections to smaller country nodes. The repulsion of the country nodes away from the center of the graph is proportional to their size. The repulsion away from the center of the graph is counterbalanced by the attraction forces due to how strongly each pair of countries are connected to each other. Once the forces of repulsion and attraction on the country nodes have been defined, the behavior of the entire graph under these forces may then be simulated as if it were a physical system. In such a simulation, the forces are applied to the country nodes, pulling them closer together or pushing them further apart. This is repeated iteratively until the system comes to a mechanical equilibrium state; i.e., their relative positions do not change from one iteration to the next. The positions of the country nodes in this equilibrium generate the graphical depiction of the network.

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