Hierarchicality of Trade Flow Networks Reveals Complexity of Products

With globalization, countries are more connected than before by trading flows, which currently amount to at least 36 trillion dollars. Interestingly, approximately 30-60 percent of global exports consist of intermediate products. Therefore, the trade flow network of a particular product with high added values can be regarded as a value chain. The problem is weather we can discriminate between these products based on their unique flow network structure. This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent $\eta$ can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, the flow networks of products with higher added values and complexity, such as machinery&transport equipment with larger exponents, are highlighted. These higher values indicate that their trade flow networks are more hierarchical. As a result, without extra data such as global input-output table, we can identify the product categories with higher complexity and the relative importance of a country in the global value chain solely by the trading network.Comment: 14 pages,7 figure

Exponents Distribution for All 4-digit SITC4 Product Categories.

<p>The stacked bar charts of different colors correspond to 1-digit SITC4 categories (left) and primary and manufacture classifications (right). For one specific 1-digit classification (say 0 for food and living animals), we can calculate the frequencies on each exponent intervals for all products with 0 prefix, then these frequencies as little bars are stacked on the tops of existing bars.</p

Distributions of Both Data Sets.

<p>The unit of is U.S.dollar.</p

Exponents of 1-digit SITC4 categories in UN data set.

<p>The categories of 8 (Miscellaneous) and 9(Not classified) are ignored in this table, The last row shows the allometry of all products as an integrated network.</p

Exponents for different products in OECD data set.

<p>The products in different industries coded by ISIC Rev.3 coding system for industries is shown. Industries of financial intermediation, business services, wholesale and retail trade, transport and storage, post and telecommunication, hotels and restaurants, and construction are ignored because their trades do not stand for goods flows. The last row shows the allometry of all industries as an integrated network.</p

Allometric exponents s of 1-digit classification products change with time.

<p>Allometric exponents s of 1-digit classification products change with time.</p

Visualization of trade flow network for power generating equipment (upper) and fruit and vegetable (lower).

<p>We use different colors to distinguish nodes as importer (import is larger than its export) and exporter (export is larger than import). The size of node denotes the total volume of trade. In these two networks, only the backbones are shown as the main parts and all other un-important links are hidden as backgrounds. The backbone extracting method is according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0098247#pone.0098247-Foti1" target="_blank">[35]</a>.</p

The allometric scaling law between (in U.S. dollar) and (in U.S. dollar) of two networks are shown.

<p>The left figure shows a super-linear scaling law (with exponent larger than 1) for power generating product, while the right one shows a sub-linear scaling law (with exponent smaller than 1) for fruit and vegetable.</p