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844841 (Harmonized System 1992 for 6-digit)

2016
World Trade $97.1kRnk 4779 / 4869
2016
Top Exporter | Top Importer $64.1k | $97.1kChina | Philippines
2016
Product Complexity 1.31Rnk 385 / 4912
2015 - 2016
Export Growth (CAGR) -75%Rnk 4823 / 4869
2016
Share of World Trade <0.01%Rnk 4779 / 4869

About

Overview This page contains the latest trade data of Shuttles for weaving machines (looms). In 2016, Shuttles for weaving machines (looms) were the world's 4779th most traded product, with a total trade of $97.1k. Between 2015 and 2016 the exports of Shuttles for weaving machines (looms) decreased by -75%, from $388k to $97.1k. Trade in Shuttles for weaving machines (looms) represent 6.2e-7% of total world trade.

Shuttles for weaving machines (looms) are a part of Knitting Machine Accessories.

Exports In 2016 the top exporters of Shuttles for weaving machines (looms)  were China ($64.1k), Austria ($9.76k), Japan ($8.75k), Chinese Taipei ($5.8k), and South Korea ($2.92k).

Imports In 2016 the top importers of Shuttles for weaving machines (looms) were Philippines ($97.1k).

Ranking Shuttles for weaving machines (looms) ranks 385th in the Product Complexity Index (PCI).

Latest Trends

Latest Data

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The following visualization shows the latest trends on Shuttles for weaving machines (looms). Countries are shown based on data availability.

For a full breakdown of trade patterns, visit the trend explorer or the product in country profile.

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* Trade values are converted to USD using each month's exchange rate. For December 2023 data, the exchange rate from December 30, 2023 is used.

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Historical Data

Exporters and Importers

Top Origin (2016)China$64.1k
Top Destination (2016)Philippines$97.1k

In 2016 Shuttles for weaving machines (looms) were the world's 4779th most traded product (out of 4,869).

In 2016, the top exporters of Shuttles for weaving machines (looms) were China ($64.1k), Austria ($9.76k), Japan ($8.75k), Chinese Taipei ($5.8k), and South Korea ($2.92k).

In 2016, the top importers of Shuttles for weaving machines (looms) were Philippines ($97.1k).

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Exporters of Shuttles for weaving machines (looms) (2016)
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Importers of Shuttles for weaving machines (looms) (2016)
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Market Dynamics

Color

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Exporters of Shuttles for weaving machines (looms) (2015 - 2016)

Importers of Shuttles for weaving machines (looms) (2015 - 2016)

Market Concentration

Value

This chart shows the evolution of the market concentration of exports of Shuttles for weaving machines (looms).

In 2016, market concentration measured using Shannon Entropy, was 1.77. This means that most of the exports of Shuttles for weaving machines (looms) are explained by 3 countries.

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Value of Exports in Shuttles for weaving machines (looms)

Net Trade

TOP NET EXPORTER (2016)China$64.1k
TOP NET IMPORTER (2016)United States$166

This map shows which countries export or import more of Shuttles for weaving machines (looms). Each country is colored based on the difference in exports and imports of Shuttles for weaving machines (looms) during 2016.

In 2016, the countries that had a largest trade value in exports than in imports of Shuttles for weaving machines (looms) were China ($64.1k), Austria ($9.76k), Japan ($8.75k), Chinese Taipei ($5.8k), and South Korea ($2.92k).

Net Trade (2016)

Country Comparison

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Ranking

This visualization shows the countries that have an important ratio of their trade related to Shuttles for weaving machines (looms).
It is possible to select the main countries that export or import Shuttles for weaving machines (looms) in the world, or by continent, as well as select the measure of interest.

Top 10 Exporters Countries of Shuttles for weaving machines (looms) by percentage of total exports

Product Complexity

Diversification Frontier

Specialization

The Complexity-Relatedness diagram compares the risk and the strategic value of a product's potential export opportunities. Relatedness is predictive of the probability that a country increases its exports in a product. Complexity, is associated with higher levels of income, economic growth potential, lower income inequality, and lower emissions.

Relatedness vs Country Complexity (2016)

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