Mapping Global Value Chains at the Product Level Article Swipe
Lea Karbevska
,
César A. Hidalgo
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.02491
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.02491
Value chain data is crucial to navigate economic disruptions, such as those caused by the COVID-19 pandemic and the war in Ukraine. Yet, despite its importance, publicly available value chain datasets, such as the ``World Input-Output Database'', ``Inter-Country Input-Output Tables'', ``EXIOBASE'' or the ``EORA'', lack detailed information about products (e.g. Radio Receivers, Telephones, Electrical Capacitors, LCDs, etc.) and rely instead on more aggregate industrial sectors (e.g. Electrical Equipment, Telecommunications). Here, we introduce a method based on machine learning and trade theory to infer product-level value chain relationships from fine-grained international trade data. We apply our method to data summarizing the exports and imports of 300+ world regions (e.g. states in the U.S., prefectures in Japan, etc.) and 1200+ products to infer value chain information implicit in their trade patterns. Furthermore, we use proportional allocation to assign the trade flow between regions and countries. This work provides an approximate method to map value chain data at the product level with a relevant trade flow, that should be of interest to people working in logistics, trade, and sustainable development.
Related Topics
Concepts
Product (mathematics)
Value (mathematics)
Supply chain
Value chain
Global value chain
Industrial organization
Aggregate (composite)
Computer science
Work (physics)
Business
International trade
Marketing
Engineering
Comparative advantage
Machine learning
Composite material
Geometry
Materials science
Mechanical engineering
Mathematics
Metadata
- Type
- preprint
- Language
- en
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385682332
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385682332Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2308.02491Digital Object Identifier
- Title
-
Mapping Global Value Chains at the Product LevelWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-06-12Full publication date if available
- Authors
-
Lea Karbevska, César A. HidalgoList of authors in order
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2308.02491Direct OA link when available
- Concepts
-
Product (mathematics), Value (mathematics), Supply chain, Value chain, Global value chain, Industrial organization, Aggregate (composite), Computer science, Work (physics), Business, International trade, Marketing, Engineering, Comparative advantage, Machine learning, Composite material, Geometry, Materials science, Mechanical engineering, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.theory | 80 |
| abstract_inverted_index.trade, | 173 |
| abstract_inverted_index.``World | 34 |
| abstract_inverted_index.between | 139 |
| abstract_inverted_index.crucial | 4 |
| abstract_inverted_index.despite | 23 |
| abstract_inverted_index.exports | 100 |
| abstract_inverted_index.imports | 102 |
| abstract_inverted_index.instead | 59 |
| abstract_inverted_index.machine | 76 |
| abstract_inverted_index.product | 156 |
| abstract_inverted_index.regions | 106, 140 |
| abstract_inverted_index.sectors | 64 |
| abstract_inverted_index.working | 170 |
| abstract_inverted_index.COVID-19 | 15 |
| abstract_inverted_index.Ukraine. | 21 |
| abstract_inverted_index.detailed | 45 |
| abstract_inverted_index.economic | 7 |
| abstract_inverted_index.implicit | 124 |
| abstract_inverted_index.interest | 167 |
| abstract_inverted_index.learning | 77 |
| abstract_inverted_index.navigate | 6 |
| abstract_inverted_index.pandemic | 16 |
| abstract_inverted_index.products | 48, 118 |
| abstract_inverted_index.provides | 145 |
| abstract_inverted_index.publicly | 26 |
| abstract_inverted_index.relevant | 160 |
| abstract_inverted_index.Tables'', | 39 |
| abstract_inverted_index.``EORA'', | 43 |
| abstract_inverted_index.aggregate | 62 |
| abstract_inverted_index.available | 27 |
| abstract_inverted_index.datasets, | 30 |
| abstract_inverted_index.introduce | 71 |
| abstract_inverted_index.patterns. | 128 |
| abstract_inverted_index.Electrical | 53, 66 |
| abstract_inverted_index.Equipment, | 67 |
| abstract_inverted_index.Receivers, | 51 |
| abstract_inverted_index.allocation | 133 |
| abstract_inverted_index.countries. | 142 |
| abstract_inverted_index.industrial | 63 |
| abstract_inverted_index.logistics, | 172 |
| abstract_inverted_index.Capacitors, | 54 |
| abstract_inverted_index.Database'', | 36 |
| abstract_inverted_index.Telephones, | 52 |
| abstract_inverted_index.approximate | 147 |
| abstract_inverted_index.importance, | 25 |
| abstract_inverted_index.information | 46, 123 |
| abstract_inverted_index.prefectures | 112 |
| abstract_inverted_index.summarizing | 98 |
| abstract_inverted_index.sustainable | 175 |
| abstract_inverted_index.Furthermore, | 129 |
| abstract_inverted_index.Input-Output | 35, 38 |
| abstract_inverted_index.``EXIOBASE'' | 40 |
| abstract_inverted_index.development. | 176 |
| abstract_inverted_index.disruptions, | 8 |
| abstract_inverted_index.fine-grained | 88 |
| abstract_inverted_index.proportional | 132 |
| abstract_inverted_index.international | 89 |
| abstract_inverted_index.product-level | 83 |
| abstract_inverted_index.relationships | 86 |
| abstract_inverted_index.``Inter-Country | 37 |
| abstract_inverted_index.Telecommunications). | 68 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile |