Corvinus
Corvinus

Estimating digital product trade through corporate revenue data

Stojkoski, Viktor ORCID: https://orcid.org/0000-0002-7594-4431, Koch, Philipp ORCID: https://orcid.org/0000-0002-3248-9686, Coll, Eva and Hidalgo, César A. ORCID: https://orcid.org/0000-0002-6977-9492 (2024) Estimating digital product trade through corporate revenue data. Nature Communications, 15 (5262). DOI https://doi.org/10.1038/s41467-024-49141-z

[img] PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
5MB

Official URL: https://doi.org/10.1038/s41467-024-49141-z


Abstract

Despite global efforts to harmonize international trade statistics, our understanding of digital trade and its implications remains limited. Here, we introduce amethod to estimate bilateral exports and imports for dozens of sectors starting from the corporate revenue data of large digital firms. This method allows us to provide estimates for digitally ordered and delivered trade involving digital goods (e.g. video games), productized services (e.g. digital advertising), and digital intermediation fees (e.g. hotel rental), which together we call digital products. We use these estimates to study five key aspects of digital trade. We find that, compared to trade in physical goods, digital product exports are more spatially concentrated, have been growing faster, and can offset trade balance estimates, like the United States trade deficit on physical goods. We also find that countries that have decoupled economic growth from greenhouse gas emissions tend to have larger digital exports and that digital exports contribute positively to the complexity of economies. This method, dataset, and findings provide a new lens to understand the impact of international trade in digital products.

Item Type:Article
Uncontrolled Keywords:economics, geography
Divisions:Corvinus Institute for Advanced Studies (CIAS)
Subjects:Geography
Information economy
Commerce and tourism
Funders:European Research Executive Agency, Agence Nationale de la Recherche, French National Research Agency, European Lighthouse of AI for Sustainability, Open access funding provided by Corvinus University of Budapest
Projects:ANR-19-P3IA-0004, 101086712-LearnData-HORIZON-WIDERA-2022-TALENTS-01, ANR-17-EURE-0010, 101120237-HOR-IZON-CL4-2022-HUMAN-02, Obs4SeaClim 101136548-HORIZON-CL6-2023-CLIMATE-01, ANITI ANR-19-P3IA-0004
DOI:https://doi.org/10.1038/s41467-024-49141-z
ID Code:10105
Deposited By: Ádám Hoffmann
Deposited On:25 Jun 2024 07:52
Last Modified:25 Jun 2024 07:52

Repository Staff Only: item control page

Downloads

Downloads per month over past year

View more statistics