ArticleScientific data2026
Chinese provinces-embedded inter-country input-output datasets with firm ownership heterogeneity.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Chinese provinces-embedded inter-country input-output datasets with firm ownership heterogeneity.Scientific data · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
Understanding how domestic and foreign-invested firms across China's provinces participate in domestic and international production networks has become increasingly important in both scientific research and policy analysis. However, existing data infrastructures, most notably input-output (IO) datasets, does not adequately support this line of inquiry. Conventional inter-country IO (ICIO) databases treat China as a single, homogeneous economy and therefore overlook substantial variation at the provincial level. Meanwhile, China's inter-provincial IO tables provide no direct linkages between individual provinces and their foreign trading partners. Moreover, firm ownership heterogeneity is largely absent in most available IO datasets. To address these limitations, we construct a new ICIO dataset that integrates China's inter-provincial IO tables-disaggregated by domestic and foreign-invested firms-with the OECD's Activities of Multinational Enterprises-ICIO data and detailed Chinese customs statistics, all within a globally consistent accounting framework. The resulting database comprises 26 sectors, 107 regions, and 2 ownership categories for the benchmark years 2007, 2012, and 2017. These data offer a new foundation for empirical work in economics, environmental assessments, network analyses, and their interdisciplinary applications, particularly for studies examining how firms with different ownership structures across Chinese provinces engage in global supply chains.
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