Upstreamness and downstreamness in input–output analysis from local and aggregate information
Abstract Ranking sectors and countries within global value chains is of paramount importance to estimate risks and forecast growth in large economies. However, this task is often non-trivial due to the lack of complete and accurate information on the flows of money and goods between sectors and coun...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-86380-6 |
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| author | Silvia Bartolucci Fabio Caccioli Francesco Caravelli Pierpaolo Vivo |
| author_facet | Silvia Bartolucci Fabio Caccioli Francesco Caravelli Pierpaolo Vivo |
| author_sort | Silvia Bartolucci |
| collection | DOAJ |
| description | Abstract Ranking sectors and countries within global value chains is of paramount importance to estimate risks and forecast growth in large economies. However, this task is often non-trivial due to the lack of complete and accurate information on the flows of money and goods between sectors and countries, which are encoded in input–output (I–O) tables. In this work, we show that an accurate estimation of the role played by sectors and countries in supply chain networks can be achieved without full knowledge of the I–O tables, but only relying on local and aggregate information, e.g., the total intermediate demand per sector. Our method, based on a rank-1 approximation to the I–O table, shows consistently good performance in reconstructing rankings (i.e., upstreamness and downstreamness measures for countries and sectors) when tested on empirical data from the world input–output database. Moreover, we connect the accuracy of our approximate framework with the spectral properties of the I–O tables, which ordinarily exhibit relatively large spectral gaps. Our approach provides a fast and analytically tractable framework to rank constituents of a complex economy without the need of matrix inversions and the knowledge of finer intersectorial details. |
| format | Article |
| id | doaj-art-aaeee68ac3a14db8b579917303e913d3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-aaeee68ac3a14db8b579917303e913d32025-01-26T12:23:43ZengNature PortfolioScientific Reports2045-23222025-01-0115111710.1038/s41598-025-86380-6Upstreamness and downstreamness in input–output analysis from local and aggregate informationSilvia Bartolucci0Fabio Caccioli1Francesco Caravelli2Pierpaolo Vivo3Department of Computer Science, University College LondonDepartment of Computer Science, University College LondonT-Division (Center for Nonlinear Studies and T4), Los Alamos National LaboratoryDepartment of Mathematics, King’s College LondonAbstract Ranking sectors and countries within global value chains is of paramount importance to estimate risks and forecast growth in large economies. However, this task is often non-trivial due to the lack of complete and accurate information on the flows of money and goods between sectors and countries, which are encoded in input–output (I–O) tables. In this work, we show that an accurate estimation of the role played by sectors and countries in supply chain networks can be achieved without full knowledge of the I–O tables, but only relying on local and aggregate information, e.g., the total intermediate demand per sector. Our method, based on a rank-1 approximation to the I–O table, shows consistently good performance in reconstructing rankings (i.e., upstreamness and downstreamness measures for countries and sectors) when tested on empirical data from the world input–output database. Moreover, we connect the accuracy of our approximate framework with the spectral properties of the I–O tables, which ordinarily exhibit relatively large spectral gaps. Our approach provides a fast and analytically tractable framework to rank constituents of a complex economy without the need of matrix inversions and the knowledge of finer intersectorial details.https://doi.org/10.1038/s41598-025-86380-6 |
| spellingShingle | Silvia Bartolucci Fabio Caccioli Francesco Caravelli Pierpaolo Vivo Upstreamness and downstreamness in input–output analysis from local and aggregate information Scientific Reports |
| title | Upstreamness and downstreamness in input–output analysis from local and aggregate information |
| title_full | Upstreamness and downstreamness in input–output analysis from local and aggregate information |
| title_fullStr | Upstreamness and downstreamness in input–output analysis from local and aggregate information |
| title_full_unstemmed | Upstreamness and downstreamness in input–output analysis from local and aggregate information |
| title_short | Upstreamness and downstreamness in input–output analysis from local and aggregate information |
| title_sort | upstreamness and downstreamness in input output analysis from local and aggregate information |
| url | https://doi.org/10.1038/s41598-025-86380-6 |
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