IDEAS home Printed from https://ideas.repec.org/p/amz/wpaper/2026-18.html

Synthetic supply networks

Author

Listed:
  • Ng, Galvin

    (Vienna University of Economics and Business)

  • Mungo, Luca
  • Bertrand, Damien

    (École Polytechnique Fédérale de Lausanne, Switzerland)

  • Lafond, François

Abstract

A good representation of the population of firms and households is essential for large-scale economic models. While there exist good methods to create synthetic populations of households, creating synthetic populations of firms and, crucially, their supply chain links, is typically much harder. Here, we introduce a flexible method to create synthetic supply networks that match both the known properties of firm-level supply networks and the properties of aggregated input-output tables used in macroeconomic models. Our method is fast, and because it uses only publicly available data, it is fully reproducible and can be easily extended.

Suggested Citation

  • Ng, Galvin & Mungo, Luca & Bertrand, Damien & Lafond, François, 2026. "Synthetic supply networks," INET Oxford Working Papers 2026-18, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    • Handle: RePEc:amz:wpaper:2026-18
    as

    Download full text from publisher

    File URL: https://oms-inet.files.svdcdn.com/production/files/Synthetic_Supply_Networks_WP_Jul_26.pdf?dm=1785504678
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fetzer, Thiemo & Lambert, Peter John & Feld, Bennet & Garg, Prashant, 2024. "AI-Generated Production Networks: Measurement and Applications to Global Trade," CEPR Discussion Papers 19708, Centre for Economic Policy Research.
    2. Luc Rocher & Julien M. Hendrickx & Yves-Alexandre de Montjoye, 2019. "Estimating the success of re-identifications in incomplete datasets using generative models," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    3. Henriet, Fanny & Hallegatte, Stéphane & Tabourier, Lionel, 2012. "Firm-network characteristics and economic robustness to natural disasters," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 150-167.
    4. Mungo, Luca & Lafond, François & Astudillo-Estévez, Pablo & Farmer, J. Doyne, 2023. "Reconstructing production networks using machine learning," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    5. Ge Zheng & Lingxuan Kong & Alexandra Brintrup, 2023. "Federated machine learning for privacy preserving, collective supply chain risk prediction," International Journal of Production Research, Taylor & Francis Journals, vol. 61(23), pages 8115-8132, December.
    6. Poledna, Sebastian & Miess, Michael Gregor & Hommes, Cars & Rabitsch, Katrin, 2023. "Economic forecasting with an agent-based model," European Economic Review, Elsevier, vol. 151(C).
    7. Fetzer, Thiemo & Lambert, Peter John & Feld, Bennet & Garg, Prashant, 2024. "AI-Generated Production Networks: Measurement and Applications to Global Trade," CAGE Online Working Paper Series 733, Competitive Advantage in the Global Economy (CAGE).
    8. Leonardo Niccol`o Ialongo & Sylvain Bangma & Fabian Jansen & Diego Garlaschelli, 2024. "Multi-scale reconstruction of large supply networks," Papers 2412.16122, arXiv.org.
    9. Marco Pangallo & Alberto Aleta & R. Maria del Rio-Chanona & Anton Pichler & David Martín-Corral & Matteo Chinazzi & François Lafond & Marco Ajelli & Esteban Moro & Yamir Moreno & Alessandro Vespignani, 2024. "The unequal effects of the health–economy trade-off during the COVID-19 pandemic," Nature Human Behaviour, Nature, vol. 8(2), pages 264-275, February.
    10. Yuya Katafuchi & Xinmeng Li & Daniel Moran & Taiki Yamada & Hidemichi Fujii & Keiichiro Kanemoto, 2025. "Construction of an enterprise-level global supply chain database," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    11. Lea Karbevska & Cesar Augusto Hidalgo, 2025. "Mapping global value chains at the product level," Post-Print hal-05472003, HAL.
    12. Norihiko Yamano & Joaquim Guilhoto & Ali Alsamawi & Colin Webb & Peter Horvát & Agnès Cimper & Carmen Zürcher & Xue Han, 2021. "Development of the OECD Inter Country Input-Output Database 2021," OECD Science, Technology and Industry Working Papers 2021/14, OECD Publishing.
    13. Andrew B. Bernard & Emmanuel Dhyne & Glenn Magerman & Kalina Manova & Andreas Moxnes, 2022. "The Origins of Firm Heterogeneity: A Production Network Approach," Journal of Political Economy, University of Chicago Press, vol. 130(7), pages 1765-1804.
    14. Massimiliano Fessina & Andrea Tacchella & Andrea Zaccaria, 2025. "Product-level value chains from firm data: mapping trophic levels into economic growth," Papers 2505.01133, arXiv.org.
    15. Samuel Wiese & Jagoda Kaszowska-Mojsa & Joel Dyer & Jose Moran & Marco Pangallo & Francois Lafond & John Muellbauer & Anisoara Calinescu & J. Doyne Farmer, 2024. "Forecasting Macroeconomic Dynamics using a Calibrated Data-Driven Agent-based Model," Papers 2409.18760, arXiv.org.
    16. Köse, Şeyda & Diem, Christian & Dervic, Elma & Friesenbichler, Klaus & Heiler, Georg & Hurt, Jan & Picatto, Hernan & Klimek, Peter, 2026. "Reconstructing temporal multi-relational firm networks at scale using large language models. The case of the semiconductor industry," INET Oxford Working Papers 2026-14, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    17. Edward Elson Kosasih & Alexandra Brintrup, 2022. "A machine learning approach for predicting hidden links in supply chain with graph neural networks," International Journal of Production Research, Taylor & Francis Journals, vol. 60(17), pages 5380-5393, September.
    18. Alexandra Brintrup & Edward Kosasih & Philipp Schaffer & Ge Zheng & Guven Demirel & Bart L. MacCarthy, 2024. "Digital supply chain surveillance using artificial intelligence: definitions, opportunities and risks," International Journal of Production Research, Taylor & Francis Journals, vol. 62(13), pages 4674-4695, July.
    19. Mitja Devetak & Antoine Mandel, 2026. "Industry Aware Firm Level Network Reconstruction," Papers 2603.21895, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Galvin Ng & Luca Mungo & Damien Bertrand & Franc{c}ois Lafond, 2026. "Synthetic supply networks," Papers 2608.03716, arXiv.org.
    2. Hommes, Cars & Poledna, Sebastian, 2026. "Forecasting economic crises: The Great Recession, the sovereign debt crisis, and COVID-19 in the euro area," Economic Modelling, Elsevier, vol. 158(C).
    3. Andrea Bacilieri & Pablo Austudillo-Estevez, 2023. "Reconstructing firm-level input-output networks from partial information," Papers 2304.00081, arXiv.org.
    4. Neave O'Clery & Ben Radcliffe-Brown & Thomas Spencer & Daniel Tarling-Hunter, 2025. "Deciphering the global production network from cross-border firm transactions," Papers 2508.12315, arXiv.org, revised Jul 2026.
    5. Bacilieri, Andrea & Borsos, András & Astudillo-Estévez, Pablo & Hoefer, Mads & Lafond, François, 2026. "Firm-level production networks: What do we (really) know?," Journal of Economic Dynamics and Control, Elsevier, vol. 187(C).
    6. Adriano Meligrana & Mitja Devetak & Steven Hoekstra & Oliver Reiter & Enrico Minnella & Sebastian Poledna & Marco Bardoscia & Aldo Glielmo, 2025. "BeforeIT.jl: High-Performance Agent-Based Macroeconomics Made Easy," Papers 2502.13267, arXiv.org, revised Oct 2026.
    7. Bacilieri, Andrea & Astudillo-Estévez, Pablo, 2023. "Reconstructing firm-level input-output networks from partial information," INET Oxford Working Papers 2023-05, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, revised Jan 2025.
    8. Schiozer, Nikolas & Lima, Gilberto Tadeu & Alexandre, Michel, 2026. "Heterogeneity in pricing behavior in hybrid DSGE-ABM macrodynamics," Economic Modelling, Elsevier, vol. 155(C).
    9. Di Noia, Jlenia & Caiani, Alessandro & Cesarini, Luigi & Arosio, Marcello & Monteleone, Beatrice, 2025. "A high resolution input–output model to assess the economic impact of floods," Journal of Economic Behavior & Organization, Elsevier, vol. 230(C).
    10. Barde, Sylvain, 2026. "Bayesian estimation of a large-scale macroeconomic policy agent-based model," Journal of Economic Dynamics and Control, Elsevier, vol. 188(C).
    11. Grugni, Elisa & Savin, Ivan & van den Bergh, Jeroen, 2026. "Carbon leakage in production networks under asymmetric climate policies," Journal of Economic Dynamics and Control, Elsevier, vol. 183(C).
    12. Cathrin Mohr & Christoph Trebesch, 2025. "Geoeconomics," Annual Review of Economics, Annual Reviews, vol. 17(1), pages 563-587, August.
    13. Tom Youngman & Tim Lennox & M. Lopes Alves & Pirta Palola & Brendon Tankwa & Emma Bailey & Emilien Ravigne & Thijs Ter Horst & Benjamin Wagenvoort & Harry Lightfoot Brown & Jose Moran & Doyne Farmer, 2026. "Agent-based macroeconomics for the UK's Seventh Carbon Budget," Papers 2602.15607, arXiv.org, revised Mar 2026.
    14. Kong, Dongmin & Pan, Yue & Tian, Gary Gang & Zhang, Pengdong, 2020. "CEOs' hometown connections and access to trade credit: Evidence from China," Journal of Corporate Finance, Elsevier, vol. 62(C).
    15. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    16. Federico Huneeus & Kory Kroft & Kevin Lim, 2021. "Earnings Inequality in Production Networks," NBER Working Papers 28424, National Bureau of Economic Research, Inc.
    17. Aleksandra Kordalska & Magdalena Olczyk, 2020. "What fosters firm-level labour productivity in Eastern European and Central Asian countries?," Bank i Kredyt, Narodowy Bank Polski, vol. 51(1), pages 91-120.
    18. Johannes Boehm & Swati Dhingra & John Morrow, 2022. "The Comparative Advantage of Firms," Journal of Political Economy, University of Chicago Press, vol. 130(12), pages 3025-3100.
    19. Felipe Brugués, 2026. "Take the Goods and Run: Contracting Frictions and Market Power in Supply Chains," American Economic Review, American Economic Association, vol. 116(2), pages 582-626, February.
    20. Samuel Wiese & Jagoda Kaszowska-Mojsa & Joel Dyer & Jose Moran & Marco Pangallo & Francois Lafond & John Muellbauer & Anisoara Calinescu & J. Doyne Farmer, 2024. "Forecasting Macroeconomic Dynamics using a Calibrated Data-Driven Agent-based Model," Papers 2409.18760, arXiv.org.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:amz:wpaper:2026-18. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: INET Oxford admin team (email available below). General contact details of provider: https://edirc.repec.org/data/inoxfuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.