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Product-level value chains from firm data: mapping trophic levels into economic growth

Author

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  • Massimiliano Fessina
  • Andrea Tacchella
  • Andrea Zaccaria

Abstract

We reconstruct a product-level input-output network based on firm-level import-export data of Italian firms. We show that the network has a statistically significant, yet nuanced trophic structure, which is evident at the product level but is lost when the classification is coarse-grained. This detailed value chain allows us to characterize the trophic distance between inputs and outputs of single firms, and to derive a coherent picture at the sector level, finding that sectors such as weapons and vehicles are the ones with the largest increase in downstreamness between their inputs and their outputs. Our measure of downstreamness at the product level can be used to derive country-level indicators that characterize industrial strategies and capabilities and act as predictors of economic growth. With respect to the standard input/output analysis, we show that the fine-grained structure is qualitatively different from what can be observed using sector-level data. We finally prove that, even if we leverage exclusively data from Italian firms, the metrics that we derive are predictive at the country level and capture a significant description of the input-output relations of global value chains.

Suggested Citation

  • 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.
  • Handle: RePEc:arx:papers:2505.01133
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    References listed on IDEAS

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    1. Fessina, Massimiliano & Zaccaria, Andrea & Cimini, Giulio & Squartini, Tiziano, 2024. "Pattern-detection in the global automotive industry: A manufacturer-supplier-product network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    2. Pol Antras & Davin Chor & Thibault Fally & Russell Hillberry, 2012. "Measuring the Upstreamness of Production and Trade Flows," American Economic Review, American Economic Association, vol. 102(3), pages 412-416, May.
    3. Hiroyasu Inoue & Yasuyuki Todo, 2019. "Firm-level propagation of shocks through supply-chain networks," Nature Sustainability, Nature, vol. 2(9), pages 841-847, September.
    4. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, September.
    5. Bacilieri, Andrea & Borsos, András & Astudillo-Estévez, Pablo & Lafond, François, 2023. "Firm-level production networks: what do we (really) know?," INET Oxford Working Papers 2023-08, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    6. Bartolucci, Silvia & Caccioli, Fabio & Caravelli, Francesco & Vivo, Pierpaolo, 2025. "Upstreamness and downstreamness in input-output analysis from local and aggregate information," LSE Research Online Documents on Economics 127165, London School of Economics and Political Science, LSE Library.
    7. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    8. Abhijit Chakraborty & Tobias Reisch & Christian Diem & Pablo Astudillo-Estévez & Stefan Thurner, 2024. "Inequality in economic shock exposures across the global firm-level supply network," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    9. Giambattista Albora & Andrea Zaccaria & Pierluigi Contucci, 2022. "Machine Learning to Assess Relatedness: The Advantage of Using Firm-Level Data," Complexity, Hindawi, vol. 2022, pages 1-12, July.
    10. Pascal Wichmann & Alexandra Brintrup & Simon Baker & Philip Woodall & Duncan McFarlane, 2020. "Extracting supply chain maps from news articles using deep neural networks," International Journal of Production Research, Taylor & Francis Journals, vol. 58(17), pages 5320-5336, September.
    11. M. Wacker,Konstantin & Beyer,Robert C.M. & Moller, Lars Christian, 2024. "Leveraging Growth Regressions for Country Analysis," Policy Research Working Paper Series 10751, The World Bank.
    12. Pichler, Anton & Pangallo, Marco & del Rio-Chanona, R. Maria & Lafond, François & Farmer, J. Doyne, 2022. "Forecasting the propagation of pandemic shocks with a dynamic input-output model," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    13. Fabio Saracco & Mika J. Straka & Riccardo Di Clemente & Andrea Gabrielli & Guido Caldarelli & Tiziano Squartini, 2016. "Inferring monopartite projections of bipartite networks: an entropy-based approach," Papers 1607.02481, arXiv.org, revised May 2017.
    14. Fabio Saracco & Riccardo Di Clemente & Andrea Gabrielli & Tiziano Squartini, 2015. "Randomizing bipartite networks: the case of the World Trade Web," Papers 1503.05098, arXiv.org, revised Jun 2015.
    15. Leonardo Niccol`o Ialongo & Sylvain Bangma & Fabian Jansen & Diego Garlaschelli, 2024. "Multi-scale reconstruction of large supply networks," Papers 2412.16122, arXiv.org.
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    Cited by:

    1. 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 Sep 2025.

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