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

2025-05-02

AI Summary

This paper addresses the loss of value-chain structural information in traditional input-output (IO) analysis due to industry-level aggregation. Leveraging Italian firm-level import-export microdata, we construct a product-level IO network and— for the first time— empirically identify and validate a robust trophic structure at the product level. Methodologically, we integrate network science, trophic theory, and structural decomposition analysis to propose a “product-level downstreamness” metric, precisely quantifying the upstream–downstream positioning of firms and industries. Our contributions are threefold: (1) We overcome industry aggregation constraints, revealing structural leaps in input–output linkages for critical sectors such as arms and automotive; (2) The metric relies solely on domestic firm-level data yet effectively captures global value chain characteristics; (3) Across multi-country samples, it significantly predicts GDP growth, outperforming conventional industry-level IO models in explanatory power.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Machine Learning: Structured Learning

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Social media analysis through the lenses of networks

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.

Problem

• Mapping product-level trophic structures in firm data
• Measuring downstreamness between firm inputs and outputs
• Predicting economic growth using fine-grained value chains

Innovation

• Reconstruct product-level input-output network
• Measure trophic distance between firm inputs-outputs
• Predict economic growth using downstreamness metrics