No. 2023-05 - Reconstructing firm-level input-output networks from partial information

Abstract:

There is a large consensus on the fundamental role of firm-level supply chain networks in macroeconomics. However, data on supply chains at the fine-grained, firm level are scarce and frequently incomplete. For listed firms, some commercial datasets exist but only contain information about the existence of a trade relationship between two companies, not the value of the monetary transaction. We use a recently developed maximum entropy method to reconstruct the values of the transactions based on information about their existence and aggregate information disclosed by firms in financial statements. We test the method on the administrative dataset of Ecuador and reconstruct a commercial dataset (FactSet). We test the method’s performance on the weights, the technical and allocation coecients (microscale quantities), two measures of firms’ systemic importance and GDP volatility. The method reconstructs the distribution of microscale quantities reasonably well but shows diverging results for the measures of firms’ systemic importance. Due to the network structure of supply chains and the sampling process of firms and links, quantities relying on the number of customers firms have (out-degrees) are harder to reconstruct. We also reconstruct the input-output table of globally listed firms and merge it with a global input-output table at the sector level (the WIOD). Differences in accounting standards between national accounts and firms’ financial statements significantly reduce the quality of the reconstruction.