🤖 AI Summary
This study addresses the challenge of reconstructing inter-firm production networks when firm-level data are unavailable and existing methods fail to accurately capture sectoral input-output structures. The authors propose a novel approach that embeds macro-level input-output constraints into a maximum entropy framework for reconstructing both binary and weighted firm-level networks. They derive, for the first time, analytical solutions that satisfy these aggregate constraints exactly. The method substantially improves fidelity to the observed macroeconomic structure, achieving near-perfect alignment with benchmark input-output tables and significantly reducing structural discrepancies compared to conventional reconstruction techniques. However, while excelling in preserving aggregate industry-level flows, the approach still faces limitations in replicating other topological properties of real-world production networks.
📝 Abstract
A number of recent contributions have put forward the topological structure of production networks as a key determinant of macro-economic dynamics. However, firm-to-firm production networks data is generally not available. Against this background, reconstruction method based on firms' size have been developed. This paper enriches this set of reconstruction methods by integrating input-output sectoral flows in the reconstruction process. We derive analytical expressions for the maximum entropy solutions to the firm network reconstruction problem with sectoral input-output constraints, first for binary networks and then for weight reconstruction. We perform a numerical analysis comparing standard and input-output based reconstruction methods using Hungarian production network data. Our results show that adding input-output constraints substantially reduces deviations from the input-output structure compared with standard methods. Our augmented method provides an almost perfect fit to input-output data, though all methods have difficulties reproducing other structural characteristics.