Synthetic supply networks
Galvin Ng, Luca Mungo, Damien Bertrand, François Lafond
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.
Create a lesson
Related papers
PPML and Heavy-Tailed Trade and Factor Flows: Why Standard Inference Fails and How to Fix It
Peter H. Egger, Ting Ji, Yulong Wang
Multitask Reinforcement Learning for Assisting Choice Model Specification
Gabriel Nova, Stephane Hess, Sander Van Cranenburgh
Why a Non-Discriminatory Royalty Surcharge Is Not Chip-Neutral: The Error in FTC v. Qualcomm
Sang-Seung Yi
Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders
Davood Wadi, Yu Ma
An Integrative Multidimensional Conceptualization of Telework Behavior: A Systematic Review and Grounded Theory Approach
Sahar Babaei, Saeed Nosratabadi, Thabit Atobishi et al.
Global Poverty Beyond the Official Line: A bounded estimate of material insufficiency
Giancarlo Crocetti