Multiplex Networks in International Trade
This project addresses the issue of economic network interdependence at the level of firms in China and their connectedness among each other as well as with the rest of the world. China accounts for about one-fifth of the world’s population and is on the way to become the world’s largest exporting economy. Since the Economic and Financial Crisis of 2007/08, researchers have become aware that understanding economic network structures at the micro-level is vital for determining the exposure and resilience of firms, jobs, sectors, and countries to adverse shocks globally. With China’s importance as a supplier of intermediate and final goods, understanding this economy as well as its internal and external economic network structure is of global importance. This project will use modern methods in data science and statistics (network and spatial econometrics) to gauge insights into the nature of the domestic and foreign network structure of Chinese firms.
Combine methods in data science, statistics/econometrics and economics to analyse:
1. the evolution of large, multilayer domestic and trade networks of firms,
2. study their consequences for economic outcomes, focusing on one of the largest economies on the globe.
Perform Bayesian non-parametric modeling to understand the behavior of Chinese firms when exporting, and the behavior of consumer countries when importing, based on the firm/region-product-consumer-destination space.
Advance probabilistic machine learning methods for network analysis and statistical/econometric methods in data with endogenous network structure.
Value of exports by countries (in US dollars)
Sophia Ding (2019). Selection Models in the Presence of Networks: Theory and Application for Panel Data (Doctoral thesis, ETH Zurich). https://doi.org/10.3929/ETHZ-B-000385927
Peter Egger, Susie Xi Rao & Sebastiano Papini (2021). A new algorithm for matching Chinese NBS firm-level with customs data, China Economic Journal, 14:3, 311-335. https://doi.org/10.1080/17538963.2021.1963046