Redefining Trade Dynamics: Incorporating Network Centrality, Spatial Heterogeneity, and Optimal Transport Theory into Models of International Trade
How do network position, within-country spatial heterogeneity, and optimal transport change the representation of international trade?
- Status
- Published dissertation · Georgia Institute of Technology doctoral dissertation
- Role
- Sole-authored doctoral dissertation
- Authors
- Ian Helfrich
- Method
- structural gravity · network centrality · optimal transport
- What this record does not establish
- This record covers the three chapters of the 2024 dissertation. Later papers and datasets that build on them have their own records and statuses.
Public artifactsPublished dissertation
Abstract
Summary
Doctoral dissertation connecting three approaches to international trade: network centrality in gravity models, nightlight- and population-weighted measures of bilateral distance, and an infinite-dimensional framework linking economic equilibrium with optimal transport.
Methods and notes
The dissertation established the longer research arc behind the current work on trade networks, spatial measurement, and optimal transport.
The first chapter augments gravity models with measures of a country’s position in the international trade network, using BACI-CEPII data from 1995 through 2015. The second constructs bilateral distance measures from the internal geography of population and nightlight intensity, with applications to U.S. interstate trade in 2017 and global trade from 2015 through 2020. The third develops a theoretical connection between equilibrium and optimal transport in infinite-dimensional spaces.
The Georgia Tech repository provides the complete dissertation and the authoritative bibliographic record.
BibTeX
Citation
@phdthesis{helfrich2024redefining,
author = {Helfrich, Ian},
title = {Redefining Trade Dynamics: Incorporating Network Centrality, Spatial Heterogeneity, and Optimal Transport Theory into Models of International Trade},
year = {2024},
school = {Georgia Institute of Technology},
url = {https://repository.gatech.edu/bitstreams/5162403c-a517-48de-b794-660a053034c9/download}
}