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Measuring dyadic trade dependence with structural gravity models

This paper asks how to measure bilateral (dyadic) trade dependence in a contemporary world economy characterized by intensive intermediate goods trade and changing availability of partners and substitutes. The authors construct measures from structural-gravity models that quantify (1) how much trade interruptions reduce a country’s welfare and (2) how much additional trade from existing partners would be required to compensate. The brief summarizes the research question, the measurement approach, and the paper’s reported comparisons with traditional aggregate measures.

structural-gravity modeling, dyadic dependence measures, foreign assistance as a case, comparisons with aggregate statistics

Data and methods

The authors build measures from theoretical models of structural gravity that explicitly incorporate intermediate goods trade and alternative partners and products, producing dyadic dependence metrics tied to welfare and compensating trade volumes. The abstract describes a modeling and measurement exercise rather than an experimental or purely descriptive dataset.

Main findings

According to the abstract, the new measures differ from traditional measures based on aggregate trade statistics and do not show a monotonic global rise in interdependence. The authors also use Chinese foreign assistance—cited as exemplified by the Belt and Road Initiative—to illustrate policy consequences, reporting that Chinese assistance increased recipients’ dependence on China without a reciprocal increase in Chinese dependence.

  • New measures are constructed from structural-gravity theory and aim to capture welfare loss and compensating trade flows.
  • The measures produce results that contrast with aggregate-trade indicators and are used to assess the effects of Chinese foreign assistance.
American Political Science Review
Trade Interdependence in the Modern Global Economy was authored by MICHAEL-DAVID MANGINI and STEPHEN CHAUDOIN. It was published in APSR in 2026. Find on Google Scholar10.1017/s0003055426101981
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