The impact of global regional trade agreement network centrality on exports

Description

1. Research hypothesis and findings. Since the 1990s, regional trade agreements (RTAs) have increasingly evolved into complex global networks. We hypothesize that a country’s embeddedness in these RTA networks-measured by network centrality - enhances its export performance by fostering technical progress and strengthening comparative advantages. Using a gravity model with global RTA data from 2010 to 2021, we find that higher RTA network centrality significantly promotes exports. The positive effects are particularly strong for countries with stable hub positions, a greater number of agreements with partner countries, and closer geographical proximity. 2. Description of the data. The dataset combines publicly available sources on global RTAs and trade flows. It contains both raw and cleaned versions of the data, as well as constructed variables that capture RTA depth, breadth, and network centrality measures. All variable definitions, construction methods, and data sources are described in detail in the accompanying Replication Package README file. 3. How to interpret and use the data. The uploaded zip archive (The impact of global regional trade agreement network centrality on exports_Readme_Replication.zip) includes four folders: (1) data - containing raw datasets and cleaned datasets used in the analysis. (2) do file - including the Stata script (Code-EMR-Replication.do), which reproduces all empirical results. (3) README Replication - providing detailed documentation of variables, data construction, sources, and replication steps. (4) results - including all empirical outputs (Tables 1-10, Figures 1-3, Appendix Tables 1-4). These materials allow other researchers to understand the structure of the dataset, replicate the analysis line by line, and extend the study to new contexts.

Steps to reproduce

1. How the data were gathered and processed. All data used in this study are publicly available from international organizations and databases. The primary sources include the World Trade Organization (WTO), the World Bank’s World Development Indicators (WDI), the Centre d’Études Prospectives et d’Informations Internationales (CEPII) Gravity Dataset, and the Design of Trade Agreements (DESTA) database of RTA provisions. These sources were downloaded in their original formats and subsequently merged and cleaned to construct the variables for the period 2010-2021. 2. Methods and workflows. The data were processed using Stata 17 for statistical analysis and replication of the gravity model estimations. Gephi was employed to compute and visualize the RTA network centrality measures. Detailed protocols, including variable definitions, construction methods, and cleaning procedures, are documented in the accompanying Replication Package README. 3. Reproducibility. Section III (“Replication steps”) of Replication Package README provides line-by-line instructions on how to reproduce all results using the provided datasets and the Stata do-file (Code-EMR-Replication.do). This ensures full transparency and replicability of the study.

Institutions

• Shanghai University of Political Science and Law

  Shanghai, Shanghai

Categories

Funders

• National Social Science Fund Youth Project

  Grant ID: 20CJY046
• Shanghai Planning Office of Philosophy and Social Science

  Shanghai

  Grant ID: 2025EJB010
• Shanghai Planning Office of Philosophy and Social Science

  Shanghai

  Grant ID: 2024BGL016
• Shanghai University of Political Science and Law

  Grant ID: 2024XQN04

Licence