Regional economic integration and machine learning: Policy insights from the review of literature

Philippe De Lombaerde, Dominik Naeher, Hung Trung Vo, Takfarinas Saber

Research output: Contribution to a Journal (Peer & Non Peer)Articlepeer-review

6 Citations (Scopus)

Abstract

Due to its focus on prediction rather than causal inference, machine learning has long been treated somewhat neglectfully in the economic literature. For several reasons, however, interest in machine learning has surged recently and is slowly finding its way into the econometric toolbox. Within the economic literature, regional integration has been one of the research areas at the forefront of this development, with various studies experimenting with different machine learning techniques to shed light on the complex dynamics governing regional integration processes. This paper provides the first systematic review of the literature that uses machine learning to study regional economic integration. The focus is twofold, first analysing studies along various thematic and methodological features (and the links between them), and then discussing the scope and nature of policy insights derived from the surveyed body of literature.

Original languageEnglish
Pages (from-to)1077-1097
Number of pages21
JournalJournal of Policy Modeling
Volume45
Issue number5
DOIs
Publication statusPublished - 1 Sep 2023

Keywords

  • Artificial intelligence
  • International trade
  • Literature review
  • Machine learning
  • Regional economic integration

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