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

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

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.

Suggested Citation

  • De Lombaerde, Philippe & Naeher, Dominik & Vo, Hung Trung & Saber, Takfarinas, 2023. "Regional economic integration and machine learning: Policy insights from the review of literature," Journal of Policy Modeling, Elsevier, vol. 45(5), pages 1077-1097.
    • Handle: RePEc:eee:jpolmo:v:45:y:2023:i:5:p:1077-1097
    DOI: 10.1016/j.jpolmod.2023.07.001
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    References listed on IDEAS

    as
    1. Saad Chiekh Ahmed Abi El Maaly, 2022. "What the Analysis of 136 Studies from 1960 to 2020 Tells Us About Comparative Regionalism Studies," Post-Print halshs-03918624, HAL.
    2. Dominik Naeher & Raghavan Narayanan, 2020. "Untapped regional integration potential: A global frontier analysis," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 29(6), pages 722-747, August.
    3. Haas, Ernst B., 1970. "The Study of Regional Integration: Reflections on the Joy and Anguish of Pretheorizing," International Organization, Cambridge University Press, vol. 24(4), pages 606-646, October.
    4. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    5. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    6. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    7. Dominik Naeher, 2015. "An Empirical Estimation of Asia's Untapped Regional Integration Potential Using Data Envelopment Analysis," Asian Development Review, MIT Press, vol. 32(2), pages 178-195, September.
    8. Naeher, Dominik, 2015. "An Empirical Estimation of Asia's Untapped Regional Integration Potential Using Data Envelopment Analysis," ADB Economics Working Paper Series 445, Asian Development Bank.
    9. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
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    More about this item

    Keywords

    Regional economic integration; International trade; Machine learning; Artificial intelligence; Literature review;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • F02 - International Economics - - General - - - International Economic Order and Integration
    • F15 - International Economics - - Trade - - - Economic Integration
    • F60 - International Economics - - Economic Impacts of Globalization - - - General

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