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Gravity Models for Global Migration Flows: A Predictive Evaluation

Author

Listed:
  • Juan Caballero Reina

    (World Data Lab
    University of Cambridge)

  • Jesus Crespo Cuaresma

    (World Data Lab
    Vienna University of Economics and Business
    International Institute for Applied Systems Analysis
    Wittgenstein Centre for Demography and Global Human Capital)

  • Katharina Fenz

    (World Data Lab
    Vienna University of Economics and Business)

  • Jakob Zellmann

    (World Data Lab
    Vienna University of Economics and Business)

  • Teodor Yankov

    (World Data Lab
    University College London (UCL))

  • Amr Taha

    (The International Organization for Migration (IOM))

Abstract

This study introduces a comprehensive econometric framework based on gravity equations and designed to forecast migrant flows between countries. The model’s theoretical underpinnings are validated through empirical data, and we show that the model has better out-of-sample predictive ability than alternative global models. We explore the quantitative effects of various socioeconomic, demographic, and geographic factors on migration and illustrate its use to obtain scenario-driven projections of bilateral migration, assessing the potential contributions of migration to population and GDP dynamics in Germany and Portugal for the period 2021–2025. Our projection results highlight the critical role of immigration in sustaining population levels and economic growth, particularly in the context of ageing populations and decreasing fertility rates across Europe.

Suggested Citation

  • Juan Caballero Reina & Jesus Crespo Cuaresma & Katharina Fenz & Jakob Zellmann & Teodor Yankov & Amr Taha, 2024. "Gravity Models for Global Migration Flows: A Predictive Evaluation," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 43(2), pages 1-17, April.
  • Handle: RePEc:kap:poprpr:v:43:y:2024:i:2:d:10.1007_s11113-024-09867-6
    DOI: 10.1007/s11113-024-09867-6
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    References listed on IDEAS

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