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Knowledge based analysis of continental population and migration dynamics

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  • Aral, Mustafa M.

Abstract

Continental migration studies range from elaborate recording of periodic migration data to regression analysis to statistical models that may combine several sources of quantitative and qualitative data. It is the writer's observation that continuous mathematical modeling has not been attempted to estimate continental migration because of the complexity of the governing mathematical models. In this study the writer proposes a continuous mathematical model to estimate continental population and migration trends. The proposed model is based on knowledge-based population dynamics model developed earlier which was used to estimate global population levels and stability of world population under various stress scenarios. In this study one of these models are extended to include continental population and continental migration concepts. Resulting mathematical model is calibrated using historical continental population data which is a reliable data source. If historical continental migration is a zero-sum process, the outcome of the calibrated model yields continental population growth and intercontinental migration estimates. Results obtained are in line with global projections that are made in other studies. The proposed model is also used for future projections.

Suggested Citation

  • Aral, Mustafa M., 2020. "Knowledge based analysis of continental population and migration dynamics," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    • Handle: RePEc:eee:tefoso:v:151:y:2020:i:c:s0040162519314350
    DOI: 10.1016/j.techfore.2019.119848
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    References listed on IDEAS

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    1. Guy Abel, 2013. "Estimating global migration flow tables using place of birth data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(18), pages 505-546.
    2. Jakub Bijak & Arkadiusz Wiśniowski, 2010. "Bayesian forecasting of immigration to selected European countries by using expert knowledge," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(4), pages 775-796, October.
    3. Dolgonosov, Boris M., 2016. "Knowledge production and world population dynamics," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 127-141.
    4. Ševčíková, Hana & Raftery, Adrian E., 2016. "bayesPop: Probabilistic Population Projections," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i05).
    5. Guy Abel, 2018. "Non-zero trajectories for long-run net migration assumptions in global population projection models," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(54), pages 1635-1662.
    6. Okuducu, Mahmut Burak & Aral, Mustafa M., 2017. "Knowledge based dynamic human population models," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 1-11.
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    Cited by:

    1. Taheri, Nima & Jahani, Hamed & Pishvaee, Mir Saman, 2024. "Modeling sustainable bioethanol supply chain in Australia: A system dynamics approach," Renewable Energy, Elsevier, vol. 227(C).

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