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Now-casting Romanian migration into the United Kingdom by using Google Search engine data

Author

Listed:
  • Andreea Avramescu

    (University of Manchester)

  • Arkadiusz Wiśniowski

    (University of Manchester)

Abstract
Background: Short-term forecasts of international migration are often based on data that are incomplete, biased, and reported with delays. There is also a scarcity of migration forecasts based on combined traditional and new forms of data. Objective: This research assessed an inclusive approach of supplementing official migration statistics, typically reported with a delay, with the so-called big data from Google searches to produce short-term forecasts (“now-casts”) of immigration flows from Romania to the United Kingdom. Methods: Google Trends data were used to create composite variables depicting the general interest of Romanians in migrating into the United Kingdom. These variables were then assessed as predictors and compared with benchmark results by using univariate time series models. Results: The proposed Google Trends indices related to employment and education, which exhaust all possible keywords and eliminate language bias, match trends observed in the migration statistics. They are also capable of moderate reductions in prediction errors. Conclusions: Google Trends data have some potential to indicate up-to-date current trends of interest in mobility, which may serve as useful predictors of sudden changes in migration. However, these data do not always improve the accuracy of forecasts. The usability of Google Trends is also limited to short-term migration forecasting and requires understanding of contexts surrounding origin and destination countries. Contribution: This work provides an example on combining Google Trends and official migration data to produce short-term forecasts, illustrated with flows from Romania to the UK. It also discusses caveats and suggests future work for using these data in migration forecasting.

Suggested Citation

  • Andreea Avramescu & Arkadiusz Wiśniowski, 2021. "Now-casting Romanian migration into the United Kingdom by using Google Search engine data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(40), pages 1219-1254.
    • Handle: RePEc:dem:demres:v:45:y:2021:i:40
    DOI: 10.4054/DemRes.2021.45.40
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    References listed on IDEAS

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    Cited by:

    1. Tjaden, Jasper & Heidland, Tobias, 2024. "Did Merkel's 2015 decision attract more migration to Germany?," Open Access Publications from Kiel Institute for the World Economy 294184, Kiel Institute for the World Economy (IfW Kiel).
    2. Nathan Wycoff & Lisa O. Singh & Ali Arab & Katharine M. Donato & Helge Marahrens, 2024. "The digital trail of Ukraine’s 2022 refugee exodus," Journal of Computational Social Science, Springer, vol. 7(2), pages 2147-2193, October.
    3. Bronitsky, Georgy & Vakulenko, Elena, 2024. "Using Google Trends to forecast migration from Russia: Search query aggregation and accounting for lag structure," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 73, pages 78-101.
    4. Bert Leysen & Pieter-Paul Verhaeghe, 2023. "Searching for migration: estimating Japanese migration to Europe with Google Trends data," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4603-4631, October.

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    More about this item

    Keywords

    international migration; time series; Bayesian analysis; Google; trends;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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