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How well can we estimate immigration trends using Google data?

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  • Philippe Wanner

    (University of Geneva)

Abstract

For a country to efficiently monitor international migration, quick access to information on migration flows is helpful. However, traditional data sources fail to provide immediate information on migration flows and do not facilitate the correct anticipation of these flows in the short term. To tackle this issue, this paper evaluates the predictive capacity of big data to estimate the current level or to predict short-term flows. The results show that Google Trends can provide information that reflects the attractiveness of Switzerland for to immigrants from different countries and predict, to some extent, current and future (short-term) migration flows of adults arriving from Spain or Italy. However, the predictions appear not to be satisfactory for other flows (from France and Germany). Additional studies based on alternative approaches are needed to validate or overturn our study results.

Suggested Citation

  • Philippe Wanner, 2021. "How well can we estimate immigration trends using Google data?," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(4), pages 1181-1202, August.
    • Handle: RePEc:spr:qualqt:v:55:y:2021:i:4:d:10.1007_s11135-020-01047-w
    DOI: 10.1007/s11135-020-01047-w
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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. Konstantin Boss & Andre Groeger & Tobias Heidland & Finja Krueger & Conghan Zheng, 2023. "Forecasting Bilateral Refugee Flows with High-dimensional Data and Machine Learning Techniques," Working Papers 1387, Barcelona School of Economics.
    3. Tjaden, Jasper Dag & Heidland, Tobias, 2021. "Does welcoming refugees attract more migrants? The myth of the "Merkel effect"," Kiel Working Papers 2194, Kiel Institute for the World Economy (IfW Kiel).
    4. 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.
    5. 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.
    6. Joop Age Harm Adema & Maitreyee Guha, 2022. "Following the Online Trail of Ukrainian Refugees through Google Trends," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 23(04), pages 62-66, July.

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