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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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    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    3. Frédéric Docquier & Giovanni Peri & Ilse Ruyssen, 2016. "The Cross-country Determinants of Potential and Actual Migration," World Scientific Book Chapters, in: The Economics of International Migration, chapter 12, pages 361-423, World Scientific Publishing Co. Pte. Ltd..
    4. Steven Ruggles, 2014. "Big Microdata for Population Research," Demography, Springer;Population Association of America (PAA), vol. 51(1), pages 287-297, February.
    5. van Dalen, H.P. & Henkens, K., 2008. "Emigration Intentions : Mere Words or True Plans? Explaining International Migration Intentions and Behavior," Discussion Paper 2008-60, Tilburg University, Center for Economic Research.
    6. Lynn Wu & Erik Brynjolfsson, 2015. "The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 89-118, National Bureau of Economic Research, Inc.
    7. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    8. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    9. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    10. Amelie Constant & Klaus Zimmermann, 2011. "Circular and Repeat Migration: Counts of Exits and Years Away from the Host Country," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 30(4), pages 495-515, August.
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    Cited by:

    1. 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.
    2. 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.
    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. 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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