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Using Google Trends to forecast migration from Russia: Search query aggregation and accounting for lag structure

Author

Listed:
  • Bronitsky, Georgy

    (HSE University, Moscow, Russian Federation)

  • Vakulenko, Elena

    (HSE University, Moscow, Russian Federation)

Abstract

This paper proposes a method for predicting migration based on search query data statistics using Google Trends Index (GTI). We improved the existing methodology in two directions: firstly, we proposed an approach for selecting key search queries and aggregating them based on various statistical criteria; secondly, we showed the importance of including in the migration model the time lag structure of search queries, depending on the goals of the migration and the associated GTIs. Based on the monthly data of the German statistical office on the volume of migration from Russia to Germany from January 2011 to August 2022, we demonstrate the efficiency of the proposed approaches. The results show that distributed lag migration models with GTI are better predict migration than SARIMA models. Average lag estimates, i.e. the response time of migration statistics to search queries on the topics “embassy”, “work” and “study”, turned out to be 5.6, 6.5 and 8 months, respectively. It is shown that in order to predict migration from Russia to Germany, it is sufficient to take into account only search queries related to the topic "embassy".

Suggested Citation

  • 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.
    • Handle: RePEc:ris:apltrx:0492
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    References listed on IDEAS

    as
    1. Böhme, Marcus H. & Gröger, André & Stöhr, Tobias, 2020. "Searching for a better life: Predicting international migration with online search keywords," Journal of Development Economics, Elsevier, vol. 142(C).
    2. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Osnat Israeli, 2007. "A Shapley-based decomposition of the R-Square of a linear regression," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 5(2), pages 199-212, August.
    8. Alexander Subbotin & Samin Aref, 2021. "Brain drain and brain gain in Russia: Analyzing international migration of researchers by discipline using Scopus bibliometric data 1996–2020," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7875-7900, September.
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    More about this item

    Keywords

    international migration; Russia; Germany; Google Trends; search queries; nowcasting; big data; forecasting.;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers

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