Volume 45 - Article 40 | Pages 1219–1254
Now-casting Romanian migration into the United Kingdom by using Google Search engine data
|Date received:||18 Jan 2021|
|Date published:||02 Dec 2021|
|Keywords:||Bayesian analysis, Google, international migration, time series, trends|
|Additional files:||readme.45-40 (text file, 4 kB)|
|demographic-research.45-40 (zip file, 8 MB)|
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.
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