Abstract
This paper examines the suitability of Google Trends data for the modeling and forecasting of interregional migration in Russia. Monthly migration data, search volume data, and macro variables are used with a set of univariate and multivariate models to study the migration data of the two Russian cities with the largest migration inflows: Moscow and Saint Petersburg. The empirical analysis does not provide evidence that the more people search online, the more likely they are to relocate to other regions. However, the inclusion of Google Trends data in a model improves the forecasting of the migration flows, because the forecasting errors are lower for models with internet search data than for models without them. These results also hold after a set of robustness checks that consider multivariate models able to deal with potential parameter instability and with a large number of regressors.
Highlights
Google Trends (GT) is an online service launched in 2008, which provides an index that reflects the relative popularity of a particular keyword by calculating the share of users’ searches for this keyword among the total Google searches
We used the F-test for seasonality based on the joint significance of seasonal dummies in a non-seasonal autoregressive integrated moving average (ARIMA) model, the Friedman [71] test, the Kruskal–Wallis test [72], the QS test by Maravall [73]—which is a variant of the Ljung–Box test computed on seasonal lags—and the Welch test [74]
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Summary
Google Trends (GT) is an online service launched in 2008, which provides an index that reflects the relative popularity of a particular keyword (or a topic) by calculating the share of users’ searches for this keyword among the total Google searches This tool has been used in various fields of research, including IT, communications, medicine, health, business, and economics; see the large [1] for a detailed review. One of the latest advances in migration research proposes the inclusion of Google Trends data to forecast migration flows In this regard, Böhme et al [2] stated that people acquire information about migration opportunities online before deciding to emigrate. Jun et al [7] provide a useful review of the research using Google Trends in a wide range of areas, including IT, communications, medicine, health, business, and economics
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