Date of Award
Master of Arts in Interdisciplinary Studies (MAIS)
Recently, the development of global network and ITC technology provided new opportunities to improve the estimations and predictability of migration flows. The activity of users of e-mail and other web-based services was compared in time and space in order to track international human mobility. At the same time, the IP based geolocation linked to Google Search proved to be efficient in geographically tracking the outbreaks of several illnesses, and also in predicting changes in economic indicators and travel patterns. This research draws from both experiences. It compares the popularity of migration-to-Spain related queries introduced to Google Search in Argentina, Colombia and Peru, to changes in a quantity of residents’ registrations in Spain, performed by immigrants proceeding from these countries between the years 2005 and 2010. Following the preliminary visual trend analysis, the time series are pre-whitened in order to formally test for a time-shifted correlation and predictability not-influenced by a general series trend. The analysis was performed on the datasets of queries popularity derived from Google Trends and anonymized micro-data of Residential Variation Statistics based on the Municipal Register of Spain. The predicted lags of one or more months that showed to be significantly correlated according to the Cross-Correlation Function have been further used to evaluate its predictability with regression analysis. The results show a significant correlation and weak to moderate predictability for the lags of several months depending on the particular country. The findings support the assumption that popularity of queries to Google Search provided by Google Trends might constitute a useful predictor of migration flows while at the same time it indicates further developments necessary in order to improve its analytical capacities.
University of Texas Brownsville