From Migration Corridors to Clusters: The Value of Google+ Data for Migration Studies

TL;DR

This paper explores how Google+ data can reveal complex migration patterns involving multiple countries, offering new insights beyond traditional bilateral migration flow analysis.

Contribution

It introduces the concept of migration clusters using social media data, demonstrating the limitations of bilateral flow analysis and proposing a model to predict triad migration patterns.

Findings

Migration clusters cannot be identified using bilateral flows alone.

Shared language and colonial ties influence migration clusters.

Web data reveals complex multi-country migration patterns.

Abstract

Recently, there have been considerable efforts to use online data to investigate international migration. These efforts show that Web data are valuable for estimating migration rates and are relatively easy to obtain. However, existing studies have only investigated flows of people along migration corridors, i.e. between pairs of countries. In our work, we use data about "places lived" from millions of Google+ users in order to study migration "clusters", i.e. groups of countries in which individuals have lived. For the first time, we consider information about more than two countries people have lived in. We argue that these data are very valuable because this type of information is not available in traditional demographic sources which record country-to-country migration flows independent of each other. We show that migration clusters of country triads cannot be identified using information about bilateral flows alone. To demonstrate the additional insights that can be gained by using data about migration clusters, we first develop a model that tries to predict the prevalence of a given triad using only data about its constituent pairs. We then inspect the groups of three countries which are more or less prominent, compared to what we would expect based on bilateral flows alone. Next, we identify a set of features such as a shared language or colonial ties that explain which triple of country pairs are more or less likely to be clustered when looking at country triples. Then we select and contrast a few cases of clusters that provide some qualitative information about what our data set shows. The type of data that we use is potentially available for a number of social media services. We hope that this first study about migration clusters will stimulate the use of Web data for the development of new theories of international migration that could not be tested appropriately before.