Abstract
Quantifying global international mobility patterns can improve migration governance. Despite decades of calls by the international community to improve international migration statistics, the availability of timely and disaggregated data about long-term and short-term migration at the global level is still very limited. In this study, we investigate the feasibility of using non-traditional data sources to fill existing gaps in migration statistics. To this end, we use anonymised and publicly available data provided by Facebook’s advertising platform. Facebook’s advertising platform classifies its users as “lived in country X” if they previously lived in country X, and now live in a different country. Drawing on statistics about Facebook Network users (Facebook, Instagram, Messenger, and the Audience Network) who have lived abroad and applying a sample bias correction method, we estimate the number of Facebook Network (FN) “migrants” in 119 countries of residence and in two time periods by age, gender, and country of previous residence. The correction method estimates the probability of a person being a FN user based on age, sex, and country of current and previous residence. We further estimate the correlation between FN-derived migration estimates and reference official migration statistics. By comparing FN-derived migration estimates in two different time periods, January-February and August-September 2018, we successfully capture the increase in Venezuelan migrants in Colombia and Spain in 2018. FN-derived migration estimates cannot replace official migration statistics, as they are not representative, and the exact methods the FN uses for classifying its users are not known, and might change over time. However, after carefully assessing the validity of the FN-derived estimates by comparing them with data from reliable sources, we conclude that these estimates can be used for trend analysis and early-warning purposes.
Highlights
In this article, we investigate the use of data from non-traditional data sources for estimating international human mobility patterns
The corrected numbers of Facebook Network (FN) migrants aged 15–64 for each gender, country of previous residence, and country of current residence are strongly correlated with the migrant stock numbers provided by official sources (R2 = 0.735, p
There are 2005 combinations of gender, country of previous residence, and current residence when there are more than 1000 migrants in the FN and the reference migration statistics
Summary
We investigate the use of data from non-traditional data sources for estimating international human mobility patterns. We chose to use the Facebook Network as the non-traditional source of data for estimating “migrants”, because it is, to the best of our knowledge, the only widely used social network that classifies its users based on their previous residence, and provides these estimates on a publicly accessible advertisement platform. Besides evaluating the level of coverage of the entire population by country, age, and gender, we relied on official statistics about international migrant stocks These statistics can be used to determine the degree to which a “FN migrant” user assimilated to the FN usage patterns of the country of current residence, and for benchmarking the results of our estimates of “FN migrants”. OECD estimates refer to the foreign-born population in all the 34 countries of destination except two, Botswana and Japan, where the migrant stock statistics identify foreign citizens
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