Measuring Labour Mobility and migration Using Big Data Data

Having up-to-date information about the nature and extent of migration within the EU is important for policy making, such as labour market policy or social services. However, timely and reliable statistics on the number of EU citizens residing in or moving across other Member states are difficult to obtain. Official statistics on EU movers are developed by national offices of statistics and published by Eurostat, but they come with a considerable time lag of about two years.
With the rise of the Internet, new data sources offer opportunities to complement traditional sources for EU mobility statistics. In particular, the availability of high quantities of data derived from social media has opened new opportunities. Therefore, we propose a statistical model that integrates data on migrant stocks within the EU from traditional sources such as census, population registers and Labour Force Survey, with new forms of data derived from Facebook. Then, we investigate the potential of the model to facilitate “now-casting”, that is, providing nearly real-time estimates that can serve as early warnings about changes in EU mobility. The model provides measures of uncertainty for the estimates of migrant stocks.

For each year between 2011 and 2018, we estimate the number of EU movers for each combination of origin and destination within the EU. In total, our model estimates just over 15 million EU citizens living in another EU member state than their country of birth in 2018, a slight increase compared to 2016 and 2017. Compared to previous years, the estimates are more uncertain (i.e. predictive intervals are wider) in 2018, the year for which we only have Facebook data available. Consistent with the Eurostat migrant stocks, our model estimates higher numbers of female migrants than male migrants. Male and female migrant stocks follow the same trend over time at different levels.

Additionally, the predictive intervals for countries such as Germany, the UK and France are wider than the predictive intervals of other countries, because their estimates are based on less information. This is due to missing values in some data sources, and the size of migrant stock in these countries being higher than the migrant stock other countries. Within the countries with high immigration, the figure shows that the increase in the numbers of migrants in the United Kingdom and Germany are slowing, while there is a decreasing trend in France, Italy and Spain.

The model estimates stocks of male and female EU movers in three age groups (15–24, 25–54 and 55–64). As expected, the numbers of male migrants aged 25 to 54 are highest in the UK and in Germany compared to other member states. The same estimates show a decreasing trend in Spain, Italy and France. With the exception of Austria, Belgium and Netherlands, the other countries are estimated to have fewer than 250,000 male migrants in this age interval.

When comparing the results with official statistics, we observe that for most of the countries the estimates are comparable to those reported by Eurostat, taking into account the missing data for some countries in the official statistics. We observe no pattern of under- or over-counting over time. However, for a handful of countries (mainly Italy and Spain) our estimates are higher than the reported number of migrants in Eurostat, suggesting that these countries may have missing observations.