Description
Having up-to-date information about the nature and extent of migration within the EU is important for policymaking, 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 statistical offices 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 large 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. Next, 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.
In the model, we assume that the data from each source are measured with bias and accuracy specific to that source and year of the measurement. For instance, we assume that census data are typically the most accurate and with the smallest bias, Facebook-derived stock data are heavily biased, whereas Labour Force Survey are subject to largest inaccuracy due to sampling errors. We correct for these inadequacies by incorporating informative prior distributions for the model parameters that allow borrowing of information across time and among countries.
| Period | 25 Apr 2022 |
|---|---|
| Event title | Big Data and Forced Migration Conference |
| Event type | Conference |
| Location | Georgetown, United States, District of ColumbiaShow on map |
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