Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States

Monica Alexander (), Kivan Polimis and Emilio Zagheni
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Monica Alexander: University of Toronto
Kivan Polimis: Center for Studies in Demography and Ecology, University of Washington
Emilio Zagheni: Max Planck Institute for Demographic Research

Population Research and Policy Review, 2022, vol. 41, issue 1, No 1, 28 pages

Abstract: Abstract Measuring and forecasting migration patterns has important implications for understanding broader population trends, for designing policy effectively and for allocating resources. However, data on migration and mobility are often lacking, and those that do exist are not available in a timely manner. Social media data offer new opportunities to provide more up-to-date demographic estimates and to complement more traditional data sources. Facebook’s Advertising Platform, for example, is a potentially rich data source of demographic information that is regularly updated. However, Facebook’s users are not representative of the underlying population. This paper proposes a statistical framework to combine social media data with traditional survey data to produce timely ‘nowcasts’ of migrant stocks by state in the United States. The model incorporates bias adjustment of Facebook data, and a pooled principal component time series approach, to account for correlations across age, time and space. We use the model to estimate and project migrants from Mexico, India and Germany, three migrant groups with varying levels and trends of migration in the US. By comparing short-term projections with data from the American Community Survey, we show that the model predictions outperform alternatives that rely solely on either social media or survey data.

Keywords: Migration; Social media data; Bayesian methods; United States (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11113-020-09599-3

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