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Forecasting Migration and Integration Trends Using Digital Demography – A Case Study of Emigration Flows from Croatia to Austria and Germany

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  • Jurić, Tado

Abstract

This paper was created as a result of the observed instability of external emigration data from Croatian official data in comparison to data from the statistical offices of the European Union (Eurostat) and Germany (DESTATIS). In this study, the author presents a descriptive analysis of alternative data sources (big data), which could be useful for determining emigration flows from Croatia to Austria and Germany, as well as for estimating and forecasting. The second goal of this paper is to show that this approach can be useful for assessing the degree of cultural assimilation/integration of Croatian emigrants using the tools of Google Trends and Facebook Analytics. To estimate the model, linear regression was used to measure the correlation between the number of searches ( x ) and the number of moves ( y ) evidenced by the official statistics.

Suggested Citation

  • Jurić, Tado, 2022. "Forecasting Migration and Integration Trends Using Digital Demography – A Case Study of Emigration Flows from Croatia to Austria and Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 70(1), pages 125-152.
    • Handle: RePEc:zbw:espost:265540
    DOI: 10.1515/soeu-2021-0090
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    References listed on IDEAS

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    1. Wilde, Joshua & Chen, Wei & Lohmann, Sophie, 2020. "COVID-19 and the Future of US Fertility: What Can We Learn from Google?," SocArXiv 2bgqs, Center for Open Science.
    2. Jurić, Tado, 2021. "Google Trends as a Method to Predict New COVID-19 Cases and Socio-Psychological Consequences of the Pandemic," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7(forthcomi).
    3. Emilio Zagheni & Ingmar Weber & Krishna Gummadi, 2017. "Leveraging Facebook's Advertising Platform to Monitor Stocks of Migrants," Population and Development Review, The Population Council, Inc., vol. 43(4), pages 721-734, December.
    4. Nina Cesare & Hedwig Lee & Tyler McCormick & Emma Spiro & Emilio Zagheni, 2018. "Promises and Pitfalls of Using Digital Traces for Demographic Research," Demography, Springer;Population Association of America (PAA), vol. 55(5), pages 1979-1999, October.
    5. Kyriaki Kalimeri & Mariano G. Beiró & Andrea Bonanomi & Alessandro Rosina & Ciro Cattuto, 2020. "Traditional versus Facebook-based surveys: Evaluation of biases in self-reported demographic and psychometric information," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 42(5), pages 133-148.
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    Cited by:

    1. Jurić, Tado, 2022. "Big (Crisis) Data in Refugee and Migration Studies – Case Study of Ukrainian Refugees," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 70(3), pages 540-553.
    2. F. F. Yusifov, 2023. "Approach for Intellectual Potential Analysis of the Scientific Institution," Digital Transformation, Educational Establishment “Belarusian State University of Informatics and Radioelectronicsâ€, vol. 29(3).

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