Tsapenko I.P., Yurevich M.A. (2022). Nowcasting migration using statistics of online queries. Economic and Social Changes: Facts, Trends, Forecast, 15(1), 74–89. DOI: 10.15838/esc.2022.1.79.4
Acostamadiedo E. et al. (2020). Assessing Immigration Scenarios for the European Union in 2030 – Relevant, Realistic and Reliable? Geneva: IOM and e Hague: NIDI.
Albertinelli A. et al. (2020). Forecasting asylum-related migration to the European Union, and bridging the gap between evidence and policy. Migration Policy Practice, X(4), 35–41.
Beduschi A. (2018). The big data of international migration: Opportunities and challenges for states under international human rights law. Georgetown Journal of International Law, 49, 982–1017.
Bengtsson L. et al. (2011). Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: A postearthquake geospatial study in Haiti. PLoS Med, 8(8), e1001083.
Bijak J. (2016). Migration forecasting: Beyond the limits of uncertainty. IOM’s GMDAC Data Briefing Series, 6, 7. Available at: gmdac.iom.int/gmdac-databriefing-migration-forecasting-beyondlimits-uncertainty
Bijak J., Czaika M. (2020). Assessing Uncertain Migration Futures: A Typology of the Unknown. QuantMig Project Deliverable D1.1. University of Southampton and Danube University Krems. Available at https://www.quantmig.eu/res/files/QuantMig%20D1.1%20Uncertain%20Migration%20Futures%20V1.1%2030Jun2020.pdf
Bijak J., Czaika M. (2020). Black swans and grey rhinos: Migration policy under uncertainty. Migration Policy Practice, 2020, X(4), 14–18. Available at: https://publications.iom.int/books/migration-policy-practice-vol-x-number-4-september-december-2020
Blazquez D., Domenech J. (2018). Big data sources and methods for social and economic analyses. Technological Forecasting and Social Change, 130, 99–113.
Bohme M. et al. (2020). Searching for a better life: Predicting international migration with online search keywords. Journal of Development Economics, 142, 14. DOI:10.1016/j.jdeveco.2019.04.002
Carammia M., Dumont J. (2018) Can we anticipate future migration flows? OECD/EASO Migration Policy Debate, 16, 9.
Carling J. (2017). How does migration arise? In: McAuliffe M., Klein Solomon M. (Conveners) Ideas to Inform International Cooperation on Safe, Orderly and Regular Migration. Geneva: IOM, 19–26.
Choi H., Varian H. (2012). Predicting the present with Google Trends. Predicting. The Economic Record, 88 (June), 2–9. DOI: 10.1111/j.1475-4932.2012.00809.x
Chudinovskikh O.S., Stepanova A.V. (2020). On the quality of the federal statistical observation of migration processes. Demograficheskoe obozrenie=Demographic Review, 7(1), 54–82 (in Russian).
Connor P. (2017). The Digital Footprint of Europe’s Refugees. Pew Research Center. Available at: https://www.pewresearch.org/global/wp-content/uploads/sites/2/2017/06/Pew-Research-Center_Digital-Footprint-of-Europes-Refugees_Full-Report_06.08.2017.pdf
Hawelka B. et al. (2014). Geo-located Twitter was proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260–271.
Lifshits M.L. (2016). Forecasting of the global migration situation based on the analysis of net migration in the countries. Prikladnaya ekonometrika=Applied Econometrics, 41, 96–122 (in Russian).
Malysheva D.B. (2017). Migration processes in Central Asian countries. In: A.B. Krylov (Ed.) Postsovetskie gosudarstva: 25 let nezavisimogo razvitiya. T. 1 [Post-Soviet States: 25 years of Independent Development. Vol. 1]. Moscow: IMEMO RAS.
Rango M. (2015). How big data can help migrants, World Economic Forum, 2 (October 5, 2015), Available at: https://www.weforum.org/agenda/2015/10/how-big-data-can-help-migrants/
Sîrbu A. et al. (2021). Human migration: The big data perspective. International Journal of Data Science and Analytics, 11, 341–360. DOI: 10.1007/s41060-020-00213-5
Sohst R. et al. (2020). The Future of Migration to Europe: A Systematic Review of the Literature on Migration Scenarios and Forecasts. Geneva: IOM and Hague: NIDI.
Sohst R., Tjaden J. (2020). Forecasting migration: A policy guide to common approaches and models. Migration Policy Practice, 4, 8–13.
Spyratos S. et al. (2019). Quantifying international human mobility patterns using Facebook Network data. PLoS One, 14(10), e0224134. https://doi.org/10.1371/journal.pone.0224134
Stewart I. et al. (2019). Rock, rap, or reggaeton? Assessing mexican immigrants’ cultural assimilation using Facebook data. In: WWW ‘19. NY: Association for Computing Machinery, 3258–3264. DOI: 10.1145/3308558.3313409
Struijs P. et al. (2014). Official statistics and big data. Big Data & Society, April–June, 1–6. DOI: 10.1177/2053951714538417
Szczepanikova A., Van Criekinge T. (2018). The Future of Migration in the European Union: Future Scenarios and Tools to Stimulate Forward-Looking Discussions. Luxembourg: Publications Office of the European Union. DOI: 10.2760/000622
Tjaden J. et al. (2021). Tale of high expectations, promising results and a long road ahead. Available at: https://medium.com/@UNmigration/using-big-data-to-forecast-migration-8c8e64703559
Tjaden J., Auer D., Laczko F. (2019). Linking migration intentions with flows: Evidence and potential use. International Migration, 57(1), 36–57. DOI: 10.1111/imig.12502
Tkachenko A.A., Ginoyan A.B. (2018). Evaluation of the migration potential of the CIS countries based on the model of international migration. Voprosy Statistiki, 25(11), 46–56 (in Russian).
Wanner P. (2021). How well can we estimate immigration trends using Google data? Quality & Quantity, 55, 1181–1202. DOI: 10.1007/s11135-020-01047-w
Wilson T. (2017). Can international migration forecasting be improved? The case of Australia. Migration Letters, 14(2), 285–299. DOI: 10.33182/ml.v14i2.333
Wladyka D. (2017). Queries to google search as predictors of migration flows from Latin America to Spain. Journal of Population and Social Studies, 2017, 25(4), 312–327. DOI: 10.25133/JPSSv25n4.002
Yurevich M.A. (2021). Inflation expectations and inflation: Nowcasting and forecasting. Journal of Economic Regulation, 12(2), 22–35 (in Russian).
Yurevich M.A., Ekimova N.A., Balatskii E.V. (2020). Digital transformation of economics. Informatsionnoe obshchestvo=Information Society, 2, 39–47 (in Russian).
Zagheni E., Weber I. (2012). You are where you e-mail: Using e-mail data to estimate international migration rates. In: WebSci ‘12: Proceedings of the 4th Annual ACM Web Science Conference. New York: Association for Computing Machinery, 348–351. DOI: 10.1145/2380718.2380764
Zagheni E., Weber I., Gummadi K. (2017). Leveraging Facebook’s advertising platform to monitor stocks of migrants. Population and Development Review, 43, 721–734. https://doi.org/10.1111/padr.12102