IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/s3ztq.html
   My bibliography  Save this paper

Stop, in the name of COVID!

Author

Listed:
  • Klein, Jordan D.
  • Weber, Ingmar

    (Qatar Computing Research Institute)

  • Zagheni, Emilio

Abstract

In the wake of the COVID-19 pandemic, travel restrictions implemented to prevent its spread, like the suspension of international transit and closure of borders, first put into place in March 2020, often suddenly, have created complex, fast-evolving networks of restrictions between the countries of origin and destination of migrants and would-be migrants. These restrictions have had a particularly noteworthy impact on migrants from North and West Africa, who have reported experiencing greater impacts from the pandemic on their journeys than migrants from any other region in the world, as flows registered through key transit points in West and Central Africa and irregular arrivals to Europe plummeted, especially along the Western Mediterranean Route key to migrants from North and West Africa. The International Organization for Migration (IOM) has postulated that international migrant stocks have fallen well short of their pre-pandemic projections in West/North Africa, Europe, and globally, by more than 2 million, due to travel restrictions. However, this is not testable with migration data from traditional sources like censuses and population surveys, which on top of pre-existing timeliness and granularity limitations, have had data collection operations delayed, canceled, interrupted, or data quality otherwise seriously compromised by the pandemic. Recognizing these challenges, key migration stakeholders, including the IOM, have called for the use of data from alternative sources, including social media, to fill in these gaps. Inspired by this call, we endeavor to test the hypothesis that COVID-related travel restrictions reduced migrant stock compared to what it would have been in the absence of such restrictions using estimates of expats, or individuals living in a given destination country who formerly lived in a given origin country, from Facebook’s advertising platform. We take advantage of the quasi-natural experiment provided by different countries’ staggered adoption of different levels of travel restrictions, which we formulate as a treatment, and attempt to control for non-travel restriction-related factors that may be simultaneously influencing migration, using the method developed by Arellano and Bond for estimating dynamic linear panel models. Looking specifically at four key origin countries in North and West Africa, Côte d’Ivoire, Algeria, Morocco, and Senegal, and their 23 key destination countries, we estimate that a destination country implementing a total entry ban over the course of a month may have expected a 3.39% reduction in migrant stock compared to the counterfactual in which no travel restrictions were implemented. However, when taking pandemic-related mortality, broader restrictions on activity and movement, and the onset of the global pandemic itself into account, we estimate that a destination country implementing an entry ban over the course of a month may expect a 5.47% increase in migrant stock. While further research is needed on both the impact of the COVID-19 pandemic on migration and using social media data to obtain accurate migration estimates, travel restrictions do not appear to have been effective in curbing migration in the countries that implement them in the context of the wider disruptions wrought by the pandemic.

Suggested Citation

  • Klein, Jordan D. & Weber, Ingmar & Zagheni, Emilio, 2022. "Stop, in the name of COVID!," SocArXiv s3ztq, Center for Open Science.
  • Handle: RePEc:osf:socarx:s3ztq
    DOI: 10.31219/osf.io/s3ztq
    as

    Download full text from publisher

    File URL: https://osf.io/download/6357f0fa0ecb42234e2ebade/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/s3ztq?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Emilio Zagheni & Ingmar Weber, 2015. "Demographic research with non-representative internet data," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 13-25, April.
    2. Emilio Zagheni & Ingmar Weber, 2015. "Demographic research with non-representative internet data," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 13-25, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barbara Brollo & Filippo Celata, 2023. "Temporary populations and sociospatial polarisation in the short-term city," Urban Studies, Urban Studies Journal Limited, vol. 60(10), pages 1815-1832, August.
    2. Barslund, Mikkel & Busse, Matthias, 2016. "How mobile is tech talent? A case study of IT professionals based on data from LinkedIn," CEPS Papers 11692, Centre for European Policy Studies.
    3. Dilek Yildiz & Jo Munson & Agnese Vitali & Ramine Tinati & Jennifer A. Holland, 2017. "Using Twitter data for demographic research," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(46), pages 1477-1514.
    4. Stefano Breschi & Francesco Lissoni & Ernest Miguelez, 2018. "Return Migrants' Self-Selection: Evidence for Indian Inventors," NBER Chapters, in: The Roles of Immigrants and Foreign Students in US Science, Innovation, and Entrepreneurship, pages 17-48, National Bureau of Economic Research, Inc.
    5. Spyridon Spyratos & Michele Vespe & Fabrizio Natale & Ingmar Weber & Emilio Zagheni & Marzia Rango, 2019. "Quantifying international human mobility patterns using Facebook Network data," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-22, October.
    6. Letizia Mencarini & Delia Irazú Hernández Farías & Mirko Lai & Viviana Patti & Emilio Sulis & Daniele Vignoli, 2019. "Happy parents’ tweets: An exploration of Italian Twitter data using sentiment analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(25), pages 693-724.
    7. Lawrence M Berger & Giulia Ferrari & Marion Leturcq & Lidia Panico & Anne Solaz, 2021. "COVID-19 lockdowns and demographically-relevant Google Trends: A cross-national analysis," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-28, March.
    8. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
    9. Jan Pablo Burgard & Joscha Krause & Ralf Münnich, 2020. "A Study of Discontinuity Effects in Regression Inference based on Web-Augmented Mixed Mode Surveys," Research Papers in Economics 2020-03, University of Trier, Department of Economics.
    10. Mingxiao Li & Song Gao & Feng Lu & Huan Tong & Hengcai Zhang, 2019. "Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data," IJERPH, MDPI, vol. 16(22), pages 1-20, November.
    11. Grow, André & Perrotta, Daniela & Del Fava, Emanuele & Cimentada, Jorge & Rampazzo, Francesco & Gil-Clavel, Sofia & Zagheni, Emilio, 2020. "Addressing Public Health Emergencies via Facebook Surveys: Advantages, Challenges, and Practical Considerations," SocArXiv ez9pb, Center for Open Science.
    12. Letizia Mencarini & Delia Irazú Hernández-Farías & Mirko Lai & Viviana Patti & Emilio Sulis & Daniele Vignoli, 2018. "Italian happy parents In Twitter," Working Papers 117, "Carlo F. Dondena" Centre for Research on Social Dynamics (DONDENA), Università Commerciale Luigi Bocconi.
    13. Sun, Xiangdong & Yuan, Ouyang & Xu, Zhao & Yin, Yanhui & Liu, Qian & Wu, Ling, 2021. "Did Zipf's Law hold for Chinese cities and why? Evidence from multi-source data," Land Use Policy, Elsevier, vol. 106(C).
    14. Michele Tizzoni & Elaine O. Nsoesie & Laetitia Gauvin & Márton Karsai & Nicola Perra & Shweta Bansal, 2022. "Addressing the socioeconomic divide in computational modeling for infectious diseases," Nature Communications, Nature, vol. 13(1), pages 1-7, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:socarx:s3ztq. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://arabixiv.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.