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Syrian Refugee Integration in Turkey: Evidence from Call Detail Records

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Guide to Mobile Data Analytics in Refugee Scenarios

Abstract

Over the past 7 years, the needs of the three and a half million Syrian refugees have shifted from emergency response to programs focused on their integration. Using D4R call detail records (CDRs), this chapter focuses on questions derived from the relevant academic literature and explores whether and how local context and service provision affect refugee integration. Unlike existing studies, we address multiple factors in a single analysis, accounting for potential confoundedness between different factors that might otherwise bias results. Our analysis supplements D4R with an array of original data sources related to refugee integration and service provision and employs linear regression and regularization techniques. We find that social integration is affected by multiple socioeconomic, welfare, and geography-related factors such as economic activity, availability of health facilities and charity foundations, network centrality, and district location. In terms of mobility, long-term over-time movement of refugees appears to be motivated by the availability of scarce welfare resources such as health clinics, as well as economic activity and the availability of religious facilities in a district. Our results suggest that policy-makers concerned with social integration of refugees must readily take into account the role of service provision in that process.

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Notes

  1. 1.

    See the supplementary material Sect. A.1.

  2. 2.

    See the supplementary material Sect. A.2 for the list of antennas associated with camps, borders, and coastal departure points. The two refugee camps Nizip 1 and Nizip 2 are in effectively the same location, so they are treated as one camp for the purposes of antenna assignment. Together they have five antennas associated with them. We note that two antennas assigned to border crossings closest cell tower rule were also assigned to refugee camps.

  3. 3.

    See the supplementary material Sect. A.3 for details.

  4. 4.

    See the supplementary material Sect. A.4 for all correlation tables.

  5. 5.

    See the supplementary material Sect. A.3 for departure points and results. We conducted a least trimmed squares regression trying different alpha values. The coefficients changed between 0.0015 and 0.002 for the possible range of alpha values, from 1 to 0.5.

  6. 6.

    The number of refugees increased from 2,880,325 to 3,049,879 between January and June 2017. It further increased to 3,381,005 by December 2017 as per statistics provided by the Turkish government.

  7. 7.

    See the supplementary material Sect. A.5 for within-group differences and for across-group differences and in call and SMS volumes.

  8. 8.

    See the supplementary material Sect. A.6 for differences in calling and SMS behavior on religious days.

  9. 9.

    See the supplementary material Sects. B.1 and B.2 for the list of variables we employ and summary statistics.

  10. 10.

    Let \(p_A\) be the probability of being from province A in the population, and \(s_A\) the probability of being from province A in the sample, the weight for province A is calculated as \(p_A/s_A\).

  11. 11.

    See the supplementary material Sect. A.4 for more detail.

  12. 12.

    See the supplementary material Sect. B.5 for detailed regression results.

  13. 13.

    We note that only a subset of the total call volume of Dataset 1 is used for this network analysis because 90.8% of the call volume is listed as having an unknown base station for either incoming or outgoing location. The antenna with the highest and lowest network centrality is shown Sect. B.3 of the supplementary material.

  14. 14.

    See the supplementary material Sect. B.4 for Lasso and Ridge results.

  15. 15.

    See the supplementary material Sect. C.1.

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Acknowledgements

We are grateful to the Republic of Turkey Ministry of Interior, the Red Crescent (Kızılay), and the Disaster and Emergency Management Authority (AFAD) for their data support. We thank Ahmet Utku Akbıyık for his research assistance. Finally, we would like to express our sincere gratitude to the D4R challenge organizers and Turk Telekom for setting up the challenge and for providing us with the datasets.

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Authors and Affiliations

  1. Department of Political Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Tugba Bozcaga & Fotini Christia

  2. IDSS, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Elizabeth Harwood

  3. CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Constantinos Daskalakis

  4. Department of Computer Science, Columbia University, New York, NY, 10027, USA

    Christos Papademetriou

Authors
  1. Tugba Bozcaga
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  2. Fotini Christia
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  5. Christos Papademetriou
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Correspondence to Tugba Bozcaga .

Editor information

Editors and Affiliations

  1. Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands

    Albert Ali Salah

  2. Massachusetts Institute of Technology, Cambridge, MA, USA

    Alex Pentland

  3. Fondazione Bruno Kessler, Trento, Italy

    Bruno Lepri

  4. Massachusetts Institute of Technology, Cambridge, MA, USA

    Emmanuel Letouzé

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Bozcaga, T., Christia, F., Harwood, E., Daskalakis, C., Papademetriou, C. (2019). Syrian Refugee Integration in Turkey: Evidence from Call Detail Records. In: Salah, A., Pentland, A., Lepri, B., Letouzé, E. (eds) Guide to Mobile Data Analytics in Refugee Scenarios. Springer, Cham. https://doi.org/10.1007/978-3-030-12554-7_12

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  • DOI: https://doi.org/10.1007/978-3-030-12554-7_12

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