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Smuggling of forced migrants to Europe: a matching model

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Abstract

This paper develops a matching model to analyze the smuggling market for forced migrants, building on the empirical evidence related to the smuggling of migrants from the Horn of Africa and the Middle East to Europe in the last decade. Comparative statics for the equilibrium solution reveal that coercion-based measures targeting the smugglers reduce the number of irregular migrants and smugglers at the expense of migrants’ overall welfare. Slightly increasing legal migration opportunities has the interesting feature of reducing irregular flows, without deteriorating migrants’ welfare or increasing the total number of migrants. An extremely restrictive asylum policy has similar effects in terms of the flows of irregular migrants as a quite loose one, with the largest flows of irregular migrants occurring under a “middle-range” policy.

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Data Availability

The authors confirm that all data generated or analyzed during this study are included in this published article. Furthermore, primary and secondary sources and data supporting the findings of this study were all publicly available at the time of submission.

Notes

  1. A significant body of high-quality reports by international organizations and research papers provide ethnographic analyses of this new migration-to-EU episode (see, for instance, Abdel Aziz et al. 2015; Achilli and Sanchez 2017; UNODC 2018; Campana 2018; Campana and Gelsthorpe 2020; Campana 2020; Sanchez 2020).

  2. The European Commission website for Statistics on Migration, accessed on July 27, 2023.

  3. In recent years, the importance of the economic motive as a determinant of migration to Europe might have increased, knowing that people exposed to extreme poverty have no other real choice but to leave the area of residence (see, for instance, The Economist, October 12, 2023, “The EU’s endless search for a migration fix”). In an extension of this model (Charlot et al. 2022), we study the case of voluntary migration in a matching framework, where economic migrants follow the same routes as forced migrants, but, additionally, carry on a cost-benefit analysis of migration, along the traditional logic in Harris and Todaro (1970).

  4. Djajić and Vinogradova (2019) also show that there may be a trade-off between the policies targeting legal and irregular migration.

  5. Another segment of the smuggling market relies on large criminal organizations, to which the “large-firm” matching model, also developed in Pissarides (2000), could be a better fit.

  6. Art. 3(a), UN Protocol Against the Smuggling of Migrants by Land, Sea and Air, supplementing the United Nations Convention against Transnational Organized Crime, 2000.

  7. See the European Commission website: Towards a Comprehensive European Migration Policy: 20 years of EU Action.

  8. See the European Commission webpage What is the New Pact on Migration and Asylum of the EU?; the expected date for final adoption of the Pact is April 2024.

  9. See for instance LeMonde, October 6, 2023, “EU states reach deal on migration reforms”.

  10. As a usual example, used later on for numerical simulations, the matching function can be of the Cobb-Douglas form, \({H}=pM_{s}^{\alpha }S_{s}^{1-\alpha }\), where \(\alpha \in (0,1)\) is the elasticity of the matching function with respect to \(M_{s}\).

  11. We implicitly assume that potential migrants simultaneously search for a smuggler and apply for a visa. Those who have the chance to obtain a visa will leave through the regular channel, the others will use the irregular channel.

  12. See https://news.un.org/en/story/2023/04/1135577.

  13. In a more general framework, this rate could be set equal to a positive parameter \(\lambda \), to acknowledge that the migration project may also be deterred by other external shocks (natural events, unexpected conflicts, accidents, etc.) and that the duration of the journey may vary from a very few days to weeks or even years (European Commission 2015). See Charlot et al. (2022) for this extension.

  14. An additional parameter might allow us to differentiate between the migrants who see their asylum status denied and accept to be sent back, and those who manage to fall between the cracks and stay as illegal migrants in the destination area.

  15. See for instance The Economist, August 8, 2019, “Migrant arrivals in Italy have tumbled” or Deutche Welle, December 1, 2019, “Germany: Thousands of migrants return after deportation”.

  16. Furthermore, the fee equations are identical when the Hosios condition is met (Hosios 1990).

  17. This imposes some restrictions on the parameters; see the online Appendix A.4.

  18. Calculations are presented in the online Appendix A.6.

  19. See the IOM News on April 2023 and the Frontex data; data reported by Pham and Komiyama (2022) reveal a similar ratio.

  20. In the simulations, a solution exists for \(D\in [0,1500]\).

  21. In the US context, Amuedo-Dorantes and Bucheli (2023) show that metering along the US-Mexico border is a restrictive asylum policy ineffective in terms of curtailing unauthorized migration, with adverse humanitarian consequences.

  22. Focusing on the case of Syrian refugees in Germany, Hannafi and Marouani (2023) show that social integration positively affects their intention to stay in Germany, which suggests that increasing the net value of migration improves the welfare of refugees.

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Acknowledgements

The authors are grateful to two anonymous referees and to the Editor, Gregory Ponthiére, for their suggestions and remarks that helped them improve the quality of this paper. They also thank participants at the LEM economic seminar in Lille in 2019, at the GLO Virtual Seminar in 2022, at the CES IELM seminar in Paris in 2022, and at the Borders workshop in Ghent in 2022 for their suggestions and constructive remarks.

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Authors and Affiliations

  1. CY Cergy Paris Université, CNRS, THEMA, Cergy, France

    Olivier Charlot

  2. Univ. Lille, CNRS, IESEG School of Management, UMR 9221 - LEM - Lille Économie Management, and ICM, Lille, France

    Claire Naiditch

  3. ESSEC Business School, CNRS, THEMA, Cergy, France

    Radu Vranceanu

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  1. Olivier Charlot
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  2. Claire Naiditch
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  3. Radu Vranceanu
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Correspondence to Claire Naiditch.

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Charlot, O., Naiditch, C. & Vranceanu, R. Smuggling of forced migrants to Europe: a matching model. J Popul Econ 37, 18 (2024). https://doi.org/10.1007/s00148-024-00993-1

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