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A Stochastic Model Of Migrant Rescue In The Mediterranean Sea

Author

Listed:
  • Amitrajeet A. BATABYAL

    (Department of Economics, Rochester Institute of Technology, 92 Lomb Memorial Drive, Rochester, NY 14623-5604, USA.)

  • Hamid BELADI

    (Department of Economics, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249-0631, USA.)

Abstract

We theoretically study the rescue of destitute migrants seeking to reach Europe on boats across the Mediterranean Sea. To this end, we first construct a discrete-time Markov chain (DTMC) model of a stylized rescue process. Second, we specify the one-step transition probabilities of the DTMC. Third, we delineate a recursive algorithm that can be used to compute the limiting or steady-state probabilities of our DTMC model. Finally, we use these limiting probabilities to compute the average throughput. This metric is an efficiency measure and it tells us the long run expected number of migrants that are rescued by the stylized rescue process under study.

Suggested Citation

  • Amitrajeet A. BATABYAL & Hamid BELADI, 2016. "A Stochastic Model Of Migrant Rescue In The Mediterranean Sea," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(1), pages 79-83, June.
    • Handle: RePEc:hrs:journl:v:viii:y:2016:i:1:p:79-83
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    References listed on IDEAS

    as
    1. Claus-Jochen Haake & Tim Krieger & Steffen Minter, 2013. "On the institutional design of burden sharing when financing external border enforcement in the EU," International Economics and Economic Policy, Springer, vol. 10(4), pages 583-612, December.
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    More about this item

    Keywords

    Markov Chain; Mediterranean Sea; Migrant; Rescue; Uncertainty;
    All these keywords.

    JEL classification:

    • J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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