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Agent-based model to rural–urban migration analysis

Author

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  • Silveira, Jaylson J.
  • Espíndola, Aquino L.
  • Penna, T.J.P.

Abstract

In this paper, we analyze the rural–urban migration phenomenon as it is usually observed in economies which are in the early stages of industrialization. The analysis is conducted by means of a statistical mechanics approach which builds a computational agent-based model. Agents are placed on a lattice and the connections among them are described via an Ising-like model. Simulations on this computational model show some emergent properties that are common in developing economies, such as a transitional dynamics characterized by continuous growth of urban population, followed by the equalization of expected wages between rural and urban sectors (Harris–Todaro equilibrium condition), urban concentration and increasing of per capita income.

Suggested Citation

  • Silveira, Jaylson J. & Espíndola, Aquino L. & Penna, T.J.P., 2006. "Agent-based model to rural–urban migration analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 445-456.
    • Handle: RePEc:eee:phsmap:v:364:y:2006:i:c:p:445-456
    DOI: 10.1016/j.physa.2005.08.055
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    References listed on IDEAS

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    1. Bardhan, Pranab & Udry, Christopher, 1999. "Development Microeconomics," OUP Catalogue, Oxford University Press, number 9780198773719.
    2. Ranis, Gustav, 1988. "Analytics of development: Dualism," Handbook of Development Economics, in: Hollis Chenery & T.N. Srinivasan (ed.), Handbook of Development Economics, edition 1, volume 1, chapter 4, pages 73-92, Elsevier.
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    Cited by:

    1. Fu, Zhaohao & Hao, Lingxin, 2018. "Agent-based modeling of China’s rural–urban migration and social network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1061-1075.
    2. García-Díaz, César & Moreno-Monroy, Ana I., 2012. "Social influence, agent heterogeneity and the emergence of the urban informal sector," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1563-1574.
    3. Zouaoui, Ahlem & Zili-Ghedira, Leila & Ben Nasrallah, Sassi, 2016. "Open solid desiccant cooling air systems: A review and comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 889-917.
    4. Enteria, Napoleon & Mizutani, Kunio, 2011. "The role of the thermally activated desiccant cooling technologies in the issue of energy and environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 2095-2122, May.
    5. Furkan Gursoy & Bertan Badur, 2021. "An Agent-Based Modelling Approach to Brain Drain," Papers 2103.03234, arXiv.org, revised Mar 2021.
    6. Cai, Ning & Ma, Hai-Ying & Khan, M. Junaid, 2015. "Agent-based model for rural–urban migration: A dynamic consideration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 806-813.
    7. Navarro, Andres & Tapiador, Francisco J., 2019. "RUSEM: A numerical model for policymaking and climate applications," Ecological Economics, Elsevier, vol. 165(C), pages 1-1.

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