Article

Contagious disruptions and complexity traps in economic development

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Abstract

Poor economies not only produce less; they typically produce things that involve fewer inputs and fewer intermediate steps. Yet the supply chains of poor countries face more frequent disruptions—delivery failures, faulty parts, delays, power outages, theft and government failures—that systematically thwart the production process. To understand how these disruptions affect economic development, we modelled an evolving input–output network in which disruptions spread contagiously among optimizing agents. The key finding was that a poverty trap can emerge: agents adapt to frequent disruptions by producing simpler, less valuable goods, yet disruptions persist. Growing out of poverty requires that agents invest in buffers to disruptions. These buffers rise and then fall as the economy produces more complex goods, a prediction consistent with global patterns of input inventories. Large jumps in economic complexity can backfire. This result suggests why ‘big push’ policies can fail and it underscores the importance of reliability and gradual increases in technological complexity.

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Acknowledgements

C.D.B. and M.H.B. acknowledge funding from the James S. McDonnell Foundation for the Postdoctoral Award and the Scholar Award (respectively) in Complex Systems. P.P. and F.V.-R. acknowledge funding from the Italian Ministry of Education Progetti di Rilevante Interesse Nazionale grant 2015592CTH. No funders had any role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

  1. Center for the Management of Systemic Risk, Columbia University, New York, NY, 10027, USA

    • Charles D. Brummitt
  2. Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, 02115, USA

    • Charles D. Brummitt
    •  & Matthew H. Bonds
  3. IMT School for Advanced Studies, Piazza San Francesco, 19, 55100, Lucca, Italy

    • Kenan Huremović
  4. Aix-Marseille School of Economics, Aix-Marseille University, 5 Boulevard Maurice Bourdet, 13001, Marseille, France

    • Kenan Huremović
  5. Department of Decision Sciences, Innocenzo Gasparini Institute for Economic Research, and Bocconi Institute for Data Science and Analytics, Università Bocconi, Via Roentgen 1, Milano, 20136, Italy

    • Paolo Pin
    •  & Fernando Vega-Redondo
  6. School of Medicine, Stanford University, Stanford, CA, 94305, USA

    • Matthew H. Bonds

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Contributions

All authors designed and performed the research and wrote the paper. C.D.B. and K.H. analysed the data.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Matthew H. Bonds.

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