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Economic Growth Under Catastrophes

  • Yuri Ermoliev
  • Tatiana Ermolieva
Chapter
Part of the Advances in Natural and Technological Hazards Research book series (NTHR, volume 32)

Abstract

The chapter analyzes effects of catastrophes on economic growth and stagnation. The economy is a complex system constantly facing shocks and changes with possible catastrophic impacts. A shock is understood as an event removing from the economy a part of the capital. We show that even in the case of well-behaving economies defined by the Harrod-Domar model, persistent in time shocks implicitly modify the economy and may lead to various traps and thresholds triggering stagnation and shrinking. The stabilization of the growth must then rely on ex-ante risk reduction and risk transfer options, such as hazard mitigation and the purchase of catastrophic insurance, as well as ex-post borrowing. The coexistence of ex-ante (risk averse) and ex-post (risk prone) options in the proposed model generates a strong risk aversion even in the case of linear utility functions. In contrast to the traditional expected utility theory, it assesses and explains trade-offs and benefits of ex-ante and ex-post management options.

Keywords

Catastrophic risks Economic growth under shock Growth stabilization Ex-ante and ex-post measures Risk aversion Two-stage stochastic optimization 

References

  1. Arrow J (1996) The theory of risk-bearing: small and great risks. J Risk Uncertain 12:103–111CrossRefGoogle Scholar
  2. Belenki VZ, Volkonski VA (1974) Iterative methods in game theory and programming. Nauka, Moscow, pp 40–73 (in Russian)Google Scholar
  3. Davis MHA (1984) Piecewise-deterministic Markov processes: a general class of Non-diffusion stochastic models. J R Stat Soc B 46:353–388Google Scholar
  4. Dupačovά J, Bertocchi M (1995) Management of bond portfolios via stochastic programming: post -optimality and sensitivity analysis. In: Doležal J, Fidler J (eds) System modeling and optimization. Proc. Of the 17-th IFIP TC7 conference, Prague. Chapmen & Hall, London, pp 574–582Google Scholar
  5. Easterly W (1994) Economic stagnation, fixed factors, and policy thresholds. J Monet Econ 33:525–557CrossRefGoogle Scholar
  6. Embrechts P, Klueppelberg C, Mikosch T (2000) Modeling extremal events for insurance and finance: applications of mathematics, stochastic modeling and applied probability, vol 33. Springer, HeidelbergGoogle Scholar
  7. Ermoliev Y, Wets R (1988) Numerical techniques of stochastic optimization. Computational mathematics. Springer, BerlinCrossRefGoogle Scholar
  8. Ermoliev Y, Ermolieva T, MacDonald G, Norkin V (1998) On the design of catastrophic risk portfolios. International Institute of Applied System Analysis, Interim report IR-98-056, Laxenburg, AustriaGoogle Scholar
  9. Ermoliev Y, Ermolieva T, MacDonald G, Norkin V, Amendola A (2000) A system approach to management of catastrophic risks. Eur J Oper Res 122:452–460CrossRefGoogle Scholar
  10. Ermoliev YM, Ermolieva TY, Norkin VI (2006), in MICRO MESO MACRO: Addressing Complex Systems Couplings; H. Liljenstroem, U. Svedin (eds), World Scientific Publishing Co. Pte. Ltd, Singapore, pp 289–302.Google Scholar
  11. Harrod RF (1939) An essay in dynamic theory. Econ J 49(193):14–33CrossRefGoogle Scholar
  12. IPCC (2011) Summary for policymakers. In: Field CB, Barros V, Stocker TF, Qin D, Dokken D, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen S, Tignor M, Midgley PM (eds) Intergovernmental panel on climate change special report on managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge University Press, CambridgeGoogle Scholar
  13. Khan MS, Montiel P, Haque NV (1990) Adjustments with growth: relating the analytical approaches of the IMF and the world bank. J Dev Econ 32:155–179CrossRefGoogle Scholar
  14. Kushner HJ, Clark DS (1978) Stochastic approximation for constrained and unconstrained systems. Springer, BerlinCrossRefGoogle Scholar
  15. Linnerooth-Bayer J, Mechler R, Hochrainer-Stigler S (2011) Insurance against losses from natural disasters in developing countries. Evidence, gaps and the way forward. J Integr Disaster Risk Manag 1(1):1–23Google Scholar
  16. MacKellar L, Ermolieva T (1999) The IIASA multiregional economic-demographic model: algebraic structure. International Institute of Applied Systems Analysis, Interim report IR-99-007, Laxenburg, AustriaGoogle Scholar
  17. Munich Re (2011) Topics geo. Natural catastrophes 2010: analyses, assessments, positions. Munich Reinsurance Company, Munich. http://www.munichre.com/publications/302-06735_en.pdf
  18. Ray D (1998) Development economics. Princeton University Press, PrincetonGoogle Scholar
  19. Sargent TJ (1978) Dynamic macroeconomic theory. Harvard University Press, CambridgeGoogle Scholar
  20. Solow R (1997) Growth theory: an exposition. Clarendon, OxfordGoogle Scholar
  21. Walker G (1997) Current developments in catastrophe modelling. In: Britton NR, Oliver J (eds) Financial risk management for natural catastrophes. Griffith University, Brisbane, pp 17–35Google Scholar
  22. Zenios SA (1993) Financial optimization. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  23. Ziemba WT, Mulvy J (1998) World Wide asset and liability modeling. Cambridge University Press, CambridgeGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.International Institute for Applied Systems Analysis (IIASA)LaxenburgAustria
  2. 2.Ecosystems, Services and Management (ESM) ProgramInternational Institute for Applied Systems Analysis (IIASA)LaxenburgAustria

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