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Empirical forecasting of slow-onset disasters for improved emergency response: An application to Kenya's arid north

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Author Info

  • Mude, Andrew G.
  • Barrett, Christopher B.
  • McPeak, John G.
  • Kaitho, Robert
  • Kristjanson, Patti

Abstract

Mitigating the negative welfare consequences of crises such as droughts, floods, and disease outbreaks, is a major challenge in many areas of the world, especially in highly vulnerable areas insufficiently equipped to prevent food and livelihood security crisis in the face of adverse shocks. Given the finite resources allocated for emergency response, and the expected increase in incidences of humanitarian catastrophe due to changing climate patterns, there is a need for rigorous and efficient methods of early warning and emergency needs assessment. In this paper we develop an empirical model, based on a relatively parsimonious set of regularly measured variables from communities in Kenya's arid north, that generates remarkably accurate forecasts of the likelihood of famine with at least 3Â months lead time. Such a forecasting model is a potentially valuable tool for enhancing early warning capacity.

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Bibliographic Info

Article provided by Elsevier in its journal Food Policy.

Volume (Year): 34 (2009)
Issue (Month): 4 (August)
Pages: 329-339

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Handle: RePEc:eee:jfpoli:v:34:y:2009:i:4:p:329-339

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Web page: http://www.elsevier.com/locate/foodpol

Related research

Keywords: Food security Food aid Early warning Emergency response Forecasting famine;

References

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  1. Christopher Barrett, 1997. "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, Taylor & Francis Journals, vol. 25(2), pages 225-236.
  2. McKenzie, D.J.David J., 2004. "Asymptotic theory for heterogeneous dynamic pseudo-panels," Journal of Econometrics, Elsevier, vol. 120(2), pages 235-262, June.
  3. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
  4. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
  5. John McPeak, 2004. "Contrasting income shocks with asset shocks: livestock sales in northern Kenya," Oxford Economic Papers, Oxford University Press, vol. 56(2), pages 263-284, April.
  6. Luseno, Winnie K. & McPeak, John G. & Barrett, Christopher B. & Little, Peter D. & Gebru, Getachew, 2003. "Assessing the Value of Climate Forecast Information for Pastoralists: Evidence from Southern Ethiopia and Northern Kenya," World Development, Elsevier, vol. 31(9), pages 1477-1494, September.
  7. Lybbert, Travis J. & Barrett, Christopher B. & Desta, Solomon & Coppock, D. Layne, 2002. "Stochastic Wealth Dynamics And Risk Management Among A Poor Population," Working Papers 14736, Cornell University, Department of Applied Economics and Management.
  8. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
  9. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
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Cited by:
  1. Lybbert, Travis J. & Sumner, Daniel A., 2012. "Agricultural technologies for climate change in developing countries: Policy options for innovation and technology diffusion," Food Policy, Elsevier, vol. 37(1), pages 114-123.
  2. Barrett, Christopher B. & Headey, Derek D., 2014. "Measuring resilience in a volatile world: A proposal for a multicountry system of sentinel sites :," 2020 Conference papers 1, International Food Policy Research Institute (IFPRI).

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