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

This paper sets out to develop an empirical forecasting model that can predict, with reasonable accuracy, the expected welfare impact of impending drought. This work is based on a set of regularly measured variables from communities in Kenya’s Arid North.

The authors argue that given the finite resources allocated for emergency response, and the expected increase in incidences of humanitarian catastrophe, there is a need for the development of rigorous and efficient methods of early warning and emergency needs assessment.

There are a number of advantages and policy implications of using such a model. These include:

  • while several early warning and emergency needs assessment guides exist, the empirical forecasting method has the advantage of demonstrable statistical rigor and out-of-sample performance
  • such a forecasting model is an invaluable tool for emergency awareness and response needs, offering rigorous, cost-effective and practical early warning capacity
  • it offers policy-makers more response leeway by forecasting three months into the future
  • while several early warning and emergency needs assessment guides exist, this empirical forecasting method has the advantage of demonstrable statistical rigor and out of sample performance
  • once developed, the model can be easily and regularly updated with new information, each time quickly re-estimating the relevant parameters in a learning process that results in improved performance.