Forecasting Instability Indicators in the Horn of Africa Region
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The forecasting of state failure and the associated indicators has been a topic of great interest to a number of different agencies. USAid, CENTCOM, the World Bank, the Center for Army Analyses, and others have all examined the subject based on their own specific objectives. Whether the goal is denying terrorists space in which to operate, deciding how to pre-position materials in anticipation of unrest, stabilizing foreign markets and trade, or preventing or mitigating humanitarian disasters, man made or otherwise, this topic has been of interest for over a decade. The Horn of Africa has been...
The forecasting of state failure and the associated indicators has been a topic of great interest to a number of different agencies. USAid, CENTCOM, the World Bank, the Center for Army Analyses, and others have all examined the subject based on their own specific objectives. Whether the goal is denying terrorists space in which to operate, deciding how to pre-position materials in anticipation of unrest, stabilizing foreign markets and trade, or preventing or mitigating humanitarian disasters, man made or otherwise, this topic has been of interest for over a decade. The Horn of Africa has been one of the least stable regions in the world over the past three decades, and a continual source of humanitarian crises as well as terrorist activity. Some of the initial modeling of instability was done in response to crises in the Horn of Africa, but research is ongoing. Current models forecasting instability suffer from lack of lead time, subjective predictions, and lack of specificity. The models demonstrated in this study provide 4 year forecasts of battle deaths per capita, refugees per capita, genocide, and undernourishment for Djibouti, Ethiopia, Eritrea, Kenya, Somalia, Sudan, and Yemen. This thesis used principal component analysis, canonical correlation, ordinary least squares regression, logistic regression, and discriminant analysis to develop models of each instability indicator using 54 variables covering 32 years of observations. The key variables within each model are identified, and the accuracy of each model is compared with current models.
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