Classifying Failing States

The US is heavily involved in the first major war of the 21st Century -- The Global War on terrorism (GWOT). As with any militant group, the foundation of the enemy's force is their people. There are two primary strategies for defeating the terrorists and achieving victory in the GWOT. First, we must root out terrorists where they live, train, plan, and recruit and attack them militarily. Second, we must suffocate them by cutting off the supply of new soldiers willing to choose aggression or even death over their current life. This thesis helps to achieve these objectives by applying Multivariate Analysis techniques to identify the states most likely to provide asylum for terrorists. Weak and Failed States are attractive to terrorist groups looking for safe haven and recruits. Governments in these states are often unable to prevent illegal activity, and are vulnerable to corruption or takeover. Citizens of failing states often experience poverty, disease, and unemployment, and may see little hope for improvement. Terrorists can meet these disenfranchised people's basic needs and promise brighter futures for families of those willing to fight and perhaps die for the cause. Current published efforts to identify failing states primarily use Ordinary Least Squares regression, which requires the analyst to predefine the degree to which a state is likely to fail. This thesis uses a Factor Analysis approach to identify the key indicators of state failure, and discriminant Analysis to classify states as Stable, Borderline, or Failing based on these indicators. Furthermore, each nation's discriminant function scores are used to determine their degree of instability. The methodology is applied to 200 countries for which open source data was available on 167 variables. Results of the classification are compared with subject matter experts in the field of state failure.