Bethany Lachina Civil War

In this research report, we shall investigate, evaluate and present the work of Bethany Lachina into the explanations for the severity of civil conflicts. Lachina’s inspiration for such an inquiry is founded upon the fact that, considering the publishing of numerous studies into civil conflicts — few, if any, focused exclusively and adequately on why some civil wars are so much deadlier than others. Given that domestic discord is the most common form of warfare, accounting for 90 percent of battle deaths (including combatant and collateral) during the period of 1990 to 2002 (Lachina and Gleditch, 2005), it is critical to understand which chief factor, or what combination of factors, influence conflict severity. Fearon interprets civil war as
Citing the work of Murdoch and Sandler, (Murdoch and Sandler, 2004), Murshed explains that a civil war can reduce a country’s economic growth potential by 31 percent in the long-term and by 85 percent in the short to medium-term. Therefore, the level of income (per capita GDP) is a “robust” predictor of severity. Collier and Hoefler highlight factors which further precipitate such severity, such as sizeable numbers of “idle, young men”, modulated capital and trade markets, “profuse” and “copious” amounts of seizable natural resources and “penetrable” borders and boundaries. Urdal explains that young males, whom are indicative of the vast majority of combatants in civil wars) are, to a lesser degree, probable to participate in insurgency when they are “getting an education” or “have a stable, secure salary”, and can “reasonably assume” that they will “prosper in the future” (Urdal,
Ordinary Least Squares (OLS) regression analysis determines the line that describes the relationship between x and y where the sum of the squared errors is the ‘least’. In all cases, the dependant variable needs to be of interval/ratio level, the independent variable can be a dummy or an interval/ratio level variable. The intercept, also known as the constant (a or α) represents the predicted value of the dependent variable (y) when the independent variable (x) is zero. The slope (b or β) represents the average change in dependent variable (y) for a one unit change in independent variable (x). For example, for hypothesis 1, the operational indicators were x = Natural log duration of conflict in years and y = Natural log best estimate battle