The Process of Political Development
John Deegan
Abstract
John Deegan
Abstract
Although a number of investigators have attempted to identify empirically a process of political development, substantial controversy still surrounds a determination of the causal factors involved. It is my contention that this state of affairs is the result of inadequacies inherent in traditional techniques of causal modeling, and aggravated when multicollinear variables are involved. To resolve this problem I first review a technique capable of reducing the confounding effects of multicollinearity. I then illustrate use of this technique, as well as a strategy for inferring causal relationships, by means of a reanalysis of published data used to construct models of political development. The strategy for causal inference utilized herein is derived from knowledge of the effects of model specification errors. On the basis of these findings a new causal model of political development, which is both theoretically and empirically consistent, is presented.
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Although a number of investigators have attempted to identify empirically a process of political development, substantial controversy still surrounds a determination of the causal factors involved. It is my contention that this state of affairs is the result of inadequacies inherent in traditional techniques of causal modeling, and aggravated when multicollinear variables are involved. To resolve this problem I first review a technique capable of reducing the confounding effects of multicollinearity. I then illustrate use of this technique, as well as a strategy for inferring causal relationships, by means of a reanalysis of published data used to construct models of political development. The strategy for causal inference utilized herein is derived from knowledge of the effects of model specification errors. On the basis of these findings a new causal model of political development, which is both theoretically and empirically consistent, is presented.
Key concepts: Causal inference, Multicollinearity, Causal model, Causal analysis, Construct (python library), Politics, Econometrics, Inference