UCLA

Parties are the main vehicles of representation in modern, democratic societies. Party systems, that is the number and the size of all the parties within a country, can vary greatly across countries. There is an ongoing debate in the political science literature about the appropriate way to reduce the dimensionality of the cross-country party system data for comparative purposes. This thesis reviews that literature and offers a new solution: Principal Component Analysis to find the most important information in the data matrix. I use data from 17 advanced democracies from 1970-2013. I conduct analyses using various related methods (Principal Component Analysis, Principal Component Analysis on the Residuals, kernel Principal Component Analysis, Non-Linear Principal Component Analysis, Principal Component Analysis on log-ratio transformed variables and Principal component Analysis on non-centered variables). I find that the most important differences across countries are: “the size of the biggest two parties”, “competition between the two biggest parties”, “existence of a third party” and “balanced multipartism.” I argue that most of the current political science literature uses summary measures that are only correlated with the first of those four dimensions. I suggest a strategy for incorporating a measure of the second dimension that relies on indices of opposition structure.