Quantitative Methods for Studying Elites: Practical Applications, Biases and Potential

Abstract

This methodological paper discusses the practical applications, advantages and disadvantages of historical event or survival analysis, compared to classic econometric strategies in social sciences such as binary logistic regressions. Accordingly, it analyses when the survival approach is preferable to logistic models, and the risks of working with observational data and potentially biased estimates. To answer these two questions, we carried out two econometric demonstrations using propensity score matching techniques, in order to analyse the effect of entrepreneurial background and ties on access to and tenure in important political positions. The data were obtained from the Chilean Elite Survey (1990¬-2010) and a data set of ministers who held office between 1990 and 2014. The findings show that business background had no significant impact on access to important positions; however, it is associated with a lower risk of being removed from the cabinet. The econometric demonstrations show that survival analysis is helpful for phenomena involving time intervals, while logistic regressions are appropriate for studying access to positions (as long as the sampling is adequate). Significant differences are also evident between unfitted models, or placebos, and models with better specifications or fitted after applying the matching algorithm.