Journal of Electoral Studies

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Home Issues Backissues Volume 31, No.1 Analyzing Electoral Stability and Change: Markov Chain Models for Longitudinal Categorical Data

Analyzing Electoral Stability and Change: Markov Chain Models for Longitudinal Categorical Data

Chi Huang
2005 / 05
Volume 12, No.1

pp.1-37
10.6612/tjes.2005.12.01.01-37
Paper

Abstract

How voters cast their votes in successive elections determines not only the fate of candidates but also the rise and fall of political parties and sometimes even causes party system changes. The subject of electoral stability and change, due to its significance in theory and practice, has long attracted the attention of political scientists around the world. Despite the voluminous publications cumulated so far, however, there are still heated debates regarding how best to model this dynamic electoral process and to estimate the amount of changes. The purpose of this paper is two-folds. First, it clarifies some confusion in the literature caused by its failing to distinguish gross change from net change and to recognize the strengths and weaknesses of various types of data in evaluating these two changes. After pointing out how panel data prevail over repeated cross-sections and aggregate data in estimating both forms of changes, we then proceed to identify a statistical model that best fits the categorical measurement of electoral changes dominant in panel surveys. The second part of this paper, therefore, pinpoints discrete-time discrete-state Markov chain models as ideal tools for describing the dynamic electoral process and further analyzing the sources of change patterns. The transition probabilities of Markov models coincide with the theoretical concepts of flow-of-the-votes and reflect the way state dependence shapes the trajectories of electoral changes. Finally, we apply a mixed Markov model to the three-wave Japanese Election Study (JES) panel data set to illustrate the potential of this technique.

Keywords

electoral stability and change

straight-and split-ticket voting

dynamic process

panel data

gross change

net change

Markov chain models