Quarterly Journal of Political Science > Vol 5 > Issue 3

A Formulation of Path Dependence with an Empirical Example

John E. Jackson, Department of Political Science, University of Michigan, USA, Ken Kollman, Department of Political Science, University of Michigan, USA
 
Suggested Citation
John E. Jackson and Ken Kollman (2010), "A Formulation of Path Dependence with an Empirical Example", Quarterly Journal of Political Science: Vol. 5: No. 3, pp 257-289. http://dx.doi.org/10.1561/100.00010001

Publication Date: 16 Dec 2010
© 2010 J. E. Jackson and K. Kollman
 
Subjects
Formal modelling,  Political organizations,  Political parties
 

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In this article:
Introduction 
Two Versions of Party Realignment 
Estimation with Simulated Data 
Empirical Example 
Is Path Dependence Ever Detectable? 
Appendix A. Derivation of Equation (2) 
Appendix B. Consistency of the Non-Linear Least Squares Estimators 
Appendix C. Power of the Non-Linear Least Squares Estimators 
References 

Abstract

We develop the empirical implications of Page's (2006) definition of path dependence as a process where the sequence of historical events affects the final outcome. A critical necessary condition for path dependence in common dynamic models is a time-varying autoregressive parameter whose value becomes 1 at some point. Failure to meet this condition results in a path independent process whose equilibrium outcome is only a function of the current exogenous conditions. This condition is illustrated with a discrete Markov Chain model and with a computational model with continuous variables, which are illustrated with models of partisan change. A Monte Carlo simulation shows how non-linear least-squares estimation can recover the parameters that distinguish path dependence from path independence. This integration of modeling and an estimation strategy is illustrated with data on civil rights attitudes and macropartisanship. The results have implications for discussions of path dependence in a wide range of social science fields.

DOI:10.1561/100.00010001