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Political Analysis Political Analysis

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A Copula Approach to the Problem of Selection Bias in Models of Government Survival

Published online by Cambridge University Press:  04 January 2017

Daina Chiba ,
Lanny W. Martin  and
Randolph T. Stevenson
Daina Chiba
Affiliation:
Department of Government, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, United Kingdom, e-mail: dchiba@essex.ac.uk
Lanny W. Martin
Affiliation:
Department of Political Science, Rice University, 6100 Main St, MS 24, Houston TX 77005
Randolph T. Stevenson
Affiliation:
Department of Political Science, Rice University, 6100 Main St, MS 24, Houston TX 77005, e-mail: stevenso@rice.edu
Corresponding
E-mail address:
dchiba@essex.ac.uk
stevenso@rice.edu
lmartin@rice.edu
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Abstract

Theories of coalition politics in parliamentary democracies have suggested that government formation and survival are jointly determined outcomes. An important empirical implication of these theories is that the sample of observed governments analyzed in studies of government survival may be nonrandomly selected from the population of potential governments. This can lead to serious inferential problems. Unfortunately, current empirical models of government survival are unable to account for the possible biases arising from nonrandom selection. In this study, we use a copula-based framework to assess, and correct for, the dependence between the processes of government formation and survival. Our results suggest that existing studies of government survival, by ignoring the selection problem, overstate the substantive importance of several covariates commonly included in empirical models.

Type
Research Article
Information
Political Analysis , Volume 23 , Issue 1 , Winter 2015 , pp. 42 - 58
Copyright
Copyright © The Author 2014. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: Supplementary materials for this article are available on the Political Analysis web site. Replication files are available on the Political Analysis Dataverse at http://dx.doi.org/10.7910/DVN/26966.

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