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A Bayesian Approach to Dynamic Panel Models with Endogenous Rarely Changing Variables*

Published online by Cambridge University Press:  20 January 2016

Tsung-Han Tsai
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Abstract

Whether democratic and non-democratic regimes perform differently in social provision policy is an important issue to social scientists and policy makers. As political regimes are rarely changing, their long-term or dynamic effects on the outcome are of concern to researchers when they evaluate how political regimes affect social policy. However, estimating the dynamic effects of rarely changing variables in the analysis of time-series cross-sectional data by conventional estimators may be problematic when the unit effects are included in the model specification. This article proposes a model to account for and estimate the correlation between the unit effects and explanatory variables. Applying the proposed model to 18 Latin American countries, this article finds evidence that democracy has a positive effect on social spending both in the short and long term.

Type
Original Articles
Information
Political Science Research and Methods , Volume 4 , Supplement 3 , September 2016 , pp. 595 - 620
Copyright
© The European Political Science Association 2016 

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Footnotes

*

Tsung-han Tsai is an Assistant Professor in the Department of Political Science, National Chengchi University, Taipei 11605, Taiwan (thtsai@nccu.edu.tw). An earlier version of this manuscript was presented at the 2011 Annual Meeting of the American Political Science Association, Seattle, WA. The author thanks Jeff Gill, Guillermo Rosas, Jacob Montgomery, Chia-yi Lee, Jamie Monogan, Keith Schnakenberg, and Justin Grimmer for helpful comments and suggestions at different stages of this manuscript. The author also thanks two anonymous reviewers and the PSRM editor for helping improve the manuscript. Any remaining errors are the author’s responsibility. To view supplementary material for this article, please visit http://dx.doi.org/10.1017/psrm.2015.81

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Supplementary material: Link

Tsai Dataset

Dataset

http://dx.doi.org/10.7910/DVN/08RCPK
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