The data used for the analysis and a replication file can be downloaded from the AJPS Data Archive on Dataverse: http://dvn.iq.harvard.edu/dvn/dv/ajps. The authors wish to acknowledge insightful comments on earlier versions from four distinctively constructive and helpful reviewers and from the editors, as well as from Leonardo Arriola, Michael Bernhard, Younhok Choe, Gary Cox, Philip Keefer, Ellen Lust, Kristin Michelitch, Susan Stokes, and participants in both the Quality of Government Institute's research group at University of Gothenburg and the Comparative Politics Colloquium at the Department of Political Science, University of Florida. A special thanks is extended to Won-Ho Park and Dominic Lisanti for initial help with the count-models. The data collection was sponsored by a grant from the Africa Power and Politics Programme funded by the UK Department for International Development (DFID). The survey was carried out by Staffan I. Lindberg in collaboration with research officers at Center for Democratic Development-Ghana, and we wish to recognize the excellent work by the 49 fieldwork assistants. Staffan I. Lindberg's time spent on the project has also been partially funded by a grant from Riksbankens Jubileumsfond (co-PIs Bo Rothstein and Sören Holmberg). As always, the content, errors, omissions, and flaws of the text are the responsibility of the authors.
What Drives the Swing Voter in Africa?
Version of Record online: 9 APR 2013
©2013, Midwest Political Science Association
American Journal of Political Science
Volume 57, Issue 3, pages 717–734, July 2013
How to Cite
Weghorst , K. R. and Lindberg , S. I. (2013), What Drives the Swing Voter in Africa?. American Journal of Political Science, 57: 717–734. doi: 10.1111/ajps.12022
- Issue online: 1 JUL 2013
- Version of Record online: 9 APR 2013
Disclaimer: Supplementary materials have been peer-reviewed but not copyedited.
Appendix A: Constructing the Dependent Vari-ables
Appendix B: Summary Statistics of the Independent Variables; Background Constituency Information; and Constructing the Index of Pervasiveness of Clientelistic Offers
Appendix C: Details and Justification of the Count Model (Negative Binomial Regression Model)
Appendix D: Dependent Variables Analysis of Model Fit
Appendix E: Robustness Checks of Results
Appendix F: Robustness Check with ZINB Model Estimation
Appendix G: Sampling Protocol
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