Published online by Cambridge University Press: 31 October 2012
This article argues that the policy uncertainty generated by elections encourages private actors to delay investments that entail high costs of reversal, creating pre-election declines in the associated sectors. Moreover, this incentive depends on the competitiveness of the race and the policy differences between the major parties/candidates. These arguments are tested using new survey and housing market data from the United States. The survey analysis assesses whether respondents’ perceptions of presidential candidates’ policy differences increased the likelihood that they would delay certain purchases and actions. The housing market analysis examines whether elections are associated with a pre-election decline in economic activity, and whether any such decline depends on electoral competitiveness. The results support the predictions and cannot be explained by existing theories.
Donald E. Stokes Professor of Public and International Affairs; Professor of Politics, Princeton University (email: email@example.com) and Assistant Professor of Methodology and American Politics, American University in Cairo. Previous presentations at Caltech, George Mason, Georgetown, Michigan, Princeton, Rutgers, Yale and the 2009 Midwest Political Science Meetings and the 2009 American Political Science Association Meetings have substantially improved this project. We are also grateful to Larry Bartels, Paul Brady, Will Bullock, Peter Buisseret, John Cogan, Hank Farber, Marty Gilens, Jason Kelly, George Krause, Adam Meirowitz, Mike Munger, Tom Romer, Harvey Rosen, Ken Shotts and Erik Snowberg for helpful comments and conversations. An online appendix is available at http://dx.doi.org/10.1017/S00071234.
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61 In the model that estimates the average effect of a gubernatorial election year, the p-value from the Difference-in-Hansen test is p = 1.00 and from the Hansen J test is p = 0.982. In the model that estimates the effect of competitiveness, the p-value from both the Difference-in-Hansen and Hansen J tests is p = 1.00.
62 We tried reducing the lag structure so that it included only the second or third lag, and we even collapsed this lag, but the Hansen tests still suggested that the models were overidentifed.
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66 For instance, if the only instrument is the collapsed second or third lag, then the Hansen J-statistic and Difference-in-Hansen test suggest the model is overidentified.
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