Out of the Sample and One Step Ahead: Forecasting Supreme Court Confirmation Votes

By Segal, Jeffrey A.; Mak, Maxwell H. H. | Justice System Journal, May 1, 2009 | Go to article overview

Out of the Sample and One Step Ahead: Forecasting Supreme Court Confirmation Votes


Segal, Jeffrey A., Mak, Maxwell H. H., Justice System Journal


We propose and test a model to forecast confirmation votes for Supreme Court nominees. Overall, the confirmation-vote model is a substantial step in the forecasting of the level of formal opposition (nay votes) in the Senate. Nominee-based characteristics-qualifications and ideological extremism-are strong predictors of the percentage of nay votes a nominee can expect during the confirmation vote. As another presidential election approaches, we provide forecasts of the formal opposition of potential nominees to the nation's highest court.

Given the fact that every four years Americans choose their commander-in-chief, presidential elections seem to be perennially on the minds of pundits, politicians, and political scientists. Another consequence of the frequency of elections is the fact that, every election, presidential and congressional forecasters attempt to predict the outcome as well as the margin of victory (the percentage of the two-party vote share). An often overlooked aspect of the horse race toward the White House is the possibility of the president being fortunate enough to place one or more of his nominees on the Supreme Court. By appointing a nominee to the nation's highest court, a president can alter the legal landscape, having an effect on decisions and legal doctrine that can far outlast a given president's administration. Thus, the repercussions of the outcome from the November election extend to the potential change in membership at the Supreme Court. As such, we propose and test a confirmation-vote model that forecasts the level of formal opposition a nominee to the nation's highest court will face.

Previous work on Supreme Court nominations has focused on the determinants of confirmation. At the most aggregate level of analysis, scholars have found that the factors that influence the binary confirmation outcome include whether the president's party controls the Senate, whether the president is in the fourth year of his term of office, and whether the justice being replaced is pivotal (Lemieux and Stewart, 1988; Palmer, 1983; Segal, 1987). At the other end of the aggregation spectrum, votes by individual senators on particular nominees, scholars have found strong influences of ideology (Cameron, Cover, and Segal 1990; Overby et al., 1992), qualifications (Cameron, Cover, and Segal 1990), interest groups (Caldeira and Wright, 1998; Segal, Cameron, and Cover, 1992), divided government, and whether president is serving in the last year of his term (Cameron, Cover, and Segal, 1990).

In between these coarse and fine levels of analysis, no scholars have examined the simple percentage of votes that a candidate has received. While the final outcome of a nominee (confirm or not confirm) as well as the vote of given senator (yea or nay) are important, the actual level of formal support or opposition of a nominee before the Senate, too, is an important aspect to the advice-and-consent process. The degree of support signals to the president what he might be able to accomplish in subsequent nominations. This is punctuated by the fact that most presidents have had the fortune of placing more than one justice on the nation's highest court. Moreover, the level of support or opposition reveals to interest groups the possible position of the Senate and as such provides information as to whether it is worthwhile to "gear up" or not. The degree of formal support or opposition for a candidate, of course, is the level of analysis that election forecasters use (i.e., the percentage of the vote), but no confirmation scholars using any of these levels of analysis have tested forecasts of their models. Forecasts are the most stringent test of a model, for forecasts guarantee out-of-sample predictions.

Overall, our model provides a solid and substantial step in the forecasting of confirmation votes. Nominee-based characteristics, such as perceived ideological extremism and qualifications, are driving determinants of the percentage of nay votes a nominee can expect before the Senate.

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Out of the Sample and One Step Ahead: Forecasting Supreme Court Confirmation Votes
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