The (Un)predictability of Primaries with Many Candidates: Simulation Evidence

It is common to describe the dynamic processes that generateoutcomes in U.S. primaries as ``unstable'' or ``unpredictable''. In fact, the way we choose candidates may amount to alottery. This paper uses a simulation approach, assuming10,000 voters who vote according to a naive, deterministicproximity rule, but who choose party affiliationprobabilistically. The voters of each party then must choose between twosets of ten randomly chosen candidates, in ``closed'' primaries.Finally, the winners of the two nominations compete in thegeneral election, in which independent voters also participate.The key result of the simulations reported here isthe complete unpredictability of the outcomes of a sequence ofprimaries: the winner of the primary, or the party's nominee,varied as much as two standard deviations from the medianpartisan voter. The reason is that the median, or any othermeasure of the center of the distribution of voters, isof little value in predicting the outcome of multicandidateelections. These results suggest that who runs may havemore to do with who wins than any other consideration. If more than two parties or candidates are expected, then thevote-maximizing position is not close to your opponents, butwell away from them. (Tullock, 1967: 55).     

Measuring Bias and Uncertainty in Ideal Point Estimates via the Parametric Bootstrap

Keith T. Poole Center for Advanced Study in the Behavioral Sciences and Department of Political Science, University of Houston, Houston, TX 77204-3011 e-mail: kpoole{at}uh.edu Over the last 15 years a large amount of scholarship in legislativepolitics has used NOMINATE or other similar methods to constructmeasures of legislators' ideological locations. These measuresare then used in subsequent analyses. Recent work in politicalmethodology has focused on the pitfalls of using such estimatesas variables in subsequent analysis without explicitly accountingfor their uncertainty and possible bias ( Herron and Shotts2003, Political Analysis 11:44–64). This presents a problemfor those employing NOMINATE scores because estimates of theirunconditional sampling uncertainty or bias have until now beenunavailable. In this paper, we present a method of forming unconditionalstandard error estimates and bias estimates for NOMINATE scoresusing the parametric bootstrap. Standard errors are estimatedfor the 90th U.S. Senate in two dimensions. Standard errorsof first–dimension placements are in the 0.03 to 0.08range. The results are compared with those obtained using theMarkov chain Monte Carlo estimator of Clinton et al. (2002,Stanford University Working Paper). We also show how the bootstrapcan be used to construct standard errors and confidence intervalsfor auxiliary quantities of interest such as ranks and the locationof the median senator.     

The Lack of Dissent in WTO Dispute Settlement

This article analyses in detail the fact that there has beenalmost no dissent in World Trade Organization (WTO) disputesettlement reports. Only a handful of articles have noted thisphenomenon, even in passing. The article first examines theempirical data with respect to dissenting and concurring opinionsat both the panel and Appellate Body levels. Fewer than 5% ofpanel reports and 2% of Appellate Body reports contain separateopinions of any kind. Second, it shows that the WTO is in factactively discouraging dissents and discusses why this mightbe the case. The article argues that dissents are valuable ingeneral and assesses whether more dissents would be a positivefor the WTO. It then reviews the few dissents that have beenpublished and demonstrates that 50% of the arguments raisedin dissents at the panel level were adopted in whole or in parton appeal by the Appellate Body, thus illustrating dissentscan and do make a difference. The article concludes that keepingthe lid on dissents may ultimately erode the strength of thedispute settlement system and hinder the ability of the WTOMembers to make appropriate changes to the Agreements.