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Many hypotheses in U.S. state politics research are multi-level, positing that state-level variables affect individual-level behavior. Unadjusted standard errors for state-level variables are too small, leading to overconfidence and possible false rejection of null hypotheses. Primo, Jacobsmeier, and Milyo (2007) explore this problem in their reanalysis of Wolfinger, Highton, and Mullin's (2005) data on the effects of post-registration laws on voter turnout. Primo et al. advocate the use of clustered standard errors to solve the overconfidence problem, but we offer an alternative solution: randomization tests. Randomization tests are non-parametric tests that do not rely on comparisons to theoretical test statistic distributions. Instead, they use distributions tailored to the data, created by randomly scrambling the data many times to simulate what would be observed under the null hypothesis. Unlike with clustering, with the randomization test, U.S. state-level reforms generally fail to be significant both as additive effects and as interactions with individual characteristics.

In this article we present a simple forecasting model that has been successful at predicting past presidential elections. The two variables included in the model are cumulative per capita income growth and presidential approval. These "fundamental" variables predict the vote especially well when measured shortly in advance of the election, when the outcome is already becoming clear in the polls. Their predictive power drops quite quickly as one steps back from the election, however; readings of income growth and approval taken early in the election year only modestly predict readings of the same variables just prior to the election. Thus we turn to leading economic indicators that allow us to forecast presidential elections by giving advance indication of changes in the economy and approval during the election year. For 1996, our model offers a cautious prediction of a Clinton victory.

We use the distribution of responses to the Gallup poll's "generic" House vote question (i.e., "If the elections for Congress were being held today, which party's candidate would you like to see win in your congressional district?") to forecast the Republican-Democratic vote split in presidential year congressional elections. Although these responses consistently offer an overly optimistic picture of the Democrats' prospects, they nonetheless serve as the basis of accurate forecasts.

This article challenges Radcliff's recent claim that state-level voter turnout strongly influences state-level vote divisions for president. It is demonstrated that Radcliff's claim is an artifact of ignoring the contribution of the Voting Rights Act to southern electoral politics. When the analysis is restricted to nonsouthern states, least squares dummy variable (LSDV) pooled time-series cross-sectional analysis shows no evidence whatsoever of a turnout effect on the vote.

This article separately investigates who registersto vote and who votes among the registered.Standard predictors of participation are much better predictors of registration than of voting among the registered. It is speculated that voting is stimulated by the act of registration.