An influential paper by Caughey and Sekhon (2011a) suggests that the outcomes of very close US House elections in the postwar era may not be as-if random, thus calling into question this application of regression discontinuity for causal inference. We show that while incumbent party candidates are more likely to win close House elections, those who win are no different on observable characteristics from those who lose. Further, all differences in observable characteristics between barely winning Democrats and barely winning Republicans vanish conditional on which party is the incumbent. Any source of a special incumbent party advantage in close elections must be due to variables that cannot be observed. This finding supports the conclusion of Eggers et al. (2015) that Caughey and Sekhon’s discovery of lopsided wins by incumbents in close races is a mere statistical fluke.

International relations scholars frequently rely on data sets with country pairs, or dyads, as the unit of analysis. Dyadic data, with its thousands and sometimes hundreds of thousands of observations, may seem ideal for hypothesis testing. However, dyadic observations are not independent events. Failure to account for this dependence in the data dramatically understates the size of standard errors and overstates the power of hypothesis tests. We illustrate this problem by analyzing a central proposition among IR scholars, the democratic trade hypothesis, which claims that democracies seek out other democracies as trading partners. We employ randomization tests to infer the correct p-values associated with the trade hypotheses. Our results show that typical statistical tests for significance are severely overconfident when applied to dyadic data.

On August 1, 2012, we prepared a forecast of the 2012 presidential vote for PS. Our model contains two variables: (1) the cumulated weighted growth in leading economic indicators (LEI) through quarter 13 of the current presidential term and (2) the incumbent party candidate's share in the most recent trial-heat polls, which were for the month of July. What mostly distinguishes our model from others is the reliance on leading indicators from the quarter ending in March of the election year. The early reading of LEI works well as a predictor because it summarizes growth in the economy leading up to the election year and also provides advance indication of changes in the economy during the election year. The exact equation and the exact forecast change as the poll readings change during the election year.

The importance of the economy in US presidential elections is well established. Voters reward or punish incumbent party candidates based on the state of the economy. The electorate focuses particularly on economic change, not the level of the economy per se, and pays more attention to late-arriving change than earlier change. On these points there is a good amount of scholarly agreement (see e.g., Erikson and Wlezien 1996; Hibbs 1987). There is less agreement, however, on what specific indicators matter to voters. Some scholars rely on income growth, others on GDP growth, and yet others on subjective perceptions (see Abramowitz 2008; Campbell 2008; Holbrook 1996b; also see Campbell and Garand 2000). In our work, we have used the index of leading economic indicators, a composite of ten variables, including the University of Michigan's index of consumer expectations, stock prices, and eight other objective indicators.

The 1969 Vietnam draft lottery assigned numbers to birth dates in order to determine which young men would be called to fight in Vietnam. We exploit this natural experiment to examine how draft vulnerability influenced political attitudes. Data are from the Political Socialization Panel Study, which surveyed high school seniors from the class of 1965 before and after the national draft lottery was instituted. Males holding low lottery numbers became more antiwar, more liberal, and more Democratic in their voting compared to those whose high numbers protected them from the draft. They were also more likely than those with safe numbers to abandon the party identification that they had held as teenagers. Trace effects are found in reinterviews from the 1990s. Draft number effects exceed those for preadult party identification and are not mediated by military service. The results show how profoundly political attitudes can be transformed when public policies directly affect citizens' lives.

Based on information available in July, we predicted that the Republicans would receive 52.9% of the total House vote and end up holding 229 seats, gaining control from the Democrats in the process (Bafumi, Erikson, and Wlezien 2010b). Our national vote forecast proved to be nearly correct, undershooting the actual Republican share (53.8%) by slightly less than one percentage point. Our seat forecast was a little less accurate. Although we did foresee the House changing hands, we did not predict such a large Republican windfall in seats—we forecast a “mere” swing of 50 seats, which was short of the actual outcome by about 13 seats. The Republican seat total of 242, however, was well within the 95% confidence interval (199 to 259).

In this article, we present a forecast of the 2010 midterm House election based on information available in early July 2010. We combine this forecast with a note of caution, explaining why electoral circumstances might lead our forecast to err. Finally, we present guidance regarding how to update the electoral forecast for 2010 based on new information that will become available leading up to Election Day.

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.

One mystery of U.S. politics is why the president’s party regularly loses congressional seats at midterm. Although presidential coattails and their withdrawal provide a partial explanation, coattails cannot account for the fact that the presidential party typically performs worse than normal at midterm. This paper addresses the midterm vote separate from the presidential year vote, with evidence from generic congressional polls conducted during midterm election years. Polls early in the midterm year project a normal vote result in November. But as the campaign progresses, vote preferences almost always move toward the out party. This shift is not a negative referendum on the president, as midterms do not show a pattern of declining presidential popularity or increasing salience of presidential performance. The shift accords with “balance” theory, where the midterm campaign motivates some to vote against the party of the president in order to achieve policy moderation.

Why did Obama defeat McCain in 2008? As with any national election outcome, the immediate culprit that comes to mind is economic performance. When the U.S. is prosperous, the electorate votes the incumbent presidential party back into office. When the U.S. economy sours, the incumbent (or incumbent party) loses. In 2008, the application of this rule led to a correct prediction once again. Economy up, Republicans out. It is difficult to challenge this conventional wisdom that the economy contributed to the transfer of the White House from the Republicans to Democrat Obama.

The October 2008 issue of PS published a symposium of presidential and congressional forecasts made in the summer leading up to the election. This article is an assessment of the accuracy of their models.

Prior to the 2008 presidential election we provided forecasts of the final vote relying on a model containing only two variables: (1) the cumulated weighted growth in leading economic indicators (LEI) through the thirteenth quarter of the sitting president's term; and (2) the incumbent party candidate's share in the most recent trial-heat polls. The novelty is the reliance on the advanced reading of the economy from the quarter ending in March of the election year. (The exact equation and the exact forecast change as the poll readings get closer to the election.) Our final forecast (Erikson and Wlezien 2008) based on trial-heat polls in August was that Barack Obama would win 52.2% of the two-party popular vote. This turned out to be quite close to the Election Day outcome of 53.5% (as of December 2), a little more than one percentage point above what we predicted.