An Improved Model for Predicting Presidential Election Outcomes

There is probably no subject which has been studied more thoroughly by political scientists than American presidential elections. A vast literature has developed examining the effects of attitudes toward the parties, candidates, and issues on voter decision-making in these quadrennial contests (for a comprehensive review of this literature see Asher, 1988). Despite the proliferation of literature on this topic, however, relatively little research has addressed what is perhaps the most basic question about presidential elections: who wins and who loses? A few scholars have developed models for predicting the national outcomes of presidential elections. Brody and Sigelman (1983) proposed a model based on the incumbent president's approval rating in the final Gallup Poll before the election. This extremely simple model yielded an unadjusted R 2 of .71. Hibbs (1982) proposed a different bivariate model based entirely on the trend in real per capita disposable income since the last presidential election. This model yielded an unadjusted R 2 of .63. Thus, neither of these bivariate models proved to be highly accurate. Lewis-Beck and Rice (1984) have developed a forecasting model which combines economic conditions and presidential popularity, Their model, which uses the president's approval rating in May and the change in real per capita GNP during the second quarter of the election year to predict the popular vote for president, yields an unadjusted R 2 of .82.