Our political economy model, as it has come to be called, has offered up forecasts of the American presidential election outcome since the early 1980s. The model, based on referendum theory, as measured by the job performance of the president and the economy (1948 to the present), yields a forecast from data available in the summer of the election year. We consider alternative specifications of this parsimonious model, examining the possible effects of other economic measures, Covid-19, and incumbency advantage on forecasting. The current point estimate of the core political economy model predicts the Democratic candidate will receive 48 percent of the two-party popular vote, which translates into a narrow Electoral College loss for the incumbent party. This point forecast, however, comes with a considerable amount of uncertainty. There is an 11-point spread around our point estimate, which effectively means we have a horserace on our hands, with both horses close to the finish line.

The conflict over abortion in the United States shows no sign of abatement. Both supporters and opponents of abortion rights have mobilized movements, engaged in collective action, suffered defeats and won victories, and survived over many decades. To understand how the current battle is likely to progress, we need to examine the movement/countermovement dynamics that drive the mobilization and outcomes of the opposing movements (Meyer and Staggenborg 1996). This article demonstrates how ongoing mobilization is fueled by the strategies and tactics of each side, which are strongly influenced by the actions of their opponents.

Election forecasting in modern democracies faces significant challenges, including increasing survey nonresponse and selection bias. Moreover, there are limitations to the current predictive approaches. Whereas structural models focus solely on macro-level variables (e.g., economic conditions and leader popularity), thereby overlooking the importance of individual-level factors, survey-based aggregation methods often rely on intuitive procedures that lack theoretical foundations. To address these gaps, this article proposes a combined (i.e., both standard and Bayesian) logistic regression approach that leverages voter-level data and incorporates a theory-based specification. By testing these models on recent waves of the American National Election Studies Time Series, this study demonstrates that the proposed approach yields notably accurate predictions of Republican popular support in each election.

The rise of populism as a global phenomenon has captured the attention of scholars and raised concerns about its impact on democracy. Thanks to a growing academic consensus around an ideational definition of populism, one can observe the generation of important cumulative knowledge on the relationship between populism and democracy. Political science has been at the forefront of this development, and this symposium seeks to both offer state-of-the art information on this topic and discuss blind spots that future studies should try to address.

When the impact of populism on liberal democracy is examined, the focus often is on populists in power. After all, when in office, populists have the possibility to change legislation, thereby negatively affecting individual freedoms and rights, and to transform the political system, often toward democratic decline and illiberalism (Pappas 2019; Ruth-Lovell and Grahn 2023).1 Far less attention has been devoted to populist parties in opposition, even though this is the position in which populists find themselves most frequently.2 Prominent examples of Western European populist parties with a decades-long position in opposition include the Rassemblement National in France and the Vlaams Belang in Belgium on the right and Die Linke in Germany and the Socialistische Partij in the Netherlands on the left. Outside of Western Europe, populist parties often have less longevity and more frequently assume office. However, many of these parties spend years in opposition before taking on government responsibility and/or have returned to the opposition benches afterwards (e.g., Partido Justicialista in Argentina and Prawo i Sprawiedliwość in Poland).

We present Iowa Electronic Markets (IEM) forecasts for the popular vote shares in the 2024 US presidential election. We discuss the differences between IEM forecasts and polls, the influence of the first presidential debate, the changes resulting from Biden dropping out of the race, and the degree of uncertainty implied by IEM forecasts. On September 29, the IEM forecast a 9-percentage-point Democratic popular vote margin according to a thinly traded vote-share market and an 85.7% chance the Democrat will receive more votes than the Republican in a thickly traded winner-takes-all market. Using a distribution derived from both markets, the forecasts are for a 6- to 7-percentage-point Democratic margin and 87.0% chance of winning. However, significant uncertainty remains.

The Partisan-Bounded Economic Model forecasts popular vote and Electoral College vote results in the 2024 presidential election based on economic growth, presidential popularity, and shifts in party identification within the electorate. Due to the partisan vote and voter inertia, presidential elections are unlikely to result in lopsided outcomes even in the face of economic turmoil or prosperity. In the Partisan-Bounded Economic Model, outliers in the annualized GDP growth are adjusted to fall within a fixed range of 5 percent to -5 percent. Any GDP growth values exceeding 5 percent or falling below -5 percent are re-coded as 5 percent and -5 percent, respectively. The model predicts that Vice President Kamala Harris will secure 52.3 percent of the party vote, 49.4 percent of the total popular vote, and 59.1 percent of the Electoral College votes.

Populism’s effects on democracy after populists gain control of government (hereafter, populist incumbents) are some of the best theorized and documented consequences. The argument that populist incumbents threaten institutions of democratic contestation—and, less frequently, that they correct some aspects of political participation and representation—has been made from multiple approaches.1 Scholars and commentators often cite specific cases of populists harming democracy and, since 2016, several large-N studies have confirmed their negative impact. Specifically, studies repeatedly show the harmful effects of populist incumbents on civil liberties, including media freedom, horizontal accountability, and electoral integrity in both electoral and liberal democracies. Research has been less consistent in showing the positive consequences of populist incumbents, especially for democratic representation and political participation.

Statesman and scholar Alexis de Tocqueville (1876) once noted, “History is a gallery of pictures in which there are few originals and many copies.” In other words, history has a habit of repeating itself, and we can deduce cycles and patterns that likely will recur. Such stability and inertia should bode well for prediction. Nevertheless, when it comes to election forecasting, especially in the United States, most prognostications rely on short-term political fundamentals measuring macroeconomic performance or government or leader popularity. In this contribution, we adopt a structural approach but depart from existing literature by focusing on historical party and governance dynamics in the vein of de Tocqueville to establish whether they offer solid guidance regarding the performance of Democrats in US congressional elections. Our ex-post political history models provide solid predictions of which party will control Congress and the Democrats’ seat tally in each chamber between 1946 and 2022. This creates conditions to assume that political history may help us forecast Campaign 2024. Our study applied this political history model to predict the 2024 congressional elections. It forecast Democrats to lose control of the Senate with a net loss of three seats and estimated an exceptionally close race for House control, with the point estimate for the House suggesting that the Democrats would fall short of winning control.

The connection between populism and democracy is widely researched. Most of the literature focuses on populist actors (e.g., parties, leaders, and governments) as it examines the intricacies of this relationship. Some of the resulting takeaways have become embedded firmly in scholarship and are currently considered accepted knowledge across the discipline. Scholars have only recently started focusing on the individual-level relationship between populism and democracy. As a result, our knowledge remains limited and is often based on the assumption that what holds for populist actors also will hold for populist citizens. The first part of this article briefly reviews the state of the art on the individual-level relationship between populism and democracy. Drawing from this review, we identify several theoretical and empirical gaps and limitations in the literature that future research should address. We conclude that contemporary scholarship has made important contributions, but more nuanced and targeted research is necessary to comprehensively understand the intricacies between populism and democracy on the individual level.

Populism is both prolific and resilient. By now, populist forces around the globe have managed to enter the highest echelons of power (Rovira Kaltwasser and Taggart 2016). It is no wonder that the contemporary academic debate has shifted its focus to exploring the consequences of populism in power, particularly its impact on democracy. Although populism and democracy are not synonymous, the representation of “the people” is a central claim to both. Most populism scholars agree that “all forms of populism without exception involve some kind of exaltation of and appeal to ‘the people’” (Canovan 1981, 94). However, depending on which democratic ideas are emphasized over others—as well as which political practices and structures are favored to institutionalize these ideas (Dahl 1991; Held 2006; Lijphart 2012)—the basic tenet of the “rule by the people” may have many different meanings.

Drawing on the case of American Muslim voter engagement in the 2024 election season, this article argues that election-forecasting models – particularly state-based models – should integrate minority populations into their analysis as crucial variables. This is of particular significance in swing states. By including minority-voter engagement and related variables relevant to them such as pressing policy concerns (e.g., anti-war sentiment and racial attitudes), forecasters can better understand and predict electoral outcomes and address the gaps identified in traditional forecasting approaches. The recommendations presented in this article help election forecasters prepare for unexpected changes, such as the American Muslim shift of support away from President Biden in the 2024 primary election season.

The initial predictions presented in this article confirm that presidential candidate vote-share estimates based on AI polling are broadly exchangeable with those of other polling organizations. We present our first two biweekly vote-share estimates for the 2024 US presidential election and benchmark them against those being generated by other polling organizations. Our post–Democratic National Convention top-line estimates for Trump (47%) and Harris (46%) closely track measurements generated by other polls during the month of August. The subsequent early September (post-debate) PoSSUM vote-share estimates for Trump (47%) and Harris (48%) again closely track with other national polling being conducted in the United States. An ultimate test for the PoSSUM polling method will be the final preelection vote-share results that we publish before Election Day on November 5, 2024.

What should we do about populism? In recent years, this question has become more urgent as populist leaders and parties have taken center stage in many countries across the globe. No longer a “minor” political phenomenon, populism has forced scholars to grapple with how to address its potential “threat” to “liberal democracy while also harnessing its “corrective” properties (Rovira Kaltwasser 2012). In this debate, the two questions of who “we” are—that is, who should respond—and how to do it often have taken different forms.

Pundits and academics alike are increasingly concerned about the health of democracy worldwide. Much of this concern is tied to the rise of populism, a global phenomenon presenting challenges to both long-established and relatively young democracies. Political science has been at the forefront of this debate, and thanks to a growing—but not universal—consensus on the ideational definition of populism, our understanding of the subject has deepened considerably. This symposium maps key debates on the complex and often ambivalent relationship between populism and democracy. In this concluding piece, we build on the arguments presented throughout the symposium and related academic discussions to outline two paths for future research on the populism and democracy nexus: a top-down and a bottom-up perspective.

Our 2020 analysis correctly forecasted Joe Biden’s victory and the outcome of every state except Georgia. That forecast relied on economic data from 125 days prior to Election Day and presidential approval data from 104 days (or more) before the election. Since 2000, our model would have correctly forecasted the winner in 95% of all states. We updated our State Presidential Approval/State Economy Model for 2024. This article summarizes the model and its historical accuracy as well as new data updates. We then generate forecasts for the overall two-party popular vote, each state’s outcome, and the Electoral College winner for the 2024 US presidential election. One hundred days prior to Election Day, our model forecasts a split two-party popular vote (50.3% for Trump, 49.7% for Harris) but a notable Trump advantage in the Electoral College, with slightly less than a three-in-four chance that Trump wins the election. This Republican advantage 100 days prior to Election Day sheds light on Biden’s abrupt decision to drop out of the race and suggests that if Harris wins, she will have overcome extremely challenging fundamentals, and/or that Donald Trump and the Republican Party will have squandered a sizeable Electoral College advantage.

Originally founded in 2004 to improve election forecasting accuracy through evidence-based methods, the PollyVote project applies the principle of combining forecasts to predict the outcome of US presidential elections. The 2024 forecast uses the same methodology as in previous elections by combining forecasts from four methods: polls, expectations, models, and naive forecasts. By averaging within and across these methods, PollyVote predicts a close race, giving Kamala Harris a slight edge over Donald Trump in both the two-party popular vote (50.8 vs. 49.2%) and the Electoral College (276 vs. 262 votes). The forecast gives Harris a 65% chance of winning the popular vote and a 56% chance of winning the Electoral College, making both outcomes toss-ups. Compared to the combined PollyVote, component forecasts that rely on trial-heat polls tend to favor Harris, whereas methods that rely on alternative measures are less optimistic about the Democratic candidate’s chances. The polls may be overestimating Harris’s lead.

Every four years, numerous election-forecasting models attempt to predict the results of the US presidential election. Regardless of the stability of any election system, such as the bipartisan system in the United States, conditions can arise (e.g., candidate resignations) that negatively impact forecasters’ ability to predict electoral outcomes. Citizen forecasting—that is, directly asking respondents who they think will win an election—has a long track record of successfully predicting presidential elections. This study proposes adapting a citizen forecasting measure originally intended for use in multiparty systems to predict the US presidential election in 2024. Using this measure, we created a forecast of the national-level popular vote and vote-share forecasts for seven swing states.