The Number of Parties and Decision-Making in Legislatures

This paper proposes a model of a legislature, formed by several parties, which has to vote for or against a certain bill in the presence of a lobbyist interested in a certain vote outcome. We show that the ease with which the lobbyist can manipulate a legislature decision increases with the number of elected parties, and, consequently, decreases with an electoral threshold. On the other hand, a lower electoral threshold increases the representativeness of a legislature. We combine these two effects in a notion of fairness. We show the existence of an electoral threshold that optimizes the fairness of a political system, which is close to 1–5%. Namely, the optimal threshold (in our sense) is close to thresholds that exist in most parliamentary democracies.

A Multivariate Analysis of the 2016 County-Level Presidential Vote and Turnout

We investigate the 2016 Presidential Election using the county as the unit of analysis to examine the variance in the percentage of votes cast for Clinton, Trump and voter turnout. Our independent variables conceptually relate to race, education, wellbeing, age, rural-urban continuum and international migration. We found that over 50% of the variance in vote outcome for Clinton and Trump is explained by race, education, economy and the physical health of the county population. Almost 50% of the variance in voter turnout is explained with the same variables plus age. The regression results showed that Trump voters tended to be more white, less educated, not poor, and unhealthy compared to Clinton voters. <br>

A Multivariate Analysis of the 2016 County-Level Presidential Vote and Turnout

We investigate the 2016 Presidential Election using the county as the unit of analysis to examine the variance in the percentage of votes cast for Clinton, Trump and voter turnout. Our independent variables conceptually relate to race, education, wellbeing, age, rural-urban continuum and international migration. We found that over 50% of the variance in vote outcome for Clinton and Trump is explained by race, education, economy and the physical health of the county population. Almost 50% of the variance in voter turnout is explained with the same variables plus age. The regression results showed that Trump voters tended to be more white, less educated, not poor, and unhealthy compared to Clinton voters. <br>

Turnout and Partisan Vote Choice

This chapter explores the link between turnout and the vote across an extended series of elections within states and electoral districts. The strength of this design is that it conforms exactly to the ordinary language that analysts and commentators use to assert a connection between turnout and vote outcome. The proponents of a turnout bias argue that increased turnout in a forthcoming election should increase the Democrat’s share of the vote compared to the previous election, while a turnout drop will erode that share. We use straightforward data for an analysis of changes through time for presidential elections from 1948 through 2016, for each Senate seat from 1966 through 2016, for each state’s gubernatorial contests from 1966 through 2016, and for all 435 congressional districts from 1972 through 2010. The analysis does not find support for the bias thesis but observes a virtually random relationship, with turnout in many states and districts helping the Republicans as often as it assists the Democrats.

The electoral threshold may swing Israeli vote outcome

Significance For the first time since 2009, Netanyahu is facing a formidable challenge from a new centrist party, Blue and White, established by Benny Gantz, a former military chief of staff, and former finance minister Yair Lapid. The election can be broadly characterised as a battle between two main blocs -- the religious right and the centre-left. Impacts Blue and White will need to articulate specific policies to maintain its positive momentum. A merger of far-right groupings may shift voters towards the New Right party. The corruption allegations against Netanyahu could prove disruptive to voting intentions.

Explaining Electoral Change in the 2018 US Midterm Elections: The Three Components of Electoral Mandates

Why did the American electorate elect a solid majority of Republicans to the House in 2016 and then 2 years later replace it with a solid majority of Democrats? This article revives the idea of an electoral mandate and applies it to the 2016 and 2018 elections. It proposes a trinity of partisan attitudes serving as the components of electoral mandates: performance, values, and leadership. The election of President Trump in 2016 depended on a mix of performance evaluations (a weak economy) favoring the Republicans and leadership evaluations (Trump’s behavior difficulties) muted by value considerations (conservative anger at being unrepresented and the necessity of a choice between Trump and Clinton). These offsetting partisan attitudes made the election close enough that a small number of votes in key states decided the electoral vote outcome. In 2018, performance evaluations again favored Republicans, but now because they presided over a stronger economy. Evaluations of Trump’s leadership remained negative. The interaction of values with these leadership assessments now favored Democrats. As the out-party, polarized liberals were motivated by anti-Trump anger. Never-Trump conservatives who had drifted back to vote Republican at the end of the 2016 campaign did not feel that same pressure without the presidency being at stake. About two-thirds of voters in 2018 said their vote was about Trump. Republicans lost to Democrats among these voters by 16 percentage points. Republicans delivered on their 2016 mandate to boost the economy, but had failed to provide leadership that many Americans could feel comfortable with.

Competing Principals and Non-Vote Decisions in the European Parliament

This article analyses the effects of competing principals on legislators’ decisions not to vote in the European Parliament. We argue that Members of the European Parliament (MEPs) are likely to decide not to vote to avoid defecting from either the national party or the European political group when both political principals disagree. Moreover, the article demonstrates that competing demands between principals interact with the expected closeness of a vote. MEPs are more likely to opt for not voting when they have few chances to influence the vote outcome and are torn between the two main principals. Based on a novel data set on individual votes in the 2009–2014 term, this article demonstrates that competing demands moderate the effect of the expected vote closeness on non-vote decisions and highlights the need to incorporate this type of legislative non-response in future research.

Brexit might trigger Scottish independence

Headline UNITED KINGDOM: Independence vote outcome uncertain

Using Online Search Traffic to Predict US Presidential Elections

Predictions of the United States presidential election vote outcome have been growing in scope and popularity in the academic realm. Traditional election forecasting models predict the United States presidential popular vote outcome on a national level based primarily on economic indicators (e.g., real income growth, unemployment), public approval ratings, and incumbency advantage. Many of these forecasting models are rooted in retrospective voting theory (Downs 1957; Fiorina 1981), essentially rewarding the party in office if times are good, punishing it if times are bad. These models have successfully predicted election results by modeling economic performance and incumbent approval ratings (Campbell 2012; Fair 1992; Fair 1996; Klarner 2012). For example, Abramowitz's (2004; 2005) “time for a change model” predicts election results using economic performance during the first half of the election year, the number of years the incumbent party has been in office, and presidential approval. For a full review of 13 presidential forecasts for the US 2012 election, seePS: Political Science and PoliticsOctober 2012 (45 (4): 610–75). Although national models are the most common, researchers have also started to use state-level predictions for presidential and congressional outcomes, with mostly positive success (Berry and Bickers 2012; Jerome and Jerome-Speziari 2012; Klarner 2012; Silver 2012). These models use similar predictors, such as incumbency, economic conditions, and home-state advantage, and predict the per-candidate percentage of popular vote. Unfortunately, with state-level models, many of the economic variables used in predicting national models are unavailable beyond 10–15 election cycles (compounded also by 1959 additions of Alaska and Hawaii), so state-level models naturally have a shorter period of analysis than do national models.

Election Forecasting: The Future of the Presidency and the House

This article is about a simple two-variable equation forecasting presidential election outcomes and a three-variable equation forecasting seat change in House elections. Over the past two decades a cottage industry of political forecasting has developed (Lewis-Beck and Rice 1992; Campbell and Garand 2000). At the 1994 meeting of the Southern Political Science Association, several participants offered their forecasts of the upcoming midterm House elections. Unfortunately, not one of the forecasters was within 20 seats of the actual outcome. If, however, these forecasts had been pooled, as Gaddie (1997) points out, then they would have come remarkably close to the actual seat change that occurred. Moving forward, at the 1996 APSA Annual Meeting the collection of forecasters did a much better job with that year's presidential election. The forecasters also got the overall popular vote outcome correct at the 2000 APSA Annual Meeting for that year's presidential election. We all forecasted a victory for Al Gore, with James Campbell coming the closest to the actual total (50.2%) at 52.8%. At the panel at the 2004 APSA Annual Meeting almost every forecaster predicted the actual outcome correctly. Forecasting elections holds us accountable—we cannot go back and change our forecast for an election after it has occurred. Moreover, if we stick with one forecast, it easy to judge the overall accuracy of our equations.