Popularity and Vote: Forecasting the 2007 French Presidential Election

2010 ◽  
Vol 43 (1) ◽  
pp. 123-136 ◽  
Author(s):  
Antoine Auberger

Abstract. The purpose of this article is to build a model that explains and forecasts the outcome of the second-round vote in the French presidential elections (with the hypothesis of a classic duel between left and right) in each department and at the national level. This model highlights the influence of the popularity of the Socialist party and a partisan variable in the explanation of the second-round vote for the candidate of the left in the French presidential elections. Its forecasts for the elections of the past (1981–1995 and 1981–2007, excluding 2002) are satisfactory and we make ex ante forecasts for the 2007 French presidential election.Résumé. L'objet de cet article est de construire un modèle qui explique et prévoit le résultat du second tour de scrutin aux élections présidentielles françaises (en supposant le duel classique entre la gauche et la droite) dans chaque département et au niveau national. Ce modèle met en lumière l'influence de la popularité du Parti socialiste et d'une variable partisane dans l'explication du vote au second tour pour le candidat de la gauche aux élections présidentielles. Les prévisions ex post pour les élections passées (de 1981 à 1995 et de 1981 à 2007, en excluant 2002) sont satisfaisantes et on établit des prévisions ex ante pour l'élection présidentielle française de 2007.

Author(s):  
Richard Adelstein

This chapter elaborates the operation of criminal liability by closely considering efficient crimes and the law’s stance toward them, shows how its commitment to proportional punishment prevents the probability scaling that systemically efficient allocation requires, and discusses the procedures that determine the actual liability prices imposed on offenders. Efficient crimes are effectively encouraged by proportional punishment, and their nature and implications are examined. But proportional punishment precludes probability scaling, and induces far more than the systemically efficient number of crimes. Liability prices that match the specific costs imposed by the offender at bar are sought through a two-stage procedure of legislative determination of punishment ranges ex ante and judicial determination of exact prices ex post, which creates a dilemma: whether to price crimes accurately in the past or deter them accurately in the future. An illustrative Supreme Court case bringing all these themes together is discussed in conclusion.


2020 ◽  
Vol 117 (45) ◽  
pp. 27940-27944 ◽  
Author(s):  
Robert S. Erikson ◽  
Karl Sigman ◽  
Linan Yao

Donald Trump’s 2016 win despite failing to carry the popular vote has raised concern that 2020 would also see a mismatch between the winner of the popular vote and the winner of the Electoral College. This paper shows how to forecast the electoral vote in 2020 taking into account the unknown popular vote and the configuration of state voting in 2016. We note that 2016 was a statistical outlier. The potential Electoral College bias was slimmer in the past and not always favoring the Republican candidate. We show that in past presidential elections, difference among states in their presidential voting is solely a function of the states’ most recent presidential voting (plus new shocks); earlier history does not matter. Based on thousands of simulations, our research suggests that the bias in 2020 probably will favor Trump again but to a lesser degree than in 2016. The range of possible outcomes is sufficiently wide, however, to even include some possibility that Joseph Biden could win in the Electoral College while barely losing the popular vote.


Author(s):  
Corwin Smidt

This article examines the role of Catholics within the 2020 presidential election in the United States. Although Catholics were once a crucial and dependable component of the Democratic Party’s electoral coalition, their vote in more recent years has been much more splintered. Nevertheless, Catholics have been deemed to be an important “swing vote” in American politics today, as in recent presidential elections they have aligned with the national popular vote. This article therefore focuses on the part that Catholics played within the 2020 presidential election process. It addresses the level of political change and continuity within the ranks of Catholics over the past several elections, how they voted in the Democratic primaries during the initial stages of the 2020 presidential election, their level of support for different candidates over the course of the campaign, how they ultimately came to cast their ballots in the 2020 election, and the extent to which their voting patterns in 2020 differed from that of 2016.


2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Trevor Torgerson ◽  
Will Roberts ◽  
Drew Lester ◽  
Jam Khojasteh ◽  
Matt Vassar

Abstract Introduction Given that 72% of internet users seek out health information using an internet search engine (Google being the most popular); we sought to investigate the public internet search interest in cannabis as a health topic when cannabis legislation appeared on state ballots and during presidential elections. Materials and methods We searched Google Trends for “cannabis” as a health topic. Google Trends data were extracted during the time period of May 1, 2008 to May 1, 2019 for the United States (US) and select states (18) within the US including: Alaska, Arizona, Arkansas, California, Colorado, Florida, Maine, Massachusetts, Michigan, Missouri, Nevada, North Dakota, Ohio, Oregon, Oklahoma, South Dakota, Utah, and Washington when cannabis was on the ballot. These state elections were referenda, not legislative votes. We then compared the internet search interest for cannabis before and after each election. To evaluate whether any associations with changes in the volume of cannabis internet searches were specific to the cannabis topic, or also occurred with other topics of general interest during an election year, the authors ran additional analyses of previously popular debated policies during Presidential Elections that may act as control topics. These policies included Education, Gun Control, Climate Change, Global Warming, and Abortion. We used the autoregressive integrated moving average (ARIMA) algorithm to forecast expected relative internet search interests for the 2012 and 2016 Presidential Elections. Individual variables were compared using a linear regression analysis for the beta coefficients performed in Stata Version 15.1 (StataCorp). Results Public internet search interest for “cannabis” increased during the voting month above the previous mean internet search interest for all 18 bills. For the US, observed internet search interest during each Presidential Election was 26.9% [95% CI, 18.4–35.4%] greater than expected in 2012 and 29.8% [95% CI, 20.8–38.8%] greater than expected in 2016. In 2016, significant state-level findings included an increase in relative internet search rates for cannabis in states with higher usage rates of cannabis in the past month (Coeff (95% CI), 3.4 (2.8–4.0)) and past month illicit drug use except cannabis rates (Coeff (95% CI), 17.4 (9.8–25.0)). Relative internet search rates for cannabis from 2008 to 2019 were also associated with increased cannabis usage in the past month (Coeff (95% CI), 3.1 (2.5–3.7)). States with higher access to legal cannabis were associated with higher relative internet search volumes for cannabis (Coeff (95% CI), 0.31 (0.15–0.46)). Of the five additional policies that were searched as topics, only two showed an increase in internet search interest during each Presidential Election. Climate Change increased by 3.5% [95% CI, − 13-20%] in 2012 and 20.1% [95% CI, 0–40%] in 2016 while Global Warming increased by 1.1% [95% CI, − 19-21%] in 2012 and 4.6% [95% CI, − 6-15%] in 2016. Conclusion Based on these results, we expect public interest in cannabis will spike prior to the Presidential election in 2020. Of the five selected control policies, only two showed an increase in internet search interest during both Presidential Elections and neither exceeded the internet search increase of cannabis. These results may indicate the growing awareness of cannabis in the US and mark a possible target for the timely dissemination of evidence-based information regarding cannabis and its usage/side-effects during future elections. Consequently, the results of this study may be important to physicians since they will likely receive an increased volume of questions relating to cannabis and its therapeutic uses during election season from interested patients. We recommend establishing a cannabis repository of evidence-based information, providing physician education, and a dosing guide be created to enable physicians to provide high quality care around the issue of cannabis.


2018 ◽  
Vol 12 (1) ◽  
pp. 2499-2504
Author(s):  
Anwar Ouassini ◽  
Mostafa Amini

One of the enduring narratives of the 2016 presidential election was the nostalgic journey President Trump took the American public on to construct his ‘Islamophobic memory’ surrounding General Pershing’s actions during the American occupation of the Philippines. While the mobilization of memory by political actors is not new in Presidential elections, the mechanism utilized to impose and mobilize pubic memory was. This paper explores how the President Trump’s tweets via the Twitter social media platform transform into ‘mediated sites of contention’ in the nurturance of public nostalgia. As a public ‘site’ that is visited by millions of people -the tweet not only memorializes events of the past but it mobilizes meaning, memory, and the society’s sense of self, which has the ability to redirect and shape public memory. We argue that Trump’s nostalgic colonial folklore via the tweet serves his ideological sentiments and larger political platforms in order to promote a vision of the past to provide his right-wing ideologies and movement supporters currency.


2020 ◽  
Author(s):  
Clemens Nollenberger ◽  
Gina-Maria Unger

Forecasts of US presidential elections have gained considerable attention in recent years. However, as became evident in 2016 with the victory of Donald Trump, most of them consider presidential elections only at the national level, neglecting that these are ultimately decided by the Electoral College. In order to improve accuracy, we believe that forecasts should instead address outcomes at the state-level to determine the eventual Electoral College winner. We develop a political economy model of the incumbent vote share across states based on different short- and long-term predictors, referring up to the end of the second quarter of election years. Testing it against election outcomes since 1980, our model correctly predicts the eventual election winner in 9 out of 10 cases – including 2016 –, with the 2000 election being the exception. For the 2020 election, it expects Trump to lose the Electoral College, as only 6.2 percent of simulated outcomes cross the required threshold of 270 Electoral Votes, with a mean prediction of 106 Electoral Votes.


2020 ◽  
Author(s):  
Didier Grimaldi ◽  
Javier Diaz ◽  
Hugo Arboleda

Abstract The avalanche of personal and social data circulating in Online Social Networks over the past 10 years has attracted a great deal of interest from Scholars and Practitioners who seek to analyse not only their value, but also their limits. Predicting election results using Twitter data is an example of how data can directly influence the politic domain and it also serves an appealing research topic. This article aims to predict the results of the 2019 Spanish Presidential election and the voting share of each candidate, using Tweeter. The method combines sentiment analysis and volume information and compares the performance of five Machine Learning algorithms. Several data scrutiny uncertainties arose that hindered the prediction of the outcome. Consequently, the method develops a political lexicon-based framework to measure the sentiments of online users. Indeed, an accurate understanding of the contextual content of the tweets posted was vital in this work. Our results correctly ranked the candidates and determined the winner by means of a better prediction of votes than official research institutes.


2020 ◽  
Author(s):  
Didier Grimaldi ◽  
Javier Diaz ◽  
Hugo Arboleda

Abstract The avalanche of personal and social data circulating in Online Social Networks over the past 10 years has attracted a great deal of interest from Scholars and Practitioners who seek to analyse not only their value, but also their limits. Predicting election results using Twitter data is an example of how data can directly influence the politic domain and it also serves an appealing research topic. This article aims to predict the results of the 2019 Spanish Presidential election and the voting share of each candidate, using Tweeter. The method combines sentiment analysis and volume information and compares the performance of five Machine Learning algorithms. Several data scrutiny uncertainties arose that hindered the prediction of the outcome. Consequently, the method develops a political lexicon-based framework to measure the sentiments of online users. Indeed, an accurate understanding of the contextual content of the tweets posted was vital in this work. Our results correctly ranked the candidates and determined the winner by means of a better prediction of votes than official research institutes.


2011 ◽  
Vol 29 (1) ◽  
pp. 123-139 ◽  
Author(s):  
Alice M. Crisp ◽  
Franklin G. Mixon

Abstract Public choice interpretations of historical events represent a growing literature in economics. This particular study follows in, and builds upon, this tradition by examining, through the public choice lens, events leading up to die U.S. presidential election of 1864. We posit that die modern theory of bureaucracy, as described in Breton and Wintrobe [ 1982], perhaps best explains die way in which Abraham Lincoln’s subordinates assisted, sometimes even manipulating the gears of die federal (Union) government in doing so, in his re-election effort. That bureaucracy, which we refer to herein as Lincoln's wartime incumbency network, was based on a system of «vertical trust», and included an incentive structure wherein subordinates provided Lincoln with «informal services» related to his re-election in 1864, and were provided in return with «informal payments», which often consisted of ex ante/ex post promotions and/or nominations for other government positions.


2020 ◽  
pp. 000276422097506
Author(s):  
Camilo Prado-Román ◽  
Raúl Gómez-Martínez ◽  
Carmen Orden-Cruz

The media and election campaign managers conduct several polls in the days leading up to the presidential elections. These preelection polls have a different predictive capacity, despite the fact that under a Big Data approach, sources that indicate voting intention can be found. In this article, we propose a free method to anticipate the winner of the presidential election based on this approach. To demonstrate the predictive capacity of this method, we conducted the study for two countries: the United States of America and Canada. To this end, we analysed which candidate had the most Google searches in the months leading up to the polling day. In this article, we have taken into account the past four elections in the United States and the past five in Canada, since Google first published its search statistics in 2004. The results show that this method has predicted the real winner in all the elections held since 2004 and highlights that it is necessary to monitor the next elections for the presidency of the United States in November 2020 and to have more accurate information on the future results.


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