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Citizen forecasting: Can UK voters see the future?
Abstract
The leading approaches to election forecasting have been statistical models, prediction markets, or vote intention polls. This paper explores a different, little used approach - citizen forecasting - a method never yet tried for the case of the United Kingdom. Citizen forecasting simply asks voters in a scientific pre-election survey who they think will win. This aggregated (averaged) result is then assessed for its accuracy in forecasting the overall election outcome. This assessment is carried out on UK general elections from 1951 to 2005, and then applied to 2010. We find, among other things, that generally speaking the majority of the electorate can foretell the winner in advance.
... In most elections, the majority of citizens are able to predict the election winner correctly, regardless of who they plan to vote for (Lewis-Beck & Skalaban, 1989;Lewis-Beck & Tien, 1999;Miller, Wang, Kulkarni, Poor, & Osherson, 2012;Murr, 2011Murr, , 2015Murr, , 2016. Most US citizens typically predict correctly not only which presidential candidate will win their state, but also who will win the presidency (e.g., Graefe, 2014); and most British citizens are usually correct about both which party will win their constituency and which will garner a parliamentary majority (e.g., Lewis-Beck & Stegmaier, 2011;Murr, 2016). How do they do it? ...
... However, a developing branch of the election forecasting literature has begun to utilize electoral expectations, measured by the question, ''who do you think will win the election?'' This approach is referred to as ''citizen forecasting'', and has been used for election prediction in both the US (Graefe, 2014;Lewis-Beck & Skalaban, 1989;Lewis-Beck & Tien, 1999;Murr, 2015) and Britain (Lewis-Beck & Stegmaier, 2011;Murr, 2011Murr, , 2016. ...
... 75% -an increase of six percentage points. Their two main findings -that most citizens forecast correctly most of the time, and that groups forecast better than individualshave subsequently been replicated at two different levels (subnational and national) and in two countries (Britain and United States); see for example Graefe (2014), Lewis-Beck and Stegmaier (2011) and Murr (2011Murr ( , 2015Murr ( , 2016. ...
Most citizens correctly forecast which party will win a given election, and such forecasts usually have a higher level of accuracy than voter intention polls. How do citizens do it? We argue that social networks are a big part of the answer: much of what we know as citizens comes from our interactions with others. Previous research has considered only indirect characteristics of social networks when analyzing why citizens are good forecasters. We use a unique German survey and consider direct measures of social networks in order to explore their role in election forecasting. We find that three network characteristics – size, political composition, and frequency of political discussion – are among the most important variables when predicting the accuracy of citizens’ election forecasts.
... In most elections, the majority of citizens are able to predict the election winner correctly, regardless of who they plan to vote for (Lewis-Beck & Skalaban, 1989;Lewis-Beck & Tien, 1999;Miller, Wang, Kulkarni, Poor, & Osherson, 2012;Murr, 2011Murr, , 2015Murr, , 2016. Most US citizens typically predict correctly not only which presidential candidate will win their state, but also who will win the presidency (e.g., Graefe, 2014); and most British citizens are usually correct about both which party will win their constituency and which will garner a parliamentary majority (e.g., Lewis-Beck & Stegmaier, 2011;Murr, 2016). How do they do it? ...
... However, a developing branch of the election forecasting literature has begun to utilize electoral expectations, measured by the question, ''who do you think will win the election?'' This approach is referred to as ''citizen forecasting'', and has been used for election prediction in both the US (Graefe, 2014;Lewis-Beck & Skalaban, 1989;Lewis-Beck & Tien, 1999;Murr, 2015) and Britain (Lewis-Beck & Stegmaier, 2011;Murr, 2011Murr, , 2016. ...
... 75% -an increase of six percentage points. Their two main findings -that most citizens forecast correctly most of the time, and that groups forecast better than individualshave subsequently been replicated at two different levels (subnational and national) and in two countries (Britain and United States); see for example Graefe (2014), Lewis-Beck and Stegmaier (2011) and Murr (2011Murr ( , 2015Murr ( , 2016. ...
Most citizens correctly forecast which party will win the election, usually with greater accuracy than voter intention polls. How do they do it? We argue that social networks are a big part of the answer: much of what we know as citizens comes from our communication with others. Previous research has considered only indirect characteristics of social networks to analyze why citizens are good forecasters. Using a unique German survey, we consider direct measures of social networks to explore their role in election forecasting. We find that three network characteristics – size, political composition, and frequency of political discussion – are among the most important variables when predicting the accuracy of citizens' election forecasts.
... This approach, which has been labeled "citizen forecasting, " dispenses with an intention item e.g., "If there were a general election tomorrow, which party would you vote for?" and replaces it with an expectation item, e.g., "Who do you think will win the next general election?" The citizen forecasting approach found its first British application, at the national level, in the 2010 general election (Lewis-Beck & Stegmaier 2011). Later, Murr (2011) went on to apply it effectively, ex post, to the constituency level for the 2010 contest and, in another article, for prior contests (Murr 2016). ...
... These efforts were extended to the British case, first in the article by Lewis-Beck & Stegmaier (2011), taking on what turned out to be the tough task of forecasting the "hung parliament" of the 2010 general election. Murr (2011) then took up the cudgel, continuing the exploration of citizen forecasting in Britain and-most noteworthy-doing so at the constituency level. ...
... To translate voter expectations into seat share forecasts, we generalise the model proposed by Lewis-Beck & Stegmaier (2011). They first estimate a linear model using historical data, regressing the winning party's seat share on the percentage of respondents who, within a month or so of the election, said that it would win. ...
Are ordinary citizens better at predicting election results than conventional voter intention polls? We address this question by comparing predictive models for British elections: one based on voters' expectations of who will win and others based on who voters themselves intend to vote for (including " cube rule " and uniform national swing models). The data come from Gallup polls and the British Election Study and Essex Continuous Monitoring Surveys, 1950–2015, yielding 446 months with both expectation and intention polls. The large sample size allows us to compare the models' prediction accuracy not just in the months prior to the election, but over the years leading up to it. In predicting both the winning party and par-ties' seat shares, we find that voter expectations outperform voter intention models. Voter expectations thus appear an excellent tool for predicting the winning party and its seat share.
... in Britain in the 2010 general election (Lewis-Beck and Stegmaier 2011). Later, Murr (2011) applied it effectively, ex post, to the constituency level for the 2010 contest and, in another article, for prior contests (Murr 2016). ...
... 7. EXP: Expectations and lagged seat share in a linear model (Lewis-Beck and Stegmaier 2011). This model regresses the seat share on the previous seat share and the vote expectations for two parties. 1 Our expectation model differs from the intention models used by others in more ways than just replacing expectations with intentions. ...
Are ordinary citizens better at predicting election results than conventional voter intention polls? The authors address this question by comparing eight forecasting models for British general elections: one based on voters' expectations of who will win and seven based on who voters themselves intend to vote for (including ‘uniform national swing model’ and ‘cube rule’ models). The data come from ComRes and Gallup polls as well as the Essex Continuous Monitoring Surveys, 1950–2017, yielding 449 months with both expectation and intention polls. The large sample size permits comparisons of the models' prediction accuracy not just in the months prior to the election, but in the years leading up to it. Vote expectation models outperform vote intention models in predicting both the winning party and parties' seat shares.
... Although the use of the expectation question in pre-election surveys goes back before the emergence of intention polling (Hayes, 1936), scholars have only recently begun to study its value for predicting election outcomes in plurality elections in the UK and the US (Graefe, 2014(Graefe, , 2015aLewis-Beck & Stegmaier, 2011;Murr, 2011Murr, , 2015aMurr, , 2015bRothschild & Wolfers, 2012). For example, one study compared the accuracy of the expectation question to polls, prediction markets, quantitative models, and expert judgment for predicting election winners and vote shares in the seven US presidential elections from 1988 to 2012. ...
... Similar to the US, the prediction task is thus reduced to a binary choice problem and most respondents are able to accurately forecast the election winner (Lewis-Beck & Stegmaier, 2011;Murr, 2011). ...
This study tests non-representative expectation surveys as a method for forecasting elections. For dichotomous forecasts of the 2013 German election (e.g., who will be chancellor, which parties will enter parliament), two non-representative citizen samples performed equally well than a benchmark group of experts. For vote-share forecasts, the sample of more knowledgeable and interested citizens performed similar to experts and quantitative models, and outperformed the less informed citizens. Furthermore, both citizen samples outperformed prediction markets but provided less accurate forecasts than representative polls. The results suggest that non-representative surveys can provide a useful low-cost forecasting method, in particular for small-scale elections, where it may not be feasible or cost-effective to use established methods such as representative polls or prediction markets.
... Several researchers have demonstrated that citizen forecasts accurately predict election outcomes in both US presidential elections and British General Elections (e.g. Lewis-Beck and Skalaban, 1989;Lewis-Beck and Tien, 1999;Lewis-Beck and Stegmaier, 2011;Murr, 2011Murr, , 2015Graefe, 2014). All of these studies find that most elections are won by the party which most citizens said would win. ...
... This finding relates to the literature on citizen forecasting (e.g. Graefe, 2014;Lewis-Beck and Skalaban, 1989;Lewis-Beck and Tien, 1999;Lewis-Beck and Stegmaier, 2011;Murr, 2011Murr, , 2015. This literature finds that most citizens correctly forecast the winner of the election most of the time. ...
British political parties select their leaders to win elections. The winning margin of the party leader among the selectorate reflects how likely they think she is to win the General Election. The present research compares the winning margins of party leaders in their party leadership elections and uses the results of this comparison to predict that the party leader with the larger winning margin will become the next Prime Minister. I term this process “the Party Leadership Model”. The model correctly forecasts 8 out of 10 past elections, while making these forecasts 4 years in advance on average. According to a Bayesian analysis, there is a 95 per cent probability that having the larger winning margin in party leadership elections increases the chances of winning the General Election. Because David Cameron performed better among Conservative MPs in 2005 than Ed Miliband did among Labour MPs in 2010, the model predicts Cameron to become Prime Minister again in 2015. The Bayesian calculation puts his chances of re-election at 75 per cent.
... The aggregate responses are then used as a forecast of who will win the election. If data on historical elections are available, the aggregate responses can also be translated to popular vote-share forecasts using simple linear regression (Lewis-Beck and Stegmaier, 2011;Lewis-Beck and Tien, 1999). ...
... Lewis-Beck and Skalaban, 1989;Lewis-Beck and Tien, 1999). Only recently have researchers begun to specifically study vote expectation surveys as a method for forecasting elections (Lewis-Beck and Stegmaier, 2011;Murr, 2011Murr, , 2014. ...
In averaging forecasts within and across four component methods (i.e., polls, prediction markets, expert judgment, and quantitative models), the combined PollyVote provided highly accurate predictions for the US presidential elections from 1992 to 2012. This research note shows that the PollyVote would have also outperformed vote expectation surveys, which prior research identified as the most accurate individual forecasting method during that time period. In addition, adding vote expectations to the PollyVote would have further increased the accuracy of the combined forecast. Across the last 90 days prior to the six elections, a five-component PollyVote (i.e., including vote expectations) would have yielded a mean absolute error of 1.08 percentage points, which is 7% lower than the corresponding error of the original four-component PollyVote. This study thus provides empirical evidence in support of two major findings from forecasting research. First, combining forecasts provides highly accurate predictions, which are difficult to beat by even the most accurate individual forecasting method available. Second, the accuracy of a combined forecast can be improved by adding component forecasts that rely on a different method and different data than the forecasts already included in the combination.
... , and general elections in the United Kingdom (UK) (Lewis- Beck & Stegmaier, 2011;Murr, 2011Murr, , 2016. These studies suggest the existence of a "wisdom of the crowd," meaning that citizens possess a type of knowledge of everyday politics that is embedded in social networks and can hardly be captured by traditional polling methods. ...
The emergent literature on citizen forecasting suggests that the public, in the aggregate, can often accurately predict the outcomes of elections. However, it is not clear how citizens form judgments about election results or what factors influence individual predictions. Drawing on an original survey experiment conducted during the campaign for the United Kingdom’s Brexit referendum, we provide novel evidence of what influences citizen forecasts in a so-far unexplored context of direct democracy. Specifically, we investigate the effect of voting preferences and political sophistication, in addition to three “exogenous factors” that we manipulate experimentally—i.e., social cues, elite cues and campaign arguments. Our findings indicate that citizens are reasonably accurate in their predictions, with the average forecast being close to the actual result of the referendum. However, important individual heterogeneity exists, with politically sophisticated voters being more accurate in their predictions and less prone to wishful thinking than non-sophisticated voters. Experimental findings show that partisan voters adjust their predictions in response to cues provided by their favorite party’s elites and partly in response to campaign arguments, and the effects are larger for low-sophisticated voters. We discuss the mechanisms accounting for the experimental effects, in addition to the implications of our findings for public opinion research and the literature on citizen forecasting.
... Ces modèles ont pour avantage d'être ancrés dans la théorieestimation du vote via des variables économiques comme la croissance de l'économie nationale, par exempletout en permettant la révision des estimations pendant la campagne. Finalement, il existe des modèles de prédiction citoyenne basés sur les résultats anticipés par les électeurs (Temporao et al., 2019;Murr 2011;Lewis-Beck et Stegmaier, 2011). ...
Résumé
Cette note de recherche a deux objectifs. Premièrement, nous présentons l'index relatif de confiance (IRC), une nouvelle mesure du potentiel de croissance et de la solidité du vote basée sur la probabilité exprimée par les électeurs de voter pour l'ensemble des partis dans leur circonscription. Nous nous penchons en détail sur les avantages de l'IRC et montrons qu'il permet d’évaluer le potentiel de croissance et la solidité du vote des partis politiques au niveau des circonscriptions et au niveau provincial. Deuxièmement, nous appliquons cet index au cas du Parti québécois à l'aide d’échantillons de grandes tailles récoltés pendant les campagnes électorales québécoises de 2012, 2014, et 2018. Cela nous permet d'illustrer et de tirer des constats sur le potentiel de croissance du Parti québécois.
... Citizen forecasting relies on the aggregation of voters' expectations about election outcomes. This approach has proven quite successful in the US and the UK (see, e.g., Boon 2012;Graefe 2014;Lewis-Beck and Stegmaier 2011;Murr 2011Murr , 2015Murr , 2016Murr et al. 2021) and it will be formally put to the test for the 2022 French presidential election (see Dufresne et al. 2022). Most studies explain the quality of citizens' forecasts by the "miracle of aggregation" theorem, which states that errors in individuals' judgments tend to cancel out in the aggregate. ...
In the last 40 years or so, scholars have proposed a vast array of models and approaches to predict election outcomes in a variety of democracies. Election forecasting has garnered increasing attention and has been the subject of multiple symposia and special issues in political science journals. This article reviews the forecasting efforts that have been deployed in the case of France since pioneering work in the late 1970s and early 1980s and discusses the peculiarities of the French political system and their consequences as well as the challenges they create for election forecasting.
... These findings are consistent with an older literature on such biases in social psychology (Shamir and Shamir, 1997;Pronin et al., 2002;Sherman et al., 2003;Todorov and Mandisodza, 2004;Chambers et al., 2006). In contrast to these findings of bias, other researchers have found that citizens' average ex-ante forecasts of aggregate electoral outcomes are often (but not always) close to accurate (Lewis-Beck and Stegmaier, 2011;Murr, 2011;Rothschild and Wolfers, 2011;Boon, 2012;Graefe, 2014;Murr, 2016), illustrating that citizens' can collectively form unbiased assessments of one another's votes in some instances. Of course there is no reason to expect a single, consistent answer to all questions of the form: "do these [citizens/representatives] have unbiased perceptions of [measure of public opinion or voting behavior]?" ...
How well do citizens understand the associations between social groups and political divisions in their societies? Previous research has indicated systematic biases in how the demographic composition of party supporters are perceived, but this need not imply that citizens misperceive the likely voting behavior of specific individuals. We report results from two experiments where subjects were provided with randomly selected demographic profiles of respondents to the 2017 British Election Study (BES) and then asked to assess either (1) which party that individual was likely to have voted for in the 2017 UK election or (2) whether that individual was likely to have voted Leave or Remain in the 2016 UK referendum on EU membership. We find that, despite substantial overconfidence in individual responses, on average citizens’ guesses broadly reflect the actual distribution of groups supporting the parties and referendum positions.
... However, the timing of the data collection has forced most studies using citizen forecasts to forecast elections ex post-that is, after they occur. Indeed, to date, only three ex ante citizenforecasting blog posts (Murr, Stegmaier, and Lewis-Beck 2016;2017;2019) and only two ex ante citizen-forecasting papers have appeared before a national election (Lewis-Beck and Stegmaier 2011;Murr 2016). Both papers forecasted British General Elections, with the Murr (2016) study relatively most accurate among 12 academic forecasts (Fisher and Lewis-Beck 2016). ...
... And citizens, perhaps surprisingly, have an excellent record as election forecasters. Previous research in the United States (Lewis- Beck and Skalaban, 1989), the United Kingdom (Lewis- Beck and Stegmaier, 2011), and Germany have all demonstrated that, when asked about who they think will win, rather than just for whom they intend to vote, citizens are generally accurate, with a record on par or even superior to other forecasting methods. ...
Citizens, especially in the aggregate, have historically been excellent election forecasters. This is, in part, due to discussing and hearing about the voting intentions of those around them, i.e., learning from their social networks. However, many people interact with networks that are ideological “echo chambers” made up of only likeminded voters. Does this absence of political disagreement decrease the ability of citizens to accurately predict an election result? Using a survey module from the 2015 Canadian Local Parliament Project, we examine how citizens living in partisan echo chambers fare at forecasting at the riding and parliamentary level and find that the reliability of echo chamber dwellers’ forecasts declines relative to those in more diverse environs. Our findings suggest that the role played by partisan composition of social networks is critical to understanding the accuracy and confidence of citizen electoral forecasts.
... Michael S. Lewis-Bec k et. al., [28] Opinion polls with statistical methods with R squared were used for forecasting. ...
The forecasting of election's outcome remained prevailed in prominence from prehistoric times and is still a delightful topic of the current era. The predictions of election results have been started from traditional methods to economic indicators and now is being swung by social media especially sentimental analysis. The present studies discuss the election forecasting methods carried out in diverse nations by the number of researchers till now. Furthermore, different number of approaches for electoral prediction using social media and economic dimensions has been investigated based on previous literature work. The main focus of this work is to study and examine various techniques, methods and parameters used for election predictions in distinct areas. Finally, we suggest some intelligent techniques which will be based upon some parameters such as the development agenda, party type and religionism etc for further modification in election prediction system, so as to enhance the accuracy of political forecasting globally Index Terms: Big data, election predictions, data mining, and forecasting.
... In most cases this was a binary questionfor example, 'which party has the best chance of winning? ' Murr (2011' Murr ( , 2015 has analysed the responses, concluding that most electors were accurate in their expectations, and he has used these expectations to forecast the outcome in each seat (see also Lewis-Beck and Stegmaier, 2011). In some ways this accuracy is unsurprising, givenas noted abovethat most seats at most elections are considered safe for one of the parties (see Curtice, 2018, on the declining Predicting election outcomes number of marginal seats over the last half-century). ...
Political scientists often debate how much information people have and deploy when making electoral decisions. Some scholars suggest that voters are aware of which party is likely to win in their local constituency at British general elections; however, this might not be the case in situations when there is substantial and spatially varying change in the relative fortunes of two or more parties between elections. That argument is evaluated here using as a case study the 2015 and 2017 general elections in Scotland: at the first, the SNP's vote share more than doubled, and it won 56 of the country's 59 seats, having won just six at the previous contest; at the second, its vote share fell by about a third, and it lost 21 of those 56 seats. Analysis of British Election Survey data collected before and during the campaigns preceding those elections shows that most respondents were aware of the SNP's surge in 2015 and expected their victory in every constituency. In 2017, most voters were aware which of the SNP's three competitors was the biggest threat in each constituency, and that awareness became clearer during the campaign; yet, voters – especially those who identified with the SNP and were contacted by it during the campaign – still (incorrectly) anticipated a local SNP victory.
... ;Lewis-Beck and Skalaban 1989;Lewis-Beck and Tien 1999;Lewis-Beck and Stegmaier 2011;Murr 2011Murr , 2016Murr , 2017, crowds can provide good estimates of complex phenomena when their members make independent choices, think diversely, are organized in a decentralized way, and an appropriate aggregation mechanism combines individual information. In a nutshell, the Wisdom of Crowds works because diversity contributes new information to the crowd's information pool and independent mistakes cancel out across individual beliefs. ...
Survey research on sensitive questions is challenging because respondents often answer untruthfully or completely refuse to answer. Existing indirect questioning techniques address the problem of social desirability bias at the expense of decreasing estimates' efficiency. We suggest the Wisdom of Crowds survey design that does not pose a tradeoff between anonymity and efficiency as an alternative. We outline the conditions necessary for the technique to work and test them empirically. Moreover, we compare the Wisdom of Crowd estimate of a right-wing populist party's vote share to alternative indirect questioning techniques' estimates as well as to the official election result in the 2017 German federal election. Provided its conditions are met, the Wisdom of Crowds design performs best in terms of both bias and efficiency. We conclude that the Wisdom of Crowds design is an important addition to social scientists' survey methodology toolbox.
... As an alternative to voter intentions, the election forecasting literature has also proposed to poll voter expectations of the election outcome (known as 'citizen forecasts'; see for e.g., Lewis-Beck and Skalaban [1989], Lewis-Beck and Tien [1999], Murr [2011Murr [ , 2015, Lewis-Beck and Stegmaier [2011]; Murr [2016]). As Graefe [2014] and Rothschild and Wolfers [2013] show, expectation polls may indeed outperform intention polls in forecasting election outcomes and vote shares. ...
Are the forecast errors of election-eve polls themselves forecastable? We present evidence from the 2008 Democratic Party nomination race between Barack Obama and Hillary Clinton showing that the answer is yes. Both cross-sectional and time series evidence suggests that market prices contain information about election outcomes that polls taken shortly before the contests do not. Conversely, election surprises relative to polls too Granger cause subsequent price movements. We then investigate whether the additional information in prices could come from the media coverage of these campaigns, and uncover a set of complex relationships between pollster's surprise, price movements, and various aspects of media coverage. Prices anticipate the balance and content of media coverage, but not the volume. On the other hand, it is the volume of media coverage, not the balance or content, that anticipates the surprise element in election outcomes. Moreover, Granger causality between prices and election surprises barely changes after controlling for media coverage, and causality from media volume to surprises persists too after controlling for price movements. Taken together, the results suggest that both prices and the volume of media coverage contain independent election-relevant information that is not captured in polls.
... The first analysis was made on 6 October, before the campaign started and the candidacies became official. It has been argued that one way to predict the election is to examine the expectations of voters about which candidate will win, rather than looking at their preferences (Lewis-Beck and Skalaban 1989;Lewis-Beck and Stegmaier 2011;Rothschild and Wolfers 2011). We measured the expectations that emerged on online discussion about which candidate could win the primary. ...
The importance of social media as a way to monitor an electoral campaign is well established. Day-by-day, hour-by-hour evaluation of the evolution of online ideas and opinion allows observers and scholars to monitor trends and momentum in public opinion well before traditional polls. However, there are difficulties in recording and analyzing often brief, unverified comments while the unequal age, gender, social and racial representation among social media users can produce inaccurate forecasts of final polls. Reviewing the different techniques employed using social media to nowcast and forecast elections, this book assesses its achievements and limitations while presenting a new technique of "sentiment analysis" to improve upon them. The authors carry out a meta-analysis of the existing literature to show the conditions under which social media-based electoral forecasts prove most accurate while new case studies from France, the United States and Italy demonstrate how much more accurate "sentiment analysis" can prove. © 2017 Andrea Ceron, Luigi Curini and Stefano M. Iacus. All rights reserved.
... Moving on to the British case, Lewis-Beck & Stegmaier (2011) show that the proportion of citizens who think that a party will win the Prime Ministership predicts its seat share. They estimate a seat equation at the national level using data from 13 British general elections between 1951 and 2005. ...
... Moving on to the British case, Lewis-Beck & Stegmaier (2011) show that the proportion of citizens who think that a party will win the Prime Ministership predicts its seat share. They estimate a seat equation at the national level using data from 13 British general elections between 1951 and 2005. ...
Most citizens correctly forecast which party will win the election, and groups of citizens forecast even better than individual citizens. Using Condorcet's jury theorem , I explain why individual citizens are better than chance at forecasting, and why in turn groups of citizens forecast better than individual citizen. The so-called 'wisdom of crowds' in election forecasting is a robust finding across countries, as the research reviewed here demonstrates. Asking citizens who they think will win results in accurate forecasts of which party will win, and of what seat or vote share a party will receive. Some of the studies reviewed here even suggest that asking citizens who they think will win is the most accurate approach to election forecasting. Similarly, asking citizens what vote share or seat number they think a party will receive accurately forecasts its actual vote share or seat number. But the analyses conducted here suggests that asking citizens what vote share or seat number they think a party will win is not the most accurate approach to election forecasting. Accordingly, when forecasting elections, academics, journalists, and pollsters should conduct more surveys asking citizens who they think will win.
... The use of the expectation question in pre-election surveys goes back before the emergence of intention polling (Hayes, 1936) and scholars have long studied the question why certain people provide more accurate forecasts than others (Lewis-Beck and Skalaban, 1989;Lewis-Beck and Tien, 1999;Dolan and Holbrook, 2001). However, only recently, scholars have begun to study the value of vote expectation surveys as a method to forecast elections in countries such as Germany, Sweden, UK, and US (Lewis-Beck and Stegmaier, 2011;Murr, 2011;Murr, 2015;Rothschild and Wolfers, 2012;Graefe, 2014;Sjöberg, 2009;Graefe, 2015a). For example, one study compared the accuracy of the expectation question to polls, prediction markets, quantitative models, and expert judgment for predicting election winners and vote shares in the seven US presidential elections from 1988 to 2012. ...
This chapter summarizes the latest research on prediction markets for political forecasting. After describing the concept of judgmental forecasting, I outline the history of political prediction markets from their predecessors in 16th century Italy to modern day online markets. Then, I describe important aspects of prediction market design, followed by a comparison of features of prediction markets and simple surveys. I also provide evidence on the relative accuracy of prediction markets and alternative methods for forecasting 44 elections in eight countries. Finally, I discuss the findings as well as their implications for future research.
... (A polling approach to forecasting not considered here relies on election result expectation, rather than vote intention. This is known as " citizen forecasting " (LewisBeck and Stegmaier 2011)). The Gallup lynchpin presidential poll occurs a day or two before the November election date. ...
In democratic nations, elections are premier political events. They require citizens to choose their leaders and leaders to be held accountable. The winners of an electoral contest are allowed to wield power, sometimes great power. Therefore, citizens follow campaigns with interest and are often eager to know who will win. In other words, they would like a forecast of the outcome. This impulse to election forecasting has been around for a long time. To forecast an election means to declare its outcome before it happens. For example, say in advance which candidate will win the race. There are many methods of forecasting elec-tions, and they can be divided into two groups: prescientific and scientific. While this distinction may seem clear, the differences are not always easy to spot. Below, we emphasize the scientific approach to election forecasting, tracing its his-torical development in the study of United States
... demonstrated that responses to the vote expectation question, also known as citizen forecasts, provide accurate predictions of election outcomes (Lewis-Beck and skalaban 1989;Lewis-Beck and Tien 1999;Lewis-Beck and stegmaier 2011;Murr 2011), few studies compare their accuracy to established benchmarks. ...
Simple surveys that ask people who they expect to win are among the most accurate methods for forecasting U.S. presidential elections. The majority of respondents correctly predicted the election winner in 193 (89%) of 217 surveys conducted from 1932 to 2012. Across the last 100 days prior to the seven elections from 1988 to 2012, vote expectation surveys provided more accurate forecasts of election winners and vote shares than four established methods (vote intention polls, prediction markets, econometric models, and expert judgment). Gains in accuracy were particularly large compared to polls. On average, the error of expectation-based vote-share forecasts was 51% lower than the error of polls published the same day. Compared to prediction markets, vote expectation forecasts reduced the error on average by 6%. Vote expectation surveys are inexpensive, easy to conduct, and the results are easy to understand. They provide accurate and stable forecasts and thus make it difficult to frame elections as horse races. Vote expectation surveys should be more strongly utilized in the coverage of election campaigns.
Das Ende der Corona-Krise rückt in immer weitere Ferne: Der Anteil derer, die ein Krisenende in mehr als sechs Monaten erwarten, stieg im Zeitraum von Ende März bis Ende April deutlich von ca. 34 Prozent auf ca. 61 Prozent an.
Wie lange genau ist ungewiss: Während ein Teil der Befragten davon ausgeht, dass die Pandemie spätestens in zwei Jahren der Vergangenheit angehört, meinen etwa genauso viele, dass die Krise noch länger dauern wird. Dabei wuchs zuletzt der Anteil, der eine mehrjährige Krise mit einer Dauer über zwei Jahre hinaus erwartet.
Optimistisch, dass die Krise bald vorbei ist, sind vor allem junge Menschen und Personen, die die Gefahr durch das Coronavirus gering einschätzen.
Introduction
According to the “wisdom of crowds” (WOC) principle, “under the right circumstances, groups are remarkably intelligent, and are often smarter than the smartest people in them. […] Even if most of the people within a group are not especially well-informed or rational, it can still reach a collectively wise decision” (Surowiecki, 2004, pp. xiii–xiv). The benefits of collective judgment have already been demonstrated multiple times in the fields of psychology, statistics, and management science (see, e.g., Clemen, 1989; Davis-Stober, Budescu, Dana, & Broomell, 2014; Dunning, 2007, pp. 84–86; Stewart, 2001, pp. 95–96; Yaniv, 2004). In political science more specifically, the WOC principle has been invoked as the main explanation underlying the accuracy of voters’ collective expectations about electoral outcomes. A number of studies in the United States, the United Kingdom, and Canada have shown that most citizens were able to correctly predict which party or candidate would win in local, regional, or national elections and that the aggregation of individual estimates increased the likelihood of a correct forecast (see, for example, Graefe, 2014; Lewis-Beck & Stegmaier, 2011; Miller, Wang, Kulkarni, Poor, & Osherson, 2012; Murr, 2011, 2015, 2016; Temporão, Dufresne, Savoie, & van der Linden, 2019). In other words, the proportion of correctly predicted outcomes was always higher than the proportion of correct individual forecasts.
The expectations of voters regarding election outcomes appear to be mostly influenced by their own political preferences. This raises two important questions. First, once partisan predispositions have been accounted for, how much do other variables like interest in the campaign, election news attentiveness, political knowledge, education or competitiveness help to explain one’s ability at predicting election outcomes? Second, does one’s level of sophistication moderate the link between political preferences and forecasting abilities? To answer these questions, I mobilize data from seven elections taken at the district and (sub)national levels. I also introduce a new measure of forecasting ability—the cumulative Brier score index. In most cases, variables other than preferences and knowledge have little or no influence on the accuracy of voters’ expectations both at the (sub)national and district levels. Political knowledge is positively associated with citizens’ forecasting abilities; however, it does not appear to moderate the preference–expectation link. This result contradicts findings from existing work and holds important implications for the study of citizen forecasting.
Incumbent city councillors have an almost insurmountable advantage in Canadian municipal elections. This article aims to improve our understanding of the municipal incumbency advantage by considering the ability of electors to correctly identify the two most competitive candidates in one's ward and the factors associated with being able to do so. Using survey data from the Canadian Municipal Election Study (CMES), we consider the case of the 2018 elections in Mississauga, a city with typically high rates of incumbent re-election. Survey respondents were asked to identify the two most competitive candidates in their local ward races. We find that comparatively few electors are able to recognize which challenger serves as the strongest threat to a sitting councillor, a finding that suggests that coordination problems may help to contribute to high rates of incumbent success. We identify several individual-level and ward-level correlates of correctly identifying the first-place and second-place finishers. We do note, however, that there is a significant amount of variation among the thousands of municipalities in Canada, so findings from this case should be tested in other settings, including larger or smaller cities where levels of information might be different.
How does perceived political corruption affect electoral preferences? Scholarship of Eastern Europe addressed this question primarily through the study of observational data. This study contributes to the existing scholarship by addressing the endogeneity problem and allows to causally interpret effects of perceived corruption on voting intentions. It combines hypotheses tested in earlier studies to investigate the impact of perceived political corruption on different electoral choices. A survey experiment in Hungary shows that exposure to political corruption makes respondents more likely to abstain from voting, less likely to support the incumbent party, and more likely to expect the anti-establishment party to win.
In recent years, the British polling industry has encountered difficulties in its attempts to measure voting intentions in important popular consultations, notably the 2014 referendum on Scottish independence, the 2015 UK general election, and the 2016 EU membership referendum. In such a context, it is extremely valuable to explore how different forecasting models that rely on political and economic variables can be used to predict the outcome of elections. In this paper, we propose such a model by introducing a political economy equation to estimate the vote share obtained by the incumbent party in UK general elections three months before the vote as well as a set of seemingly unrelated regressions (SUR) to forecast the results of the Official Opposition, the Liberal Democrats, and the remaining parties.
There is a growing literature suggesting that the result for each constituency at British general elections can be predicted using ‘citizen forecasts’ obtained through voter surveys. This may be true for the majority of constituencies where the result at previous contests was a substantial majority for one party’s candidates: few ‘safe seats’ change hands. But is it true in the marginal constituencies, where elections are won and lost? Analysis of such ‘citizen forecast’ data for the Labour-Conservative marginal constituencies in 2017 indicates not. Although respondents were aware of the seats’ relative marginality and of general trends in party support during the campaign, they could not separate out those that were eventually lost by each party from those that were won again, even in seats where the elected party won comfortably.
Die Wahlabsichtsfrage, populärwissenschaftlich auch als „Sonntagsfrage“ bezeichnet, wird kritisiert, weil mit ihr der Stimmenanteil der „Alternative für Deutschland“ (AfD) nicht valide zu messen sei. Wir argumentieren, dass alternative Messinstrumente, die Verzerrungen aufgrund von sozialer Erwünschtheit berücksichtigen, besser geeignet sind. Dazu testen wir erstmalig drei alternative Messmethoden – das doppelte Listenexperiment, die kreuzweise Randomisierte-Antwort-Technik und die Weisheit-der-Vielen-Methode – hinsichtlich des geschätzten AfD-Stimmenanteils und vergleichen sie mit der klassischen „Sonntagsfrage“. Unsere Ergebnisse zeigen, dass insbesondere die Weisheit-der-Vielen-Methode eine kostengünstige und gute Erweiterung der politikwissenschaftlichen Fragebatterie ist.
Research on election forecasting suggests there are benefits from combining different sources of information. This paper discusses the experience of a combined forecasting method that was developed for the UK’s EU referendum in 2016. The sources included opinion polls, vote expectation surveys, prediction and betting markets, expert and volunteer forecasts, and various forecasting models based on polling and other kinds of data. Averages of sources within each of these categories all, in our final forecast, suggested Remain was more likely to win but with varying degrees of certainty. Combining them produced a forecast that beat some but not others. Opinion polls and citizen forecasts came closest to the true outcome. Betting and prediction market participants and volunteer forecasters were the most overconfident that the UK would vote Remain. This may have been because they were distrustful of the polls following the 2015 general election miss and had too strong an expectation of a late swing towards the status quo similar to those in Scotland in 2014 and Quebec in 1995.
The aim of this article is to determine how well pre-election polls can predict the results of parliamentary elections, and what determines the accuracy of these predictions. The dependent variables are 1) the correct indication of the winning party and 2) the accuracy of election surveys in forecasting voters' support. The first independent variable is the time between the poll and the date of the election. The second explanatory variable is the difference in results between the two parties with the greatest support. This study uses data from all publicly available polls that took place in the 12 months before every parliamentary election in Poland from 1993 to 2015. The analysis uses Bayesian hierarchical modeling and Markov Chain Monte Carlo simulation. The results show that the average probability that a pre-election poll will correctly predict the winning party is around 80%, whereas the probability that it will correctly predict the distribution of voters' support (with 3% error margin) is around 50%. The evidence partially proved that the forecasting accuracy of an election poll is the better the closer the poll is taken to the date of the election. It was also proved that the ability of a poll to predict the winner is better the greater the gap between the survey results of the two leading parties.
This paper explores the feasibility of using social surveys to detect electoral manipulation in authoritarian regimes. It compares official results from the July 2013 elections in Zimbabwe with findings from a nationally representative pre-election survey. The comparison confirms that the dominant incumbent party won the elections but by far smaller margins than officially reported. This discrepancy provides analytic leverage to identify the possible presence of coercive mobilization and vote suppression and to pinpoint their geographic location. The election results are re-estimated using a set of voting simulations based on novel proxy indicators and an original list experiment designed to reveal the political preferences of fearful voters. The paper concludes by discussing why autocrats manipulate elections and whether or not they succeed in their objectives.
This paper applies the Seats-Votes Model to the task of forecasting the outcome of the 2015 election in Britain in terms of the seats won by the three major parties. The model derives originally from the ‘Law of Cubic Proportions’ the first formal statistical election forecasting model to be developed in Britain. It is an aggregate model which utilises the seats won by the major parties in the previous general election together with vote intentions six months prior to the general election to forecast seats. The model was reasonably successful in forecasting the 2005 and 2010 general elections, but has to be modified to take into account the ‘regime shift’ which occurred when the Liberal Democrats went into coalition with the Conservatives in 2010.
Who do you think will win in your constituency? Most citizens correctly answer this question, and groups are even better at answering it. Combining individual forecasts results in the ‘wisdom of crowds’ explained by Condorcet's jury theorem. This paper demonstrates the accuracy of citizen forecasts in seven British General Elections between 1964 and 2010, and reports what citizens interviewed in February and March forecasted for the election in May 2015. ‘Citizen forecasting’ predicts vote shares and winners in constituency elections, and seat numbers and governments in national elections. The paper also introduces a new method for predicting vote shares from citizen forecasts. Citizen forecasts are direct, accurate, and comprehensible. Pollsters should collect them and communicate their results more often.
We add to the literature on citizen forecasting by examining the 2013 German federal election and, for the first time, trying to predict vote shares. A random sample of voters was asked to predict the vote shares for each party in telephone interviews. We examine the accuracy of individuals' expectations and analyze the influence of wishful thinking and published vote intention polls on voters' expectations. Individual forecasts are aggregated, assuming a wise crowd will make a precise forecast. Expectations do not yield a forecast of satisfactory accuracy and they are inferior to vote intention polls. High-ability subgroups do not yield better forecasts than the whole sample. Issues of sampling and measurement are addressed. We conclude that asking voters to predict eight interdependent vote shares is probably too difficult a task.
Increasingly, professional forecasters rely on citizen forecasts when predicting election results. Following this approach, forecasters predict the winning party to be the one which most citizens have said will win. This approach predicts winners and vote shares well, but related research has shown that some citizens forecast better than others. Extensions of Condorcet’s jury theorem suggest that naïve citizen forecasting can be improved by delegating the forecasting to the most competent citizens and by weighting their forecasts by their level of competence. Indeed, doing so increases both the accuracy of vote share predictions and the number of states forecast correctly. Allocating the state’s electoral votes to the candidate who the most weighted delegates say will win yields a simple but successful forecasting model of the US Presidency. The ‘wisdom of crowds’ model predicts eight presidential elections out of nine correctly. The results suggest that delegating and weighting provide easy ways to improve citizen forecasting.
The present study reviews the accuracy of four methods for forecasting the 2013 German election: polls, prediction markets, expert judgment, and quantitative models. On average, across the two months prior to the election, polls were most accurate, with a mean absolute error of 1.4 percentage points, followed by quantitative models (1.6), expert judgment (2.1), and prediction markets (2.3). In addition, the study provides new evidence for the benefits of combining forecasts. Averaging all available forecasts within and across the four methods provided more accurate predictions than the typical component forecast. The error reductions achieved through combining forecasts ranged from 5% (compared to polls) to 41% (compared to prediction markets). The results conform to prior research on US presidential elections, which showed that combining is one of the most effective methods to generating accurate election forecasts.
We analyze individual probabilistic predictions of state outcomes in the 2008 U.S. presidential election. Employing an original survey of more than 19,000 respondents, we find that partisans gave higher probabilities to their favored candidates, but this bias was reduced by education, numerical sophistication, and the level of Obama support in their home states. In aggregate, we show that individual biases balance out, and the group's predictions were highly accurate, outperforming both Intrade (a prediction market) and fivethirtyeight.com (a poll-based forecast). The implication is that electoral forecasters can often do better asking individuals who they think will win rather than who they want to win.
Many studies report the “wonders of aggregation” and that groups (often) yield better decisions than individuals. Can this “wisdom of crowds”-effect be used to forecast elections? Forecasting models in first-past-the-post systems need to translate vote shares into seat shares by some formula; however, the seat–vote ratio alters from election to election. To circumvent this problem, this paper proposes citizen forecasting, which aggregates citizens’ local expectations to directly forecast constituencies. Using data from the 2010 British Election Study, this paper finds (1) that groups are better forecasters than individuals, (2) that citizen forecasting correctly predicts a hung parliament, and (3) that marginality and group size are important predictors for “getting it right”.
the innovation of internet cause too many important things to be possible. But one of the main goals of internet was bringing people together. As internet provides infrastructure for different people to connect each other their diversities come to play and brings some mentionable cases to study. Some studies show "wonders of aggregation" meaning that decision of a group of people can be more accurate than individuals. In this paper we examines a theory that says crowd of people can predict an event or for example they could votes which nominee will win and award and most of the times it could be true and this result can come from the social interaction between friends and people.
This article investigates methodologies for translating data from constituency betting markets in each of the UK's 650 constituencies into national-level predictions of parties' seat shares for the 2010 House of Commons election. We argue that information from betting markets is highly disaggregated (offering candidate-level predictions), adjustable throughout the campaign, and free to access – meaning that such data should be a useful resource for electoral forecasters. However, we find that constituency-market gambling data from the site Betfair.com proved to be a relatively poor basis for predicting party seat shares, and we also find evidence suggesting that the data were systematically biased in several ways. Nonetheless, we argue that future research in this area should compensate for these biases to harness the potential of constituency prediction markets for electoral forecasting.
Election forecasting in the United Kingdom has been experiencing a renaissance, as work travels from popularity function to vote function models. One strain of recent work stresses political economy explanations, while another stresses parsimonious prediction. Both approaches have their strengths, and are combined here in a two‐step approach. First, a prediction equation is offered, based on a powerful empirical proxy variable for the election outcome itself. Second, an explanatory equation is offered, accounting theoretically for that proxy variable. This recursive system of equations is estimated, evaluated and found, by various diagnostics, to be extremely robust. Then, forecasts are rendered for multiple measures of UK election outcomes, in order to bring together the various measures that have appeared in the literature: government vote (and seats) share, opposition vote (and seats) share, government vote (and seats) lead. Finally, as a test, the model is applied to forecast the next UK general election under two scenarios. Using poll data from August 2007 and May 2009, we conclude that Gordon Brown missed a golden opportunity when he became leader of his party. While a snap election at that time would have led the sitting government to another victory, an election in the autumn of 2009 would result in the worst score for the Labour Party since 1992 and a return to the opposition benches.
We apply a dynamic perspective to forecasting votes and seats in British elections. Our vote model captures the swing of the electoral pendulum between the two major parties while using prime ministerial approval as the (sole) short-run predictor of vote choice. The seat model incorporates the inertia of the previous seat distribution while translating votes into seats. The models forecast the lead of one major party over the other (percentage for votes and number for seats). The statistical estimation includes data on British elections since 1945, although the test for cycles (swing of the electoral pendulum) goes as far back as 1832. The vote model picks the winner of every one of the 1945–2005 elections (out-of-sample forecasts) and is rarely off by more than 2 percentage points. The seat model does almost as well, rarely missing the seat lead by more than 25.
'Times they are a-changing...' is at the source of the Westminster model. Or are they? Electoral reformers are still trying to replace Britain's single-member plurality electoral system with some form of PR, as they have been for 150 years. The system has maintained the parliamentary dominance of two parties, but elections have become increasingly multi-party and disproportional since 1974. However, at sub-state, local, and European elections, Britain has become a very active laboratory for electoral system design and implementation.
During the nineteenth century, a presidential voter actually selected a party-prepared candidate list, casting it in full view of others. The "Australian" ballot, adopted in nearly all states by 1900, took away party preparation of the ballot. State officials now prepared overall candidate lists from which the voter picked in secret. The introduction of the Australian ballot was heralded as a blow against political corruption and for "good government". But practical questions arose. With the state itself responsible for the ballot, how should it decide which candidates to list? Some barriers to entry seemed necessary, otherwise the list would be unwieldy. Each of the states began to pass laws restricting ballot access, often aimed at third parties.
This paper examines two rival forecasting models of election outcomes for Britain. The first is a model based on a revised version of the ‘cube rule’ called the seats–votes model which is designed to predict the number of seats won by parties in the House of Commons. The second is based on the idea that incumbent parties are rewarded by the electorate for a good performance in government, particularly in relation to the economy, and punished by a poor performance. The seats–votes model appears to have an edge over its rival, when it comes to forecasting elections. However, tests show that neither model encompasses the other, so that both models can learn from each other when it comes to improving the accuracy of forecasts. Insights from a revised model which incorporates variables from both versions are then used to simulate the outcome of the next general election in Britain in 2009 or 2010. A wide range of possible outcomes suggested by these simulations produce a hung Parliament, where no party has an overall majority in the House of Commons.
This is a plea for a dynamic perspective in forecasting British elections. Autoregressive models are capable of making forecasts in their own right (ex-ante, early, and unconditionally). Their large forecast errors, however, suggest that these models be used in combination with structural models of the vote. Lewis-Beck has identified the key short-term predictors of the vote such as government approval and economic conditions. The performance of such a vote model can only be helped by the inclusion of a dynamic element that captures the undeniable return of the British vote to equilibrium.
Addresses criticisms of the authors' previous linking of emotion and intelligence by explaining that many intellectual problems contain emotional information that must be processed. Using P. Salovey and J. D. Mayer's (1990) definition of emotional intelligence as a type of social intelligence that involves the ability to monitor one's own and others' emotions, to discriminate among them, and to use the information to guide one's thinking, it is argued that intelligence is an appropriate metaphor for the construct. The abilities and mechanisms that underlie emotional intelligence are described. These mechanisms are (1) emotionality itself, (2) facilitation and inhibition of emotional information flow, and (3) specialized neural mechanisms. Emotionality contributes to specific abilities, and emotional management influences information channels and problem solving. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The advent of three-party politics in Britain with the February 1974 general election has introduced an uncertainty into electoral and parliamentary politics unprecedented in the post-war period. In these circumstances, election forecasting has assumed a special interest and significance for academics, politicians, political commentators, and the like. This article presents and assesses the performance of three forecasting instruments, the ‘incremental’, ‘opinion polling’ and ‘economic’ models. They are estimated over the period 1951–1983 and are then used to predict the share of the vote won by the governing, opposition and Alliance parties in the 1987 general election. All are successful in the sense that they forecast the continuation of the Conservative party's electoral dominance. with Labour and the Alliance a poor second and third. Only the economic model, however, generates a reasonable forecast of the gap separating the major parties and it is used to predict the distribution of parliamentary seats between them. It is seen to be substantially more accurate for the government than for the opposition, which is itself a reflection of the uncertainty introduced into British politics by the emergence of a significant third party in recent elections.
Forecasting elections has long been regarded by political scientists as an interesting problem in its own right. But it assumes special importance for those countries that do not have fixed election dates. In Britain, for example, it is up to the prime minister to choose the date, within the statutory five year limit. Correct timing can clearly be crucial to the outcome, and the prime minister can be expected to go to considerable lengths to ensure that the election is called for the date most favourable to his party. But there lies the prime minister's problem: elections must be called three to four weeks before polling day. With what degree of accuracy can the result be forecast at the time the election is called?
A small but interesting literature on election forecasting has emerged in recent years. The forecasting techniques used in this literature vary widely, from crude extrapolation to sophisticated model building. Up to now the emphasis has been on election night forecasting, in which the basic problem involves extracting the maximum amount of information from electoral returns, in order to forecast the outcome a few hours before it is finally known. For obvious reasons the techniques utilized in this context are of little use prior to election day.
Emotional intelligence is introduced in the field of advertising and marketing communications. An overview of the construct, in terms of its historical perspective, models and applications is provided, together with suggestions for future research.
Parliamentary boundary reviews in the UK are undertaken to remove — as far as is practicable — inter-constituency variations in the number of electors. Their impact has almost invariably favoured the Conservative party — largely because population shifts between reviews tend to favour Labour with the movement of electors away from the inner cities and old industrial areas. That has been the case again with the Fifth Periodical Reviews conducted by the Boundary Commissions for England and Wales. The next general election will thus be slightly easier for the Conservatives to win than if the boundaries used for the 2005 contest were to be retained. But not much easier. Recent elections have seen very substantial biases operating in the translation of votes into seats favouring Labour. The biases are the result of the interaction of several geographies — of constituency size, abstentions and party support- only one of which (size) is directly tackled by the reviews. Unless those other geographies are changed the next two or three UK general elections are likely to see a continuation of these marked biases.
Although neural networks are increasingly used in a variety of disciplines there are few applications in political science. Approaches to electoral forecasting traditionally employ some form of linear regression modelling. By contrast, neural networks offer the opportunity to consider also the non-linear aspects of the process, promising a better performance, efficacy and flexibility. The initial development of this approach preceded the 2001 general election and models correctly predicted a Labour victory. The original data used for training and testing the network were based on the responses of two experts to a set of questions covering each general election held since 1835 up to 1997. To bring the model up to date, 2001 election data were added to the training set and two separate neural networks were trained using the views of our original two experts. To generate a forecast for the forthcoming general election, answers to the same questions about the performance of parties during the current parliament, obtained from a further 35 expert respondents, were offered to the neural networks. Both models, with slightly different probabilities, forecast another Labour victory. Modelling electoral forecasts using neural networks is at an early stage of development but the method is to be adapted to forecast party shares in local council elections. The greater frequency of such elections will offer better opportunities for training and testing the neural networks.
The article provides a set of contingent forecasts for the forthcoming UK general election. The forecasts are based on popularity function derived from monthly time series data covering the period 1997–2004. On most likely assumptions, the forecasts produce a clear Labour victory in the early summer of 2005, with the Liberal Democrats increasing their vote share by roughly four percentage points.
To forecast an election means to declare the outcome before it happens. Scientific approaches to election forecasting include polls, political stock markets and statistical models. I review these approaches, with an emphasis on the last, since it offers more lead time. Consideration is given to the history and politics of statistical forecasting models of elections. Rules for evaluating such models are offered. Examples of actual models come from the United States, France and the United Kingdom, where this work is rather new. Compared to other approaches, statistical modelling seems a promising method for forecasting elections.
This article develops a forecasting model of seat shares in the House of Commons applied to general election outcomes. The model utilises past information about party seat shares, together with data from the polls gathered prior to the election, to forecast the number of seats won by the parties. Once it has been estimated the model will be used to make a forecast of the outcome of a possible general election in May 2005. The article starts by focusing on research into translating votes into seats, or the cube rule and its modifications. It then goes on to develop the forecasting model, which is based on electoral and poll data from 1945 to 2001.
This paper first offers a final forecast for the May 2010 UK general election based on our “two-step model” [Nadeau, R., Lewis-Beck, M.S., Bélanger, É., 2009. Election forecasting in the United Kingdom: a two-step model. Journal of Elections, Public Opinion and Parties 19, 333–358.]. That model is then used to explore a new technique, “nowcasting.” We examine our model “nowcasts,” comparing it to the classic forecasting strategy of the vote intention poll, across the contemporary election period, 2005–2010. As is shown, nowcasting offers forecasting advantages that simple polling does not. Most notably, the nowcast provides variation that appears much more subject to substantive explanation of the electoral cycle, and it provides predictions with a three-month lead.
Political science, unlike economics, does not have a long tradition of forecasting models. However, this is changing. Currently, there is considerable interest in election forecasting. The basis for the interest is a flurry of related publications on House, Senate and presidential elections. A common goal for these studies is the development of a model, inevitably based on aggregate time-series data, which predicts election returns. The resulting models, some of which are quite accurate, can differ a good deal in specification and estimation. Also, they vary in complexity, making them more or less accessible to the engaged voter.
Much presidential election forecasting research employs macromodels based on national economic and political fluctuations. Micromodels based on surveys of individuals exist, but they are almost entirely pre-election explorations of vote intention. What has been neglected are micromodels derived from vote expectations. We show, by analysis of the American National Election Surveys 1956–1996, that voters themselves can forecast who will win the presidential election. We go on to explain some of sources of this forecasting ability, and to evaluate its precision. Voter forecasting models emerge as a useful alterative to current approaches.
Political and Election Forecasting
- Michael S Lewis-Beck
- Charles Tien
Lewis-Beck, Michael S. and Charles Tien. 2010. "Political and Election Forecasting." In
Michael Clements and David Hendry (eds). Oxford Handbook on Economic Forecasting.
Oxford: Oxford University Press. forthcoming.
From Votes to Seats: The Operation of the UK Electoral System since 1945
- R Johnston
- C Pattie
- D Dorling
- D Rossiter
Johnston, R., Pattie, C., Dorling, D., Rossiter, D., 2001. From Votes to Seats:
The Operation of the UK Electoral System since 1945. Manchester-University Press, Manchester, UK.
Election forecasting
- M S Lewis-Beck
- C Tien
Lewis-Beck, M.S., Tien, C., Election forecasting. In Clements, M., Hendry, D.
(Eds.), Oxford Handbook on Economic Forecasting. OxfordUniversity
Press, Oxford. forthcoming.
From Votes to Seats: The operation of the UK electoral system since 1945
- Ron Johnston
- Charles Pattie
- Danny Dorling
- David Rossiter
Johnston, Ron, Charles Pattie, Danny Dorling and David Rossiter. 2001. From Votes to Seats:
The operation of the UK electoral system since 1945. Manchester, UK: Manchester
University Press.