In This Article Election Forecasting

  • Introduction
  • General Overviews
  • Journals
  • Polling Forecasts
  • Election Markets
  • Other Approaches to Election Forecasting

Political Science Election Forecasting
by
Mary Stegmaier, Helmut Norpoth
  • LAST REVIEWED: 04 May 2015
  • LAST MODIFIED: 27 June 2017
  • DOI: 10.1093/obo/9780199756223-0023

Introduction

Election forecasting appeals to a basic human urge to peek into the future. Ever since elections were invented to choose leaders, humans have been tempted to find ways that would tell them with some degree of certainty who would win an election. The highly quantitative nature of elections aids them in such an endeavor. Few phenomena of interest to a social scientist lend themselves so readily to forecasting than electoral contests. In a two-party competition they produce a clear winner, and in multiparty settings they produce numerical shares for each of the contenders. With the advent of statistical techniques, electoral data have become increasingly easy to handle. It is no surprise, then, that election forecasting has become a big business, for polling firms, news organizations, and betting markets as well as academic students of politics. There are three major types of election forecasting. Perhaps the best known to the general public involves polls of the voting public. The growth of polling is supplying a near-endless stream of data on candidate and party support that can be marshaled for predictions of electoral outcomes. During a national election campaign in the United States and many other countries, a new poll is reported every day showing the current state of the “horse race.” Though not strictly a forecast, the poll result, or an average of the latest polls, is widely seen as the best guess of who is going to win and by how much. On election night, as the nation eagerly awaits the result, news organizations rely on exit polls to project the winner of a particular electoral contest. Alongside predictions of outcomes from samples of voters (or polls taken beforehand), academic scholars have constructed models of voting behavior to forecast the outcomes of elections. These forecasts are derived from theories and empirical evidence about what matters to voters when they make electoral choices. The forecast models typically rely on a few predictors in highly aggregated form, with an emphasis on phenomena that change in the short-run, such as the state of the economy, so as to offer maximum leverage for predicting the result of a specific election. Finally, betting markets provide forecasts of election outcomes based on the buying and selling of candidate futures with real money. These markets have witnessed a resurgence with the advent of the Internet, but their operations face legal obstacles in the United States.

General Overviews

The author of Bean 1948 may have been the first to publish a book using the phrase, “how to predict elections” in the title. Bean is best remembered for an approach that searches for the locality (state, county) whose vote matches most closely the national vote division. Such “bellwethers,” he believed, would provide a highly efficient way of forecasting the overall outcome, with little lead time, if all people vote on the same day. With an article published in the 1970s, Fair founded the econometric school of forecasting presidential elections, using mainly aggregate economic measures along with political variables and extending the time horizon back to the 1916 election. This approach is detailed in Fair 2002. In addition, Fair has kept his model up to date and lets users calculate their own forecasts on his website. Lewis-Beck and Rice 1992 is the first book-length overview of the various approaches to election forecasting. It establishes clear statistical criteria for building forecast models with proven determinants of the vote choice, such as economic conditions and presidential approval. The book is also valuable for applying the forecast model to congressional elections, as well as elections outside the United States. Jones 2002 extends the scope of forecasting to trial heats (polls), exit polls, expert judgments, cycles, and the nomination process. Norpoth 2014 demonstrates the predictive utility of electoral cycles with an analysis of nearly 200 years of US presidential elections. Holbrook 2010 covers forecasts through betting markets and state-level models. The template for using all the states in presidential election forecasting was first presented in Rosenstone 1983. Such a model would be able to forecast not only the national popular vote, but also the Electoral College vote, which ultimately elects a president. The introduction to an issue on a symposium, Lewis-Beck and Stegmaier 2014 presents a recent assessment of the various approaches to election forecasting.

  • Bean, Louis H. How to Predict Elections. New York: Knopf, 1948.

    E-mail Citation »

    A pioneering work in election forecasting, using past election results to identify states that are most typical for the national outcome of presidential elections (“bellwethers”). Maine’s early vote at that time put that state in a special role (“As Maine goes, so goes the nation”).

  • Fair, Ray C. Predicting Elections and Other Things. Stanford, CA: Stanford University Press, 2002.

    E-mail Citation »

    A pioneer in election forecasting presents his model, which heavily relies on measures of economic performance and covers elections from 1916 on. Also discusses the theoretical and methodological foundations of forecasting elections as well as other things. Fair’s website lets users calculate their own forecasts of upcoming elections.

  • Holbrook, Thomas. “Forecasting US Presidential Elections.” In The Oxford Handbook of American Elections and Political Behavior. Edited by Jan Leighley, 346–371. Oxford: Oxford University Press, 2010.

    DOI: 10.1093/oxfordhb/9780199235476.001.0001E-mail Citation »

    An assessment of the state of election forecasting as of 2010. Examines and compares the accuracy of presidential election forecasts from 1996 to 2004, with special attention to the lessons learned from the 2000 election; also considers alternatives such as political markets and state-level forecasting.

  • Jones, Randall J. Who Will Be in the White House? Predicting Presidential Elections. New York: Longman, 2002.

    E-mail Citation »

    A comprehensive and highly readable overview of the various approaches to election forecasting, from bellwethers to trial heats, presidential approval, and economic models, to name a few, with their respective forecast scenarios for the 2004 presidential election.

  • Lewis-Beck, Michael S., and Tom Rice. Forecasting Elections. Washington, DC: CQ, 1992.

    E-mail Citation »

    A classic book-length work of election forecasting. It presents an overview of approaches to election forecasting, focusing on the theory and statistical procedures of forecasting while covering models of presidential elections, but also including House, Senate, and state contests as well as elections abroad (France).

  • Lewis-Beck, Michael S., and Mary Stegmaier. “US Presidential Election Forecasting: Introduction.” PS: Political Science and Politics 47.2 (April 2014): 284–288.

    E-mail Citation »

    An introduction for a symposium in this issue on forecasting presidential elections, with coverage of approaches using fundamentals, polls, prediction markets, and cycles.

  • Norpoth, Helmut. “The Electoral Cycle.” PS: Political Science and Politics 47.2 (April 2014): 332–335.

    E-mail Citation »

    An autoregressive model for presidential elections from 1828 to 2012 shows the predictive power of the two previous outcomes, allowing for an early forecast. The White House party is favored after one term while change is more likely after two terms.

  • Rosenstone, Steven J. Forecasting Presidential Elections. New Haven, CT: Yale University Press, 1983.

    E-mail Citation »

    This book presented the first state-level model to predict the vote in presidential elections, using a pooled cross-sectional design, and covering elections from 1948 to 1972. Key predictors are issues, state of the economy, and incumbency. The model was used to forecast both the 1976 and 1980 presidential elections.

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