Political Forecasting, Good Governance

Improving Election Prediction Internationally

Author(s): Ryan Kennedy, David Lazer, Stefan Wojcik
Date: February 3, 2017
Publication Type: Journal Article
Research Topics: Political Forecasting, Good Governance

Overview:

Assumptions underlying election result predictions have been encountering wide criticism. This study, published in the journal Science, reports the results of a multiyear program to predict direct executive elections in a variety of countries from globally pooled data. The authors analyzed a variety of potential predictors theorized to be of importance, ranging from economic performance to polling data.They developed prediction models with an election data set of 86 countries and more than 500 elections, and a separate data set with extensive polling data from 146 election rounds. They participated in two live forecasting experiments. 

Key Findings:

  • Elections were about 80 to 90% predictable, despite uncertainties with available data.
  • Polling data were very important to successful prediction, although it was necessary to correct for systematic biases.
  • Unexpectedly, economic indicators were only weakly predictive.
  • As data sources improve and grow, predictive power is expected to increase.

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