Professor in Political Science and Computer and Information Science

David Lazer

Improving Election Prediction Internationally

Journal Article
Publication date: 
02/2017
Authors: 
Ryan P. Kennedy
Stefan J. Wojcik
David Lazer
Improving Election Prediction Internationally

This study reports the results of a multiyear program to predict direct executive elections in a variety of countries from globally pooled data. We developed prediction models by means of an election data set covering 86 countries and more than 500 elections, and a separate data set with extensive polling data from 146 election rounds. We also participated in two live forecasting eperiments. Our models correctly predicted 80 to 90% of elections in out-of-sample tests. The results suggest that global elections can be successfully modeled and that they are likely to become more predictable as more information becomes available in future elections. The results provide strong evidence for the impact of political institutions and incumbent advantage. They also provide evidence to support contentions about the importance of international linkage and aid. Direct evidence for economic indicators as predictors of election outcomes is relatively weak. The results suggest that with some adjustments, global polling is a robust predictor of election outcomes, even in developing states. Implications of these findings after the latest U.S. presidential election are discussed.

Research Areas TOC

Computational Social Science, Collective Cognition

Computational Social Science, 21st Century Democracy, Political Networks

Computational Social Science, Political Networks

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21st Century Democracy, Political Networks