Improving election prediction internationally.

Area of Science:

• Political Science
• Computational Social Science
• International Relations

Background:

• Predicting election outcomes is crucial for understanding democratic processes.
• Previous models often lacked global scope or comprehensive data.
• The impact of various factors on direct executive elections requires further investigation.

Purpose of the Study:

• To develop and validate models for predicting direct executive elections globally.
• To identify key predictors of election outcomes across diverse countries.
• To assess the role of polling data in election forecasting.

Main Methods:

• Utilized a multiyear dataset of over 500 elections from 86 countries.
• Employed a separate dataset with extensive polling data from 146 election rounds.
• Validated models through out-of-sample testing and live forecasting experiments.

Main Results:

• Models achieved 80-90% accuracy in predicting election outcomes.
• Political institutions, incumbent advantage, and international linkage/aid were significant predictors.
• Economic indicators showed relatively weak predictive power.
• Global polling data demonstrated robustness as a predictor, even in developing nations.

Conclusions:

• Global elections can be successfully modeled and are becoming more predictable.
• Political institutions and incumbent advantage are strong determinants of election results.
• International factors and polling data are vital for accurate election forecasting.