A Unified Bayesian Model for Voter Turnout Estimation: Combining Surveys, Aggregate Data, and Selection Correction

Abstract

Accurate small-area estimation of voter turnout for demographic subgroups is crucial for political analysis but methodologically challenging. Survey data suffer from over-reporting, non-representativeness, and non-ignorable non-response, while ecological inference (EI) from aggregate data is vulnerable to the ecological fallacy. We propose a Bayesian hierarchical framework that jointly integrates individual-level survey data, official aggregate turnout margins, and census cell counts. The framework comprises three models: a multilevel ecological inference (EI) model that extends MRP-style structure to aggregate data, a Poll-and-Margin (PM) model that jointly fits survey and margins in one coherent likelihood, and a Full Selection (FS) model that adds a Heckman-style selection correction under a random-contact assumption and uses informative priors for identification. In simulations with controlled selection mechanisms based on empirical census demographics, FS substantially improves over established benchmarks, particularly under strong selection bias or heavy censoring, but is sensitive to unmodeled selection noise. In an application to the 2023 Estonian parliamentary election, PM performs similarly to the current state of the art on available validation margins, with FS showing a modest improvement that depends on the informative priors.