Synthetic Area Weighting for Measuring Public Opinion in Small Areas

TL;DR

This paper introduces a synthetic area weighting method for small area public opinion estimation, which improves upon existing models by directly using survey-only variables and national weights, without requiring outcome models.

Contribution

The paper proposes a novel synthetic area estimator that enables direct use of survey variables and weights, enhancing small area opinion estimates without outcome modeling.

Findings

The method effectively captures opinion heterogeneity across districts.

It outperforms traditional multilevel regression and poststratification in small area estimates.

Empirical application to Florida shows improved district-level opinion estimates.

Abstract

The comparison of subnational areas is ubiquitous but survey samples of these areas are often biased or prohibitively small. Researchers turn to methods such as multilevel regression and poststratification (MRP) to improve the efficiency of estimates by partially pooling data across areas via random effects. However, the random effect approach can pool observations only through area-level aggregates. We instead propose a weighting estimator, the synthetic area estimator, which weights on variables measured only in the survey to partially pool observations individually. The proposed method consists of two-step weighting: first to adjust differences across areas and then to adjust for differences between the sample and population. Unlike MRP, our estimator can directly use the national weights that are often estimated from pollsters using proprietary information. Our approach also clarifies the assumptions needed for valid partial pooling, without imposing an outcome model. We apply the proposed method to estimate the support for immigration policies at the congressional district level in Florida. Our empirical results show that small area estimation models with insufficient covariates can mask opinion heterogeneities across districts.