How Should We Measure District-Level Public Opinion on Individual Issues?

Due to insufficient sample sizes in national surveys, strikingly little is known about public opinion at the level of Congressional and state legislative districts in the United States. As a result, there has been virtually no study of whether legislators accurately represent the will of their constituents on individual issues. This article solves this problem by developing a multilevel regression and poststratification (MRP) model that combines survey and census data to estimate public opinion at the district level. We show that MRP estimates are excellent predictors of public opinion and referenda results for both congressional and state senate districts. Moreover, they have less error, higher correlations, and lower variance than either disaggregated survey estimates or presidential vote shares. The MRP approach provides American and Comparative Politics scholars with a valuable new tool to measure issue-specific public opinion at low levels of geographic aggregation.