Towards Principled Unskewing: Viewing 2020 Election Polls Through a Corrective Lens from 2016

Abstract

We apply the concept of the data defect index Meng (2018) to study the potential impact of systematic errors on the 2020 pre-election polls in 12 presidential battleground states. We investigate the impact under the hypothetical scenarios that (1) the magnitude of the underlying nonresponse bias correlated with supporting Donald Trump is similar to that of the 2016 polls, (2) the pollsters' ability to correct systematic errors via weighting has not improved significantly, and (3) turnout levels remain similar to 2016. Because survey weights are crucial for our investigations but are often not released, we adopt two approximate methods under different modeling assumptions. Under these scenarios, which may be far from reality, our models shift Trump's estimated two-party voteshare by a percentage point in his favor in the median battleground state, and increases twofold the uncertainty around the voteshare estimate.