Correction for nonignorable nonresponse bias in the estimation of turnout using callback data

Overestimation of turnout has long been an issue in election surveys, with nonresponse bias or voter overrepresentation regarded as one of the major sources of bias. However, the adjustment for nonignorable nonresponse bias is substantially challenging. Based on the ANES Non-Response Follow-Up Study concerning the 2020 U.S. presidential election, we investigate the role of callback data in adjusting for nonresponse bias in the estimation of turnout. Callback data are the records of contact attempts in the survey course, available in many modern large-scale surveys. We propose a stableness of resistance assumption to account for the nonignorable missingness in the outcome, which states that the impact of the missing outcome on the response propensity is stable in the first two call attempts. Under this assumption and by leveraging covariates information from the census data, we establish the identifiability and develop estimation methods for turnout, including a doubly robust estimator. Our methods produce estimates very close to the official turnout and successfully capture the trend of declining willingness to vote as response reluctance or contact difficulty increases. This work hints at the importance of adjusting for nonignorable nonresponse bias and exhibits the promise of callback data for political surveys.