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The Current Population Survey is the gold-standard data source for studyingwho turns out to vote in elections. However, it suffers from potentiallynonignorable unit and item nonresponse. Fortunately, after elections, the totalnumber of voters is known from administrative sources and can be used to adjustfor potential nonresponse bias. We present a model-based approach to utilizethis known voter turnout rate, as well as other population marginaldistributions of demographic variables, in multiple imputation for unit anditem nonresponse. In doing so, we ensure that the imputations producedesign-based estimates that are plausible given the known margins. We introduceand utilize a hybrid missingness model comprising a pattern mixture model forunit nonresponse and selection models for item nonresponse. Using simulationstudies, we illustrate repeated sampling performance of the model underdifferent assumptions about the missingness mechanisms. We apply the model toexamine voter turnout by subgroups using the 2018 Current Population Survey forNorth Carolina. As a sensitivity analysis, we examine how results change whenwe allow for over-reporting, i.e., individuals self-reporting that they votedwhen in fact they did not.

Using auxiliary marginal distributions in imputations for nonresponse  while accounting for survey weights, with application to estimating voter  turnout