# Leveraging Auxiliary Information on Marginal Distributions in Nonignorable Models for Item and Unit Nonresponse

## TL;DR

This paper introduces a model-based approach that uses auxiliary marginal information to improve handling of nonresponse in surveys, enhancing imputation accuracy and allowing sensitivity analysis.

## Contribution

It develops a framework for incorporating auxiliary marginal data into nonresponse models, enabling tailored missingness mechanisms and better imputation strategies.

## Key findings

- Improved imputation of voter turnout data.
- Sensitivity analysis of nonresponse assumptions.
- Framework applicable to various survey contexts.

## Abstract

Often, government agencies and survey organizations know the population counts or percentages for some of the variables in a survey. These may be available from auxiliary sources, for example, administrative databases or other high quality surveys. We present and illustrate a model-based framework for leveraging such auxiliary marginal information when handling unit and item nonresponse. We show how one can use the margins to specify different missingness mechanisms for each type of nonresponse. We use the framework to impute missing values in voter turnout in a subset of data from the U.S.\ Current Population Survey (CPS). In doing so, we examine the sensitivity of results to different assumptions about the unit and item nonresponse.