SHARELIFE Imputations

TL;DR

This paper presents the SHARELIFE-MI project, which develops multiple imputation methods to address missing data in SHARELIFE life-course surveys, ensuring data quality and validity.

Contribution

It introduces a new imputation model based on fully conditional specification for handling missing values in complex life-history data.

Findings

Imputation model effectively reduces missing data bias.

Validations show consistency with observed data and external benchmarks.

Method improves data completeness for longitudinal analysis.

Abstract

This report describes the SHARELIFE-MI project, which aims to generate multiple imputations for missing values in the life-course data collected in SHARELIFE Waves 3 and 7. The SHARELIFE study reconstructs individual life histories through retrospective questions covering key biographical domains such as partnerships, fertility, employment, and residence. As in the regular SHARE waves, item nonresponse represents an important source of nonsampling error - particularly for monetary variables, which require conversions across multiple currencies and long time periods. We document the preliminary data recoding and harmonization steps, as well as the design, specification, and implementation of an imputation model based on the fully conditional specification approach. Finally, we assess the internal and external validity of the resulting imputations through comparisons with the observed data, alternative nonresponse adjustments based on inverse propensity weighting, and external benchmarks from the regular SHARE waves.