Reducing cross-wave variability in survey measures of household wealth

Abstract

Survey measures of household wealth often incorporate measurement error. The resulting excess variability in the first difference in wealth makes meaningful statistical inference difficult on changes in household-level wealth. We study the effects of two methods intended to reduce this problem: Asset verification confronts respondents with large discrepancies between wealth reports from the current wave and from the previous wave. Cross-wave imputation uses adjacent wave information in the imputation procedures for missing data. In the U.S. Health and Retirement Study, the corrections from asset verification substantially reduced wave-to-wave changes in wealth. The cross-wave imputations also reduced variation, but to a lesser extent.