%0 Journal Article %J Journal of Economic and Social Measurement %D 2020 %T Reducing cross-wave variability in survey measures of household wealth %A Michael D Hurd %A Erik Meijer %A Moldoff, Michael %A Susann Rohwedder %K household income %K Panel data %K social structure %K survey design %K United States %X 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. © 2019-IOS Press and the authors. All rights reserved. %B Journal of Economic and Social Measurement %V 44 %P 117-139 %G eng %R 10.3233/JEM-190465 %0 Report %D 2016 %T Improved Wealth Measures in the Health and Retirement Study: Asset Reconciliation and Cross-Wave Imputation %A Michael D Hurd %A Erik Meijer %A Moldoff, Michael %A Susann Rohwedder %K Demographics %K Methodology %X In this report, we present improved wealth measures for the Health and Retirement Study (HRS), which aim to reduce the effect of observation error on wealth levels and changes in wealth. The new wealth measures take account of the asset verification section in the HRS and use cross-wave information, most notably the value of the same asset in adjacent waves, in the imputation models, so imputed values better preserve serial correlation in the asset values. We document how we dealt with several methodological challenges in the implementation of these improvements. The corrections from the asset verification data reduce the standard deviations of wave-to-wave changes by substantial amounts (up to 57 percent for total wealth). The most important effect of the cross-wave imputations is a considerable reduction of the number of spikes and trenches (large changes in value followed by large changes back). %G eng %4 Methodology/Retirement and Retirement Benefits/Socioeconomic Status %$ 999999