Estimating the Impacts of Program Benefits: Using Instrumental Variables with Underreported and Imputed Data

• 成果类型: Article
• 署名作者: Stephens, Melvin, Jr.; Unayama, Takashi
• 署名单位: University of Michigan System; University of Michigan; National Bureau of Economic Research; Hitotsubashi University
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_00769
• 发表日期: 2019-07
• 页码: 468-475
• 关键词: social-security; measurement error; Missing Data; match bias; consumption; income; wages

摘要

Survey nonresponse has risen in recent years, which has increased the share of imputed and underreported values found on commonly used data sets. While this trend has been well documented for earnings, the growth in nonresponse to government transfers questions has received far less attention. We demonstrate analytically that the underreporting and imputation of transfer benefits can lead to program impact estimates that are substantially overstated when using instrumental variables methods to correct for endogeneity or measurement error in benefit amounts. We document the importance of failing to account for these issues using two empirical examples.