基于调查报告和行政报告的盈余不稳定性指标比较

Chinhui Juhn, Kristin McCue
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引用次数: 13

摘要

在Celik, Juhn, McCue和Thompson(2009)中,我们发现基于当前人口调查(CPS)和收入和计划参与调查(SIPP)的数据估计的收入不稳定水平彼此相当接近,并且与其他来自收入动态小组研究(PSID)的估计接近,但来自失业保险(UI)收入的估计要大得多。考虑到UI数据来自行政记录,通常被认为比调查报告更准确,这引起了人们的担忧,即基于调查数据的措施低估了真实的收入不稳定性。为了解决这个问题,我们使用了来自SIPP和LEHD数据库中UI收入记录的调查样本之间的联系,以确定工作经历和收入信息差异的来源。已经做了大量的工作来比较来自行政记录的收入水平与SIPP和CPS中收集的收入水平,但我们对收入不稳定性的理解将受益于进一步研究不同来源的收入变化特性的差异。我们首先比较整体和匹配样本的特征,以解决匹配过程中的选择问题。然后,我们比较了SIPP和LEHD数据中的收入水平和工作,以确定它们之间的差异。最后,我们开始研究这些差异如何影响对收益不稳定性的估计。我们的初步发现表明,在收入分布的下尾,收入变化的差异在很大程度上解释了不稳定性估计的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparing Measures of Earnings Instability Based on Survey and Administrative Reports
In Celik, Juhn, McCue, and Thompson (2009), we found that estimated levels of earnings instability based on data from the Current Population Survey (CPS) and the Survey of Income and Program Participation (SIPP) were reasonably close to each other and to others’ estimates from the Panel Study of Income Dynamics (PSID), but estimates from unemployment insurance (UI) earnings were much larger. Given that the UI data are from administrative records which are often posited to be more accurate than survey reports, this raises concerns that measures based on survey data understate true earnings instability. To address this, we use links between survey samples from the SIPP and UI earnings records in the LEHD database to identify sources of differences in work history and earnings information. Substantial work has been done comparing earnings levels from administrative records to those collected in the SIPP and CPS, but our understanding of earnings instability would benefit from further examination of differences across sources in the properties of changes in earnings. We first compare characteristics of the overall and matched samples to address issues of selection in the matching process. We then compare earnings levels and jobs in the SIPP and LEHD data to identify differences between them. Finally we begin to examine how such differences affect estimates of earnings instability. Our preliminary findings suggest that differences in earnings changes for those in the lower tail of the earnings distribution account for much of the difference in instability estimates.
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