Effects of Nonresponse and Measurement Error on Earnings Volatility and Inequality: Evidence from Survey and Administrative Data

由于各种原因，工人的收入每年都在变化。这种收入波动与贫困、收入不平等加剧、经济流动性下降以及社会福利计划的使用有着重要的联系。人们对波动性和不平等的了解大多来自调查数据，这些数据通常提供了广泛的变量集合。然而，调查数据存在数据质量问题，如不回应（例如，拒绝回答有关收益的调查问题）和测量误差（未能准确报告收益）。这些数据质量问题造成了从调查数据中得出收益结论的障碍。另一方面，关于收入和其他相关主题的行政数据避免了调查的一些测量缺陷。然而，行政数据本身并不包括教育、种族和家庭结构等重要变量，这些变量是充分调查收入波动的原因和趋势所必需的。该项目旨在通过将广泛用于从收入和收入了解美国贫困率的大型调查数据与有关工人收入的行政数据联系起来，调和调查和行政数据之间的分歧结果。通过将这两个数据集联系起来，本项目探讨了有关盈利波动趋势的重要问题，导致盈利波动的潜在人口因素，经济冲击对盈利的影响以及薪酬结构如何导致盈利变化。总的来说，这些问题促进了我们对收入如何变化的理解，这对于解释不平等加剧和设计社会福利计划至关重要。该项目由四项研究组成，这些研究利用了限制访问的调查和来自当前人口调查（CPS）年度社会经济补编（ASEC）和社会保障管理局的行政管理。详细收益记录（DER）。能够观察多个收益报告（行政和调查），结合CPS的短面板结构和DER中可用的完整收益历史，可以识别永久收入，以及测量误差结构。对于那些在CPS中不是应答者的人来说，获得DER收入可以确定非应答者的收入分配情况。将这两个方面结合起来，就可以以单独的调查或行政数据无法完成的方式调查收益水平和波动性。第一个项目指定了收益反应的有限混合模型，以检查连续调查响应者与连续无响应者和从响应转向无响应者之间的差异，反之亦然。第二个项目检查波动性水平和趋势是否存在差异，调整面板磨损，ASEC和DER之间的非联系以及测量误差。第三个项目提供了永久性和暂时性收益冲击的新（半参数）估计。最后，第四个项目是关于波动性的方差分解，以隔离多少水平和趋势差异是由年工作时间、小时工资或小时和工资的协方差的差异驱动的。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

AbstractEarnings of workers change from year to year for various reasons. This earnings volatility has important links to poverty, rising income inequality, declining economic mobility, and the use of social welfare programs. Much of what is known about volatility and inequality comes from survey data, which generally offers a broad collection of variables. However, survey data suffers from data quality issues such as non-response (for example, refusing to answer survey questions about earnings) and measurement error (failing to report earnings accurately). These data quality issues create obstacles in drawing conclusions about earnings from survey data. Administrative data on earnings and other related topics, on the other hand, avoids some of the measurement pitfalls of surveys. However, administrative data alone do not include important variables such as education, race, and family structure necessary to fully investigate the reasons and trends of earnings volatility. This project seeks to reconcile the diverging results from survey and administrative data by linking a large survey data that is widely used to understand U.S. poverty rate from income and earnings to administrative data on worker earnings. By linking these two datasets, this project explores important questions on earnings volatility trends through time, underlying demographic elements that cause earnings volatility, the effects of economic shocks on earnings and how compensation structure may lead to shifts in earnings. Overall, these questions advance our understanding of how earnings change, which is crucial in explaining rising inequality and designing social welfare programs. The project consists of four studies that utilize restricted-access survey and administrative from the Current Population Survey (CPS) Annual Social Economic Supplement (ASEC) and Social Security Administration?s Detailed Earnings Records (DER). The ability to observe both multiple reports of earnings (administrative and survey) combined with the short panel structure of the CPS and the full earnings history available in the DER, allows identification of permanent income, as well as measurement error structure. The availability of DER earnings for those who are non-respondents in the CPS allows identification of the distribution of income for non-respondents. Combining these two aspects allows the investigation of earnings levels and volatility in ways that neither survey nor administrative data alone could accomplish. The first project specifies a finite mixture model of earnings response to examine differences between continuous survey responders to both continuous non-responders and switchers from response to non-response or vice versa. The second project examines whether there are differences in levels and trends in volatility, adjusting for panel attrition, non-linkage between the ASEC and DER, and measurement error. The third project provides new (semiparametric) estimates of permanent and transitory shocks to earnings. Finally, fourth project is on variance decomposition of volatility to isolate how much of the level and trend differences are driven by differences in annual hours of work, hourly wages, or the covariance of hours and wages.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.