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

无答复和测量误差对盈利波动和不平等的影响：来自调查和管理数据的证据

• 批准号: 1918828
• 负责人: James Ziliak
• 金额: $ 37.26万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1918828&HistoricalAwards=false
• 关键词: Effects; Nonresponse; Measurement; Error; Earnings

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.

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部分来自调查数据，这些数据通常提供了广泛的变量。然而，调查数据存在数据质量问题，如不回答（例如，拒绝回答有关收入的调查问题）和测量错误（未能准确报告收入）。 这些数据质量问题给从调查数据中得出收益结论造成了障碍。 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在计量方面的一些缺陷。 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工人收入的行政数据联系起来，协调调查和行政数据的不同结果。通过将这两个数据集联系起来，该项目探讨了有关收入波动趋势的重要问题，包括时间、导致收入波动的基本人口因素、经济冲击对收入的影响以及薪酬结构如何导致收入变化。总的来说，这些问题促进了我们对收入变化的理解，这对于解释日益加剧的不平等和设计社会福利计划至关重要。该项目包括四项研究，利用限制访问的调查和行政从目前的人口调查（CPS）年度社会经济补充（ASEC）和社会保障管理局？详细收入记录（DER）。观察多份收入报告（行政和调查）的能力，结合CPS的短面板结构和DER中提供的完整收入历史，可以确定永久收入以及测量误差结构。 在CPS中，非应答者可以获得DER收入，从而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，就可以对收入水平和波动性进行调查，而这是调查或行政数据本身所无法实现的。第一个项目指定了一个有限的混合模型的收益反应，以检查连续调查响应者之间的差异，连续无响应者和切换器从响应到无响应，反之亦然。第二个项目审查是否有波动的水平和趋势的差异，调整面板损耗，ASEC和DER之间的非联系，测量误差。第三个项目提供了新的（半参数）估计的永久性和暂时性的冲击收益。最后，第四个项目是波动性的方差分解，以分离出多少水平和趋势差异是由年度工作时间、小时工资或小时和工资协方差的差异驱动的。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Trends in Earnings Volatility Using Linked Administrative and Survey Data

使用关联的管理和调查数据的收益波动趋势

• DOI: 10.1080/07350015.2022.2102023
• 发表时间: 2023
• 期刊: Journal of Business & Economic Statistics
• 影响因子: 3
• 作者: Ziliak, James P.;Hokayem, Charles;Bollinger, Christopher R.
• 通讯作者: Bollinger, Christopher R.

Reconciling Trends in U.S. Male Earnings Volatility: Results from Survey and Administrative Data

美国男性收入波动趋势的调和：调查结果和行政数据

• DOI: 10.1080/07350015.2022.2102020
• 发表时间: 2023
• 期刊: Journal of Business & Economic Statistics
• 影响因子: 3
• 作者: Moffitt, Robert;Abowd, John;Bollinger, Christopher;Carr, Michael;Hokayem, Charles;McKinney, Kevin;Wiemers, Emily;Zhang, Sisi;Ziliak, James
• 通讯作者: Ziliak, James