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

University Of Kentucky Research Foundation, Lexington KY

Investigators

Abstract

Abstract Earnings 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.