Earnings Inequality and Mobility Trends in the United States: Nationally Representative Estimates from Longitudinally Linked Employer-Employee Data

Abowd, John M.; McKinney, Kevin L.; Zhao, Nellie

Earnings Inequality and Mobility Trends in the United States: Nationally Representative Estimates from Longitudinally Linked Employer-Employee Data

Abstract

Using earnings data from the U.S. Census Bureau, this paper analyzes the role of the employer in explaining the rise in earnings inequality in the United States. We first establish a consistent frame of analysis appropriate for administrative data used to study earnings inequality. We show that the trends in earnings inequality in the administrative data from the Longitudinal Employer-Household Dynamics Program are inconsistent with other data sources when we do not correct for the presence of misused SSNs. After this correction to the worker frame, we analyze how the earnings distribution has changed in the last decade. We present a decomposition of the year-to-year changes in the earnings distribution from 2004-2013. Even when simplifying these flows to movements between the bottom 20%, the middle 60% and the top 20% of the earnings distribution, about 20.5 million workers undergo a transition each year. Another 19.9 million move between employment and nonemployment. To understand the role of the firm in these transitions, we estimate a model for log earnings with additive fixed worker and firm effects using all jobs held by eligible workers from 2004-2013. We construct a composite log earnings firm component across all jobs for a worker in a given year and a non-firm component. We also construct a skill-type index. We show that, while the difference between working at a low- or middle-paying firm are relatively small, the gains from working at a top-paying firm are large. Specifically, the benefits of working for a high-paying firm are not only realized today, through higher earnings paid to the worker, but also persist through an increase in the probability of upward mobility. High-paying firms facilitate moving workers to the top of the earnings distribution and keeping them there.

Sponsorship

This work received support from NSF Grants SES-0339191, CNS-0627680, SES-0922005, TC-1012593, and SES- 1131848. This paper was written while the the third author was a Pathways Intern at the U.S. Census Bureau. We have benefited from discussions with David Card, John Eltinge, Patrick Kline, Francis Kramarz, Kristin McCue, Ian Schmutte, Lars Vilhuber, participants at the NBER conference that preceded this volume, the editors of this volume, Edward Lazear and Kathryn Shaw, and two anonymous referees. Sara Sullivan edited the final manuscript. Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau or other sponsors. All results have been reviewed to ensure that no confidential information is disclosed. This research uses data from the Census Bureau's Longitudinal Employer-Household Dynamics Program, which was partially supported by the following National Science Foundation Grants: SES-9978093, SES-0339191 and ITR-0427889; National Institute on Aging Grant AG018854; and grants from the Alfred P. Sloan Foundation.

Date Issued

2017-03-01

Publisher

Journal of Labor Economics

Keywords

income inequality; mobility; Longitudinal Employer-Household Dynamics Program; distribution

Related DOI

https://doi.org/10.1086/694104

Related To

http://www.nber.org/papers/w23224
http://dx.doi.org/10.5281/zenodo.51603

Previously Published As

John M. Abowd, Kevin L. McKinney, and Nellie L. Zhao, "Earnings Inequality and Mobility Trends in the United States: Nationally Representative Estimates from Longitudinally Linked Employer-Employee Data," Journal of Labor Economics 36, no. S1 (January 2018): S183-S300.
John M. Abowd, Kevin L. McKinney, and Nellie L. Zhao, "Earnings Inequality and Mobility Trends in the United States: Nationally Representative Estimates from Longitudinally Linked Employer-Employee Data," NBER Working Paper No. 23224. Online: http://www.nber.org/papers/w23224.

Types

article
preprint