ports the results of research and analysis undertaken by U.S. Census Bureau staff. It has undergone a Census Bureau review more limited in scope than that given to official Census Bureau publications. This document is released to inform interested parties of ongoing research and to encourage discussion of work in progress. The views expressed herein are attributable only to the authors and do not represent the views of the U.S. Census Bureau, its program sponsors, Cornell University, or data providers. Some or all of the data used in this paper are confidential data from the LEHD Program. The U.S. Census Bureau supports external researchers' use of these data through the Research Data Centers (see www.ces.census.gov). For other questions regarding the data, please contact Jeremy S. Wu, Program Manager,

Bureau is preparing to support external researchers' use of these data under a protocol to be released in the near future. 154 John M. Abowd et al. 156 John M. Abowd et al. 157 160 John M. Abowd et al. 4. BHY specify a linear relation and emphasize the departure of coefficients from 1. Hall (1998) discusses an alternative log-linear relation that may be relevant. We use the log-linear specification in our analysis in part because our human capital measures are not on the same inherent scale and metric as the measures of assets and market value.

We reconsider the potential for explaining inter-industry wage differences by decomposing those differences into parts due to individual and employer heterogeneity, respectively. Using longitudinally linked employer-employee data, we estimate the model for the United States and France. The part arising from individual heterogeneity can be theoretically and empirically related to the worker's opportunity wage rate. The part arising from employer heterogeneity can similarly be related to product market quasi-rents and relative bargaining power. We find that these two variables are highly correlated with both parts of the differential in France. Although the U.S. inter-industry wage differentials are strongly correlated with those in France, the decomposition is more nuanced in the American data, where the opportunity wage rate and the product market conditions are related to both the personal and employer heterogeneity.

Information on firm dynamics is critical to understanding economic activity, yet is fundamentally difficult to measure. In this article we introduce a new way of capturing dynamics: following clusters of workers as they move across administrative entities. We show that a worker flow approach improves linkages across firms in longitudinal business databases. The approach also provides conceptual insights into the changing structure of businesses and employer-employee relationships. Many worker-cluster flows involve changes in industry particularly movements into and out of personnel supply firms. Another finding, that a nontrivial fraction of firm entry is associated with such flows, suggests that a path for firm entry is a group of workers at an existing firm starting a new firm.

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][2005][2006][2007][2008][2009][2010][2011][2012][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. Abowd acknowledges direct 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. AbstractUsing earnings data from the U.S. Census Bureau, this paper analy...