NSF-Census Research Network - Abowd, John M.

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

admin — Mon, 01 Jan 2018 00:00:00 +0000

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
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.

Author:

Abowd, John M.

McKinney, Kevin L.

Zhao, Nellie

External Link:

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

Nodes:

NCRN Coordinating Office

Publication Status:

Published

Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Secure the Future of the Federal Statistical System?

admin — Wed, 11 Oct 2017 00:00:00 +0000

Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Secure the Future of the Federal Statistical System?
Weinberg, Daniel; Abowd, John M.; Belli, Robert F.; Cressie, Noel; Folch, David C.; Holan, Scott H.; Levenstein, Margaret C.; Olson, Kristen M.; Reiter, Jerome P.; Shapiro, Matthew D.; Smyth, Jolene; Soh, Leen-Kiat; Spencer, Bruce; Spielman, Seth E.; Vilhuber, Lars; Wikle, Christopher
The National Science Foundation-Census Bureau Research Network (NCRN) was established in
2011 to create interdisciplinary research nodes on methodological questions of interest and
significance to the broader research community and to the Federal Statistical System (FSS),
particularly the Census Bureau. The activities to date have covered both fundamental and applied
statistical research and have focused at least in part on the training of current and future
generations of researchers in skills of relevance to surveys and alternative measurement of
economic units, households, and persons. This paper discusses some of the key research findings
of the eight nodes, organized into six topics: (1) Improving census and survey data collection
methods; (2) Using alternative sources of data; (3) Protecting privacy and confidentiality by
improving disclosure avoidance; (4) Using spatial and spatio-temporal statistical modeling to
improve estimates; (5) Assessing data cost and quality tradeoffs; and (6) Combining information
from multiple sources. It also reports on collaborations across nodes and with federal agencies,
new software developed, and educational activities and outcomes. The paper concludes with an
evaluation of the ability of the FSS to apply the NCRN’s research outcomes and suggests some
next steps, as well as the implications of this research-network model for future federal
government renewal initiatives.
This paper began as a May 8, 2015 presentation to the National Academies of Science’s Committee on National Statistics by two of the principal investigators of the National Science Foundation-Census Bureau Research Network (NCRN) – John Abowd and the late Steve Fienberg (Carnegie Mellon University). The authors acknowledge the contributions of the other principal investigators of the NCRN who are not co-authors of the paper (William Block, William Eddy, Alan Karr, Charles Manski, Nicholas Nagle, and Rebecca Nugent), the co- principal investigators, and the comments of Patrick Cantwell, Constance Citro, Adam Eck, Brian Harris-Kojetin, and Eloise Parker. We note with sorrow the deaths of Stephen Fienberg and Allan McCutcheon, two of the original NCRN principal investigators. The principal investigators also wish to acknowledge Cheryl Eavey’s sterling grant administration on behalf of the NSF.
The conclusions reached in this paper are not the responsibility of the National Science Foundation (NSF), the
Census Bureau, or any of the institutions to which the authors belong

Author:

Levenstein, Margaret C.

External Link:

Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Secure the Future of the Federal Statistical System?

Nodes:

NCRN Coordinating Office

Publication Status:

Published

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

admin — Wed, 01 Mar 2017 00:00:00 +0000

Author:

Abowd, John M.

McKinney, Kevin L.

Zhao, Nellie

External Link:

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

Nodes:

Cornell University

Publication Status:

Preprint

Modeling Endogenous Mobility in Earnings Determination

admin — Sun, 01 Jan 2017 00:00:00 +0000

Modeling Endogenous Mobility in Earnings Determination
Abowd, John M.; McKinney, Kevin L.; Schmutte, Ian M.
We evaluate the bias from endogenous job mobility in fixed-effects estimates of worker- and firm-specific earnings heterogeneity using longitudinally linked employer-employee data from the LEHD infrastructure file system of the U.S. Census Bureau. First, we propose two new residual diagnostic tests of the assumption that mobility is exogenous to unmodeled determinants of earnings. Both tests reject exogenous mobility. We relax the exogenous mobility assumptions by modeling the evolution of the matched data as an evolving bipartite graph using a Bayesian latent class framework. Our results suggest that endogenous mobility biases estimated firm effects toward zero. To assess validity, we match our estimates of the wage components to out-of-sample estimates of revenue per worker. The corrected estimates attribute much more of the variation in revenue per worker to variation in match quality and worker quality than the uncorrected estimates.
Replication code can be found at DOI: http://doi.org/10.5281/zenodo.zenodo.376600 and our Github repository endogenous-mobility-replication .

Author:

Abowd, John M.

McKinney, Kevin L.

Schmutte, Ian M.

External Link:

Modeling Endogenous Mobility in Earnings Determination

Nodes:

Cornell University

Publication Status:

Preprint

How Will Statistical Agencies Operate When All Data Are Private?

admin — Tue, 06 Sep 2016 00:00:00 +0000

How Will Statistical Agencies Operate When All Data Are Private?
Abowd, John M.
The dual problems of respecting citizen privacy and protecting the confidentiality of their data have become hopelessly conflated in the “Big Data” era. There are orders of magnitude more data outside an agency’s firewall than inside it—compromising the integrity of traditional statistical disclosure limitation methods. And increasingly the information processed by the agency was “asked” in a context wholly outside the agency’s operations—blurring the distinction between what was asked and what is published. Already, private businesses like Microsoft, Google and Apple recognize that cybersecurity (safeguarding the integrity and access controls for internal data) and privacy protection (ensuring that what is published does not reveal too much about any person or business) are two sides of the same coin. This is a paradigm-shifting moment for statistical agencies.

Author:

Abowd, John M.

External Link:

How Will Statistical Agencies Operate When All Data Are Private?

Nodes:

Cornell University

Publication Status:

Preprint

Presentation: NCRN Fall 2015: Formal Privacy Protection for Data Products Combining Individual and Employer Frames

admin — Mon, 14 Dec 2015 00:00:00 +0000

Presentation: NCRN Fall 2015: Formal Privacy Protection for Data Products Combining Individual and Employer Frames
Abowd, John M.; Haney, Samuel; Machanavajjhala, Ashwin; Kutzbach, Mark; Graham, Matthew; Vilhuber, Lars
Published tabular summaries of linked employer-employee data usually use a job frame (statutory employer linked to a specific employee) but include characteristics of both the individual (employee) and workplace (employer establishment). Formal privacy protection of these characteristics requires defining the sensitivity of the published statistic to variation in a single individual or a single workplace (establishment). We propose a model that simultaneously protects individuals and establishments using parameters that control the conventional differential privacy for individuals and a generalization that provides a similar privacy guarantee for the employment magnitudes associated with an employer establishment. We implement our model using three alternative noise distributions. We present results for cross-sectional employment summaries for combinations of employer industry, geography, and ownership; and employee sex, age, race, ethnicity, and education. The system is illustrated using the LEHD Origin-Destination Employment Statistics (LODES) database displayed in the U.S. Census Bureau’s OnTheMap application.

Author:

Abowd, John M.

Haney, Samuel

Machanavajjhala, Ashwin

Kutzbach, Mark

Graham, Matthew

Vilhuber, Lars

External Link:

Presentation: NCRN Fall 2015: Formal Privacy Protection for Data Products Combining Individual and Employer Frames

Nodes:

NCRN Coordinating Office

Publication Status:

Published

Document type:

Presentation

NCRN Meeting Spring 2015: Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods

admin — Fri, 08 May 2015 00:00:00 +0000

NCRN Meeting Spring 2015: Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods
Abowd, John M.; Schmutte, Ian
Presentation at the NCRN Meeting Spring 2015

Author:

Abowd, John M.

Schmutte, Ian

External Link:

NCRN Meeting Spring 2015: Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods

Nodes:

NCRN Coordinating Office

Publication Status:

Published

Document type:

Presentation

NCRN Meeting Spring 2015: Can Government-Academic Partnerships Help Secure the Future of the Federal Statistical System? Examples from the NSF-Census Research Network

admin — Fri, 08 May 2015 00:00:00 +0000

NCRN Meeting Spring 2015: Can Government-Academic Partnerships Help Secure the Future of the Federal Statistical System? Examples from the NSF-Census Research Network
Abowd, John M.; Fienberg, Stephen E.
May 8, 2015 CNSTAT Public Seminar

Author:

Abowd, John M.

Fienberg, Stephen E.

External Link:

NCRN Meeting Spring 2015: Can Government-Academic Partnerships Help Secure the Future of the Federal Statistical System? Examples from the NSF-Census Research Network

Nodes:

NCRN Coordinating Office

Publication Status:

Published

Document type:

Presentation