Bibliography
See also the RePEc Author page, the Google Scholar page, or the ORCID page.
Table of contents
Bibliography by Year
2022
Vilhuber, Lars; Son, Hyuk Harry; Welch, Meredith; Wasser, David N.; Darisse, Michael
Teaching for Large-Scale Reproducibility Verification Journal Article
In: Journal of Statistics and Data Science Education, vol. published online, 2022.
@article{vilhuber2022c,
title = {Teaching for Large-Scale Reproducibility Verification},
author = {Lars Vilhuber and Hyuk Harry Son and Meredith Welch and David N. Wasser and Michael Darisse},
url = {https://arxiv.org/abs/2204.01540v1},
doi = {10.1080/26939169.2022.2074582},
year = {2022},
date = {2022-06-01},
journal = {Journal of Statistics and Data Science Education},
volume = {published online},
abstract = {We describe a unique environment in which undergraduate students from various STEM and social science disciplines are trained in data provenance and reproducible methods, and then apply that knowledge to real, conditionally accepted manuscripts and associated replication packages. We describe in detail the recruitment, training, and regular activities. While the activity is not part of a regular curriculum, the skills and knowledge taught through explicit training of reproducible methods and principles, and reinforced through repeated application in a real-life workflow, contribute to the education of these undergraduate students, and prepare them for post-graduation jobs and further studies.},
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We describe a unique environment in which undergraduate students from various STEM and social science disciplines are trained in data provenance and reproducible methods, and then apply that knowledge to real, conditionally accepted manuscripts and associated replication packages. We describe in detail the recruitment, training, and regular activities. While the activity is not part of a regular curriculum, the skills and knowledge taught through explicit training of reproducible methods and principles, and reinforced through repeated application in a real-life workflow, contribute to the education of these undergraduate students, and prepare them for post-graduation jobs and further studies.
Schmutte, Ian; Vilhuber, Lars
An Interview with John M. Abowd Journal Article
In: International Statistical Review, vol. 90, no. 1, pp. 1–40, 2022, ISSN: 0306-7734, 1751-5823.
@article{schmuttevilhuber2022,
title = {An Interview with John M. Abowd},
author = {Ian Schmutte and Lars Vilhuber},
url = {https://onlinelibrary.wiley.com/share/author/FGGIFIHDMM4I8FYHRA2Y?target=10.1111/insr.12489},
doi = {10.1111/insr.12489},
issn = {0306-7734, 1751-5823},
year = {2022},
date = {2022-02-01},
urldate = {2022-02-21},
journal = {International Statistical Review},
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2021
Abowd, John M.; Schmutte, Ian M.; Vilhuber, Lars
Disclosure Limitation and Confidentiality Protection in Linked Data Incollection
In: Chun, Asaph Young; Larsen, Michael D.; Durrant, Gabriele; Reiter, Jerome P. (Ed.): Administrative Records for Survey Methodology, Wiley, 2021, ISBN: 978-1-119-27204-5.
@incollection{abowd_disclosure_2021,
title = {Disclosure Limitation and Confidentiality Protection in Linked Data},
author = {John M. Abowd and Ian M. Schmutte and Lars Vilhuber},
editor = {Asaph Young Chun and Michael D. Larsen and Gabriele Durrant and Jerome P. Reiter},
doi = {https://doi.org/10.1002/9781119272076.ch2},
isbn = {978-1-119-27204-5},
year = {2021},
date = {2021-01-01},
booktitle = {Administrative Records for Survey Methodology},
publisher = {Wiley},
series = {Survey Research Methods & Sampling},
abstract = {Confidentiality protection for linked administrative data is a combination of access modalities and statistical disclosure limitation. We review traditional statistical disclosure limitation methods and newer methods based on synthetic data, input noise infusion and formal privacy. We discuss how these methods are integrated with access modalities by providing three detailed examples. The first example is the linkages in the Health and Retirement Study to Social Security Administration data. The second example is the linkage of the Survey of Income and Program Participation to administrative data from the Internal Revenue Service and the Social Security Administration. The third example is the Longitudinal Employer-Household Dynamics data, which links state unemployment insurance records for workers and firms to a wide variety of censuses and surveys at the U.S. Census Bureau. For examples, we discuss access modalities, disclosure limitation methods, the effectiveness of those methods, and the resulting analytical validity. The final sections discuss recent advances in access modalities for linked administrative data.},
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Confidentiality protection for linked administrative data is a combination of access modalities and statistical disclosure limitation. We review traditional statistical disclosure limitation methods and newer methods based on synthetic data, input noise infusion and formal privacy. We discuss how these methods are integrated with access modalities by providing three detailed examples. The first example is the linkages in the Health and Retirement Study to Social Security Administration data. The second example is the linkage of the Survey of Income and Program Participation to administrative data from the Internal Revenue Service and the Social Security Administration. The third example is the Longitudinal Employer-Household Dynamics data, which links state unemployment insurance records for workers and firms to a wide variety of censuses and surveys at the U.S. Census Bureau. For examples, we discuss access modalities, disclosure limitation methods, the effectiveness of those methods, and the resulting analytical validity. The final sections discuss recent advances in access modalities for linked administrative data.
2018
Abowd, John M.; Schmutte, Ian M.; Vilhuber, Lars
Disclosure Limitation and Confidentiality Protection in Linked Data Technical Report
Center for Economic Studies, U.S. Census Bureau no. 18-07, 2018.
@techreport{RePEc:cen:wpaper:18-07,
title = {Disclosure Limitation and Confidentiality Protection in Linked Data},
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abstract = {Confidentiality protection for linked administrative data is a combination of access modalities and statistical disclosure limitation. We review traditional statistical disclosure limitation methods and newer methods based on synthetic data, input noise infusion and formal privacy. We discuss how these methods are integrated with access modalities by providing three detailed examples. The first example is the linkages in the Health and Retirement Study to Social Security Administration data. The second example is the linkage of the Survey of Income and Program Participation to administrative data from the Internal Revenue Service and the Social Security Administration. The third example is the Longitudinal Employer-Household Dynamics data, which links state unemployment insurance records for workers and firms to a wide variety of censuses and surveys at the U.S. Census Bureau. For examples, we discuss access modalities, disclosure limitation methods, the effectiveness of those methods, and the resulting analytical validity. The final sections discuss recent advances in access modalities for linked administrative data.},
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Confidentiality protection for linked administrative data is a combination of access modalities and statistical disclosure limitation. We review traditional statistical disclosure limitation methods and newer methods based on synthetic data, input noise infusion and formal privacy. We discuss how these methods are integrated with access modalities by providing three detailed examples. The first example is the linkages in the Health and Retirement Study to Social Security Administration data. The second example is the linkage of the Survey of Income and Program Participation to administrative data from the Internal Revenue Service and the Social Security Administration. The third example is the Longitudinal Employer-Household Dynamics data, which links state unemployment insurance records for workers and firms to a wide variety of censuses and surveys at the U.S. Census Bureau. For examples, we discuss access modalities, disclosure limitation methods, the effectiveness of those methods, and the resulting analytical validity. The final sections discuss recent advances in access modalities for linked administrative data.
Pistner, Michelle; Slavkovi'c, Aleksandra; Vilhuber, Lars
Synthetic Data via Quantile Regression for Heavy-Tailed and Heteroskedastic Data Incollection
In: Domingo-Ferrer, Josep; Montes, Francisco (Ed.): Privacy in Statistical Databases, 2018.
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2013
Abowd, John M.; Schneider, Matthew J.; Vilhuber, Lars
Differential Privacy Applications to Bayesian and Linear Mixed Model Estimation Journal Article
In: Journal of Privacy and Confidentiality, vol. 5, no. 1, 2013, (Article 4).
@article{AbowdSchneiderVilhuber2013,
title = {Differential Privacy Applications to Bayesian and Linear Mixed Model Estimation},
author = {John M. Abowd and Matthew J. Schneider and Lars Vilhuber},
url = {https://doi.org/10.29012/jpc.v5i1.627},
year = {2013},
date = {2013-01-01},
journal = {Journal of Privacy and Confidentiality},
volume = {5},
number = {1},
abstract = {We consider a particular maximum likelihood estimator (MLE) and a computationally intensive Bayesian method for differentially private estimation of the linear mixed-effects model (LMM) with normal random errors. The LMM is important because it is used in small-area estimation and detailed industry tabulations that present significant challenges for confidentiality protection of the underlying data. The differentially private MLE performs well compared to the regular MLE, and deteriorates as the protection increases for a problem in which the small-area variation is at the county level. More dimensions of random effects are needed to adequately represent the time dimension of the data, and for these cases the differentially private MLE cannot be computed. The direct Bayesian approach for the same model uses an informative, reasonably diffuse prior to compute the posterior predictive distribution for the random effects. The empirical differential privacy of this approach is estimated by direct computation of the relevant odds ratios after deleting influential observations according to various criteria.},
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We consider a particular maximum likelihood estimator (MLE) and a computationally intensive Bayesian method for differentially private estimation of the linear mixed-effects model (LMM) with normal random errors. The LMM is important because it is used in small-area estimation and detailed industry tabulations that present significant challenges for confidentiality protection of the underlying data. The differentially private MLE performs well compared to the regular MLE, and deteriorates as the protection increases for a problem in which the small-area variation is at the county level. More dimensions of random effects are needed to adequately represent the time dimension of the data, and for these cases the differentially private MLE cannot be computed. The direct Bayesian approach for the same model uses an informative, reasonably diffuse prior to compute the posterior predictive distribution for the random effects. The empirical differential privacy of this approach is estimated by direct computation of the relevant odds ratios after deleting influential observations according to various criteria.
2012
Abowd, John M.; Gittings, Kaj; McKinney, Kevin L.; Stephens, Bryce E.; Vilhuber, Lars; Woodcock, Simon
Dynamically Consistent Noise Infusion and Partially Synthetic Data as Confidentiality Protection Measures for Related Time Series Technical Report
Federal Committee on Statistical Methodology 2012.
@techreport{AbowdEtAl2012,
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Abowd, John M.; Vilhuber, Lars; Block, William
A Proposed Solution to the Archiving and Curation of Confidential Scientific Inputs Incollection
In: Privacy in Statistical Databases, pp. 216-225, 2012, ISBN: 978-3-642-33626-3.
@incollection{AbowdVilhuberBlock2012,
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2011
Abowd, John M.; Vilhuber, Lars
National Estimates of Gross Employment and Job Flows from the Quarterly Workforce Indicators with Demographic and Industry Detail Journal Article
In: Journal of Econometrics, vol. 161, pp. 82-99, 2011.
@article{AbowdVilhuber2010,
title = {National Estimates of Gross Employment and Job Flows from the Quarterly Workforce Indicators with Demographic and Industry Detail},
author = {John M. Abowd and Lars Vilhuber},
doi = {10.1016/j.jeconom.2010.09.008},
year = {2011},
date = {2011-01-01},
journal = {Journal of Econometrics},
volume = {161},
pages = {82-99},
abstract = {The Quarterly Workforce Indicators (QWI) are local labor market data produced and released every quarter by the United States Census Bureau. Unlike any other local labor market series produced in the US or the rest of the world, QWI measure employment flows for workers (accession and separations), jobs (creations and destructions) and earnings for demographic subgroups (age and gender), economic industry (NAICS industry groups), detailed geography (block (experimental), county, Core-Based Statistical Area, and Workforce Investment Area), and ownership (private, all) with fully interacted publication tables. The current QWI data cover 47 states, about 98% of the private workforce in those states, and about 92% of all private employment in the entire economy. State participation is sufficiently extensive to permit us to present the first national estimates constructed from these data. We focus on worker, job, and excess (churning) reallocation rates, rather than on levels of the basic variables. This permits a comparison to existing series from the Job Openings and Labor Turnover Survey and the Business Employment Dynamics Series from the Bureau of Labor Statistics (BLS). The national estimates from the QWI are an important enhancement to existing series because they include demographic and industry detail for both worker and job flow data compiled from underlying micro-data that have been integrated at the job and establishment levels by the Longitudinal Employer-Household Dynamics Program at the Census Bureau. The estimates presented herein were compiled exclusively from public-use data series and are available for download.},
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The Quarterly Workforce Indicators (QWI) are local labor market data produced and released every quarter by the United States Census Bureau. Unlike any other local labor market series produced in the US or the rest of the world, QWI measure employment flows for workers (accession and separations), jobs (creations and destructions) and earnings for demographic subgroups (age and gender), economic industry (NAICS industry groups), detailed geography (block (experimental), county, Core-Based Statistical Area, and Workforce Investment Area), and ownership (private, all) with fully interacted publication tables. The current QWI data cover 47 states, about 98% of the private workforce in those states, and about 92% of all private employment in the entire economy. State participation is sufficiently extensive to permit us to present the first national estimates constructed from these data. We focus on worker, job, and excess (churning) reallocation rates, rather than on levels of the basic variables. This permits a comparison to existing series from the Job Openings and Labor Turnover Survey and the Business Employment Dynamics Series from the Bureau of Labor Statistics (BLS). The national estimates from the QWI are an important enhancement to existing series because they include demographic and industry detail for both worker and job flow data compiled from underlying micro-data that have been integrated at the job and establishment levels by the Longitudinal Employer-Household Dynamics Program at the Census Bureau. The estimates presented herein were compiled exclusively from public-use data series and are available for download.
2010
Abowd, John M.; Vilhuber, Lars
National Estimates of Gross Employment and Job Flows from the Quarterly Workforce Indicators with Demographic and Industry Detail (with color graphs) Technical Report
Center for Economic Studies, U.S. Census Bureau no. 10-11, 2010.
@techreport{ces-wp-10-11,
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The Quarterly Workforce Indicators (QWI) are local labor market data produced and released every quarter by the United States Census Bureau. Unlike any other local labor market series produced in the U.S. or the rest of the world, the QWI measure employment flows for workers (accession and separations), jobs (creations and destructions) and earnings for demographic subgroups (age and gender), economic industry (NAICS industry groups), detailed geography (block (experimental), county, Core- Based Statistical Area, and Workforce Investment Area), and ownership (private, all) with fully interacted publication tables. The current QWI data cover 47 states, about 98% of the private workforce in those states, and about 92% of all private employment in the entire economy. State participation is sufficiently extensive to permit us to present the first national estimates constructed from these data. We focus on worker, job, and excess (churning) reallocation rates, rather than on levels of the basic variables. This permits comparison to existing series from the Job Openings and Labor Turnover Survey and the Business Employment Dynamics Series from the Bureau of Labor Statistics. The national estimates from the QWI are an important enhancement to existing series because they include demographic and industry detail for both worker and job flow data compiled from underlying micro-data that have been integrated at the job and establishment levels by the Longitudinal Employer-Household Dynamics Program at the Census Bureau. The estimates presented herein were compiled exclusively from public-use data series and are available for download.
Abowd, John M.; Vilhuber, Lars
VirtualRDC - Synthetic Data Server Online
Cornell University, Labor Dynamics Institute 2010.
@online{AbowdVilhuber,
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2009
Abowd, John M.; Stephens, Bryce E.; Vilhuber, Lars; Andersson, Fredrik; McKinney, Kevin L.; Roemer, Marc; Woodcock, Simon D.
The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators Book Chapter
In: Dunne, Timothy; Jensen, J. Bradford; Roberts, Mark J. (Ed.): Producer Dynamics: New Evidence from Micro Data, University of Chicago Press, 2009.
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Abowd, John M.; McKinney, Kevin L.; Vilhuber, Lars
The link between human capital, mass layoffs, and firm deaths Book Chapter
In: Dunne, Timothy; Jensen, J. Bradford; Roberts, Mark J. (Ed.): Producer Dynamics: New Evidence from Micro Data, University of Chicago Press, 2009.
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2008
Abowd, John; Vilhuber, Lars
How Protective Are Synthetic Data Incollection
In: Domingo-Ferrer, Josep; Saygin, Yücel (Ed.): Privacy in Statistical Database, vol. 5262, pp. 239-246, Springer Berlin Heidelberg, 2008.
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2005
Abowd, John M.; Vilhuber, Lars
The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers Journal Article
In: jbes, vol. 23, no. 2, pp. 133-152, 2005.
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Abowd, John M.; Stephens, Bryce E.; Vilhuber, Lars
Confidentiality Protection in the Census Bureau's Quarterly Workforce Indicators Technical Report
U.S. Census Bureau, LEHD and Cornell University 2005.
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2004
Margolis, David N.; Plug, Erik; Simonnet, Véronique; LarsVilhuber,
Early Career Experiences and Later Career Outcomes: An InternationalComparison Book Chapter
In: Chapter 5, pp. 90-117, 2004.
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Abowd, John M.; Vilhuber, Lars
VirtualRDC Online
Cornell University, Labor Dynamics Institute 2004.
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2002
Abowd, John M.; Lengermann, Paul A.; Vilhuber, Lars
The Creation of the Employment Dynamics Estimates Technical Report
LEHD, U.S. Census Bureau no. TP-2002-13, 2002.
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Abowd, John M.; Vilhuber, Lars
The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers Technical Report
LEHD, U.S. Census Bureau no. TP-2002-17, 2002.
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