24 citations · 64 across the 10 of their papers we have counts for
10 papers
Estimating the Potential Impact of Combined Race and Ethnicity Reporting on Long-Term Earnings Statistics
Kevin L. McKinney, John M. Abowd
We use place of birth information from the Social Security Administration linked to earnings data from the Longitudinal Employer-Household Dynamics Program and detailed race and et…
Noisy Measurements Are Important, the Design of Census Products Is Much More Important
John M. Abowd
McCartan et al. (2023) call for "making differential privacy work for census data users." This commentary explains why the 2020 Census Noisy Measurement Files (NMFs) are not the be…
A Simulated Reconstruction and Reidentification Attack on the 2010 U.S. Census
John M. Abowd, Tamara Adams, Robert Ashmead +12
We show that individual, confidential microdata records from the 2010 U.S. Census of Population and Housing can be accurately reconstructed from the published tabular summaries. Ni…
Disclosure Avoidance for the 2020 Census Demographic and Housing Characteristics File
Ryan Cumings-Menon, Robert Ashmead, Daniel Kifer +4
In "The 2020 Census Disclosure Avoidance System TopDown Algorithm," Abowd et al. (2022) describe the concepts and methods used by the Disclosure Avoidance System (DAS) to produce f…
An In-Depth Examination of Requirements for Disclosure Risk Assessment
Ron S. Jarmin, John M. Abowd, Robert Ashmead +11
The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to m…
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census
Daniel Kifer, John M. Abowd, Robert Ashmead +5
The purpose of this paper is to guide interpretation of the semantic privacy guarantees for some of the major variations of differential privacy, which include pure, approximate, R…
Confidentiality Protection in the 2020 US Census of Population and Housing
John M Abowd, Michael B Hawes
In an era where external data and computational capabilities far exceed statistical agencies' own resources and capabilities, they face the renewed challenge of protecting the conf…
Geographic Spines in the 2020 Census Disclosure Avoidance System
Ryan Cumings-Menon, John M. Abowd, Robert Ashmead +5
The 2020 Census Disclosure Avoidance System (DAS) is a formally private mechanism that first adds independent noise to cross tabulations for a set of pre-specified hierarchical geo…
Reconciling Trends in U.S. Male Earnings Volatility: Results from Survey and Administrative Data
Robert Moffitt, John Abowd, Christopher Bollinger +6
There is a large literature on earnings and income volatility in labor economics, household finance, and macroeconomics. One strand of that literature has studied whether individua…
U.S. Long-Term Earnings Outcomes by Sex, Race, Ethnicity, and Place of Birth
Kevin L. McKinney, John M. Abowd, Hubert P. Janicki
This paper is part of the Global Income Dynamics Project cross-country comparison of earnings inequality, volatility, and mobility. Using data from the U.S. Census Bureau's Longitu…