Zobrazeno 1 - 10
of 273
pro vyhledávání: '"Abowd, John"'
Autor:
McKinney, Kevin L., Abowd, John M.
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 ethnicity data from the 2010 Census to study how long-term earnings diff
Externí odkaz:
http://arxiv.org/abs/2407.12775
Autor:
Abowd, John M.
Publikováno v:
Harvard Data Science Review, Volume 6, Number 2 (Spring, 2024)
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 best focus for that plea. The August 2021 letter from 62 prominent resea
Externí odkaz:
http://arxiv.org/abs/2312.14191
Autor:
Abowd, John M., Adams, Tamara, Ashmead, Robert, Darais, David, Dey, Sourya, Garfinkel, Simson L., Goldschlag, Nathan, Kifer, Daniel, Leclerc, Philip, Lew, Ethan, Moore, Scott, Rodríguez, Rolando A., Tadros, Ramy N., Vilhuber, Lars
Using only 34 published tables, we reconstruct five variables (census block, sex, age, race, and ethnicity) in the confidential 2010 Census person records. Using the 38-bin age variable tabulated at the census block level, at most 20.1% of reconstruc
Externí odkaz:
http://arxiv.org/abs/2312.11283
Autor:
Cumings-Menon, Ryan, Ashmead, Robert, Kifer, Daniel, Leclerc, Philip, Spence, Matthew, Zhuravlev, Pavel, Abowd, John M.
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 formally private output in support of the 2020 Census data product rele
Externí odkaz:
http://arxiv.org/abs/2312.10863
Autor:
Jarmin, Ron S., Abowd, John M., Ashmead, Robert, Cumings-Menon, Ryan, Goldschlag, Nathan, Hawes, Michael B., Keller, Sallie Ann, Kifer, Daniel, Leclerc, Philip, Reiter, Jerome P., Rodríguez, Rolando A., Schmutte, Ian, Velkoff, Victoria A., Zhuravlev, Pavel
Publikováno v:
PNAS, October 13, 2023, Vol. 120, No. 43
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 measure the disclosure risks and societal benefits of the published dat
Externí odkaz:
http://arxiv.org/abs/2310.09398
Autor:
Abowd, John M., McKinney, Kevin L.
We study mixed-effects methods for estimating equations containing person and firm effects. In economics such models are usually estimated using fixed-effects methods. Recent enhancements to those fixed-effects methods include corrections to the bias
Externí odkaz:
http://arxiv.org/abs/2308.15445
Autor:
Abowd, John M., McKinney, Kevin L.
Publikováno v:
Revue économique, 2024 Jan 01. 75(1), 55-72.
Externí odkaz:
https://www.jstor.org/stable/48767053
Autor:
Abowd, John M, Hawes, Michael B
This chapter examines the motivations and imperatives for modernizing how statistical agencies approach statistical disclosure limitation for official data product releases. It discusses the implications for agencies' broader data governance and deci
Externí odkaz:
http://arxiv.org/abs/2303.00845
Autor:
Kifer, Daniel, Abowd, John M., Ashmead, Robert, Cumings-Menon, Ryan, Leclerc, Philip, Machanavajjhala, Ashwin, Sexton, William, Zhuravlev, Pavel
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\'enyi, zero-concentrated, and $f$ differential privacy. We interpret
Externí odkaz:
http://arxiv.org/abs/2209.03310
This article is an edited transcript of the session of the same name at the 38th Annual NABE Economic Policy Conference: Policy Options for Sustainable and Inclusive Growth. The panelists are experts from government and private research organizations
Externí odkaz:
http://arxiv.org/abs/2208.05252