The Role of Chance in the Census Bureau Database Reconstruction Experiment.


Journal

Population research and policy review
ISSN: 0167-5923
Titre abrégé: Popul Res Policy Rev
Pays: Netherlands
ID NLM: 8309372

Informations de publication

Date de publication:
Jun 2022
Historique:
entrez: 13 6 2022
pubmed: 14 6 2022
medline: 14 6 2022
Statut: ppublish

Résumé

The Census Bureau plans a new approach to disclosure control for the 2020 census that will add noise to every statistic the agency produces for places below the state level. The Bureau argues the new approach is needed because the confidentiality of census responses is threatened by "database reconstruction," a technique for inferring individual-level responses from tabular data. The Census Bureau constructed hypothetical individual-level census responses from public 2010 tabular data and matched them to internal census records and to outside sources. The Census Bureau did not compare these results to a null model to demonstrate that their success in matching would not be expected by chance. This is analogous to conducting a clinical trial without a control group. We implement a simple simulation to assess how many matches would be expected by chance. We demonstrate that most matches reported by the Census Bureau experiment would be expected randomly. To extend the metaphor of the clinical trial, the treatment and the placebo produced similar outcomes. The database reconstruction experiment therefore fails to demonstrate a credible threat to confidentiality.

Identifiants

pubmed: 35692262
doi: 10.1007/s11113-021-09674-3
pmc: PMC9186495
mid: NIHMS1812071
doi:

Types de publication

Journal Article

Langues

eng

Pagination

781-788

Subventions

Organisme : NICHD NIH HHS
ID : P2C HD041023
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD043392
Pays : United States

Références

Proc Natl Acad Sci U S A. 2020 Jun 16;117(24):13405-13412
pubmed: 32467167

Auteurs

Steven Ruggles (S)

Institute for Social Research and Data Innovation, University of Minnesota, Minneapolis, Minnesota, USA, 55455.

David Van Riper (D)

Institute for Social Research and Data Innovation, University of Minnesota, Minneapolis, Minnesota, USA, 55455.

Classifications MeSH