CREAT: Census Research Exploration and Analysis Tool

Non-Random Assignment of Individual Identifiers and Selection into Linked Data: Implications for Research

January 2026

Working Paper Number:

CES-26-06

Abstract

The U.S. Census Bureau's Person Identification Validation System facilitates anonymous linkages between survey and administrative records by assigning Protected Identification Keys (PIKs) to person records. While PIK assignment is generally accurate, some person records are not successfully assigned a PIK, which can lead to sample selection bias in analyses of linked data. Using the American Community Survey (ACS) and the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) between 2005 and 2022, we corroborate and extend existing findings on the drivers of PIK assignment, showing that the rate of PIK assignment varies widely across socio-demographic subgroups. Using earnings as a test case, we then show that limiting a survey sample of wage earners to person records with PIKs or successful linkages to W-2 wage records tends to overestimate self-reported wage earnings, on average, indicative of linkage-induced selection bias. In a validation exercise, we demonstrate that reweighting methods, such as inverse probability weighting or entropy balancing, can mitigate this bias.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
estimating, survey, respondent, earnings, bias, matching, race, socioeconomic, census bureau, irs, sampling, household surveys, earner, linkage

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

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:
Internal Revenue Service, Bureau of Labor Statistics, Social Security Administration, Current Population Survey, American Community Survey, Social Security Number, Protected Identification Key, W-2, National Academy of Sciences, Social and Economic Supplement, Census Bureau Disclosure Review Board, PIKed, Person Validation System, Person Identification Validation System, Individual Taxpayer Identification Numbers, SSA Numident, COVID-19, CPS ASEC

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