The Nature of the Bias When Studying Only Linkable Person Records: Evidence from the American Community Survey
April 2014
Working Paper Number:
carra-2014-08
Abstract
Record linkage across survey and administrative records sources can greatly enrich data and improve their quality. The linkage can reduce respondent burden and nonresponse follow-up costs. This is particularly important in an era of declining survey response rates and tight budgets. Record linkage also creates statistical bias, however. The U.S. Census Bureau links person records through its Person Identification Validation System (PVS), assigning each record a Protected Identification Key (PIK). It is not possible to reliably assign a PIK to every record, either due to insufficient identifying information or because the information does not uniquely match any of the administrative records used in the person validation process. Non-random ability to assign a PIK can potentially inject bias into statistics using linked data. This paper studies the nature of this bias using the 2009 and 2010 American Community Survey (ACS). The ACS is well-suited for this analysis, as it contains a rich set of person characteristics that can describe the bias. We estimate probit models for whether a record is assigned a PIK. The results suggest that young children, minorities, residents of group quarters, immigrants, recent movers, low-income individuals, and non-employed individuals are less likely to receive a PIK using 2009 ACS. Changes to the PVS process in 2010 significantly addressed the young children deficit, attenuated the other biases, and increased the validated records share from 88.1 to 92.6 percent (person-weighted).
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:
data,
data census,
census data,
survey data,
survey,
minority,
ethnicity,
bias,
record,
population,
associate,
citizen,
census bureau,
sampling,
resident,
datasets,
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linkage
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identified to contain references to specific institutions, datasets, and other organizations.
:
Social Security Administration,
American Community Survey,
Social Security Number,
Protected Identification Key,
National Opinion Research Center,
PIKed,
Person Validation System,
Federal Poverty Level,
Person Identification Validation System,
Individual Taxpayer Identification Numbers
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