The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers
October 2002
Working Paper Number:
tp-2002-17
Abstract
In this paper, we describe the sensitivity of small-cell flow statistics
to coding errors in the identity of the underlying entities. Specifically,
we present results based on a comparison of the U.S. Census Bureau's
Quarterly Workforce Indicators (QWI) before and after correcting for
such errors in SSN-based identifiers in the underlying individual wage
records. The correction used involves a novel application of existing
statistical matching techniques. It is found that even a very conservative
correction procedure has a sizable impact on the statistics. The
average bias ranges from 0.25 percent up to 15 percent for flow statistics,
and up to 5 percent for payroll aggregates.
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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.
:
payroll,
statistical,
report,
quarterly,
survey,
agency,
earnings,
employee,
employ,
employed,
statistician,
tenure,
workforce,
hire,
matching,
labor statistics,
employment count,
clerical,
employment statistics,
wage data,
ssa,
employee data,
workforce indicators
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Bureau of Labor Statistics,
Social Security Administration,
National Science Foundation,
Bureau of Labor,
Employer Identification Numbers,
Cornell University,
Social Security,
Economic Census,
Social Security Number,
National Institute on Aging,
Alfred P Sloan Foundation,
Longitudinal Employer Household Dynamics,
Cornell Institute for Social and Economic Research,
LEHD Program,
Quarterly Workforce Indicators
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