The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers

John Maron Abowd · Lars Vilhuber

58 citations0/0 files storedJournal of Business and Economic Statistics

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Data Quality and Management Survey Methodology and Nonresponse Census and Population Estimation

Abstract

In this article 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 before and after correcting for such errors in Social Security Number-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 .25% up to 15% for flow statistics, and up to 5% for payroll aggregates.

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