Cover: Measurement Error and Misclassification

Measurement Error and Misclassification

A Comparison of Survey and Register Data

Published Jan 19, 2007

by Arie Kapteyn, Jelmer Yeb Ypma

Download eBook for Free

FormatFile SizeNotes
PDF file 0.3 MB

Use Adobe Acrobat Reader version 10 or higher for the best experience.

The authors provide both a theoretical and empirical analysis of the relation between administrative and survey data. By distinguishing between different sources of deviations between survey and administrative data they are able to reproduce several stylized facts in the literature. In doing so, they deviate from the almost universal assumption that the administrative data represent the truth. They illustrate the implications of different error sources for estimation in (simple) econometric models and find potentially very substantial biases, both when using survey data and when using administrative data. The analysis is applied to Swedish data that have been collected for a validation study as part of a larger European health and retirement study (SHARE: Survey of Health, Ageing, and Retirement in Europe). Thus this paper makes two contributions: (1) it adds to the limited number of empirical validation studies of earnings measurement in surveys and (2) it shows the sensitivity of some findings in the literature for the assumption that administrative data represent the truth. They find in particular that the common finding of substantial mean reversion in survey data largely goes away once we allow for a richer error structure.

The research described in this report was performed under the auspices of RAND Labor and Population.

This report is part of the RAND working paper series. RAND working papers are intended to share researchers' latest findings and to solicit informal peer review. They have been approved for circulation by RAND but may not have been formally edited or peer reviewed.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.