On the Extent, Correlates, and Consequences of Reporting Bias in Survey Wages

Marco Caliendo, Katrin Huber, Ingo E. Isphording and Jakob Wegmann

No 103, Berlin School of Economics Discussion Papers from Berlin School of Economics

Abstract: We study the extent, correlates, and consequences of reporting bias in survey wages using German linked survey-administrative data (SOEP-CMI-ADIAB). Survey wages differ systematically from administrative records: mean survey wages are 7% lower, and discrepancies follow a mean-reverting pattern. Individual characteristics explain little of the reporting bias, whereas firm context explains most of the variation. Since neither data source alone is sufficient, we construct a hybrid wage that combines their respective strengths. Measurement choice matters, but in ways that depend on how administrative top-coding is handled across the full wage distribution: when wages are outcomes, censoring administrative wages at the assessment limit understates returns to education by 4–11% and the gender wage gap by up to 23%, while imputation reverses the bias for returns to education. When wages are regressors, wage-satisfaction gradients are 9–28% steeper with survey than with administrative wages below the assessment limit, a pattern inconsistent with classical attenuation bias and pointing to non-classical, context-dependent misreporting. We provide guidance for choosing between administrative, survey, and hybrid wages depending on the application, with lessons that extend to any setting where self-reported wages are collected alongside top-coded administrative records.

Keywords: reporting bias; measurement error; wage; income; administrative data; survey data; data linkage (search for similar items in EconPapers)
JEL-codes: C81 D31 J30 (search for similar items in EconPapers)
Pages: 65 pages
Date: 2026-07-10
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Persistent link: https://EconPapers.repec.org/RePEc:bdp:dpaper:0103

DOI: 10.48462/opus4-6296

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