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

Marco Caliendo (), Katrin Huber, Ingo Isphording () and Jakob Wegmann ()
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Marco Caliendo: University of Potsdam
Ingo Isphording: Max Planck Institute for Behavioral Economics
Jakob Wegmann: Rockwool Foundation Berlin

No 18794, IZA Discussion Papers from IZA Network @ LISER

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, with mean-reverting discrepancies that firm context explains far better than individual characteristics. Since neither source alone is sufficient, we construct a hybrid wage combining their strengths. Measurement choice matters mainly through the treatment of administrative top-coding: when wages are outcomes, censoring 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. When wages are regressors, wage-satisfaction gradients are 9-28% steeper with survey than administrative wages below the assessment limit, indicating non-classical, context-dependent misreporting. We provide guidance for choosing between administrative, survey, and hybrid wages, with lessons for 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)
Date: 2026-07
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Related works:
Working Paper: On the Extent, Correlates, and Consequences of Reporting Bias in Survey Wages (2026)
Working Paper: On the Extent, Correlates, and Consequences of Reporting Bias in Survey Wages (2025)
Working Paper: On the Extent, Correlates, and Consequences of Reporting Bias in Survey Wages (2024)
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