@TechReport{iza:izadps:dp18794,
            	author={Caliendo, Marco and Huber, Katrin and Isphording, Ingo E. and Wegmann, Jakob},
            	title={On the Extent, Correlates, and Consequences of Reporting Bias in Survey Wages},
            	year={2026},
	month={Jul},
            	institution={Institute of Labor Economics (IZA)},
            	address={Bonn},
            	type={IZA Discussion Paper},
            	number={18794},
            	url={https://www.iza.org/index.php/publications/dp18794},
	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},
            }