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Surveys are an indispensable source of data for applied economic research; however, their reliance on self-reported information can introduce bias, especially if core variables such as personal income are misreported. To assess the extent and impact of this misreporting bias, we compare self-reported wages from the German Socio-Economic Panel (SOEP) with administrative wages from social security records (IEB) for the same individuals. Using a novel and unique data linkage (SOEP-ADIAB), we identify a modest but economically significant reporting bias, with SOEP respondents underreporting their administrative wages by about 7.3%. This misreporting varies systematically with individual, household, and especially job and firm characteristics. In replicating common empirical analyses in which wages serve as either dependent or independent variables, we find that misreporting is consequential for some, but not all estimated relationships. It turns out to be inconsequential for examining the returns to education, but relevant for analyzing the gender wage gap. In addition we find that misreporting bias can significantly affect the results when wage is used as the independent variable. Specifically, estimates of the wage-satisfaction relationship are substantially overestimated when based on survey data, although this bias is mitigated when focusing on interpersonal changes. Our findings underscore that survey-based measures of individual wages can significantly bias commonly estimated empirical relationships. They also demonstrate the enormous research potential of linked administrative-survey data.

We study the long-term effects of the 2015 German minimum wage introduction and its subsequent increases on regional employment. Using data from two waves of the Structure of Earnings Survey allows us to estimate models that account for changes in the minimum wage bite over time. While the introduction mainly affected the labour market in East Germany, the raises are also increasingly affecting low-wage regions in West Germany, such that around one third of regions have changed their (binary) treatment status over time. We apply different specifications and extensions of the classic difference-in-differences approach as well as a set of new estimators that enables for unbiased effect estimation with a staggered treatment adoption and heterogeneous treatment effects. Our results indicate a small negative effect on dependent employment of 0.5 percent, no significant effect on employment subject to social security contributions, and a significant negative effect of about 2.4 percent on marginal employment until the first quarter of 2022. The extended specifications suggest additional effects of the minimum wage increases, as well as stronger negative effects on total dependent and marginal employment for those regions that were strongly affected by the minimum wage in 2015 and 2019.

We study the accuracy of job seekers' wage expectations by comparing subjective beliefs to objective benchmarks using linked administrative and survey data. Our findings show that especially job seekers with low objective earnings potential and those predicted to face a penalty compared to their pre-unemployment wage display overly optimistic wage expectations. Moreover, wage optimism is amplified by increased job search incentives and job seekers with overoptimistic wage expectations tend to overestimate their reemployment chances. We discuss the labor market implications of wage optimism, as well as the role of information frictions and motivated beliefs as sources of overoptimism.