Abstract

The covid-19 pandemic has accelerated a trend in survey research to use online data collection for general population samples. High quality web surveys recently achieved response rates comparable to or even exceeding those of telephone surveys. However, selection bias with respect to education is often more pronounced. Most web surveys offer weights to adjust for education bias that rely on the assumption that nonresponse is random conditional on the variables in the model. In 2023, the institute for employment research in Germany launched a new online panel survey of the German workforce (IAB-OPAL), using a push-to-web approach. Addresses were sampled from a powerful database comprising compulsory social insurance notifications by employers as well as unemployment insurance and welfare benefit records. We utilize this unique opportunity of a sampling frame containing detailed individual level information on complete employment biographies. This allows us to assess not only how education bias develops over the recruitment process, but whether response propensities within education strata differ by usually unobserved attributes like benefit receipt experience, occupations or wages.