Analysing Establishment Survey Non-Response Using Administrative Data and Machine Learning

Abstract

Abstract Declining participation in voluntary establishment surveys poses a risk of increasing non-response bias over time. In this paper, response rates and non-response bias are examined for the 2010–2019 IAB Job Vacancy Survey. Using comprehensive administrative data, we formulate and test several theory-driven hypotheses on survey participation and evaluate the potential of various machine learning algorithms for non-response bias adjustment. The analysis revealed that while the response rate decreased during the decade, no concomitant increase in aggregate non-response bias was observed. Several hypotheses of participation were at least partially supported. Lastly, the expanded use of administrative data reduced non-response bias over the standard weighting variables, but only limited evidence was found for further non-response bias reduction through the use of machine learning methods.