Analysing establishment survey non-response using administrative data and machine learning

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.

Automated summary

This paper examines the response rates and non-response bias of the 2010-2019 IAB Job Vacancy Survey and evaluates the potential of machine learning algorithms for adjusting non-response bias. The findings show that while the response rate decreased, there was no concomitant increase in non-response bias. The use of administrative data reduced non-response bias, but limited evidence was found for further reduction through machine learning methods.