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

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." (Author's abstract, IAB-Doku) ((en))

Cite article

Küfner, B., Sakshaug, J. & Zins, S. (2022): Analysing establishment survey non-response using administrative data and machine learning. In: Journal of the Royal Statistical Society. Series A, Statistics in Society, Vol. 185, No. Suppl. 2, p. S310-S342., accepted on July 23, 2022. DOI:10.1111/rssa.12942