Artikel
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.
- Sprache
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Englisch
- Erschienen in
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Journal: Journal of the Royal Statistical Society: Series A (Statistics in Society) ; ISSN: 1467-985X ; Volume: 185 ; Year: 2022 ; Pages: S310-S342 ; Hoboken, NJ: Wiley
- Thema
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data quality
IAB Job Vacancy Survey
non‐response bias
survey participation
weighting adjustment
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Küfner, Benjamin
Sakshaug, Joseph W.
Zins, Stefan
- Ereignis
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Veröffentlichung
- (wer)
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Wiley
- (wo)
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Hoboken, NJ
- (wann)
-
2022
- DOI
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doi:10.1111/rssa.12942
- Letzte Aktualisierung
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10.03.2025, 11:44 MEZ
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- Küfner, Benjamin
- Sakshaug, Joseph W.
- Zins, Stefan
- Wiley
Entstanden
- 2022
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