Comparing Panel Retention among Ukrainian and Other Refugees in a German Probability-Based Panel: Evidence from a Decomposition Analysis

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Johannes Schütt, Elena Sommer

In: Survey Methods : Insights from the Field (2026), im Ersch. [online first. 2026-06-29]

Abstract

Despite the rise in global refugee migration, probability panels surveying refugees remain rare. The German IAB-BAMF-SOEP Survey of Refugees, conducted annually since 2016, provides valuable panel data for research. In 2023, following Russia’s invasion of Ukraine, a sample of Ukrainian refugees was added, along with a refreshment sample of refugees from other countries. This paper examines household-, individual-, and interview-related determinants of panel retention across these two groups of refugees. As a proof of concept, we apply the Kitagawa-Blinder-Oaxaca decomposition to assess whether it offers analytical insight for comparative panel retention analysis beyond standard group-indicator regression by separating compositional from non-compositional sources of retention differences. Although initial participation was similar, Ukrainian refugees demonstrated significantly higher panel retention in the second wave. To assess the source of this retention gap, we first analyse the determinants of panel retention between the first and the second survey wave and then decompose the retention difference to quantify how much of it stemmed from sample composition. Our findings indicate that the disparities in panel retention between the two groups are almost entirely attributable to differences in sample composition rather than to other factors, as confirmed by the decomposition analysis. Beyond providing insights into panel retention determinants and the sources of retention gaps, the paper discusses the additional value and challenges of applying the Kitagawa-Blinder-Oaxaca decomposition in comparative panel retention analysis.



Keywords: decomposition, hard to reach populations, panel retention, probability-based household panel, surveying refugee population
DOI:
https://doi.org/10.13094/SMIF-2026-00004

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