Establishing a probability sample in a crisis context: the example of Ukrainian refugees in Germany in 2022

Steinhauer, Hans Walter; Décieux, Jean Philippe; Siegert, Manuel; Ette, Andreas; Zinn, Sabine Article — Published Version Establishing aprobability sample in acrisis context: theexample of Ukrainian refugees in Germany in 2022 AStA Wirtschaftsund Sozialstatistisches Archiv Provided in Cooperation with: Springer Nature Suggested Citation: Steinhauer, Hans Walter; Décieux, Jean Philippe; Siegert, Manuel; Ette, Andreas; Zinn, Sabine (2024) : Establishing aprobability sample in acrisis context: theexample of Ukrainian refugees in Germany in 2022, AStA Wirtschaftsund Sozialstatistisches Archiv, ISSN 1863-8163, Springer, Berlin, Heidelberg, Vol. 18, Iss. 1, pp. 77-97, https://doi.org/10.1007/s11943-024-00338-0 This Version is available at: https://hdl.handle.net/10419/315634 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ ORIGINALVERÖFFENTLICHUNG https://doi.org/10.1007/s11943-024-00338-0 AStA Wirtschaftsund Sozialstatistisches Archiv (2024) 18:77–97 Establishing a probability sample in a crisis context: the example of Ukrainian refugees in Germany in 2022 Hans Walter Steinhauer · Jean Philippe Décieux · Manuel Siegert · Andreas Ette · Sabine Zinn Received: 6 July 2023 / Accepted: 19 February 2024 / Published online: 4 March 2024 © The Author(s) 2024 Abstract Following Russia’s invasion of Ukraine in early 2022, more than one million refugees have arrived in Germany. These Ukrainian refugees differ in many aspects from Germany’s past forced migration experiences and there exists an urgent need for sound data and information for politics, practitioners, and academics. In response, the IAB-BiB/FReDA-BAMF-SOEP study was established to provide highquality longitudinal data following a register-based probability sample. We detail on an approach for sampling refugees in brief time, making use of two different registers—the German population register and the central register of foreigners—and discuss the quality of the final sample with respect to potential selectivity of participation in the panel. Overall, we demonstrate the benefits and feasibility of establishing register-based samples even in the context of a geopolitical crisis and the necessity of sound data within brief time horizons. We provide guidance that can be followed for similar events in the future. Keywords Ukrainian Refugees · Probability Sampling · Central Register of Foreigners · Population Registers Hans Walter Steinhauer · Sabine Zinn Socio-Economic Panel, German Institute for Economic Research, Mohrenstraße 58, 10117 Berlin, Germany E-Mail: [email protected] Jean Philippe Décieux · Andreas Ette Migration and Mobility, Federal Institute for Population Research, Friedrich-Ebert-Allee 4, 65185 Wiesbaden, Germany Manuel Siegert Research Centre, Federal Office for Migration and Refugees, Neumeyerstraße 22–26, 90411 Nürnberg, Germany Sabine Zinn Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany K 78 H. W. Steinhauer et al. 1 Introduction On 24 February 2022, Russia started its military invasion of Ukraine; the escalation of the armed conflict continues to affect large parts of the country. In response to the loss of security and protection, millions of Ukrainians fled the country into neighboring countries and member states of the European Union (EU). Refugees mostly fled to Poland as neighboring country. Germany is hosting the second largest community of Ukrainian refugees within the EU. Before the Russian invasion in February 2022, immigrants from Ukraine constituted a comparatively small group in Germany. At the end of 2021, about 155,000 Ukrainian citizens were living in Germany; this reflects a long-term, decade-long increase—of about 2.6% per year—in the number of Ukrainians. Their migration volume was comparatively small, with an average yearly immigration of around 13,000 Ukrainian citizens arriving in Germany between 2012 and 2021 (see Fig. 1). This pattern changed fundamentally in 2022. February 2022 saw 14,000 Ukrainian citizens fleeing the war (most of them around the beginning of the war end of February). In March 2022, about 417,000 Ukrainian citizens arrived. Although numbers of arriving Ukrainian refugees quickly decreased thereafter, almost 64,000 refugees arrived in Germany during June 2022. In less than five months, the stock of Ukrainian citizens registered increased almost seven times, reaching 1.02 million Ukrainian citizens registered in Germany at the end of June 2022. Fig. 1 Development of immigrant flows (EMR) of Ukrainian citizens to Germany and stocks (AZR) of Ukrainian citizens in Germany, 2012–2022 (2012–2021: annual figures, 2022: monthly figures). (Source: Special analysis from the German Central Register of Foreigners (AZR) (reporting date 30 November 2022) and German Population Register (EMR)) K Establishing a probability sample in a crisis context: the example of Ukrainian refugees in... 79 The reception of Ukrainian refugees and the provision of options for integration pose major challenges for policymakers, administration, and society. While EU member states, including not least Germany, have learned a lot from previous largescale arrivals of refugees, the influx of Ukrainians differs from the past in at least five key characteristics (Brücker et al. 2023): First, the demographic composition differs with women, children, and elderly people dominating recent forced migration flows from Ukraine, because adult men must remain in Ukraine for military service. Second, refugees from Ukraine are granted a special legal status by European law (Article 5(1) of European Council Directive 2001/55/EC adopted in July 2001), which enables them to obtain a residence title (residence permit according to § 24 of the Residence Act (temporary protection)) in Germany without an asylum procedure (Federal Office for Migration and Refugees 2022). Third, the acceptance of Ukrainian refugees in the host society seems to be higher than in previous largescale arrivals of refugees (Dražanová and Geddes 2022). Fourth, refugees arriving in the past were usually allocated (Steinhauer et al. 2019). Ukrainian refugees, on the other hand, had the opportunity to choose their own place of residence, provided they were able to find their own accommodation—for example with relatives, friends, or acquaintances. Only refugees who were unable to provide themselves with accommodation were distributed geographically (Adam et al. 2021). Fifth, the short distance between Ukraine and Germany—compared to origin countries in the Middle East and Central Asia, for example—reduces the costs and threats of travel routes. The greater ease of traveling is also supported by waiving train fares between Ukraine and Germany for refugees. Additionally, geographical proximity makes circular migration between origin and host countries more likely to happen frequently. The volume and speed of forced migration from Ukraine, together with the substantial differences existing between recent Ukrainian refugees compared to the arrival of refugees in the past, require comprehensive knowledge and sound (longitudinal) data to understand its individual and societal consequences. Whereas several initiatives quickly responded to those data requirements with the implementation of ad-hoc surveys of Ukrainian refugees based on readily available non-probability samples, the IAB-BiB/FReDA-BAMF-SOEP survey was launched with the aim of generating high-quality probability-based longitudinal data on Ukrainian refugees recently arriving in Germany. The project is a joint work between the German Institute for Employment Research (Institut für Arbeitsmarkt und Berufsforschung, IAB), the German Federal Institute for Population Research (Bundesinstitut für Bevölkerungsforschung, BiB), the Research Center of the German Federal Office for Migration and Refugees (Forschungszentrum des Bundesamtes für Migration und Flüchtlinge, BAMF-FZ), and the German Socio-Economic Panel (SOEP). The success of this study depends on the quick and effective creation of a probability sample of Ukrainian refugees in Germany. Only with a probability-based approach it is possible to generalize the results of the study to Ukrainian refugees in Germany. This paper provides details on how we create a random sample using two different administrative registers: the German population register (Einwohnermelderegister, EMR) and the German Central Register of Foreigners (Ausländerzentralregister, AZR). Using both registers in combination allowed for benefitting from their adK 80 H. W. Steinhauer et al. vantages while balancing their disadvantages. Specifically, centrally available basic information from the AZR about newly registered Ukrainian citizens from 24 February 2022, onwards provided the basis for sampling municipalities in Germany hosting Ukrainian refugees in a first phase. Here, the AZR provides timely information on the overall number of Ukrainian refugees registered with reception facilities, the police, or foreigners’ authorities at the municipal level. In contrast, these numbers are not centrally available from the EMRs, because EMRs are maintained decentral at the level of municipalities. The advantage of the EMR, however, is that it contains individual address data for people registered within the municipality, which was not yet the case in the AZR at that time. For this reason, we draw a sample of municipalities in a first phase using information provided by the AZR on the number of Ukrainian refugees. Within the sampled municipalities, we ask the EMRs to list all Ukrainian nationals aged 18 to 70 who registered after 24 February 2022 together with their addresses. This procedure builds on the example of the “Refugees in the German Educational System (ReGES)” study (Steinhauer et al. 2019), but extends it to a sample covering all German federal states and responding to immediate migration flows. The paper presents the sampling approach then discusses its strengths and weaknesses with particular emphasis on potential bias due to consent to panel participation. Section 2 provides an overview of general sampling techniques for refugee populations, while Sect. 3 offers an overview of recent surveys of Ukrainian refugees. Details of our approach on sampling refugees are discussed in the following two sections: Sect. 4 provides information on the registration and allocation of Ukrainian refugees in Germany and the sampling of municipalities from the AZR. In Sect. 5, the design of the IAB-BiB/FReDA-BAMF-SOEP is introduced with respect to sampling Ukrainian refugees. Section 7 details the fieldwork and the response rates of the study before Sect. 7 concludes. The paper shows, first, that Germany has by now implemented an efficient system of administrative registration of refugees that can be successfully used for sampling in the context of geopolitical crises and resulting large-scale refugee or migration flows. Second, it shows that the combination of both registers—the EMR and the AZR—allows for establishing high-quality probability samples despite complicated (mostly data protection related) regulations for accessing those registers and even in contexts when information is urgently needed. 2 Sampling techniques for migrant and refugee populations Refugees are forced migrants. This distinguishes them in essential aspects from other migrants, such as labor migrants or migrants due to family reunification. Voluntary migration usually happens after a long decision-making process (Kley 2017). People leaving their home country to live elsewhere represent only a very small proportion of the home population (worldwide, about 3% in 2015; see Willekens et al. 2016). Refugees, however, flee in a hurry and do not follow a (purely) rational plan regarding their escape route, place of refuge, or about their further life course (Hunkler et al. 2022). Refugees often flee to neighbouring or nearby countries, where they constitute a major proportion of the refugee population. The reason is that war, exK Establishing a probability sample in a crisis context: the example of Ukrainian refugees in... 81 tensive persecution, or displacement makes more people fear for life and limb, thus driving them to act. Focusing on recent examples, 30% of Syrian citizens and almost 20% of Venezuelan citizens have left their home country to flee violent unrest and destruction (United Nations High Commissioner for Refugees 2022a). In the hosting country, nevertheless, refugees are usually still a small group compared to the native population. Moreover, they are highly likely to change their residence frequently (Bloch 1999); although local legal residence requirements often play a key role in refugees’ freedom of movement (see El-Kayed and Hamann (2018) for the German case). Hence, according to the definition of Tourangeau (2014), they are a hard-to-reach population (see also Massey 2014; Wenzel et al. 2022). Tourangeau (2014) defines a population as being hard to reach if it is either hard to sample,hardtoidentify, hard to find or contact,hardtopersuade,hardtointerview, or a combination of these aspects. Specifically, a group is hard to sample when there is either no sampling frame available for the group or the group is small with respect to their fraction in the population. Another reason for a population to be hard to sample is mobility. Highly mobile people cannot be easily located at a certain place of residence. A population is hard to identify when it is stigmatized, sensitive, or if screening questions miss members of the population resulting in under-coverage. Groups are hard to find or contact when their members are mobile, not willing to be identified as part of a group, or simply protected by gatekeepers. Given contact is established, some individuals are hard to persuade to participate in the survey. This is often related to busyness, alternatives to spend their time, the survey topic, or the authority issuing the survey. Finally, some individuals are hard to interview because of language problems or because of their cognitive or physical abilities. In the literature, several methods are proposed to draw samples of hard-to-reach populations (see Andreß and Careja (2018) for migrant populations). The most prominent strategies are location sampling, snowball sampling, respondent driven sampling, convenience sampling, and (screened) register samples. The first two approaches provide non-probability samples. The latter lead to probability samples. In location sampling respondents are recruited in places where they spend a notable amount of time. In snowball sampling a target person gives access to the survey questionnaire to other target persons he or she can contact. Both approaches are commonly used to recruit migrant groups, including refugees (e.g., Agadjanian and Zotova 2012; McKenzie and Mistiaen 2009). However, they yield non-probability samples whose survey statistics require a model-based framework to be extrapolated to the population level. Compared to the design-based approach the methodology for estimation becomes more complex. Because information on the target population is usually not available, it is also not possible to compensate for selection biases (see Groves 2006 and Kalton 2014), e.g., regarding groups that are commonly not well covered by migrant surveys such as uneducated migrants (Amior 2020). Furthermore, research shows that the quality of non-probability samples is significantly worse and less robust compared to random-based samples (see Cornesse et al. 2020; MacInnis et al. 2018). Respondent driven sampling (RDS) is applied in various migrant studies (e.g., Lattof 2018). The basic idea of RDS is to sample people from a hard-to-reach population and make use of their social networks, thus relying on the sampled person to be well connected to other people of the population. Compared K 82 H. W. Steinhauer et al. to snowball sampling, RDS does allow for drawing a random sample, when certain assumptions are met, e.g., the referral chains become long enough. However, the conditions for generating a random sample of migrants are difficult to achieve with this technique (especially passing on survey questions through long chains of respondents); see also (Tyldum 2021). Switching RDS to a web mode does not really overcome its obstacles. On the contrary, it makes it vulnerable to misuse and poses a threat to data quality (Sosenko and Bramley 2022). In convenience sampling, interviewers select participants for a survey, for example based on their proximity (e.g., being at a reception center) or certain characteristics (e.g., being user of Facebook). But also, respondents can select themselves into the survey, e.g., an online survey promoted on social media. To overcome these drawbacks, random samples based on registers are, not only for migrants or refugees, seen as a superior method of generating a sample. This requires, first, register data and, secondly, access to register data. Whether such data exists and is also accessible for scientific purposes varies across countries. In general, the data situation is better in Scandinavian countries than in other European countries or around the world (see Weber and Saarela 2019;Belletal.2015). In Germany, the two most comprehensive registers for sampling migrants and refugees are the German population register and the central register of foreigners. In general, the population register constitutes the most comprehensive sampling frame with the legal obligation to register in the local registration office within two weeks after changing address in Germany. However, a major obstacle for sampling individuals at the federal level is that the German population register is not organized centrally. The register, maintained at the level of the municipality, contains addresses and basic personal demographic information (e.g., gender, date of birth, nationality, date of migration, see § 3 Bundesmeldegesetz for the full list) of almost all persons who are officially residing in Germany; thus, also of all officially registered refugees and migrants. The register can be used and accessed for scientific purposes based on § 34 and § 46 Bundesmeldegesetz. Here, the information accessible is limited and each information must be substantiated. Each registration office can decide whether to provide the desired information. Recent examples of using the population register for establishing migrant samples include the project “Socio-cultural integration processes among New Immigrants in Europe” (Diehl et al. 2016), the “German Emigration and Remigration Panel Study” (Ette et al. 2021), and the panel of the German Centre for Integration and Migration Research “DeZIM-Panel” (Dollmann et al. 2022). However, because of the decentralized structure of Germany’s population register, its use must always rely on two-stage sampling. In this sampling technique, a random sample of municipalities (serving as primary sampling units, PSUs) with enough migrants is selected at the first stage and individual addresses (representing the secondary sampling units, SSUs) are drawn at the second stage. The second register used for sampling migrants and refugees in Germany is the central register of foreigners, which documents all persons who are not German nationals and stay in Germany for more than 90 days (Babka von Gostomski and Pupeter 2008). It receives its information mostly from (local) immigration offices in Germany (“Ausländerbehörden”), whose area of responsibility coincides with that of the German municipalities (“Gemeinde”) and districts (“Kreise”). Thus, two-stage K Establishing a probability sample in a crisis context: the example of Ukrainian refugees in... 83 samples can be selected from this register. Recent examples for using the AZR include the study “Forced Migration and Transnational Family Arrangements” (Sauer et al. 2022) as well as the establishment and refreshing of the IAB-BAMF-SOEP study (Brücker et al. 2016; Kühne et al. 2019 and Steinhauer et al. 2022). Studies using the AZR as a sampling frame also follow a two-stage sampling technique, usually sampling local immigration offices (PSUs) before sampling the individuals registered at those offices (SSUs). When drawing our sample, however, addresses were only available in the AZR for refugees who were undergoing asylum proceedings. Because Ukrainian refugees do not have to undergo an asylum procedure, no addresses were available for them. Moreover, obtaining registered addresses from the immigration authorities would have taken a comparatively long time. 3 Review of existing surveys of Ukrainian refugees Currently, most of the few existing surveys about Ukrainian refugees use non-probability sampling to create their sample—with all the drawbacks mentioned earlier. A brief overview of studies on Ukrainian refugees is presented in Table 1providing details on the host country, the field period the survey was conducted, the number of respondents as well as the sampling design applied by the study. With respect to comparative samples of Ukrainian refugees across different hosting countries, the United Nations High Commissioner for Refugees (UNHCR) and partners collected data in Czech Republic, Hungary, Moldova, Poland, Romania, and Slovakia from 4871 Ukrainian refugees using a location sampling approach between 16 May and 15 June 2022. Here, interviews were mostly conducted at locations such as border areas and transit zones or information and assistance points (United Nations High Commissioner for Refugees 2022b). Similarly, the European Union Agency Table 1 Brief overview on existing studies on Ukrainian Refugees Study/Conductors Country Field period Respondents Sampling design UNHCR & partners Czech Republic, Hungary, Moldova, Poland, Romania, and Slovakia 16 May to 15 June 2022 4871 Location sampling EUAA & OECD European Union 11 April to 7 June 2022 2369 Convenience sampling BMI Germany March 2022 Approx. 2000 Location sampling, convenience sampling GESIS Germany, Poland April to May 2022 Approx. 1300 Convenience sampling UkrAiA Austria March to June 2022 Approx. 1000 Convenience sampling UkrAiA Kraków, Poland March to June 2022 500 Location sampling Ukrainian Refugees in Poland Survey 2022 Poland June to August 2022 Approx. 1800 Stratified twostage random sampling K 84 H. W. Steinhauer et al. for Asylum (EUAA) together with the Organization for Economic Co-operation and Development (OECD) collected information on 2369 Ukrainian refugees via convenience sampling using an online mode of data collection during the time between 11 April and 7 June 2022 (European Union Agency for Asylum 2022). In Germany, the Federal Ministry of the Interior and Community (BMI) launched an early survey in March 2022 at the registration offices of three central hubs in Berlin, Hamburg, and Munich, where they interviewed refugees. Additionally, the survey was advertised on the homepages of the BMI, BAMF, and by the mobile phone app Germany4Ukraine.de (Federal Ministry of the Interior and Community 2022). This multisource convenience sampling approach resulted in almost 2000 interviews. Additionally, a web-based survey on Ukrainians staying in Germany or Poland was run by the Leibniz Institute for the Social Sciences (GESIS) in April and May 2022 (Pötzschke et al. 2022). The study recruited around 1300 refugees through adverts on social media. Both studies provide an initial picture of the fates, living situations, attitudes, problems, and needs of the Ukrainian refugees in Germany. However, as both studies do not use random samples, neither can make general statements about the situation of Ukrainian refugees in Germany. Further, both studies consist of only one wave. Such designs cannot provide any insights concerning the situation of Ukrainian refugees and how it is changing over time. With respect to other major host countries of Ukrainian refugees, Austria launched a rapid response survey (Ukrainian arrivals in Austria, UkrAiA) using convenience sampling to quickly learn about the socio-demographics of Ukrainian refugees as well as their educational resources and intentions to stay in Austria or return. The survey was conducted between March and June 2022 using both pen and paper interviews (PAPI) and computer assisted web interviews (CAWI). The survey conducted more than 1000 interviews with adult respondents, also collecting information on their partners and children (Kohlenberger et al. 2022). Parallel to the study conducted in Austria, 500 Ukrainian refugees were surveyed in Kraków, Poland, also using location sampling at different registration spots (P˛edziwiatr et al. 2022). Moreover, Poland together with the World Health Organization (WHO) implemented the Ukrainian Refugees in Poland Survey 2022. They use a stratified two-stage random sampling design to sample Ukrainian refugees. At the first stage, locations (i.e., PSUs) were stratified by border crossing regions and PSUs were randomly sampled. Persons (i.e., SSUs) aged 18 years and older were sampled using systematic sampling within the PSU, if they had already stayed in Poland for at least two weeks. Roughly 1800 sampled persons also provided information third persons they were traveling with, thus yielding information on about 5000 Ukrainian refugees (Beqiri and Cierpiał-Wolan 2022). 4 Registration procedure of Ukrainian refugees in Germany Knowing that comprehensive registers are the best choice for creating a random sample of Ukrainian refugees is one side of the coin, but finding and accessing such a register within a reasonable amount of time and creating an appropriate sample in a timely manner is the other. A fundamental issue here is how official authorities K Establishing a probability sample in a crisis context: the example of Ukrainian refugees in... 91 Table 3 Final disposition codes for the gross sample according to AAPOR. (Source: IAB-BiB/ FReDA-BAMF-SOEP-Data; Authors’ own calculation) Final disposition code Number 1.1 Complete interview 10,395 2.11 Refusal 63 2.12 Break-off or partial 2008 2.2 Non-Contact 24,992 2.31 Death 7 2.32 Physically or mentally unable/ incompetent 26 2.4 New address after field period 259 3.1 Nothing known about address 10,076 4.1 Screen out 97 4.2 Moved abroad 77 in the interim. Further details on final disposition codes are available in Table 3. A total of 8695 interviews were submitted online, with an additional 1700 submitted as PAPI. The following Table 4provides the number of refugees in the gross sample as well as for the first wave in 2022 by federal state, sex, age group, and marital status. The information displayed for the gross sample and the first wave in 2022 is the information as provided by the EMR. Certainly, the data highlights a notable concentration of refugees in North Rhine-Westphalia and Bavaria, particularly attributed to significant populations in Düsseldorf and Munich, respectively. These urban centers, being major destinations for Ukrainian refugees, contribute substantially to the overall numbers in their respective federal states. For a huge portion of the sample the marital status is unknown. This is mainly because EMRs did not provide this information. Among known statuses, there is diversity, with a majority of the sample being single or married. For the gross sample, information provided by the EMR (see Table 4)isthe only information available to compare refugees who decided to participate in the panel and those who refused to do so. We model the decision to participate in the panel using a logit model. The dependent variable (y) for the model is the decision to participate in the panel (y= 1 if final disposition code is 1.1 and y= 0 if final disposition code is one of 2.11, 2.12, 2.2, 2.32, 2.4, and 3.1). We exclude refugees who died (code 2.31), who were screened out of the population (code 4.1), and who moved abroad (code 4.2) from the analysis because they do not belong to the desired population. In the model we estimate, we control for federal state, age, sex, and marital status by inserting dummy-variables. Figure 5displays the coefficient plot for the model including the dummy variables which significantly influence the decision to participate in the panel study. The model finds male refugees and refugees sampled in Berlin and Hamburg to be less likely to participate in the panel. Refugees being sampled in Saarland and refugees being married are more likely to participate in the panel study. The weights accompanying the data are generated through a three-step process. Initially, design weights for the two-phase design are calculated following the methodology outlined by Särndal et al. (2003, Chap. 9). The design weights are K 92 H. W. Steinhauer et al. Table 4 Total number of refugees in the gross sample and the first wave of the IAB-BiB/FReDA-BAMFSOEP-Survey. (Source: IAB-BiB/FReDA-BAMF-SOEP-Data; Authors’ own calculation) Gross sample Refusals Panel Federal state Schleswig Holstein 971 767 204 Hamburg 3397 2743 654 Lower Saxony 2625 2054 571 Bremen 341 267 74 North Rhine-Westphalia 9801 7560 2241 Hesse 3404 2692 712 Rhineland-Palatinate 2036 1559 477 Baden-Württemberg 3473 2669 804 Bavaria 8584 6613 1971 Saarland 289 195 94 Berlin 7421 6059 1362 Brandenburg 642 517 125 Mecklenburg-Western Pomerania 734 559 175 Saxony 1785 1434 351 Saxony-Anhalt 1354 1046 308 Thüringen 1143 871 272 Sex Unknown 10 9 1 Male 10,086 8110 1976 Female 37,904 29,486 8418 Age group Unknown 1604 1233 371 Older than 17 up to 20 2459 1875 584 Older than 20 up to 25 4025 3127 898 Older than 25 up to 30 4586 3681 905 Older than 30 up to 35 6318 4930 1388 Older than 35 up to 40 7387 5758 1629 Older than 40 up to 45 5886 4588 1298 Older than 45 up to 50 4203 3251 952 Older than 50 up to 55 3196 2519 677 Older than 55 up to 60 2681 2137 544 Older than 60 up to 65 3318 2656 662 Older than 65 up to 70 2337 1850 487 Older than 70 0 0 0 Marital status Unknown 28,323 22,346 5977 Single 8445 6609 1836 Civil partnership 6 4 2 Married 8548 6515 2033 Divorced 1949 1534 415 Widowed 729 597 132 K Establishing a probability sample in a crisis context: the example of Ukrainian refugees in... 93 Fig. 5 Coefficient Plot for the model estimating the decision to participate in the panel of the IAB-BiB/ FReDA-BAMF-SOEP-Survey. (Source: IAB-BiB/FReDA-BAMF-SOEP-Data; Authors’ own calculation) intended to account for the complexities introduced by the two-phase sampling as well as the systematic (pps) selection. Design weights are then adjusted to account for non-response. This adjustment considers characteristics provided by the EMR (referenced in Table 4). This step addresses potential biases introduced by non-response. Subsequently, raking is applied to margins for sex, age, federal state, and month of immigration, to ensure that the sample distribution aligns with the population distribution, as provided by the AZR effective November 2022. The resulting weights, computed through this process, yield an effective sample size of 8231 and a reasonable design effect of 1.26, computed according to Kish (1992). 7 Conclusions After Russia’s invasion of Ukraine in early 2022, more than one million refugees arrived in Germany. These Ukrainian refugees differ in many aspects from Germany’s past forced migration experiences including their demographics, legal status, perceived acceptance, allocation, and the continuity of circular migration flows, not least because of greater geographical proximity. To learn about these recently arriving refugees, their needs, resources, as well as challenges ahead, a survey allowing for generalization to this population was urgently needed. To meet this need quickly, four institutions joined their expertise and resources to create a probability sample for the population of Ukrainian refugees using two different registers: the German K 94 H. W. Steinhauer et al. population register and the central register of foreigners. The approach we presented in this paper can be used to draw a sample in the same way for any third country nationals. The sampling design encompasses a two-phase methodology involving systematic (pps) sampling. This approach may be deemed intricate due to certain drawbacks associated with systematic (pps) sampling, notably the inability to compute secondorder inclusion probabilities, thus impeding classical variance estimation (Wolter 2007). Nonetheless, alternative methodologies, such as jackknife or bootstrap methods, emerge as viable options for variance estimation within the context of complex designs. While utilizing the AZR and the EMR in a two-phase sampling approach, our study is confined to the population of Ukrainian refugees aged 18–70, registered between 24 February 2022 and the time of sampling in the EMR. A comparison between the gross sample drawn from the EMR and the corresponding population registered in the AZR (effective November 2022) reveals a large congruence. The presented analyses show that, even within a geopolitical crisis resulting in large inflows of refugees, the existing registers in Germany constitute a comprehensive sampling frame. Following time and resource constraints, a deliberate decision was taken to concentrate on more populated areas instead of rural areas, aiming towards a larger gross sample. Comparing the gross and the net sample of Ukrainian refugees with the target population of Ukrainian refugees aged 18–70 that were registered by the end of May 2022 in Germany provides evidence of the overall success of this sampling approach and the high-quality of this probability sample. The paper shows the benefits and feasibility of establishing register-based samples even in contexts of geopolitical crisis and providing sound data within brief time horizons. For politics and practitioners, the data provides comprehensive information for evidence-based decisionmaking much earlier than in the past. For migration scholars the resulting survey data is highly valuable because this surveying of the target population already starts before selective return and onward movements by the forced migrants have taken place. Moreover, the data can be used in future research and compare results of our study with those of others for comparable measurements. This will be of particular interest to researchers comparing findings from probability and non-probability samples. Acknowledgements The authors thank all other members of the IAB-BiB/FReDA-BAMF-SOEP project team (in alphabetical order): Herbert Brücker, Martin Bujard, Adriana Cardozo, Markus M. Grabka, Yuliya Kosyakova, Amrei Maddox, Nadja Milewski, Robert Naderi, Wenke Niehues, Nina Rother, Lenore Sauer, Sophia Schmitz, Silvia Schwanhäuser, C. Katharina Spieß, and Kerstin Tanis as well as the members of the project team at infas consisting of Doris Hess, Michael Ruland, and Sabrina Torregroza. Additional thanks to Jan Eberle from the Federal Statistical Office and many colleagues from local registration offices providing invaluable insights into the details as well as their local specifics of the registration procedures in Germany. Finally, the authors thank two anonymous reviewers for their valuable comments and suggestions improving the paper considerably. Funding Open Access funding enabled and organized by Projekt DEAL. Conflict of interest H.W. Steinhauer, J.P. Décieux, M. Siegert, A. Ette and S. Zinn declare that they have no competing interests. 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