Handbook on Irregular Migration Data: Concepts, Methods and Practices

HANDBOOK ON IRREGULAR MIGRATION DATA CONCEPTS, METHODS AND PRACTICES Edited by Denis Kierans & Albert Kraler [email protected] https://irregularmigration.eu/ © MIrreM, 2025 Images: Philipp Lammer & Visuality Acknowledgements The Handbook has benefited from the collegial support, critical feedback and contributions coming from the MIrreM project team, which form the backbone of the Handbook. We also thank the members of the MIrreM’s advisory board who not only gave critical feedback but also opened doors to relevant stakeholders outside the project. Moreover, we are indebted to the wider ‘community of practice’ engaged in this field for often critical yet helpful feedback on the Handbook and getting us involved in different fora where we could test our ideas. We would especially like to thank the contributors to this Handbook who have been instrumental in extending the scope of this Handbook beyond the confines of the MIrreM project, and allowing us to cover a much broader ground than we would have been able to do and turning this into a truly collective endeavour. We also wish to thank colleagues who have taken time to review drafts of chapters and textboxes, including Julia Descamps, Frank Laczko, Arjen Leerkes, Fran Meisner, Rocco Molinari, Laura Peitz, Ann Singleton, Randy Stache, Georgina Sturge, Jennifer Van Hook, Peter Walsh and Teddy Wilkin. Last but not least, the Handbook would not have been possible without the support from Adriana Harm and Franziska Klauser at the Department for Migration and Globalisation at the University for Continuing Education (Danube University) Krems. Special thanks also to Chloë Bouvier and Imanol Legarda of MIrreM’s project partner PICUM (Platform for International Cooperation on Undocumented Migrants). Editors: Denis Kierans & Albert Kraler Cover Illustration: Franziska Klauser & Philipp Lammer Layout and Design: Franziska Klauser & Philipp Lammer, based on a template created by Chloë Bouvier Publisher: University of Krems Press, © 2025 ISBN: 978-3-903470-24-8 DOI: https://doi.org/10.48341/g31s-vq79 Product safety according to EU regulation: [email protected].at Suggested citation: Kierans, D. & Kraler, A. (eds.). Handbook on Irregular Migration Data. Concepts, Methods and Practices. Krems: University of Krems Press, 2025. https://doi.org/10.48341/g31s-vq79 FUNDING ACKNOWLEDGEMENT This book has benefited from financial support from the European Union’s Horizon Europe research and innovation programme, UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee and the Canada Excellence Research Chairs Program of the Government of Canada. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union, the Research Executive Agency, UKRI and the Government of Canada. Co-funded by: This work is openly licensed under Creative Commons Attribution-Non-Commercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) Table of Contents Foreword Frank Laczko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Executive Summary Denis Kierans and Albert Kraler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Preface Albert Kraler and Denis Kierans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 Chapter 1 Introduction – Making the case for better data on irregular migration Denis Kierans and Albert Kraler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17 Box 1.1: A history of interest: Irregular migration data in Europe Albert Kraler and Denis Kierans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Chapter 2 What is irregular migration? Albert Kraler, Tuba Bircan and Ann Singleton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23 Box 2.1: “Words matter” – Terms used to describe irregular migration. Albert Kraler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Box 2.2: Applying a mixed migration lens to irregular migration Roberto Forin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Box 2.3: Pathways in and out of irregularity Albert Kraler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30 Box 2.4: Defining “missing migrants” Julia Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Chapter 3 Ethics and data on irregular migration Jill Ahrens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41 Box 3.1: Making undocumented migrant children visible: A balanced approach to data collection, analysis and use Marzia Rango, Naomi Lindt, Sebastian Palmas and Danzhen You . . . . . . . . . . . . . . . 43 Box 3.2: Addressing ethical challenges in surveying irregular migrants – The MIMAP survey on the im-/mobility of rejected asylum seekers Randy Stache . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Box 3.3: Linkage of administrative data in a data protection sensitive way – The case of Austria Albert Kraler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Chapter 4 What are good quality data on a phenomenon that is hard to measure? Denis Kierans and Lalaine Siruno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55 Box 4.1: Uncertainty in irregular migration data Denis Kierans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Box 4.2: Irregular migration to the UK: A Home Office statistical overview JonSimmonsand Lucy Swinnerton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Chapter 5 Innovations in methodological approaches to estimate irregular migrant stocks and flows Alejandra Rodríguez-Sánchez and Jasper Tjaden . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .65 Box 5.1: Traditional and innovative approaches Alejandra Rodríguez-Sánchez and Jasper Tjaden . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67 Chapter 6 Data traces and the inevitable visibility of irregular migration Alejandra Rodríguez Sánchez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75 Box 6.1: Metadata in the context of migration Alejandra Rodríguez Sánchez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Chapter 7 Register data sources on migrant stocks Laura Peitz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83 Box 7.1: Applying the ‘Signs of Life’ method: The case of Italy Marco Marsili and Francesca Licari . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Box 7.2: Chile’s experiences in integrating data for estimating the foreign population with irregular migration status Julibeth Rodríguez and Felipe Mallea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Box 7.3: Using AZR data to analyse pathways out of irregularity: An application example Laura Peitz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Chapter 8 Getting into the flow - what do we know now, 15 years since CLANDESTINO? Lalaine Siruno and Arjen Leerkes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .93 Box 8.1: Frontex data on “illegal border crossings” and the political construction of “illegal” immigration Filip Savatic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Box 8.2: Understanding asylum data in the context of irregular and regular migration TeddyWilkin and Petya Alexandrova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Box 8.3: Understanding 4Mi data Francesco Teo Ficcarello . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Chapter 9 Irregular migration and informal work Aslı Salihoğlu and Carlos Vargas-Silva . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .105 Chapter 10 Surveying irregular migrants: Challenges and approaches Rocco Molinari and Livia Elisa Ortensi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .111 Box 10.1: The Centre Sampling Technique Rocco Molinari and Livia Elisa Ortensi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Box 10.2: Surveying irregular migrants with an existing sampling frame – The IAB-BAMF-SOEP survey of refugees Randy Stache . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Box 10.3: Reliability in measuring migrants’ legal trajectories and experiences of irregularity in a retrospective survey: The case of “Trajectories and Origins 2” Julia Descamps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Chapter 11 Towards the more effective use of irregular migration data Adèle Appriou, Jasmijn Slootjes and Ravenna Sohst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .121 Box 11.1: GDPR and the limits of data access Adèle Appriou, Jasmijn Slootjes and Ravenna Sohst . . . . . . . . . . . . . . . . . . . . . . . . . . . .124 Box 11.2: Spain’s padrón system Adèle Appriou, Jasmijn Slootjes and Ravenna Sohst . . . . . . . . . . . . . . . . . . . . . . . . . . . .127 Chapter 12 Progress, limits, and the need for sustained effort Denis Kierans and Albert Kraler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .129 List of contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .133 6 Foreword Foreword Frank Laczko What is the scale of irregular migration across Europe? What have been the recent trends in irregular migration? How good are the data on irregular migration? What data are needed to improve our understanding of irregular migration? These are some of the key questions addressed in this new book based on research conducted in 20 countries in Europe and North America. Irregular migration is a topic which receives a vast amount of policy, media and public attention. Yet reliable, timely and comparable data on the subject are often hard to find. Even when data are available, they may be misinterpreted and misused by policymakers and the media who do not understand fully how migration statistics are produced. There is a lack of guidance on how best to measure irregular migration. In response to this challenge, the European Commission launched the MIrreM project in 2022 to strengthen the understanding and use of irregular migration data across Europe. The aim of the project is not just to produce more data, but to support more informed and transparent policy conversations, helping to ensure that decisions reflect evidence, not assumptions. This Handbook is one of the key outputs of this project. The Handbook provides a user-friendly resource for navigating irregular migration data – highlighting what is available, how to interpret it, and where the limits lie. It speaks to policymakers, journalists, researchers, and advocates for those who want to use data more responsibly and effectively in a domain often dominated by uncertainty and speculation. Irregular migration intersects with border management, asylum systems, labour markets, and social integration. Yet the data underpinning these discussions are often patchy, politicised, or poorly understood. This book provides tools to critically assess available estimates and encourages a more nuanced debate around what irregular migration numbers can (and cannot) tell us. This Handbook builds directly on the CLANDESTINO project (2007-2009), which offered one of the first systematic attempts to estimate irregular migration in Europe. One of the key headline figures from the MIrreM project is the estimate that there were between 2.6 million and 3.2 million irregular migrants living in 12 European countries over the period 2016-2023. However, the quality of data on irregular migration in many countries is poor or outdated. Indeed, 5 countries studied by MIrreM have not produced any estimates in 7 Foreword recent years. Countries also tend to collect data on irregular migration in very different ways making comparisons difficult. This Handbook offers guidance on how to interpret statistics on irregular migration. It offers a framework for navigating complexity rather than eliminating it – recognising that some uncertainty is inevitable, but that it can still be managed thoughtfully. The book clarifies complex concepts and the technical aspects of irregular migration data. Examples of data innovation are highlighted in the book and there is a discussion of the potential of using non-traditional sources of data to understand irregular migration trends. The book provides examples of insights gained from analysing data produced by the private sector and through the analysis of social media data. The Handbook provides examples of how irregular migration data are used in practice — from policymaking to service provision — helping to anchor abstract concepts in the real world. The book suggests practical tools for interpreting irregular migration data, supporting more informed and responsible use of estimates and indicators. The book frames data as a process, not just a product, drawing attention to how data are shaped by legal categories, institutional priorities, methodological decisions, and real-world constraints. What comes next? This publication is a step forward, not the destination. The long-term goal is to foster a more integrated and strategic approach to irregular migration data – one that combines the rigour of official statistics with the innovation of alternative data sources. The MIrreM project has taken important steps in this direction, but sustained progress will require ongoing collaboration across governments, civil society, academia, and the private sector – particularly in Europe, where MIrreM found irregular migration data especially uneven. It is hoped that this Handbook will serve as a contribution for that continued work. 8 Executive Summary Executive Summary Denis Kierans and Albert Kraler Irregular migration is a persistent feature of mobility to and within Europe, yet the evidence base remains fragmented, inconsistent, and often misunderstood. The very notion of ‘irregular migration’ is vague, ambiguous, and ultimately a legal and policy category that requires careful, context-sensitive interpretation. This Handbook distils lessons from the Measuring Irregular Migration (MIrreM) project and contributions from colleagues in research, government, and civil society. Focused on Europe, it offers practical guidance on how to compile, interpret, and use irregular migration data, bringing together conceptual clarifications, ethical safeguards, methodological advances, and examples of good practice. This Handbook is intended for policymakers, statisticians, journalists, researchers, and practitioners. It can be used to: understand the current state of knowledge; recognise opportunities and pitfalls when working with data; identify promising approaches for producing estimates; learn from practical examples; and inform strategies for improving responsible use of data in policymaking. Concepts and definitions Conceptual clarity is essential because definitions shape what is measured and compared. Irregular migration is not a fixed fact but a policy category that varies across countries and over time. • In this Handbook, ‘irregular migration’ refers to the phenomenon, ‘irregular migrants’ to people in that situation, and ‘migrant irregularity’ to the condition of lacking legal status under national law. Because definitions differ, comparability is limited. • Terms such as ‘illegal’, ‘undocumented’, and ‘irregular’ carry connotations that shape perceptions and policies. The Mixed Migration Centre shows that rigid categories like asylum seeker or economic migrant often fail to capture overlapping motivations and vulnerabilities. • The MIrreM taxonomy distinguishes irregularity as a subset of precarious immigration status and separates pathways into and out of irregularity from stocks and flows. For example, asylum seekers may enter a country irregularly but gain a legal right to stay once granted protection. • The taxonomy also captures how individuals’ legal status changes over time as they move into, through, and out of irregularity. • Definitional choices shape what is visible in data and policy debates. The IOM Missing Migrants Project, for instance, records deaths in transit, highlighting border risks while not capturing deaths linked to irregular status after arrival. 15 Preface Chapter 6: Data Traces and the Inevitable Visibility of Irregular Migration Analyses how irregular migrants appear in conventional and alternative data sources, challenging the assumption of invisibility and highlighting how visibility is shaped by institutional and legal contexts. Chapter 7: Register data sources on migrant stocks Assesses how administrative registers, such as Germany’s AZR and Spain’s padrón, can help derive indicators of irregular residence, while also noting gaps, biases, and data quality challenges. Chapter 8: Getting into the flow - what do we know now, 15 years since CLANDESTINO? This chapter takes stock of how irregular migration flows are measured, noting changes over time in the availability and accessibility of flow indicators, particularly at EU level, but also persistent challenges related to validity, scope, and interpretation. Chapter 9: Irregular migration and informal work Proposes a method to estimate the overlap between irregular residence and informal employment using labour force survey data. Chapter 10: Surveying irregular migrants: challenges and approaches Reviews strategies for including irregular migrants in survey research, including regularisation surveys, retrospective trajectory data, and targeted sampling approaches. Chapter 11: Towards the More Effective Use of Irregular Migration Data Explores how institutional, legal, and political factors shape the use of irregular migration data, identifying key barriers to uptake. Chapter 12: Progress, limits, and the need for sustained effort Summarises the Handbook’s core insights and outlines practical steps to improve the production, interpretation and application of irregular migration data across Europe. 16 Preface References Ahrens, J., Kraler, A., Legarda, I. & Levoy, M. (eds) (2025), Handbook on Regularisation Policies: Practices, Debates and Outcomes. Krems: University of Krems Press. https://doi.org/10.48341/chqk-ey86. Belmonte, M., Pingsdorf, J., Cortinovis, R., Nedee, A. & Tintori, G. (2025). Managing Irregular Migration and Return: Lessons from National Practices. Luxembourg: Publication Office of the European Union. European Commission and Eurostat (2018). Expert group on refugee and internally displaced persons statistics – International recommendations on refugee statistics. Luxembourg: Publications Office of the European Union. https://data.europa.eu/doi/10.2785/517815. European Commission. Eurostat. and United Nations Organisation (2020) International Recommendations on Internally Displaced Persons Statistics (IRIS): March 2020. LU: Publications Office. https://data.europa.eu/doi/10.2785/18809. Searle, J. R. (2011): Making the Social World: The Structure of Human Civilization. Oxford: Oxford University Press. Chapter 1 Introduction – Making the case for better data on irregular migration Denis Kierans and Albert Kraler 18 Irregular migration is a subset of overall migration, typically only making up a small share of migration stocks and flows. Yet it warrants individual attention when it comes to methods for collecting, analysing and using data. We present this Handbook to assist ongoing and future efforts with the hope that it adds to this field of research in three ways. The first is by bringing much needed guidance on interpreting different types of data on irregular migration, some of which are delivered on an almost daily basis in policy debates and the media. These data may be border apprehensions, interceptions at sea, deportations, or migrant deaths. Often, they are released and reported on without much contextual information and lack detail about their quality, the assumptions that underpin them and what the data actually show (Kraler & Reichel, 2022). Definitions are fuzzy, terms are conflated. Flow data may be presented as stocks, or vice versa. From time to time, estimates on the number of irregular migrants present in a particular country, a group of countries, or another area make it to headline news. Again, this is typically with little attention to the quality of the estimate or the context in which the estimate was produced, such as the population group covered, the reference year or the methodology used. Sometimes these data are specifically collected to inform policy debates in Introduction – Making the case for better data on irregular migration Chapter 1 Key points • Irregular migration data are often of low quality and misinterpreted by those who use it, when they are used at all. This Handbook provides clear and easy-to-understand guidance on how to improve the quality of these data and an understanding of them. • Despite ongoing public and political interest, there has been a notable lack of investment in improving the methods and capacities for generating irregular migration estimates, particularly in European National Statistical Offices (NSOs). By synthesising key findings from the MIrreM project and highlighting good practices and promising innovations, this Handbook seeks to help bridge that gap. • Ultimately, this Handbook makes the case that these challenges can only be met – and much risk mitigated in the process – through strengthened leadership on, coordination around and long-term investment in a Europe-wide infrastructure capable of producing, disseminating and fostering responsible and appropriate use of irregular migration data 19 Making the case for better data on irregular migration response to the presence (or perceived presence) of irregular migrants in a specific area. When data are used in these debates, it matters not just whether they are accurate, but whether they are well understood and used appropriately. As discussed in Chapter 11, the responsible use of migration data depends as much on interpretation and communication as on technical quality. This Handbook is intended to support both: offering tools for better measurement and clearer thinking about what these numbers do and do not tell us. In short, the production of these data and their use in different types of debates are here to stay. We hope this Handbook brings clarity to some of these recurring policy, operational and social challenges. Many of the technical problems that emerge in this area – e.g., small populations, partial visibility, reliance on administrative proxy data instead of or in addition to traditional data sources – are shared by those working on other ‘hard-to-reach’ groups. As such, the insights offered here may also be relevant to researchers and practitioners working in related areas. Box 1.1: A history of interest: Irregular migration data in Europe Albert Kraler and Denis Kierans Irregular migration has been an issue of high salience in Europe since at least the 1990s, when migration flows to Western Europe surged following the collapse of Communist regimes in Eastern Europe and the displacement following the violent break-up of Yugoslavia. Beyond these major turning points, the primary receiving countries in Europe had already experienced a longer-standing increase in asylum-related inflows from beyond Europe, traditionally the main source of refugees in Europe. At the same time, legal migration increased considerably, facilitated in Europe by freedom of movement policies in the European Union and the Eastern enlargement. These developments fuelled a broader interest in migration, which in turn led to increased efforts to improve migration statistics at the national, European-wide and global levels (Kraler, Reichel, & Entzinger, 2015). The political interest in irregular migration also went hand-in-hand with more systematic administrative data collection, such as on apprehensions, smuggling and deportation. At the European level, the first such effort was the Centre for Information, Discussion and Exchange on the Crossing of Frontiers and Immigration (CIREFI) data collection initiative, launched in 1996 to support, and initially conducted on a confidential basis (See Kraler & Jandl, 2006). This formed the basis for the Enforcement of Immigration Legislation (EIL) Statistics collected by Eurostat under the 2007 Regulation on Migration Statistics. Yet there was also growing interest in irregular migrants who have not come into contact with state authorities, but constituted an important part of the migrant population – and workforce – especially in Southern EU Member States. A study commissioned by the European Commission in 1991 appears to have been the first to examine the scale of the irregular migrant population in a European comparative perspective (Werth & Körth, 1991). Another study commissioned by Eurostat a few years later placed greater emphasis on conceptual and methodological aspects (Delaunay & Tapinos, 1998). In some ways, this laid the groundwork for the first systematic European effort to collect, assess and produce estimates for a larger number of European countries, and to elaborate an estimate of the overall irregular migrant population in the EU as a whole: the CLANDESTINO project (CLANDESTINO, 2009). Building on this foundation, MIrreM refines the CLANDESTINO methodology and adds important new elements. One is an exploration of innovative methods (Chapter 5). Another is a sustained effort to involve relevant stakeholders, raising awareness about the opportunities and limitations of data on irregular migration and encouraging more better practices in collecting, analysing and using these data. 20 The second aim of this Handbook is to bring attention to the need for more high-quality European-focussed research on irregular migration data. Indeed, there has been a relative neglect in Europe of the irregular migrant population by demographers in general and by National Statistical Offices (NSOs) specifically. While irregular migration data ‘suddenly were everywhere’3, irregular migrants as a population group and subject of demographic analysis are conspicuously absent from the statistical work of Other recent initiatives address this issue as well. At the European level, negotiations on a new Regulation on Population and Housing Statistics have led to the creation of a “task force on implementation guidelines for a harmonised population base”, which considers irregular migrants alongside other hard-to-count groups. Under the Conference of European Statisticians (CES), hosted by the United Nations Economic Commission for Europe (UNECE), two further task forces – ‘Measuring Hard-to-Reach Groups in Administrative Sources’1 and ‘Defining and Measuring New Forms of International Migration’2 – have collected practices from NSOs on how to account for irregular migrants in population statistics, including methodological approaches (UNECE, 2025). References: CLANDESTINO. (2009). Undocumented migration: Counting the uncountable. Data and trends across Europe (Final report). Foundation for European and Foreign Policy (ELIAMEP). https://emnbelgium. be/sites/default/files/publications/CLANDESTINO-final-report.pdf Delaunay, D., & Tapinos, G. (1998). La mesure de la migration clandestine en Europe: 1. Rapport de synthèse 2. Rapport des experts. Eurostat. https://www.documentation.ird.fr/hor/fdi:010016012 Kraler, A., & Jandl, M. (2006). Statistics on refusal, apprehensions and removals: An analysis of the CIREFI data. In M. Poulain, N. Perrin, A. Singleton, & THESIM Project (Eds.), THESIM: Towards harmonised European statistics on international migration (pp. 271–285). Presses universitaires de Louvain. Kraler, A., Reichel, D., & Entzinger, H. (2015). Migration statistics in Europe: A core component of governance and population research. In P. Scholten, H. Entzinger, R. Penninx, & S. Verbeek (Eds.), Integrating immigrants in Europe (pp. 39–58). Springer. https://doi.org/10.1007/978-3-319-16256-0_3 United Nations Economic Commission for Europe. (2025). Measuring hardto-reach groups in administrative sources (Draft). Conference of European Statisticians, seventy-third plenary session, Geneva, 16–18 June 2025. https://unece.org/sites/default/files/2025-04/HardToReachGroups%20Consult.pdf Werth, M., & Körth, H. (1991). Immigration of citizens from third countries into the southern member states of the EEC: A comparative survey of the situation in Greece, Italy, Spain, and Portugal. Office for Official Publications of the European Communities. Chapter 1 1 https://unece.org/statistics/documents/2023/11/working-documents/terms-reference-task-force-hard-reach-groups 2 https://unece.org/statistics/documents/2024/02/working-documents/task-force-defining-and-measuring-new-forms 3 Paraphrasing Kathleen Newland’s (2010) observation on the ascendancy of migration as a key concern on the international level in the early 2000s. 21 many European governments, despite their salience in public and political discourse. As a result, our knowledge on the demography and socioeconomics of irregular migrants is limited, and often biased. In contrast, demographers and sociologists in the United States have for decades produced regular estimates of the irregular migrant population and its demographic characteristics, supported in part by the availability of population-wide surveys (see chapter 5). The US also has a higher proportion of irregular migrants relative to its total (3% in 2022) and foreign-born (25%) population than European countries which in some respects makes these estimation exercises more feasible (Kierans & Vargas-Silva, 2024). That said, the relatively small size of the irregular migrant population in Europe – estimated at less than 1% of the total population and between 8% and 10% of the total foreign-born population since 2008 (see Chapter 4)4 – should not be interpreted as grounds for inaction. Nor is Europe devoid of good practice, as evidenced by the many case studies featured this Handbook. However, compared to the US, quantitative estimates of irregular migration are infrequent, and – with a few exceptions – limited to assessing the overall scale of the irregular migrant population, with limited to no detail about demographic or socioeconomic characteristics. Part of the reluctance within European statistical institutions may reflect discomfort with publishing estimates that carry high uncertainty and diverge from the conventions of register and census based population statistics. But this caution comes with its own risks. In the absence of official figures, governments leave a vacuum that can be filled by unreliable or agenda-driven figures. These numbers can have outsized influence, and may, ironically, further discourage NSOs from stepping into the debate and improving the state of the art. We hope this Handbook encourages more NSOs to take up efforts to produce reliable and well communicated information on irregular migration. To this end, this Handbook introduces several approaches that may be helpful, including capturerecapture methods, model-based simulations, residual estimation, and innovative uses of administrative irregularities. The third contribution of this Handbook is building a case for investment into the infrastructure needed to support long-term improvements in irregular migration data. A recurring theme across MIrreM’s work is that improving irregular migration data and their use is not only a technical matter, but an institutional one as well (see Chapter 11). Eurostat, has already taken important steps coordinating irregular migration flow data through its enforcement of immigration legislation (EIL) statistics. It is well positioned to play a Europeanwide convening role around stock estimates. Longer-term funding, knowledge exchanges between NSOs and researchers, annual national updates and standardised reporting templates are all relatively low hanging fruits, which have the potential to transform the irregular migration data landscape in Europe for the better. Conclusion This Handbook does not offer a blueprint for improving the quality and use of irregular migration data in every context. But it does offer tools, examples and a case for long-term investment in the infrastructure needed to produce, disseminate and support the responsible use of these data. In doing so, we hope to reduce the risks of misuse, foster greater consistency and transparency, and ultimately improve the capacity of governments and institutions to engage meaningfully with one of the most contested issues in European migration policy. Making the case for better data on irregular migration 4 The foreign-born population excludes those born in countries covered by free movement agreements. 22 Chapter 1 References Kierans, D. & Vargas-Silva, C. (2024). The Irregular Migrant Population of Europe. MIrreM Working Paper No.10. Krems: University for Continuing Education Krems (Danube University Krems). https://doi.org/10.5281/zenodo.13857073 Kraler, A., & Reichel, D. (2022). Migration statistics. In P. Scholten (Ed.), Introduction to migration studies (pp. 439–462). Springer. https://doi.org/10.1007/978-3-030-92377-8_27 Newland, K. (2010). The governance of international migration: Mechanisms, processes, and institutions. Global Governance: A Review of Multilateralism and International Organizations, 16(3), 331–343. https://doi.org/10.1163/19426720-01603004 Chapter 2 What is irregular migration? Albert Kraler, Tuba Bircan and Ann Singleton 24 What is irregular migration? Chapter 2 Key points • This chapter explores how ‘irregular migration’ is defined and why the concept is contested, showing the tension between using existing categories for measurement and critically interrogating them. • It highlights that terms such as ‘irregular’, ‘illegal’, or ‘undocumented’ are not neutral but historically and politically charged. • The chapter explains that ‘irregular migration’ may denote different phenomena, legal status, border crossings, or policy violations, and stresses the need for precise definitions. • It shows that irregularity is not fixed but shaped by laws, administrative practices, and political contexts, varying between states and over time. • Understanding irregular migration requires both snapshots of populations and trajectories of status change. The MIrreM taxonomy maps pathways into and out of irregularity, while making visible the limits of classification. This chapter addresses an irresolvable challenge: how to discuss ‘irregular migration’ in a reflexive way, whilst necessarily using language and terminology that reproduces contested narratives and categories. It is in itself an area of study in need of the ‘demigranticization’ advocated by Dahinden (2016). The chapter addresses this challenge by exploring how ‘irregular migration’ is conceptualised, used, and measured and by proposing an approach that allows quantifications without falling into the pitfall of reifying problematic categories. At first glance, it appears to describe a clearly defined phenomenon, often equated with ‘undocumented’, ‘clandestine’, ‘unauthorised’, ‘unlawful’ or ‘illegal’ migration (see on the terms used Box 2.1 below). Yet, in practice, the term is used in divergent and often ambiguous ways. It features prominently in academic, policy, and media discourse, but rarely with consistent meaning. 31 What is irregular migration? perspective also helps to overcome the limitations of a “presentist” perspective. For example, Jasso et al. (2008) were able to demonstrate that almost a third of all persons granted permanent residence in the United States in 2016 (around 900,000 persons) had experienced periods of irregularity previously, suggesting a considerable regularity of irregularity, but above all demonstrating the extent to which irregular migrants were able to regain a legal status even in the absence of an explicit regularisation policy. Yet we also acknowledge that our analytical framework misses important quantifiable aspects of irregular migration, that are nevertheless relevant to assess policies addressing irregular migration. The issue of migrant deaths in transit is a case in point: while not relevant to describe the population of migrants with precarious legal status, and pathways into or out of a precarious legal status in Europe, it is an important measure of mortality risks, and more broadly, violence at the EU’s external borders (See Carling, 2007). Yet, how migrant deaths are conceptualised is also contested: which deaths should be considered, and which should not? (see Box 2.4). Box 2.4: Defining “missing migrants” Julia Black Since 2014, the International Organization for Migration’s Missing Migrants Project has documented more than 75,000 deaths and disappearances during migration worldwide, but many more remain undocumented and largely invisible. The population of “missing migrants” is challenging to define, given the politicization of the topic and the lack of visibility of the largely irregular movements in which deaths and disappearances during migration occur. IOM’s Missing Migrants Project was created in response to the October 2013 shipwrecks off the coast of Lampedusa which claimed more than 300 lives. Perhaps because of its inception in the trans-Mediterranean space, it includes only deaths which occurred in the process of international migration, as well as those who go missing during maritime crossings and who are presumed dead. This definition is aimed at identifying the risks that occur during transit, but necessarily excludes many other types of missing migrants, such as deaths of labour migrants, deaths in detention or reception centres, and deaths related to internal displacement. It also excludes the hard-to-measure population of missing persons who have lost contact with their families during their migration journey. Other datasets, including those from UNITED (UNITED for Intercultural Action, 2025), ICRC (IRC, 2022), and the Border Deaths Database (T. K. Last, 2015; see also T. Last et al 2017), use different definitions in the production of their data that include or exclude these sub-groups of missing migrants. Much of the variance in these definitions stems from the interpretation of state boundaries. A narrow definition of “missing migrants” includes only those deaths that take place at state border crossings as viewed on a map. A broader definition includes those that are linked to any “manifestation of state-made boundaries in any space,” (Cuttitta Last 2019) such as suicides linked to lengthy asylum application processing times. The production of data is key to policymaking—notably, the word “statistics” is derived from “state”—as well as forming public opinion on migration and many other topics. Different definitions of “missing migrants” make certain population groups visible, while skipping over others entirely. Different definitions of “missing migrants”, and the data they entail, illuminate specific aspects of the risks of migration. These different definitions may be used constructively by data producers and users to illustrate how policy and practice contribute to preventable deaths and disappearances of migrants. 32 Chapter 2 References Cuttitta, P., & Last, T., eds. (2019). Border Deaths: Causes, Dynamics and Consequences of MigrationRelated Mortality. Amsterdam University Press. doi:10.2307/j.ctvt1sgz6. IRC (2022). “Counting the Dead: How Registered Deaths of Migrants in the Southern European Sea Border Provide Only a Glimpse of the Issue.” https://missingpersons.icrc.org/library/counting-deadhow-registered-deaths-migrants-southern-european-sea-border-provide-only. Last, T. K. (2015). “Deaths at the Borders Database.” https://research.vu.nl/en/datasets/deaths-at-the-borders-database. Last, T., Mirto, G., Ulusoy, O., Urquijo, I., Harte, J., Bami, N., Pérez, M. P., et al. (2017). “Deaths at the Borders Database: Evidence of Deceased Migrants’ Bodies Found along the Southern External Borders of the European Union.” Journal of Ethnic and Migration Studies 43(5): 693–712. DOI:10.1080/1369183X.2016.1276825. UNITED for Intercultural Action (2025). “List of Refugee Deaths.” https://unitedagainstrefugeedeaths.eu. Defining irregular migration and precarity of status What is migrant irregularity? From a legal perspective, irregular migration is defined by its opposite – what in a given state and at a given point in time is defined as legal migration, or more precisely, what the conditions are for admission and residence. Irregular migration thus is a residual category whose meaning may vary considerably over time and space. During the period of ‘guest worker’ recruitment, for example, post-entry regularisation was quite common across Europe, as the majority of labour migrants were recruited through informal channels outside the formal frameworks established by labour recruitment agreements and entered on tourist visa or in fact lacked any authorisation. For instance, in 1968, 82 per cent of residence permits issued in France were issued to migrants already present in the territory, highlighting both the massive scope of informal recruitment and the scale of post-entry regularisations (Descamps, 2024:5). Today, this option is no longer available or used in most countries and application from abroad has been established as the default requirement for obtaining a residence permit. Another example of the changing meaning of irregularity is the expansion of free movement rights within the European Union since the Treaty of Rome in 1957, its extension to family members, students and other categories and its geographical extension by successive waves of EU enlargement. While EU citizens also need to comply with certain residence requirements when moving to other EU Member States, they enjoy a wide-ranging right to movement and settlement in other EU Member States, until Brexit exposed the consequences for those citizens who had not obtained permission to stay. Otherwise non-compliance with rules is usually only sanctioned with mild penalties, for example with a fine in the case of the requirement to obtain a ‘registration certificate’5 (a type of 5 See for a summary of rules for EU citizens moving to another EU Member State https://europa.eu/youreurope/citizens/residence/documents-formalities/registering-residence/index_en.htm. 33 What is irregular migration? residence permit that documents, rather than authorises lawful residence). , only under certain circumstances – notably lack of means, a criminal conviction or on grounds of public security can – EU citizens be expelled or issued with a residence ban. A third example concerns the differential conditions of entry based on visa regimes. Citizens from some states may enter visa-free, thus avoiding the risk of unlawful entry, while others require prior authorisation, making them more vulnerable to irregularisation. These distinctions are not merely technical, and reflect deeper global hierarchies of mobility rooted in postcolonial relations and geopolitical inequalities. These examples are striking reminders of the importance of context. They also highlight that irregular migration cannot be understood as a simple binary (regular vs. irregular), as migrant irregularity is often debated in public and policy debates. The binary approach often masks the complex and diverse experiences of migrants who do not easily fit into legal categories (Triandafyllidou & Bartolini, 2020). Moreover, immigration policy itself is highly differentiated within and between countries, foreseeing different rules for different categories of people, for example between those requiring a visa and those who do not, or EU citizens and third country-nationals. Some of these distinctions are fundamental in terms of migrants’ legal status. EU citizenship is one of these key distinctions within the European Union and associated states. Another important distinction is between irregular migrants ‘known to authorities’ (in the sense that identities and address are known) and ‘undetected’ irregular migrants (European Union Agency for Fundamental Rights, 2011). Irregular migrants known to authorities are migrants who have been apprehended, whose asylum claim was rejected or whose permit has been withdrawn and currently are awaiting return. Some of these migrants may be in a situation of unlawful stay and being known to authorities only for very brief periods of time until their return is effected. In other cases, return may be suspended and they may remain in this limbo situation of receiving some legal recognition of their stay, but in principle obliged to return for years, such as in the case of persons receiving a ‘Toleration’ (Duldung) status in Germany (see chapter 7). In other cases, migrants may abscond , thus turning into irregular migrants not known to authorities again. Among individuals in irregular residence situations who are not known to the authorities, further distinctions can be drawn. Crucially, it is not the person who is ‘irregular’, but rather their legal status, an administrative condition produced through state processes. Irregularity arises from specific legal and procedural determinations, often shaped by gaps in documentation, delayed decisions, or breaches of immigration conditions. Referring to individuals as ‘irregular migrants’ risks essentialising a status that is contingent, contested, and often temporary. Within this group, we can differentiate between those who lack any authorisation of stay and those who violate the conditions of an otherwise valid permit. The latter may include, for instance, tourists or students who engage in unauthorised employment or who overstay their permitted duration of stay (see Chapter 9). In both cases, the condition of irregularity is not automatic: It is formally established only once a legal process has identified an individual being in breach of immigration rules. Another category of interest are asylum seekers. In public debates, asylum related migration has long been associated with irregular migration. Indeed, given the absence of legal pathways for admission for refugees, the large majority of asylum seekers enter European states irregularly. Yet according to Article 31 of the Geneva Refugee Convention, unlawful entry is irrelevant in the case of refugees. Also, asylum seekers’ stay is lawful during the time their claim is assessed. At the same time, if their claim is rejected, they become unlawfully staying. The status of asylum seekers therefore is of a special kind. In the MIrreM project, we have included asylum seekers in a broader category or ‘class’6 of ‘provisionally staying migrants’, alongside other categories of migrants, notably migrants with a suspended return decision, or migrants awaiting the outcome of the regularisation procedure. This 6 In MIrreM we use the term ‘class’ as we have sought to define mutually exclusive groupings of migrants within a broader taxonomy of migrant irregularity (Kraler and Ahrens 2023). 34 Chapter 2 category reflects the fact that the residence rights of migrants subsumed in the category are limited and that there is a strong link to migrant irregularity, despite a temporary lawful stay. A key conclusion that we have drawn from this reflection on different types of irregularity and associated phenomena is that it is useful to place irregularity within a wider concept of legal status precariousness as an overarching category of analysis comprising irregular migrants narrowly speaking, those with a provisional right to stay, and finally, in the EU, EU citizens who have lost free movement rights (Vargas-Silva et al., 2025). Migrants with a precarious legal status can be defined as those “individuals who lack regular immigration or residence status or, having a conditional or temporary status, are vulnerable to the loss of that status. They are therefore deprived of or run the risk of losing the most basic social rights and access to services.” (Homberger et al., 2022). Table 2.1 provides an overview of the three main types of migrants with a precarious legal status we have distinguished in the MIrreM project, how we defined these, and concrete examples. The MIrreM taxonomy of migrants with a precarious legal status Table 2.1, below, focuses on stocks. Combining this perspective with a flow perspective, provides a scheme for analysing pathways into and out of irregularity and how these relate to different types of legal status precariousness, presented in Figure 2.2, overleaf. Importantly, this scheme only provides a snapshot at a given point in time – and within a given period of time in relation to flows. Nevertheless, it also provides a basis for conceptualising legal status trajectories over longer periods of time by considering how individuals move through different pathways and obtain or lose particular statuses, in a reiteration of the ‘static’ snapshot. The main purpose of the MIrreM taxonomy is a systematic mapping of available statistical indicators and estimates – and providing a conceptual framework for the collection of original data. 35 What is irregular migration? Class Definition ExamplesExamples Irregular MigrantsRelated classes to irregular migrants Migrants without residence rights Migrants with a provisional residence status or a reasonable claim to a provisional status Mobile EU citizens with a revoked right to stay Non-nationals (i.e. third-country in the EU) without any legal residence status in the country where they reside, including those whose presence in the territory – if detected – may be subject to termination through an order to leave and/or an expulsion order because of their activities. Non-nationals (i.e. third-country in the EU) who enjoy a provisional right to stay subject to a review of their case Mobile EU citizens who have lost residence rights and no longer enjoy the right to movement and/or settlement in the EU and are liable to be removed •Non-nationals (i.e. third-country nationals in the EU) without any status • Non-nationals (i.e. third-country nationals in the EU) Persons engaged in an activity that violates the terms of their permission to remain in the country, which, if detected could result in the revocation of their permission to remain in the country and/or their expulsion from it. • Unregistered persons with false papers and identities •Persons issued with a return decision who do not return •Persons whose removal has been formally suspended • Individuals awaiting status determination •Unaccompanied minors whose asylum claim has been rejected •Third-country nationals in the EU who are victims of trafficking or exploitation with a provisional permit to stay •Mobile EU citizens with a residence ban on public order or security grounds or criminal charges •Mobile EU citizens without a long-term residence and without sufficient means Table 2.1: Migrants with a precarious legal status (Source: Kraler and Ahrens 2023, p.23f) 36 Inflows Pathways into irregularity Birth Parents without status / Failure to obtain a residence status for the child Inmigration Not authorised to travel / Not meeting conditions for entry Loss of status Overstaying / status withdrawal / negative decisions Demographic flows Geographic flows Status - related flows Pathways out of irregularity Outflows Migration related deaths/ deaths not related to migration Death Mandatory return; Voluntary return; Onward migration Outmigration Formal regularisation; In - country application, incl. informal regularisation; Regularisation by operation of the law; Change in personal circumstances entitling to stay; provisional status Status ad - justment Stocks of irregular migrants Non -nationals without any status (i.e. third -country nationals in the EU) Visibility registered / not registered by authorities Non -nationals without any status (i.e. thirdcountry nationals in the EU) who breach conditions of their stay Unregistered persons with false papers and identities Persons issued a return decision who are not removed (non -removed). Migrants with a provisional status Non-removed whose removal is formally suspended (e.g.„Duldung“ [Toleration]) Individuals awaiting status determination Unaccompanied minors Victims issued short-term permits to ensure criminal procedures Mobile EU citizens with a revoked right to stay EU migrants with a residence ban on public policy, public order or security grounds including convicition for criminal charges EU migrants without permanent residence not meeting conditions of stay Legal migrants EU migrants Third-country nationals Visibility registered/ not registered by authorities Visibility registered/ not registered by authorities Chapter 2 Figure 2.2: MIrreM taxonomy of migrants with a precarious legal status and pathways in and out of irregularity (Source: Kraler and Ahrens 2023, p.31.) 37 What is irregular migration? Conclusion The term irregular migration occupies a central position in contemporary migration debates, yet its meaning is anything but settled. As this chapter has shown, it is a contingent, politically loaded, and administratively unstable construct. Its usage varies across institutional, national, and disciplinary contexts, often conflating legal status with racialised and gendered assumptions about social worth, deservingness, or security risk. It remains a term of operational importance for statisticians, demographers, and policymakers tasked with monitoring population movements, allocating resources, and designing policy responses. From a scientific standpoint, treating irregular migration as a discrete, countable population is both analytically problematic and ethically fraught. People move in and out of irregularity through a range of legal, administrative, and life-course events. Their status may be ambiguous, temporary, or contested, conditions that are poorly captured by static categories. For this reason, this chapter has argued for a shift away from binary framings toward a trajectory-based understanding of legal status. This approach not only reflects the empirical realities of status transitions but also aligns with a more nuanced, longitudinal perspective on migration dynamics. Equally important is the recognition that categories such as ‘irregular’, ‘unauthorised’, or ‘undocumented’ are not neutral descriptors. They are produced and reproduced within legal systems, institutional logics, and discursive fields that are themselves shaped by imperial and colonial histories of inequality, racialisation, and state power. The very effort to define and measure irregular migration thus becomes entangled with the politics of boundary-making, between citizen and non-citizen, insider and outsider, legitimate and illegitimate mobility. At the same time, it is necessary to acknowledge the use of legal categories in migration governance. States regulate entry and residence, and these regulations inevitably generate distinctions, which in turn generate concrete outcomes. Scientific integrity requires that we do not take these distinctions at face value. Instead, we must interrogate the assumptions on which they rest, examine the consequences they produce, and remain attentive to their evolution over time. The MIrreM framework proposed in this chapter is intended as a tool for navigating these tensions. It provides a structured yet flexible taxonomy that allows researchers, officials, and civil society actors to engage with the various phenomena of legal status precariousness in a more systematic and transparent way. It is not a final answer, but a starting point for methodological development, dialogue, data improvement, and policy reflection. In short, irregular migration is not a property of individuals, but a product of institutional arrangements and political decisions. Measuring it (if this is possible) demands methodological rigour, definitional clarity, and above all, critical awareness. As social scientists, our task is not only to describe the world as it is but to understand the dynamics of social phenomena and make visible the ways in which categories, measurements, and narratives shape that world—and, in turn, to question whether they ought to. 38 Chapter 2 References Beauchemin, C., Descamps, J., & Dietrich-Ragon, P. (2023). “Sans papiers ou sans logement : les aléas des trajectoires des immigrés « installés » en France.” https://www.ined.fr/fr/publications/editions/documenttravail/sans-papiers-ou-sans-logement-aleas-des-trajectoires-des-immigres-installes-france/#tabs-2. Carling, J. (2007). “Migration Control and Migrant Fatalities at the Spanish-African Borders.” International Migration Review 41(2): 316–43. https://doi.org/10.1111/j.1747-7379.2007.00070.x. Chauvin, S., & Garcés-Mascareñas, B. (2012). “Beyond Informal Citizenship: The New Moral Economy of Migrant Illegality.” International Political Sociology 6(3):241–59. https://doi.org/10.1111/j.1749-5687.2012.00162.x. Dahinden, J. (2016). “A Plea for the ‘de-Migranticization’ of Research on Migration and Integration.” Ethnic and Racial Studies 39(13): 2207–25. https://10.1080/01419870.2015.1124129. De Genova, N. P. (2002). “Migrant ‘Illegality’ and Deportability in Everyday Life.” Annual Review of Anthropology 31(1):419–47. https://doi.org/10.1146/annurev.anthro.31.040402.085432. Descamps, J. (2024). “Can We See Their ID? Measuring Immigrants’ Legal Trajectory: Lessons From a French Survey.” International Migration Review 01979183241295995. https://doi.org/10.1177/01979183241295995. European Union Agency for Fundamental Rights (2011). Fundamental Rights of Migrants in an Irregular Situation in the European Union - Comparative Report. European Union Agency for Fundamental Rights. https://fra.europa.eu/sites/default/files/fra_uploads/1827-FRA_2011_Migrants_in_an_irregular_situation_ EN.pdf. Goldring, L. (2022). “Precarious Legal Status Trajectories as Method, and the Work of Legal Status.” Citizenship Studies 26(4–5):460–70. https://doi.org/10.1080/13621025.2022.2091228. Homberger, A., Kirchhoff, M., Mallet-Garcia, M., Ataç, I., Güntner, S., & Spencer, S. (2022). “Local Responses to Migrants with Precarious Legal Status: Negotiating Inclusive Practices in Cities Across Europe.” Zeitschrift Für Migrationsforschung/ Journal of Migration Studies 2(2):93-116 Seiten. https://doi.org/10.48439/zmf.179. 39 What is irregular migration? Jasso, G., Massey, D. S., Rosenzweig, M. R., & Smith, J. P. (2008). “From Illegal to Legal: Estimating Previous Illegal Experience among New Legal Immigrants to the United States.” International Migration Review 42(4):803–43. https://doi.org/10.1111/j.1747-7379.2008.00148.x. Kraler, A.,, & Ahrens, J. (2023). Conceptualising Migrant Irregularity for Measurement Purposes. MIrreM Working Paper No. 2 / 2023. Krems: University for Continuing Education (Danube University Krems). https://doi.org/10.5281/zenodo.7868237. Kraler, A., & Reichel, D. (2022). “Migration Statistics.” Pp. 439–62 in Introduction to Migration Studies - An Interactive Guide to the Literatures on Migration and Diversity, IMISCOE Research Series, edited by P. Scholten. Springer. https://doi.org/10.1007/978-3-030-92377-8_27 Pijnenburg, A., & Rijken, C. (2021). “Moving beyond Refugees and Migrants: Reconceptualising the Rights of People on the Move.” Interventions 23(2):273–93. doi:10.1080/1369801X.2020.1854107. Rheindorf, M., Vollmer, B., Van Liempt, I., & Sigona, N. (2025). Understanding and Reframing Migration Narratives: Towards an Evidence-Based Policy Discourse in Europe. Policy brief. I-Claim. Sironi, A., Bauloz, C., & Emmanuel, M. (2019). Glossary on Migration. No.34. International Migration Law. International Organization of Migration. https://publications.iom.int/system/files/pdf/iml_34_glossary.pdf. Smellie, S., & Boswell, C. (2024). “Comparative Analysis of Migration Narratives in Political Debate and Policymaking.” doi:10.5281/ZENODO.10590197. Triandafyllidou, A., & Bartolini, L. (2020). “Understanding Irregularity.” Pp. 11–31 in Migrants with Irregular Status in Europe, IMISCOE Research Series, edited by S. Spencer and A. Triandafyllidou. Cham: Springer International Publishing. United Nations Secretariat. Department of Economic and Social Affairs. Statistics Division. (2025). Recommendations on Statistics of International Migration and Temporary Mobility. https://unstats.un.org/go/migrrec. Vargas-Silva, C., Leerkes, A., Kierans, D., Siruno, L., & Kraler, A. (2025). “Tools for Collecting Information on Irregular Migration Estimates and Indicators.” Open Research Europe 5:176. doi:10.12688/openreseurope.20695.1. 40 Chapter 2 47 Ethics and data on irregular migration such biases are relatively minor. She argues that migration status should more often be included in surveys, because this would enrich theoretical understandings of migrants’ experiences and inform policy development. However, this assumes that migrants know their status and would report it openly and accurately. This highlights the importance of reflexivity in interpretation, recognising whose experiences are represented and whose are overlooked. Such critical awareness should guide transparent communication to policymakers in order to prevent decisions based on incomplete or skewed evidence, which may further marginalise already vulnerable populations. Safeguarding rights and responsible data use Trust and mistrust Trust is a foundational element in the collection and use of migration data. Descamps and Boswell (2018) show how institutional mistrust (e.g. fuelled by rivalries, lack of transparency, conflicting incentives, etc.) undermines coordination and data sharing. Mistrust between agencies can lead to fragmented systems, duplicated efforts, and ultimately weaker evidence for policymaking. At the same time, migrants themselves may deeply distrust data collection efforts. Fear of surveillance, deportation, or misuse of personal information reduces willingness to participate or share accurate data (Kraler et al., 2015). This affects not only research quality but also the credibility of policy responses. However, when trust is established through robust safeguards and ethical practice, data collection and use can serve positive purposes. Responsible data use can inform the design of social inclusion programmes, improve service provision, and support policies that protect migrant’s rights. Researchers need to recognise that trust cannot be demanded but must be earned through ethical practice, including respecting autonomy, ensuring confidentiality, negotiating consent to participate, and demonstrating commitment to protecting research participants from harm. These principles must guide both data collection and the wider institutional relationships on which migration data systems depend (see Box 3.2, for an example). Box 3.2: Addressing ethical challenges in surveying irregular migrants – The MIMAP survey on the im-/mobility of rejected asylum seekers Randy Stache When no sampling frame exists (e.g. when studying irregular migrants unknown to the authorities) or when particularly sensitive topics are being explored, conventional survey methods quickly reach their limits. Irregular migrants are hard-to-reach and hard-to-survey: The group is blurry and elusive (hard to identify, highly mobile with mistrust against authorities and researchers). The group also is socially and legally marginalised, vulnerable and typically lacks prior engagement with empirical research. Many are familiar with interviews only in the context of authorities, such as police or asylum proceedings. These conditions raise ethical challenges, including data protection, informed consent, and the positionality of researchers. In consequence, innovative and adaptive methodological approaches are needed. 48 Chapter 3 One example for such an approach is the MIMAP project (“Feasibility study on the im-/mobility of rejected asylum seekers”). Conducted between 2022 and 2025 by the Research Centre of the Federal Office for Migration and Refugees in Germany, a part of the project focused on irregular migrants from Anglophone West Africa who had undergone an asylum procedure in Germany. It employed an innovative mixed-methods design, combining quantitative survey research with in-depth ethnographic fieldwork. Ten rejected asylum seekers were repeatedly interviewed and accompanied in their everyday lives. This ethnographic engagement facilitated trust-building and enabled the identification of key community individuals who acted as gatekeepers for the quantitative study. The survey applied Respondent-Driven Sampling (RDS), implemented via a custom-designed mobile application. The app hosted the survey, ensured full anonymity by collecting no personal data, and enabled participants to digitally refer the survey to up to three peers. Participants received a digital €10 shopping voucher both for completing the survey and for each successful referral. To explore the sensitive issue of mobility aspirations (staying, returning, or migrating onward) the survey incorporated a factorial survey. Participants evaluated four hypothetical profiles of individuals with a ‘tolerated’ status, whose characteristics (e.g., length of stay: 1, 4, or 10 years) were experimentally varied. Respondents were asked to recommend whether each fictional individual should stay in Germany, return to the country of origin, or migrate to another country. The experimental variation enabled the identification of factors that shape im-/mobility aspirations. In line with the contextualizing qualitative interviews, the quantitative findings show that employment status, conditions in the country of origin, and the location of own children strongly influence (im) mobility aspirations. In contrast, migration enforcement policies such as deportation pressures and return assistance play minor roles (Stache et al., 2025). Combining qualitative interviews and ethnographic trust-building with a respondent-driven sampling featuring an anonymous, app-based survey and a survey experiment, enabled the systematic investigation of sensitive topics among a highly inaccessible population – while maintaining ethical rigor and contextual depth. References: Peitz, L. and Stache, R. and Johnson, L. (2024) How to Survey Hard-to-Reach Populations: A Practical Guide to App-Based Respondent-Driven Sampling. Robert Schuman Centre for Advanced Studies Research Paper No. 2024/23, http://dx.doi.org/10.2139/ssrn.4901241 Stache, R., Johnson, L., Peitz, L., & Carwehl, A.-K. (2025). Bleibeund Rückkehrabsichten von Geduldeten: Erkenntnisse aus einem Umfrageexperiment mit westafrikanischen Schutzsuchenden. (BAMF-Kurzanalyse, 2-2025). Nürnberg: BAMF. https://doi.org/10.48570/bamf.fz.ka.02/2025.d.2025. bleibrueckabsicht.1.0 Transparency, contestability and responsibility Quantifying irregular migration can lend findings a veneer of objectivity and authority that masks their contingent, uncertain nature. Numbers often carry persuasive power in policy debates, but when poorly communicated or misinterpreted estimates can mislead decision-makers or the public. Ethical responsibility demands that researchers clearly communicate the limits and assumptions of 49 Ethics and data on irregular migration their methods. Transparency here is substantive: it requires explaining potential sources of error, methodological assumptions, and the ways findings can and cannot be interpreted. For algorithmic methods, transparency can help other experts (and it is important to acknowledge this facet) to contest the assumptions and biases embedded into computational analysis. By doing so, policymaker and researchers help ensure that data supports informed, balanced policy decisions rather than fuelling sensationalism or punitive responses. Considerations for data linkage and anonymisation strategies Data protection law, especially the GDPR, imposes clear limits on how personal data may be collected, used, and shared. While these rules are crucial for protecting individual rights, they can also pose practical challenges for research, particularly in linking datasets across sources or countries. It is necessary to respond to these challenges through careful anonymisation strategies. Pseudonymisation of individuals’ identities is a standard practice, with participants given choices about the level of disclosure they are comfortable with. Researchers can use coded protocols for interviews, workshops, and surveys to minimise identifiability. Anonymisation should not be treated as a one-off exercise but as an ongoing obligation to protect participants’ rights as data is processed, analysed, and shared. This also involves putting in place technical safeguards, for example: access controls that limit who can view or process data; and secure environments supported by encryption (see for an innovative example of pseudonymisation by design, Box 3.3). Box 3.3: Linkage of administrative data in a data protection sensitive way – The case of Austria Albert Kraler On the national level, a wide range of statistical indicators on irregular migration are available from different administrative databases, including those on migration enforcement (apprehensions, return orders, rejections at the border, migrant smuggling, etc.), asylum databases, and residence permit databases. Despite some inherent limitations associated to their administrative purpose, the anchoring of measurement concepts in operational and legal categories and their specific scope linked to domain specific regulatory frameworks, administrative databases provide a rich source for scientific analysis. This is particularly true when they contain historical data and allow examining migrants’ trajectories (chapter 7) or when they allow linkage of different databases (record linkage). In both cases, questions about data protection arise. For example, in compliance with the privacy regulations databases generally foresee a certain timeframe after which personal data needs to be deleted, if no longer necessary for the particular administrative purpose they are meant to serve. Sometimes, specific events will lead to the deletion of records from registers. For example acquisition of citizenship will result in the deletion of that person’s records from residence permit registers). Similarly, record linkage can be restricted by law, as is the question of who has access to different types of data. The case of Austria is a good example of database linkage and the preservation of historical records are possible in a data protection compliant way. In Austria, the pseudonymisation of register data for statistical purposes is achieved through the use of (encrypted) sector specific personal identifiers (verschlüsselte Bereichsspezifische Personenkennzahl Amtliche Statistik – bPK-AS). The bPK-AS is generated by the Stammzahlenregisterbehörde (Central Register Authority). It is a cryptographically derived identifier derived from the personal identifier used in a specific domain (for example social security, or the population register code) and a code for the domain.3 It is unique to each individual 3 The principle of encryption used for the generation of the sector specific identifiers is described (in German) here : https://www.bundeskanzleramt.gv.at/agenda/digitalisierung/stammzahlenregisterbehoerde/bereichsspezifischepersonenkennzeichen/beschreibung.html. The encryption procedure is based on Central Register Authority Ordinance (Stammzahlenregisterbehördenverordnung) 2022, see https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=20011934. 50 Chapter 3 and serves as a key to match data from different registers with each other. Crucially, the bPK-AS is not reversible, meaning it cannot be traced back to the original personal identification number (Statistics Austria 2024). Statistics Austria uses these anonymised personal identifiers to link data from various sources – such as social insurance records, employment data, and education registers – through deterministic linkage and without revealing personal identities. While Statistics Austria gets updates from administrative databases in real time, it uses anonymized statistical mirror databases for statistical purposes (Fuchs et al. 2024). All register data is stored in a historicised way, allowing longitudinal analysis. Since 2022, all statistical databases based on data collected by Statistics Austria itself (through surveys and other statistical reporting systems) as well as a wide range of administrative databases from different public bodies are assembled in the “Austria Micro Data Centre” (AMDC).4 By mid-2026, all public administrative database – with the exception of security related databases – should be made available by the AMDC. In addition, researchers can link their own datasets to the AMDC by obtaining a sector specific identifier from the Central Register Authority for their own dataset, which in turn enables Statistics Austria to include this dataset in the AMDC, making it linkable to all datasets contained in the AMDC. A precondition for including a dataset in the AMDC is that researchers collect personal information (notably name, date of birth, place of residence) to enable pseudonymisation by the Central Register Authority. The AMDC is open for researchers in accredited institutions, which need to meet a number of criteria for accreditation (such as scientific purpose of the organisation, research quality, independence). While immigration and migration enforcement related databases are not (yet) linked to the AMDC and therefore cannot be used to analyse legal status trajectories, the design of the system nevertheless can serve as a model for balancing data utility and privacy protection. References Fuchs, R., Göllner, T., Hartmann, S. & Thomas, T. (2024). “Fostering Excellent Research by the Austrian Micro Data Center (AMDC)” Jahrbücher für Nationalökonomie und Statistik, 244(4), 433-445. https://doi.org/10.1515/jbnst-2023-0043 Statistik Austria (2024): Standard-Dokumentation Metainformationen (Definitionen, Erläuterungen, Methoden, Qualität) zu den Registerbasierten Erwerbsverläufen. https://www.statistik.at/fileadmin/shared/QM/Standarddokumentationen/B_1/std_b_erv.pdf Importantly, ‘special categories’ of personal data, such as ‘race’, ethnic origin or political opinions, carry heightened risks. The MIrreM project applies the ‘data minimisation principle’ by deliberately limiting data collection to what is strictly necessary, while ensuring individuals are fully informed of their rights and protections. When applied carefully, these practices allow data to be used constructively: for example, to understand migration patterns, design inclusive services and improve resource allocation without compromising individual privacy. 4 https://www.statistik.at/en/services/tools/services/center-for-science/austrian-micro-data-center-amdc 51 Ethics and data on irregular migration Secondary use of data MIrreM also uses existing datasets to estimate irregular migrant populations. Even if these are anonymised or aggregated, ethical issues remain. Researchers and policymakers must consider the conditions under which data were originally collected, and if this included informed consent, voluntariness and transparency, and how linking datasets may create new risks or reinforce surveillance logics. To address this, researchers need to commit to clear documentation of data sources, ethical review of any secondary use, and a critical assessment of how data linking may affect the rights and perceptions of the populations concerned. Policymakers must be wary of normalising data practices that reinforce securitisation narratives, where migrants are framed primarily as risks to be managed rather than individuals with rights. At the same time, responsible linking and analysing of secondary data can yield valuable insights for planning services, understanding the characteristics of migrant populations and evaluating the effectiveness of policies. This requires a careful balance between administrative utility and respect for fundamental human rights. Inclusive governance and legal safeguards The use of data about irregular migration should complement, not replace, engagement with migrants themselves or with civil society organisations that work with them directly. Policymakers should strive for inclusive governance in migration data systems, ensuring that policy proposals reflect diverse perspectives and do not solely rely on technocratic or quantitative assessments. Policymakers must also consider the need for updated legal frameworks to regulate the use of linked or repurposed datasets, especially when applied to groups that may lack formal protections. This includes reviewing data protection laws and institutional safeguards to ensure that they cover the specific vulnerabilities associated with an irregular residence status. When governance frameworks are inclusive and transparent, data can be used proactively to identify gaps in protection, target resources effectively and support interventions that benefit migrants and wider communities. Conclusion Research ethics in the context of irregular migration cannot be reduced to a checklist. Compliance with legal frameworks such as GDPR and the EU AI Act is necessary, but only as a baseline. What is required instead is an ongoing reflexive approach about the risks, responsibilities, and power relations involved at every stage – from research design and data collection to analysis and communication. For those involved in data collection and processing, such as researchers, statisticians, public sector officials and those working in migrant support organisations, this means embedding ethics awareness in all activities, recognising the rights and dignity of those whose lives are studied, and promoting transparency and accountability in the production and use of migration data. By treating ethics as an integral, continuous process, researchers can help ensure that their work contributes to more just, humane, and evidence-informed migration policy. 52 Chapter 3 References Anderson, B. (2019). New directions in migration studies: Towards methodological de nationalism.Comparative Migration Studies,7(1), 1-13. https://doi.org/10.1186/s40878-019-0140-8 ALLEA. (2023). The European Code of Conduct for Research Integrity (Revised ed.). Berlin: All European Academies. https://doi.org/10.26356/ECOC Bakewell, O. (2008). Research beyond the categories: The importance of policy irrelevant research into forced migration. Journal of Refugee Studies, 21(4), 432–453. https://doi.org/10.1093/jrs/fen042 Cyrus, N. (2023). Ethical Benchmarking for the Measurement of Irregular Migration. In MIrreM Policy Brief No.1. Krems: University for Continuing Education Krems (Danube University Krems). https://doi.org/10.5281/zenodo.10042022 Dahinden, J. (2016). A plea for the ‘de-migranticization’ of research on migration and integration.Ethnic and racial studies,39(13), 2207-2225. https://doi.org/10.1080/01419870.2015.1124129 Descamps, J. (2024). Can We See Their ID? Measuring Immigrants’ Legal Trajectory: Lessons From a French Survey.International Migration Review,https://doi.org/10.1177/01979183241295995 European Union (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. Official Journal of the European Union, L, 2024(1689), 1–127. https://eur-lex.europa.eu/eli/reg/2024/1689/oj European Union. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation). Official Journal of the European Union, L119, 1–88. https://eur-lex.europa.eu/eli/reg/2016/679/oj Floridi, L., Taddeo, M., & Turilli, M. (2018). Group privacy: A defence and an interpretation. In L. Taylor, L. Floridi, & B. van der Sloot (Eds.), Group privacy: New challenges of data technologies (pp. 83–100). Cham, Switzerland: Springer. 53 Ethics and data on irregular migration Hendow, M., Wagner, M., Ahrens, J., Cherti, M., Kierans, D., Kraler, A., Leerkes, A., Leon, L., Rodríguez Sánchez, A., Siruno, L., Tjaden, J., & Vargas-Silva, C. (2024). How fit is the available data on irregular migration for policymaking?. In MIrreM Policy Brief No. 3. Krems: University for Continuing Education Krems (Danube University Krems).https://doi.org/10.5281/zenodo.13757685 Kraler, A., Reichel, D., & Entzinger, H. (2015). Migration statistics in Europe: A core component of governance and population research.Integrating immigrants in Europe: Research-policy dialogues, 39-58. https://doi.org/10.1007/978-3-319-16256-0_3 Jasso, G., Massey, D. S., Rosenzweig, M. R., & Smith, J. P. (2008). From illegal to legal: Estimating previous illegal experience among new legal immigrants to the United States. International Migration Review, 42(4), 803–843. https://doi.org/10.1111/j.1747-7379.2008.00148.x Mohan, S. S., Mountz, A., Romero, M., & Visan, A. (2023). How to research ‘irregular’ migration: approaches and perspectives from the field. InResearch Handbook on Irregular Migration(pp. 36-48). Edward Elgar Publishing. https://doi.org/10.4337/9781800377509 Reed-Berendt, R., Dove, E. S., Pareek, M., & Group, U.-R. S. C. (2022). The Ethical Implications of Big Data Research in Public Health: “Big Data Ethics by Design” in the UK-REACH Study. Ethics & Human Research, 44(1), 2–17. https://doi.org/10.1002/eahr.500111 Scheel, S., & Tazzioli, M. (2022). Who is a migrant? Abandoning the nation-state point of view in the study of migration.Migration Politics,1(1), 002. https://doi.org/10.21468/MigPol.1.1.002 Taylor, L., & Meissner, F.(2024).Migration statistics in times of large-scale mobility data: ethical concerns and concerns with ethics. In W. L. Allen, & C. Vargas-Silva (Eds.),Handbook of research methods in migration(2nd edition, pp. 280-295). Edward Elgar Publishing.https://doi.org/10.4337/9781800378032.00031 54 Chapter 3 Chapter 4 What are good quality data on a phenomenon that is hard to measure? Denis Kierans and Lalaine Siruno 56 Introduction: Measuring the unmeasurable? Policymakers often point to data to justify their decisions, particularly in contested policy spaces, such as immigration (Boswell, 2009; Kraler & Reichel, 2022). Irregular migration, while a point in case, poses distinct challenges to this practice. Although irregular migrants are the subject of intense political and media scrutiny in many countries, information about them is notoriously scant and unreliable (Vollmer, 2011). This chapter examines what constitutes “good quality” data in this complex landscape. It introduces the MIrreM framework for assessing irregular migration estimates and indicators and discusses how uncertainty (Box 4.1) and other criteria shape data quality. Rather than assuming these figures are fit for purpose, we suggest users interrogate them, asking whether the data are credible, transparent, and suited to question at hand. We conclude with a checklist based on the five MIrreM criteria to support this process. What are good quality data on a phenomenon that is hard to measure? Chapter 4 Key points • Irregular migration is difficult to measure, and the data that exist are often limited, inconsistent, or outdated. This chapter introduces a practical framework to help users assess the quality and credibility of such data, rather than taking estimates at face value. • It distinguishes between key data types—stocks vs flows, estimates vs indicators—and highlights how conceptual ambiguity, observational gaps, and poor documentation can undermine how irregular migration data are interpreted and used. • When applied to over 250 estimates across 14 countries, the framework reveals significant variation in quality. While some countries produce relatively robust and transparent figures, many rely on outdated, methodically weak or poorly documented estimates. Still, pockets of good practice exist across North America and Europe, which can be built on. • The chapter argues that responsible use of irregular migration data depends not only on improving data systems, but also on the ability of users to critically assess what data mean, how they were produced, and whether they are fit for purpose. 63 Measuring the unmeasurable? Conclusion Measuring and estimating irregular migration will always be difficult. But better data and their use is possible. A step in the right direction is to incorporate critical appraisals of the data as a matter of course, especially for those shaping policy and the public debate. To this end, the MIrreM quality criteria can be used or adapted as a preliminary, rapid-fire assessment tool for irregular migration data. Not all estimates or indicators will meet all criteria fully. However, if you are unable to answer the relevant questions on the checklist, we recommend learning more about the data before you use it in your work or draw conclusions from the data. To conclude, we emphasise that assessing the quality of irregular migration data is not merely an academic or technical matter. Given irregular migration data’s uneven quality and limited availability – combined with the political and public sensitivity of the issue – it is easy to misinterpret, with potentially serious consequences. Avoiding this requires investment not only in data systems, but also in the capacities of individuals and institutions to interpret and use irregular migration data responsibly. Figure 4.1: Checklist for rapid assessment irregular migration estimates and indicators 64 Chapter 4 References Boswell, C. (2009). The Political Uses of Expert Knowledge: Immigration Policy and Social Research. Cambridge University Press. Kierans, D. & Vargas-Silva, C. (2024). The Irregular Migrant Population of Europe. MIrreM Working Paper No. 11. Krems: University for Continuing Education Krems (Danube University Krems). https://doi.org/10.5281/zenodo.13857073 Kraler, A. & Reichel, D. (2022). Migration Statistics. In: Scholten, P. (eds) Introduction to Migration Studies. IMISCOE Research Series. Springer, Cham. https://doi.org/10.1007/978-3-030-92377-8_27 Siruno, L., Leerkes, A., Hendow, M. & Brunovksá, E. (2024). Working Paper on Irregular Migration Flows. MIrreM Working Paper No. 9. Krems: University for Continuing Education Krems (Danube University Krems). https://doi.org/10.5281/zenodo.10702228 Vargas-Silva, C., Leerkes A., Kierans, D., Siruno, L. & Kraler, A. (2025). Tools for collecting information on irregular migration estimates and indicators [version 1; peer review: 2 approved]. Open Res Europe 2025,5:176. https://doi.org/10.12688/openreseurope.20695.1  Vollmer, B. A. (2011). Policy Discourses on Irregular Migration in the EU-‘Number Games’ and ‘Political Games’. European Journal of Migration and Law, 13(3), 317-339. https://doi.org/10.1163/157181611X587874 Wilkinson, M., Dumontier, M., Aalbersberg, I.et al.(2016). The FAIR Guiding Principles for scientific data management and stewardship.Sci Data3. https://doi.org/10.1038/sdata.2016.18 Chapter 5 Innovations in methodological approaches to estimate irregular migrant stocks and flows Alejandra Rodríguez-Sánchez and Jasper Tjaden 66 Introduction The scientific study of irregular migration, its description, and its estimation are closely connected with the search for appropriate measurements and empirical observations related to this form of migration. Aside matters related to the definition of irregular migration, treated in other chapters in the book, and especially in Kraler and Ahrens (2023), there are multiple challenges associated with measuring irregular migration stocks and flows and attaining estimates of the size of these quantities. Some of these challenges were highlighted by the seminal CLANDESTINO project – an EU–funded project (2007-2009) which reviewed data and methodologies on irregular migration over a decade ago (Jandl, 2011). Key obstacles include irregular migrants’ reluctance to disclose their status in surveys or censuses, the absence of adequate sampling frameworks, and their elevated mobility patterns—all necessitating alternative research approaches. In MIrreM, we seek to update this review with advances in literature in terms of data Innovations in methodological approaches to estimate irregular migrant stocks and flows Chapter 5 Key points • This chapter builds on Rodríguez-Sánchez and Tjaden (2023), who reviewed the main methods for estimating irregular migrant stocks and flows, spanning both established and more experimental approaches. • Measuring irregular migration remains a fundamental challenge: the population is difficult to observe, and even widely used methods such as residual estimation or capture–recapture provide only partial pictures. • Traditional techniques continue to form the backbone of the field, but improvements have often come from incremental innovations, such as using mortality data to refine life-course approaches, or expanding residual methods with large government databases and machine learning. • More novel directions, such as exploiting consular registers, driver’s licence data, or online search behaviour, show promise in filling gaps, though these remain context-specific and experimental. 67 Innovations in estimating irregular migration sources and methodologies. For a comprehensive and detailed overview of each method, see the review paper by Rodríguez-Sánchez and Tjaden (2023). In that review, a detailed explanation of how the different methods work, typical databases used, and their strengths/weaknesses are documented. Depending on which definition of irregular migration we employ, some methods might be better suited than others to capture the different operationalizations, especially as these will be based on different data sources Box 5.1: Traditional and innovative approaches Alejandra Rodríguez-Sánchez and Jasper Tjaden By “Traditional” we refer to approaches covered by previous methodological overviews (Jandl, 2011; Pinkerton et al., 2004) on which our overview builds. These are well established methods that are used to estimate irregular migration across the world. We included these traditional methods in our review out of a desire to be comprehensive, but also because some of the innovations build from well-established methods, like the residual approach. We defined “Innovative” approaches as those methods that either use novel data sources (e.g., digital behavioural data) or apply a new estimation method to standard data sources. These approaches improve upon some of the limitations of established methods. The innovative approaches were identified through literature review and discussions with experts. Estimating irregular migrant stocks and, especially, irregular migration flows, despite important advances, remains a challenging endeavour. Methods estimating stocks measure the total number of irregular migrants residing in a country at a specific point in time (e.g., the year 2022), whereas methods estimating flows capture changes in that population over a defined period (e.g., 2015– 2020), attaining measures of inflows or outflows. MIrreM’s innovative pilot studies which are summarized in the next chapter, and which were based on this overview, are aimed at tackling some of these challenges. This chapter of the Handbook is intended to highlight some of the most innovative aspects of approaches we found through our scoping review for measuring both stocks and flows. A quick overview of the methodologies we found in this search can be found in Table 1. We grouped the methods based on their core data (e.g., government data, non-government data, survey, mixed data, and digital data) and estimation strategies, plus a brief description of their main idea, rather than focusing on minor differences across methods. Our review encompasses both traditional and innovative methods (see Box 1). 68 Chapter 5 69 Innovations in estimating irregular migration Table 5.1: Overview of methodological approaches covered in Rodríguez-Sánchez & Tjaden (2023) review (Note: Author’s own elaboration) 70 Chapter 5 Review criteria Our review of each methodology was based on a series of criteria we deemed fundamental to understanding the scope of each method, meaning which population the method is able to produce estimates for, and the quality of its estimates. The selection of which features of the methodologies to highlight was based on existing common standards for the evaluation of scientific evidence, such as the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the Grading of Recommendations, Assessment, Development and Evaluation (GRADE), among others (Guyatt et al., 2008; Page et al., 2021). Each of the methods was evaluated in RodríguezSánchez and Tjaden (2023) according to the following criteria: • Main Idea: Explains the core concept of the method in simple terms for non-experts. • Data Source: Identifies main data types (e.g., administrative, surveys, census) and gives examples. • Coverage / Definition: Describes which subgroups of irregular migrants are included or excluded, highlighting potential biases. • Estimation Assumptions: Outlines key assumptions needed for the method to estimate the total irregular migrant population accurately. • Reliability: Assesses whether the method gives consistent results over time. • Scalability: Evaluates whether the method can be applied in different countries. • Ethical Issues: Flags ethical concerns in data use, collection, and potential risks to migrants. • Examples: Provides references to studies that apply the approach. What we found Among the traditional approaches, covering both indirect and direct approaches as classified by Jandl (2011), we review multiplier or simple extrapolation, the capture-recapture or multiple system estimation approach, the residual estimation method, self-identification in surveys, and expert or Delphi surveys. Although these methods possess important drawbacks we highlight in the literature, these methods are well-known and considered standard. We found important innovations regarding the multiplier, in Drbohlav and Lachmanová (2023), which document the results of implementing the multiplier in practice; and also, innovative work in the residual method, with the use of machine learning, larger government databases on social programs, and the evaluation of robustness of the method to core methodological assumptions (van Hook et al., 2021). Moreover, among traditional approaches, we included the use of specific events, such as largescale regularization and formal status adjustment programs (Sabater & Domingo, 2012), and life course events. Changes in legislation have offered the opportunity to understand the number of individuals lacking legal status in the past (Kraler, 2019). In turn, the life course events, in which administrative or register data sources on births, deaths, or hospitalizations, can offer important clues as to the sizes of populations as long as these can be extrapolated to the larger population. In particular, we highlight the potential of data on mortality (Surkyn et al., 2023), an approach which holds promise to be implemented in various countries relying on similar data. Another class of traditional approaches we cover follows statistical modelling practices. For example, labour demand and the flow-stock modelling. In the labour demand modelling (Hess, 2006), only irregular migrant workers are estimated on the basis of reported economic output based on administrative data. In the flowstock model (Fazel-Zarandi, Feinstein & Kaplan, 71 Innovations in estimating irregular migration 2018; Rodilitz & Kaplan, 2021), in turn, used in the US, and which have been criticized for providing implausible estimates that go orders of magnitude beyond existing estimations (Capps et al., 2018), information on cumulative inflows (visa overstayers, irregular border crossings/apprehensions) minus cumulative outflows (deportations, voluntary emigration, mortality, status changes) is used to derive an estimate. Finally, among traditional approaches we also included the use of official, administrative, and commercial databases that allow for the estimation of irregular flows or stocks. Although this could be considered partly innovative, as new data sources have become available, irregular border crossings (Savatic et al., 2021; FRONTEX, 2022), data bases on asylum claims and refugee status (Ghui & Blangiardo, 2019), migrant deaths and apprehensions, and database systems enabling identification of visa overstayers. In the US, for example, overstayer events are estimated by considering all arrivals through air, sea, and land, matched to records of exits (Department of Homeland Security, 2022; Warren, 2017). Advances and challenges Among the innovative approaches, we document the development of important approaches. For example, in terms of databases, institutional registers on college enrolment (Hsin & Reed, 2020), as well as driver license register data (Lueders & Mumper, 2022), and consular registers of migrant communities (Bhandari et al., 2021). On their own, these databases cannot, on their own, be used to estimate the total number of irregular migrants, but when put in combination with other data sources they have the potential to provide important clues about irregular migrant population size. On the more methodological side, the use of statistical imputation in large databases, often in connection to the residual method (Gálvez-Iniesta, 2020), constitutes an important innovation worth mentioning (Borjas & Cassidy, 2019; Ro & van Hook, 2022). Also, the use of innovative data sources on consumption and online search behaviour can be highlighted among the most important innovations (Nixon, 2022; Böhme, Gröger, & Stöhr, 2020). New databases that also inform irregular migrant flows, for example, have been created as a result of important citizen-driven projects (UNITED for Intercultural Action. (n.d.), or the creation of more encompassing surveillance programs. An example of the first is the Missing Migrants Project collecting data on the population of migrants dead and presumed missing while en route (GarcíaBorja & Black, 2022). Knutson (2021) and Molnar (2019) discuss the uses of socio-technical systems based on artificial intelligence that enable facial recognition in the enforcement of migration. Significant progress has been achieved in refining estimation methods to address the shortcomings of traditional techniques like the residual method. These improvements have been driven by the adoption of novel methodologies and the growing availability of diverse data sources (Vespe et al., 2017). However, estimation approaches remain largely fragmented, often shaped by the specific type of data source employed. 72 Chapter 5 References handari, R., Feigenberg, B., Lubotsky, D., & Medina-Cortina, E. (2021). Projecting Trends in Undocumented and Legal Immigrant Populations in the United States (No. NB21-16). NBER. https://www.nber.org/programsprojects/projects-and-centers/retirement-and-disability-research-center/center-papers/nb21-16 Borjas, G. J., & Cassidy, H. (2019). The wage penalty to undocumented immigration. Labour Economics, 61, 101757. https://doi.org/10.1016/j.labeco.2019.101757 Böhme, M. H., Gröger, A., & Stöhr, T. (2020). Searching for a better life: Predicting international migration with online search keywords. Journal of Development Economics, 142, 102347. https://doi.org/10.1016/j.jdeveco.2019.04.002 Capps, R., Gelatt, J., Van Hook, J., & Fix, M. (2018). Commentary on “The number of undocumented immigrants in the United States: Estimates based on demographic modeling with data from 1990-2016”. PloS one, 13(9), e0204199. https://doi.org/10.1371/journal.pone.0204199 Department of Homeland Security. (2022). FY 2020 Entry/Exit Overstay Report (Entry/Exit Overstay Report). Department of Homeland Security. https://www.dhs.gov/sites/default/files/2021-12/CBP%20-%20FY%20 2020%20Entry%20Exit%20Overstay%20Report_0.pdf FRONTEX. (2022, October 7). Risk Analysis for 2022/2023. Publications. https://frontex.europa.eu/publications/risk-analysis-for-2022-2023-RfJIVQ Gálvez Iniesta, I. (2020). The size, socio-economic composition and fiscal implications of the irregular immigration in Spain [WorkingPaper]. https://e-archivo.uc3m.es/handle/10016/30643 Ghio, D., & Blangiardo, G. C. (2019). Exploring the link between irregular migration and asylum: the case of Italy. Genus, 75(1), 14. https://doi.org/10.1186/s41118-019-0060-3 Guyatt, G. H., Oxman, A. D., Vist, G. E., Kunz, R., Falck-Ytter, Y., Alonso-Coello, P., & Schünemann, H. J. (2008). GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 336(7650), 924–926. https://doi.org/10.1136/bmj.39489.470347.AD 79 Data traces & visibility of irregular migration Pilot studies in MIrreM In a series of pilot studies developed in the context of MIrreM, we have made use of a similar intuition regarding metadata to develop innovative methodologies to estimate irregular migration. We used data from the following sources: Facebook users (Rodríguez-Sánchez & Tjaden, 2025a); digital surveys on US based Mexicans and Venezuelan users of social media platforms (Tjaden & RodríguezSánchez, 2025); air passenger data collected on all flights across the world towards and outside of Europe (Bernasconi & Recchi, 2025); information on employment conditions of immigrants in the UK (Salihoğlu & Vargas-Silva, 2025); the effects of changes in laws governing access to healthcare through the National Health Institute (NHS) in the UK (Rodríguez-Sánchez & Tjaden, 2025b); and a matching of official registers capturing mortality and population in Belgium (Surkyn & Bircan, 2025), a method that shares many more commonalities with traditional approaches as discussed in Chapter 5). The results of these pilot studies advance the literature in this domain and connect to a growing literature employing innovative data sources and methodologies to tackle the challenges of estimating irregular migration (Rodríguez-Sánchez & Tjaden, 2023). Tjaden and Rodríguez-Sánchez (2025) demonstrated that Facebook ads can effectively reach irregular migrants and that list experiments provide more reliable estimates of legal status than direct questions in the United States context, particularly among well-establish immigrant groups such as Mexicans residing in the US. Salihoğlu and Vargas-Silva (2025) demonstrated that some of the conceptual and measurement challenges in studying irregular migrants can be addressed through analysis of the informal economy and migrants’ characteristics, using a clear conceptual framework and probabilistic tools such as national labour force surveys from Turkey and the UK to estimate their presence within it. Rodríguez-Sánchez and Tjaden (2025b), in turn, showed that healthcare reforms in the UK resulted in a slight decline in new GP registrations at practices serving large migrant populations, which, when combined with arrival data at local levels, could be used to estimate undocumented migration flows using a multiplier approach. Bernasconi and Recchi (2025) analysed net air travel flows in the Schengen area, using passenger data and adjusted official net migration figures, providing estimates of irregular inflows by region of origin for 2019, closely matching the notion of visa overstayers. Drawing on Belgian population register data, Surkyn and Bircan’s study (2025) shows that mortality rates can serve as a robust indicator for estimating the size and changes of irregular migrants over time, providing detailed insights by gender, age group, and even region of origin. Finally, Rodríguez-Sánchez and Tjaden (2025a), in turn, showed that Facebook stocks of migrants, when examined by means of predictive modelling and machine learning, can provide hints at the hidden numbers not measured by official migrant stocks, offering a global comparison of irregular migrant stocks. 80 Chapter 6 Conclusion Computational approaches, of which digital data is an important component, have already enriched migration research (Drouhot et al., 2023). Thinking about data about migrants without a legal status as meta-data is helpful in understanding the information and in the development of further methods to estimate the number of irregular migrants. This type of data also comes with important limitations. The definition of what constitutes “migration”, “place of birth”, “migration status” etc. may vary across the different sources of metadata that exist and, importantly, may not be comparable to official or research standard definitions. These alternative data sources have not been created for research purposes. Despite the insights generated by metadata, one major limitation is the inability to learn something more about the demographics and living conditions of the population of irregular migrants thus estimated. Moreover, there are important risks associated with employing what previous research as defined as “footprints” when not put into the larger context of statistical information on other population statistics (Gelatt, Fix, & van Hook, 2018), such as information on birth, death, school enrolment, housing, and other records. Assessing when metadata is generated, whether it results from voluntary or involuntary actions, and understanding the potential coverage of such alternative data sources are key steps in determining how much insight can be gained from using innovative data in migration research. While each of these data traces can only offer a partial view of migrants with an irregular status, together these different sources of information underscore the inescapable visibility of irregular migrants and the potential to better understand their presence and the challenges such communities are facing. 81 Data traces & visibility of irregular migration References Bachmeier, J. D., Van Hook, J., & Bean, F. D. (2014). Can we measure immigrants’ legal status? Lessons from two US surveys. International Migration Review, 48(2), 538-566. https://doi.org/10.1111/imre.12059 Borjas, G. J., & Cassidy, H. (2019). The wage penalty to undocumented immigration. Labour Economics, 61, 101757. https://doi.org/10.1016/j.labeco.2019.101757 Drouhot, L. G., Deutschmann, E., Zuccotti, C. V., & Zagheni, E. (2023). Computational approaches to migration and integration research: promises and challenges. Journal of Ethnic and Migration Studies, 49(2), 389-407. https://doi.org/10.1080/1369183X.2022.2100542 Enríquez, C. G. (2019). Inmigración en España: una nueva fase de llegadas. Análisis del Real Instituto Elcano (ARI),(28), 1. Savatic, F., Thiollet, H., Mesnard, A., Senne, J. N., & Jaulin, T. (2024). Borders Start With Numbers: How Migration Data Create “Fake Illegals”. International Migration Review, 59(3), 1432-1463. https://doi.org/10.1177/01979183231222169 Rodriguez Sanchez, A., & Tjaden, J. (2023). Estimating Irregular Migration – A Review of Traditional and Innovative Methods. In MIrreM Working Paper No.4 (version 2). Krems: University for Continuing Education Krems (Danube University Krems). https://doi.org/10.5281/zenodo.8380854 Sîrbu, A., Andrienko, G., Andrienko, N., Boldrini, C., Conti, M., Giannotti, F., ... & Sharma, R. (2021). Human migration: The big data perspective. International Journal of Data Science and Analytics, 11, 341-360. https://doi.org/10.1007/s41060-020-00213-5 Tjaden, J. (2021). Measuring migration 2.0: a review of digital data sources. Comparative Migration Studies, 9(1), 59. https://doi.org/10.1186/s40878-021-00273-x Van Hook, J., Bachmeier, J. D., Coffman, D. L., et al. (2015). Can we spin straw into gold? An evaluation of immigrant legal status imputation approaches. Demography, 52, 329–354. https://doi.org/10.1007/s13524-014-0358-x 82 Chapter 6 Velasco, F. R. (2021). El arduo camino hacia la universalidad de la asistencia sanitaria de los inmigrantes irregulares en España. e-Revista Internacional de la Protección Social, 6(1), 343-369. http://dx.doi.org/10.12795/e-RIPS.2021.i01.16 Young, M. E. D. T., & Madrigal, D. S. (2017). Documenting legal status: A systematic review of measurement of undocumented status in health research. Public Health Reviews, 38, 1–25. https://doi.org/10.1186/s40985-017-0073-4 Zagheni, E., Weber, I., & Gummadi, K. (2017). Leveraging Facebook’s advertising platform to monitor stocks of migrants. Population and Development Review, 721-734. https://doi.org/10.1111/padr.12102 Chapter 7 Register data sources on migrant stocks Laura Peitz 84 Introduction Counting the number of irregular migrants or more closely investigating this target group using administrative data presents profound challenges. In most national contexts, registration in population registers or similar systems is tied to a legal residence status. Consequently, migrants without the legal right to stay are typically excluded from such databases altogether. Moreover, even if they could be registered, irregularly staying migrants might deliberately avoid contact with public authorities to minimize the risk of detection and possible deportation. This further limits their visibility in administrative data systems. Single administrative data sources may contain data on irregular migrants interacting with particular public institutions – for example for schooling or urgent healthcare, or upon regularization or police force encounters – but this data is oftentimes incomplete, fragmented, inconsistent, and usually not linked to broader administrative registers. While these obstacles pose significant barriers to the statistical inclusion of irregular migrant populations, some efforts have recently emerged to provide details on irregular migrant stocks based on administrative data sources (see also UNECE, 2025). In Italy, for instance, irregularly staying migrants can be identified by comparing data from various administrative sources and applying the Signs of Life method (see Box 7.1). Register data sources on migrant stocks Chapter 7 Key points • Analysing irregular migrant stocks using register or other administrative data can prove challenging given the usually undocumented nature of the phenomenon; yet, some recent efforts highlight the potential of register data. • The German Central Register of Foreigners (AZR) provides longitudinal data on nonGermans staying or having previously stayed in the country, including subsets of irregularly staying migrants, allowing for in-depth analyses of various research questions. • Throughout, the chapter provides real-life examples of how register data has been used in irregular migration research across different countries. 85 Register data sources on migrant stocks A similar approach has recently been applied in Chile, where the number of irregular migrants is estimated by integrating data from post-census administrative records on education, tourist stays, and police reports, and comparing this data against the baseline of residence permit applicants (see Box 7.2). In Spain, everybody is encouraged to register in the municipal population registers of their municipality (Padrón Municipal). The registration is a prerequisite for accessing basic rights and public services, such as health care and schooling. It is independent of legal status and – crucially – is not used for immigration control. As a consequence, the padrones even include irregularly staying migrants. When comparing or linking the padrón data to other administrative datasets, it is possible to assess questions around migrant irregularity, such as deriving the number of irregularly staying migrants from a comparison of the padrones with the database of legal stay permits (González-Enríquez, 2016). In the UK and Poland, recent efforts have been undertaken to produce a time series of the number of irregularly staying migrants based on the ethnic economies approach and non-linear count regression models. The assumption is that regularly settled ethnic groups provide support for individuals of similar ethnicity from their countries of origin to circumvent national restrictions on migration rules regarding work. Based on this, the numbers of detentions extracted from official police and border enforcement data are scaled up to the regularly residing foreign population using non-linear count regression models to estimate the number of irregularly staying migrants per country of origin (Beręsewicz, 2024). 1 Box 7.1: Applying the ‘Signs of Life’ method: The case of Italy Marco Marsili and Francesca Licari In Italy, the National Institute of Statistics (Istat) identifies the number of irregularly staying migrants by applying the Signs of Life (SoL) approach. To this end, in a first step, data on migration (changes of residence) are drawn from the centralized population register (ANPR, managed by the Ministry of the Interior). These data are subjected to standard control and correction procedures. In general, the quality of the data is quite high; in case of partial non-response, the information is filled using donor hot-deck methods of imputation or by retrievals from the previous year’s census, where available. In a second step, the information of the ANPR is integrated into a demographic data system (MideaAnvis, MIcro-DEmographic Account - Virtual Statistical register of the population) which, in addition to migrations, also incorporates data of other population changes (births, deaths, acquisitions of citizenship). Midea-Anvis is a counting system based on micro-data, in which all data are integrated with each other and with respect to the population of the last census, in order to verify the stock-flow coherence of the information acquired. The last step is comparing Midea-Anvis with a large set of administrative archives (AIDA, Integrated Archive of Administrative Data), including, among others, the tax, social security, energy consumption, and education registers as well as the Cadastre of buildings and constructions. Each administrative archive in AIDA provides life signals on habitually resident persons who have spent a significant amount of time in Italy over the last three years. The comparison between AIDA and Midea-Anvis produces three distinct datasets: 1. individuals present in Midea-Anvis and confirmed as residents through the life signals system in AIDA (the so-called “usual resident population”); 2. individuals not present in Midea-Anvis but with strong life signals in AIDA (under-coverage); 3. individuals present in Midea-Anvis but without life signals in AIDA (over-coverage). 1 This research was led by Brendan Georgeson (Office for National Statistics, UK) and Maciej Beręsewicz (Poznań University of Economics and Business, Poland). 86 Chapter 7 The second of these datasets is relevant information about irregular migrants. It comprises all those individuals who, despite not having specific authorization to reside in the national territory (for example, because they have an expired residence permit) show signs of administrative life in Italy (e.g. because of working, studying or avwaiting residence permit renewal). Hence, as regards irregular or undocumented migrants, the current structure of the data production system allows to correctly focus on a specific group of irregularly staying migrants. the other side, the evaluation of fully undocumented migrants is most challenging as, by definition, they do not show any sign of life. Nonetheless, Istat also produces national estimates of fully undocumented migrants on a yearly basis. The sources used to produce these estimates have varied over the years, depending on data availability, including sample surveys and data from administrative sources. In recent years, the methodology has been improved by also integrating data of the Ministry of the Interior relating to police stops on the territory or at the border, as well as data relating to actual repatriations to countries of origin. In contrast, Germany presents a unique case in this regard, as it has been using a Central Register of Foreigners (AZR) for over 70 years, which includes comprehensive data on the majority of non-nationals staying in the country, even parts of those without legal residency status. The reason is that in Germany, many migrants who are formally obliged to leave the country are issued a Duldung (tolerated status) while their removal is temporarily suspended due to either actual obstacles (e.g. missing travel documents or illness) or legal reasons (e.g. family unity) preventing deportation. These migrants are well captured in the Central Register of Foreigners, meaning that the register is suited for detailed analyses aimed at specific subgroups of irregular migrants. The rest of this chapter will present the Central Register of Foreigners and the potentials and pitfalls of using its administrative data in irregular migration research. Box 7.2: Chile’s experiences in integrating data for estimating the foreign population with irregular migration status2 Julibeth Rodríguez and Felipe Mallea Since 2014, Chile has witnessed an increasing migrant flow, which has meant that the country must assume the challenge of officially measuring the phenomenon. To this end, the National Statistics Institute (INE) and the National Migration Service (Sermig) have developed a methodology for estimating the number of foreign nationals residing in Chile between censuses by linking bordercontrol data with residency applications after the 2017 Census. This study employs a methodology that integrates baseline data from the census with data from postcensus administrative records by linking microdata of various government institutions. A critical component is using administrative records to identify populations with a potentially irregular migration status. 2 This work was conducted by the Studies Department at the National Migration Service of Chile and the Demography Subdepartment at the National Institute of Statistics of Chile (team members: Gabriel Santander, Consuelo Salas, Marisol Opazo, Pablo Roessler, Felipe Hugo, Luis Rodríguez, Miguel Ojeda, Francisco González). More details are available at: https://serviciomigraciones.cl/estudios-migratorios/estimaciones-de-extranjeros/ and https://www.ine.gob.cl/estadisticas/ sociales/demografia-y-vitales/demografia-y-migracion. 87 Register data sources on migrant stocks The methodology acknowledges the complexities in precisely quantifying irregular migration, which are due to the multifaceted character and the dynamic fluidity of migration status. Consequently, the scope of the estimation is deliberately confined to a specific subset of the population with irregular status, a delimitation necessitated by the availability of relevant data sources and by the objective of clearly distinguishing the populations with regular and irregular migration status. The estimation of the population with irregular status specifically includes individuals who do not possess any type of residency permit application, who have been in the country for at least six months, and who have no recorded departure for the period ending on December 31, 2023. The sources for the estimation are as follows: 1. The biometric control system (between June and December 2023) 2. Expired tourist visa extensions or police reports (including both formal denunciations for unauthorized entry and self-reported clandestine entries) 3. Primary and secondary student enrollment in Chilean educational institutions of those who are assigned a provisional identifier because they lack a national identification number (RUN) By including a wide range of administrative records, we can account for the two main areas that form the basis of irregular migration of foreign nationals in Chile: (1) those who enter the country clandestinely and who cannot apply for a residency permit, and (2) those who enter the country legally and who cannot apply for a residency permit. With these two areas and their combination with records from border control, it can be determined whether the person was in the country for the period ending on December 31, 2023. In processing the data, 33,251 people who left the country were excluded. In contrast, we included those whose presence and residence in Chile were shown by the records of their administrative acts to be subsequent to their exit from the country. The final dataset for the population with irregular migration status comprised 336,984 individuals, whose information was categorized by primary source: 261,449 from police reports, 10,217 from expired tourist visas, and 65,318 from official enrollments without a national identification number (RUN). In conclusion, this study presents a methodology that contributes to international migration statistics by integrating census data with diverse administrative data. While recognizing the inherent uncertainties in estimating the population with irregular migration status, the results offer valuable insights for targeted public policy design and demonstrate potential for adaptation in other countries, which would thereby improve the quality and comparability of regional migration data. Structure and contents of the AZR The Ausländerzentralregister (AZR, Central Register of Foreigners) is Germany’s primary administrative register for non-German nationals living in Germany. Established in 1953 and governed by law (Gesetz über das Ausländerzentralregister), the AZR plays a central role in federal and local migration governance, strategic planning as well as in daily migration-related administrative activities. The data is entered into the register primarily by local immigration offices (Ausländerbehörden) as well as other public institutions such as the Federal Office for Migration and Refugees (Bundesamt für Migration und Flüchtlinge, BAMF) or federal and state police forces. Various public authorities use 88 Chapter 7 the data stored in the AZR to support their caseby-case decision-making, operational planning, and political decision-making. Over the past years, the register has become an important, though complex, information source on various migrationrelated questions for public administration, the government, the media, the broader public, and for researchers interested in understanding patterns and dynamics of migration and residence (Brückner, 2019; Peitz, 2025; Tanis, 2022; Weber, 2022). The AZR covers all non-German nationals who reside in or have resided in Germany for more than three months. In addition, it includes data of individuals who have filed an asylum claim and of those who have been issued residence law decisions, such as expulsion or deportation orders. The data recorded is stored in the AZR for the duration of an individual’s stay in Germany, and usually for ten years after their departure (five years after death). All data entries are deleted from the AZR upon naturalization, without the possibility of further tracking these individuals given the lack of a central population register in Germany. The AZR contains various data attributes per individual. Which types of attributes are stored depends on the specific group of migrants. Only rudimentary information is stored in the case of EU citizens, while the most comprehensive data is collected on individuals entering the asylum system. The variables contained include: • Personal data: unique AZR identifier, full name, date of birth, gender, nationality, marital status • Border crossings: entries, voluntary departure, forced return • Residence status: temporary and permanent residence titles, Duldung, obligations to leave the country • Asylum procedure: application filed, asylum status, rejection In addition to this “core” data, the AZR has in recent years been expanded by multiple additional variables, including language skills, education and profession, postal address, and integration course information, but the quality of these variables varies (see below). With exception of time-invariant personal data, data entries in the AZR are usually locationand timestamped: They contain the date of the respective data entry as well as the municipal level of the executive authority (which usually corresponds to individuals’ place of residence), along with the respective federal state. Importantly, whenever new information is entered for many of the ‘core’ variables, the previous data entry is not overwritten. Instead, all previous information on these variables is kept as long as an individual’s data is stored in the AZR (see Gleiser & Hinz, 2024, p.8). This way, the AZR data allows for longitudinal and flow analyses. Irregular migration stocks and flows based on AZR data The AZR can provide indicators on irregular migration stocks and flows. However, one needs to carefully delineate the groups of irregular migrants who are, and who are not, included in AZR data. Being an administrative register utilized and filled by public authorities, the AZR, virtually by definition, contains only data on migrants with contact to the authorities. Based on the MIrreM taxonomy (Kraler, 2023), the following groups of irregularly staying migrants (migrants with an obligation to leave the country) can be identified using the AZR: individuals who are issued a return decision, whose status is expired or revoked, and whose removal is formally suspended.3 The following flows into and out of irregularity can be traced based on AZR data: inmigration, being born into an obligation to 3 Due to the specific filter functions in the AZR, identifying these groups is possible in the most current cross-sectional dataset. It is, however, not necessarily possible for all these groups retrospectively in the longitudinal dataset. 95 15 years after CLANDESTINO: what do we know? trends). The first large-scale EU-funded project to do so was CLANDESTINO (Undocumented Migration: Counting the Uncountable: Data and Trends Across Europe)1, which ran from 2007 to 2009. The final report presented the following conclusions (Jandl et al., 2008, p. 17): The review of efforts to estimate the size of irregular migration on a European level has shown that the numbers indicated are based on very rough estimates. Often, we do not know which groups of irregular migrants are in [sic] included in a stock estimate, nor we do not know whether a flow estimate is meant to measure net inflows or gross inflows (without substraction [sic] of outflows). Jandl (2008, p. 20) further pointed out that compared to stocks, flows are generally not wellmeasured: …Given the highly volatile nature of migration flows, the scarcity of reliable indicators on illegal migration flows, and the dearth of appropriate methods for estimating such flows, most efforts have concentrated on estimating stocks of undocumented migrants rather than flows. Now over a decade since CLANDESTINO, and with managing irregular migration flows a mainstay policy priority in the EU and other countries, this chapter outlines the main findings from the MIrreM Project’s Work Package on Flows (WP4). More specifically, it provides a summary of the current approaches to measuring irregular migration flows, and addresses the question: what do we know now about irregular migration flows, 15 years since CLANDESTINO?2 Expanding the temporal and geographic scope, improving the quality assessment criteria MIrreM is a follow-up to CLANDESTINO, and the following Table shows a basic comparison between the two projects in terms of timelines and geographic coverage: 1 https://irregular-migration.net/ 2 This chapter draws mainly from the following WP4 deliverable, which was published in 2024, hence, 15 years since the conclusion of the CLANDESTINO Project in 2009: Siruno, L., Leerkes, A., Hendow, M., & Brunovská, E. (2024). MIrreM Working Paper on Irregular Migration Flows. University for Continuing Education Krems (Danube University Krems). https://doi.org/10.5281/zenodo.10702228 Table 8.1: Basic comparison between the CLANDESTINO and MIrreM Projects 96 Chapter 8 A notable difference is the inclusion of non-EU countries in the MIrreM project. And as discussed in detail in Chapter 4 of this Handbook, MIrreM has developed and used a more structured set of criteria to assess the quality of irregular migration data. In addition, for irregular migration flows in particular, the MIrreM project highlighted the distinction between estimates and indicators. Estimates refer to statistical calculations or approximations that quantify both observed and non-observed or unknown irregular migration flows. Indicators, on the other hand, refer to metrics or variables that relate only to observed or measured irregular migration flows. In other words, indicators of irregular migration flows show the number of actual observations or cases, such as detections of illegal border crossings, whereas estimates use indicators to come to conclusions about a broader trend, including non-observed components, such as the total number of adults, detected and undetected, who crossed into a country without the legal right to do so. Two related but different sets of criteria were developed to assess the quality of irregular flow estimates and indicators.3 What we know now about irregular migration flows, 15 years since CLANDESTINO Post-CLANDESTINO, scholars observe that available migration data often remain “inaccurate, inconsistent and incomplete” as they are based on differing definitions (Bijak et al., 2019, p. 471). In addition to differing definitions and measures, there are persistent and interlinked gaps based on the drivers or reasons behind migration, geographic coverage, demographic characteristics, and time lag in the availability of data (Ahmad-Yar & Bircan, 2021). International migration flows are particularly difficult to measure, and this is the case even with advancements in technology and data science (McAuliffe & Ruhs, 2017). Several international organisations, including UN DESA and the OECD, have been collecting and publishing international migration flows data, but different definitions and data collection methods present challenges in harmonisation and comparability (Yildiz & Abel, 2021). As there is an inherent challenge in collecting data on clandestine or irregular processes, the difficulties are even more pronounced when capturing data on irregular migration flows (McAuliffe & Sawyer, 2021, p. 48, emphasis added). So, while many countries have available stock estimates, there is persistence in the scarcity of available flow estimates as observed in the CLANDESTINO Project. Because of this, irregular migration flows are more often measured through statistical indicators, particularly geographic and status-related flow indicators. Table 8.2 below provides a summary of the findings from the CLANDESTINO and MIrreM projects related to different types of irregular migration flows. In view of findings from CLANDESTINO, the conclusion reached then, namely that the methodologies for analysing irregular border crossings, visa overstays, and overall irregular migration flows lag behind the study of irregular resident stocks (Vogel et al., 2008), still rings true. However, the MIrreM Project has found that there are now more irregular flow indicators, particularly for geographic flows, and to some extent, also asylum-related status flows. 3 However, and as this piece underscores, compared to stocks, there is a notable lack of available estimates on irregular migration flows. 97 15 years after CLANDESTINO: what do we know? Table 8.2: Summary of flow trends from the CLANDESTINO and MIrreM Projects 98 Chapter 8 Box 8.1: Frontex data on “illegal border crossings” and the political construction of “illegal” immigration Filip Savatic Since 2009, Frontex, the Border and Coast Guard Agency of the European Union (EU), has published a dataset on “illegal border crossings” (IBCs) into the EU and Schengen Area which is publicly accessible through the institution’s website.4 This dataset was initially labelled “irregular border crossings” until 2022, with the change reflecting a striking shift. Over time, particularly after the so-called “migration crisis” of 2015, this dataset has been increasingly referenced by mainstream media, researchers, international organizations, and other actors as a measure of “illegal” migration to Europe. However, the use of these data as an indicator of irregular migration is problematic for several reasons. First, they capture only detected entries, and may, depending on type of border and context, represent an undercount of actual crossings. Second, they represent crossings and not people and thus may record repeat crossings made by the same individual multiple times, leading to an overcount of movements. Most importantly, the database does not consider valid protection claims of those detected while irregularly crossing a border. As article 31 of the Geneva Refugee Convention states, irregular entry is permitted when individuals are fleeing persecution (United Nations, 1951/1967). Given the absence of legal pathways for refugees to reach Europe, most asylum seekers reach the continent without any prior authorization, with many subsequently obtaining refugee status. Deploying a novel method, Savatic et al. (2024) use data on asylum adjudications across 31 European states to divide Frontex data on IBCs into those who would likely obtain refugee status (or not) given their nationality. The average acceptance rate is weighted given the number of first instance asylum decisions by nationality made in each of the 31 states. First instance data are used to ensure comparability given that asylum appeals procedures vary across states; using these data generates a conservative estimate of asylum acceptances as only rejections are overturned. This division of IBCs reveals that, between 2009-2021, 55.4% can be considered “likely refugees,” a proportion that rises to 75.5% at the peak of arrivals in 2015. With most IBCs representing forced migration flows considering the asylum policies implemented domestically within Europe, the use of data on border crossings as an objective measure of “illegal” migration is misplaced. Overall, this analysis exposes how data can be – and are – deployed to further certain public narratives and thereby represent political constructions rather than objective truths. In the case of data on border-crossings collected by law enforcement agencies such as Frontex, narratives of “illegal” migration flows construct an understanding of border crossings as something which requires a securitized response – one that law enforcement bodies can provide. Alternative labelling such as “forced” migration would imply that humanitarian responses to migration flows would be more appropriate. Thus, it is imperative for news media, researchers, and all other public authorities to adopt a critical approach to data, questioning what they represent and what purpose they serve for those who collect and publish them. References Savatic, F., Thiollet, H., Mesnard, A., Senne, J.-N., & Jaulin, T. (2024). Borders Start With Numbers: How Migration Data Create “Fake Illegals”. International Migration Review, 59(3), 1432-1463. https://doi.org/10.1177/01979183231222169 (Original work published 2025) United Nations (1951/1967). Convention relating to the status of refugees and protocol relating to the status of refugees. United Nations Treaty Series, 189 U.N.T.S. 137; 606 U.N.T.S. 267. https://www.unhcr.org/media/1951-refugee-convention-and-1967-protocol-relating-status-refugees 4 See https://www.frontex.europa.eu/what-we-do/monitoring-and-risk-analysis/migratory-map/ 99 15 years after CLANDESTINO: what do we know? Conclusion While there is still hardly any data available for demographic flows, available EU-level indicators for irregular flows are generally of good quality, particularly with respect to accessibility and documentation. But there are, unsurprisingly, some limitations in terms of validity and reliability. In terms of external validity, the data available often only describe an aspect of the phenomenon of irregular migration instead of being representative of the whole (e.g., asylum data only capture statusrelated flows). Among others, there are also issues with double-counting5 or missing data, particularly when disaggregating by age and sex, which pose a challenge to measurement precision. As for internal validity, it is difficult to independently assess since the data are generated by bureaucracies with limited oversight; the indicators used are crosssectional and not linked in any way; and there are not many opportunities to cross-validate the numbers with other information. Eurostat and EU Agencies work hard to harmonise data collection among member states, but currently, limitations continue to be evident, particularly with regard to doubleor under-counting, geographical and temporal comparability (including time lags), and finally, interoperability across EU systems. Good quality data are essential for effective migration governance. On the one hand, it can be in the best interests of irregular migrants to be counted, particularly if they need protection. However, the same data can also be used for the enforcement of migration legislation, including apprehension, detention, or deportation. As such, the interest in enhancing data collection on irregular migration and generating estimates must be carefully weighed against privacy considerations and societal interests. This balance is crucial so as not to impede trust on the part of irregular migrants and hinder the public service mission of providers or support groups, civil servants, and other streetlevel bureaucrats who regularly come into contact with them. In view of these, we recommend the following main ways forward to advance research on irregular migration flows and to prevent misuse of migration data: • Define irregularity well and, when needed, be clear about different types of irregularity; • Continue improving data quality for (selected) flow indicators, for example, by investing more resources into quality checks and making cohort data across multiple indicators available (without compromising privacy considerations); • Acknowledge that supplemental qualitative information is essential for the validation and triangulation of quantitative data; incorporating qualitative studies6 into the collection of migration data should be the norm; and finally, • Consider using accessible informational resources, such as educational videos, to mitigate the misuse of migration data for political purposes; knowing the importance of a fact-based discourse can help ensure that statistics on migrant populations are not manipulated or misrepresented to serve political agendas. The salience and problematisation of irregular migration in policy and everyday discourse increase the risks associated with the use of irregular migration data for political purposes. The potential for misuse7 cannot be underestimated – from the presentation of statistics to the utilisation of such statistics in political decisions and policymaking. Immigration, particularly irregular migration, has become a divisive, even polarising topic. As such, all the more is good quality data – accurate, frequent and timely – of critical importance. 5 For example, if an individual attempts to cross the border multiple times within a short period, each attempt is likely recorded as a separate incident. There is also potential double counting between indicators as one person might generate a detection at one border, then an application for asylum, then a withdrawal, then another detection at another border, another asylum application, a Dublin hit, a negative asylum decision etc. All these data concerning one individual may be recorded within a year on Eurostat. 6 For example, conducting anonymous interviews with irregular migrants themselves and collecting testimonies that describe their situations and intentions in more detail rather than relying solely on a simple counting exercise. 7 See for example, ECRE. (2022). Asylum statistics and the need for protection in Europe: Updated Factsheet https://ecre.org/wp-content/uploads/2022/12/Asylum-statistics-and-the-need-for-protection-in-Europe-final.pdf. Also, Mouzourakis, M. (2014). ‘Wrong number?’ The Use and Misuse of Asylum Data in the European Union. https://www.ceps.eu/ceps-publications/wrong-number-use-and-misuse-asylum-data-european-union/ 100 Chapter 8 References Ahmad-Yar, A. W., & Bircan, T. (2021). Anatomy of a misfit: International migration statistics. Sustainability, 13(7), 4032. https://doi.org/10.3390/su13074032 Bijak, J., Disney, G., Findlay, A. M., Forster, J. J., Smith, P. W., & Wiśniowski, A. (2019). Assessing time series models for forecasting international migration: Lessons from the United Kingdom. Journal of Forecasting, 38(5), 470-487. https://doi.org/10.1002/for.2576 Cantat, C., Pécoud, A., & Thiollet, H. (2023). Migration as Crisis. American Behavioral Scientist, 1-23. https://doi.org/10.1177/00027642231182889 CLANDESTINO Project (2009). CLANDESTINO Project Final Report. https://cordis.europa.eu/docs/publications/1266/126625701-6_en.pdf De Genova, N. P. (2002). Migrant “illegality” and deportability in everyday life. Annual Review of Anthropology, 31(1), 419-447. https://doi.org/10.1146/annurev.anthro.31.040402.085432 ECRE (2022). Asylum statistics and the need for protection in Europe: Updated Factsheet. https://ecre.org/ wp-content/uploads/2022/12/Asylum-statistics-and-the-need-for-protection-in-Europe-final.pdf Eurostat. (n.d.). Enforcement of immigration legislation statistics introduced. https://ec.europa.eu/eurostat/ statistics-explained/index.php?title=Enforcement_of_immigration_legislation_statistics_introduced Jandl, M. (2008). Methods for estimating stocks and flows of irregular migrants. Chapter 3 of CLANDESTINO Report on methodological issues. https://migrant-integration.ec.europa.eu/library-document/clandestinoproject-report-methodological-issues_en Jandl, M., Vogel, D., & Iglicka, K. (2008). Report on methodological issues (Research Paper, CLANDESTINO Undocumented Migration: Counting the Uncountable, Issue. https://migrant-integration.ec.europa.eu/ library-document/clandestino-project-report-methodological-issues_en McAuliffe, M., & Ruhs, M. (2017). World Migration Report 2018. https://www.iom.int/sites/g/files/tmzbdl486/ files/country/docs/china/r5_world_migration_report_2018_en.pdf 101 15 years after CLANDESTINO: what do we know? McAuliffe, M., & Sawyer, A. (2021). The roles and limitations of data science in understanding international migration flows and human mobility. In M. McAuliffe (Ed.), Research Handbook on International Migration and Digital Technology (pp. 42-57). Edward Elgar Publishing. Mouzourakis, M. (2014). ‘Wrong number?’ The Use and Misuse of Asylum Data in the European Union. https://www.ceps.eu/ceps-publications/wrong-number-use-and-misuse-asylum-data-european-union/ Sassen, S. (1999). Guests and Aliens. The New Press. Siruno, L., Leerkes, A., Hendow, M., & Brunovská, E. (2024). MIrreM Working Paper on Irregular Migration Flows. University for Continuing Education Krems (Danube University Krems). https://doi.org/10.5281/zenodo.10702228 Vogel, D., Jandl, M., Kraler, A., & Vogel, D. (2008). Report on methodological issues (Report prepared for the research project CLANDESTINO Undocumented Migration: Counting the Uncountable. Data and Trends across Europe funded under the 6th Framework Programme of the European Union, Issue. https://migrantintegration.ec.europa.eu/library-document/clandestino-project-report-methodological-issues_en Yildiz, D., & Abel, G. (2021). Migration stocks and flows: data concepts, availability and comparability. In M. McAuliffe (Ed.), Research handbook on international migration and digital technology (pp. 29-41). Edward Elgar Publishing. 102 Chapter 8 Box 8.2: Understanding asylum data in the context of irregular and regular migration TeddyWilkin and Petya Alexandrova Data on asylum applications are used widely used as indicators of mixed migration to and within the EU+.8 Yet interpreting these figures in relation to irregular and regular migration requires careful nuance. Many asylum seekers cross borders undetected, some enter legally, and others apply repeatedly in the same country or move between EU+ countries. This complexity creates challenges for measurement, interpretation and policy. As of mid-2025, there were 1.3 million asylum applications in the EU+ still awaiting a final decision. This highlights the scale of people currently staying with unresolved legal status—many of whom may eventually find themselves in an irregular position if their claim is rejected. In 2024, EU+ countries issued around a third of a million negative asylum decisions. While some appeal such decisions, many abscond and remain without legal residence. Visa policy provides a direct link between asylum and regular migration. In 2024, around a quarter of all asylum applications in the EU+ were lodged by persons originating from visa-exempt countries. Such persons can enter the EU for touristic reasons without needing to apply for visa. Many do so, and then claim asylum. Conversely, those from visa-obliged countries may apply for a visa and then arrive regularly and apply for asylum. The share of visa holders among asylum applicants is quite important in some EU+ countries. However, irregular entry remains extremely important for asylum applications. EUAA estimates suggest that in 2024, detected illegal border-crossings by land and sea accounted for about 1 in 7 asylum applications overall, rising to a third of all asylum applications in frontline Member States. However, these only reflect actual detections at the border. Undetected irregular arrivals are, by definition, not counted—meaning any analysis based solely on detections risks underestimating the scale. This makes it even more important to triangulate asylum data with other sources. Asylum applications can also reveal secondary movements—people applying sequentially in more than one EU+ country or applying in EU+ countries other than the one they initially entered. In 2024, nearly 150,000 decisions were issued in response to outgoing Dublin requests, which, we estimate, relates to about 14% of total applications. Such requests are made under the Dublin III Regulation which establishes which Member State is responsible for examining an asylum application. Most of these requests were for reasons related to secondary movements. Even persons with refugee status have been known to move and reapply elsewhere. Data from Eurodac, the EU’s biometric database for asylum and irregular entry, provide additional insights. In 2023, there were more than 276,000 instances of asylum applications being linked to recent irregular border-crossings. Just over half applied for asylum in the same Member State where they were detected, while the rest applied for asylum in another Member State. These matches illustrate the link between irregular entry and asylum applications, but the Eurodac data have limitations including potential double counting, the exclusion of children under 14, and the lack of breakdowns by nationality. Repeated asylum applications add another layer of complexity. According to eu-LISA,9 only 55% of applications lodged in 2023 were first-time claims, indicating that nearly half of all applicants had already lodged previous asylum applications somewhere in the EU+. EUAA estimates suggest that nearly a tenth were individuals reapplying in the same EU+ country (in both 2023 and 2024), often after remaining in the country for an extended period—typically in an irregular or tolerated status. 8 EU+ = EU Member States plus Norway and Switzerland 9 E-LISA stands for the European Union Agency for the Operational Management of Large-Scale IT. 103 15 years after CLANDESTINO: what do we know? Box 8.3: Understanding 4Mi data Francesco Teo Ficcarello What is 4Mi? 4Mi, developed by the Mixed Migration Centre (MMC), is an innovative and global data collection platform10 that provides independent and in-depth insights into the experiences of migrants moving along mixed migration routes. Since 2014, 4Mi has become the world’s largest globally comparable primary data collection system focused specifically on people on the move, with more than 130,000 interviews conducted in over 30 countries across Africa, Asia, Europe and Latin America. Rationale and scope 4Mi was created to fill a major evidence gap around the realities faced by migrants and refugees in transit—populations often invisible in traditional migration statistics due to their irregular or noncamp-based status. The initiative contributes directly to the Global Compact for Migration’s Objective 1 by providing accurate and disaggregated data for informed policy-making. Methodology Data are collected through one-on-one, structured interviews using standardized yet adaptable survey tools, administered by a large network community-based enumerators (approx. 130 as of 2025), most of whom are migrants or refugees themselves. These enumerators are embedded in local contexts, ensuring high trust and access to hard-to-reach groups. In countries where MMC is not established, data are collected through local partners rooted in the countries. Surveys are quantitative, enabling statistically robust analysis, but also include open-ended questions to capture personal narratives. Sampling is purposive, with enumerators operating in migration hubs identified through scoping and mapping exercises. While not statistically representative, the data are highly indicative, enabling rich, contextual understanding of profiles, drivers, journeys,, vulnerabilities, and aspirations of people on the move. Asylum data can also reflect demographic patterns. Some applicants are children born in the EU+ to an asylum-seeking parent, in some cases making up more than 10% of all applicants. These figures reflect how status can persist intergenerationally without clear legal resolution. Looking ahead, under the Interoperability Regulation, the Central Repository for Reporting and Statistics (CRRS), currently under development, is expected to deliver cross-system statistics that will significantly improve our understanding of these dynamics. It will enable anonymous tracking across databases and provide more precise insights into how individuals move through stages of irregular entry, legal stay, asylum and status withdrawal. In short, asylum data provide a valuable but incomplete window into migration stocks. They reflect both regular and irregular situations but must be interpreted with care. Analysts should consider visa status, secondary movements, repeated applications and related demographic patterns. When triangulated with detections at the border, visa records, and Dublin statistics, asylum data help clarify not only the scale of irregular presence, but also how individuals engage with EU+ migration and protection frameworks. 10 See https://mixedmigration.org/4mi/4mi-faq/ 104 Chapter 8 Tools and flexibility The model is flexible, allowing for add-ons on topics such as youth migration, climate mobility, and urban integration. Innovations include longitudinal follow-ups, remote data collection, and interactive dashboards for public data exploration. This flexibility was key to rapidly launching Covid-19-specific modules, through which 25,500 interviews were conducted in 2020 alone. Data use and outputs 4Mi data feed into MMC’s research publications,11 interactive dashboards12 and presentations towards evidence-based programming and policy-making. The data are also shared with partners such as UN agencies and NGOs under data-sharing agreements. Outputs include statistical analyses in the form of research reports, briefing papers, snapshots, infographics and policy briefings, as well as real-time response tools for humanitarian actors. A unique complement to flow data By providing in-depth, qualitative insights into the human dimension of migration, 4Mi complements other data collection and flow monitoring systems (e.g., IOM’s DTM), which focus more on volumes. 4Mi captures lived experiences, decisions, and risks in a globally comparable format, enabling crossregional and route-based analysis. Its integration of quantitative scale with qualitative depth ensures that the perspectives of (irregular) migrants—often missing from mainstream migration discourse— are not only heard but systematically analyzed. In doing so, 4Mi plays a vital role in providing an evidence base for the development of more humane, inclusive, and responsive migration policy and practice worldwide.13 11 See https://mixedmigration.org/resources/ 12 See https://mixedmigration.org/4mi/4mi-interactive/ 13 More information on 4Mi can be found at https://mixedmigration.org/wp-content/uploads/2021/08/4Mi-Introduction.pdf Chapter 10 Surveying irregular migrants: Challenges and approaches Rocco Molinari and Livia Elisa Ortensi 112 Introduction Understanding irregular migration processes is crucial in contexts where legal barriers to longterm immigration are prominent, such as Western migrant-receiving countries. Policymakers not only need techniques to estimate irregular migration flows and stocks, but also data on the lived experiences of undocumented migrants. This includes how legal status interacts with various dimensions of settlement (e.g., health, labour market, family formation, crime, attitudes). Surveying undocumented migrants is one way to investigate these issues. However, while traditional migration surveys are already challenging (Vickstrom and Beauchemin, 2024), these challenges are amplified when the target population lacks legal status, due to structural, methodological, and ethical issues that distinguish this population from most others. A fundamental difficulty is that irregular migrants are not generally included in official population registers or sampling frames, leading Surveying irregular migrants: Challenges and approaches Chapter 10 Key points • Irregular migrants are difficult to capture in statistics because of their absence from official sampling frames, mobility, and fear of detection. Surveying them requires tailored approaches, including non-probability sampling, trust-building strategies and ethical safeguards. • This chapter reviews three types of surveys that can yield data on irregular migrants: those that explicitly include them in the sampling design, those that target applicants of regularisation programmes, and retrospective surveys that reconstruct past legal trajectories. • Drawing on examples from France, Italy, Spain and the United States, this chapter shows how innovative designs and context-specific adaptations can improve coverage and data quality. • Each approach has its own strengths and limitations. A combination of methods, applied thoughtfully, is needed to strengthen the evidence base and support more accurate data collection and analysis. 113 Surveying irregular migrants: Challenges & approaches to identification challenges for researchers. Without a known universe from which to draw a representative sample, it is not possible to apply standard probability sampling methods. Moreover, the lives of irregular migrants tend to be embedded in informal networks and practices. Mistrust can be a pervasive issue: irregular migrants often avoid contact with entities perceived as linked to official institutions due to fear of detection, detention, or deportation. This leads to high levels of non-response and answers shaped by mistrust, especially if anonymity is not fully guaranteed. Building trust requires time, cultural sensitivity, and in many cases, collaboration with communitybased actors or mediators. Even when undocumented migrants are—whether by design or by chance—included in a survey sample, legal status is rarely collected, and if it is, the data are often unreliable. High mobility and precarious living conditions further complicate data collection. Frequent changes in housing and employment, geographic mobility, and periods of complete inaccessibility due to informal work patterns make it extremely difficult to trace respondents over time, particularly in longitudinal studies (Peitz et al., 2024). Finally, undocumented migrants are likely to differ from the other migrants on the basis of observable and unobservable characteristics. This selectivity can affect the representativeness of any resulting sample. Altogether, these factors combine to make irregular migrants one of the most difficult populations to study using conventional social science methods. Accurately capturing their living conditions requires not only adapted methodological tools, but also a deep ethical commitment to protection, confidentiality, and respectful engagement. For all these reasons, surveys that include undocumented migrants are generally scarce, small, locally based, and targeted to specific migrant subgroups (Bachmeier et al., 2014). However, a limited number of studies have succeeded in targeting undocumented migrants or including them within broader samples of migrant populations. This chapter examines the most commonly used approaches to surveying undocumented migrants and reviews promising practices. Although most of the research has traditionally been conducted in the United States, the chapter places greater emphasis on Europe, where several innovative approaches have recently emerged. What types of irregular migration surveys are there? Surveys that include information on the life conditions of current or former undocumented migrants can be broadly grouped into three main categories, based on their methodological approach and target population: 1. The first category comprises surveys that explicitly include undocumented migrants in their sampling design. These are the only surveys that can be used to understand the life conditions of current irregular migrants. They typically compare irregular with regular migrants. These surveys use specific data collection techniques—such as centre-based sampling or other network-based methods— designed also to reach undocumented individuals, or they rely on existing sources that indirectly capture segments of the undocumented population without targeting them explicitly. 2. A second category consists of surveys conducted in the context of regularisation programmes. These surveys focus on people applying for legal status and often gather information on their legal trajectories and socio-economic conditions. Some include a longitudinal component, following applicants over time to assess the impact of regularisation on their lives. 3. A third type includes retrospective surveys conducted with migrants who currently hold a legal status, but which collect data on their past experiences of irregularity, thereby reconstructing their legal trajectory and capturing temporary phases of undocumented residence. These surveys can be used to understand the situation of migrants who have recently regularised and to understand the medium and long-term consequences of irregularity among regularised migrants. 114 Chapter 10 Surveys that explicitly include undocumented migrants in their sampling design Some surveys designed to collect information on undocumented migrants avoid the use of a conventional sampling frame altogether. A leading example in Europe is the Regional Observatory for Integration and Multiethnicity (ORIM) in Lombardy, Italy. Active from 2001 to 2021, the program collected data on the living conditions of people with a migration background. Explicit efforts were made to include irregular migrants, who – particularly in ORIM’s early years – made up a substantial portion of the foreign-origin population in the area. Every year, ORIM conducted retrospective, faceto-face interviews with a representative sample of foreign residents in the region using the Centre Sampling Technique (CST; see Box 10.1; Baio et al., 2011). A cornerstone of the ORIM model was its participatory and inclusive approach to fieldwork: interviews were conducted by trained culturallinguistic mediators of migrant background, enhancing trust and communication, which was particularly important when engaging with undocumented individuals. Over the course of two decades, ORIM generated a unique cross-sectional data series that supported academic research and informed evidence-based policies in integration, social inclusion, and rights protection. Although the program was discontinued in 2021, it has remained a methodological benchmark for research on hard-to-reach populations and a model for how undocumented migrants can be ethically and effectively surveyed. CST has also been used at the national level in Italy and outside the Italian context (e.g. the Immigrant Citizenship Survey ICS). Some surveys have successfully reached undocumented migrants by exploiting administrative sources that, by their nature, include them. One prominent example in Europe is the Spanish National Immigrant Survey (ENI; Reher and Requena, 2009), carried out by Spain’s National Statistics Institute (INE) in 2006–07. The ENI drew its sample from the municipal population register (Padrón Municipal), which grants all registered residents—including irregular migrants—access to public health care and other services and is considered representative of immigrants living in Box 10.1: The Centre Sampling Technique Rocco Molinari and Livia Elisa Ortensi The Centre Sampling Technique (CST) is a probabilistic sampling method developed to reach hard-to-survey populations, particularly undocumented migrants who are typically excluded from standard household surveys due to the lack of a sampling frame. The method was first implemented systematically in Italy. CST is based on the idea that migrants—regardless of their legal status—tend to frequent specific centres or aggregation points in their everyday lives, such as religious institutions, cultural and community associations, consulates, NGOs, migrant help desks, public spaces, and informal meeting places. The method proceeds in three stages. First, a mapping phase is conducted to identify and classify existing centres that are expected to be regularly visited by the target population within the geographic area of interest. Centres are categorised by type (e.g., religious, cultural, associative, consular), estimated relevance (e.g., estimated average attendance) and population specificity (e.g., open to all migrants or nationalityspecific), and then stratified accordingly. Then, a sample of centres is drawn, and some individuals are selected in each centre either randomly (e.g., systematic sampling upon entry) or via controlled quota sampling if the flow is not randomizable. The unit of analysis is the individual migrant. After the end of the interview phase, weights are calculated based on the number of centres attended and their importance, which allows for correcting potential overrepresentation of more socially active individuals. 115 Surveying irregular migrants: Challenges & approaches Spain irrespective of their legal status. It collected information on the type of respondents’ residence permit and immigration status (e.g., asylum applicant). Similarly, Germany’s IAB-BAMF-SOEP1 (see Box 10.2) and the Feasibility Study on the Im-/Mobility of Rejected Asylum Seekers (MIMAP; Stache et al., 2024) include groups such as rejected asylum seekers with temporary suspension of removal (‘Duldung’), capturing segments of the population who experience forms of de facto irregularity. The MIMAP Survey, in particular, was explicitly designed to target irregular migrants through its sampling strategy and questionnaire items. Box 10.2: Surveying irregular migrants with an existing sampling frame – The IAB-BAMF-SOEP survey of refugees Randy Stache As in any survey, a suitable sampling frame that includes the entire target population and enables sample selection as well as contact details is crucial for reliable survey data collection on irregular migrants and for generalizing empirical results. In Germany, the Central Register of Foreigners (see Chapter 7) offers such a sampling frame for subgroups of irregular migrants, enabling representative samples and the use of traditional survey methods. Since 2016, the IAB-BAMF-SOEP Survey of Refugees is annually surveying refugees who arrived in Germany since 2013 in a panel study, regardless of the outcome of their asylum procedures. As a result, the data include irregular migrants known to the authorities whose deportation has been temporarily suspended (tolerated/Duldung). The dataset offers several advantages to analyse the living situation of irregular migrants: 1) Accessibility to external researchers via a data usage agreement. 2) Broad thematic coverage, including migration trajectories, housing, employment, language acquisition, health, attitudes, religion. 3) Longitudinal design, allowing for the observation of individual developments over time. 4) A heterogeneous group of irregular migrants in terms of age, gender country of origin, and other characteristics. 5) Comparative potential, enabling systematic analyses of differences between individuals with tolerated status and other groups (recognized refugees or migrants and natives - when using the compatible SOEP-CORE and IAB-SOEP MIG data), and the identification of influencing factors across domains. However, when using the data for research on irregular migrants some limitations arise: 1) The dataset includes only a specific subgroup of irregular migrants – those with tolerated status following an asylum application. Additionally, this group tends to participate less often in follow-up surveys and had higher non-response. 2) Additionally, not all topics are covered in every survey wave. 3) As a result, representativeness and reliable estimations may be limited for certain research questions. However, statistical techniques such as weighting, pooling of waves, or propensity score matching can help mitigate vthese issues. 4) There is inherent selectivity: irregular migrants who have returned, moved to another country, or gone into hiding are not captured in the data. 5) Some questions central to the lived experiences of irregular migrants – such as work permits, life in irregularity, coping with the threat of deportation, or expectations regarding their country of origin – are either absent or not asked in a way that avoids possible bias, like social desirability. 1 This survey is undertaken by the Research Centre of the Federal Office for Migration and Refugees (BAMF-FZ) in cooperation with the Institute for Employment Research (IAB) and the Socio-Economic Panel (SOEP) at German Institute for Economic Research (DIW Berlin). Further information can be found at https://www.diw.de/en/diw_01.c.930532.en/iabbamf-soep_survey_of_refugees.html 116 Chapter 10 In the US, nationally representative surveys have been used to identify ‘likely undocumented’ immigrants through imputation. For example, using the Survey on Income and Program Participation (SIPP), a longitudinal study investigating occupational-related aspects in the US, some scholars exploited limited information on visa status (concerning citizenship and legal permanent resident (LPR) status) and participation in welfare programs to infer immigrant respondents’ current legal status (Hall et al., 2010). Other studies have developed imputation methods based on observable characteristics unrelated to legal status, which have been applied to the Current Population Survey (CPS), the American labour force survey (Passel and Cohen, 2014). Surveys targeted to applicants of regularisation programmes Surveys targeting applicants of regularisation programmes are a key source of empirical evidence on migrants who have experienced irregularity. However, they only capture information on those who successfully applied, and therefore exclude non-applicants or rejected cases. These surveys are typically conducted in the process of major legalisation programmes and are designed to capture individuals’ socioeconomic characteristics, labour market trajectories, and integration patterns. One of the most prominent examples is the Legalized Population Survey (LPS), a longitudinal survey launched in the US after the 1986 Immigration Reform and Control Act (IRCA), which granted legal status to nearly 2.7 million undocumented migrants. Conducted in two waves, the LPS collected detailed data on preand postlegalisation employment, mobility, income, and legal trajectories, and remains a foundational source for studying the economic impacts of legalisation. The first wave of the survey (LPS1) gathered data from 6,193 individuals who had applied for temporary residence status by January 31, 1989. Respondents were asked to report their employment status during the week preceding the submission of their amnesty application. In the second wave (LPS2), conducted in 1992, a followup was carried out with 4,012 participants from LPS1 who had since obtained lawful permanent residence. While the sample is not representative of all individuals who received amnesty under IRCA, the longitudinal design remains a major strength for analysing changes in employment outcomes over time, specifically around the critical transition from undocumented to legal status. The Brief Analysis 3/2024 published by the Research Centre of the Federal Office for Migration and Refugees illustrates how this data can be used to study the living conditions of tolerated persons in comparison to recognized refugees, using propensity score matching. The comparison shows that both groups are similarly integrated in terms of language skills and employment. However, the tolerated are more likely to live in shared accommodations and report much lower life satisfaction, which further declines over time (Stache, 2024). References: Stache, R. (2024). Auswirkungen einer Duldung auf Lebenssituation und Lebenszufriedenheit. (BAMF-Kurzanalyse, 3-2024). Nürnberg: BAMF. https://doi.org/10.48570/bamf.fz.ka.03/2024.d.2024.duldung.1.0 117 Surveying irregular migrants: Challenges & approaches Another smaller scale example is the Parchemins Study, a prospective, mixed-methods panel survey conducted alongside Operation Papyrus, the 2017– 2018 regularisation scheme for undocumented economic migrants in the Swiss canton of Geneva. It tracked approximately 400 individuals up to 3 years after regularisation, focussing on the effects of regularisation on their health and well-being (Lives Centre, 2020). Retrospective surveys on migrants who currently hold a legal status collecting data on their past experiences of irregularity A third type of survey focuses on the past irregular experiences of migrants who now hold legal status. By working with immigrants holding legal status, these surveys simplify sampling design, but rely on respondents’ recall and willingness to disclose prior undocumented residence through direct questions (e.g., ‘Have you ever been irregular?’) and collecting information on how their legal status changed over time (e.g., the types and timings of residence permits). Examples include the Social Condition and Integration of Foreign Citizens (SCIF) survey, conducted by the Italian National Statistical Office (Istat) in 2011-12, and Trajectories and Origins 2 (TeO2), carried out by the French Institute for Demographic Studies (INED) and the National Institute of Statistics and Economic Studies (INSEE) in 2019-20 (see Box 10.3). One of the main limitations of these studies lies in their exclusive focus on the initial phase of irregularity (i.e., between arrival in the destination country and the acquisition of a first permit) without reconstructing respondents’ full legal status trajectory. To address this limitation, one could extend the time frame by combining retrospective questions about past legal status with longitudinal or prospective data that track respondents over time. Box 10.3: Reliability in measuring migrants’ legal trajectories and experiences of irregularity in a retrospective survey: The case of “Trajectories and Origins 2” Julia Descamps In a retrospective survey, how much can we rely on the data collected on legal status and past episodes of irregularity? Drawing on the example of the French Trajectories and Origins survey (Ined, INSEE, 2019-2020), the potential biases were considered (Descamps, 2024). Two of these are particularly challenging in the context of surveying irregular migration. Memory bias, which occurs when the content of a response depends on the ability to recall information, could affect migrants with insecure and bumpy legal trajectory. Social desirability bias, a tendency to present oneself in a favorable light to others, might be more prevalent among migrants who have experienced irregularity, an experience on the legal margins, therefore particularly sensitive. Those biases are tested using TeO2 survey, by examining the non-response rates, and quantifying the under-reporting of irregularity, on a sample of 7,057 immigrants arrived to France after the age of 18. Non-response to the question “Have you ever been irregular?” is low (1%), and does not increase with the length of time since arrival, unlike the non-response rate on the first legal permit in France. Regarding irregularity, memory bias appears to be minimal: respondents found it more difficult to recall events from the early stages of their legal journey, but were less hesitant when it came to irregularity. 118 Chapter 10 The length of time that respondents declare they spent as irregular migrants is then compared with a proxy for irregular status on entry: the time it took them to obtain their first residence permit (from the year they entered France to the year they obtained their first residence permit). Positive differences between the two figures (reported time with undocumented status inferior to time before first residence permit obtained) are taken as evidence of under-reporting of periods of irregular status by respondents. Taking only those respondents with a gap between accessing France and obtaining their first permit – who could therefore underreport this situation – 70% of cases match within one year. The proportion of under-reported irregularity is 27%. This rate is an estimate of the social desirability bias. This bias appears to be more prevalent among educated migrants. The feeling of downward social mobility associated with irregular status, stronger when the social status in the home country is high, can lead respondents to regain control over their migratory narrative. The same is true of asylum applicants who were denied refugee status: they also tend to under-report irregularity. Their experience of administrative domination could lead them to modify their account of their irregular status. Social desirability bias could also overlap with memory bias, with partial answers being due to the often precarious and rocky migration trajectories of asylum seekers. These results highlight the importance of statistically surveying migrants about their various legal statuses and experiences of irregularity. Particular attention should be paid to the effects of categorisation and the leeway it provides. References: Descamps, J. (2024). Can We See Their ID? Measuring Immigrants’ Legal Trajectory: Lessons From a French Survey. International Migration Review, 0(0). https://doi.org/10.1177/01979183241295995 11 See https://mixedmigration.org/resources/ 12 See https://mixedmigration.org/4mi/4mi-interactive/ 13 More information on 4Mi can be found at https://mixedmigration.org/wp-content/uploads/2021/08/4Mi-Introduction.pdf Another notable example is the ELIPA 2 French panel, conducted by the Ministère de l’Intérieur et des Outre-Mer in three waves (2019, 2020, and 2022) with a representative sample of immigrants who obtained their first residence permit in France in 2018. In addition to other topics, the survey collected both retrospective and ongoing information on the administrative process of respondents, allowing researchers to reconstruct their legal status trajectories over a four-year period. A common limitation of these surveys is that they only include immigrants who have obtained legal status at some point, thereby excluding those who remain undocumented. However, retrospective surveys also offer several advantages. First, instead of treating legal status as a fixed condition, they make it possible to investigate specific phases of irregularity, which is particularly valuable in contexts characterised by recurrent regularisations. Second, by relying on large samples and rich questionnaires, they enable long-term analyses of the consequences of irregular status over multiple time periods and dimensions of migrants’ lives. 119 Surveying irregular migrants: Challenges & approaches Conclusion Efforts to survey irregular migrants will always face trade-offs between coverage, data quality, and ethical safeguards. No single method can fully overcome the challenges of sampling, trust, and mobility, so mixed approaches tailored to specific contexts are essential. Well-designed surveys can generate robust evidence to inform more balanced debates and better-targeted policies, but only if they are grounded in careful methodological choices and genuine engagement with the communities concerned. 120 Chapter 10 References Bachmeier, J.D., Van Hook, J. & Bean, F.D. (2014) Can we measure Immigrants’ legal status? Lessons from two U.S. surveys. International Migration Review, 48(2), 538–566 Baio, G., Blangiardo, G. C., & Blangiardo, M. (2011). Centre Sampling Technique in foreign migration surveys: A methodological note. Journal of Official Statistics, 27(3), 451–465. Hall, M., Greenman, E. & Farkas, G. (2010) Legal status and wage disparities for Mexican immigrants. Social Forces, 89(2), 491–513. Lives Centre (2020). “Parchemins” Project - Assessing the health and well-being of undocumented migrants. Online Resource https://www.centre-lives.ch/en/project/parchemins-project-assessing-health-and-wellbeing-undocumented-migrants?chapter=195-project-description-parchemins Passel, J.S. & Cohn, D. (2014) Unauthorised immigrant totals rise in 7 states, fall in 14: decline in those from Mexico fuels Most states decreases. Washington, DC: Pew Research Centre’s Hispanic Trends Project. November. Peitz, L. and Stache, R. & Johnson, L. (2024) How to Survey Hard-to-Reach Populations: A Practical Guide to App-Based Respondent-Driven Sampling. Robert Schuman Centre for Advanced Studies Research Paper No. 2024/23, http://dx.doi.org/10.2139/ssrn.4901241 Reher, D., & Requena, M. (2009). The National Immigrant Survey of Spain: A New Data Source for Migration Studies in Europe. Demographic Research, 20(12): 253–278. https://doi.org/10.4054/DemRes.2009.20.12 Stache, R., Peitz, L., & Johnson, L. (2024). The MIMAP Survey on Im-/Mobility Aspirations of Rejected Asylum Seekers: Survey Instruments & Codebook. Nürnberg: Bundesamt für Migration und Flüchtlinge. https://doi.org/10.48570/bamf.fz.fragebogen.mimap.en.2024.1.0 Vickstrom, E. & Beauchemin, C. (2024). Quantitative surveys on migration. In Sciortino, G., Cvajner, M., & Kivisto, P. (Eds.) Research Handbook on the Sociology of Migration, Edward-Elgar, pp 227-242. 127 Towards better use of migration data Box 11.2: Spain’s padrón system Adèle Appriou, Jasmijn Slootjes and Ravenna Sohst Spain’s padrón municipal de habitantes (municipal register of inhabitants) is a notable example of how local registration systems can support the inclusion of irregular migrants while generating valuable data for public planning and service provision. All residents, regardless of their status, are required to register with the padrón, which grants them access to municipal services such as education, health care, libraries, and language courses. The main advantage for irregular migrants registering in the padrón system is the possibility of obtaining arraigo social (legal residence) if they provide proof that they have lived in Spain for at least three years. Registration requires minimal documentation—typically an ID and proof of address—which many municipalities apply flexibly to reduce barriers for irregular migrants. For instance, Barcelona actively encourages registration even for those without a fixed address, with city officials conducting field visits to verify the residence of individuals unable to provide formal proof. Another key feature of this public formation is its separation from other policy functions (e.g., immigration enforcement), which, along with outreach by civil society actors, helps build trust and encourage participation. This initiative enables the country to gather valuable information about all residents, including their age, country of origin, nationality, gender, and family or marital status. While the padrón fosters inclusion, challenges remain. Registration requirements and practices vary between municipalities, with some cities facilitating registration for irregular migrants more actively than others. Issues around data accuracy—such as residents failing to de-register when they move— have also been noted. Nevertheless, Spain’s padrón offers valuable lessons on how local initiatives can improve data collection and service access for irregular migrants. Conclusion While data on irregular migration has the potential to drive more effective, transparent, and responsive policymaking, this potential remains limited by persistent gaps and structural barriers. Enhancing the quality, accessibility, and responsible use of data is not only feasible, but necessary for fair and informed migration governance. 128 Chapter 11 References Delvino, N. (2017). City initiative on migrants with irregular status in Europe: Overview of good practices. Centre on Migration, Policy and Society, University of Oxford. https://www.compas.ox.ac.uk/wp-content/uploads/City-Initiative-on-Migrants-with-Irregular-Status-inEurope-CMISE-report-November-2017-FINAL.pdf European Union Agency for Fundamental Rights (2018). FRA Opinions Biometrics. https://fra.europa.eu/en/content/fra-opinions-biometrics Slootjes, J., & Sohst, R. (2024). Towards the More Effective Use of Irregular Migration Data in Policymaking. Brussels: Migration Policy Institute Europe. https://doi.org/10.5281/zenodo.14276892 Slootjes, J., Sohst, R., & Kokkelmans, R. (2023). Mapping Stakeholders’ Needs and Usage of Irregular Migration Data. MIrreM Briefing Paper No. D2.1. Brussels: Migration Policy Institute Europe. https://doi.org/10.5281/zenodo.7589494 Chapter 12 Progress, limits, and the need for sustained effort Denis Kierans and Albert Kraler 130 Chapter 12 Progress, limits, and the need for sustained effort Denis Kierans and Albert Kraler Irregular migration remains one of the most politically salient and technically challenging areas of migration data and policy in Europe. A range of stakeholders from academia to NGOs to government ministries collect and analyse data on irregular migration and are actively improving upon the evidence base in important respects. Still, quantitative information on irregular migration remains marked by significant gaps, inconsistencies, and contested interpretations. Too often, service provision, public discourse and decisions on migration management are made with reference to numbers that are partial, outdated, or biased, and presented without clear explanation of their scope and limitations. These problems persist in part because there is no European body tasked with sustaining cooperation, building capacity, or coordinating knowledge on irregular migration data. Overcoming this gap is essential if progress is to become cumulative rather than fragmented and short-lived. Many of the implications set out in this Handbook will be familiar to those who have worked on improving statistics on irregular migration – or migration more generally – for years. Calls for clearer concepts, more robust quality assessments, scalable methodologies, greater transparency, stronger ethical safeguards, and closer alignment between data producers and users are not new. Yet to say there has been no change would overlook the progress of recent years. Across Europe, there is growing use of administrative registers to capture aspects of irregular migration and produce publicly available analysis; greater openness to innovative estimation methods; more ambitious and thoughtful surveys to boost coverage of hardto-reach populations; and increased awareness of the importance of trust-based engagement. The examples featured in this Handbook illustrate that such progress is possible and can be sustained. Spain’s padrón system continues to register all residents regardless of status, enabling both service provision and valuable local-level statistics. The United Kingdom’s Home Office publishes regular operational statistics and analysis on irregular arrivals and enforcement activity, providing an accessible view of specific flow indicators. Austria’s Austrian Micro Data Centre offers a model for privacy-compliant linkage of administrative datasets to support longitudinal analysis. Italy’s Regional Observatory for Integration and Multiethnicity (ORIM) survey in Lombardy demonstrates how inclusive, community-engaged data collection can be maintained over decades. The Mixed Migration Centre’s global 4Mi survey shows how community-based enumerators can gather detailed information from people on the move at a global scale. Various innovative methods have worked well in specific settings. In some places, promising approaches remain at the pilot level, dependent on individual champions, short-term funding, or local conditions unlikely to be replicable elsewhere. We also recognise that many of the implications set out in this Handbook are necessarily broad. We resist detailed prescription because context matters: initiatives need to be adapted to local legal frameworks, institutional arrangements and operational realities, drawing on the expertise of those who work closest to the data and the communities the data concern. There is a balance 131 Conclusion to strike. Too much rigidity makes it difficult for a concept or method to travel; too much generality reduces its practical value. The case studies in this Handbook, particularly the innovative estimation methods and data-collection practices, are intended to provide concrete examples that, while rooted in specific contexts, are well suited to adaptation – if not immediately, then over time – into other settings. Notwithstanding this need for techniques to be context-appropriate, it is clear that one thing which could help maintain and grow this body of innovation would be more coordination at the European level. The work documented in this Handbook sits within a longer trajectory of European and international efforts to improve irregular migration data. Earlier initiatives – in particular the CLANDESTINO project, which ended in 20091 – laid the groundwork for this Handbook and many of the good practices highlighted in it. While the past 15 years have seen new innovations and pilots, the lack of a consistent, Europe-wide mechanism for maintaining and building on these advances has limited their cumulative impact. A sustained, coordinated investment over that period would have undoubtedly produced a more harmonised, institutionalised, and widely adopted set of approaches across the continent.2 Such coordination would need to be mindful of ethical considerations. Techniques such as probabilistic matching of administrative records, capture–recapture analysis, mixed-method survey designs, and the integration of digital trace data have broad applicability when adapted with care. Local and municipal practices that build trust, such as the inclusivity of Spain’s padrón regardless of migration status or the Regional Observatory for Integration and Multiethnicity’s (ORIM) use of cultural mediators, show that the findings from integration research can go hand in hand with data quality. These case studies also underscore a broader point: effective irregular migration data systems are as much about relationships, governance, and institutional trust as they are about statistical methods. Looking ahead, one of the hopes for this Handbook is that it will help to spur greater Europe-wide coordination on irregular migration data. This should take place under the leadership of key stakeholders, such as Eurostat, the Directorate General for Migration and Home Affairs of the European Commission (DG Home), the European Border and Coast Guard Agency (Frontex), the EU Agency for Asylum (EUAA) and the EU Agency for the Operational Management of Large-Scale IT Systems in the Area of Freedom, Security and Justice (eu-LISA). This type of leadership, coupled with long-term funding, would go a long way to ensuring that improvements in irregular migration data are sustained rather than episodic. This should include knowledge exchange and technical cooperation between researchers, NSOs, ministries and international organisations from different countries. Irregular migration will never be fully knowable. Uncertainty is inherent in a phenomenon shaped by mobility, with strong incentives to remain “under the radar,” and prone to shifting policies 1 See https://cordis.europa.eu/project/id/44103. Results are also available from the archived project website at https://www.uni-bremen.de/fb12/irregular-migration-1. 2 The CLANDESTINO team sought additional funding from the European Commission, proposing a cooperation with the European Migration Network to undertake regular updates of the CLANDESTINO database and undertake related analyses, but their attempt was unsuccessful. 132 Chapter 12 and legal frameworks. More information does not automatically reduce uncertainty or lead to greater insights. In some cases, an abundance of data can be more damaging than a scarcity of information, fuelling misinterpretation, selective use, or misplaced confidence in the numbers. The aim is not to eliminate uncertainty, but to manage it, grounding policy and public debate in evidence that is as reliable, transparent, and context-aware as possible. The examples and approaches in this Handbook show that there is significant potential in activities already under way, that better data are achievable, and that their careful use can strengthen both understanding and governance of irregular migration. The challenge is to move from promising but isolated or short-lived initiatives towards a Europe-wide infrastructure for irregular migration data that is durable, well-resourced and collaborative. This will not be achieved quickly, but the building blocks already exist, providing a foundation for longerterm investment in more informed, transparent and credible data on irregular migration. List of contributors 134 List of contributors Petya Alexandrova, European Union Agency for Asylum, Malta Jill Ahrens, Department for Migration and Globalisation, University for Continuing Education (Danube University) Krems, Austria Adèle Appriou, Migration Policy Institute Europe, Belgium Tuba Bircan, Brussels Institute for Social and Population Studies, Vrije Universiteit Brussel (VUB), Belgium Julia Black, Missing Migrants Project, International Organization for Migration, Germany Francesco Teo Ficcarello, Mixed Migration Centre Global, Uganda Roberto Forin, Mixed Migration Centre Europe, Switzerland Denis Kierans, Centre on Migration, Policy and Society (COMPAS), University of Oxford, United Kingdom Albert Kraler, Department for Migration and Globalisation, University for Continuing Education (Danube University) Krems, Austria Frank Laczko, former Head of IOM’s Global Migration Data Analysis Centre (GMDAC), Germany, and series editor, Routledge books on “Global Migration Issues” Arjen Leerkes, Maastricht Graduate School of Governance, Maastricht University, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, and Research and Documentation Centre (WODC) of the Dutch Ministry of Justice and Security Francesca Licari, National Institute of Statistics, Italy Naomi Lindt, UNICEF and International Data Alliance for Children on the Move (IDAC), United States Felipe Malle, National Migration Service, Chile List of contributors . 135 List of contributors Marco Marsili, National Institute of Statistics, Italy Rocco Molinari, Department of Statistical Sciences “Paolo Fortunati”, University of Bologna, Italy Livia Elisa Ortensi, Department of Statistical Sciences “Paolo Fortunati”, University of Bologna, Italy, University of Bologna, Italy Sebastián Palmas, UNICEF and International Data Alliance for Children on the Move (IDAC), Spain Laura Peitz, Migration, Asylum and Integration Research Centre, Federal Office for Migration and Refugees (BAMF), Germany Marzia Rango, UNICEF and International Data Alliance for Children on the Move (IDAC), Italy and the United States Julibeth Rodríguez, National Migration Service, Chile Alejandra Rodríguez-Sánchez, Department of Administrative and Political Sciences, University of Potsdam, Germany Aslı Salihoğlu, Centre on Migration, Policy and Society (COMPAS), Oxford Filip Savatic, Sciences Po, France Randy Stache, Federal Office for Migration and Refugees (BAMF), Germany Jon Simmons, UK Home Office Analysis & Insight directorate, United Kingdom Ann Singleton, School for Policy Studies, University of Bristol, United Kingdom Lalaine Siruno, United Nations University Maastricht Economic and Social Research Institute on Innovation and Technology and Maastricht University, the Netherlands Jasmijn Slootjes, Migration Policy Institute Europe, Belgium Ravenna Sohst, Migration Policy Institute Europe, Belgium Lucy Swinnerton, UK Home Office Analysis & Insight directorate, United Kingdom Jasper Tjaden, Department of Administrative and Political Sciences, University of Potsdam, Germany Carlos Vargas-Silva, Centre on Migration, Policy and Society (COMPAS), University of Oxford, United Kingdom Teddy Wilkin, European Union Agency for Asylum, Malta Danzhen You, UNICEF and International Data Alliance for Children on the Move (IDAC), United States This Handbook brings together concepts, findings, methods, and case studies to offer a clear, practical understanding of irregular migration data. It addresses the challenges of conceptualising, measuring, interrogating, and using data on one of Europe’s most politically sensitive migration issues. Drawing on examples from across Europe and beyond, it provides guidance on concepts and definitions, ethics, estimation methods, data innovation, and policy application. It is designed to support policymakers, practitioners and researchers seeking more informed, transparent, and coordinated approaches to irregular migration data.